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POLYNYAS WINDOWS TO THE WORLD
Edited by WALKER O. SMITH, JR. Virginia Institute of Marine Sciences, College of William & Mary, Gloucester Pt., USA and DAVID G. BARBER Department of Geography, University of Manitoba, Winnipeg, Canada
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Contents List of Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xi
Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xiii
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xv
1
The Role of Sea Ice in Arctic and Antarctic Polynyas D.G. Barber and R.A. Massom
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1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 2. Polynyas, Sea Ice and Climate Variability/Change . . . . 3. An Inventory of Arctic and Antarctic Polynyas . . . . . . 3.1. Northern Hemisphere Polynyas . . . . . . . . . . . 3.2. Southern Hemisphere Polynyas . . . . . . . . . . . 4. Detailed Case Studies . . . . . . . . . . . . . . . . . . . . 4.1. The North Water (NOW) Polynya (NW Greenland) 4.2. The Mertz Glacier Polynya (East Antarctica) . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
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Physical Oceanography of Polynyas W.J. Williams, E.C. Carmack and R.G. Ingram 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2. Polynya Formation Processes . . . . . . . . . . . . . . . 2.1. Mechanically Forced Polynyas . . . . . . . . . . . 2.2. Convectively-Forced Polynyas . . . . . . . . . . . 2.3. Feedback Processes Within Polynyas . . . . . . . 2.4. Marginal Ice Zone ‘Polynyas’ . . . . . . . . . . . 3. Physical Oceanography . . . . . . . . . . . . . . . . . . 3.1. Water Mass Transformation Within Polynyas . . . 3.2. Transport of Dense Water Away from the Polynya 4. Biological Importance of Polynyas . . . . . . . . . . . . 5. Future Research in a Changing Environment . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
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Polynya Modelling A.J. Willmott, D.M. Holland and M.A. Morales Maqueda v
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Contents 1. 2. 3. 4.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flux Model Approach . . . . . . . . . . . . . . . . . . . . . . . . . Parameterisations for the Collection Thickness H . . . . . . . . . . Opening of a One-Dimensional Polynya . . . . . . . . . . . . . . . 4.1. Constant Collection Thickness . . . . . . . . . . . . . . . . . 4.2. Parameterisation (3.2) for H . . . . . . . . . . . . . . . . . . 4.3. Parameterisation (3.3) for H . . . . . . . . . . . . . . . . . . 4.4. Discussion of the Opening Models . . . . . . . . . . . . . . 5. Two-Dimensional Steady-State Solutions . . . . . . . . . . . . . . 6. Two-Dimensional Steady-State Solutions with Ocean Currents . . 7. Unsteady 2-Dimensional Flux Models . . . . . . . . . . . . . . . . 8. Polynya Closing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9. A Polynya Flux Model with a Prognostic Frazil Ice Concentration 10. Coupled Atmosphere–Polynya Flux Model . . . . . . . . . . . . . 11. General Circulation Modelling Approach . . . . . . . . . . . . . . 12. Ice GCM Equations . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1. Mass Conservation . . . . . . . . . . . . . . . . . . . . . . . 12.2. Momentum Conservation . . . . . . . . . . . . . . . . . . . . 13. Numerical Methods in Ice GCMs . . . . . . . . . . . . . . . . . . . 14. Regional Ice GCM Applications . . . . . . . . . . . . . . . . . . . 14.1. Coastal-Ocean Polynya . . . . . . . . . . . . . . . . . . . . . 14.2. Open-Ocean Polynya . . . . . . . . . . . . . . . . . . . . . . 15. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
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Meteorology and Atmosphere–Surface Coupling in and around Polynyas P.J. Minnett and E.L. Key 1. Introduction . . . . . . . . . . . . . . . . . 1.1. Polynya Formation . . . . . . . . . . 2. Measurements of Meteorological Variables 2.1. Winds . . . . . . . . . . . . . . . . . 2.2. Surface Temperature . . . . . . . . . 2.3. Surface Humidity . . . . . . . . . . . 2.4. Profiles . . . . . . . . . . . . . . . . . 2.5. Clouds . . . . . . . . . . . . . . . . . 2.6. Aerosols . . . . . . . . . . . . . . . . 2.7. Radiation . . . . . . . . . . . . . . . 3. NWP Models . . . . . . . . . . . . . . . . . 4. Surface Interactions . . . . . . . . . . . . . 4.1. Radiative Fluxes . . . . . . . . . . . 4.2. Turbulent Fluxes . . . . . . . . . . . 5. Outlook for the Future . . . . . . . . . . . . 6. Summary . . . . . . . . . . . . . . . . . . . Appendix A: Acronyms . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . .
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Contents 5
vii
Gas Fluxes and Dynamics in Polynyas L.A. Miller and G.R. DiTullio
163
1. Introduction . . . . . . . . . . . . . . . . . . . . . 2. Gases in Cold, Salty Water . . . . . . . . . . . . . 3. Carbon . . . . . . . . . . . . . . . . . . . . . . . . 3.1. The Ross Sea . . . . . . . . . . . . . . . . . 3.2. The North Water . . . . . . . . . . . . . . . 3.3. The Northeast Water . . . . . . . . . . . . . 3.4. Other Polynyas . . . . . . . . . . . . . . . . 4. Sulfur . . . . . . . . . . . . . . . . . . . . . . . . . 5. Methylhalides . . . . . . . . . . . . . . . . . . . . 6. The Future for Air–Sea Gas Exchange in Polynyas Acknowledgements . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . 6
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Biogeochemistry of Polynyas and Their Role in Sequestration of Anthropogenic Constituents M. Hoppema and L.G. Anderson . . . . . . . . . . . . . . . . .
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Physical Control of Primary Productivity in Arctic and Antarctic Polynyas K.R. Arrigo 1. Introduction . . . . . . . . . . . . . . . . . . . . 2. Formation of the Four Major Polynyas . . . . . . 2.1. The NEW polynya (Arctic) . . . . . . . . 2.2. The NOW Polynya (Arctic) . . . . . . . . 2.3. The RSP Polynya (Antarctic) . . . . . . . 2.4. The MGP Polynya (Antarctic) . . . . . . . 3. Role of Physicochemical Properties of Polynyas 3.1. Effect of Temperature . . . . . . . . . . . .
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163 164 166 170 173 175 177 178 180 180 182 182
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1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Antarctic Polynyas . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Weddell Polynya in the 1970s . . . . . . . . . . . . . . . . 2.2. Recurrent Offshore Polynyas . . . . . . . . . . . . . . . . . 2.3. Coastal Polynyas in the Weddell Sea . . . . . . . . . . . . 2.4. Ross Sea and Terra Nova Bay Polynyas . . . . . . . . . . . 2.5. East Antarctic Coastal Polynyas . . . . . . . . . . . . . . . 2.6. Summary and Concluding Remarks for Antarctic Polynyas 3. Polynyas in the Arctic Ocean . . . . . . . . . . . . . . . . . . . . 3.1. Storfjorden Polynya . . . . . . . . . . . . . . . . . . . . . . 3.2. Northeast Water Polynya . . . . . . . . . . . . . . . . . . . 3.3. North Water Polynya . . . . . . . . . . . . . . . . . . . . . 3.4. Cape Bathurst Polynya . . . . . . . . . . . . . . . . . . . . 3.5. St. Lawrence Island Polynya . . . . . . . . . . . . . . . . . 3.6. Laptev Sea Polynya . . . . . . . . . . . . . . . . . . . . . . 3.7. Comparison of the Different Arctic Polynyas . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
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viii
Contents 3.2. Effect of Light and Nutrients . . . . . . . . . . . . . . 3.3. Effect of UV Radiation . . . . . . . . . . . . . . . . . 4. Cloud Reduction over Polynyas with High Latent Heat Flux 5. Timing of Polynya Expansion . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
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Primary Production and Nutrient Dynamics in Polynyas J.-E. Tremblay and W.O. Smith 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 2. Phytoplankton Characteristics of Individual Polynyas 2.1. North Water Polynya . . . . . . . . . . . . . . . 2.2. Northeast Water Polynya . . . . . . . . . . . . . 2.3. Cape Bathurst Polynya . . . . . . . . . . . . . . 2.4. Other Arctic Polynyas . . . . . . . . . . . . . . 2.5. Ross Sea Polynya . . . . . . . . . . . . . . . . . 2.6. Mertz Glacier Polynya . . . . . . . . . . . . . . 2.7. Terra Nova Bay Polynya . . . . . . . . . . . . . 3. Ecological Consequences of Polynya Production . . . 3.1. North Water Polynya . . . . . . . . . . . . . . . 3.2. St. Lawrence Island Polynya . . . . . . . . . . . 3.3. Ross Sea Polynya . . . . . . . . . . . . . . . . . 4. Comparison of Arctic and Antarctic Polynyas . . . . . 5. Summary and Conclusions . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
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Zooplankton Processes in Arctic and Antarctic Polynyas D. Deibel and K.L. Daly 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Zooplankton in Arctic Ocean Polynyas . . . . . . . . . . . . . . . . . . . 2.1. Species Composition and Abundance . . . . . . . . . . . . . . . . 2.2. Individual and Community Biomass . . . . . . . . . . . . . . . . . 2.3. Individual Feeding Rates, Diet and Community Grazing . . . . . 2.4. Faecal Pellet Production and Vertical Flux . . . . . . . . . . . . . 2.5. Seasonal Energy Storage and Egg Production Rates . . . . . . . . 2.6. Secondary Production and Generation Time . . . . . . . . . . . . 3. Zooplankton of the Southern Ocean . . . . . . . . . . . . . . . . . . . . 3.1. Species Composition and Abundance . . . . . . . . . . . . . . . . 3.2. Individual and Community Biomass . . . . . . . . . . . . . . . . . 3.3. Individual Feeding Rates and Diet . . . . . . . . . . . . . . . . . . 3.4. Faecal Pellet Production and Vertical Flux . . . . . . . . . . . . . 3.5. Seasonal Life History, Energy Storage and Egg Production Rates . 3.6. Secondary Production and Generation Time . . . . . . . . . . . . 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
239 243 243 247 248 248 249 255 256 256 257 257 258 259 261 263 263
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ix
Pelagic Bacterial Processes in Polynyas H.W. Ducklow and P.L. Yager
323
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Overview: Microbial Food Webs in Polar Seas . . . . . . . . 2.1. Methods and Terminology . . . . . . . . . . . . . . . . 2.2. Food Web Structure and Function . . . . . . . . . . . . 2.3. Bacterial Growth in Cold Water . . . . . . . . . . . . . 3. Bacterial Processes in the Ross Sea Polynya (RSP) . . . . . . 4. Bacterial Processes in Greenland Polynyas (NEW and NOW) 4.1. The Northeast Water (NEW) Polynya . . . . . . . . . . 4.2. The North Water (NOW) Polynya . . . . . . . . . . . . 5. Other Polynyas . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Summary and Prospects . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
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Benthic Processes in Polynyas J.M. Grebmeier and J.P. Barry . . . . . . . . . .
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The Impact and Importance of Production in Polynyas to Top-Trophic Predators: Three Case Histories N. Karnovsky, D.G. Ainley and P. Lee 1. Introduction . . . . . . . . . . . . . . . 2. Ross Sea Polynya . . . . . . . . . . . . 2.1. Physical Characteristics . . . . . 2.2. Organic Production . . . . . . . . 2.3. Middle and Upper Trophic Levels 3. North Water Polynya . . . . . . . . . . 3.1. Physical Characteristics . . . . . 3.2. Organic Production . . . . . . . . 3.3. Middle and Upper Trophic Levels
323 326 326 328 330 331 338 338 346 349 349 350 350
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1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. General Polynya Patterns and Processes . . . . . . . . . . . . . . . . . . 2.1. St. Lawrence Island Polynya (SLIP) . . . . . . . . . . . . . . . . . 2.2. Ross Sea Polynya (RSP) . . . . . . . . . . . . . . . . . . . . . . . 3. Means to Assess Interpolynya Differences . . . . . . . . . . . . . . . . . 4. Rate Processes and Their Controls . . . . . . . . . . . . . . . . . . . . . 4.1. Benthic Oxygen Demand . . . . . . . . . . . . . . . . . . . . . . . 4.2. Retention Versus Export of Carbon to the Benthos . . . . . . . . . 4.3. Depth as the Major Factor for Variance in Benthic Carbon Cycling 4.4. Comparison of SLIP and RSP to other Polynyas . . . . . . . . . . 4.5. Comparison of Polynyas to Retreating Marginal Ice Zones and Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
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Contents 4. Northeast Water Polynya . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Polynyas and Climate Change: A View to the Future W.O. Smith and D.G. Barber
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1. Introduction . . . . . . . . . . . . . . . . . . 2. Polynyas and Climate Change: The Arctic . . 3. Polynyas and Climate Change: The Antarctic 4. Conclusions . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . .
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Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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List of Contributors Ainley, D.G. Anderson, L.G. Arrigo, K.R. Barber, D.G. Barry, J.P. Carmack, E.C. Daly, K.L. Deibel, D. DiTullio, G.R. Ducklow, H.W. Grebmeier, J.M. Holland, D.M. Hoppema, M. Ingram, R.G. Karnovsky, N. Key, E.L. Lee, P. Massom, R.A. Miller, L.A. Minnett, P.J. Morales Maqueda, M.A. Smith Jr., W.O. Tremblay, J.-E. Williams, W.J. Willmott, A.J. Yager, P.L.
(391) (193) (223) (1, 411) (363) (55) (271) (271) (163) (323) (363) (87) (193) (55) (391) (127) (391) (1) (163) (127) (87) (391, 411) (239) (55) (87) (323)
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Foreword Polynyas are large regions of open water and thin ice that recur from year to year at the same geographic locations in both summer and winter. Winter polynyas are astonishing, in that it seems counter-intuitive that large areas of open water and thin ice can exist under conditions of very cold strong winds. Such polynyas are physically impressive natural phenomena, akin to tornadoes, severe storms or flash floods. My first encounter with a winter polynya occurred on a cold windy day in March, south of Nome, Alaska. We had flown by helicopter over the 2-km wide coastal polynya to film the frazil ice plumes that occurred in the open water. We then landed on the first year ice downwind of the polynya, where our objective was to sample the frazil ice thickness at the polynya edge. Standing on the ice surface a few tens of meters from the polynya edge, I realized that the combination of the strong cold winds, blowing snow and ice crystals, and the crashing of the waves breaking on the first year ice meant that our sampling plan was too dangerous to carry out. Because of such hazardous conditions combined with the isolated nature of the polynyas, most winter studies are done using remote sensing, over-winter moorings, or icebreakers. Within the polynya, the presence of thin ice and open water means that a large winter heat exchange can occur between the ocean and atmosphere, and if the water is at the freezing point, the polynya also serves as a strong source of ice and produces a salt flux to the underlying ocean. In Ross and Weddell Seas, the dense water formed within the polynyas interacts with the ice shelves in such a way as to generate the cold Antarctic Bottom Water. Descriptions of such physical processes and their impact on the ocean dominated the early polynya modeling. Biologists then realized that because of solar insolation, the large thin ice regions generated in winter by, for example, the Ross Sea polynya, melted before the surrounding pack ice and provided in nutrient-rich waters, large areas for the early onset of primary production. Studies of this enhanced production at a number of Arctic and Antarctic locations yielded a number of successful multi-disciplinary studies. This book provides the first comprehensive, multidisciplinary look at polynyas, and their interactions with the ocean, atmosphere and biology. It does this through a systematic examination of the geographic distribution of polynyas, how they form, the factors that maintain them, and from the results of a series of field and theoretical investigations, their physical and biological properties. The editors represent both the biological and physical aspects of the field: Walker Smith studies biological interactions within polynyas and David Barber studies their physical properties. The thirteen chapters divide into three themes: the first five discuss the physical properties, the next seven describe the biological, and the last discusses polynyas and global warming. Each chapter includes an introduction for the non-specialist, a description of potential future research, and an excellent bibliography. The physics chapters describe the distribution of the polynyas and the diversity of mechanisms that drive them, the difference between northern and southern hemisphere polynyas, the associated oceanography, the polynya modeling efforts, and the atmospheric coupling and gas exchange across the interface. The biology chapters describe the biogeochemistry, the primary productivity, the nutrient dynamics, the zooplankton bacterial and benthic processes and the role of predators. xiii
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Many of the chapters contain case studies that describe the physics and biology within specific polynyas. In the Arctic, these include the St. Lawrence Island polynya in the Bering Sea, the Northeast Water polynya off northeast Greenland, the North Water polynya in Baffin Bay and the Kashevarov polynya in the Okhotsk Sea. In Antarctica, they include the Mertz Glacier, Ross Sea and Cosmonaut polynyas. The book concludes with a disturbing discussion of the future of polynyas that would occur from global warming, namely that as the air temperatures warm and the amount of sea ice decreases, polynyas will become less prominent and their role in the regional biology and physical oceanography will be reduced. Seelye Martin Washington, DC March 2007
Preface Polynyas have been recognized as unique regions within polar regions—areas with reduced ice cover (relative to the surrounded areas) that confer unusual physical, chemical and biological characteristics and processes, and often enhanced exhibit enhanced rates. Their importance to global processes (e.g., gas ventilation, deep water formation, support of polar food webs) is indisputable, and in recent years a number of national and international programs have sought to understand their features in the context of the entire polar system. A variety of meetings and symposia have been held on these regions in the past two decades, and research within polynyas continues today. Indeed, the idea for the book originated from a symposium in Quebec City, Canada in September, 2001. The meeting was perhaps overshadowed by world events, but its importance was long-lasting. The objective of this volume is to synthesize our knowledge and to suggest avenues of future research. The volume consists of 13 independent papers, covering a wide range of research within polynyas, including physical, meteorological, chemical, biological, and modeling studies. Polynyas are in ways enigmatic, in that they are ephemeral features, and difficult to study in situ. Despite this, they have significance far beyond their geographic confines, and are critically important components of polar systems. It also has been suggested that they may be among the first of polar systems to be impacted by climate change, and as such are convenient regions to monitor the impacts of large-scale change. The encouragement and support of all of those who attended the Quebec meeting, and who greatly helped bring this volume to completion, is acknowledged and appreciated. This book is dedicated to all those who have worked, struggled and loved working in the harsh reality of Arctic and Antarctic polynyas. Walker Smith
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Chapter 1
The Role of Sea Ice in Arctic and Antarctic Polynyas D.G. Barber1 and R.A. Massom2 1 Clayton H. Riddell Faculty of Environment, Earth, and Resources; Canada Research Chair in Arctic System
Science; Centre for Earth Observation Science, 476 Wallace Building, Fort Gary Campus, University of Manitoba, Winnipeg, MB R3T 2N2, Canada 2 Australian Government, Australian Antarctic Division and Antarctic Climate and Ecosystems Cooperative Research Centre, Private Bag 80, c/o University of Tasmania, Hobart, Tasmania 7001, Australia
Abstract Polynyas are persistent and recurrent regions of open water and/or thin ice or reduced ice concentration, tens to tens of thousands of square kilometers in areal extent, that occur within the sea ice zones of both hemispheres at locations where a more consolidated and thicker ice cover would be climatologically expected. Rather than simply constituting recurrent “windows” in the sea ice, polynyas are profoundly affected by, and intimately linked to, local and even regional ice conditions (i.e., the “icescape”). They respond sensitively to thermodynamic and dynamic forcing by the ocean and atmosphere and entail ecologically important “oases” that enable birds and mammals to overwinter at high latitudes and encourage enhanced primary production in the spring. In this review, we introduce the concept of polynyas from the perspective of the sea ice conditions/processes that define them. We discuss the unique characteristics of polynyas in both polar regions, and assess their possible response/contribution to climate variability and change. An inventory of Northern Hemisphere polynyas is presented, based primarily of satellite data analysis but also on information from the literature and aboriginal peoples. Summary statistics on polynya opening and closing dates are also provided, along with information on the availability of light relative to the seasonal cycles of sea ice. In the Southern Hemisphere, we present an update of an inventory of Antarctic polynyas and discuss how coastal, glacial and deep-ocean processes affect their and distribution. Two important polynyas are examined in more detail, i.e., the North Water (NOW) polynya in the north and the Mertz Glacier polynya in the south. These case studies focus on details of the different physical processes driving their creation, maintenance and dissolution. Each of these polynyas has been the focus of dedicated in situ research programmes in recent years.
1 Introduction Polynyas are important features of the sea ice covers of both polar regions. Their overall importance, not only locally but also globally, lies in their unique characteristics. Polynyas Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74001-6
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are persistent and recurrent regions of open water and/or thin ice, tens to tens of thousands of square kilometers in extent, that occur at locations where a more consolidated and thicker ice cover would be climatologically expected (Smith et al., 1990; Martin, 2001). They differ from leads in that they recur at approximately the same location each year and persist for periods of weeks to months, with intermittent openings and closings with intermittent openings and closings due mainly to changes in atmospheric forcing. Leads, on the other hand, are linear openings that occur ephemerally on synoptic or shorter time-scales and tend not to recur in the same location (an exception being flaw leads—see below). While Arctic and Antarctic polynyas are fundamentally similar, striking hemispheric differences exist in the processes and phenomena responsible for their formation and maintenance. Moreover, considerable intra-hemispheric differences occur. These differences strongly reflect the contrasting environmental and particularly sea ice conditions present. Rather than simply constituting recurrent “windows” in the sea ice, polynyas are profoundly affected by, and intimately linked to, local and even regional ice conditions, which we term the “icescape”. In this review, we gather together available information and present some new material to construct the first combined inventory of Arctic and Antarctic polynyas, in an effort to highlight these characteristics. Emphasis is placed on modes of formation and maintenance, illustrated by more-detailed case studies of two key polynyas. Consideration is also given to inter-annual variability, possible causes and impacts of change in polynya characteristics, and the potential contribution and sensitive response of polynyas to climate change and/or variability. We begin this synthesis with an assessment of the setting of polynyas—the sea ice covers of both polar regions. In the Arctic ice forms annually throughout most of the oceanic areas north of the Arctic Circle. Maximum extent occurs around the end of March with an area of about 14 × 106 square kilometres (km2 ), with summer time melt resulting in a minimum of about 7 × 106 km2 towards the end of September (Gloersen et al., 1992). The “southward” growth of the perennial pack and “northward” advance of annual ice from the continental shelves means that changes in areal extent occur most often over the ocean shelves. These regions are the realm of Arctic polynyas. Several modeling studies predict a reduction in sea ice areal extent over the next several decades, resulting in a seasonally ice-free Arctic as early as 2050 (Vinnikov et al., 1999; Flato and Boer, 2001). Observational studies, based on the passive microwave record, confirm these predictions for both rates of reduction and, to a certain extent, geographic location (Rigor et al., 2002; Comiso, 2003a; Drobot and Maslanik, 2003). Parkinson et al. (1999) showed that an average reduction of approximately 3% per decade occurred in the areal extent of Arctic sea ice occurred over the period 1978 to 1998. Major anomalous reductions in summer-ice extent were observed in 1998, followed by record lows in 2002 (Serreze et al., 2003), 2003 (Barber and Hanesiak, 2004), 2004 (Stroeve et al., 2005), with the minimum extent on record being 2005 (M. Serreze, pers. comm.). The Arctic sea ice zone has experienced an associated shortening of the sea ice season over the past 25 years (Parkinson, 2000). Reports of a substantial reduction in Arctic ice thickness (volume), however, are more controversial. While analysis of submarine sonar data suggested a decline in thickness over the past 40 years (Rothrock et al., 1999), this may be due to a sampling bias (Holloway and Sou, 2002). Yu et al. (2004) provide compelling evidence for an overall Northern Hemisphere volume decrease of 32%, most of which resulted from a reduction in thickness of ice over two metres (m) thick. This coincided with an increase in the areal extent of open water and young ice forms of 20–30% (Yu et al., 2004). It is expected that this rate of change will increase due to the ice-albedo feedback mechanism (Curry et al., 1995). The export of sea ice from the
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Arctic basin via Fram Strait may also be increasing as the central Arctic pack becomes more mobile (Kwok et al., 2004). Pressure patterns driven, at least in part, by the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) modes play a role in this export (Kwok et al., 2004). In the Southern Hemisphere sea ice areal extent varies annually by a factor of about 5, from a maximum of 18–20 × 106 km2 in September–October to 3–4 × 106 km2 each February (Gloersen et al., 1992; Comiso, 2003b). As such, the Antarctic pack comprises a high proportion (∼80%) of first-year ice. It is therefore largely a seasonal sea ice zone, with areas of perennial ice limited to the Ross, Amundsen and western Weddell Seas. A major difference between the Southern and Arctic Oceans is that the former is unrestricted by land masses equatorward and is bounded at its southern extremity by a vast frozen continent. Its sea ice cover extends from a maximum southerly extent of about 75◦ S northwards as far as approximately 55◦ S at maximum extent, with a meridional width ranging from a few hundred kilometres in the Indian Ocean sector to approximately 1600 km in the Weddell Sea (Gloersen et al., 1992). The largely-divergent drift behaviour of the Antarctic pack combines with relatively high vertical ocean-heat fluxes (Gordon and Huber, 1990; Martinson, 1993) to produce an ice cover that is lower in concentration and thinner on average than in the Arctic, i.e., approximately 1 vs 3 m (Dieckmann and Hellmer, 2003). A significant hemispheric contrast also exists in the relative lengths of the annual sea ice growth and decay seasons (Gloersen et al., 1992). The Arctic growth and decay cycle typically follows a symmetrical pattern with ice-extent maxima and minima occurring approximately six months apart (i.e., in March and September, respectively). That of Antarctic sea ice is asymmetrical, however, with the autumn–winter growth period exceeding the spring–summer decay period (i.e., February–September vs. September–February, respectively). While total Antarctic sea ice extent has increased slightly over the satellite era (i.e., the past 30 years; Zwally et al., 2002; Parkinson, 2004), strong regional contrasts are apparent. The only Antarctic sector to have exhibited a strong negative trend over this period is that to the west of the Antarctic Peninsula (Smith and Stammerjohn, 2001; Comiso, 2003b). This decrease has coincided with an extraordinary regional-scale warming trend of >2◦ C since the 1940s (Vaughan et al., 2003; King et al., 2004). By the same token, a positive ice extent trend has occurred in the Ross Sea (Zwally et al., 2002). De la Mare (1997) inferred a decline of about 25% in the area covered by Antarctic sea ice in summer from the 1950s to 1970s, based on whaling ship locations. While such assertions have been questioned (Ackley et al., 2003), research using methanesulfonic acid (MSA) in ice-sheet cores as a proxy indicator of ice extent suggests that the East Antarctic ice edge may indeed have retreated by >1◦ of latitude since 1950 (Curran et al., 2003). Modeling studies by Wu and Budd (1998) and Wu et al. (1999) also indicate that Antarctic sea ice was more extensive in the last century. As with the Arctic, a complex picture is emerging of atmospheric variability as it affects sea ice distribution. Dominant modes of atmospheric variability occur on a range of scales (Kidson, 1999; Simmonds, 2003; Simmonds and King, 2004), and with teleconnections to lower latitude phenomena (Yuan and Martinson, 2000; Liu et al., 2002a, 2002b; White et al., 2002; Carleton, 2003). On the decadal scale, recent change has been observed in large-scale tropospheric circulation in the Southern Ocean in the form of a strengthening and contraction of the circumpolar vortex, and a concomitant strengthening of the circumpolar westerlies (Hurrell and van Loon, 1994; Thompson and Solomon, 2002; Gillett and Thompson, 2003). This is associated with the Southern Annular Mode (SAM), or Antarctic Oscillation (Fyfe et al., 1999; Gong and Wang, 1999; Hall and Visbeck, 2002). The SAM is analogous to the Arctic Oscillation (AO) and is the dominant mode of variability in atmospheric circulation
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in the Southern Hemisphere. The importance of this and other modes of atmospheric variability on sea ice distribution e.g., the El Niño-South oscillation is becoming increasingly apparent (Kwok and Comiso, 2002; Liu et al., 2004). Changes in these dominant modes of atmospheric variability likely have a profound impact on polynya dynamics and thermodynamics, but one that is only just beginning to be addressed.
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Polynyas, Sea Ice and Climate Variability/Change
Polynyas form as a result of complex interactions of the ocean, atmosphere and sea ice, and are intimately linked to ice conditions, water-mass dynamics/thermodynamics and atmospheric circulation patterns. This is a critical characteristic of polynyas, and one that underlies their potential sensitivity to, and modulation of, climate change/variability. Polynyas are thought to constitute highly sensitive, and informative, windows into polarocean processes and their response to regional- to global-scale climate variability/change. Their use as sentinels of high-latitude environmental change is strongly dependent on our level of understanding of the complex processes responsible for their formation and maintenance and their role in driving and responding to that change. Examination of the suite of forcing variables associated with different polynyas provides us with key information on how oceanic and atmospheric forcing may be changing within particular areas of both polar regions. Polynyas lend themselves well to long-term and focused monitoring in that they typically occur at scales that are suitable for both detailed surface observations and observation from space. Observatories that utilize these polynyas can also tell us a great deal about the complexities of the physical-biological coupling occurring within them. While synoptic- and meso-scale meteorological processes play a fundamental role in coastal polynya formation (Pease, 1987), the impact of longer-term atmospheric modes and the role of ocean-to-atmosphere heat fluxes (Minnett and Key, 2007) are much less well understood, as are feedback effects. An example of the latter is the sea ice–cloud–albedo feedback, whereby clouds emanating from polynyas due to enhanced ocean-atmosphere moisture fluxes (Dare and Atkinson, 2000; Morales Maqueda et al., 2004) create an atmosphere-tosurface radiative flux that ablates sea ice. This in turn enhances water-vapor flux and cloud formation, leading to a positive feedback to further enhance ice melt. Polynyas have generally been broadly categorized into latent- and sensible-heat forms. Latent-heat polynyas occur in areas in which ice is removed from the region of origin by winds and/or ocean currents as it forms. The heat required to balance loss to the atmosphere, and hence maintain the area of “open water”, is provided by the latent heat of fusion of the continually-forming ice (Smith et al., 1990; Lemke, 2001). Production of new ice is also a significant contributor to the downstream advection of latent heat within the polynya and the formation of deep water through thermohaline processes involving brine rejection. Classical sensible-heat polynyas, on the other hand, form where the transport of oceanic heat to the surface by upwelling, vertical mixing or deep-ocean convection prevents ice formation and/or enhances melt of an existing ice cover (Smith et al., 1990). Following Gordon and Comiso (1988) and Morales Maqueda et al. (2004), we broadly categorise polynyas as either shelf water or deep water, given that some have both sensible- and latent-heat components. As their name suggests, deep-water polynyas occur offshore from the continental shelf which is the domain of shelf-water polynyas. Because the ocean-to-atmosphere heat fluxes through a polynya are several orders of magnitude greater than those through the surrounding ice pack in winter, polynyas dominate the regional heat budget and also influence the atmospheric circulation (Kottmeier and
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Engelbart, 1992; Gallée, 1997). Immense heat and salt fluxes in Arctic polynyas play a key role in coupling atmospheric heat loss to regional ice-mass balance and oceanic salt production (Martin et al., 2004). In the Northern Hemisphere, shelf water polynyas in the Chukchi, Bering, Beaufort, and Barents Seas contribute to the maintenance of the cold halocline layer of the Arctic Ocean (Martin and Cavalieri, 1989; Cavalieri and Martin, 1994; Winsor and Björk, 2000). Moreover, Arctic polynyas are important sites of dense shelf-water formation, which contribute to the production of Arctic intermediate and deep water (Martin et al., 1998; Weingartner et al., 1998; Gladyshev et al., 2000; Golovin, 2002; Signorini and Cavalieri, 2002; Winsor and Chapman, 2002). Given these attributes, Arctic polynyas are not only highly sensitive to climate change/variability, but also have the potential to have a profound wider impact on climate. Polynyas are a common feature of the circumpolar Antarctic sea ice zone. While superficially similar to their Arctic counterparts, they differ in a number of important respects, both in terms of their mode of formation and their overall impact. Most Antarctic polynyas are encountered adjacent to the coastline and on the narrow continental shelf (Massom et al., 1998), and are of the latent-heat variety. In simple terms, they form on the downwind side of morphological blocking features in response to divergent ice conditions forced by the prevailing wind field (Zwally et al., 1985). Katabatic winds also play a major role in certain locations and close to the coast. These gravity-driven winds are generated by intense radiative cooling of air masses above the inland ice-sheet plateau, with their strength being derived from down-slope acceleration of the air as they drain seawards (Tauber, 1960). They are persistent in both strength and direction, and are channeled by the ice-sheet topography to typically emerge over the coastal sea ice zone via outlet glacier valleys and ice shelves (Parish and Bromwich, 1989; Yu et al., 2005). Indeed, their strength is largely determined by local orographic conditions (Tauber, 1960), but also involves interaction with patterns of large-scale atmospheric circulation (Parish and Bromwich, 1998). On coming into contact with the ocean surface in polynyas, strong cold winds promote high rates of sea ice formation and also continually advect the new ice away from the polynya leading edge as quickly as it forms. This results in a thin ice cover and substantially higher ice production rates than those that occur in adjacent areas of consolidated sea ice by a factor of ten or more (Cavalieri and Martin, 1985; Zwally et al., 1985; Ushio et al., 1999). Indeed, Antarctic latent-heat polynyas have often been described as “ice factories” of the pack ice zone. Where polynyas are elongated, linear features that occur over continental-shelf regions at the interface between moving pack ice and landfast ice, they are termed flaw/leads (as noted above). Flaw leads form in response to prolonged periods of offshore winds, and tend to remain open more intermittently than most recurrent polynyas. Antarctic examples are found to the north of Dumont d’Urville in Adélie Land (Massom et al., 2001) and off the Prince Olav Coast between approximately 40◦ and 50◦ E (Ishikawa et al., 1996.) In the Arctic the Circumpolar Flaw lead (CFL) polynya system recurs in the East Siberian, Laptev Sea, Kara Sea (Dethleff et al., 1998) and off the Mackenzie Shelf in the southern Beaufort Sea (Barber and Hanesiak, 2004). Generic physical processes encountered in latent-heat polynyas, and related to frazil ice formation, are described by Martin et al. (1992) and Martin (2001). A number of factors determine shelf-water polynya size and shape (see Willmott et al., 2007). Polynya dimension in the cross-wind direction is determined by the configuration of the coastline/blocking feature over which the wind-/current-driven ice divergence occurs (Martin, 2001). Model studies suggest that shelf-water polynya size is controlled on synoptic time-scales by the balance between sea ice production, its downwind export/advection to
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the polynya edge, and the possible upwind “back-accumulation” of the piled-up new ice (Pease, 1987; Ou, 1988; Darby et al., 1995). Intermittent polynya closure occurs on synoptic scales with the cessation of strong winds and/or a reversal of synoptic-wind direction, i.e., by polynya freezing and infilling, respectively (Massom et al., 2001). Inter-annual variability relates to changes in the mean patterns of zonal wind velocity in the coastal zone (Cavalieri and Martin, 1985). Although relatively small compared to the Antarctic sea ice zone overall (in terms of areal extent), coastal shelf polynyas play a disproportionately large role in a range of key physical, biological and biogeochemical processes (Bromwich et al., 1998). For one thing, intense heat loss to the atmosphere through polynya “windows” can range from 200 to >500 W m−2 depending on wind speed and temperature (Fahrbach et al., 1994; Markus et al., 1998; Budillon et al., 2000; Dare and Atkinson, 2000; Roberts et al., 2001). This combines with high ice production and associated brine rejection rates to significantly increase the density of the water column underlying polynyas (Bindoff et al., 2000a). A key factor in this respect is their persistence and recurrence. As a result, Antarctic coastal polynyas play a central role in the formation of dense shelf waters (Gordon et al., 1993; Bindoff et al., 2001). Moreover, a small number of polynyas are key sites of Antarctic Bottom Water (AABW) formation. Recent analysis of available data by Rintoul (1998) suggests that three polynya systems are largely responsible for total global AABW formation—Adélie Land (contributing 24% by volume of the global ocean volume cooler than θ = 0◦ C, where θ is potential temperature), the southern Weddell Sea (68%) and the Ross Sea (8%). The significance of these polynyas is underlined by the fact that Orsi et al. (1999) estimate that AABW occupies 3.5% of the volume of the global ocean, while Worthington (1981) estimates that it affects >41% of the global oceanic volume via advection and mixing. Antarctic polynyas have also been proposed as key sites of deep-ocean ventilation and possibly the sequestration of atmospheric CO2 into the deep ocean (Goodison et al., 1999). Recent modeling studies have confirmed the critical importance of polynyas to the maintenance of global-ocean thermohaline circulation (Marsland et al., in press), and have demonstrated that the presence of open water within modeled sea ice contributes significantly to the sensitivity of the climate response (Grigg and Holbrook, 2001; Wu et al., 2003). As noted above, Antarctic polynyas are themselves likely to be highly sensitive to any change in forcing parameters, related to climate change/variability (Wu et al., 2003). A major concern, based upon recent model projections of global warming, is the possibility of a cessation of thermohaline circulation as a result of changing sea ice conditions (Broecker et al., 1998). Any long-term change in Antarctic polynya behavior has major implications not only for high-latitude physical, biological and biogeochemical processes but also the global climate system, involving complex and poorly understood feedback mechanisms. The wider implications are immense, given that processes occurring in the Southern Ocean have a profound influence on regional and global ocean circulation and climate (Rintoul et al. 2001a, 2001b; Jacobs, 2004). Uncertainties are currently large, however, and the inter-annual variability in polynya contributions is poorly understood. While polynya characteristics potentially contain important signals related to possible global climate change, a major challenge relates to the difficulty in distinguishing long-term trends from short-term variability related to the major modes of atmospheric circulation. This is compounded by the spatio-temporal complexity of the variations and processes involved, and the short-term nature of the satellite data record.
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An Inventory of Arctic and Antarctic Polynyas
In the Northern Hemisphere, polynyas can be broadly categorized into three distinct types: (a) ice bridge polynyas, (b) land bridge polynyas, and (c) ocean strait polynyas. We provide an overview of each below as a means of illustrating the similarities and differences between the various ice conditions (“icescapes”) responsible: (a) Ice bridge polynyas. Recent work on the North Water (NOW) polynya off north west Greenland showed that the presence of an “ice bridge” in Nares Strait is critical to its formation (Wilson et al., 2001). If the ice bridge does not form, then the region continues as a marginal ice zone with advection of sea ice from the Lincoln Sea south through Kennedy Channel and into northern Baffin Bay. In fact, it has been suggested that the NOW polynya actually creates a polynya north of the Nares Strait in the Lincoln Sea when the ice bridge is not formed in the North Water (NOW) polynya region (e.g., Kozo, 1991). When the ice bridge forms, strong atmospheric flow from the north removes ice as quickly as it is formed to create the North Water polynya (Barber et al., 2001). When the bridge breaks in spring, the area again returns to a marginal-ice zone. Processes controlling the formation of this ice bridge are not well understood. It appears, however, to involve a complex interplay between the source of sea ice types available within the Nares Strait and the dynamic and thermodynamic processes acting upon the sea ice. In the case of an ice-bridge polynya, it is important that the downstream region has sufficient room to accept the advection of newly-formed ice. (b) Land bridge polynyas. A process very similar to (a) occurs when a strong atmospheric flow across the ocean surface is bounded by a land “bridge”. The St. Lawrence Island polynya (e.g., Pease, 1987) in the Bering Sea is a good example. The east–west trend of the land provides a bridge against which the strong northerly atmospheric flow advects young ice south faster than it can consolidate. Newly-formed ice then accumulates in the southern (downwind) reaches of the polynya. Feedbacks are generated within the polynya through increasing cloudiness and buoyancy fluxes to enhance both the longwave flux to the surface and the wind flow into the polynya from the north. (c) Ocean strait polynyas. These occur when a strong oceanic (e.g., tidal) flow is present in regions restricted by land such as straits, e.g., in the Canadian Archipelago. A good example of this is the Fury and Hecla Strait polynya at the northern end of Foxe Basin. This polynya is recurrent and exists as long as strong oceanic fluxes keep the immediate downstream area of the polynya (Foxe Basin) clear of ice. If the ice is mobile and can be advected southward, then newly-formed ice is swept into the receptor area downstream. It is also important that the “upstream” source region of ice (in this case the Gulf of Boothia) provides little or no ice to the strait. Thus, an ice bridge also plays an important role in the formation and maintenance of this type of polynya. This polynya categorization is not directly applicable to Antarctic polynyas. Due to the relative absence of multiyear sea ice in the Southern Hemisphere, other “icescape” features play a more important role in polynya formation. These include coastal promontories, icebergs and glacier tongues, with associated fast ice. The latter is perennial in certain regions. Antarctic deep-water polynyas have a dominant sensible-heat component and tend to form in areas where cold and fresh surface waters are separated from underlying warmer, saltier waters by a weak pycnocline (Morales Maqueda et al., 2004). Only three deep-water polynyas occur or have recently occurred in Antarctica—the Weddell Sea polynya (originally
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centered on the Greenwich meridian and 66◦ S), the smaller Maud Rise polynya (at approximately 65◦ S, 2◦ E) and the Cosmonaut Sea polynya (centered on approximately 65◦ S, 45◦ E). Hypotheses of specific processes responsible for their formation and maintenance are discussed in a later section. In creating this polynya inventory, we used a variety of techniques, including satellite data and, in the Arctic, published literature (including Inuit knowledge) and historical (e.g., whaling) records. Arctic polynya characteristics were derived from Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) data using the Polynya Signature Simulation Method (PSSM; Markus and Burns, 1995). This measures sub-pixel scale polynyas using 85- and 37-GHz brightness-temperature data, creating an enhanced resolution of about 6.25 km. This technique takes advantage of the higher resolution at 85 GHz (about 15 km) while using the lower frequency data (at about 30 km resolution) to compensate for the sensitivity of the higher frequency to atmospheric effects. We use these satellite datasets to determine polynya average opening and closing dates, sea ice areal extent, amount of light available at that particular latitude and the role of changing sea ice cover on that available light. The light fields were modeled using a one-dimensional radiative-transfer model coupled to the sea ice concentration and areal extent information generated from the PSSM. The light estimate is based on clear-sky conditions. Some of the polynyas have virtually no published information, other than their existence, while others are too small to be resolved in satellite passive-microwave data. Due in large part to their remoteness, relatively little is known about Antarctic polynyas compared to their northern counterparts, with a few notable exceptions. Our knowledge of their distribution and behaviour is based almost exclusively on analysis of satellite data. In this section, we synthesize these data and summarize available information to underline the over-arching role of sea ice and other ice, i.e., the “icescape”, in the formation and maintenance of each polynya. This inventory cannot be exhaustive, given limitations on the length of this chapter; rather, it represents a compendium of polynya characteristics and driving processes. Additional information on atmosphere-polynya interactions, surface heat and moisture fluxes over polynyas, and observational and modeling studies is provided by Morales Maqueda et al. (2004). 3.1
Northern Hemisphere Polynyas
We were able to identify 61 distinct and recurring polynyas in the Northern Hemisphere (Figure 1). Some are well known, e.g., the North Water and Cape Bathurst polynyas, while others are referenced infrequently within the scientific literature or historical whaling records. In this section, we provide tabular summaries of existing information about each polynya and new information derived from satellite passive microwave data as a means of assessing icecover information and available radiation. Numbers in parentheses refer to the numbers of polynya locations shown in Figure 1. The North Water (NOW) polynya (41) (Table 1) is the largest polynya in the Canadian Arctic and one of the most biologically-productive polynyas in the Northern Hemisphere. Although this polynya is primarily created by latent-heat processes, upwelling of warm water also contributes (Barber et al., 2001; Melling et al., 2001). It has the largest per-unit-area biological production of any waters in the Northern Hemisphere. We provide a detailed synthesis of its function later in this chapter. The St. Lawrence Island polynya (SLIP) (6) (Table 2) is a latent-heat polynya that forms on the southern coast of St. Lawrence Island (Bering Sea) in winter. It is located over a shallow continental shelf, with depths averaging about 50 m. The areal extent is typically
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Figure 1: Distribution map showing the number and names of Northern Hemisphere polynyas detected and identified from (a) an analysis of DMSP SSM/I data using the PSSM method (Markus and Burns, 1995) and (b) a literature review. This listing provides a minimum estimate of the number of recurrent polynyas. Some of these polynyas no longer exist in a fashion analogous to their recent history (e.g., the NEW polynya).
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Table 1: NOW polynya summary statistics. In all tables Stdev = standard deviation, and OW is open water Polynya variables Location (lat/long) Mean opening date Mean closing date Mean opening date/Stdev Mean merging date/Stdev Maximum area Minimum area January OW area July OW area Sea ice types
Sensible heat process
Latent heat process
Mean daily solar insolation
76–79◦ N, 70–80◦ W (estimate from map) November May Week 19.83; Stdev = 1.52 Week 28.92; Stdev = 2.47 Maximum extent in July (ca. 80,000 km2 )/max. spatial extent 20,000 km2 N/A ∼4659 km2 ∼27,1641 km2 In January, the North Water area is almost completely covered with thin drifting ice, 50% of which is <30 cm thick. In late March or early April, the polynya starts to expand along the Greenland coast and begins to spread southward and westward Sensible-heat fluxes do not appear to reach the ice base with the exception of perhaps the autumn period ((Melling et al., 2001)). Atmospheric sensible heat also appears to play a role both in the autumn and spring (Barber et al., 2001) The most important factor keeping the polynya open is the mechanical removal of ice by currents and winds south of an ice bridge that forms across Nares Strait in winter ∼1108 W m−2
Table 2: Saint Lawrence Island polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
63◦ N, 170◦ W November–December May–June Late November–December forming; May–June disappearing 5000 km2 Incorporated with the Bering Sea ∼7185 km2 ∼57,880 km2 Ice cover is typically composed of 50% first-year ice N/A The polynya is large enough to allow wind dynamics to be significant in its circulation ∼1438 W m−2
approximately 100 km by 20–50 km. Most rapid ice growth mainly occurs along the southfacing coast in response to prevailing northerly winds, which advect the sea ice southward (Drucker et al., 2003). While the polynya may appear as early as November, it usually develops in December and persists until May or June (Pease, 1987). The North East Water (NEW) polynya (38) (Table 3) is highly-variable in both its location and size (maximum area approximately 44,000 km2 ). It is created over Ob Bank (depth less than 200 m) by northerly winds in winter combined with local ocean currents associated with an anticyclonic gyre over the shelf to the south and concentrated southeasterly flow
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Table 3: Northeast Water (NEW) polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Mean opening date/Stdev Mean merging date/Stdev Variability (code or weeks)
Maximum area Minimum area January OW area July OW area Sea ice types
Sensible heat process Latent heat process
Mean daily solar insolation
North of 79◦ , 5◦ to 15◦ W and 77◦ –81◦ N, 5◦ –15◦ W May September Week 23.25; Stdev = 1.43 Week 29.03; Stdev = 2.64 Throughout the spring and summer, this area is ice-free or covered in open pack. Starts to open about April/May, steadily growing in size, then closing in approximately September ∼44,000 km2 N/A ∼107 km2 ∼18,445 km2 Thin ice through winter, open water by April–May. 85% of the ice being transported by the East Greenland Current is multi-year and second-year ice Thought to have a sensible-heat component. As melt alone does not create the NEW open-water area, it cannot be termed a pure sensible-heat polynya As well as latent-heat processes, the interaction of the northward coastal current with the Norske Øb Ice Barrier is a major factor in creating the NEW summer polynya. It cannot be termed a pure latent-heat polynya, as freezing conditions are not experienced, and therefore a balance between freezing rate and ice export is not present. The grounding of deep-draft icebergs/ice islands can prevent ice advection into the polynya from the north, leading to significant polynya expansion ∼1034 W m−2
along the shelf break (Minnett et al., 1997). These remove ice from the coastal region. The grounding of deep-draft icebergs or ice islands can prevent ice advection into the polynya from the north (Massom, 1984), leading to significant polynya expansion. This polynya typically remains open from May–September and has occasionally appeared in late autumn or early winter (October–November). It is highly-important biologically, with elevated levels of primary production as well as a strong higher trophic dependency. In recent years, the NEW polynya has ceased to exist due to a non-occurrence of the ice bridge at its northern end (Minnett, pers. comm.). The reasons for this change are currently unknown. The Coburg Island polynya (42) (Table 4) is annually persistent and regular in shape and size. Open water is visible from January and is typically horseshoe-shaped. Freeze-up in late September or early October starts in Jones Sound and includes Coburg Island by mid-to late October. Ice in this area is mainly annual. A flaw lead appears first in Lady Ann Strait, with the development occurring between the fast ice of Jones Sound and the pack ice of Baffin Bay. The open-water area of this polynya may at times become fairly extensive, with leads reaching the North Water polynya. This polynya eventually amalgamates with the North Water polynya to form a complex of polynyas in Northern Baffin Bay. The Bylot Island polynya (44) (Table 5) varies in specific location and size, but is always located along the eastern coast of Bylot Island and extends southward. The flaw lead first appears in February each year and disappears in April, forming between shore-fast ice near Bylot Island and the pack ice of Baffin Bay. Wind forcing and associated pack-ice dynamics maintain the lead and drive its opening and closing. This polynya eventually merges
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Table 4: Coburg Island polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
76◦ N, 80◦ W (estimates from map) January Mid-to late October Open water in January—a horseshoe-shaped lead in Lady Ann Strait, southwest of Coburg Island N/A N/A ∼175 km2 ∼12,648 km2 Annual ice is prevalent N/A Very regular in form and location—may be correlated with the southeastern current from Jones Sound ∼1095 W m−2
Table 5: Bylot Island polynya summary statistics Polynya variables Location (lat/long)
Mean opening date Mean closing date
Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process
Mean daily solar insolation
Always located along the eastern coast of Bylot Island and extending southwards across the entrance to Pond Inlet and past Cape MacCulloch on Baffin Island.; 73◦ N, 75◦ W (estimates from map) February Freeze-up in late October, although the polynya disappears in April or early May. This polynya fuses with the North Water polynya occurring as early as June or as late as August, when break-up occurs in Baffin Bay Known to appear in March; varies in size and location from year to year N/A N/A N/A N/A N/A N/A A lead develops between the shorefast ice near Bylot Island and pack ice of Baffin Bay. The opening and closing of the lead is primarily maintained by the winds and the general motion of pack ice in Baffin Bay ∼1162 W m−2
with the North Water polynya when ice breakup occurs in Baffin Bay between June and August. The Lancaster Sound polynya (43) (Table 6) is located along the northern and southern sides of Lancaster Sound and extends southwards into Prince Regent Inlet. Flaw leads appear around mid-November to mid-December. The western limit of the polynya is usually aligned with Maxwell Bay, but can occur to the west of Griffith or Lowther Islands. Breakup occurs in early to mid-May, with land-fast ice formation in late September to early October resulting in complete ice coverage of the Sound by late October. Prince Regent Inlet polynya (54) (Table 7). In January each year, a characteristic pattern of cracks and leads develops along the east and west coasts of Prince Regent Inlet.
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Table 6: Lancaster Sound polynya summary statistics Polynya variables Location (lat/long)
Mean opening date Mean closing date Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
The leads extent along the northern and southern sides of Lancaster Sound and runs southwards into Prince Regent Inlet; 74◦ N, 80◦ W (estimates from map) Mid-November to mid-December Breakup occurs in early to mid-May; Landfast ice forms in late September to early October. The Sound is totally ice-covered by late October Known to appear in March, varies in size and location from year to year N/A N/A ∼108 km2 ∼36,969 km2 Landfast ice and pack ice of Baffin Bay N/A Pattern of leads change due to currents and winds ∼1126 W m−2
Table 7: Prince Regent Inlet polynya summary statistics Polynya variables
Comments
Location (lat/long) Mean opening date
73◦ N, 90◦ W (estimates from map) In January, a typical pattern of cracks and shore leads develop along the east and west sides of Prince Regent Inlet. These leads also connect with the leads on the southern side of Lancaster Sound and follow local currents Freeze-up usually occurs by mid-October N/A N/A N/A ∼22 km2 ∼7070.55 km2 First-year ice in Prince Regent Inlet and multiyear ice in Gulf of Boothia N/A The submarine sill across the Sound may induce the counter-clockwise currents in Prince Regent Inlet ∼1174 W m−2
Mean closing date Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
These are aligned with prevailing ocean-currents and eventually connect with the Lancaster Sound polynya. Freeze-up typically occurs by mid-October. There is a distinct separation of annual ice in Prince Regent Inlet and multiyear ice in the Gulf of Boothia. The cyclonic ocean currents that occur in Prince Regent Inlet may be due to the submarine sill across the sound. The Bellot Strait polynya (53) (Table 8) is a typical “ocean-strait polynya” and is created by strong oceanic forcing through the narrow channel between Somerset Island and Boothia Peninsula. Open water occurs in the centre of Bellot Strait from late December until March. Ice break-up occurs between late June to late September. The polynya remains open and fuses with the open water of Prince Regent Inlet, and becomes ice- covered between late September and December. Strong currents on the eastern side of the strait produce an east-
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Table 8: Bellot Strait polynya summary statistics Polynya variables Location (lat/long) Mean opening date
Mean closing date
Variability (code or weeks)
Maximum area
Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
72◦ N, 95◦ W (estimates from map) Open water occurs in the centre of Bellot Strait from late December to March. In late June to late September, Bellot Strait stays open and connects with open water in Prince Regent Inlet Bellot Strait is ice-covered from late September to December. Break-up typically occurs in June, but significant variability occurs between years—depends on ice break-up in Prince Regent Inlet and Franklin Strait Break-up is variable between years—during break-up, Bellot Strait may be completely open, completely frozen over or open in the centre with ice at either end Strong currents on the eastern side of the Strait create an eastward extension of the polynya. In April–May, the polynya migrates eastwards and expands into Brentford Bay N/A ∼6 km2 ∼420 km2 N/A N/A Polynya formation is due to ocean currents flowing through narrow channel between Somerset Island and Boothia Peninsula ∼1182 W m−2
ward extension of the polynya. From April through May, the polynya propagates eastwards and expands into Brentford Bay. The Committee Bay polynya (51) (Table 9) is highly variable in terms of its opening date but has a characteristic form by at least April. Timing of freeze-up is also highly variable, with dates ranging from early to mid-September to early October. The polynya is part of a shoreline lead system that extends between Prince Regent Inlet, the Gulf of Boothia and Committee Bay. The sea ice is typically land fast along the shore while ice in the middle of the bay is made up of second-year and multiyear ice. There is no bathymetric- or ocean current-related explanation for this polynya, although wind and tidal dynamics are believed to determine its formation and maintenance. Once formed, this polynya persists until early June, when the land-fast ice begins to break up. Hells Gate and Cardigan Strait polynyas (58) (Table 10) are highly recurrent features in narrow passages through which strong currents flow. Freeze-up is usually in September, with new ice forming in the bays and fjords. Although this area is almost completely covered with ice through October and November, the ice remains mobile. Open water is typically present from early-December until July. Ice concentrations and conditions are highly variable throughout the winter as well as between years. Due to the submarine shelf that runs across Jones Sound between Cape Storm on Ellesmere Island and Cape Svarten on Devon Island, the polynya tends to remain ice-free along its southeastern flank. During its maximum extent in May through July, it displays an ice-free core that is isolated from Jones Sound. July brings the break up of ice in Norwegian Bay, with ice advecting from here southwards to block Hells Gate and Cardigan Strait. As a result, this area never becomes ice-free in summer. Queens Channel and Penny Strait polynya (56) (Table 11). Due to shallow water depths and strong tidal and wind-driven currents, a polynya forms near Dundas Island Strait and
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Table 9: Committee Bay polynya summary statistics Polynya variables
Comments
Location (lat/long)
Polynya between Wales Island and Simpson Peninsula 68◦ N, 86◦ W (estimates from map) Appears in January/February/April, developing a characteristic form by April Freeze-up occurs in early to mid-September or early October. Breakup occurs in early June, and the polynya fuses. At max break-up, there is open water along the coast but Committee Bay is never completely ice-free N/A N/A N/A ∼2 km2 ∼216 km2 Landfast ice forms along the shore, while second-year and multiyear ice occurs in the middle of the Bay (drifts in from the Gulf of Boothia). Forms part of a shorelead system between Prince Regent Inlet, Gulf of Boothia and Committee Bay N/A Polynya creation is possibly by wind and tidal dynamics ∼1277 W m−2
Mean opening date Mean closing date
Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types
Sensible heat process Latent heat process Mean daily solar insolation
Table 10: Hells Gate—Cardigan Strait polynya summary statistics Polynya variables
Comments
Location (lat/long) Mean opening date
76.5◦ N, 90◦ W (estimates from map) December to July—open water. Break-up occurs in July, originating in Norwegian Bay. Ice drift patterns create a blockage in Hell Gate and Cardigan Strait. As a result, this area never becomes ice-free, even in the summer Freeze up occurs in September. In October/November, the area is covered with 90–100% concentrations of mobile ice. In early December, open water occurs on both sides of North Kent Island Usually, Jones Sound is open throughout August, while Hell Gate and Cardigan Strait have a 60–80% concentration ice cover Maximum extent occurs though May/June/July N/A N/A N/A New ice, first-year ice and multiyear ice N/A This polynya forms as a result of strong ocean currents flowing from Norwegian Bay to Jones Sound ∼1078 W m−2
Mean closing date
Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
northwest of Cornwallis Island in Queens Channel and McDougall Sound. In January, a section of open water appears and is maintained through the winter months. The polynya maximum extent occurs at the end of April or early May, with breakup taking place in June. The shallow waters and islands create a bottleneck of jammed ice that eventually melts by early to mid-July. Several small polynyas are visible in May or early June along the eastern
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Table 11: Queens Channel–Penny Strait polynya summary statistics Polynya variables Location (lat/long) Mean opening date
Mean closing date
Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
76.5◦ N, 97◦ W (estimates from map) January—open water develops between Dundas Island and Baillie-Hamilton Island. Break-up starts in June, when shallow waters and islands create a bottleneck of jammed ice. This ice melts in early to mid-July. By late June/early July, open water from Penny Strait joins with Dundas Islands Freeze-up occurs in late Sept to early August, originating in Penny Strait and Northwest of Cornwallis Island in Queens Channel. Two weeks later, remaining areas freeze up to create a typical ice pattern N/A Maximum extent occurs at the end of April, with waters remaining entirely open N/A N/A N/A Annual ice N/A Driven by strong ocean currents and shallow waters in the region ∼1080 W m−2
Table 12: Dundas Island polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
78.1◦ N, 94.8◦ W. The polynya is 1 km north of Dundas Island (between Cornwallis and Devon Islands) N/A N/A N/A N/A N/A ∼41 km2 ∼4272 km2 Annual and multiyear ice Known as a sensible-heat polynya N/A ∼1084 W m−2
side of Penny Strait. These develop before breakup and appear to occupy a constant annual location. As breakup progresses throughout June, the separate polynyas fuse together until the entire strait is free of ice, typically by mid-June. By late June to mid-July, the open-water areas of Penny Strait coalesce with those adjacent to Dundas Island. The Dundas Island polynya (57) (Table 12), which is located about 1 km north of Dundas Island between Cornwallis and Devon Islands, is formed by sensible-heat processes. A consistent source of warm water is transported to the surface in this area at specific phases of the tidal cycle. This warmer water tends to be concentrated on the southern side of Pioneer Channel. The ocean within the polynya region has a depth of 30 m, with some shallower areas on the eastern edge being 18 m. Currents follow a semi-diurnal tidal cycle and flow
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Table 13: Northern Bering Sea polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Mean opening date/Stdev Mean merging date/Stdev Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
65◦ N, 70◦ W Opens in response to northerly to easterly winds Closes with southerly winds, which transport pack ice into the area, thereby closing the polynya Week 50.33; Stdev = 1.75 Week 16.26; Stdev = 4.41 N/A N/A N/A ∼1151 km2 ∼78,605 km2 Typically rough fractured ice, enabling significant wind drag on the ice surface No oceanic heat source is available to melt ice due to the cooling/freezing of shelf waters by mid-winter Latent heat polynyas. These are the simplest, and result from north-northeasterly winter winds transporting ice away from the lee coast ∼1325 W m−2
primarily to the east. As a result, the ocean-temperature profiles display a large amount of mixing down to 20 m. This polynya occurs where the currents are strongest and the water is warmest. This polynya has also displayed a strong connection with increasing air temperatures, i.e., an increased areal extent, in this region of the Arctic. The Northern Bering Sea polynyas (Table 13) are latent-heat polynyas which are simple in terms of their formation processes and relatively well studied. They open with northerly to easterly winds, and close with southerly winds that transport pack ice back into the polynya area. Rough fractured ice in this region allows it to be easily moved by wind forcing. Sensible heat is not available to create the polynya as a result of the cooling/freezing of shelf waters by mid-winter. Currents flow northward through the Anadyr and Bering Straits throughout the winter. Brine rejection from heavy ice production conditions the water and plays a key role in maintaining the Arctic Ocean halocline (Aagaard et al., 1981; Cavalieri and Martin, 1994). Cold air and reduced wind speeds allow ice formation, which reduces the polynya size. Strong northerly winds, on the other hand, allow the polynya to reform. The Cape Bathurst polynya (61) (Table 14) is a recurrent polynya in the southeastern Beaufort Sea that appears to be an enlargement of the circumpolar flaw lead (CFL) system. The polynya extends into the Amundsen Gulf by a combination of sensible- and latent-heat mechanisms. It appears that the Beaufort Gyre acts like a classical “ice bridge” in holding back multiyear sea ice and allowing atmospheric forcing to remove sea ice from the polynya within Amundsen Gulf. The polynya contains young ice throughout the annual cycle, with significant areas of open water usually occurring in April but with significant inter-annual variability in polynya dynamics (Arrigo and van Dijken, 2004a). Other polynyas are also listed (i.e., Foxe Basin polynya, the Lambert Channel polynya (Smith and Rigby, 1981), the Franklin Strait polynya, Whaler’s Bay polynya, the Sea of Okhotsk Sea polynya (Kimura and Wakatsuchi, 2004), and the Laptev Sea flaw polynya (Dmitrenko et al., 2001), Kara Sea and East Siberian Sea flaw polynyas and the Storfjorden polynya (Haarpaintner et al., 2001, Tables 15–20). The remaining polynyas in the Northern
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Table 14: Cape Bathurst polynya summary statistics Polynya variables Location (lat/long) Mean opening date
Mean closing date Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
71◦ N, 126◦ W (estimate from map) Open water may appear in December, although the polynya is visible in April. Open water may be found throughout the year. In January, leads occur off the eastern side of Cape Bathurst and north of Cape Perry. Open water is visible here until late May/early June Freeze-up occurs between mid-October and early to mid-November and usually takes 2–3 weeks N/A N/A N/A ∼52 km2 ∼47,127 km2 Mainly annual ice Warmer water may upwell onto the shelf in the vicinity of the polynya The shorelead polynya extent is dictated by wind. A local eastward-flowing current into Amundsen Gulf creates open water along the fast ice boundary ∼1214 W m−2
Table 15: Foxe Basin polynya summary statistics Polynya variables Location (lat/long) Mean opening date
Mean closing date
Variability (code or weeks)
Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process
Mean daily solar insolation
69◦ N, 77◦ W (estimates from map) Early January in Northern Foxe Basin—typical cracks, leads and open water areas develop. In mid-February/early March—open shoreleads occur around Koch Island, Rowley Island, Baird Peninsula, Foley Island, and the Spicer Islands. Leads also develop between the pack and fast ice on the southeast side of Prince Charles Island and southern side of Air Force Island. In January—a typical horseshoe-shaped polynya occurs between Jens Munk Island and Igloolik Island, and remains through the winter. Another typical polynya occurs in February/March, from Cape Wilson south past Winter Island, and lasts until regional ice break-up Freeze-up in mid-October starts in the northwestern corner and grows south to Southampton Island by early/mid-November. On the other hand Foxe Basin usually cleared of ice by late August/late September Open water may appear as early as December. In January, a typical horseshoe-shaped polynya forms between Jens Munk Island and Igloolik Island, and remains through the winter Mid-May—open water occurs in northwest, growing towards the southeast N/A N/A N/A First-year ice with fast ice zones around islands and the shoreline N/A The central pack ice is continually moving due to winds, tides and currents. Winds from the northwest create the polynyas off Rowley Island, Spicer Islands, Prince Charles Island, Air Force Island and Cape Wilson. An early opening of Foxe Basin (northwestern region) is attributed to the northwesterly winds and southward currents ∼1265 W m−2
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Table 16: Lambert Channel polynya summary statistics Polynya variables Location (lat/long) Mean opening date
Mean closing date
Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
Southern Dolphin and Union Strait, between Victoria Island and the mainland; 68.5◦ N, 114.5◦ W (estimates from map) Over a 5-year period, the polynya opened in February, mid-March, mid-April and June, and stayed open until July. Open water first occurs on the southwestern side of Lambert and Camping Islands. This polynya usually remains open until regional ice break-up begins in early July, and fuses with Amundsen Gulf in mid-to late July or early August Freeze-up begins in mid-October to early November. It starts in the Coronation Gulf and along the mainland coast. Dolphin and Union Strait are frozen over by the end of October to the start of November Highly variable N/A N/A N/A N/A Annual ice N/A This area has very shallow spots and many shoals, along with strong ocean currents and heavy tidal rips ∼1271 W m−2
Table 17: Franklin Strait polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
71.5◦ N, 96◦ W Eastern side of Franklin Strait (estimates from map) Open water is visible in April N/A N/A Approximately 1.5–2 km in diameter N/A N/A N/A Young unstable ice prevails, due to strong currents passing through the strait Known as a sensible-heat polynya N/A ∼1197 W m−2
Hemisphere are very small and/or highly ephemeral, and we summarize the information from these to complete the inventory of this hemisphere in Table 21. 3.2
Southern Hemisphere Polynyas
In this section we collate recent findings on the distribution and characteristics of Antarctic polynyas and highlight ice conditions and processes responsible for their formation and maintenance. We begin with 28 shelf-water polynyas identified along the East Antarctic coast between 40◦ E and 160◦ E in winter-time (June–October) SSM/I imagery from 1987 to 1994 (Massom et al., 1998). Locations are shown superimposed on a map of streamlines of near-
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Table 18: Whaler’s Bay polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process
Latent heat process Primary productivity Secondary productivity Nutrients Mean daily solar insolation
81◦ N, 14◦ E Northern coast of Svalbard N/A N/A N/A N/A N/A ∼35,227 km2 ∼47,670 km2 N/A This is a sensible-heat polynya. It involves significant mixing of warm Atlantic water flowing northwards in the West Spitsbergen Current and colder ambient water. Aagaard et al. (1987) calculated heat loss to be >200 W m−2 in 100–200 m deep waters, i.e., sufficient heat to avert ice development N/A N/A N/A N/A ∼1003 W m−2
Table 19: Okhotsk Sea polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process
Latent heat process
Mean daily solar insolation
Head at 55.7◦ N, 149.9◦ E and tail intersects 55.0◦ N at ∼146.5◦ E December March/April N/A Tadpole-shaped with 50 km diameter “head” N/A 73,110 km2 100,044 km2 N/A This polynya also has an oceanic heat source due to upwelling. Tidally-driven heat flux from intermediate layers to surface helps to maintain the polynya Primarily wind-driven. Wind is a substantial influence on the dynamics of the polynya, as seen from the highly variable ice concentrations over the area ∼1753 W m−2
surface gravity drainage winds (after Parish and Bromwich, 1987) in Figure 2 to highlight the clear association. Information on the location, mean areal extent, month of maximum extent and degree of recurrence and persistence is given in Table 22. Available data on dominant modes of formation of each polynya is also included in this table. Primary determinants of polynya formation are coastal configuration, relative to prevailing winds and ocean currents, ocean bathymetry (largely via its impact on grounded iceberg and resultant pack- and fast-ice
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Table 20: Laptev Sea polynya summary statistics Polynya variables Location (lat/long) Mean opening date Mean closing date Mean opening date/Stdev Mean merging date/Stdev Variability (code or weeks) Maximum area Minimum area January OW area July OW area Sea ice types Sensible heat process Latent heat process Mean daily solar insolation
Centre of Laptev Sea 76.4◦ N, 125.8◦ E Mid-March Remains until the beginning of May. May last longer? Week 16.26; Stdev = 2.90 Week 25.42; Stdev = 2.64 N/A Up to 100 km wide N/A ∼108 km2 ∼116,659 km2 N/A Primarily created by continuous southerly winds throughout the winter ∼1127 W m−2
distributions), and ice-sheet topography as it affects the channeling and outlet emergence of katabatic winds from ice sheet to ocean. Of the 28 East Antarctic shelf-water polynyas identified, all apart from those in Porpoise Bay and adjacent to the Amery Ice Shelf occur on the downwind (typically western) side of islands (e.g., Drygalski, Mill, Bowman and Terra Nova Islands) and coastal/near-coastal protrusions or blocking features (see Table 22 for details and locations), and are driven by prevailing easterly winds poleward of the Antarctic Circumpolar Trough and/or katabatic winds. Blocking features include ice-sheet promontories, ice shelves (e.g., the West, Shackleton and Voyeykov Ice Shelves), iceberg tongues (e.g., the Dalton, Blodgett and Dibble Iceberg Tongues), icebergs grounded on shoals together with areas of fast ice (e.g., at Cape Darnley), and “ice tongues”. An ice tongue forms when a glacier flowing into the sea does not immediately fracture into icebergs, but rather floats into the ocean. Examples associated with large polynyas are the Mertz Glacier (at approximately 67.7◦ S, 145◦ E) and the Drygalski Ice Tongue (at 75.5◦ S, 163.5◦ E). There is a strong relationship between polynya formation and katabatic winds (Figure 2), with at least 18 of the 28 East Antarctic polynyas being associated with outlet confluence zones of these winds. The influence of katabatic winds can extend over 100 km offshore (Adolphs and Wendler, 1995; Wendler et al., 1997), although they typically lose momentum some tens of kilometres from the coast (Bromwich and Kurtz, 1984). Notable examples of katabatic wind-driven polynyas in Antarctica are those in Terra Nova Bay in the Ross Sea (Bromwich et al., 1992; van Woert, 1999a, 1999b), adjacent to the Ross Ice Shelf (Bromwich et al., 1998; Fichefet and Goosse, 1999), in Commonwealth Bay (Adolphs and Wendler, 1995) and adjacent to the Mertz Glacier tongue (Bindoff et al., 2000a, 2001; Massom et al., 2001). Significant variability occurs not only in East Antarctic polynya size but also in the degree of recurrence and persistence (Table 22). Eight of the smaller polynyas occur only occasionally. These are (from west to east), the Lützow-Holm Bay (Syowa), Taylor Glacier, Amery Ice Shelf, Paulding Bay, Porpoise Bay, Blodgett Iceberg Tongue, Ninnis Glacier, and Slava Bay. The mean size of the remaining 20 shelf-water polynyas ranged from about 1000 km2 for the Cape Hudson polynya to about 23,000 km2 for the Mertz Glacier polynya (based upon an ice-concentration threshold of 75%). In general, the larger polynyas tend to
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Table 21: Summary characteristics of several of the smaller polynyas in the Northern Hemisphere Name
Polynya variable
Cumberland Sound
Location (lat/long) Mean opening date/Stdev Mean merging date/Stdev January OW area July OW area Mean daily solar insolation Location (lat/long) January OW area July OW area Mean daily solar insolation Location (lat/long) Mean opening date/Stdev Mean merging date/Stdev January OW area July OW area Mean daily solar insolation Location (lat/long) Mean opening date Mean closing date Maximum area January OW area July OW area Mean daily solar insolation Location (lat/long) Mean opening date/Stdev Mean merging date/Stdev January OW area July OW Area Mean daily solar insolation Location (lat/long) January OW area July OW area Mean daily solar insolation Location (lat/long) Mean daily solar insolation
Frobisher Bay
Roes Welcome Sound
Lincoln Sea
Barents Sea
Kara Sea
Ob Bank
65◦ N, 65◦ W (estimates from map) Week 13.89; Stdev = 2.75 Week 28.16; Stdev = 2.03 ∼34 km2 ∼15,942 km2 ∼1363 W m−2 62.5◦ N, 65◦ W (estimates from map) ∼1014 km2 ∼18,477 km2 ∼1439 W m−2 64◦ N, 88◦ W (estimates from map) Week 48.91; Stdev = 1.49 Week 25.41; Stdev = 1.44 ∼1281 km2 ∼107,107 km2 ∼140 W m−2 82.5◦ N, 55◦ W (estimates from map) November, stabilizing in Jan., mid-Sept./Oct. April 150 × 200 km (golf-club shaped) ∼65 km2 ∼500 km2 ∼986 W m−2 75◦ N, 40◦ E (estimate from map) Week 4.18; Stdev = 5.04 Week 21.87; Stdev = 1.61 ∼752,166 km2 ∼984,779 km2 ∼1205 W m−2 70–80◦ N, 60–80◦ E (estimates from map) ∼1113 km2 ∼240,521 km2 ∼1116 W m−2 80.3◦ N, 14◦ W ∼1012 W m−2
be more stable and exhibit less inter-annual variability (i.e., a higher degree of recurrence). In all cases, polynya maximum areal extent occurs in winter/early spring, varying from June to October. For a circumpolar picture, we draw upon the work of Arrigo and van Dijken (2003a). While the icescapes responsible for polynya formation are not examined in this mainly biological study, it represents the first detailed circumpolar Antarctic polynya inventory. Based upon analysis of satellite PSSM-derived daily images of sea ice distribution, Arrigo and van Dijken (2003a) identified 52 polynyas. This number was subsequently reduced to 37 postpolynyas which, as their name suggests, occur each year after “conventional” winter polynya activity. Although not polynyas in the strictest sense, these spring-time features are included here by virtue of their biological and biogeochemical importance and their intimate rela-
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Table 22: Locations, mean size (in km2 ), month of maximum extent, degree of recurrence (REC) and persistence (PER), and formation mode of 28 shelf (latent-heat) polynyas in East Antarctica (from 40–160◦ E), based upon the 75% ice-concentration threshold in the winter months of 1987 to 1994. The mean areas have been calculated considering only the months when the polynya was present. Recurrence refers to the number of years from 1987 to 1994 that the polynya appears during winter with an ice-concentration core of <75%. Persistence is the number of winter months (in this case July–October 1987, June–October 1988–93 and June–August 1994) that the polynya is present (i.e., the maximum is 37). For formation mode, K is katabatic winds, NS is north-south oriented coastline, U is possible upwelling of warm deep water, IS is offshore islands, IT is grounded iceberg tongue, GB is grounded icebergs and GT is floating glacier tongue. Although a polynya typically forms in the lee of Iceberg B-9B, it did not become grounded off the Mertz Glacier Tongue until June 1992 and is not included. From Massom et al. (1998) Location
Syowa Casey Bay Enderbv Land Taylor Glacier Cape Darnley Mackenzie Bay Amery Ice Shelf Prydz Bay Barrier Bay West Ice Shelf Drygalski Island Shackleton Ice Shelf Denman Glacier Mill Island Bowman Island Vincennes Bay Cape Poinsett Dalton Iceberg Tongue Paulding Bay Voyeykov Ice Shelf Porpoise Bay Blodgett Iceberg Tongue Dibble Iceberg Tongue Mertz Glacier Ninnis Glacier Cape Hudson Slava Bay Terranova Island
◦S
◦E
Long., Mean area (75%)
Month of maximum extent
Recurrence
Persistence
Formation mode
68.5 67.9 66.0 67.1 67.6 68.8 69.8 67.1 67.4 66.3 65.6 66.0 66.0 65.4 65.5 66.4 65.2 66.5
42.0 47.9 54.6 61.7 69.1 71.4 74.2 78.1 81.9 86.6 92.1 95.4 99.8 101.2 102.8 110.3 110.6 121.5
2500 1950 3670 2690 17,850 3460 490 13,750 5430 3450 5380 31,640 2590 1270 4980 9170 12,150 5460
June June October October October October June October June/October June October June July July/October June June/October October June/July
4 5 8 1 8 8 4 8 8 8 8 8 8 8 8 8 8 8
4 5 30 1 37 37 10 33 35 36 29 37 34 36 34 37 37 36
NS NS, GB, K GB, U GB, IS GB GB, K K GB, U NS, GB NS GB, NS, IS NS, GB, K GB, K NS, K, U, IS NS, K, GB, IS GB, K, NS GB IT
66.7 66.1 66.9 66.6
123.8 124.8 128.5 130.0
180 3140 550 2720
June June June June
1 8 3 3
2 21 5 6
NS, K K, GB K IT, K
66.9
134.2
6830
June
8
34
IT, K
66.5 68.2 68.3 68.9 69.2
145.4 149.1 153.1 155.1 158.8
23,300 1880 1060 450 1110
October August August September August
8 4 5 4 6
37 8 9 10 12
K, GT, IT, U K, GT GB, K GB, K K, IS
Lat.,
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D.G. Barber and R.A. Massom
Figure 2: A map of coastal shelf-water polynya locations in East Antarctica derived from analysis of DMSP SSM/I ice concentration data (1987–1994). Grey shading indicates the approximate maximum areal extent of each polynya. Over the Antarctic Ice Sheet, thin lines denote contours of surface elevation, while thick lines represent streamlines of near-surface gravity drainage winds (after Parish and Bromwich, 1987). From Massom et al. (1998). © 1998 International Glaciological Society, reproduced with permission.
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Figure 3: Map of the Antarctic showing locations of the 37 “post-polynyas” derived from SSM/I data (names are given in Table 23). (a) The scaled coloured region indicates the percentage of days between 1 June and 31 October (from 1997 to 2001 inclusive) that a particular pixel location was ice-free. Coastal areas with low winter sea ice cover (i.e., 50% ice-free days in winter) were identified as polynyas. (b) Masks (shown as variable coloured areas) used to associate a given pixel location with a particular polynya and extend at most as far north as the continental shelf (depth <1000 m). Continental Shelf areas not associated with polynyas are shown in white. From Arrigo and van Dijken (2003a). © 2003 AGU, reproduced with permission.
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D.G. Barber and R.A. Massom
Figure 4: Expanded view of Figure 3a showing the size and distribution of Antarctic coastal polynyas in greater detail. Black areas are those that remain ice-free all winter, while red areas are those that remained ice covered throughout the winter. From Arrigo and van Dijken (2003a). Copyright AGU, reproduced with permission. tionship to winter polynyas (Figures 3, 4). The smaller number of post-polynyas relates to the coalescence of certain polynyas in spring. For example, three distinct winter polynyas in Prydz Bay (centered on approximately 67.7◦ S, 78.0◦ E) typically merge to form a single large post-polynya in early spring. Locations, mean winter-areas and degree of recurrence of the 37 post-polynyas are given in Table 23. The smallest Antarctic polynya identified occurs in the West Lazarev Sea, and averages only 130 km2 in winter (and 1040 km2 as a post-polynya in summer) over the period 1997–2002. By far the largest, on the other hand, is the Ross Sea polynya, with a mean openwater area of 20,230 km2 in winter (and a post-polynya area of 396,500 km2 in summer). The formation of this polynya adjacent to the Ross Ice Shelf is attributed to ice divergence forced by both katabatic and synoptic winds (Jacobs and Comiso, 1989; Bromwich et al., 1993, 1998), combined with the on-shelf incursion and upwelling of relatively warm Modified Circumpolar Deep Water (MCDW) which induces sea ice melt (Pillsbury and Jacobs, 1985; Jacobs and Giulivi, 1998; Budillon et al., 2000). As a result of its size and high degree of recurrence and persistence, it has an over-riding impact on regional ice formation and melt
The Role of Sea Ice in Arctic and Antarctic Polynyas
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Table 23: Physical characteristics of 37 Antarctic polynyas based on an analysis of a 5year average satellite passive-microwave dataset (1997 through 2002). Polynya locations are mapped in Figures 2 and 3. From Arrigo and van Dijken (2003a) #
Polynya name
1 2 3 4 5 6 7 8 9 10 11 12 13 14 10 16 17 18 19
Ross Sea Sulzberger Bay Hull Bay Wrigley Gulf Amundsen Sea Pine Island Bay Eltanin Bay Latady Island Marguerite Bay Larsen Ice Shelf Ronne Ice Shelf Halley Bay Lyddan Island Maudheim Jelbart Ice Shelf W. Lazarev Sea E. Lazarev Sea Breid Bay Lützow-Holm Bay Amundsen Bay Cape Borle Utstikkar Bay Cape Darnley Mackenzie Bay Prydz Bay West Ice Shelf Davis Sea Shackleton Ice Shelf Vincennes Bay Cape Poinsett Henry Bay Paulding Bay Porpoise Bay Davis Bay Dumont d’Urville Mertz Glacier Ninnis Glacier
20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Summer Summer: Shelf Number Peak Latitude Longitude Winter area area, area, winter width, of days month ×103 km2 ×103 km2 ratio km >50% maximum size
◦S
75.51 76.79 74.30 73.70 73.35 73.16 72.81 71.19 68.10 66.75 74.96 75.28 74.22 71.01 70.16 69.94 69.58 70.06 69.03
179.19◦ E 20.23 152.37◦ W 1.65 135.06◦ W 1.41 128.96◦ W 2.07 112.91◦ W 3.67 104.25◦ W 1.09 81.22◦ W 2.99 76.90◦ W 1.11 68.06◦ W 0.97 59.94◦ W 1.18 46.43◦ W 5.39 26.82◦ W 2.74 19.46◦ W 2.12 10.88◦ W 0.35 3.06◦ W 0.22 7.85◦ W 0.13 12.24◦ E 1.09 25.86◦ E 1.01 38.20◦ E 0.66
66.31 65.76 67.06 67.24 68.40 67.69 66.17 65.73 65.28
50.06◦ E 54.22◦ E 61.24◦ E 67.87◦ E 71.12◦ E 77.73◦ E 84.84◦ E 93.06◦ E 104.22◦ E
65.97 65.57 66.26 65.97 66.15 65.56 66.11 66.37 67.02
Number of years formed
396.5 3.29 10.04 13.40 38.00 16.89 21.48 18.90 9.16 4.04 121.4 21.62 7.57 3.30 3.15 1.04 9.84 2.97 2.25
19.6 2.0 7.1 6.5 10.4 15.5 7.2 17.0 9.5 3.4 22.5 7.9 3.6 9.4 14.6 8.2 9.0 2.9 3.4
715 127 65 119 263 104 371 257 301 309 477 255 237 54 26 28 95 36 146
100 144 92 103 103 91 103 142 173 162 88 119 87 97 109 103 128 128 74
Feb Oct Feb Feb Jan Jan Feb Mar April Jan Feb Feb Feb Feb Mar Mar Feb Mar Apr
5 5 5 5 5 5 5 5 5 3 2 4 4 5 5 4 5 5 1
0.23 1.32 0.39 6.83 1.23 8.28 0.27 7.81 0.70
5.35 8.92 10.15 29.52 3.42 66.20 1.86 32.00 2.89
22.8 6.8 26.3 4.3 2.8 8.0 7.0 4.1 4.2
62 58 106 114 228 179 82 163 96
102 135 84 133 125 116 122 124 93
Mar Feb Feb Jan Feb Feb Feb Mar Feb
5 5 5 5 5 5 3 5 4
108.30◦ E 113.63◦ E 119.85◦ E 123.31◦ E 128.61◦ E 132.92◦ E 139.31◦ E
2.92 1.34 1.57 0.54 0.46 3.43 0.96
21.88 9.55 13.10 3.90 4.46 16.20 13.02
7.5 7.2 8.4 7.2 9.8 4.7 13.6
175 72 153 74 209 157 124
124 144 135 104 74 145 121
Feb Feb Feb Feb Mar Jan Feb
5 5 5 5 2 5 5
144.04◦ E 147.62◦ E
5.91 1.41
26.60 12.28
4.5 8.7
129 182
133 124
Dec Feb
5 5
and marine ecology, and is also an important site of AABW production (Jacobs et al., 1985; Rintoul, 1998; Koshlyakov and Tarakanov, 2003). It has been postulated that a significant freshening of High-Salinity Shelf Water (HSSW) in the region since the 1960’s (Jacobs and Giulivi, 1998; Jacobs et al., 2002) may relate to changes in polynya location and (Morales Maqueda et al., 2004) with brine production
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varying with the geometry of upstream ice fronts, ice tongues and icebergs. Furthermore, polynya maintenance may be affected by changes in coastal configuration via its impact on adjacent low salinity coastal currents, upwelling and sensible-heat sources. Recent studies have examined polynya behaviour in response to different modes of large-scale atmospheric variability, i.e., climate “state”. For example, Arrigo and van Dijken (2004b) related the large inter-annual variability in the size of the Ross Sea polynya to El Niño-Southern Oscillation (ENSO) events. During El Niño years the low-pressure system usually centered over East Antarctica shifts eastwards over the Ross Sea. Wind speeds diminish markedly in the vicinity of the Ross Sea polynya as a result of this transition (Kwok and Comiso, 2002), and may switch from predominantly offshore to onshore. Rather than being blown northwards in spring, sea ice remains in the South West Ross Sea during El Niño events to create anomalously heavy ice conditions and diminish polynya activity, e.g., in 1997–1998. This was the same year that the Ronne polynya (see below) attained its maximum size (Ackley et al., 2001; Hunke and Ackley, 2001). A smaller polynya recurs in Terra Nova Bay in the western Ross Sea at about 75◦ S, ◦ 164 E (van Woert, 1999a; van Woert et al., 2001). It is maintained by the blocking effect of the Drygalski Ice Tongue in preventing ice incursion into the polynya from the south, combined with strong katabatic winds that blow newly-forming ice away from the shore (Kurtz and Bromwich 1983, 1985; Bromwich and Geer, 1991; Manzella et al., 1999). Van Woert (1999a) further shows that long-wave and sensible heat fluxes explain up to 50% of the observed variance in polynya area. Fluctuation of the eastern (seaward) polynya boundary is determined by northward ice advection past the tip of the ice tongue, while offshore extent is approximately equivalent to the tongue length (Bromwich and Kurtz, 1984). While this typically changes by calving, the tip of the tongue was removed in mid-April 2005 by a collision with giant iceberg B15A, which calved from the Ross Ice Shelf in March 2000. Two other significant polynyas occur near the continental-shelf break in the NW Ross Sea—the Pennell Bank polynya (at approximately 72◦ S, 174◦ E) and the Ross Passage polynya (at approximately 71.2◦ S, 172◦ E). These polynyas Which form part of the larger Ross Sea polynya in Figure 3b, appear to be maintained by upwelling of relatively warm Circumpolar Deep Water (CDW) at the shelf-break coupled with ice divergence above a submarine bank (Jacobs and Comiso, 1989). A number of other Antarctic polynyas exhibit both latent- and sensible-heat characteristics, an example being the Prydz Bay polynya system (centered on about 67.7◦ S, 77.7◦ E). This polynya forms on the western side of a North East to South West trending coastline (Streten and Pike, 1984), with sea ice advection into the region being blocked by grounded icebergs and fast ice. Incursions of CDW (Vaz and Lennon, 1996; Wong et al., 1998) may also play a role in polynya maintenance (Flocco et al., unpublished). With a high degree of persistence and being the second largest Antarctic polynya (mean approximately 8000 km2 based on SSM/I data from 1997–2002), this polynya plays a major role in regional primary production (Arrigo and van Dijken, 2003a) and potentially in water-mass modification/formation (Rintoul, 1998). The apparent discrepancy in winter polynya areal extents in Tables 22 and 23 is attributable to a number of factors. While close agreement exists in East Antarctic polynya locations determined by the two studies, polynya areas calculated by Massom et al. (1998) are significantly greater. This is largely attributable to the fact that Arrigo and van Dijken (2003a) calculate open-water area only, while polynya area determined using the ice-concentration threshold approach includes the area of thin ice present (and is also sensitive to the threshold used). The latter method in effect demarcates the overall polynya “règime”. In general,
The Role of Sea Ice in Arctic and Antarctic Polynyas
29
the open-water fraction proper is relatively small compared to the polynya règime, which includes ice newly-formed within the polynya. Ambiguities remain in the determination of the boundary between the thin polynya ice and the thicker surrounding pack ice. As such, “polynya size” can be a somewhat misleading term unless qualified. 3.2.1
Shelf-Water Polynyas in the Weddell Sea (65–77◦ S, 60◦ W–10◦ E)
An extensive series of narrow shelf-water polynyas, with mean widths of <10 km, forms adjacent to ice shelves and in the lee of headlands around the coastal periphery of the Weddell Sea (Zwally et al., 1985; Comiso and Gordon, 1998; Markus et al., 1998). Due to their different characteristics, these polynyas are often segregated into an eastern region, a western region extending northwards along the east Antarctic Peninsula coast, and a southern region near the Filchner-Ronne Ice Shelf. The Ronne polynya centred on approximately 76◦ S, 50◦ W is driven primarily by strong wind outbursts from the ice shelf (Gill, 1973; Renfrew et al., 2002). According to Foldvik et al. (2001) and Makinson and Nichols (1999), this effect is enhanced by tidal influences, which lead to periodic convergence and divergence of the near-shore sea ice cover. While coastal polynyas in the southern sector represent only 0.2% of the total ice-covered area of the Weddell Sea (Markus et al., 1998), they account for an estimated 2.5–9% of total sea ice production (Renfrew et al., 2002). As a result of high rates of ice formation combined with suitable bathymetric configuration, this sector is of key global significance as a major site of AABW formation (Carmack and Foster, 1975; Gordon et al., 1993; Jacobs, 2004). Polynyas open on the eastern side of the Antarctic Peninsula in winter in response to persistent strong winds (up to 20 m s−1 ). In spring–summer, however, a weakening of the offshore-wind component combines with ice convergence against the Peninsula within the western limb of the Weddell Gyre to greatly diminish polynya size (Markus et al., 1998). In the eastern Weddell Sea (30◦ W to 20◦ E), polynyas form in the lee of coastal promontories in response to both meso-/synoptic-scale winds and strong coastal ocean currents. The frequency of polynyas in this sector is similar in winter and spring to that encountered along the ice-shelf fronts (Kottmeier and Engelbart, 1992). For all sectors, Comiso and Gordon (1998) note coherence between peak maximum coastal-polynya area and total ice extent in the Weddell Sea. Inter-annual variability in coastal-polynya area and activity around the Weddell Sea is related to meso- and synoptic-scale atmospheric phenomena, namely katabatic winds, cyclones and their tracks, and barrier winds (Renfrew et al., 2002). 3.2.2
The Importance of Icebergs in Antarctic Polynya Formation
Grounded icebergs are particularly abundant at high southern latitudes, and play a key direct and indirect role in polynya formation and maintenance when grounded—much more so than in the Arctic (with the exception of the NEW polynya; see above). From Table 22, it can be seen that at least 16 of 28 East Antarctic polynyas are intimately associated with icebergs. Grounding occurs in water shallower than about 400 m, mainly within approximately 100 km of the Antarctic coast but up to approximately 300 km away in certain locations. Moreover, grounded icebergs can reside for up to decades (Frezzotti et al., 1998; Massom, 2003) to exert a dominant impact on regional pack-ice, fast-ice, and polynya distributions. Anomalous polynya conditions can also result from the incursion of icebergs. In recent years, the size and characteristics of the Ross Sea polynya have also been dramatically affected by a series of enormous icebergs from the Ross Ice Shelf. Iceberg B-15A (area about 6400 km2 ) calved in March 2000 then drifted westwards to ground at the western face of the Ross Ice Shelf,
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Figure 5: (a) Map of the Southwest Ross Sea showing the drift tracks over time of giant icebergs B-15A and B-15B as determined from sequential cloud-free EOS Terra Moderate-resolution Imaging Spectro-radiometer (MODIS) images. While B-15B drifted approximately northwards to exit the Ross Sea almost a year after calving, B-15A drifted westwards along the Ross Ice Shelf front to ground between Ross Island and Franklin Island in mid-2001. (b) Map of the Southwest Ross Sea in mid-December 2000 (∼9 months after the calving) showing the locations of the B-15 iceberg fragments as well as icebergs B-17 and B-19 (labeled white structures), and how these blocked the advection of sea ice (shown as grey) out of the region (open water is black). (c) Map of the Southwest Ross Sea showing the normal pattern of ice advection in spring (determined from satellite data). The oval demarcates the approximate location of the iceberg fragments shown in (b). (d) Changes in open-water area derived from satellite SSM/I data using the PSSM algorithm (Markus and Burns, 1995), and defined as an area with <10% sea ice cover, in the Southwest Ross Sea (73.5–79◦ S, 160–162◦ W) between October and March for the years 1997 to 2001 inclusive. After Arrigo and van Dijken (2004b). © 2004 Elsevier 2004, reproduced with permission. forming a barrier to pack-ice advection (Arrigo and van Dijken, 2004b). The drift pattern of this and associated icebergs is shown in Figure 5a. Even prior to this, the icebergs had an immediate effect by forming a barrier that greatly restricted the typical (climatological) northwest drift pattern of pack ice out of the region (Figures 5b and 5c). This led to one of the heaviest ice years on record in the Southwest Ross Sea (i.e., a four-fold increase in seaice area retained, or 165,000 vs. 40,000 km2 at the usual time of maximum open-water area (Arrigo et al., 2002; Arrigo and van Dijken, 2004b). This resulted in a much smaller
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Figure 6: Time series from 1978–2003 of the size of the Ross Ice Shelf polynya, with open water fraction determined by applying the PSSM algorithm (Markus and Burns, 1995) to Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and DMSP SSM/I data (Arrigo et al., 2003). Note that 2000/01 was the “iceberg B-15” year, and 1997/8 a strong El Niño year. © 2003 AGU, reproduced with permission. polynya (Figures 5d and 6), comparable in size to that during the strong El Niño event of 1997–1998 (see above). Another vast iceberg, C-19, calved in May 2002 and joined B-15A to create unusually-high ice concentrations through summer of 2002–2003 (Arrigo and van Dijken, 2003b). In fact, open-water area in the SW Ross Sea at this time attained its lowest value over the 25-year record shown in Figure 6, amounting to only 25% of the typical icefree area. This in turn resulted in a dramatic decrease in annual primary production within the polynya, which is normally one of the most productive of all Antarctic polynyas (Arrigo and van Dijken, 2003a; Tremblay and Smith, 2007). The presence of locally-produced large grounded icebergs on the shallow Berkner Shelf have also recently had a major impact on the size of the Ronne polynya system (Comiso and Gordon, 1998; Nicholls and Makinson, 1998; Markus, 1996). In this case, locally-produced icebergs block the prevailing westward drift of sea ice, causing a build-up (decrease in polynya size) to the east and a decreased concentration (polynya enlargement) to the west. The opening/closing of this polynya in the wake of icebergs is largely controlled by synoptic weather systems. In the absence of icebergs, off-shore winds create a coastal polynya that runs the length of the ice-shelf front (Nøst and Østerhus, 1998). The above examples focus on the impact of enormous icebergs on regional “icescape” conditions and polynya formation. Equally important are myriads of small grounded icebergs, numbering in their thousands around the near-coastal Antarctic zone. This important yet often over-looked factor will become apparent in the Antarctic case study given below. 3.2.3
Antarctic Deep-Water Polynyas
In this section, we examine the only three major deep-water polynyas that occur or have recently occurred in the Southern Ocean. While the classical sensible-heat mechanism involves the upward entrainment of heat by deep-ocean convection (Smith et al., 1990), an alternative mechanism involves the upwelling of warm deep water induced by the complex interaction of ocean circulation and local seafloor topography (Comiso and Gordon, 1996). Another prerequisite is that divergent sea ice drift prevents ice incursion into the polynya (Lemke, 2001; Morales Maqueda et al., 2004).
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Figure 7: Monthly mean Southern Ocean ice concentration contour from July to October 1974, derived from Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR) brightness-temperature data, showing the formation of the deep-ocean Weddell Sea Polynya (marked WP) centred on the Greenwich Meridian. At its largest extent, the polynya measured 1000 × 350 km. After Zwally et al. (1985). © 1985 AGU, reproduced with permission. The Weddell Sea and Maud Rise Polynyas. Perhaps the most famous deep-water polynya occurred in the eastern Weddell Sea during the winters of 1974–1976 but has not been observed since. With a maximum area of approximately 350,000 km2 (Zwally and Gloersen, 1977), this is the largest polynya encountered to date. After first forming near Maud Rise (Figure 7), a seamount centered near 65◦ S, 2◦ E that rises from a depth of 5000 m to within 2000 m of the surface, this feature slowly drifted westward with the mean oceanic flow at a speed of about 1 km d−1 into the central Weddell Sea (Carsey, 1980). A number of hypotheses have been proposed to explain its formation–cessation, as reviewed by Morales Maqueda
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Figure 8: A sequence of sea ice concentration images derived from DMSP SSM/I data and showing the formation of the western mode of the Cosmonaut Sea polynya (Antarctica) over the period June 25 through July 19, 1999. Images are from (a) 25–29 June, (b) 5–9 July, (c) 10–14 July, and (d) 15–19 July (all 1999). In (c), the polynya has an areal extent of 130,000 km2 . CA is Cape Ann. Original images courtesy of Kent Moore, University of Toronto, Canada. Reproduced with permission. et al. (2004). These include deep-ocean convection forced by static instability (Martinson et al., 1981; Gordon, 1991; Martinson and Ianuzzi, 1998), meteorological forcing effects (Parkinson, 1983; Kämpf and Backhaus, 1998; Goosse and Fichefet, 2001), and interactions between oceanic circulation/stratification and Maud Rise (Holland, 2001; Muench et al., 2001). While the ephemeral nature of this major sea ice anomaly remains a mystery, a polynya with characteristics similar to those modeled by Holland (2001) has been intermittently observed over Maud Rise, e.g., in 1980 (Comiso and Gordon, 1987) and 1994 (Drinkwater, unpublished). According to Comiso and Gordon (1987), the main mechanism behind the polynya disappearance is wind-driven influx of ice into the polynya, where it melts to produce a low-salinity cap to suppress ocean vertical mixing. The Cosmonaut Sea Polynya. First identified in satellite passive-microwave data by Comiso and Gordon (1987), a large deep-ocean polynya also forms annually at approximately 65◦ S and 45◦ E in the Cosmonaut Sea in water 3000–4000 m deep (Arbetter et al., 2004; Bailey et al., 2004; Comiso and Gordon, 1987, 1996; Gordon and Comiso, 1988; Takizawa et al., 1994). According to Comiso and Gordon (1987, 1996) and Gordon and Comiso (1988), the Cosmonaut Sea polynya system has two distinct modes of formation. The first develops in early winter, is located west of 45◦ E, and appears to be related to strong cyclone activity leading to ocean upwelling and the formation of a large-scale embayment in the sea ice by divergence (Figure 8). The second mode occurs farther to the east (near
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Cape Ann) in both winter and early spring, and may have a strong ocean interaction of the Antarctic Coastal Current and the Antarctic Circumpolar Current (ACC) and the upwelling of CDW. It is also preceded by the formation of a coastal, wind-driven polynya. While the two elements can remain separate, they often merge to form one polynya. Although intraand inter-annual shifts in the polynya centroid are only slight, its maximum area ranged from 35,000 and 115,000 km2 between 1973 and 1993 (mean approximately 70,000 km2 and interannual variability about 65%), with open water accounting for an estimated 30–45% of the total area (Comiso and Gordon, 1987, 1996). Individual polynya “event” duration ranges from days to weeks, with considerable inter-annual variability in size and timing (between July and October). Recent work has suggested that atmospheric divergence is a primary mechanism driving the Cosmonaut Polynya formation (Arbetter et al., 2004, Bailey et al. (2004). Once again, the exact mechanisms involved in the formation and maintenance of this polynya remain unclear, and are the focus of continuing research using both observations and modeling. The ice-sheet coastal margin in this region is also an area of frequent shelf-water polynya formation due to strong katabatic wind activity (Ishikawa et al., 1996).
4
Detailed Case Studies
In this section we present a more comprehensive overview of sea ice and associated processes involved in the formation and maintenance of one polynya from the Northern Hemisphere (the North Water polynya) and one from the Southern Hemisphere (the Mertz Glacier polynya). Both illustrate the often complex role of the regional icescape and, in the case of the Mertz Glacier polynya, the importance of remote iceberg calving events. 4.1
The North Water (NOW) Polynya (NW Greenland)
The North Water (NOW) polynya recurs annually, spanning northern Baffin Bay and southern Smith Sound between Ellesmere Island and Greenland, from approximately 76◦ N to 80◦ N (Figure 9). An ice bridge spanning Nares Strait forms in winter to prevent sea ice in the Lincoln Sea and Kane Basin from being advected southwards into northern Baffin Bay (Steffen, 1985; Barber et al., 2001). Once the ice bridge forms, sea ice within the polynya is blown southwards to open it by the latent-heat mechanism. Upwelling near the Greenland coast also brings warm water to the base of the surface mixed layer, which may be entrained by convection, particularly in the autumn (Melling et al., 2001). This sensible-heat flux to the sea ice-ocean interface slows ice growth on the Greenland side of the polynya. The result is thinner sea ice on the eastern side and thicker ice on the western side of the North Water region (Mundy and Barber, 2001). While removal of sea ice by drift dominates the formation of the polynya itself, thermodynamic ice-ocean-atmosphere processes determine the properties of annual land-fast ice surrounding the polynya. This ice has a major impact on the properties of the water column in spring and summer on both the Ellesmere Island and Greenland coasts (Melling et al., 2001). The polynya remains small throughout the early spring (February through April), producing substantial sea ice that is exported from the polynya southwards (Wilson et al., 2001). In spring the polynya enlarges rapidly due to atmospheric forcing; warm air is advected northwards along the eastern edge of the polynya, creating a southeast to northwest gradient in melt onset and pond onset (Barber et al., 2001; Ingram et al., 2002). Recently Marsden et al. (2004) argued that the polynya might also be “self-sustaining” after its initial formation reaches a threshold size. They proposed that buoyancy forcing supplied by the polynya to the atmosphere may induce a low-pressure cell over
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Figure 9: Map of the NOW polynya region showing how sea ice concentration (SIC) anomalies, derived from satellite passive-microwave data, evolved over the period 1979 to 1996. The slopes show statistically-significant trends in a best-fit linear model, with decreases (increases) in SIC anomaly in dark (light) tones. the polynya, the rotation of which maintains the polynya by preventing cold ambient air of terrestrial origin from entering the region and refreezing the ocean surface. Barber et al. (2001) examined the meteorological and sea ice conditions within the NOW region from 1979–1996. The mean surface air temperature is characterized by an east–west gradient across the NOW region, with colder atmospheric temperatures (−14◦ C) associated with the Ellesmere coast and warmer temperatures (−9◦ C) on the Greenland coast. This average temperature pattern was presumed to result from warm air advection from over the polynya towards the Greenland coast due to prevailing atmospheric flows. Seasonally, the atmospheric temperatures were lower in winter and exhibited the same gradient (east to west) throughout the annual cycle. The mean sea-level pressure (SLP) showed a pronounced inverted trough extending into northern Baffin Bay due to a quasi-static stationary low-pressure system situated south of Greenland. The 1000–500 hPa mean thickness suggests a slight advection of warm air within Greenland waters. Seasonally, the SLP analysis showed a deepening of the trough and associated increases in the pressure gradient with a winter minimum. Mean wind speeds (1983–96) in the northern NOW region are near 1.5 m s−1 from the N and NE, with a minimum of <0.5 m s−1 centered roughly in the middle of Baffin Bay and extending down the Greenland coast to Disko Island (along the trend of the surface-pressure
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trough). The wind-speed minimum separates the N to NNW flow in Canadian waters and a S to SSE flow along the Greenland coast that is typified by a cyclonic flow within the NOW polynya and a low-level convergence zone from Melville Bay down the Greenland coast. We note here that the surface wind speeds are likely to be considerably higher in magnitude and reasonably equivalent in direction relative to these model-forecast winds. The general meteorological setting determines, and mirrors, the spatial pattern of the annual mean sea ice concentration. The polynya is formed some time between November and March with the creation of the ice bridge in Nares Strait/Smith Sound. This bridge, combined with a northerly flow of strong surface winds, creates a latent-heat polynya early in the winter. A distinct temporal lag occurs between the ice concentration within Nares Strait and that situated south of the ice bridge. This pattern exhibits reasonable interannual stability and reflects the basic operations of the latent-heat component of the NOW polynya. The general formation and decay pattern closely follows the average temperature pattern over the area, including the observed onset of melt in the fast ice surrounding the polynya (Yackel et al., 2001). The average dates of formation and decay show that the two patterns are approximately symmetrical with the gradient occurring from NW to SE across the NOW polynya region. This result indicates that melt onset occurs earliest in the SE corner of the NOW polynya region and progresses northwestwards. Historical data on ice thickness along the Greenland Coast are very sparse, but show that the ice thickness was, on average, thinner on the Greenland coast relative to the Ellesmere Island coast by about 30–50 cm. This thickness difference, combined with the advection of warm air from the south, may result in the observed early melt on the Greenland side. This melt pattern also coincides with the observed pattern of primary production within NOW, illustrating the link between melt onset and stabilization of the ocean mixed layer (Galley et al., unpublished). Analysis of sea ice anomalies in the NOW region indicates that a distinct spatial structure occurs within the annual sea ice anomalies. Several clusters of significant anomalies are evident in the annual averages, which suggests that the sea ice anomalies are structured geographically within the NOW region (Figure 9). It also appears that these clusters are related in a bipolar fashion, including pairs of clusters with positive anomalies (in-phase) and pairs with different signs (out-of-phase). Computation of the time series slopes from the full 18-year period, presented as a surface, confirms the existence of these anomaly clusters. This is surprising, given that the slope-surface clusters are statistically significant (p < 0.01) at five different locations within the NOW region. The cluster interrelationships are explained as a dipole relationship (Figure 9), where the sign of the trend in one cluster is related to the sign of the trend in another cluster (separated geographically). In summary, a “bridge dipole” operates out of phase, e.g., as ice anomalies increase (decrease) in Nares Strait they decrease (increase) in Smith Sound. This dipole appears to respond to increased surface temperatures both at the meso- and macro-scales. Inter-annual analysis suggests that this dipole has in fact been operating more often with negative anomalies in Nares Strait and positive anomalies in Smith Sound. This suggests that the duration of the ice bridge has been reduced annually over the 18-year record. The “gyre dipole” operates in-phase with the Smith Sound area. When ice-concentration anomalies are negative (positive) in Smith Sound, they are also negative (positive) along the Southwest coast of Greenland. Physically, we related this feedback to the role of the Baffin Bay Gyre in redistributing southward flowing ice back into the SE quadrant of the polynya. Details on this redistribution mechanism, as observed in the 1998 ice motion data, are described in Wilson et al. (2001).
The Role of Sea Ice in Arctic and Antarctic Polynyas 4.2
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The Mertz Glacier Polynya (East Antarctica)
Centered on approximately 67◦ S and 145◦ E in Adélie Land, the Mertz Glacier polynya (MGP) is a major feature of the East Antarctic sea ice cover, with mean open-water and polynya-règime areas of about 6,000 km2 and 23,000 km2 respectively (Tables 22 and 23). While the MGP is a classical “leeside” polynya, complex regional “icescapes” contribute to its formation, size and maintenance. Key factors are: • the floating Mertz Glacier tongue, which forms a morphological barrier (about 100 km long) to the westward drift of sea ice within the Antarctic Coastal Current; • ice-sheet topography in the Commonwealth Bay to Buchanan Bay regions, which funnels strong and persistent katabatic winds (with an average speed of greater than 25 m s−1 and a temperature typically less than −30◦ C) seawards via topographic outlet depressions (Ball, 1957; Wendler et al., 1997); and • the distribution of grounded icebergs and associated fast ice, as determined by complex series of shoals and banks, and the resultant distribution of pack ice. Rather than forming a single entity, the MGP comprises two elements (Figure 10). These are: (i) a southern règime driven by both katabatic and synoptic winds (from Buchanan Bay to Commonwealth Bay), and (ii) a northern règime driven by synoptic winds (with a predominantly easterly component) and extending as far north as point Y in Figure 10 (Massom et al., 2001). The northern element responds rapidly to changes in wind speed and direction associated with the passage of storms (Massom et al., 2001). A second large polynya, the Mertz Deep or Ninnis polynya, occurs about 100–150 km to the east of the MGP and on the leeward (western) side of the grounded iceberg barrier made up of iceberg B-9B and an assemblage of hundreds of small closely-spaced icebergs (marked SGB1). Iceberg B-9B calved from the Ross Ice Shelf in October 1987 (Keys, 1994), and grounded in approximately its current position in June 1992. As this polynya responds rapidly to changes in the synoptic wind field, its opening and closing behaviour closely mirrors that of the MGP northern “règime” to the west. This polynya is number 37 in Figures 3 and 4. Particularly high rates of sea ice production and removal occur within the southern MGP règime. As a result, the MGP constitutes a major regional “ice factory” (Cavalieri and Martin, 1985; Williams and Bindoff, 2003; Marsland et al., 2004). Recent estimates of ice production rates in winter are about 4–8 cm d−1 over the entire polynya (Lytle et al., 2001; Bindoff et al., 2001), with approximately equal contributions from thermodynamic and dynamic growth. Roberts et al. (2001) reported ice-growth rates as high as 25 cm d−1 (equivalent to less than 50 m a−1 ), but in the more extreme conditions of Southeast Buchanan Bay (i.e., in the core of the katabatic règime). Due to the high rates of ice production and associated heat loss to the atmosphere, the MGP is a major site of high-density water formation that contributes to AABW production (Rintoul, 1998; Bindoff et al., 2000a, 2001). Other important factors include glacier tongueocean interaction, a suitable bathymetric configuration and an on-shelf intrusion of MCDW in the region (Williams and Bindoff, 2003; Marsland et al., 2004). The latter increases shelf water salinity, and potentially supplies sufficient heat to help maintain ice-free conditions (Jacobs and Comiso, 1989). This may also lead to increased ice production near the coast by reducing the amount of ice to the north, thereby enhancing the offshore removal of newlyforming ice by winds. The biological significance is highlighted by Arrigo and van Dijken (2003a), who reported chlorophyll a concentrations of more than 2 mg m−3 . The behaviour of the MGP is determined not only by local wind and ice conditions, but also by complex and remote “icescapes” some tens to hundreds of kilometres to both the
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Figure 10: Part of a Radarsat ScanSAR Wide image (resolution 100 m) of the Mertz Glacier polynya, East Antarctica, on 4 August 1999, and showing an extensive polynya comprising two règimes (see the text). Strong wind conditions on this day (from the SE) produced high rates of sea ice formation in the form of frazil ice streamers (the streaked regions in the polynya) which are aligned with the wind direction—classic polynya “ice-factory” conditions. CB is Commonwealth Bay, and WB is Watt Bay. SGB1 through SGB3 are assemblages of small icebergs grounded on shoals/banks. The important impact on polynya extent of the floating Mertz Glacier Tongue (MGT) and the associated line of small grounded icebergs to the north (SGB2) is clearly apparent. Synoptic-scale changes in wind forcing typically result in cyclical opening and closing of the northern règime at a frequency of 3 to 7 days. Note also the recurrent polynya in the lee (downwind) of grounded iceberg B-9B and myriads of small-grounded bergs to the north (SGB1). Icebergs B-4 and C-08 are also grounded. After Massom (2003). Radarsat data copyright Canadian Space Agency/Agence Spatiale Canadienne 1999, processed and distributed by Radarsat International, used with permission. Copyright Commonwealth of Australia; reproduced with permission.
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Figure 11: A NOAA AVHRR image of the Mertz Glacier polynya region, acquired at Casey Station on 14 June 1999 (16:57 UTC), showing features referred to in the text. SGB1 through SGB4 are groups of small grounded icebergs. Lines mark the approximate boundaries between the different ice regimes as they affect the Mertz Glacier polynya. Image courtesy of the Australian Bureau of Meteorology and Australian Antarctic Division. After Massom et al. (2001). © 2001 International Glaciological Society, reproduced with permission. east and west (Figures 11 and 12). To the east, iceberg B-9B and small grounded-iceberg assemblage SGB1 combine with icebergs to the south from a major calving of the Ninnis Glacier tongue (NGT) in 2000 (Massom, 2003) and associated fast ice to form an immense meridional barrier to westward ice advection within the Antarctic Coastal Current. This blocking feature, which we term the “promontory”, extends from NGT to point X in Figures 11 and 12—a distance of approximately 220 km across a sea ice zone that is generally only approximately 500 km wide (at this longitude) at maximum extent (October; Massom et al., 2003). Massom et al. (2001) further noted from analysis of a US satellite image from October 1963 that similar “icescape” conditions prevailed then. In fact, the promontory was virtually identical in 1963, despite the fact that B-9B did not drift into the region for another 30 years. This indicates that the myriads of small grounded rather than vast tabular icebergs are the key building blocks of the promontory, each constituting an “anchor point” for fast-ice growth. The promontory, which is supplemented by the interception of ice from the east, has the effect of steering pack ice drifting westwards in the coastal zone to the northwest and away from the Mertz Glacier. A parallel line of grounded small icebergs, approximately 100 km to the west of SGB1, marked SGB2 in Figures 10–12 and seasonally-connected by fast ice, further prevents the advection of thick first-year ice into the polynya from the East/North east.
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Figure 12: A Radarsat ScanSAR Wide image (resolution 100 m) of the Mertz Glacier polynya region, 8 March 1998 (∼10.24 GMT), illustrating the complex “icescape” surrounding the Mertz Glacier polynya and affecting its formation and behaviour. MGT-Mertz Glacier tongue, BB Buchanan Bay, WB Watt Bay, NGT Ninnis Glacier tongue, SGB1, SGB2 and SGB3 assemblages of small grounded bergs, CIS the Cook Ice Shelf, and CF Cape Freshfield. The inset map of Antarctica shows the location of the image. After Massom (2003). © 2003 Cambridge Journals, reproduced with permission. It also effectively doubles the size of the polynya in the north-south direction and extends the northern synoptic règime. By deflecting the pack ice drifting westwards along the George V Land coast to the northwest, the promontory also creates a relatively narrow but compact “stream” of thick (more than 5 m) floes. This feature is typically 50–100 km across and hundreds of kilometres long (Massom et al., 2001). It is a recurrent feature that typically forms from February to March each year (Figure 12) to persist through the ice season and become more fully-developed as ice supply from the east increases (Figure 11). After emerging to the Northwest (at point X), the stream drifts westwards along the trend of the shelf break and across the northern flank of the polynya. Convergence in ocean-surface currents (Heil and Allison, 1999; Bindoff et al., 2000b) maintains its coherent nature, although sporadic breakups do occur (particularly when strong south/southwesterly winds persist, e.g., in October–November 1999; Massom et al., 2003). The stream has two impacts on the MGP. It protects the polynya from destructive wave– ice interaction processes by forming a band between it and the marginal ice zone. Together with an annual fast-ice “buttress” to the west, it also effectively narrows the outlet zone available for ice export away from the polynya “ice-factory” (Figure 11). As a result of the restricted outlet zone and high ice production rates within the polynya core in autumn-winter, the polynya periodically “back fills” to reduce the open-water area until storms flush out the region and the process repeats itself (Massom et al., 2001). Synoptic-scale decreases in
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Figure 13: Radarsat-1 synthetic aperture radar images of the Mertz Glacier polynya region in 1999 acquired under (a) calm conditions (wind speed <5 m s−1 from the SW) on 14 August (10:41 GMT), and (b) under windy conditions (>10 m s−1 from the S/SE) on 21 August (18:16 GMT). The spatial resolution is 200 m. In (a), the dark area adjacent to the coastline, Mertz Glacier tongue and grounded icebergs (SGB2) comprises calm open water/nilas. In (b), the grey streaks in the enlarged polynya result from wind-roughening of the ocean surface and the presence of frazil-ice streamers. This further illustrates the inherent difficulty in determining polynya size, i.e., given the highly variable conditions within a given polynya “regime”. After Williams (2004). Radarsat data copyright Canadian Space Agency/Agence Spatiale Canadienne 1999, processed and distributed by Radarsat International, used with permission. Figure reproduced with permission. polynya règime extent also occur with the periodic cessation of strong winds and/or reversals in wind direction (Figure 13). The buttress again owes its presence to lines of grounded icebergs (SGB3 and 4 in Figure 11), which anchor fast-ice growth and also intercept pack ice drifting into the region from the east. Resultant eastward growth of the buttress into Commonwealth Bay decreases the westward extent of the polynya to limit its size. This additional back-propagation or back-fill (denoted by black arrows in Figure 11) is again fed by ice from the polynya itself. Icebergs grounded in the region to the east of the Mertz Glacier have a long residence time (5–20 years; Frezzotti et al., 1998). When they finally float clear, however, they can increase the meridional extent of the northern MGP regime by temporarily re-grounding at point Y in Figures 10–12 (Massom, 2003). Iceberg C-08, which was produced by a calving of the Ninnis Glacier tongue in 1980–1982, grounded along the north-eastern flank of the Mertz Glacier terminus in 1989–1991 (Wendler et al., 1996; Frezzotti et al., 1998). It dislodged itself from this location in January, 2002 to re-ground at point Y in July 2002, where it
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remained until May 2003 before drifting westwards with the stream. Inter-annual variations in the atmospheric forcing field and associated changes in the complex set of “icescape” processes and interactions outlined above lead to variations in polynya behaviour. For example, the timing of buttress formation varies from year to year, e.g., from mid-March in 2001 to early-May in 1998 (Massom et al., 2001). Massom et al. (1998) observed significant inter-annual variability both in the timing of polynya maximum extent and timing from i.e., June in 1992 to October in 1989 and 1993. In an example from 1999, Massom et al. (2003) showed how a large-scale anomalous change in atmospheric circulation in the austral spring (October–November), and related to an unusually-persistent blocking anticyclone in the South Tasman Sea thousands of kilometres to the Northeast, had a profound impact on the MGP. An enduring shift in the near-coastal prevailing wind field from East/Southeasterly to South/Southwesterly led to a dispersal of the enclosing “stream”. This in turn opened the polynya up to the marginal ice zone, swept ice northeastwards out of the polynya and led to an unusually early spring time melt-back of ice in the region. In general, Antarctic shelf-water polynyas play a major role in the annual melt-back of the seasonal sea ice cover (Massom et al. 1998, 2003), once the surface heat budget switches from negative to positive in the austral spring–summer and new sea ice formation is replaced by processes of melt and advection (Nihashi and Ohshima, 2001).
5 Conclusions This review has shown that polynyas are complex “windows” in the sea ice cover, and that a wide range of processes are responsible for their formation and maintenance. Moreover, significant hemispheric differences and similarities occur, with variability both within and between polynyas. In particular, this chapter has highlighted the intimate relationships that exist between polynyas and their physical setting, and the complex and often subtle role of the regional “icescape”, both local and remote. This comprises not only pack ice and glacial tongues/ice-sheet promontories, but also grounded icebergs and associated fast ice. Polynya variability is in turn intimately related to variability in “icescape” conditions driven by large-scale atmospheric and oceanic variability. While ice-bridge, land bridge and oceanstrait mechanisms are predominant in the Arctic, this is not true for the Antarctic. Similarly, icebergs play a major role in polynya formation in the Southern Ocean, but much less so in the Arctic. Under a climate-warming scenario, it may be that the predicted increase in ice discharge rates from the Antarctic Ice Sheet may, for example, affect polynya distribution and behaviour by leading to enhanced iceberg production. In some cases, polynya behavior is affected not only by the local “icescape” but also by ice conditions hundreds of kilometres away. Moreover, iceberg calving events can have a major impact on polynyas hundreds to thousands of kilometres “downstream”, and multiple years later. This study has also underlined major deficiencies in our current understanding of polynya-ice-atmosphere-ocean interaction processes. For example, little detailed information is available from many polynyas (e.g., those over the Eurasian continental shelf and most around Antarctica, with a few notable exceptions like Mertz Glacier, Ross Sea and Terra Nova Bay polynyas). The exact mechanisms responsible for the formation, maintenance and episodic nature of Antarctic deep-water polynyas also remain largely unresolved. Moreover, little is known about polynya interannual to decadal variability in response to recently-identified modes of variability and anomalous patterns in large-scale atmospheric circulation in both hemispheres. Polynya modeling has a key role to play in addressing these
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and related issues (Morales Maqueda et al., 2004; Willmott et al., 2007), supported by integrated field research and long-term monitoring by satellite remote sensing. Polynya models enable examination of polynya sensitivity to changes in boundary conditions, yet require improved observational data for both input and validation. It is anticipated that improved estimates of the open-water area in polynyas, and its variability, will be achieved using data from new satellites. These include higher-resolution data from the EOS Aqua Advanced Scanning Microwave Radiometer-E (AMSR-E) and polarimetric data from the new synthetic aperture radar (SAR) missions, e.g., Envisat, the Japanese Advanced Land Observing System (ALOS) satellite and Radarsat-2 (Lubin and Massom, 2005). In summary, polynyas constitute very effective and informative windows into polarocean processes and their response to regional- to global-scale climate variability and/or change. Their use as sentinels of such change is, however, dependent on our improved understanding of the complex processes responsible for their formation and maintenance, the feedbacks involved, and their role in driving and responding to that change.
Acknowledgements This work was supported by NSERC, CFI and CRC grants to DGB, and by support from the Australian Government’s Cooperative Research Centres Programme through the Antarctic Climate and Ecosystems Cooperative Research Centre (ACE CRC) to RM. We thank W. Chan and C. Blouw for assistance in the Arctic data analysis and literature review, and to D. Fast for figure preparation; and to T. Maconachie for editorial assistance. Grateful thanks are also given to two anonymous reviewers for their thoughtful and helpful comments, and to S. Marsland and K. Michael of the ACE CRC for their excellent internal reviews.
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Parish, T.R., Bromwich, D.H., 1998. A case study of the Antarctic katabatic wind interaction with large-scale forcing. Monthly Weather Review 126, 199–209. Parkinson, C.L., 1983. On the development and cause of the Weddell Polynya in a sea ice simulation. Journal of Physical Oceanography 13, 501–511. Parkinson, C.L., 2000. Variability of Arctic sea ice: The view from space, an 18-year record. Arctic 53, 341–358. Parkinson, C.L., 2004. Southern Ocean sea ice and its wider linkages: Insights revealed from models and observations. Antarctic Science 16, 387–400. Parkinson, C.L., Cavalieri, D.J., Gloersen, P., Zwally, H.J., Comiso, J.C., 1999. Arctic sea ice extents, areas and trends, 1978–1996. Journal of Geophysical Research 104, 20837– 20856. Pease, C.H., 1987. The size of wind-driven coastal polynyas. Journal of Geophysical Research 92, 7049–7059. Pillsbury, R.D., Jacobs, S.S., 1985. Preliminary observations from long-term current meter moorings near the Ross Ice Shelf, Antarctica. In: Jacobs, S.S. (Ed.), Oceanology of the Antarctic Continental Shelf. In: Antarctic Research Series, vol. 43. Antarctic Geophysical Union, Washington, DC, pp. 87–107. Renfrew, I.A., King, J.C., Markus, T., 2002. Coastal polynyas in the southern Weddell Sea: Variability in the surface energy budget. Journal of Geophysical Research 107, 3063, doi:10.1029/2000JC000720. Rigor, I.G., Wallace, J.M., Colony, R.L., 2002. Response of sea ice to the Arctic Oscillation. Journal of Climate 15, 2648–2662. Rintoul, S.R., 1998. On the origin and influence of Adélie Land bottom water. In: Jacobs, S.S., Weiss, R.F. (Eds.), Ocean, Ice, and Atmosphere: Interactions at the Antarctic Continental Margin. In: Antarctic Research Series, vol. 75. Antarctic Geophysical Union, Washington, DC, pp. 151–171. Rintoul, S.R., Church, J., Wijffels, S., Fahrbach, E., Garcia, M., Gordon, A., King, B., Morrow, R., Orsi, A., Speer, K., 2001a. Monitoring and understanding Southern Ocean variability and its impact on climate. In: Smith, N.R., Koblinsky, C.J. (Eds.), Observing the Ocean in the 21st Century. Australian Bureau of Meteorology, Melbourne, pp. 486– 508. Rintoul, S.R., Hughes, C., Olbers, D., 2001b. The Antarctic Circumpolar System. In: Siedler, G., Church, J., Gould, J. (Eds.), Ocean Circulation and Climate. Academic Press, New York, pp. 271–302. Roberts, A., Allison, I., Lytle, V.I., 2001. Sensible and latent heat flux estimates over the Mertz Glacier Polynya from inflight measurements. Annals of Glaciology 33, 377–384. Rothrock, D.A., Yu, Y., Maykut, G.A., 1999. Thinning of the Arctic sea ice cover. Geophysical Research Letters 26, 3469–3472. Serreze, M.C., Maslanik, J.A., Scambos, T.A., Fetterer, F., Stroeve, J., Knowles, K., Fowler, C., Drobot, S., Barry, R.G., Haran, T.M., 2003. A record minimum Arctic sea ice extent and area in 2002. Geophysical Research Letters 30, 1110, doi:10.1029/2002GL016406. Signorini, S.R., Cavalieri, D.J., 2002. Modelling dense water production and salt transport from Alaskan coastal polynyas. Journal of Geophysical Research 107, doi:10.1029/2000JC000491. Simmonds, I., 2003. Modes of atmospheric variability over the Southern Ocean. Journal of Geophysical Research 108, 10, doi:10.1029/2000JC000542. Simmonds, I., King, J.C., 2004. Global and hemispheric climate variations affecting the Southern Ocean. Antarctic Science 16, 401–413.
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Smith, M., Rigby, B., 1981. Distribution of polynyas in the Canadian Arctic. In: Stirling, I., Cleator, H. (Eds.), Polynyas in the Canadian Arctic. Environment Canada, Ottawa, Canada, pp. 7–28. Canadian Wildlife Service Occasional Paper Number 45. Smith, R.C., Stammerjohn, S.E, 2001. Variations of surface air temperature and sea ice extent in the western Antarctic Peninsula (WAP) region. Annals of Glaciology 33, 493–500. Smith, S.D., Muench, R.D., Pease, C.H., 1990. Polynyas and leads: An overview of physical processes and environment. Journal of Geophysical Research 95, 9461–9479. Steffen, K., 1985. Warm water cells in the North Water, northern Baffin Bay during winter. Journal of Geophysical Research 90, 9129–9136. Streten, N.A., Pike, D.J., 1984. Some observations of the sea ice in the southwest Indian Ocean. Australian Meteorological Magazine 32, 195–206. Stroeve, J.C., Serreze, M.C., Fetterer, F., Arbetter, T., Meier, W., Maslanik, J., Knowles, K., 2005. Tracking the Arctic’s shrinking ice cover: Another extreme September minimum in 2004. Geophysical Research Letters 32, L04501, doi:10.1029/2004GL021810. Takizawa, T., Ohshima, K.I., Ushio, S., Kawamura, T., Enomoto, H., 1994. Temperature structure and characteristics appearing on SSM/I images of the Cosmonaut Sea, Antarctica. Annals of Glaciology 20, 298–306. Tauber, G.M., 1960. Characteristics of Antarctic katabatic winds on Antarctic meteorology. In: Papers Proceedings of a Symposium. Pergamon Press, Kidlington, pp. 52–64. Thompson, D.W.J., Solomon, S., 2002. Interpretation of recent Southern Hemisphere climate change. Science 296, 895–899. Tremblay, J.E., Smith, W.O. Jr., 2007. Primary production and nutrient dynamics in polynyas. In: Smith, W.O. Jr., Barber, D.G. (Eds.), Polynyas: Windows to the World. Elsevier, Amsterdam. Ushio, S., Takizawa, T., Ohshima, K.I., Kawamura, T., 1999. Ice production and deep-water entrainment in shelf break polynya off Enderby Land, Antarctica. Journal of Geophysical Research 104, 29771–29780. van Woert, M.L., 1999a. The wintertime expansion and contraction of the Terra Nova Bay polynya. In: Spezie, G., Manzella, G.M.R. (Eds.), Oceanography of the Ross Sea: Antarctica. Springer, New York, NY, pp. 145–164. van Woert, M.L., 1999b. Wintertime dynamics of the Terra Nova Bay polynya. Journal of Geophysical Research 104, 7753–7769. van Woert, M., Meier, W.N., Zhou, C.Z., Archer, A., Pellegrini, A., Grigioni, P., Bertoia, C., 2001. Satellite observations of upper-ocean currents in Terra Nova Bay, Antarctica. Annals of Glaciology 33, 407–412. Vaughan, D.G., Marshall, G.J., Connolley, W.M., Parkinson, C., Mulvaney, R., Hodgson, D.A., King, J.C., Pudsey, C.J., Turner, J., 2003. Recent rapid regional climate warming on the Antarctic Peninsula. Climate Change 60, 243–274. Vaz, R.A.N., Lennon, G.W., 1996. Physical oceanography of the Prydz Bay region of Antarctic waters. Deep-Sea Research I 43, 603–641. Vinnikov, K.Y., Robock, A., Stouffer, R.J., Walsh, J.E., Parkinson, C.L., Cavalieri, D.J., Mitchell, J.F.B., Garrett, D., Zakharov, V.F., 1999. Global warming and Northern Hemisphere sea ice extent. Science 286, 1934–1937. Wendler, G., Ahlnas, K., Lingle, C.S., 1996. On Mertz and Ninnis Glaciers, East Antarctica. Journal of Glaciology 42, 447–453. Wendler, G., Gilmore, D., Curtis, J., 1997. On the formation of coastal polynyas in the area of Commonwealth Bay, Eastern Antarctica. Atmospheric Research 45, 55–75.
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Zwally, H.J., Comiso, J.C., Parkinson, C.L., Cavalieri, D.J., Gloersen, P., 2002. Variability of Antarctic sea ice 1979–1998. Journal of Geophysical Research 107, 3041, doi:10.1029/2000JC000733.
Chapter 2
Physical Oceanography of Polynyas W.J. Williams1 , E.C. Carmack2 and R.G. Ingram1 1 Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada 2 Institute of Ocean Sciences, Sidney, BC V8L 4B2, Canada
Abstract Physical processes related to polynya formation are reviewed and selected examples from both the Arctic and the Antarctic seas are given. Polynyas are categorized by dividing them into mechanically and convectively forced systems, recognizing that most polynyas are formed by a confluence of two or more physical factors, and that positive feedback processes also impact formation. Polynyas strongly impact the regional oceanography. Those that are initiated by mechanical forcing from the wind, for example, may produce large quantities of ice and brine. Dense water formed in this manner can then migrate via Ekman layers, gravity currents, and eddying motions across the shelf, and drain into the deep ocean. Under scenarios of global warming, a climatologically retreating ice edge will alter the size and distribution of polynyas. In the Arctic and on the Antarctic Peninsula, the general pattern of polynyas relative to the ice edge is likely to be similar.
1 Introduction The word polynya originates from a Russian term for “ice hole”, and thus polynyas are traditionally defined as regions of open water that are found in otherwise ice-covered waters. The area of open water can be up to 105 square kilometres (km2 ) and tends to re-occur at specific locations each year. The timing, duration and size of the open water at these locations may display large inter-annual variation, a characteristic that relates to the general variability of interacting sea-ice, atmospheric and oceanic conditions. Still, the re-occurrence of open water at specific locations suggests that the processes that cause it are regionally robust. The geographical distribution of polynyas in the Arctic and Antarctic regions is described in Barber and Massom (2007). For a polynya region to maintain open water when air temperatures are less than the freezing point of the surface water there needs to be either physical removal of sea ice (seaice divergence) or sufficient heat input from oceanic flows and insolation or a combination of these factors. If ice-divergence is dominant, the surface of the ocean is at the freezing point and large quantities of sea ice, initially in the form of frazil ice, are formed. However, this newly formed ice is rapidly removed by the ice divergence, keeping the open water Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74002-8
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Figure 1: Cross-section of a polynya defining 4 different widths: (1) the region of open water, (2) the region of open and partially open water, (3) the practical width of the polynya, which encompasses all ice over which is too thin for safe travel, and (4) the full width of the polynya, which encompasses all ice that is thinner than the surrounding pack. ice-free. There is a large sensible-heat flux from the warm ocean to the cold atmosphere, due to the release of the latent heat of freezing as frazil ice forms; thus polynyas formed in this way have been termed ‘latent-heat’ polynyas. If oceanic heat flux is dominant, the open water is kept from freezing by transport of warm water to the surface. Again, there is a large sensible-heat flux from the ocean to the atmosphere and thus polynyas formed in this way have been termed ‘sensible-heat’ polynyas. In general, open water in a polynya region is formed by a confluence of geographical and physical factors and ice-divergence and oceanic heat flux both contribute in varying degrees. Polynyas are also highly seasonal, with oceanic heat flux, ice divergence and solar radiation varying in importance at various times throughout the freeze-melt cycle. As noted below, the combination of processes contributing to polynya formation and maintenance also allow for the potential for positive feedback effects. The types of regional physics that lead to polynya formation may cause thinner ice or reduced coverage but not open water. This leads to a more encompassing and useful definition of a polynya as “a geographically fixed region in the aquatic cryosphere where ice cover is, on average, thinner or reduced in concentration compared to the surrounding pack or fast ice”. This definition allows us to also better define the extent of a polynya as is illustrated by a schematic of a polynya caused by ice divergence (Figure 1). The four defining areas are: (1) the region of open water, (2) the region of open and partially open water, (3) the practical width of the polynya which encompasses all ice over which is too thin for safe travel, and (4) the full width of the polynya which encompasses all ice that is thinner than the surrounding pack. Once this definition is adopted we are free to focus on processes that cause localized reduced or thinner ice cover. A distinction also needs to be made between polynyas and leads. This is sometimes blurred, but in general leads are quasi-linear openings in the pack ice that can be metres to 100’s of metres wide and kilometers to tens of kilometres long. Like mechanically forced polynyas, they are due to local ice divergence and occur at weak points in the pack ice. Unlike polynyas, however, they generally have no fixed or recurrent location, although their statistics are often predictable by region and season. For example, even the central Arctic
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pack ice in winter has about 1% leads by area, whereas the Antarctic pack ice in winter has very roughly 10% leads by area (Gloersen et al., 1992). Because of the insulating effect of sea ice and snow and its high albedo, air-sea heat exchange is roughly two orders of magnitude greater within leads and polynyas than over the Arctic ice making them important to the surface heat balance in the Arctic Ocean (Maykut and Perovich, 1987; Alam and Curry, 1998; Andreas and Cash, 1999). As the winter ice pack retreats in spring the frequency of leads and polynyas increases, allowing a greater diffuse input of heat to the surface of the ocean due to leads and localized input at polynyas (Maykut and McPhee, 1995; Wadhams, 2000). This important positive feedback is discussed below, and may play a major role in the response of polynyas to climate change. Comprehensive reviews have been published on the physics of sea ice by Untersteiner (1986) and Wadhams (2000), and on polynyas and leads by Smith et al. (1990) and Morales Maqueda et al. (2004). We do not attempt to duplicate these excellent references, or provide a detailed review of polynya research. Instead, we provide a classification of polynya types based on the physical processes that cause them that we feel is more useful than the original latent-/sensible-heat classification (Section 2). We also describe the physical oceanography associated with polynyas (Section 3) and a few aspects of their importance to biological oceanography (Section 4). We conclude with remarks on emerging research questions, including climate change (Section 5).
2 Polynya Formation Processes A distinction between latent- and sensible-heat polynyas is commonly made. Morales Maqueda et al. (2004) suggest, however, that most polynyas are a combination of the two types, and argue that polynyas should be distinguished as either shelf- or deep-water polynyas. We focus on the combination of geographical features and oceanic and atmospheric processes that cause recurrent polynyas, and therefore discuss polynyas in terms of their forcing mechanisms. These mechanisms are diverse but are grouped here into either mechanical forcing (ice-divergence) or convective forcing (oceanic heat flux). We then discuss the feedback mechanisms that cause most polynyas to be maintained by a combination of factors. Well-studied examples of each type of polynya are noted. 2.1
Mechanically Forced Polynyas
A polynya caused by ice-divergence is a result of the ice being forced apart by either windstress or ocean currents. The location of the polynya relative to some geographic feature (e.g., an island) is controlled by this forcing. Wind-driven polynyas—Polynyas initiated by the wind are the most common type of polynya. They form against the coast or at the edge of land-fast ice, ice shelves and ice tongues when offshore wind pushes the ice pack away. Once the polynya is open, frazil ice quickly forms in the exposed water and is then transported downwind, typically in the form of ‘tadpole-like’ wind-rows associated with Langmuir circulation. This frazil ice consolidates against the pack ice at the offshore and downwind edge of the polynya and covers some of the exposed water (Figure 2a). The accumulation of frazil ice tends to make the polynya narrower whereas the offshore wind-forcing of the pack ice tends to make the polynya wider. The total rate of production of frazil ice is proportional to the surface area of open water exposed so that as the polynya
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Figure 2: Physical processes within wind-driven polynyas: (a) a wind-driven polynya forming in the lee of an island (e.g., St. Lawrence Island); (b) the flaw polynya that forms on the Mackenzie Shelf in the southern Beaufort Sea; (c) a flaw polynya in the Antarctic. All diagrams are vertical sections across the polynya.
widens both the production of ice and its rate of accumulation downwind increase. This leads to a possible steady state width in which the polynya is sufficiently wide so that its widening due to the wind is offset by narrowing due to consolidation of frazil ice (Lededev, 1968). Pease (1987) combined these ideas in a one-dimensional flux model of an idealized polynya; and while this model is highly simplified, it lucidly illustrates interactions among a
Physical Oceanography of Polynyas number of important processes. With this model the width of the polynya is VH 1 − e(−tF /H ) , W = F
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(1)
where W is the polynya width, V is the offshore speed of the solidified ice, F is the rate of production of new ice in the polynya, H is the accumulation depth of the new ice at the down-wind side of the polynya, and t is time. The maximum width of the polynya is then V H /F and the exponential time scale is H /F . This initial model of a wind-forced polynya has been refined to include two-dimensional effects (such as an irregular coastline, difference in wind and ice velocity direction), unsteady forcing (such as temporal variation in the winds and air temperature) (Ou, 1988; Morales Maqueda and Willmott, 2000), variations in ice collection depth (Biggs et al., 2000), stratification and dense-water production and dispersal (Chapman, 1999) and the closing of polynyas due to onshore ice movement and ice growth (Markus and Burns, 1995; Winsor and Björk, 2000). These models are discussed in Wilmott et al. (2007). The formation of polynyas due to ice-divergence is strongly influenced by internal icestress that tends to inhibit their formation. For example, when large-scale ice dynamics cause compressive internal ice stress against the coast, then local offshore wind-stress events may not be sufficient to open the ice. Ice strength is also a factor: higher wind-stress is required to fracture stronger ice. Ice strength is a function of temperature and thickness (Timco and O’Brien, 1994), so that wind-forced polynyas that open in the spring might do so because the ice has weakened from seasonal warming. Wind-forced polynyas therefore tend to occur at times and locations where there is little resistance to offshore ice motion during offshore wind-stress events. A classic example is the St Lawrence Island polynya in the Bering Sea (Schumacher et al., 1983; Pease, 1987), which forms on the southern side of the island. This polynya opens up during periods of northerly to easterly winds, as there is little internal resistance to ice motion forced towards the south and west into deeper water and towards the edge of the pack ice (Figure 3). This is also generally true in the Antarctic where the ice velocity is generally northward, moving ice offshore and towards the ice-edge. A divergent ice pack is created, which coupled with lower ice concentration, smaller fractions of multiyear ice and thinner ice than in the Arctic (Wadhams, 2000), should reduce internal ice stress and be conducive to polynya formation. Flaw lead polynyas in the Arctic—In the Arctic wind-driven polynyas generally occur where offshore wind pushes ice away from the coast or edge of land-fast ice creating the so called flaw lead. Two types of flaw leads can be distinguished (Reimnitz et al., 1994). If occasional winds drive pack ice toward the coast, as is in the case of the Beaufort Sea, the compression forms grounded pressure ridges known as stamukhi (Reimnitz et al., 1978). If the prevailing winds drive ice offshore, as in the Laptev Sea, then the ice regime dilates and the edge of the flaw lead is smooth (Barnett, 1991). This points out that polynyas and leads should not be studied in isolation, but rather are part of the larger ice ocean system. For example, the flaw polynya off the Mackenzie River delta in the Canadian Beaufort Sea is part of the full landfast/stamukhi/polynya/pack system, all of which respond to local wind forcing and geometry (Macdonald and Carmack, 1991; Figure 2b). These flaw polynyas form almost continuously around the Arctic basin, extending across the Kara, Laptev, East Siberian, Chukchi and Beaufort Seas. They display a tendency to form between anchor points (e.g., islands, capes) and/or near the 10–20 m isobath (Zubov, 1943, H. Melling, pers. comm.). Conditions in the Kara Sea are described by Pfirman et al. (1995) and Buzov (1991), in the Laptev Sea and East Siberian Seas by Timokhov (1994) and
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Figure 3: A superposition of 2 satellite images of the St. Lawrence Island polynya. The light-dark content is a Synthetic Aperture Radar (SAR) image (courtesy of the Canadian Space Agency) while the color is an AVHRR image (courtesy of NOAA). The color is proportional to the ice surface temperature, so that cold regions are blue and warm regions are red. The images were taken on 9 January 1999 when ∼15 m/s winds from the northeast kept the polynya open in air temperatures of −14◦ C. Within the polynya, wind-rows of newly formed frazil ice and its accumulation downwind can be seen. The image was assembled by R. Drucker (Drucker et al., 2003). Reimnitz et al. (1994), in the Chukchi Sea by Stringer and Groves (1991) and Martin et al. (2004a, 2004b) and in the Beaufort by Stirling and Cleator (1981). There are also numerous flaw leads that form within the straits of the Canadian Arctic Archipelago, large flaw leads forming in Hudson Bay and Lancaster Sound (Stirling and Cleator, 1981). Ice production and brine rejection estimates for this group of polynyas are given in Winsor and Björk (2000) and for the subset of the Siberian Shelf by Martin and Cavalieri
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(1989), which allow us to assess the relative importance of each region to the Arctic ice pack and halocline. The Bering and Okhotsk Seas also have flaw polynyas. In the Okhotsk Sea there are coastal wind-driven polynyas in the north and off Sakhalin Island in the east. Ice cover in the region is described by Alfultis and Martin (1987) and production of dense water from the coastal polynyas described by Martin et al. (1998) and Shcherbina et al. (2004). In the Bering Sea hydrography and ice conditions of the St Lawrence Island polynya in late winter are described by Clement et al. (2004) and detailed observations of the ice thickness and frazil ice within the polynya are given in Drucker et al. (2003). In addition, flaw polynyas in the Bering Sea can be identified on the northern sides of the Gulf of Anadyr and Norton Sound (Winsor and Björk, 2000; Barber and Massom, 2007). Wind-driven polynyas in the Antarctic—Like in the Arctic, flaw polynyas are common around the Antarctic continent. Flaw polynyas in the Weddell Sea have been discussed by Markus (1998), Markus et al. (1998) and Comiso and Gordon (1996), in the Ross Sea by Zwally et al. (1985) and Gloersen et al. (1992) and in eastern Antarctica by Adolphs and Wendler (1995) and Massom et al. (1998). In the Antarctic wind-driven ice-divergence is greatly enhanced by katabatic winds that descend from the cold Antarctic continent and push ice offshore and away from ice-shelves and fast ice (Adolphs and Wendler, 1995; Figure 2c). In many places these winds are focused along the valley of a glacier which ends as an icetongue protruding from the coast. The combination of an ice tongue blocking the general westward movement of ice and offshore wind forces a polynya on the western side of the ice tongue (Figure 4a). Two of the most intensively studied polynyas in the Antarctic are initiated in this way. These are the Terra Nova Bay polynya alongside the Drygalski Ice Tongue in the Ross Sea (Kurtz and Bromwich, 1983; Bromwich and Kurtz, 1984; van Woert, 1999) and the Mertz polynya alongside the Mertz Glacier ice tongue off of Adelie Land (e.g., Massom et al., 2001; Williams, 2004). Additional blockage of the westward ice flow can also be caused by grounded icebergs so that polynyas can form on their western side (Massom et al., 2001). Although largely due to the wind, the input of heat is also a factor for Antarctic flaw polynyas. Most of these polynyas are reported to grow as solar radiation increases in the spring due to the low albedo of open water relative to sea ice, and some are partially maintained by upwelling of warm Circumpolar Deep Water (CDW) (see below). An example is the Ross Sea polynya, which runs along the edge of the Ross Ice Shelf. With an average area of ∼27,000 km2 (Morales Maqueda et al., 2004), it forms the largest recurrent polynya in Antarctica, and rapidly increases in size during spring (Gloersen et al., 1992). Ice bridges—Wind-initiated polynyas may also form at the edge of ice bridges, which are particular features of the Canadian Arctic Archipelago. For example, the North Water polynya forms to the south of an ice bridge that usually forms in winter across northern Smith Sound in Baffin Bay (Dunbar, 1969; Melling et al., 2001; Ingram et al., 2002; Figure 4b). The dominant northerly winds channeled by the land topography of Ellesmere Island and Greenland, as well as the southward directed ocean circulation provide a flux of sea ice southward, away from the rigid ice bridge, forming the open water/light ice conditions associated with the polynya (Melling et al., 2001). A composite view of the ice bridge and its geographical relationship to the North Water is given by Kawamura et al. (2001). The circulation pattern of the North Water polynya region and northern Baffin Bay is described by Melling et al. (2001), Barber et al. (2001), and Wilson et al. (2001), using different approaches. The strongest southward flow occurs along the western side of Smith Sound and east of Devon Island, while relatively warm waters, associated with the West Greenland Current enter the area from the southeast. Much of this warm flow crosses Baffin Bay along
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Figure 4: Diagrams of physical processes in which blockage by ice is a factor in polynya formation: (a) a polynya formed in the lee of an ice tongue, such as the Terra Nova Bay polynya in the Ross Sea and the Mertz Glacier polynya off of Adelie Land, Antarctica; (b) a wind-driven polynya forming in a strait with an ice bridge, such as the North Water in the Canadian Arctic Archipelago and (c) a current-driven polynya, in this case the Northeast Water off northeastern Greenland (OBIS is the Ob Bank Ice Shelf, NOIS is the Norske Øer Ice Shelf, NGCC is the Northeast Greenland Counter Current and EGC is the East Greenland Current). All diagrams are views from above.
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latitude 75◦ N to the mouth of Lancaster Sound, but the remaining part of the current moves north, hugging the steep topography near the Greenland coast and enters the North Water region. This suggests the possibility of a convective component to the North Water, (Steffen, 1985a; Lewis et al., 1996), but Melling et al. (2001) and Bâcle et al. (2002) found little evidence for upwelled warm water off Greenland. However, the persistence of young ice on the Greenland side of the North Water throughout the fall and winter season implicates sensibleheat flux to the surface either from the atmosphere or by oceanic entrainment processes (Ingram et al., 2002). The average air temperature is warmer on the Greenland side than on the Canadian side (Barber et al., 2001), which may lead to the reduced sea ice cover along Greenland. This melt gradient has been previously observed (Lewis et al., 1996) and forms an important coupling mechanism between the oceanic stratification and primary production within the polynya area. Current-driven polynyas—An opening in the ice can also be due to ice-divergence forced by ocean currents, but this is less common since ocean currents, typically, are not strongly horizontally divergent, unless affected by special geography. An example of this polynya type is the Northeast Water, a polynya located off the northeastern coast of Greenland between two coastal features: the Ob Bank to the north and the Norske Øer Bank to the south (Schneider and Budéus, 1995, 1997a, 1997b; Johnson and Niebauer, 1995). Thick ‘floebergs’ ground over the Ob Bank and block the stream of ice from the north; landfast ice is present over the Norske Øer Bank, thus blocking the influx of ice from the south. The Northeast Greenland Coastal Current flows northward under the landfast ice and into the Northeast Water and then removes any new frazil ice from the polynya (Figure 4c). While current-driven processes explain the location of the Northeast Water, the polynya shows strong seasonality and other physical processes come into play throughout the year (cf. Minnett et al., 1997; Holland et al., 1995). In the Northeast Water the area of reduced sea ice cover is variable, but the polynya usually opens in the May–June period and reaches maximal extent in August. Schneider and Budéus (1995), Budéus and Schneider (1995) and Minnett et al. (1997) discuss its generation and local hydrography. Satellite imagery (Parkinson et al., 1987) shows complex ice movement within the polynya and ice floe motion, observed in 1993, showed considerable inertial/tidal motion in drift tracks (P. Galbraith and G. Ingram, pers. comm.). In regard to the under-ice current regime, little is known in the Northeast Water. For example, Johnson and Niebauer (1995) used Accoustic Doppler Current Profiler (ADCP) data averaged over a few hours as snapshot observations of the circulation and estimated that the tidal contribution to the flow is small. Topp and Johnson (1996) include tidal spectral analysis in their examination of current meter data gathered in the Northeast Water from August 1992 to 1993, but only their three shallowest moorings at 75 metres (m) depth (a depth chosen to protect the moorings from ice ridges) are above the summer pycnocline. 2.2
Convectively-Forced Polynyas
Polynyas that are largely due to a flux of warm water to the surface do not require ice motion for their formation, and theoretically could be independent of ice dynamics and internal ice stress. However, there is generally some ice motion present, so that ice is advected into the polynya area. The presence of open water is then dependent on the rate of melting of the sea ice being greater than the rate of advection. Locations of warm water flux to the surface can therefore be regions of low ice concentration, or thin ice rather than open water. Vertical buoyancy flux can either be due to free or forced convection. Free convection is the sinking of dense plumes (for example, cold brine rejected from forming ice), and forced
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convection is the lifting of dense water (e.g., turbulent mixing due to flow shear or vertical advection in upwelling). Both free and forced convection are observed to support polynya formation. With respect to an underlying interface, free convection may be non-penetrative or penetrative, the latter referring to the special case in which sinking plumes entrain underlying water back into the upper layer (Farmer, 1975). Both free and forced convection can occur in association with buoyancy loss, but they are only effective as a heat transport mechanism when they penetrate an underlying layer of water that is above the freezing temperature. Free convection—Heat flux associated with free convection follows from the formation of unstable, dense water at the surface, which sinks as convective plumes and penetrates and entrains the underlying warm water. Because the density of seawater at low temperatures is almost entirely determined by salinity and because the polar oceans are salt-stratified (see Carmack, 2000) free convection relies on a source of salt, usually brine distilled by ice formation, to overcome ambient stratification (see Gawarkiewicz and Chapman, 1995). Generally winter ice formation increases the salinity of the upper mixed-layer by only 1 to 2 psu (Aagaard et al., 1981; Yamamoto-Kawai et al., 2005). Free convection is thus favored at sites where some form of pre-conditioning has occurred to either (1) locally reduce the stability of the water or (2) locally focus the build-up of brine (Killworth, 1983). One form of preconditioning occurs in the center of cyclonic gyres where the divergence of surface waters reduces local stability by drawing more weakly stratified water near to the surface. For example, regions of reduced stability occur in the cyclonic gyres of the Greenland and Labrador Seas, and these are known to be significant sources of deep water that feed the global thermohaline circulation (Reid and Lynn, 1971; Talley and McCarthy, 1982). Alternately, pre-conditioning may occur on shelves when wind conditions the previous autumn result in the offshore transport of surface waters freshened by summer runoff and ice melt, resulting in more saline surface water at the onset of freezing (Melling and Lewis, 1982; Omstedt et al., 1994). An example of locally-reduced stability also occurs in the cyclonic Weddell Gyre, where Martinson et al. (1981) have proposed that pre-conditioning, brine-driven convection and the intermittent break-down of the halocline may explain the deep-water Maud Rise Polynya (Figure 5a). This polynya attained the largest size (area ∼350,000 km2 ) of any polynya (Carsey, 1980). It appeared between 1974 and 1976, but it has not reappeared since, and the reasons for its appearance and disappearance are widely debated (Morales Maqueda et al., 2004). Possible explanations include penetrative convection in a system oscillating between states of static instability (ice formation and brine rejection) and static stability (convection and ice melt; Martinson et al., 1981), convection triggered by flow topography interactions (Ou, 1991; Holland, 2001a, 2001b; Muench et al., 2001), and the role of freshwater fluxes on deep convection (Marsland and Wolff, 2001). Akitomo et al. (1995) suggested that convection was aided by thermobaric instability, wherein the differential compressibility of water favors the sinking of cold elements, a hypothesis also supported by McPhee (2000). Parkinson (1983) used a coupled ice/ocean model to simulate polynya formation, and Goosse and Fichefet (2001) modeled the polynya through wind-driven icedivergence. Forced-convection—Heat can also be brought to the surface by turbulence generated by the flow. Such heat flux may be associated with tidal forcing, upwelling or throughflow. Of this type, tidal forcing is most frequently studied. Tidally-forced polynyas: The interaction of tidal flow with topography (e.g., continental shelves, channels and banks) can cause amplification and rectification of tidal currents,
Physical Oceanography of Polynyas Figure 5: Diagrams illustrating a variety of physical processes causing convectively forced polynyas: (a) free convection polynya at Maud Rise in the Weddell Sea (Q represents the heat flux to the atmosphere); (b) tidal mixing at Kashevarov Bank in the Okhotsk Sea (adapted from Rogachev et al., 2001); (c) a polynya maintained by upwelling in the Cosmonaut Sea, Antarctica (adapted from Comiso and Gordon, 1996; AD is the Antarctic divergence); and (d) a polynya forced by river throughflow such as Lake Laberge in the Yukon, Canada (site of Sam Magee’s cremation). Diagrams (a), (b) and (d) are vertical sections across the polynya, (c) is a view from above. 65
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enhanced vertical mixing and rectified ice motion, all of which may generate polynyas in icecovered seas. Kashevarov Bank in the Okhotsk Sea is the location of a tidally-driven polynya which opens and closes in phase with the fortnightly spring-neap tidal cycle (Alfultis and Martin, 1987; Martin et al., 1998, 2004a, 2004b) (Figure 5b). The dominant tides over the bank are the diurnal K1 and O1, and the size and shape of the bank is such that these tides resonate with trapped wave modes of the bank leading to local amplification and an attendant increase in vertical mixing (Kowalik and Polyakov, 1998; Polyakov and Martin, 2000). There is a tidally well-mixed region over the top of the bank that is surrounded by a tidal mixing front on the sides of the bank (Rogachev et al., 2000, 2001). Non-linear interaction of the tide with the bank also leads to an anticyclonic tidally-rectified flow around the bank. The difference in frequency between the K1 and O1 tides is a fortnightly cycle. Due to the resonant and non-linear interaction of the diurnal tide with the bank, this cycle is amplified leading to strong variability over a fortnightly cycle. In particular, vertical mixing over the bank is much larger during ‘spring’ tides than during ‘neap’ tides; Rogachev et al. (2001) speculate that this fortnightly sequence of mixing followed by relaxation and re-stratification introduces a strong biological periodicity. There are other examples of tidally-forced polynyas at constrictions or sills in the straits of the Canadian Arctic Archipelago (den Hartog et al., 1983; McLaughlin et al., 2006). Tidal flows at these locations are strong and vertical mixing is enhanced. It is expected that the polynyas here are largely due to the swift tidal currents breaking up ice that forms at the sill (mechanical forcing); however, there is also likely a contribution due to vertical mixing of heat from warmer subsurface layers (Smith et al., 1983). An excellent overview of the polynyas in this region is given in Stirling and Cleator (1981). Examples include polynyas at Hells Gate and Cardigan Strait, and at Dundas Island (Smith et al., 1983; Topham et al., 1983) where tidal flows force convection to bring warmer water to the surface during winter. A simple flux model for forced-convection polynya formation over sills, analogous to the flux model for flaw leads (Pease, 1987) can be formulated. Consider a two-dimensional, two-layer system of uniform flow U directed over a mixing region (e.g., sill); initially the upper layer of thickness hu is at the freezing temperature and the lower layer of thickness hl is some temperature T above freezing. Then the heat H (in watts m−1 ) advected into the mixing region, per metre of width at the sill of the channel, is Hin = ρcp T h1 U.
(2)
For a surface flux Q from an overlying polynya of length L, the loss of heat to the atmosphere is Hout = QL.
(3)
A polynya of length L can be maintained as long as Hin > Hout . Q is seasonally variable, with large contributions coming from shortwave radiation in the spring and early summer, so that L increases as the season progresses. Upwelling polynyas: Transfer of heat from deep to surface waters can occur as a result of upwelling, either at the coast or in the middle of a gyre. Comiso and Gordon (1987, 1996) have examined the formation and variability of the Cosmonaut Polynya, which forms in early winter off Cape Ann (50◦ E) in Enderby Land, Antarctica. Gordon and Comiso (1988) argue that flow/topography interactions compress streamlines at Cape Ann leading to vortex stretching and strong cyclonic flow, thus driving upwelling that supplies the heat flux to form an embayment in the ice edge (Figure 5c). These authors also note that a second pattern of
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polynya formation develops farther to the east in winter and early spring as a wind-driven flaw polynya, and several processes are operative throughout the annual cycle. Circumpolar Deep Water surrounds the Antarctic continent and upwells near the continental shelf break due to the wind-driven circulation associated with the Antarctic Circumpolar Current. Circumpolar Deep Water is relatively warm, and its intrusion onto the Antarctic continental shelf brings heat to polynyas. An example is the Ross Sea polynya (Pillsbury and Jacobs, 1985; Jacobs and Comiso, 1989). Throughflow polynyas: Another example of forced-convection generating polynyas occurs in ice-covered lakes, where the turbulent jet of riverine inflow entrains warmer deep water to the surface (Stigebrandt, 1978). A similar process occurs at the outflow of icecovered lakes where accelerated flow and selective withdrawal of lake water to the outflow draws warmer deep water closer to the surface, where it is mixed to the surface by turbulence to create a polynya (Hamblin and Carmack, 1990). Thus ice-covered lakes often exhibit two polynyas: a small one at the inflow and a larger one at the outflow. An example of this is Lake Laberge in northwestern Canada (Figure 5d; Carmack et al., 1987). Because of the vast number of lakes and reservoirs with seasonal ice cover, this type of polynya is numerically dominant compared to oceanic polynyas and offer excellent and accessible sites for process studies; however, they remain largely ignored. 2.3
Feedback Processes Within Polynyas
Polynyas are complex phenomena and most if not all are formed and maintained by a confluence of physical processes and feedbacks. A diagram of known feedback processes is shown in Figure 6. For example, wind-driven ice-divergence directly causes open water but also results in stress at the surface of the ocean and brine rejection. The stress can cause shelf-wide upwelling/downwelling circulations and local upwelling/downwelling circulations at the ice edge due to differential stress on the water between the open water and the ice covered water. The brine rejection causes entrainment of underlying water to the surface mixed layer.
Figure 6: A wiring diagram of polynya forcing, system response and structural attributes. Boxes along the top of the diagram denote various forcing mechanisms (e.g., wind, currents, tides, upwelling, free convection and insolation). Solid (downward-directed) arrows depict system responses to the various forcing mechanisms; dashed (upward-directed) arrows depict positive feedback links.
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These circulations can bring heat to the polynya resulting in a convectively-forced component. Alternatively, convective processes that locally thin the ice will also weaken it, perhaps allowing wind-forcing or internal ice stress to open a hole in the ice so that the open water is again now due to the result of a combination of convective- and mechanical-forcing. Because of the many feedbacks it is unlikely that only one mechanism contributes to any polynya. A hybrid polynya is one in which multiple processes dominate either in parallel or in series. One such example is the polynya that forms in northern Nares Strait, where an ice bridge forms and prevents ice from entering from the Lincoln Sea, allowing a latent heat effect, while strong tides in the strait enhance vertical mixing and allow a sensible heat effect (Kozo, 1991). Hybrid processes are especially important, from a climate change perspective, if they occur in temporal sequence or if one process serves to trigger or set favorable preconditions for another. 2.4
Marginal Ice Zone ‘Polynyas’
In our definition of a polynya, it is implied that the polynya region is bounded by ice or a mixture of ice and land, but not open water. Allowing open water as a boundary to the polynya region leads us to embayments in the Marginal Ice Zone that are caused by similar physical processes to those causing polynyas. Examples of these in the Arctic are the mechanically-driven Nordbutka and convectively-driven Whalers Bay ‘polynyas’ Examples in the Antarctic are the Cosmonaut Polynya (see above) which can begin as an embayment in the ice edge (Moore et al., 1999) and the western side of the north Antarctic Peninsula (Barber and Massom, 2007). The Nordbutka (North Bay) forms within an open ocean ‘embayment’ formed by a northeast retroflection of the East Greenland ice stream along the cold, low salinity Jan Mayen Current (Figure 7a). Comiso et al. (2001) proposed that new frazil ice formed with the embayment is subsequently transported to the Odden by westerly winds, thus maintaining the polynya and providing a source of new ice to the Odden. In Whalers Bay, north of Spitzbergen (Aagaard et al., 1987; Falk-Petersen et al., 2000), heat is advected to the ice edge by the West Spitzbergen Current, an extension of the warm Norwegian-Atlantic Current (Figure 7b). Upon encountering the westward moving pack ice, the eastward moving current melts ice to form the Bay (Untersteiner, 1988). Water mass transformation is also associated with these phenomena, leading to the formation of one member of the Arctic halocline complex (Steele et al., 1995; Rudels et al., 1996). Because of this advective heat and salt source, this region may be particularly sensitive to global warming (Arctic Climate Impact Assessment, 2005). Solar radiation is especially important during spring transition. The albedo of open water in a polynya is far less than that of the surrounding snow and ice so that open polynyas receive proportionally more solar heat input. Therefore, as day length and air temperatures increase during the Arctic and Antarctic spring, a polynya begins to gain heat from the atmosphere before the ice pack does. At this point the character of the polynya changes: ice-divergence and convection are no longer necessary to prevent ice from growing in the polynya and the heat absorbed can enlarge the polynya by melting ice (e.g., the Ross Sea Polynya, the North Water Polynya). As polynyas enlarge in the spring and the ice edge retreats, many polynya regions likely become embayments in the marginal ice zone. A similar transition also occurs for the flaw lead in the Arctic, which widens in the spring to become open water and the marginal ice-zone (Figure 8).
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Figure 7: Physical processes in 2 marginal ice zone polynyas: (a) the Nordbutka and Odden, a wind-driven polynya that forms an open ocean embayment in the Greenland Sea (Q represents heat lost to the atmosphere as frazil ice forms); and (b) Whalers Bay off northwestern Spitzbergen, a free-convection polynya with lateral advection (AW is Atlantic Water which brings heat to the region, BHF is buoyancy lost to the atmosphere via sensible heat flux and BMW is buoyancy added to the water column by melt water). Both diagrams are vertical sections across the polynya.
Figure 8: Diagram of the season cycle of ice cover in the southern Beaufort Sea. The flaw polynya expands to open water over the summer. MYI is multi-year ice, SIZ is the seasonal ice zone, and MIZ is the marginal ice zone. INY is the ice new year when seasonal ice that has survived the summer is defined to graduate to the multi-year ice class.
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3 Physical Oceanography Our focus in this section is on the processes that occur internal to the ocean. Mechanisms associated with air/sea/ice interaction and the physics of sea ice are reviewed by Morales Maqueda et al. (2004) and by Barber and Massom (2007) and Minnett and Key (2007). Two main topics are examined: firstly, the creation and transformation of water masses within the polynya by brine rejection, heat loss and free and forced-convection; and secondly, the transport of these new water masses away from the polynya via density-driven flows that are subsequently modified by rotation, bottom friction and entrainment/detrainment, and which interact with shelf/slope topography, including canyons and glacial depressions. 3.1
Water Mass Transformation Within Polynyas
Little is known about mixed layer processes within wind-driven polynyas owing to difficulties of making direct observations (see Meunch et al., 1995 for related observations of convection within an Arctic lead). As frazil-ice forms in the polynya, free convection of the rejected brine must cause an increase in salinity and deepening of the surface mixed layer. In the open water, wind-rows of the frazil ice are evident from airborne photography and satellite images, and this is suggestive of the presence of Langmuir circulations. Observations of frazil ice within the St. Lawrence Island polynya were presented in Drucker et al. (2003) using both satellite images and moored upward-looking sonars. Their data shows evidence of frazil ice 5–20 m deep in the water column, and this is consistent with both in situ formation of ice at depth in a marginally super-cooled water column or with downward advection of ice by Langmuir circulation. During a winter opening of a wind-driven polynya, intense surface buoyancy loss and large wind stresses occur together. The combined effect of this forcing on Langmuir circulations and mixed layer dynamics is worth further examination. In convectively, driven polynyas, new water masses are formed by vertical mixing of relatively warm and salty water underlying the fresher and colder surface water. The result is a water mass of intermediate salinity and hence intermediate density. Heat loss at the surface also cools this water, possibly close to its freezing point. In this two-layer scenario the newly created water mass leaves the polynya region by intruding between the upper and lower water masses. Flows of this type are to be found near the sill polynyas in the Canadian Arctic Archipelago, where tidal flows are strong enough to produce this mixing effect yearround (Melling, 2000), and over Kashevarov Bank in the Okhotsk Sea, for similar reasons (Rogachev et al., 2000). 3.2
Transport of Dense Water Away from the Polynya
Brine rejection associated with ice formation is most efficient where there is strong icedivergence and these regions are predominately the coastal- and flaw-lead polynyas (e.g., Melling and Lewis, 1982; Markus, 1998). Thus, large quantities of brine enriched water tend to be produced near shore over the continental shelf and, because it is dense, it eventually crosses the shelf and shelf-break and descends the slope. This brine drainage from shelves has long been implicated as a mechanism for ventilation of intermediate and deep waters in both the Arctic (Aagaard et al., 1981, 1987; Melling and Lewis, 1982) and Antarctic (Gill, 1973; Foster and Carmack, 1976) seas. Observations of Dense Water Descent: In the Antarctic numerous observations indicate that dense brine-modified water descends on the continental slope (Baines and Condie, 1998). The three major sites of these down-slope flows are the Western Weddell Sea (Foster
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and Carmack, 1976), the Ross Sea (Jacobs et al., 1970) and the Adelie Depression (Rintoul, 1998). Other sites of down-slope flows include Enderby Land (Killworth, 1983) and Wilkes Land (Carmack and Killworth, 1978). When flow down the Antarctic slope reaches the bottom it becomes Antarctic Bottom Water. Of the approximately 8–9.5 Sv of Antarctic Bottom Water formation calculated from CFC-11 inventories (Orsi et al., 1999, 2002), 66% generates from the Western Weddell Sea, 7% from the Ross Sea and 25% from the Adelie Depression (Rintoul, 1998). If the descending plume is not dense enough to reach the bottom, it detaches from the slope, forming a distinct layer at intermediate depth (Baines and Condie, 1998). Evidence of formation of intermediate water by detraining, rather than detaching, down-slope flow has also been found (e.g., Carmack, 1973, Figure 2). In the Arctic the most compelling observations of brine drainage derive from the Storfjorden, Svalbard and downstream (Schauer, 1995; Schauer and Fahrbach, 1999; Haarpaintner et al., 2001; Fer et al., 2003, 2004; Skogseth et al., 2004). This water is dense enough to descend beneath the Atlantic layer in the Arctic Ocean, and so contributes to deep water there. Elsewhere in the Arctic, production of dense water in flaw-lead polynyas is the major source of dense Arctic shelf water (Martin and Cavalieri, 1989). This dense water is thought to contribute to the cold halocline of the Arctic Ocean; and Winsor and Björk (2000) estimate a production rate of 0.2 Sv of 32.85 psu water for these flaw-lead polynyas which would provide about 30% of the flux necessary to maintain the halocline complex. It is unlikely that the dense water produced in the flaw-lead system is dense enough to penetrate the Atlantic layer because of entrainment of lighter water (Melling and Lewis, 1982), but an exception to this may be the Chukchi Sea (Winsor and Björk, 2000). In the Okhotsk Sea the hydrographic effects of dense-water production by polynyas have been described by Gladyshev et al. (2003) and Shcherbina et al. (2003, 2004). Here dense water produced by flaw polynyas in the north contributes to Okhotsk Sea Mode Water (Fukamachi et al., 2004). While they do not specifically implicate polynyas as the source, Talley et al. (2003) note that brine drainage from the Tartar Strait region is responsible for deep-water renewal in the Sea of Japan. Physical Mechanisms of Dense Water Descent: Reviews of the processes involved with cross-shelf and down-slope flows are provided by Killworth (1983), Rudels (1993), and Baines and Condie (1998). Models of the descent of water have focused on geostrophic adjustment, bottom Ekman layers, entrainment/detrainment, instability, thermobaricity and the effect of canyons. These models also can be loosely grouped by whether they address a localized source from a sill or a broad source at the shelf-break. There are several types of shelf bathymetry over which dense polynya water forms, and this affects the characteristics of the descending water. The Arctic shelves are typically 100– 400 km wide and have a shelf-break that is only about 80 m. On these shelves, the flaw lead is located at roughly the 20 m isobath, and dense water produced there must traverse a wide, shallow and gently sloping shelf before descending the slope. In contrast, the western Weddell Sea in Antarctica has a shelf that is approximately 400 m and 200 km wide. Dense water that collects on this shelf is thought to cross the shelf-break and flow down the slope as a about 300 m thick ‘sheet’ (Baines and Condie, 1998). Finally, a common feature of the Antarctic is a depression or basin in the shelf, formed by glaciation. Dense water collects in the depression before spilling over a relatively narrow sill near the shelf-break to create a somewhat localized source of dense water at the top of the slope. The Adelie Depression and the Ross Sea are examples of this type of Antarctic bathymetry, and Storfjorden and the Barents Sea (Midttun, 1985) are Arctic examples.
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As dense water descends it is acted upon by the Coriolis force. Geostrophic adjustment of a dense shelf-water front at the shelf break is considered by Condie (1995) for a crossshelf section with no along-shelf variation. Down-slope flow of the dense shelf water occurs during the geostrophic adjustment but the final adjusted steady state has only along-slope velocities. Further down-slope flow can then be achieved via continual re-adjustment of the front down-slope to accommodate more dense water (Baines and Condie, 1998). This model is likely to be most applicable where the thickness of the descending plume is much greater than the bottom Ekman layer thickness, such as in the Western Weddell Sea. The effects of bottom friction can be included by adding a thin bottom Ekman layer to the model. In this layer, dense water descends the slope obliquely creating a down-slope transport that can balance the production rate of dense shelf water. Bottom Ekman transport will be more important if the descending plume is thin, such as might be anticipated on the Arctic slope. If the descending dense water is thick enough for geostrophic adjustment to be important, baroclinic instability of the front between the dense water and the ocean interior leads to the development of eddies. Indeed, a common feature of both laboratory and numerical models is the eddying of the dense water as it traverses the shelf and slope. Such eddies have a signature throughout the water column with the dense water turning anticyclonically and water above turning cyclonically (e.g., Gawarkiewicz and Chapman, 1995). Evidence of the surface expression of eddies of this type has been found in satellite images of the Denmark Strait overflow (Jiang and Garwood, 1996). Numerical modeling indicates that this type of eddy motion can dominate the cross-shelf flux of dense water away from a shallow polynya. In contrast, over the slope, down-slope transport in a bottom Ekman layer can dominate over the eddying of the thicker layer near the top of the slope (Baines and Condie, 1998). The rotational adjustment of dense, brine-modified waters within a coastal polynya and their flow across a continental shelf and slope to deeper waters has been considered in detail in a series of papers which use both theory and idealized numerical models (Chapman and Gawarkiewicz, 1995, 1997; Chapman, 1999, 2000). The scenario is one where brine rejection causes water within the polynya to become denser than the surrounding ocean so that a surface-to-bottom density front develops at the edge of the polynya. This front quickly adjusts to geostrophic balance and then eventually becomes unstable. The eddies which subsequently develop from the instabilities cause a flux of dense, brine-modified water across the shelf away from the polynya. There is also a compensating flux of lighter, ambient water into the polynya. The eddy fluxes are able to roughly balance the brine rejection, so that the density within the polynya region does not change much after the onset of instability. Chapman and Gawarkiewicz (1997) developed scale-based estimates for the maximum density of the polynya water and the time at which it is achieved. Gawarkiewicz (2000) included the effects of a shelf-break, slope and stratification on the eddy propagation. The along-shelf scale of the resulting down-slope flow was set by the scale of eddies at the shelf-break. For weak stratification the down-slope flow continued to the bottom. For strong stratification the flow is injected over the slope at intermediate depth. As dense water continues down-slope it entrains overlying water via turbulent mixing which increases the volume of the plume and decreases its density. This ultimately reduces the depth at which the descending plume finds its equilibrium density in the stratified ocean, the point at which it detaches from the slope and flows into the interior. Turbulent entrainment is evident in hydrographic sections across the Antarctic slope. As well as making the plume less dense and more voluminous, turbulent entrainment creates stratification within the plume, so that the plume becomes less dense further away from the seabed. As the plume descends though the stratified ocean, detrainment of the outer edge of the plume, rather than
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complete detachment of the plume, can then occur as the fluid at the outer edge of the plume finds its equilibrium density (cf. Aagaard et al., 1985). The descent of dense water likely involves a continual turbulent entrainment and detrainment process at the boundary between the plume and the interior. Models that include entrainment need to specify entrainment coefficients. These are not well known from ocean measurements and have been based on the Richardson number (Baines and Condie, 1998) or on can be slope dependent (Melling and Lewis, 1982). A descending cold plume also experiences differential compressibility. One of the consequences of the non-linear equation of state for seawater is that cold seawater is more compressible than warm seawater. Dense polynya water is very cold, so as is descends the slope it becomes denser relative to warmer ambient water. This ‘thermobaric’ effect increases the penetration depth of the down-slope flow in the Antarctic (Gill, 1973; Foster and Carmack, 1976; Killworth, 1977). Along-shelf/slope variations in bathymetry can also lead to increased cross-shelf or down-slope flow. For example, a cross-shelf/slope canyon can channel flow cross-shelf and down-slope by interrupting geostrophic balance and Ekman layers and causing a downcanyon, gravity current component to the flow (Baines and Condie, 1998). Indications of down-canyon flow of dense water have been found in data from Kugmallit Valley on the Mackenzie Shelf, after a large flaw-lead polynya event (Williams et al., 2006) and have also been simulated in laboratories (Baines and Condie, 1998; Kampf, 2005). Chapman and Gawarkiewicz (1995) investigate a polynya located at the head of a canyon using a numerical model. In this case, when eddying motions encounter the canyon, a gravity current of dense water forms and it flows down the canyon, enhancing cross-shelf transport there. Many slope regions in the Arctic and Antarctic are crossed by canyons or are ‘corrugated’ so that their modification of down-slope flow is possibly significant. However, there are also many areas where slope topography is not well known so that the overall effect of canyons on deep-water formation is uncertain. The behavior of sinking plumes can also be altered by interaction with ambient circulation. For example, surface-stress due to wind and ice motion can cause both shelf-wide upwelling/downwelling circulations and localized circulation at the ice edge due to differential surface stress between open and ice covered water (Hakkinen, 1986; Carmack and Chapman, 2003). Since many polynyas are mechanically forced by the wind the effect of the mean circulation generated by surface stress on dense water produced in these polynyas is a complex and integral part of polynya dynamics. Chapman (2000) extended his numerical polynya model to include the effects of a mean along-shelf flow and found that the eddying dense water was advected downstream by the mean flow and that the maximum density anomaly attained within the polynya region was reduced. Data from the St Lawrence Island Polynya area show only slight evidence of geostrophic adjustment, exhibit no cross-shelf eddy density fluxes, and suggest that dense polynya water may be advected away from the polynya region before the equilibrium eddy generation time scale is attained (S. Danielson, pers. comm.).
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Biological Importance of Polynyas
The strong physical forcing within polynyas impacts biological processes. Mechanicallyforced polynyas that are maintained by latent-heat flux are often called “ice factories” because the continual removal of new ice maintains large heat losses and corresponding ice
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growth rates. This is especially true of the Siberian flaw polynyas, which supply vast quantities of ice to the Trans-Polar Drift (Pfirman et al., 1997). At the same time such polynyas could be called “brine factories” owing to their disproportionate inputs of salt to the water column, and the corresponding link to ventilation of intermediate and deep waters. The “factory” concept can also be applied to convectively-forced polynyas that are maintained by sensible heat flux; here, however, it is not ice or brine formation that is enhanced, but rather nutrient flux to the euphotic zone, for the same physical processes that bring warm waters from depth also supply nutrients (cf. Tremblay et al., 2002). With the increased solar radiation in spring and summer, the ice/water albedo effect comes into play, and both major polynya types become sites of disproportionate warming and ice melting (Ohshima et al., 1998); at this time of year, as noted by Morales Maqueda et al. (2004), polynyas could be called “meltwater factories”. Early input of solar radiation and the differing stability/water mass characteristics advances the timing of biological production and vertical fluxes of biogenic materials (Tremblay and Smith, 2007). The polar seas are everywhere salt-stratified, and this insulates the surface waters and ice from the underlying warm waters (e.g., Atlantic and Pacific Summer waters in the Arctic and Circumpolar Deep Water in the Antarctic; Carmack, 2000). This same stratification acts to constrain the depth of haline convection in winter, and thus suppress the seasonal resetting of nutrients from underlying waters, a situation that potentially limits new production (Carmack et al., 2004). However, enhanced winter nutrient fluxes can be expected in polynyas. Elevated brine production associated with mechanical forcing and latent heat fluxes penetrates deeper into the underlying halocline than would otherwise occur from without ice divergence. Likewise, turbulent mixing associated with convective-forcing and sensible heat fluxes will enhance the upward flux of nutrients; if sufficient solar radiation enters the polynya before the waters with elevated nutrients are removed by currents, then enhanced production should occur (Melling et al., 2001; Mei et al., 2002). But, polynyas are not, by necessity, bottom-up hot spots of elevated primary production, as has been noted in the case of the Saint Lawrence Island Polynya (J. Grebmeier and L. Cooper, pers. comm.), so each must be evaluated individually.
5 Future Research in a Changing Environment A central goal for future research is to improve our theoretical understanding of polynyas so that better models can be developed (see Morales Maqueda et al., 2004, for review). This requires effort in advancing both process models (e.g., Chapman, 1999; Winsor and Björk, 2000) and coupled simulation models (e.g., Darby et al., 1994; Biggs and Willmott, 2001). But new advances in modeling will require major efforts in field observations, both to advance new theories and to validate model predictions. Because of their limited size (generally sub-grid scale) and disproportionate influence in air-sea exchanges (heat and moisture fluxes) their parameterization in climate simulation models is especially critical and challenging (Morales Maqueda et al., 2004). The social importance of this challenge is elucidated in the Arctic Climate Impact Assessment (Arctic Climate Impact Assessment, 2005) and the key to this challenge is the ability to predict the size of a polynya, how it evolves in time and how it will respond to altered forcing. In particular, our knowledge of the physical oceanography and fluid dynamics of polynyas is limited. We know little about the mixed layer within a wind-driven polynya, how that mixed layer deepens from brine rejection and how the density front around the polynya
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boundaries is maintained. Also, the flow of dense water masses away from the polynya, their descent across the shelf and slope and ultimate integration into oceanic water masses is complex, involving eddies, gravity currents and turbulent entrainment, all of which will benefit from more detailed observations. How will a changing climate impact polynyas? A conceptual schematic for the response of polynyas to climate warming along an idealized meridional section from low to high latitudes is shown in Figure 9. For simplicity a comparison is made for a series (numbered 1 to 5) of forced-convection (sensible heat) polynyas wherein locally-intensified mixing processes transport heat from warm deep waters through the halocline (solid line) to the surface. Figure 9a depicts present day conditions in winter with both seasonal ice (SI) and perennial ice (PI). No polynya can exist outside the halocline (salt-stratification) boundary as this is the tabla rasa for sea-ice formation; any advance or retreat of the northern or southern halocline will constrain polynya formation in either a warming or cooling climate. Vertical mixing at location 1 transports heat to the surface, but because the attendant atmospheric heat loss is insufficient to form ambient ice, there obviously is no polynya. Above location 2 the atmospheric heat loss is sufficient to form ice in winter and the oceanic heat flux is sufficient to maintain open water; an example here is the Kashevarov Bank polynya (Polyakov and Martin, 2000; Rogachev et al., 2001). At location 3 the oceanic heat flux is sufficient to maintain a wintertime polynya in perennial ice, while at location 4 the oceanic heat flux results in a substantial thinning and weakening of ice but no open water; the Northeast Water polynya appears to lie somewhere between 3 and 4 (Johnson and Niebauer, 1995; Schneider and Budéus, 1995). Above location 5 the oceanic heat flux effects a minor and likely unnoticed thinning of the overlying perennial ice. Figure 9b depicts present day conditions in summer. With the melting of seasonal ice, no polynya occurs at location 2, but the same mixing that transports heat will be associated with nutrient fluxes, leading to a biological ‘hot spot’ rather than a physical one. At location 3 the polynya may be expected to become larger due to diminished atmospheric heat loss and increased solar radiation (ice-albedo feedback). Above location 4 the oceanic heat flux that resulted in thinning during winter may now erode ice to the surface, forming a summer polynya in the perennial ice, while at location 5 seasonal thinning is expected. The same idealized meridional section under scenarios of climate warming will exhibit decreases in ice extent, thickness and duration (Arctic Climate Impact Assessment, 2005). Figure 9c depicts hypothetical wintertime conditions with retreat and thinning of both seasonal and perennial ice. The polynya above location 2 has now vanished altogether. At location 3 the perennial ice has retreated to higher latitudes and only a seasonal polynya now forms. At location 4, formerly a site of local thinning, a year-round polynya now forms. At location 5, formerly a site of minor thinning, the oceanic heat flux now maintains substantially thinner ice. Finally, Figure 9d depicts future summertime conditions under scenarios of climate warming. No polynya is observed at location 3, a much larger polynya is maintained at location 4, and a new seasonal polynya is formed at location 5. A direct and potentially fruitful approach to predicting the sites of future (potential) polynyas, as well as the associated biological hot spots, may lie in the application of tidal dissipation models (H. Simmons, pers. comm.). The above argument is most applicable to the Canadian Arctic Archipelago which spans a wide range of latitude and contains tidally-driven polynyas at constrictions in the straits. In the Antarctic the locations of polynyas found around the Antarctic Peninsula may also move poleward under climate change. However, for most Antarctic polynyas a poleward shift in position is blocked by the Antarctic land mass, and so different effects of climate change will likely occur there.
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Figure 9: Diagram of the response of polynyas to climate warming along a hypothetical meridional transect from low to high latitudes and a sequence (1 to 5) of regions of enhanced vertical mixing (indicated by oval arrows): (a) present day conditions in winter; (b) present day conditions in summer; (c) under warming conditions for winter; and (d) under warming conditions for summer. Here SI is seasonal ice and PI is perennial ice. The ice/water–albedo feedback mechanism is shown by arrows entering the water or reflecting off snow and ice.
Acknowledgements We would like to thank the Canadian Program of Energy Research and Development, Canadian NSERC and the Canadian Arctic Shelf Exchange Study (CASES) for providing the
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funding. Patricia Kimber provided the figures. S.S. Jacobs, H. Melling, R. Muench and W.O. Smith gave helpful advice and criticism. We acknowledge the late Malcolm Ramsay and his inspiration in all things Arctic.
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Winsor, P., Björk, G., 2000. Polynya activity in the Arctic Ocean from 1958 to 1997. Journal of Geophysical Research 105, 8789–8803. Yamamoto-Kawai, M., Tanaka, N., Pivovarov, S., 2005. Freshwater and brine behaviors in the Arctic Ocean deduced from historical data of δ 18 O and alkalinity (1935–2002 AD). Journal of Geophysical Research 110, doi:10.1029/2004JC002793. Zubov, N.N., 1943. Arctic Ice. Glavsevmorputi, Moscow, 491 pp. Zwally, H.J., Comiso, J.C., Gordon, A.L., 1985. Antarctic offshore leads and polynyas and oceanographic effects. In: Jacobs, S.S. (Ed.), Oceanology of the Antarctic Continental Shelf. In: Antarctic Research Series, vol. 43. AGU, Washington, DC, pp. 203–226.
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Chapter 3
Polynya Modelling A.J. Willmott1,* , D.M. Holland2 and M.A. Morales Maqueda2,* 1 Department of Mathematics, Keele University, Keele, Staffordshire ST5 5BG, UK 2 Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
Abstract Polynya models can be divided into two categories: polynya flux models and general circulation polynya models. Flux models predict the location of the polynya edge by postulating mass and momentum balances at the polynya edge that are based on the dynamic equations for frazil and consolidated new ice. The general circulation approach uses comprehensive dynamic-thermodynamic sea ice models to predict the ice concentration in a certain region and, from this, the evolution of the polynya, which is characterised as the oceanic area within which ice concentration is smaller than a given threshold (10%, say). In this chapter, we review the most recent literature on both polynya modelling approaches.
1 Introduction From the perspective of the climate modelling and physical oceanographic communities, recurrent polynyas are worthy of study because they are sites where, (1) water mass transformation takes place through the combined effects of cooling and frazil ice formation, (2) large ocean-to-atmosphere heat (several hundred Watts per square metre (m2 )) and moisture fluxes occur, (3) atmosphere CO2 is sequestered into the ocean by physical-chemical processes and biological activity. These three phenomena associated with polynyas are discussed in Morales Maqueda et al. (2004). Recurrent polynyas occur most frequently along coastlines, and they are most often driven mechanically by winds or ocean currents, that push the ice offshore. Recurrent polynyas can appear as well in the open ocean, although this type of polynya is less common than the coastal one. The existence of open ocean polynyas is normally due to deep warm water reaching the surface through upwelling or vertical mixing, thus melting the ice, although theories have as well been proposed for the mechanical creation of some of these features (e.g. Holland, 2001a). * Now at Proudman Oceanographic Laboratory, Liverpool, L3 5DA, UK. Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74003-X
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Figure 1: Schematic representation of physical process taking place in deep water and shelf water polynyas (Morales Maqueda et al., 2004). Deep water polynyas occur beyond the continental shelf break and are frequently created and maintained by a sensible-heat mechanism, whereby upwelling or vertical mixing of subsurface water brings about sea ice melting or prevents ice from forming. Strong surface buoyancy losses through cooling can lead to deep water formation. Shelf water polynyas appear over the relatively shallow continental shelves. The majority of these polynyas are wind-driven. Offshore winds push the pack ice away from the coast, exposing the freezing surface waters to the cold atmosphere. Frazil ice is thus formed and herded downwind along Langmuir windrows. Brine-rich, cold water associated with sea ice formation will accumulate over the shelf and eventually flow down the shelf break slope to form deep and bottom water. In order to address the above three phenomena associated with the presence of a coastal polynya, we first require the ability to predict how the area of a polynya will evolve in time. Of course, being able to calculate the evolution of a polynya area is not in its own right sufficient for determining process such as water mass transformation, for example. Knowledge about the stratification of the water column prior to the polynya opening is also required to calculate dense water formation. Unfortunately, we rarely have in-situ hydrographic data within a polynya (e.g. Haarpainter et al., 2001), and instead we have to rely upon climatological data sets (e.g. Steele et al., 2001), which are unlikely to accurately reflect the stratification of waters within a polynya. The purpose of this chapter is to review the range of models that are available for predicting how the area of a polynya evolves in time. Broadly speaking, we can group the models into: (a) process, or reduced physics models, (b) models based on coupled ice–ocean general circulation models (GCMs). Before reviewing models in these two categories, it is instructive to review the physical processes that take place within a polynya. These processes involve coupling between the atmosphere, ocean and sea ice, and currently they are not well represented in process models of coastal polynya evolution. Indeed, the relative importance of these processes in polynya dynamics is still open to debate. Figure 1 schematically represents the physical processes operating in a coastal polynya, namely:
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1. Frazil ice motion within the polynya influenced by the unsteady circulation of the shelf waters, such as Langmuir circulations. 2. Frazil ice production is accompanied by brine input into the upper ocean which modifies the baroclinic flow of the shelf circulation, and this in turn feeds back to influence the trajectories of the frazil ice. 3. As air flows across the polynya (e.g. katabatic winds) it experiences warming from the relatively warm water within the polynya, and this in turn reduces the frazil ice production, primarily through a reduction of the ocean-to-atmosphere sensible heat flux. 4. Warming the surface atmospheric boundary layer above a polynya potentially leads to the generation of perturbation winds that in turn will modify both the frazil ice production and the frazil ice trajectories. 5. Evaporation over the polynya increases the air moisture content over the polynya, thus reducing latent-heat losses, and this promotes the creation of clouds that increase downward longwave radiation. Both processes lead to a reduction in ice growth within the polynya. 6. Tidal and inertial motions and tidal residual currents over the shelves also affect the production of frazil ice by transporting heat in and out the polynya area and causing strong vertical mixing in the water column. For polynyas extending beyond the shelf break, Ekman pumping may as well play an important role in advecting relatively warm deep waters onto the shelf, thus hindering frazil ice production. 7. By increasing the salinity of the shelves, coastal polynyas contribute to the formation of intermediate and deep waters in the Arctic and Southern oceans. Down-slope flow of dense waters created in polynyas is a key, although poorly understood, process in the thermohaline circulation of the polar oceans. The dynamics of these flows are unique in the ocean because they are controlled by thermobaric effects: as the cold water sinks, it tends to remain denser than the warmer ambient water because of the non-linear dependence of the thermal expansion coefficient of sea water on pressure. Thermobaricity is thus important in determining the velocity and entrainment of cold down-slope plumes. In developing simplified physical models for polynya area evolution in time (hereafter we refer to such models as “polynya models”) it would be a daunting task to incorporate all the feedback processes cited above. Indeed, it is reasonable to enquire whether all these feedback processes are equally important in polynya modelling. Intuitively, we would expect the answer to this question to depend upon the maximum extent of the polynya. For a relatively narrow (10 to 20 kilometres (km) wide) coastal polynya maintained by offshore katabatic winds, say, we might expect the rapid transit time (of the order of 30 minutes or less) of air particles across the polynya to lead to weak coupling between the atmosphere and the ocean–sea ice. In contrast, the Giant Weddell Polynya that occurred during the winters of 1974 to 1976 (Zwally et al., 1985) had a typical horizontal length scale of 200 km and we would expect processes (3), (4) and (5) to be operating more strongly in this case. In the following discussion of simplified physical polynya models we will review them in order of increasing complexity.
2 Flux Model Approach A frequently adopted approach to model polynyas is to use flux models. Polynya flux models were first developed by Pease (1987) and Ou (1988), embracing an idea of Lebedev (1968)
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that a wind-generated coastal polynya attains a maximum size determined by the balance between the ice production within the polynya and the flux of ice out of the polynya. If such a balance does not occur the polynya area will be evolving. Using this simple concept, a variety of flux models have been developed over the last fifteen years, or so, incorporating many of the feedback processes discussed above. All these flux models either enable the polynya area to be predicted analytically (under certain assumptions) or numerically. The main advantages of using a flux modelling approach compared with models based on ocean and sea ice GCMs are: (1) it is easier to identify the dominant physical processes operating throughout the evolution of the polynya area, (2) these simplified models have their strength in isolating the basic polynya equilibriums and looking at asymptotic solutions, (3) the computational time required for calculating flux model solutions is significantly smaller than for fully dynamicthermodynamic ice models. Another reason for pursuing the polynya flux model approach concerns the parameterisation of dense water production within polynyas not resolved by coarse resolution coupled ocean–sea ice GCMs. Morales Maqueda et al. (2004) discuss parameterisations of this type that utilise the ability of a flux model to predict polynya area. We state the governing equations describing the opening of a two-dimensional coastal polynya which is initially closed. In the theory below we are incorporating the coupling between given surface current and the frazil ice trajectories. However, we are assuming that the atmospheric forcing fields are prescribed; in other words, that there is no coupling between atmosphere and the polynya. We shall relax this constraint in Section 10. Let C(R, t) = 0 describe the polynya edge, where R is the position vector of any point on the edge and t is time. Morales Maqueda and Willmott (2000) show that C satisfies ∂C H U − hc uc (2.1) + · ∇C = 0. ∂t H − hc In (2.1) U is the consolidated ice velocity, uc is the frazil ice velocity at the polynya edge, H is the thickness of newly formed consolidated ice at the polynya edge formed when the frazil ice “piles-up” against C, hc denotes the thickness of frazil ice arriving at the polynya edge, immediately prior to conversion into consolidated new ice, and ∇ = i ∂/∂x + j ∂/∂y, where i and j are unit vectors in the x and y directions, respectively, denotes the two-dimensional Nabla operator. Clearly, if the offshore drift of frazil ice is less than that of consolidated ice, the polynya will open without bound because frazil ice can never “catch-up” with the offshore pack ice. Two points should be noted at this point regarding (2.1). First, we are characterising frazil ice only in terms of its thickness, rather than thickness and concentration. Clearly, it is desirable to incorporate variable ice concentration in a polynya model because satellite observations of these features characterise them as regions of non-zero (but low) ice concentration (e.g. Liu et al., 1997). We shall return to this point in Section 9. Second, there is considerable debate about how to represent H , a topic deferred to the next section. Conservation of frazil ice mass is governed by ht + ∇(hu) = F,
(2.2)
where F (r, t) is the given frazil ice production rate, and we are assuming the density of frazil ice to be constant. Within the polynya the frazil ice concentration is low (typically ≤ 30%) and we assume that the frazil ice velocity can be determined from the free-drift ice momentum balance ρi f k × hu = τ − τ iw ,
(2.3)
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Figure 2: Schematic diagram illustrating the polynya model (adapted from (Willmott et al., 1997)). The evolution of any point P on the polynya ice edge C is determined according to (2.1). τ , uo , u, and U denote the wind stress, the surface velocity of the ocean, the frazil ice velocity, and the consolidated ice velocity respectively. In general, all these fields depend on position. where ρi is the frazil ice density, k is an upward unit vector, f is the Coriolis parameter (assumed constant for shelf flows), τ (r, t) is the prescribed wind stress and τ iw is the ice– water shear stress. It is common (e.g. McPhee, 1979) to adopt a quadratic representation for the ice–water shear stress: τ iw = ρi ciw |u − uo |(u − uo ),
(2.4)
where uo (r, t) represents the prescribed surface current (Figure 2) and ciw is the ice–water shear stress drag coefficient. We again stress that we are not incorporating coupling between brine production, associated with frazil ice formation, and the baroclinic circulation of the shelf water. Such a coupling would mean that uo is not prescribed, but instead would have to be calculated as part of the polynya opening problem. Although u can be determined analytically from (2.3) and (2.4) in terms of τ and uo (Willmott et al., 1997), a simple scale analysis of the terms in (2.3) reveals that the Coriolis term is usually negligible. The exception to this rule is when the depth h becomes several tens of centimeters, or more, but for polynya applications we expect the thickness of frazil ice to not exceed 20 cm. Neglecting the Coriolis term in (2.3), we find that 1
u = uo + (ρi ciw |τ |)− 2 τ ,
(2.5)
upon using (2.4). Initially we shall assume that the polynya is closed, in which case C(R, 0) = L(R),
(2.6)
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where L is the given coastline (Figure 2) and R is the position vector of any point on the polynya edge. On the coastline we also demand that the frazil ice thickness is zero at all times: h(L(r), t) = 0.
(2.7)
To make further progress in solving (2.1)–(2.7) for the polynya edge C we require information about H , uo , τ and F . Certainly, we expect F , τ and uo to vary in space and time, in which case a numerical approach must be used to solve the problem. However, we can learn a great deal about polynya opening by assuming that τ and F are constant and neglecting the effects of surface currents (i.e. uo = 0). Note that we must also parameterise H before we can solve (2.1)–(2.7). We would expect the pile-up of frazil ice against the polynya edge, forming consolidated new ice of thickness H , to be a quantity that has to be determined as part of the polynya opening problem. In other words, we might expect H to be a function of u and U , and this forms the topic of the next section.
3
Parameterisations for the Collection Thickness H
Early polynya flux models (Pease, 1987; Ou, 1988; Darby et al., 1994, 1995) assumed that H is a constant. There are two major drawbacks with this simple parameterisation. First, the polynya opening time and steady-state area are extremely sensitive to the value of H . To understand why, note that in a steady state the flux of frazil ice arriving normal to the polynya edge (referring to Figure 2, hc u · n) balances the flux of consolidated ice away from the polynya in the direction normal to the edge C (i.e. H U · n). We see that H partly determines the latter flux. Second, in deriving (2.1) we assumed that throughout the entire polynya evolution h < H , and in particular at the polynya edge C, hc < H . Indeed, the presence of the singularity in (2.1) when hc = H is indicative that the flux model formulation “breaks down” in this limit. This point is discussed further in Ou (1988) and Biggs et al. (2000). Clearly, for any choice of H , we cannot guarantee, a priori, that h < H will be satisfied throughout the entire polynya evolution. Alternative empirical parameterisations of H as a function of wind speed and polynya width (Alam and Curry, 1998) and wind velocity only (Winsor and Björk, 2000) have been proposed. For a constant wind stress, the latter parameterisation yields a constant value for H , with the associated problems identified above. The former parameterisation for H has not been introduced in a two-dimensional polynya flux model. In the one-dimensional opening problem (see Section 4), the Alam and Curry (1998) parameterisation couples the collection depth to the evolving polynya width, a problem that has yet to be addressed. If however, the instantaneous polynya width is replaced by the steady-state width (a quantity that can be calculated a priori) in the Alam and Curry (1998) parameterisation, we shall again produce a constant value for H . Biggs et al. (2000) propose the following collection thickness parameterisation: H = hc + c|(u − U ) · n|2 ,
(3.1)
where c ≈ 0.665 m−1 s2 , and n is a unit vector normal to C pointing away from the polynya. Otherwise, the notation in (3.1) follows that in Section 2. Parameterisation (3.1) is obtained following the studies of Martin and Kauffman (1981) and Bauer and Martin (1983), who consider the pile-up of grease ice at the downwind edge of a lead. The reader is referred to Biggs et al. (2000) for details of the derivation of (3.1).
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Parameterisation (3.1) has two major advantages over the constant collection thickness approach. First, the inequality H > hc is certain to hold throughout the polynya opening regime. Second, H is calculated as part of the polynya opening problem, rather than being prescribed. For two-dimensional polynyas the thickness H will vary along the edge C because hc and n vary, even when u and U are uniform. In contrast, for a one-dimensional opening case, (3.1) reduces to H = hc + c(u − U )2 ,
(3.2)
where u and U are the frazil and consolidated ice speeds, respectively. Clearly, H is constant along the polynya edge C when u and U are uniform in the one-dimensional case. Although steady-state solutions can be calculated using (3.1) (see Biggs et al., 2000), it is shown by Biggs and Willmott (2004) that (3.1) is not robust in the unsteady opening problem. The origin of the problem with (3.1) occurs when opening to a steady-state polynya edge that has one (or more) corners. At a corner point on C, n is not defined. Thus, as we approach a corner point from either side along C the value of H will be discontinuous at the corner point. In summary, if the steady-state polynya edge associated with (3.1) does not have a corner (see Biggs et al., 2000) then we can solve the opening problem. Biggs and Willmott (2004) propose a modification of (3.1) of the form H = hw + hc + c|(u − U ) · n|2 ,
(3.3)
where hw is a constant thickness associated with the effect of wave radiation stress (Martin and Kauffman, 1981). An estimate of the wave radiation stress collection thickness is of the order of 5 cm based on the fact that surface waves within a polynya are of high frequency type with wavelength lying in the range 0.06 to 1.0 m (Biggs and Willmott, 2004). Parameterisation (3.3) is more robust than (3.1) in the sense that the number of cases in which the polynya cannot open to its steady-state solution are significantly reduced when using (3.3) compared to (3.1). The problem with parameterisation (3.1) resides in the dependence of H on the orientation of the polynya edge C, that leads to discontinuous collection thicknesses at points where C does not have a unique tangent. However, the polynya edge described by C is only an idealisation. In reality, the boundary between the frazil ice region and the offshore consolidated new ice region is made of discontinuous ice floes of many different shapes and whose edges are oriented in arbitrary directions. From this perspective, the polynya edge C described by the flux-model theory can be construed as an abstract line that smooths out the jagged structure of the actual polynya edge over length scales much larger that those of individual ice floes. Frazil ice hitting upon an ice floe will pile-up along its walls and its collection thickness will be given by (3.1) but with n representing now the normal to floe edge. One could argue that it is the average collection thickness of frazil ice along the walls of individual ice floes that should be used as a value for H in (2.1). If we assume, for the sake of the argument, that ice floes are approximately cylindrical then the average collection thickness of frazil ice along the walls of an ice floe, H , will be given by 1 H = hc + c|u − U |2 . (3.4) 2 We note that H in this expression is a function of hc and the modulus of the relative velocity u − U only. Expression (3.4) does not depend on the orientation of the polynya, and therefore this collection thickness parameterisation is free of the problems associated with (3.1) discussed above.
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4 Opening of a One-Dimensional Polynya In this section we shall examine the simplest one-dimensional opening problem. Consider a straight coastline and let X(t) denote the width of the polynya. Then at any time C(R, t) = 0 is equivalent to the straight edge X = X(t). Initially, the polynya is assumed to be closed so that X(0) = 0. The governing equation for the polynya width follows immediately from (2.1), and we obtain dX H U − hc uc . = dt H − hc
(4.1)
Note that in (4.1), U and uc denote the offshore speeds of consolidated and frazil ice at the polynya edge, respectively. A further simplification will be to neglect surface ocean currents and to assume that the ice speeds are uniform and satisfy u > U > 0,
(4.2)
where the subscript c has now been omitted on the frazil ice velocity, for convenience. Inequality (4.2) ensures that the polynya will open to a steady-state width. The frazil ice mass conservation equation (2.2) simplifies to ht + uhx = F,
(4.3)
subject to h(0, t) = 0, t ≥ 0. To understand the fundamental behaviour of the polynya opening problem we further assume that F is constant. 4.1
Constant Collection Thickness
When H is constant the solution of (4.1) and (4.3) subject to the initial condition and coastal boundary condition is X 1 X 1 t= (4.4) − Lp − ln 1 − , u U u Lp where Lp = H U/F is the steady-state width discussed by Pease (1987). The width Lp can be calculated immediately from (4.1) by noting that in the steady-state case H U = hc u
(4.5)
at x = Lp , and that hc = F (Lp /u), where the transit time for frazil ice across the polynya is Lp /u. Clearly (4.5) states that the flux of frazil ice arriving at the polynya edge balances the flux of consolidated new ice away from the polynya edge. During opening, the latter flux exceeds the former because the frazil ice depth is small, and the polynya edge moves offshore. We define the polynya opening time Tp to be the time taken for the polynya to open to a width (1 − )Lp , where typically = 0.05. From (4.4) we see that Lp 1 1 Tp = (4.6) (1 − ) + Lp − ln −1 . u U u
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Parameterisation (3.2) for H
Biggs et al. (2000) show that X = L 1 − exp(−U t/L) ,
(4.7)
where cuU (u − U ) L= (4.8) F is the steady-state width associated with collection thickness (3.2). Notice that L depends cubically on ice drift speeds and will be extremely sensitive to measured errors in these quantities. Once again the width (4.8) can be deduced immediately from (4.5) at X = L. Using the fact that hc = F (L/U ) we find that the flux balance (4.5) becomes FL U (4.9) + c(u − U )2 = F L u from which (4.8) follows. The polynya opening time is given by T =
L ln −1 . U
4.3
Parameterisation (3.3) for H
(4.10)
Biggs and Willmott (2004) show that collection thickness parameterisation (3.3) leads to the following solution for the polynya width X = L˜ 1 − exp −U t/L˜ , (4.11) where the steady-state width L˜ is given by hu cuU (u − U ) ˜ 1+ . L= F c(u − U )2
(4.12)
The opening time in this case is found to be L˜ T˜ = ln −1 . U 4.4
(4.13)
Discussion of the Opening Models
To compare the opening times predicted by the model using a constant collection thickness with that using parameterisation (3.2) we demand that the steady-state width in each model is identical. This can be achieved by requiring H = cu(u − U ),
(4.14)
in the constant collection thickness model. In deriving (4.14) we are of course assuming the ice velocities and ice production rates are identical in each model. Biggs et al. (2000) show that T > Tp and that the relative difference between the opening times T − Tp U 1− U = +1 ≤ < 1, T u ln u
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Figure 3: Temporal evolution of a one-dimensional polynya edge for two values of the ratio U/u: (a) 0.2, (b) 0.6. After Biggs et al. (2000). depends solely on the ratio of the ice drift velocities. Figure 3 shows the evolution of the polynya to its steady state for both models with U/u as a parameter. Figure 4 shows contour plots of Lp , L, Tp and T in air temperature (Ta )–wind speed (Ua ) space. The solutions plotted in Figures 3 and 4 use an expression for F proposed by Pease (1987) which linearly depends on Ta via the sensible heat term. From this figure we deduce that 1. L is a more sensitive function than Lp of Ua . The weak dependence of Lp on Ua is explained by the fact that both U and F increase almost linearly with increasing Ua . Increasing Ua tends to open the polynya, while increasing F tends to close it and the two processes cancel. However, L grows quadratically with Ua (see (4.8)). 2. Both Tp and T are strong functions of Ua and Ta . Note that Tp increases as Ua increases, while the opposite is true for T , and this is due to the increased dependence of L (compared with Lp ) on Ua . In a constant H model, an increase in Ua leads to a linear increase in both U and u and (4.6) shows that Tp depends on Ua via the factor F −1 . However, T grows linearly with Ua (see (4.10)). We see from (4.8) and (4.12) that L˜ > L for given U , u and F . Hence (4.10) and (4.13) show that T˜ > T . For example, with U = 0.3 m s−1 , u = 2 U and F = 0.27 m day−1 we find that L = 11.5 km, while L˜ = 21 km. The corresponding opening times are T = 31.9 h and T˜ = 59 h.
5 Two-Dimensional Steady-State Solutions Polynya flux models in two dimensions were first introduced by Darby et al. (1994) and the model representation of the influence of coastline orientation on steady-state polynya shape was investigated in Darby et al. (1995). With the notation introduced in Section 2, the problem solved by Darby et al. (1995) can be formulated as (H U − hc uc ) · n = 0,
(5.1)
which simply states that a balance between the mass fluxes of frazil and consolidated new ice must exist at the steady-state polynya edge in a direction normal to the edge. Darby et al. (1995) introduced the concept of an alongshore polynya length scale La that measures the alongshore distance over which a polynya adjusts to its asymptotic width. In this paper the frazil and consolidated ice velocities are assumed constant. The length scale La depends
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Figure 4: Contour plots of the new and H -constant equilibrium widths L and Lc , (a) and (b) respectively (in km), and the corresponding spin-up times t 0.05 and tc0.05 , (c) and (d) respectively (in hours), as a function of air temperature Ta (◦ C) and wind speed Ua (m s−1 ). For plots (b) and (d), the constant collection thickness Hc is chosen so that L = Lc at the control wind speed of Ua = 20 m s−1 . After Biggs et al. (2000). on the direction of travel of both frazil ice and the consolidated new ice. When frazil ice and consolidated ice both drift due offshore, La is zero. Variations in the coastline shape that occur over length scales much smaller than La are not reproduced in the steady-state polynya edge. However, the steady-state polynya reproduces the shape of capes and coastal embayments provided their alongshore lengthscale is greater than or equal to La . A simple theory was developed by Morales Maqueda and Willmott (2000) for the derivation of La by considering the polynya edge response to small coastline departures from a straight line. Biggs et al. (2000) revisited the steady-state polynya problem of Darby et al. (1995), instead using (3.1) as the collection thickness. They study polynya formation for two simple coastline configurations, namely a semi-infinite and a finite-length coastal barriers. In all
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cases, their steady-state solutions are similar to those obtained with constant H , although, for identical u, U and F , polynya areas are normally larger when H is not a constant. Note that in (3.1) the subscript c has been dropped on the frazil ice velocity because it is assumed to be constant. A peculiarity of the steady non-constant H solutions is that corners in the polynya edge can form. As discussed above, the frazil ice collection thickness is not uniquely defined at these points.
6 Two-Dimensional Steady-State Solutions with Ocean Currents So far, we have neglected in our discussion the effects of coastal currents on the drift of frazil and consolidated sea ice on a polynya, a problem that was investigated by Willmott et al. (1997). These authors calculate free-drift frazil ice velocities within a polynya using (2.3) and (2.4) and derive analytical expressions for the polynya shape for the case of a semi-infinite straight coastline when uo , U and τ are uniform in the alongshore direction. They also determine the analytical formula for the alongshore polynya length scale La in this case. In Darby et al. (1995) frazil ice trajectories were a family of straight lines and this fact was exploited in the derivation of La . In the present problem, frazil ice trajectories are generally curved and are not parallel to each other (Figure 5). As a result, La will depend on the particular geometry of these trajectories. This is illustrated in Figure 5, where three steady-state polynya solutions are shown for identical wind stress and frazil ice production rates but different alongshore ocean current distribution. Note how the polynya shape varies significantly from a situation in which the coastal current has no shear (panel a) to cases in which there is a marked shear in the x-direction (∂vo /∂x = 0.3310−5 s−1 in panel b and ∂vo /∂x = −0.3310−5 s−1 in panel c). The Willmott et al. (1997) model is applied to the simulation of the Northeast Water Polynya (NEW), which sometimes form off the northern Greenland coast during winter and early spring between the Henrik Krøyer Islands and Ob Bank (Figure 6). Among the many sensitivity experiments described in this paper, two are specially worthy of note. In the first one, frazil ice production rates are calculated as a function of frazil ice thickness. This is a potentially important process, as surface heat losses are expected to drastically diminish when frazil ice depths and concentrations are high. This experiment is in some way a precursor of the coupled atmosphere–polynya model described in Section 10. In the second one, the sensitivity of the polynya shape to spatially varying consolidated new ice drift is investigated. Not surprisingly, the polynya edge is shown to be highly responsive to variations in this flux.
7 Unsteady 2-Dimensional Flux Models Morales Maqueda and Willmott (2000) developed the first 2-dimensional model for the opening of a coastal polynya. In this study, (2.1) is solved using the method of characteristics for the case when H is constant and ocean currents are neglected. Morales Maqueda and Willmott (2000) allow both the offshore wind stress and the frazil ice production rate to vary spatially and temporally. However, the authors obtain analytical solutions for the opening of the polynya adjacent to an island, represented as a straight coastline of finite length, in the case when the wind stress (and hence ice drift velocities) and frazil ice production rate are constant. In this special case a simple expression is obtained for the steady-state area, Ae , of an island polynya, namely H |U | Def . Ae = (7.1) F
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Figure 5: Polynya solutions along a straight coastline located at x = 0 for uniform wind stress (bold arrow on the y axis) and frazil ice production (Willmott et al., 1997). The arrows within the polynya indicate the direction and relative magnitude of the oceanic currents. In the top panel, the longshore ocean currents are uniform; in the middle panel, the current speed increases offshore; in the bottom panel, the speed decays offshore. The dotted lines within the polynya represent selected frazil ice trajectories. The length and direction of the heavy arrows on the consolidated new ice region denote the Lebedev–Pease width (H |U |/F ) and the direction of motion of the consolidated ice, respectively.
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Figure 6: Simulation of the NEW polynya (Willmott et al., 1997). The point (0 km, 100 km) is located at (82.1◦ N, 21◦ W). The heavy line delineates the coast of Greenland and idealised landfast ice boundaries are marked by the dashed lines. The thin dotted lines correspond to selected frazil ice trajectories. The thin dashed line is the contour of the polynya observed on 6 May 1991.
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Figure 7: Simulated St. Lawrence Island Polynya (SLIP, located at approximately 170W–63N) at a time, t, (counted from the moment the polynya started to open) when the polynya has reached 99% of its steady-state area in February (using climatological data). Also shown is the consolidated new ice region (non-hatched area). Within the polynya, the dashed lines are frazil ice trajectories drawn about 10 km apart. The thick (thin) vector represents the consolidated new ice (frazil ice) velocity. The small blank area adjacent to the coast corresponds to the land fast ice. After Morales Maqueda and Willmott (2000). In (7.1), Def is the “effective cross-sectional length of the island”, defined as the maximum separation between the coastal points in a direction perpendicular to the consolidated ice velocity (see Figure 7). The remaining notation in (7.1) is defined in earlier sections. Expression (7.1) demonstrates that the steady-state area of a polynya depends on not only the wind speed and the air temperature (they are both required to calculate F , and the former sets |U |) but also on Def . In the neighbourhood of polynyas it is usually assumed that the consolidated and frazil ice are in free-drift (i.e. internal ice stresses are negligible within the sea ice). The frazil ice velocity is frequently related empirically to the surface wind velocity U a according to u ≈ 0.06U a . Zubov’s law is often invoked to determine the consolidated ice velocity U = ε[cos U a − sin k ∧ U a ], where k is an upward unit vector, ε ≈ 0.03 and ≈ 28◦ is a turning angle, positive to the right of the wind in the Northern Hemisphere. Thus, changing the direction of U a leads to a change in the direction of U and hence in the magnitude of Def (see Figure 7). In the case of St. Lawrence Island Def can theoretically be as large as 150 km when U is oriented to the east-southeast. However, in practice the climatological winter wind stress over St. Lawrence Island exhibits a small variation in orientation (a northerly wind) and Def ≈ 116 km to within ±5 km (Morales Maqueda and Willmott, 2000). Recognising the drawbacks with assigning a constant value to H (see Section 3), 2-dimensional polynya opening models have been developed that incorporate parameterisations (3.1), (3.3) (Biggs and Willmott, 2004) and (3.4) (Walkington and Willmott, 2005b).
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Biggs and Willmott (2004) show that (3.1) is not robust in models for the opening of 2-dimensional polynyas. The origin of the problem is the following. For certain ice drift velocities the steady-state polynya edge exhibits a corner. When opening to such solutions it is found that the characteristic curves associated with (2.1) do not completely span the polynya domain. As a consequence there are segments of the evolving polynya edge that cannot be reached by characteristics leaving the coast. Recognising the drawbacks of (3.1), Biggs and Willmott proposed an alternative collection depth parameterisation (3.3), and use it to study the opening of an island polynya. A “prototype island” is represented by a straight coastline of length D, and the polynya opening time T is compared with that in Morales Maqueda and Willmott (2000) (T¯ , say), when the steady-state polynya areas are identical. When D is short (long) it is found that T¯ > T (T¯ < T ). Furthermore, Biggs and Willmott (2004) show that (3.3) performs well in simulations of the opening of the St. Lawrence Island polynya compared with satellite observations of the opening time. Finally, at the stage of writing this chapter, the study by Walkington and Willmott (2005b) is not yet complete.
8
Polynya Closing
In many Arctic and Antarctic regions the ice drift direction alternates offshore in response to the changing wind stress, leading to polynya opening and then closing. Consider a polynya that has opened under the action of offshore wind-stress. At the instant when the wind stress becomes onshore, there will be a distribution of frazil ice within the polynya. During an onshore wind stress regime two processes contribute to the closure of a polynya. Frazil ice will drift onshore and continue to form thermodynamically. This ice will “pile-up” at the coast to form a coastal boundary of consolidated ice that moves offshore. Second, the offshore consolidated ice pack will now drift onshore under the action of the wind stress. It is clear that there will be two distinct phases during the polynya closing process. In the initial stage 0 < t < tcrit frazil ice arriving at the coast originated from the polynya interior at the onset of closure (see Figure 8). In other words, during this phase all the frazil ice that was inside the polynya at the instant it begins to close (t = 0) moves onshore, while continuing to grow thermodynamically, to pile-up at the coast. This first phase takes place over the time tcrit , a quantity that must be determined as part of the closing problem. In the final phase of the closing regime (tcrit < t < Tclose ) frazil ice arriving at the coast originated from the polynya edge that is travelling onshore with speed U under the action of the wind stress. Thus, the closing time to be calculated is Tclose . Tear et al. (2003) study the one-dimensional closing problem under the following assumptions, (1) the onshore and offshore ice drift rates are uniform, possibly distinct during opening and closing, (2) the frazil ice production rate is constant, (3) surface ocean currents are neglected. These three assumptions enable Tear et al. (2003) to obtain a large number of analytical solutions in the cases when the collection thickness is either constant or parameterised by (3.2). Consequently, the closing solutions obtained by Tear et al. (2003) convey the fundamental behaviour of the polynya closing problem. Relaxing assumptions (1) and (2) will not alter the formulation of the closing problem although solutions in this case will almost certainly have to be calculated numerically. The salient points to note about the solutions of the polynya closing problem are:
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Figure 8: Schematic of polynya closure (Tear et al., 2003). Diagrams of the polynya for (a) 0 < t < tcrit and (b) tcrit < t. The width of the consolidated ice region adjacent to the coast, formed by frazil ice drifting onshore, is s(t). 1. The closing time is shorter than the opening time. There are two situations which can violate this conclusion. The first is when the ice drift rates onshore during polynya closure are significantly smaller than the offshore drift rates during opening. This situation is perhaps less likely to occur in reality. The second situation is when a polynya opens to a small fraction of its steady-state width. In this case, the opening and closing time are almost identical because there is very little frazil ice inside the polynya at the onset of closure to pile-up at the coast. 2. If a constant collection thickness is used, it is possible that during the closing cycle the thickness of frazil ice will exceed the named value of H (of course, this could happen during the opening cycle, in which case the closing problem becomes irrelevant) thereby invalidating the governing equations. The parameterisation (3.2) for H avoids this problem. In their investigation of the polynya closure problem with constant collection thickness, Tear et al. (2003) calculate the ratio Tclose /Topen of closing and opening times as a function of the parameters ε, μ = Uc /uc = Uo /uo , λ = −Uc /Uo = −uc /uo and γ = Fc /Fo , where Uc (uc ) and Uo (uo ) are the consolidated (frazil) ice velocities during closure and opening, respectively, and Fc and Fo are the corresponding frazil ice production rates. The parameter ε
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Figure 9: Contours of Tclose /Topen for γ = 1, and (a) λ = 0.5; (b) λ = 0.75; (c) λ = 1; (d) λ = 1.25. The broken line shows the behaviour of μcrit . After Tear et al. (2003). determines the width (1−ε)Ls of the polynya, where Ls is the steady state width. They show that this ratio is virtually insensitive to γ but varies considerably with λ, decreasing significantly as λ increases, and that for 1 ≤ λ the ratio is always smaller than 1 (Figure 9). Note that in Figure 9 there are regions where no valid solution is obtained (blank region enclosed by the broken line) because the frazil ice thickness exceeds the prescribed consolidated ice thickness at some instant during the polynya closure. Tear et al. (2003) subsequently examine this quotient when parameterisation (3.2) is used (Figure 10). Qualitatively, the behaviour is similar to that in Figure 9. The authors also compare closure times for both collection depth new in ε–μ space, with λ as a paparameterisations. Figure 11 shows contours of Tclose /Tclose new rameter and γ = 1. From this figure it is clear Tclose > Tclose when λ ≥ 1. When λ < 1, new < T Figure 11 reveals that Tclose close when μ is sufficiently small (i.e. consolidated ice speeds significantly smaller than frazil ice speeds), but the inequality in closing times reverses for larger values of μ. The closing time of a 2-dimensional polynya is considered by Biggs et al. (2004) when the collection depth H of consolidated new ice at the polynya edge (during opening) and at the coast (during closing) are assumed to be constant. For a polynya adjacent to an island, represented by a straight line barrier of length D, Biggs et al. (2004) demonstrate that the closing time T 2D is:
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new /T new for γ = 1, and (a) λ = 0.5; (b) λ = 0.75; (c) λ = 1; (d) Figure 10: Contours of Tclose open λ = 1.25. After Tear et al. (2003).
• shorter than the opening time, in agreement with the 1-dimensional case studied by Tear et al. (2003); • relatively insensitive to ice drift orientation and the quotient |U |/|u| where u and U are the frazil and consolidated ice velocities respectively; • weakly dependent on D, except when the initial polynya area, prior to closing, is much smaller than the steady-state area and when D ≤ La , where La is the alongshore adjustment length scale (see Section 5); • sensitive to F /FO , where F and FO are the constant frazil ice production rates during polynya closing and opening respectively. Biggs et al. (2004) exploit these results to derive an approximate expression for T 2D (Ta2D , say) that can be readily evaluated in terms of H , u, U , F and FO . The caveat is that Ta2D is only valid for a polynya with area, prior to closing, that is close to the steady-state value. It must be stressed that Ta2D can be calculated without recourse to running a numerical polynya flux model.
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new for γ = 1, and (a) λ = 0.5; (b) λ = 0.75; (c) λ = 1; Figure 11: Contours of Tclose /Tclose (d) λ = 1.25. The broken line shows the behaviour of μcrit . After Tear et al. (2003).
9
A Polynya Flux Model with a Prognostic Frazil Ice Concentration
The fact that we readily identify polynyas as regions of low sea ice concentration provides the incentive to extend the flux model formulation to include variable frazil ice concentration. Within a 1-dimensional context a model of this type has been developed by Walkington and Willmott (2005a). The authors show that the generalisation of the equation (4.1) governing the width X of an opening coastal polynya is AH U − ac hc uc dX = . (9.1) dt AH − ac hc In (9.1) A is the concentration of the consolidated ice pack in the neighbourhood of the polynya edge, and is assumed to be constant. Concentration of frazil ice at the polynya edge is denoted by ac . The remaining notation is defined in Section 4. Walkington and Willmott (2005a) consider the case when the consolidated and frazil ice drift rates are both constant, with u > U > 0. Note the subscripts c is dropped on the frazil ice velocity for notational convenience. The frazil ice depth and concentration fields are determined from the equations ht + uhx = Fw (1 − a) + aFi
(9.2)
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Fw (1 − a), h0
107 (9.3)
where Fw and Fi denote the frazil ice production rates in open and ice covered regions, respectively, and h0 is a constant ice accumulation depth (see Section 12 of this article for a further discussion of this term and Lemke et al., 1990). In general, the solution for X is numerically calculated by Walkington and Willmott (2005a). However, exploiting the fact that |Fi /Fw | ≈ 10−2 1 allows Walkington and Willmott (2005a) to calculate an approximate analytical solution for X when; (a) A and H are specified constants; (b) AH = ac hc + c(u − U )2 . Case (a) is analogous to the collection depth parameterisation used by Pease (1987), while case (b) generalises the parameterisation of Biggs et al. (2000). Walkington and Willmott (2005a) demonstrate that the introduction of a variable frazil ice concentration reduces the net ocean to atmosphere heat flux within the polynya, thereby increasing both the opening time and steady-state polynya width, compared with flux models that neglect concentration.
10 Coupled Atmosphere–Polynya Flux Model The large net ocean to atmosphere heat flux within a coastal polynya has the potential to increase the temperature of the atmosphere in the lower convective boundary layer. A consequence of this atmospheric warming will be to reduce the sensible heat flux from the ocean to the atmosphere, and this in turn will decrease the frazil production rate. We therefore anticipate that the polynya opening time and the steady-state area could be significantly altered in a coupled atmosphere–polynya model. Renfrew and King (2000) considered the impact of a typical, prescribed, heat flux within a Weddell Sea polynya on a one-dimensional convective boundary layer model of the lower atmosphere. This atmospheric boundary layer model is used as the starting point for the development of a coupled atmosphere–polynya model by Walkington and Willmott (2005a). The polynya model described in Section 9 is coupled by Walkington and Willmott (2005a) to the atmospheric boundary layer model of Renfrew and King. In the coupled model, frazil ice production is found to decrease with offshore distance in response to warming of the lower atmosphere above the polynya. Further, a new qualitative feature is found in a coupled atmosphere–polynya model, that is absent in a polynya model where the coupling is absent, namely the concept of a critical wind speed, above which a steady-state polynya cannot exist. The existence of the critical wind speed is most clearly illustrated in the case when the prescribed air temperature at the coast is near the freezing point. In this case, a relatively small amount of heat into the atmospheric boundary layer creates a large relative change in the net heat flux, allowing the frazil ice production rate to vanish completely at some offshore location. Now, the net ocean to atmosphere heat flux is predominately due to the sensible heat flux which is directly proportional to the (prescribed) wind speed 10 m above the ocean surface, u10 . Thus, increasing u10 increases the warming of the atmospheric boundary layer, making it more likely that frazil ice production will be suppressed beyond some offshore location in the polynya. In the absence of frazil ice production a polynya will open indefinitely. In summary, the polynya extent and opening time in a coupled model is increased compared with these quantities in a decoupled coastal polynya model. Finally, we note that the
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temperature in the atmospheric boundary layer above a polynya increases with the offshore distance, thereby creating an offshore atmospheric gradient. Over relatively narrow coastal polynyas (order 20 km, or less) the perturbation wind generated by this buoyancy forcing is found to be insignificant, although this conclusion is likely to change over features such as the Weddell Polynya (Timmerman et al., 1999).
11
General Circulation Modelling Approach
Here we switch from discussing flux polynya models to general circulation (GCM) polynya models. A sea-ice GCM is a numerical model—it uses discretized versions of the conservation equations of mass and momentum of sea ice, coupled with a numerical technique, to generate solutions to these equations. The solutions are obtained over a discrete, regular spatial grid that covers the region under study, and the solutions are generated at discrete, regular time intervals. The numerical solution then provides a spatio-temporal description of the evolution of the sea-ice cover. A sea-ice GCM receives its forcing from atmospheric and oceanic components, either of which may in themselves be GCMs, or simply as prescribed data sets. In the latter case, the modelling system is referred to as a “stand-alone” ice GCM. In the former case, the system is referred to as a “coupled” GCM. A coupled system will also contain a discretisation and solution technique for the atmosphere and/or ocean mass and momentum conservation equations. The distinguishing characteristic of a GCM polynya model, compared to a flux polynya model, is that the GCM describes a polynya in terms of the concentration of sea ice as distributed over some regular spatial grid, although the former is being developed to include variable concentration. Here we define the sea-ice concentration to be a number between zero and one, describing the fraction of a grid cell covered by sea ice. The non-covered fraction is referred to as a lead. When many neighbouring grid cells have, collectively, a low sea-ice concentration, the patch of grid cells would be identified as a polynya within a GCM simulation. By contrast to GCM polynya modelling, a flux polynya model describes a polynya in terms of a contour line, where the ice concentration switches sharply from a near zero value to a near unity value. Again, as a point of difference, the GCM approach does not have this concept of a sharp edge, nor of a collection thickness at that edge.
12
Ice GCM Equations
The two key mathematical principles governing an ice GCM are that mass and momentum are conserved. We consider the ice as a two-dimensional fluid floating upon the ocean surface and underlying the atmosphere. The ice has a vertical thickness h(r, t), an areal concentration c(r, t), and a horizontal velocity u(x, t), where r = (x, y) is a two-dimensional position vector, and x, y, and t have their usual meanings. The coordinate system used in the derivations that follow is an Eulerian one—the x and y axes are fixed in space. The density of the ice is denoted ρ and is assumed to be a constant. The mass of ice per unit area is the product quantity m = ρhc. 12.1
Mass Conservation
A mass conservation relation for the ice is now derived. A conservation law, expressed in an Eulerian framework, demands that the time rate of change of a property is equal to the
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difference in flux of that property entering and leaving an arbitrarily small element of area. For ice mass, this statement reduces to ∂m (12.1) + · (mu) = 0, ∂t and substituting the earlier definition of m, while noting that ρ is a constant, gives the result ∂hc + · (hcu) = 0. (12.2) ∂t Assuming for the moment that u is known (we will determine some aspects of its behaviour when we discuss the momentum equation in Section 12.2), we then have a single equation describing the behaviour of two unknowns, h and c. This single equation is, in a sense, an underdetermined system—it has no unique solution for h or c. Even if we specify initial condition on h and c, we will only know how the product of these fields evolve in space and time, but not the individual fields themselves. Clearly, we need to impose a second, auxiliary relation governing the behaviour of one or both of these variables. Writing down a statement of mass conservation requires no specific knowledge of the behaviour of the fluid being studied (i.e., all fluids conserve mass). To further progress, we have to rely on specific knowledge of the ice behaviour. More precisely, we have to make some reasonable assumptions about its behaviour. We assume that as ice moves about its domain, being advected by the velocity field u, the ice thickness does not change. This is equivalent to the statement that the ice thickness behaves as a Lagrangian field, and so ∂h Dh = + u · h = 0. (12.3) Dt ∂t Part of the basis for this statement is that ice, being a rigid material, tends to keep its shape and form, unless forced to deform by an overwhelming mechanical stress. In any instance that the ice does not behave according to (12.3), then the ice must obviously become thinner or thicker. Because we are going to treat ice as a plastic material (as in the context of the momentum equation to be discussed in Section 12.2), there is no mechanical process by which the ice can become thinner. That is, in a divergent ice flow field (∇ · u > 0), ice will not undergo thinning. However, there is a mechanical process by which ice can become thicker in a convergent ice flow field (∇ · u < 0)—the ice can undergo plastic failure. This means we have to refine the conditions under which (12.3) is valid and only apply it when ice is not being thickened by flow deformation. Still, (12.3) is not yet a satisfactory equation to use as a second auxiliary relation in an ice GCM as it is not valid then, under all flow regimes. Following with our goal of deriving such an auxiliary relation, we can nonetheless use the thickness equation (12.3) to derive a relation for the ice concentration. By multiplying (12.3) by c and subtracting the resulting equation from our mass-conservation equation (12.2), we arrive at the concentration-conservation statement ∂c + · (cu) = 0. (12.4) ∂t It is the equation pair (12.2) and (12.4) that are commonly used to solve for the two variables h and c in an ice GCM. While equation (12.2) provides a solution for the product quantity hc, in a GCM we divide this quantity by the now-known quantity c from (12.4), to arrive at knowing h. The equation pair (12.2) and (12.4) certainly describe the behaviour of ice that is not undergoing flow deformation. We derived this pair under the assumption (i.e., equation (12.3)
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that ice thickness does not change as ice is advected about a domain. The ice concentration field, of course, can change depending on the nature of the flow field driving the ice. Specifically, the concentration can range over the physically meaningful set of values [0, 1]. Less obvious, the equation pair (12.2) and (12.4) also describes the evolution of the thickness and concentration fields even when ice is undergoing flow deformation, and that the ice is thickening. Given a physically meaningful initial ice concentration, this concentration cannot evolve to become less than zero, because (12.4) cannot produce a negative value of c. That is, once c decreases from a positive value and reaches zero, then the flux-divergence term becomes identically zero and the time rate of change of c also becomes zero. Thus negative concentrations cannot be realised. At the other end of the [0, 1] concentration range, enforcing concentration to be less than or equal to unity is more problematic. There is nothing inherent about equation (12.4) that causes it to enforce such a condition. Instead it must be imposed as a separate constraint c ≤ 1.
(12.5)
Once this constraint is reached, i.e., c = 1, the mass-conservation equation (12.2) effectively transforms into the thickness-conservation equation: ∂h (12.6) + · (hu) = 0. ∂t We now have a situation where the thickness is controlled by equation (12.3) when the ice concentration is less than unity and by equation (12.6) when it is equal to unity. In the former case, the ice thickness does not change due to mechanical forcing. In the latter case, it may indeed change, depending on the details of the mechanical forcing imposed by the flow field u. In an ice GCM, this ‘switching’ behaviour in terms of ice thickness evolution is automatically guaranteed to occur as long as we impose the constraint (12.5) as we solve the equation pair (12.2) and (12.4). A temporary oversight in the derivation of the mass-conservation equation was the lack of treatment of sources and sinks of mass. We now redress this by considering the forcing terms that arise from ice thermodynamics—sources and sinks of heat energy that create and destroy ice mass. Symbolically, we add forcing terms hc and c to the right-hand side of the governing mass (12.2) and concentration (12.4) equations as: ∂hc + · (hcu) = hc , (12.7) ∂t ∂c (12.8) + · (cu) = c . ∂t The formulation of the term hc is relatively straightforward as it simply demands that one carries out a careful heat energy budget at the interfaces between the ice and atmosphere, between the ice and ocean, and subsequently determine the amount of freezing and or melting that occurs for the ice mass. The treatment of c is more problematic. This term describes how external heat energy changes the areal concentration of the ice. In essence, while the hc term describes the change in mass of the ice, the c term describes the ratio of change in ice concentration c to that of thickness h. As one can well imagine, the c term is not derivable from first principles, but instead must be formulated to best parameterise those process which specifically contribute to lateral melt (i.e., changes in c) versus those that contribute to vertical melt (i.e.,
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changes in h). The development of these parameterisations is an active area of research. Basic details of the formulation of the thermodynamic forcing terms as commonly used in ice GCMs is given by Holland (1998). 12.2
Momentum Conservation
We now derive the principle of momentum conservation for the ice. For momentum, which is the product quantity mu, the statement begins as ∂(mu) + · ((mu)u) = 0, (12.9) ∂t which states that the time rate-of-change of momentum in an arbitrarily small element of area is equal to the difference in momentum advected into and out of the area. In ice GCMs it is more common to formulate the momentum conservation law in terms of the time rateof-change of velocity rather than momentum, as in (12.9). To arrive at such a formulation, we manipulate the mass-conservation equation (12.2) by multiplying it by the vector u and subtracting the resulting equation from the momentum-conservation equation (12.9). The resulting equation is ∂u + mu · u = 0. (12.10) ∂t This relation is valid in an inertial frame of reference, that is one moving at a constant velocity (possibly zero), in a straight line. To use this equation in an Eulerian reference frame that is attached to the rotating Earth, a coordinate transformation must be employed (Stommel and Moore, 1989). This leads to the form
m
∂u (12.11) + mu · u + mf k × u = 0, ∂t involving the Coriolis force. Here, f denotes the Coriolis parameter, which is a function of latitude φ according to f = 2 sin φ, and is the angular rotation rate of the Earth. A unit vector k points in the local-vertical direction. The coordinate transformation also involves a centrifugal force, but that force occurs only in the local-vertical direction (along with the gravity force), and here we are describing only forces in the local horizontal direction. The description of the momentum conservation law, up to this point, has only involved body forces (i.e., ones that are proportional to the mass of the ice). There are also surface and interfacial forces acting on the ice. On the top surface there is a stress due to the velocity shear between the ice motion and the wind; on the bottom surface there is an analogous stress due to the ocean current. These stresses are formulated as quadratic drag laws, using empirical coefficients. The usual forms of these atmospheric τ and oceanic τ iw drags are (McPhee, 1976): m
τ
= ρa ca |ua − u|(ua − u),
τ iw = ρi ciw |u − uo |(u − uo ),
(12.12)
where ρa and ρi are the air and ice densities, respectively, ca and ciw the drag coefficients, and ua and uo the velocities, of the air and ocean, respectively. The remaining interfacial forces acting on sea ice are those due to internal stresses. The detailed description of these forces is often referred to as an ice rheology. Recall that the ice momentum equation has been derived for an arbitrary small horizontal area element. Then, in a Cartesian coordinate frame, one can describe normal and shear stresses acting along
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each edge of a horizontal, rectangular area element. These stresses are collected into a stress tensor, σ , which appears in the present setting as a rank-2 tensor: σxx σxy . σ = (12.13) σyx σyy The normal stresses are represented by the diagonal elements σxx and σyy , and the shear stresses are represented by the σxy and σyx . As equal stresses on opposite faces effectively cancel one another out, it is the divergence of the stress tensor ∇ · σ that determines the net contribution of the interfacial stresses to the ice momentum balance. In the instance that both the normal and shear stresses are ignored, then there is no rheology in place and the flow is described as being in “free drift”. Where only the normal stresses are defined, and the stresses are finite for convergent flow (∇ · u < 0) and zero in divergent flow (∇ · u > 0), then the rheology is typically called a “cavitating fluid” (Flato and Hibler, 1992). Finally, when both the normal and shear stresses are included, and the shear stress is finite and proportional to the normal stress, then the rheology is referred to as “plastic”. There are elaboration on this latter type, namely “viscous-plastic” (Hibler, 1979), “granular-flow” (Tremblay and Mysak, 1997), and “elastic-viscous-plastic” (Hunke and Dukowicz, 1997), but such rheologies represent relatively minor modifications to the physical principles underlying a plastic rheology. The astute reader may have noticed that, by introducing interfacial stresses σ , we have introduced another closure problem. We have no explicit relationship between the stresses and the other known variables. The typical closure in this instance is to mathematically formulate the ice as having plastic behaviour. This implies that the interfacial stresses are independent of the velocity field and their gradients, and that the ice can support only a limited amount of interfacial stress before it undergoes failure. We denote that maximum stress as σmax and chose to relate it as (Hibler, 1979): σmax = P ∗ h e−k(1−c) ,
(12.14)
P∗
and k are empirical constants that describe the strength of the ice as functions where of thickness h and concentration c. Notice that the strength parameterisation is a strongly varying function of ice concentration. For concentration just slightly under unity, the ice will have almost no strength. This implies, by contrast, that only ice near unity concentration will experience non-zero interfacial stresses. The manner in which the rheology operates is that under convergent flow (∇ · u < 0) the normal stresses will be able to reach to a value of σmax , and the stresses will tend to resist ice converging. If the ice is forced sufficiently hard, for example by the a strong wind stress τa , then it is possible that the interfacial stresses will not be sufficiently strong to oppose the forcing. In this case the ice undergoes plastic failure, and the convergence of the flow goes ahead unhindered. Returning to the formulation of our ice momentum-conservation relation, and adding these surface and interfacial stresses, we arrive at the final expression ∂u + mu · u + mf k × u = τ − τ iw + ∇ · σ . (12.15) ∂t This completes our brief discussion of the equations governing the ice behaviour in a GCM. The reader should pay special attention, in the context of polynya modelling, to the equations we have derived for ice concentration (12.4) and velocity (12.15). It is principally these two equations that, working in tandem, produce the essential features of the polynya m
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simulations that we will discuss in Section 14. The simulation of concentration is, after all, fundamental to the existence of a polynya, which in an ice GCM context is simply an area of grid cells having a low ice concentration as compared to a surrounding area of cells having a higher one. While state-of-the-art ice GCMs employ a wide variety of treatments of ice thermodynamics and dynamics, including those having a thickness distribution (which applies the above equations individually to each thickness category within a grid cell), these elaborations and many others are well discussed in the literature (see reference list). Our objective has been to provide the reader with an introduction to the most fundamental laws and assumptions that underlie most ice GCMs currently in use, and that are directly relevant to polynya dynamics.
13
Numerical Methods in Ice GCMs
There are many approaches to solving the ice GCM equations on a digital computer. Most approaches to date have involved the finite-difference method, whereby the dependent variables c, h, and u, are evaluated at discrete grid points. Other approaches are possible, for example, the finite element and spectral methods, but have not been employed as often as their finite-difference counterpart. For the finite-difference method, the continuum variables are mapped onto discrete grid points in both space and time. Clearly, the finer the underlying grid, the better the resolution, and the more accurate and relevant the numerical solution. Presently available computing resources dictate the upper limit on grid resolution. Most ice GCM polynya studies to date have been performed over regional domains, typically of expanse of about 103 km in each horizontal direction, and have been able to therefore use relatively high grid resolution of 10 km, and in some cases finer. Some studies have been performed within the context of global domains and have accordingly used much more modest resolution in the region of polynyas, leading to a sometimes poor simulation of the polynya. One method to overcome this is to use a warped domain whereby the underlying mesh is stretched to provide highest resolution in the known geographical area of a polynya (e.g., Martin et al., 2004). So as to provide a concrete example on how to move from the continuum equations of Section 12 into their discrete-domain analogs, and how to obtain their numerical solutions, we here succinctly describe one particular numerical scheme (Holland, 2001c and references therein). To further limit the scope of the discussion, while still giving an overview of that one particular numerical technique, we restrict our attention to just the ice concentration equation (12.4), and further limit that equation to its one-dimensional, x-direction counterpart as ∂c ∂(cu) (13.1) + = 0. ∂t ∂x As a starting point for obtaining a numerical solution of (13.1), we represent the continuum dependent variables in terms of Taylor series expansions in both their time and space independent variables. For the time discretisation, we represent the time-derivative term in (13.1) starting with the expansion: 1 ∂ 2c ∂c (13.2) (x)δt + (r)δt 2 + . . . , ∂t 2 ∂t 2 where we have discretized continuum time according to t = kδt, and k is a discrete time index and δt is a discrete time increment. The discrete concentration is then referred to by ck+1 (r) = ck (r) +
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Figure 12: One-dimensional grid showing discrete locations of scalar type variables (at “O” positions, marked by full-integer indices) and vector type variables (at “X” positions, marked by half-integer indices). The two types of variables are spatially staggered with respect to one another. The spatial indexing is “i” and increases to the right. the k superscript index as, for example at the forward time as ck+1 . Formulating an analogous expression for the ice concentration at discrete time index k −1, we can subtract the resulting expression from (13.2) and arrive at a finite difference estimate of the time derivative as: ck+1 (r) − ck−1 (r) ∂c(r, t) = + O(δt 2 ), ∂t 2δt
(13.3)
where O(δt 2 ) represents neglected terms of order δt 2 and higher. We state then that the accuracy of this approximation is second-order in δt. This particular formulation of time discretisation is usually referred to as the “leap-frog” approach. The approach does have a drawback—it provides two solutions, one the physically correct solution and the other an oscillating incorrect one. The latter can easily be removed by application of a low-pass filter in time (Asselin, 1972). For spatial discretisation, we first note that the spatial derivative term in (13.1) involves both the concentration c and velocity u. For spatial discretisation, it is common practice to stagger the grids upon which scalar (i.e., concentration) and vector (i.e., velocity) quantities are defined. One such staggered arrangement is show in Figure 12. We represent the xderivative of the product term cu in (13.1) starting with the expansion
k k k ∂cu δx 1 ∂ 2 cu δx 2 k + ci+1/2 ui+1/2 = ci ui + (13.4) + ..., ∂x 2 2 ∂x 2 2 where we have discretized continuum space according to x = iδx, and i is a discrete space index and δx is a discrete space increment. The index can also have half-integer values, for example i + 1/2, corresponding to the location of the velocity grid points. Formulating an analogous expression for the product cu at discrete time index i − 1/2, we can subtract the resulting expression from (13.4) and arrive at a finite difference estimate of the space derivative as k
k uki−1/2 ci+1/2 uki+1/2 − ci−1/2 ∂cu (13.5) = + O(δx), ∂x δx which is a first-order in δx approximation. One difficulty with (13.5) is that unlike the velocity field, the concentration is not defined at half-integer points. It is defined at full-integer points. To overcome this, we estimate the discrete concentration, at half-integer indices, using the “upwind” values k ci+1/2 = cik k ci+1/2
=
k ci+1
where uki+1/2 > 0, where
uki+1/2
and
(13.6)
< 0.
This approximation involves another first-order error in δx. Our estimate of ∂(cu)/∂x still remains as being first-order accurate. An attractive property of the upwind scheme is that it is positivity-preserving and so it is monotone. It does not create any artificial extrema. An
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unattractive property is its low-order accuracy and its generally overly diffusive behaviour. This deficiency can be overcome by the application of an “anti-diffusive” corrections step which essentially eliminates the first-order truncation error, thus leaving the solution as being non-diffusive and second-order accurate (Smolarkiewicz, 1984). Putting together the temporal (13.3) and spatial (13.5) discretisations, our original continuum equation for ice concentration (13.1) becomes the computer-usable, discrete version k
δt k k+1 k−1 k k ci+1/2 ui+1/2 − ci−1/2 ui−1/2 + O δt 3 + O(δxδt). ci = ci + 2 (13.7) δx This numerical equation can be demonstrated to be consistent with the original continuum relation. By consistent we mean that in the limit as the grid resolution δx and time step δt both approach zero, the numerical equation reproduces exactly the continuum equation. In other words, consistency means that we are, at least, solving the correct equation. An important practical aspect of implementing the time and space discretisation is that the solution be numerically stable—that is it does not “blow up”. In the present case, our discrete equation will be stable, provide that the condition is met: δx
k δt u
i+1/2
1,
(13.8)
where uki+1/2 denotes the maximum velocity encountered anywhere on the grid (Durran, 1998). Stability is more than a practical requirement as it also gives confidence that the numerical solution obtained will ultimately converge to the solution of the original continuum equation. Our concentration equation is a linear equation in the sense that we are considering the velocity variable u to be a given quantity, i.e., akin to a forcing field. In that instance, the Lax-Equivalence theorem tells us that provided that our numerical equation is consistent with the continuum equation, and that the numerical solution is stable, then we have a necessary and sufficient condition for convergence (see details in Durran, 1998). Convergence simply means that in the limit as the grid resolution δx and time step δt both approach zero, the numerical solution becomes closer and closer to the continuum solution (which formally we do not know, and hence is why we use a numerical method). In other words, convergence means that we are obtaining the correct solution, albeit with a finite accuracy. This completes the description of the numerical solution of the one-dimensional concentration equation (13.1), and by generalisation, to the two-dimensional version (12.4). An analogous discretisation and solution technique is applied to the mass equation (12.2). The numerical treatment of the momentum equation (12.15) follows along much of the same treatment, but there are important, additional considerations pertaining to this equation. First, the treatment of the terms involving the Coriolis force require some spatial averaging, at least for staggered grids (as would be the case here) where the u and v components of u are not collocated. When solving the u velocity component, the Coriolis term involves the orthogonal velocity component v, which on a staggered, two-dimensional grid, will not be collocated with u. Additionally, for numerical stability, when using an explicit scheme like the leap-frog, the constraint f δt 1 must be adhered (Durran, 1998). Secondly, the numerical implementation of the rheology can lead to unrealistically small time steps, if not treated carefully. The plastic behaviour of ice has fast timescales associated with it, particularly during the transition from divergent to convergent flows, and thus can require excessive time resolution. The general technique for dealing with this problem is to add additional non-physical terms to the ice momentum equation, either of a viscous nature
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or an elastic one, such that the plastic behaviour is over taken by the viscous or elastic behaviour in problematic circumstances. The numerical trick lies in the ability to set the viscous or the elastic constants such that sufficiently large times steps can be taken. Having completed our brief sketch of the ice conservation laws and their numerical solution, we now review some past applications of these to the study of various polynya of the polar regions.
14
Regional Ice GCM Applications
While observed polynya range widely in their spatial and temporal scales, most ice GCM studies have concentrated on the larger-scale, more persistent variety. The names and locations of some of these are presented in Figure 13. The figure is not comprehensive, nor the discussion that follows, as modelling studies other than those to be presented been carried out for some of these same polynya. As well, there have been studies of polynya in areas other than those marked in the figure. The studies chosen, however, reflect those that have as their fundamental focus the explicit goal of modelling the ice cover. There are many polynyarelated studies that assess the impacts of a specified polynya shape and size in an atmosphere and/or ocean GCM. That genre of indirect polynya modelling, including the description of a multitude of feedback processes, will not be addressed here. We now describe a number of studies aimed at simulating polynya. For the most part, they solve ice equations similar to those outlined in Section 12 and use a numerical technique analogous to that of Section 13.
Figure 13: Locations of regional Arctic (left panel) and Antarctic (right panel) polynya which have been simulated with ice GCMs. The light yellow areas represent continent; the dark blue represent ocean areas that have frequent ice cover; and the light blue represent ocean areas that rarely have ice cover. The six Arctic polynya displayed are: 1, Storfjorden (Svalbard); 2, North-East Water (NEW); 3, North Water (NOW); 4, Hudson Bay; 5, St. Lawrence Island; and 6, Okhotsk Sea. The six Antarctic polynya displayed are: 7, Mertz Glacier; 8, Terra Nova Bay; 9, Ross Sea; 10, Ronne Shelf; 11, Weddell Sea; and 12, Cosmonaut Sea.
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Coastal-Ocean Polynya
The following is a brief survey of ice GCMs used in predicting the shape of coastal-ocean polynyas, both in the Arctic and Antarctic. There are ten such polynya discussed in this subsection. Their geographic locations are noted in Figure 13. They are referred to below by the numbers that appear on the maps in the figure. 1. Storfjorden (Svalbard): Remote sensing observations have shown a persistent polynya in Storfjorden. This polynya is a relatively small scale feature, but is included here as it serves as a typical example of a wind-driven coastal polynya. A simplified mathematical model of the ice dynamics and thermodynamics (Zyranov et al., 2003), similar to the ice GCM approach outlined in the previous sections, has shown that the opening and development of the polynya is reasonably accurately simulated. The study clearly illustrates that wind forcing is the dominant mechanism responsible for the existence of this polynya. 2. North-East Water (NEW): The waters off the northeast coast of Greenland are fed massive amounts of sea ice by the arctic ice advecting southward through the Fram Strait. Despite that feed, observations have indicated pockets of low ice concentration along this coast and a weakness in concentration across the adjacent Fram Strait. A coupled ice–ocean model (Holland et al., 1995), forced by monthly climatological atmospheric data, reproduced a polynya in this region, as well as a trough in ice concentration across the Fram Strait (see Figure 14). The key finding in the study was that the inclusion of a plastic rheology was fundamental to the creation and maintenance of the polynya. In a follow-on numerical sensitivity study, with the model ice in “free drift”, no polynya was formed. 3. North Water (NOW): Located in northern Baffin Bay, juxtaposed by Northern Greenland and Ellesmere Island, the north water polynya is among the more permanent, regularly recurring of polynya. An ice–ocean model forced by monthly climatological atmospheric data (Yao and Tang, 2003) was able to reproduce localised regions of thin ice where winds forced the model ice away from coastlines or fast ice zones. Although the modelling study did not specifically address the role of ice rheology in the maintenance of the polynya, satellite observations strongly suggest that an upstream blocking effect due to ice rheology also contributes to the low ice concentrations that constitute the NOW polynya. 4. Hudson Bay: Although it forms in an inland sea, the recurring polynya of the northwestern Hudson Bay, is a coastal polynya. A coupled ice–ocean model (Saucier et al., 2004) forced by inter-annually varying atmospheric data over the period 1996–1998 was able to reproduce aspects of the observed polynya. The simulation showed that wind forcing is the dominant mechanism responsible for the opening and the maintenance of this polynya. 5. St. Lawrence Island: In the Bering Sea, in the lee of the St. Lawrence Island, there is a regularly occurring polynya. A coupled atmosphere–ice model (Lynch et al., 1997) was able to simulate aspects of this polynya over the four-day period February 24–27, 1992. The persistence in direction of northerly winds was found to be the key mechanism in establishing this polynya downstream of the wind and in the lee of the island. Of secondary but notable importance, feedback processes between the opening of the modelled polynya and the stability of the modelled lower atmosphere were found to be important in producing a more accurate simulation of the polynya as compared to the satellite-observed one.
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Figure 14: An ice GCM simulation of a coastal polynya located in the northeast waters (NEW) of the Greenland Sea (Holland et al., 1995). The colours represent ice concentration, with dark blues being of 34% or lower and deep reds being of 86% or higher. The green colours represent land (Greenland on the left and the island of Spitsbergen near the center). The polynya are the low-ice concentration features along the northeast coast of Greenland.
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6. Okhotsk Sea: Along the northwestern shoals of the Okhotsk Sea, a variety of satellite observations have provided direct evidence of a polynya. A coupled ice–ocean model (Martin et al., 2004), forced by atmospheric data sets as well as by tides, was applied to this area. Under conditions of offshore wind-forcing, a polynya was simulated. While the winds provided a key mechanism for opening the ice cover, the tidal forcing was also noted to play an important role. In particular, the model simulations demonstrate that tidal mixing allowed deep, warm waters to reach the ocean surface and to contribute to polynya maintenance, via ice melting. 7. Mertz Glacier: In the lee of the Mertz Glacier floating ice tongue, to the north of Adelie Land, East Antarctica, there forms a persistent polynya. The polynya exists both due to the effect of the floating glacier as a barrier which prevent the sea ice from gathering on the western side of the glacier, but also due to the strong offshore katabatic winds in this area. A global coupled ice–ocean model (Marsland et al., 2004), using a warped grid that focused model resolution on the polynya area, was able to well reproduce the location and extent of this polynya. 8. Terra Nova Bay: Located off the east coast of Victoria Land, is a relatively small 0.005 × 106 km2 but persistent polynya. Its size is poorly correlated with the largescale wind forcing, suggesting that local katabatics are the main forcing mechanism. This polynya is in strong contrast to many Antarctic coastal polynyas which are forced by large, synoptic-scale winds. A coupled atmosphere–ice model (Gallee, 1997) has been able to simulate the dominantly katabatic-forced nature of this polynya. A secondary result from the modelling is that a feedback process between the polynya and the katabatic winds was seen. The feedback provided for a strengthening of the katabatics through an “ice-breeze” effect. 9. Ross Sea: In late spring, a polynya usually develops on the western Ross Sea continental shelf immediately north of the Ross Ice Shelf. A global ice–ocean model (Fichefet and Goosse, 1999) was able to simulate some aspect of this polynya, despite the relatively coarse resolution of the model in the area of the polynya. As a primary mechanism of forcing, strong winds were identified in the simulation. However, the import of warm deep waters from far offshore was also noticed to have measurable impact on the polynya through sensible heat effect. 10. Ronne Shelf: North of the Ronne Ice shelf, a polynya is usually evident during summer. It was particularly well pronounced in the satellite observational record during summer 1997–1998. A coupled ice–ocean model (Hunke and Ackley, 2001), forced by interannual winds of the 1997–1998 period, was able to reproduce the anomalously low ice cover of that summer. While offshore-winds were clearly the dominant factor controlling the polynya, it was also noticed that an ocean-albedo effect contributed substantially to the further development of the polynya. 14.2
Open-Ocean Polynya
The following is a brief survey of ice GCMs used in predicting the shape of open-ocean polynya, for the Antarctic only. There are two such polynya discussed in this subsection. Open-ocean polynya have a more rare occurrence than their coastal-ocean counterparts. Their geographic locations are noted in Figure 13. 11. Weddell Sea: This polynya is the largest ever observed. And it is among the most variable of all polynya. During the period 1974–1976, it persisted as a large area
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Figure 15: An ice GCM simulation of an open-ocean polynya, located near the Maud Rise seamount in the Weddell Sea (Holland, 2001a). This a plan view of the ocean surface, of horizontal dimensions 500 km in both directions. The black, dashed oval represents the outline of the Maud Rise, a topographic feature located well below the sea surface. The colours represent ice concentration, with dark greens being of 85% or lower, and deep reds representing up to full, 100% ice coverage. The blue patch represents an area of zero ice concentration—it is the polynya.
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of 0.20 × 106 km2 of low ice concentration. Since then, it has only reappeared as an occasional, transient area of 0.02×106 km2 (Lindsay et al., 2004). While, this polynya forms in the open ocean, well away from any coastal land boundary, the presence of a large seamount is believed to play a central role in its formation. A coupled ice– ocean simulation (Holland, 2001a, 2001b) has demonstrated that ocean currents near the seamount can induce a stress onto the ice cover causing it to open (Figure 15). In another coupled ice–ocean study (Beckmann et al., 2001), tidal currents were also demonstrated to have an impact on weakening the ice concentration in the vicinity of the seamount. 12. Cosmonaut Sea: Located in the Cosmonaut Sea, in an area to the east of the Weddell Polynya, is another open-ocean polynya. It’s appearance is also of a transient nature, having reached a peak size of 0.13 × 106 km2 in the winter of 1980, when it formed an embayment of open water in the surrounding ice. A regional atmosphere– ice model (Bailey et al., 2004) has demonstrated that the opening of the sea ice is highly correlated with the divergence in atmospheric flow associated with the passage of atmospheric, synoptic systems. From this brief survey, we can see that polynya occur for a complex of reasons, ranging from direct wind influences including strong katabatics, ocean currents, upwelling of ocean sensible heat, tidal influence, seamounts, and ice rheology, as a minimal list of factors. Future polynya research will undoubtedly refine this list as well as the details of the physics necessary for improved parameterisation. That will ultimately lead to more accurate and robust simulation of polynya in GCMs.
15
Conclusions
We have presented two modelling approaches now in common use to predict the shape of polynya. We have nominally labelled these approaches as ‘flux’ and ‘GCM’. These approaches, although quite distinct, are each in principle capable of describing the two archetype polynya, that is, those that form in the coastal ocean and those in the open ocean. Precisely which approach a researcher may wish to employ in practice, depends very much on the details of the application at hand. The flux modelling approach has, as its central goal, the description of a contour edge that delineates the separation of a frazil ice zone from a consolidated ice zone. This modelling approach, as described, uses specified states of the atmosphere and ocean. These states effectively serve as forcing fields to drive the governing ice equations. The equations themselves are conservation laws for mass and momentum of the frazil and consolidated ice. The laws are simplified, as for example, terms in the equations relating to Coriolis and ice rheology are omitted, as such terms are not judged to be of key importance in the simulation of the polynya contour edge. A key element in the solution of these equations is to first formulate an appropriate closure relating the thickness of the consolidated ice to that of other, known system parameters, such as the frazil ice thickness and the velocity of the frazil and consolidated ice. With such a parameterisation in hand, one is able to describe the temporal and spatial evolution of the polynya contour edge. The GCM modelling approach seeks to describe, not the exact contour delineating icefree and consolidated-ice waters, but rather the spatial variation in ice concentration. As such, the exact location of a polynya edge is inferred, based on some prior specification of what
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particular concentration distinguishes ice-free waters from those of consolidated-ice waters. This approach relies on solving the complete mass and momentum equations, including the specification of Coriolis and ice-rheology forces, over a spatial grid. For adequate resolution of a polynya edge, a relatively high-resolution grid is required. This can lead to substantial computing overhead, and is certainly one drawback to using this approach. On the other hand, the ability of a GCM to easily deal with arbitrary forcing fields and geometries, as well as a complete momentum balance, are among its main strengths. Each of these modelling approaches has strengths and weaknesses. We further elaborate on the most crucial aspects of these. A main strength of the flux approach is that it provides a relatively straightforward manner to describing the shape of a polynya edge. Using but a few simple conservation laws and forcing fields, one can readily describe the shape of a polynya edge with only modest computing effort, and in some instances the solution may even be obtained analytically. Because of its ease of application, and the relative transparency of its governing equations, this approach readily lends itself towards gaining conceptual insight into polynya dynamics. A weakness is that one has to parameterise the collection thickness, which involves some uncertainty because the nature of the parameterised relationship between model dependent variables is not at all obvious. The approach also has restricted application in terms of complexity. That is, with spatially varying forcing fields embedded in a spatially complex geometry, a purely numerical approach is ultimately needed, and at that point the GCM approach may indeed become far more practical. We have focused on the modelling of the shape of polynya, and have paid particular attention to the formulation of the mass and momentum balances of the ice. In doing so, we have assumed knowledge of many external factors, such as the state of the atmosphere and the ocean. These external factors provide the thermodynamic and dynamic forcing onto the sea ice. While the existence of a polynya does indeed modify the state of the atmosphere and ocean, the description and modelling of that level of feedback process is beyond that explored in this chapter. There is an evolving literature describing such interactive polynya modelling, but it is outside of the present scope. This chapter is intended to serve as an introduction to researchers interested in polynya modelling, and as such, it has focused on presenting those elements of polynya modelling felt to be of foundational importance to describing the shape of a polynya. We hope that the reader is now sufficiently familiar with the basic principles underlying the flux and GCM approaches to be able to begin using them to predict the shape of observed polynya, as well as to predict how such polynya may evolve in a changing global climate.
Acknowledgements AJW is grateful for the support of the UK NERC via research grants NER/T/S/2002/00425 (Rapid climate change programme) and NER/T/S/2000/00585 (Autosub under ice).
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Lemke, P., Owens, W.B., Hibler III, W.D., 1990. A coupled sea ice-mixed layer-pycnocline model for the Weddel Sea. J. Geophys. Res. 95, 9513–9525. Lindsay, R.W., Holland, D.M., Woodgate, R.A., 2004. Halo of low ice concentration observed over the Maud Rise seamount. Geophys. Res. Letters 31, L13302. Liu, A.K., Martin, S., Kwok, R., 1997. Tracking of ice edges and ice floes by wavelet analysis of SAR image. J. Atmos. Ocean. Technol. 14, 1187–1198. Lynch, A.H., Glueck, M.F., Chapman, W.L., Bailey, D.A., Walsh, J.E., 1997. Satellite observation and climate system model simulation of the St. Lawrence Island polynya. Tellus 49, 277–297. Marsland, S.J., Bindoff, N.L., Williams, G.D., Budd, W.F., 2004. Modeling water mass formation in the Mertz Glacier Polynya and Adelie Depression, East Antarctica. J. Geophys. Res. 109, C11003, doi:10.1029/2004JC002441. Martin, S., Kauffman, P., 1981. A field and laboratory study of wave damping by grease ice. J. Glaciol. 27, 283–313. Martin, S., Polyakov, I., Markus, T., Drucker, R., 2004. Okhotsk Sea Kashevarov Bank polynya: Its dependence on diurnal and fortnightly tides and its initial formation. J. Geophys. Res. 109, C09S04, doi:10.1029/2003JC002215. McPhee, M.G., 1976. Ice–ocean momentum transfer for the AIDJEX ice model. AIDJEX Bulletin 29, 93–111. McPhee, M.G., 1979. The effect of the oceanic boundary layer on the mean drift of pack ice: application of a simple model. J. Phys. Oceanogr. 9, 388–400. Morales Maqueda, M.A., Willmott, A.J., 2000. A two-dimensional time dependent model of a wind-driven coastal polynya: application to the St. Lawrence Island Polynya. J. Phys. Oceanogr. 30, 1281–1304. Morales Maqueda, M.A., Willmott, A.J., Biggs, N.R.T., 2004. Polynya dynamics: A review of observations and modelling. Rev. Geophys. 42, RG1004, doi:10.1029/2002RG000116. Ou, H.W., 1988. A time-dependent model of a coastal polynya. J. Phys. Oceanogr. 18, 584– 590. Pease, C.H., 1987. The size of wind-driven coastal polynyas. J. Geophys. Res. 92, 7049– 7059. Renfrew, I.A., King, J.C., 2000. A simple model of the convective internal boundary layer and its application to surface heat flux estimates within polynyas. Boundary Layer Meteorol. 94, 335–356. Saucier, F.J., Senneville, S., Prinsenberg, S., Roy, F., Smith, G., Gachon, P., Caya, D., Laprise, R., 2004. Modelling the sea ice–ocean seasonal cycle in Hudson Bay, Foxe Basin and Hudson Strait, Canada. Climate Dynamics 23, 303–326. Smolarkiewicz, P.K., 1984. A fully multidimensional positive definite advection transport algorithm with small implicit diffusion. J. Comp. Phys. 54, 325–362. Stommel, H.M., Moore, D.W., 1989. An Introduction to the Coriolis Force. Columbia University Press, New York. 297 pp. Steele, M., Morfley, R., Ermold, W., 2001. PHC: A global ocean hydrography with a highquality Arctic Ocean. J. Climate 14, 2079–2087. Tear, S., Willmott, A.J., Biggs, N.R.T., Morales Maqueda, M.A., 2003. One-dimensional models for the closure of a coastal latent heat polynya. J. Phys. Oceanogr. 33, 329–342. Timmerman, R., Lemke, P., Kottmeier, C., 1999. Formation and maintenance of a polynya in the Weddell Sea. J. Phys. Oceanogr. A 29, 1251–1264. Tremblay, L.B., Mysak, L.A., 1997. Modeling sea ice as a granular material, including the dilatancy effect. J. Phys. Oceanogr. 27, 2342–2360.
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Walkington, I.A., Willmott, A.J., 2005a. A coupled coastal polynya-atmosphere boundary layer model. J. Phys. Oceanogr. (submitted). Walkington, I.A., Willmott, A.J., 2005b. An unsteady, two-dimensional model for the opening of a coastal latent polynya. Ocean Modelling (in preparation). Willmott, A.J., Morales Maqueda, M.A., Darby, M.S., 1997. A model for the influence of winds and oceanic currents on the size of a steady-state latent heat coastal polynya. J. Phys. Oceanogr. 27, 2256–2275. Winsor, P., Björk, G., 2000. Polynya activity in the Arctic Ocean from 1958 to 1997. J. Geophys. Res. 105, 8789–8803. Yao, T., Tang, C.L., 2003. The formation and maintenance of the north water polynya. Atmosphere-Ocean 41, 187–201. Zwally, H.J., Comiso, J.C., Gordon, A.L., 1985. Antarctic offshore leads and polynyas and oceanographic effects. In: Jacobs, S. (Ed.), Oceanology of the Antarctic Continental Shelf. In: Ant. Res. Ser., vol. 43. American Geophysical Union, Washington, DC, pp. 203–223. Zyranov, D., Haarpaintner, J., Korsnes, R., 2003. Storfjorden (Svalbard): Modeling of the polynya development and the sea ice ridging process. Model. Ident. Control 24, 37–48.
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Chapter 4
Meteorology and Atmosphere–Surface Coupling in and around Polynyas P.J. Minnett and E.L. Key Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL 33149, USA
Abstract Polynyas and the overlying atmosphere interact through a series of feedback mechanisms which impart a distinctive polar maritime character to the boundary layer over and downwind of the open water area. Enhanced turbulent fluxes across the ice-free interface introduce heat and moisture into the otherwise cold, dry polar atmosphere, modifying clouds through plume formation and radiative exchanges between the atmosphere and underlying surface. Anthropogenic aerosols of remote origin and local biogenic emissions provide additional direct and indirect radiative forcing, which may also influence precipitation rates, cloud optical depth, and ozone concentration. These combined effects modulate the efficacy of polar regions’ ability to act as a “heat sink” for the climate system, establishing a link between the regional polynya meteorology and global conditions. Models, gridded analyses, and remotely-sensed and validating measurements which describe the meteorology and feedback mechanisms in and around polynyas are discussed in this chapter, with an outlook toward future efforts and novel measurement and analytical techniques.
1 Introduction Polynyas are intimately linked to the state of the atmosphere above and about them. Surface winds and heat exchanges, both turbulent and radiative, directly couple polynya formation and maintenance with the overlying atmosphere (Andreas and Ackley, 1982). Above and downwind of the open water, increased turbulent fluxes facilitate heat and moisture transfer into the boundary layer, leading to cloud and plume formation, which influences cloud frequency, type, and distribution (Arbetter et al., 2004; Key et al., 2004). This, in turn, modifies the components of the surface radiation through increased emission in the longwave from cloud base and scattering in the shortwave by cloud droplets, and, thus, the cloud radiative forcing (CRF; Ramanathan et al., 1989). Variations in the surface albedo resulting from polynya formation, when the relatively dark, absorbing water surface replaces bright, reflective snow and ice, also affect the radiative profile (Andreas and Ackley, 1982), which feeds back into cloud radiative forcing and ultimately polynya growth (Kottmeier and Engelbart, 1992). Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74004-1
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Due to inherent cloud and ice cover variability, surface meteorological and radiative measurements within polynyas are limited by technological and logistical constraints and provide only a fragmented history. Rapidly transiting ice, icebergs, and currents preclude the use of above-surface mooring instrumentation, while the demolition of on-ice buoys by wildlife, ridging, and melt shortens data transmission and makes deployment cost-prohibitive. In situ, surface measurement is usually accomplished from ice-breaking, or ice-strengthened, research vessels; however, repeat deployments to polynyas are few, hindering robust study of seasonal or inter-annual variability, or boundary layer characterization during recent ice retreat and oscillating pressure patterns. Aircraft surveys provide additional measurements, particularly within-cloud microphysical fields and radiative fluxes necessary for accurate radiative transfer modeling. Naturally, these data sets are range- and time-limited and rarely visit polynyas or provide multi-year measurements within the same polynya. Such frequent, regional polynya coverage falls within the domain of a few high- and moderate-resolution polar orbiting satellites which image polynya areas with rapid orbital repeat sampling. The majority of polynyas, however, occupy spatial scales (O(1–10 kilometres (km))) which many current and most heritage spaceborne sensors are unable to adequately resolve. A few active sensors, e.g. SAR (acronyms are defined in the Table at the end of this chapter), are capable of penetrating multi-layered, persistent cloud and image the polynya surface at high resolution (O(10 metres (m))), but have relatively narrow swaths (O(10–100 km)) and infrequent coverage (3–5 days). This sampling issue not only affects the satellite time series, but also those analyses and models which utilize retrieved meteorological fields. The small size of most polynyas relative to the grid scale of Numerical Weather Prediction (NWP) and climate models, the frequency of polynya formation close to coasts, the scarcity of polar measurements to be assimilated by the models, and also those independent measurements necessary to validate their predictions, mean that the model fields are of uncertain accuracy and validity in the vicinity of polynyas. Since polynyas and many sub-grid scale features (e.g., leads, straits, islands of the Canadian Archipelago) are inadequately captured by the large model grids and their meteorology insufficiently sampled by in situ and space-borne instrumentation, their influence on local and regional atmospheric fields is largely unknown and limited to a few representative polynyas. There is a great temptation to extend the meteorological measurements taken at coastal and inland reporting stations or central Arctic buoys (http://iabp.apl.washington.edu) to the polynyas themselves. While this is a good first approximation, and one that works better for some variables (e.g., barometric pressure) than others, it is likely to fail to account for the influence of the polynya on the local meteorology. Furthermore, in polar regions, meteorological reporting stations and buoys are sparsely distributed, often influenced by local topography or ice conditions, and in some cases sporadically maintained, or only seasonally operational. A handful of specialized land-based installations, such as the ARM site at the North Slope of Alaska (Stokes and Schwartz 1994; Stamnes et al., 1999), feature instruments that do not constitute part of standard measurement suites, such as lidars and wind profilers, which add to our knowledge of the vertical distribution of certain atmospheric fields. Extensive observations from drifting ice camps (Gordienko, 1962; Arctic Climatology Project, 2000) and ice stations contribute significantly to the meteorological archive; but by definition characterize conditions over-ice which are not necessarily representative of polynya meteorology. Over the past two decades, sampling efforts within polynyas have increased, furthered by national and international committees, such as the International Arctic Polynya Programme. Repeat expeditions to the Northeast Water, North Water, and Cape Bathurst polynyas provide
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foundation datasets on which much of our understanding of polynya meteorology is based. In the Antarctic, a combination of opportunistic data from re-supply missions to coastal stations and polynya-centric field programs (e.g., ROPEX; Mertz Polynya Experiment: Roberts et al., 2001; Terra Nova Bay expeditions: Parish and Bromwich, 1989) describe the boundary layer over the larger, recurring southern hemisphere polynyas. The surface meteorological forcing provides a unifying mechanism for the formation and maintenance of polynyas in both hemispheres. In situ measurements of atmospheric properties and related surface fluxes require data from multiple sensors, mounted on a stable platform, such as an ice-breaking research vessel. Due to changing ice and light conditions, however, the majority of available shipboard meteorological time series are limited to April to October in the Arctic and September to March near Antarctica when ease of passage and the number of daylight hours are maximized. Even during spring and summer, polar regions present particular challenges to accurate meteorological measurements. The environment is harsh to both instruments and observers alike, both often functioning at the limits of their working ranges and endurance. Instruments can freeze or suffer the effects of ice build up or ice melt. Under conditions of blizzard and frost, instruments can be covered by an insulating or obscuring blanket. While careful maintenance of the instruments can help mitigate these effects, sometimes it is impractical or unsafe to try to clean or service the sensors when they are most in need of attention. Additional problems in shipboard measurement may arise from the ship’s motion, flow distortion around the ship, and icing. Some steps can be taken to limit these effects, such as careful siting of the instruments on foremast towers and mounting radiometers on gimbals, but sometimes it is necessary to simply discard segments of time-series of measurements where the measurements are known to be uncorrectable, e.g. when an anemometer is in the lee of the ship’s superstructure, or a pyranometer is in shadow. 1.1
Polynya Formation
The positions of the recurrent polynyas are shown in an earlier chapter (Barber and Massom, 2007). Many of these open water areas occur close to coasts and are influenced by local winds and surface heat fluxes. Polynyas have been partitioned into two types according to the physical mechanisms for their formation and maintenance: sensible-heat and latent-heat polynyas (Smith et al., 1990). These designations indicate the primary source of heat used to melt the ice, or limit its formation. Sensible-heat polynyas require a source of heat in the water column that is brought to the surface by upwelling, possibly in response to an alongshore or offshore wind. Paradoxically, latent-heat polynyas are a result of ice formation caused by heat loss to the atmosphere: this releases the latent heat of fusion, and surface winds or currents move the newly formed ice downstream. For this mechanism to work there has to be a barrier that prevents, or restricts, the inflow of ice into the open water. In the North Water, this is an ice arch that causes a log-jam in Smith Sound, to the north, and prevents the inflow of ice from Nares Strait (Barber et al., 2001; Vincent and Marsden, 2001). In the Northeast Water Polynya, there are two barriers: to the south an ice shelf that extends as a glacier tongue which is grounded on Belgica Bank, and which obstructs inflow of ice by the anticyclonic circulation over the bank, and to the north a land-fast ice shelf over the Ob Bank that deflects the flow out of the Arctic Ocean through Fram Strait (Schneider and Budéus, 1995; Minnett et al., 1997; Willmott et al., 1997). Winds blowing over St. Lawrence Island Polynya open a polynya on the downwind side of the island (Pease, 1987) while the Sea of Okhotsk Polynya is formed by offshore winds blowing from Siberia over the shallow
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continental shelf (Martin et al., 1998). Some polynyas, such as the Hells Gate Polynya and Lady Anne Strait Polynya in the Canadian archipelago (Smith and Rigby, 1981), Kashevarov Bank Polynya in the Sea of Okhotsk (Martin et al., 2004), and the Great Siberian Polynya in the East Siberian and Laptev Seas (Kowalik and Proshutinsky, 1994), maintain ice-free conditions through association with very strong tidal flows that presumably mix the heat in the otherwise stratified water column to the surface; although, log-jam type blockages of ice flow in the narrows between islands may be important in the case of Hells Gate and Lady Anne Strait. There are those that may exhibit different characteristics at different times or under different conditions. For example, there is evidence that the North Water has a sensible heat component during winter (Steffen, 1985), while in summer it is a latent heat polynya (Ingram et al., 2002). Similar mechanisms give rise to recurrent polynyas on the Antarctic coast and continental shelves (König-Langlo et al., 1998). For example, the Ross Bay Polynya is formed by katabatic winds removing coastal ice and maintained by latent heat release of ice crystals forming in the open water area (Bromwich and Kurtz, 1984). Other open water areas, such as the Brunt Ice Shelf (Markus and Burns, 1995), Mertz Glacier (Massom et al., 1998), Prydz Bay (Nunes Vaz and Lennon, 1996), and Terra Nova Bay (Kurtz and Bromwich, 1983) polynyas, form in response to similar wind forcing events and are maintained through a combination of winds and barriers to ice inflow, such as grounded ice and promontories. Tidal flows in shallow coastal areas also deform ice cover leading to the creation of open water areas like the Ronne Ice Shelf Polynya in the Weddell Sea (Renfrew et al., 2002). Further offshore, sensible-heat mechanisms dominate the Cosmonaut and Weddell Sea polynyas, which are seemingly sustained by local thermohaline convection driven by a sinking cold, brine layer under newly-forming ice and consequent upwelling of relatively warm water from depth (Martinson et al., 1981; Comiso and Gordon, 1996). Vertical motion induced by current flow around bottom topography, such as the Maud Rise Seamount, can also lead to recurrent offshore polynya formation (Holland, 2001). While the physical processes that cause polynyas to form and be sustained can vary and may involve the overlying atmosphere to differing degrees, they are united by a common definition: sizeable areas of open water in otherwise ice-covered seas. It is this lack of an insulating ice layer that permits robust air-sea interaction throughout the open water area and complex air–ice–ocean feedbacks at its periphery.
2
Measurements of Meteorological Variables
While the instruments used to take measurements of surface meteorology in and around polynyas are not described in detail here, an overview of common sensors and operational considerations is provided. Further information about advanced instrumentation is outside the scope of this chapter but available in DeFelice (1998), http://www.arm.gov/sites/nsa.htm, and papers cited within this text. Generally, measurement of polar meteorology is achieved through shipboard expeditions, aircraft surveys, or manned observing stations. These sampling platforms, however, are relatively sparsely distributed, expensive to maintain, and are being replaced by automatic stations. Meteorology recorded by unmanned sensors incur additional uncertainty as weather conditions or biological fouling may affect operations and data quality. A good example is the effects of freezing rain on anemometers, which may become inoperative once they are encased in ice. This is frequently seen in the data from the automated station on the low-lying island of Henrik Krøyer Holme in the Northeast Water
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Polynya (H. Valeur, pers. comm., 1994; Minnett et al., 1997). Descriptions of some standard sensors and more advanced, research instrumentation at the ARM site at Barrow, Alaska, are accessible at http://www.arm.gov/sites/nsa.stm. Additional information about instruments, such as those deployed on aircraft, is provided in the appropriate references cited below. Sampling meteorological fields from a ship requires a number of sensors, which are sited in such a way as to minimize structural contamination—e.g., cloud imagers are placed in areas devoid of overhanging or obscuring deck or antennas, while anemometers are located on masts which raise the sensor off the deck to minimize shadowing and flow distortion around the ship’s superstructure. Aerosol sensors aboard ship are placed away from the smokestack, though screening of the data is necessary for periods of shifting wind direction when the smoke may affect not only particle count but also tandem radiation measurements. Usually, basic meteorological variables, such as surface air temperature, relative humidity, barometric pressure, and wind speed and direction are recorded by an instrument suite, such as can be found both on ships and at weather stations at high latitudes. However, the accuracy of these measurements in freezing conditions and sub-zero temperatures is low, requiring specialized sensors for the most reliable values. For instance, relative humidity, as measured by a frost point hygrometer, is not susceptible to freezing failures and provides a highly accurate measurement of relative humidity over ice, not provided by traditional dielectric film sensors. Wind vanes, sonic anemometers, and 2π -radiometer domes are also prone to malfunction at cold temperatures should seaspray or hoarfrost coat the instrument. Unfortunately, new technology has not addressed these measurements. Thus, rigorous maintenance becomes necessary for shipboard operations because of the added threats of salt and moisture, which do not affect most land-based weather stations. Instrumentation for measuring cloud is manifold, ranging in complexity from the robust all-sky camera unit (Figure 1) used for capturing time-lapse images of hemispheric cloud cover to the combined radar-lidar array currently only deployed at the ARM NSA-AAO site for cloud microphysical studies. Autonomous all-sky imaging units, also used at the NSA site, though practical, are subject to misclassification errors by cloud identification algorithms (Buch et al., 1995). To assess the influence of cloud cover on the underlying snow, ice, or ocean surface, coincident broadband measurements of hemispheric downwelling shortand longwave radiation must be collected. Calculations of net cloud forcing, which is measure of the radiative flux and surface response, require highly accurate, rapid sampling of upwelling radiation, and thus, albedo. Despite the importance of albedo and radiative fluxes to estimates of the surface heat budget, few measurements are available in the Arctic, and even less within dynamic polynya areas. 2.1
Winds
Wind serves both to open and maintain some polynyas first through divergence, then through enhanced ice formation and redistribution; the combination of latent heat release from freezing and the advection of new ice downwind is crucial to the expansion of open water. Since few in situ measurements of polynya wind speed and direction are readily available and satellite retrievals of wind do not provide adequate spatio-temporal resolution, most studies of wind-related effects on polynya size, shape, and duration have been based on models (e.g., Lynch et al., 1997; Goosse and Fichefet, 2001). Many of these assume geostrophic wind patterns, though several regional or box models incorporate the prevailing flow to study the optimal ice removal and thus polynya size, as a function of wind speed and small deviations in wind direction. In reality, the wind fields over polynyas are quite complex, as the wind
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(a)
(b)
Figure 1: Examples of meteorological sensors used to make measurements in polynyas. The instruments mounted on the mast on the foredeck of the Canadian Coast Guard Ship Pierre Radisson (left) measure wind speed and direction, air temperature and humidity and atmospheric pressure. The cross-beam supports 2π-radiometers to measure hemispheric incident short- and long-wave radiation. These are mounted on gimbals, with pendula, to keep the sensors pointing upwards and remove the effects of ship motion. The all-sky camera (right) consists of a down-looking video camera pointing at a hemispheric mirror that provides an image of the entire dome of the sky. The images are recorded by a time-lapse recorder for subsequent analysis. stress over bare, snow-covered, and ponded ice and open water may differ markedly (Chao, 1999). Katabatic flow off nearby ice shelves may contribute additional complexity, particularly along and offshore the Antarctic coast; however, in the Arctic, these wind events are often unsampled over polynyas and must be extrapolated from point measurements at coastal weather stations. Since many coastal weather stations are located in sheltered areas, ill-exposed to the prevailing wind, the wind time series are not necessarily representative of large-scale flow offshore. Extrapolating measurements from shore to a polynya may bias the wind speed and favor directions along prominent topographic axes. For instance, shipboard measurements within the North Water Polynya reveal dominant northerly winds averaging speeds 1.5–4.5 m s−1 greater than those measured at coastal stations, most of which are situated in fjords or leeward of low-relief (O(100 m)) topography. While wind directions within these sheltered locales did capture much of the northerly flow, secondary peaks in wind speed manifested in directions coincident with local topography. It is possible that in some of these cases, the data record has captured katabatic drainage off of Ellesmere or Greenland ice fields and glaciers. In situ measurements and modeling studies of the Greenlandic wind field (Bromwich et al., 1996; Heinemann, 1999) characterize katabatic flow as a boundary layer jet, increasing in speed from the central ice cap towards the coast, reaching speeds of up to 25 m s−1 , and exerting influence over long horizontal scales. Although these thermallyinduced jets occur year-round, either in response to nighttime radiative cooling or daytime ice melt, they exhibit pronounced seasonal and diurnal variability.
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Figure 2: OLS Image of katabatic winds blowing over open water in the Ross Sea, Antarctica. Streamers of frazil ice forming at the surface are oriented in the direction of air flow. (Courtesy of arcane.ucsd.edu). In Antarctica, a combination of coastal katabatic wind forcing and synoptic cyclones accounts for the wind-driven component of polynya formation along the coast (Figure 2). The measured ice-free area fluctuates with the passage of these wind events unless assisted by grounded-ice features, which restrict or block the return flow of ice into the polynya. The Ronne Ice Shelf and Terra Nova Bay Polynyas are formed by the combination of highspeed wind drainage from converging, steeply-sloping coastal topography, semi-permanent cyclonic upper air flow, and upwind ice tongues within the Ross and Weddell Seas. A number of polynyas have also been identified under the Atmospheric Convergence Line, at approximately 60◦ S, where ice divergence and subsequent oceanic upwelling facilitates the formation of hybrid latent-sensible heat polynyas. Similar strong wind conditions are also noted in the Arctic, particularly in the Bering Sea in the vicinity of St. Lawrence Island, where a latent-heat polynya forms in shallow inshore waters in response to local wind forcing. The polynya may take shape either on the northern or southern shore of St. Lawrence Island, and shows a distinct size dependence on not only wind speed but also direction. While the island itself is of low relief (less than 600 m), the two largest mountains on the island shadow the only two weather stations, and thus distort the only generally available wind data from this area. Assumptions of geostrophy over a large scale have been made in the Bering Sea, using meteorological station data from Siberian and Alaskan coasts, although topographic effects may introduce error into the calculation—Siberian stations, such as Anadyr and Uelen, are located in enclosed bays and on icy capes while Alaskan stations may either be found in valleys or on exposed coastal plains.
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Figure 3: Schematic of surface ice drift during negative (low) and positive (high) phases of the Arctic Oscillation. From AMAP (2002). Even distant topography can influence wind measurements. For example, the Brooks Range in northern Alaska sits 150 km from Barter Island, a coastal weather station near to the Beaufort Flaw Lead and Banks Island and Cape Bathurst Polynyas. A narrow range of wind directions introduce strong forcing into the data record at this station, where winds reach more than 20 m s−1 in both the easterly and westerly directions as channeled by the orientation of the mountain range (Dickey, 1961). Using these measurements as an indicator of at-sea wind is at odds with the large-scale flow of the Beaufort atmospheric anticyclone, which favors primarily alongshore (easterly) winds over the open water, except when the wind pattern is shifted southward during positive phases of the Arctic Oscillation (Figure 3). At that time, the flow becomes onshore near the US–Canadian border while an offshore arm blows over Wainwright, to the west, bringing with it Pacific air masses, cyclones, and aerosols. Analyses by Adolphs and Wendler (1995), Andreas and Cash (1999), and Marsden et al. (2004) address the role of wind forcing in setting up a polynya microclimate which aids maintaining the open water area. Assumptions of geostrophy and boundary layer length scales guide these analyses, which proffer that cyclonic/anticyclonic wind fields develop over the center of polynyas, guiding ice redistribution and encouraging upwelling of relatively warm subsurface water along the periphery. 2.2
Surface Temperature
As strong winds blow over sea ice, creating areas of ice divergence, open water surfaces are exposed to the overlying atmosphere. Continued wind forcing enhances heat transfer from the relatively warm upper ocean into the atmospheric boundary layer. Despite this negative heat exchange from ocean to atmosphere, the air–sea temperature differences may be large (|T | > 20 K), especially in comparison to air–ice temperature differences. Analyses of similar differences in polynya and land-based air temperatures reveal more than simple divergence in heat capacity and content: seasonal and episodic variations, the latter related to katabatic drainage events and topographic shadowing of coastal weather stations, also factor into polynya–land meteorological differences. Although topographic influences and downsloping flow may account for a small fraction of land–sea air temperature differences,
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Figure 4: Evolution of surface air temperature over the North Water Polynya (blue line) and nearby coastal stations (all other colors). The polynya-coastal difference in air temperature shown at the right captures the progression in ice and snow melt in late spring and early summer. Air temperatures over the coast rise as bare land is exposed, while the open water and continued ice melt within the polynya modulates temperatures around 0◦ C. From Key (2004). it is mostly the disparity in heat capacities of soil, bare rock, snow, ice, and ocean that produce a near-uniform negative temperature gradient between land and sea in summer. Over the polynya, cooler surface air temperatures prevail with smaller diurnal ranges than measured at neighboring coastal sites. Whereas the increase in solar radiation over summer melts snow and warms bare soil and rock, most of this energy within polynyas is expended on ice melt, further moderating polynya temperatures to generally within a few degrees of 0◦ C (Figure 4; Anderson, 1993; Key, 2004). In areas where open water is extensive and semi-permanent, land–sea air temperature differences from the oceanic surface layer, whereas coastal land margin heat content quickly dissipates. During transitional seasons—spring and fall—when the rapid radiation changes and ice development are anti-phase, other factors may govern the land–sea air temperature differences. In the St. Lawrence Island Polynya during spring, for example, advection of warm sub-polar air by large-scale southerly flow and convective heat release from the shallow Bering Sea under intense wind forcing may account for the warm air temperatures over the polynya with respect to land. Also, at this time of year, cloud cover over the Bering Sea is extensive and persistent, so that the local snow cover at coastal weather stations does not receive adequate solar radiation to accelerate the melt. Late summer and early fall observations of the Barrow Flaw Lead, after significant summertime insolation had induced wide-scale melt and a large open water area in the Beaufort Sea, show a high amplitude diurnal signal of several degrees in surface air temperature. Although these daily fluctuations are still smaller than those measured at coastal weather stations, their appearance suggests that under minimal cloud and ice conditions, Arctic air surface temperatures exhibit diurnal behavior similar to that measured over sub-polar ice-free oceans. Both the Arctic and Antarctic share a proclivity towards near-surface temperature inversions (King, 1990; Serreze et al., 1992), which, though occurring year-round, manifest most strongly in winter months. These inversions complicate remote sensing retrievals of surface air temperature and cloud cover, since fog, stratus, and stratocumulus clouds (Kottmeier and Engelbart, 1992) within the inversion tend to have warmer cloud top temperatures than the underlying surface. Space-borne sounder and in situ radiosonde profiles indicate that the depth of the boundary layer beneath the inversion layers varies from 300–500 m, while the inversion layer depth itself ranges seasonally, reaching a maximum depth in early spring (Anderson, 1993). Turbulent mixing, which increases with decreasing sea ice cover, erodes this maximum over and downwind of polynyas and leads (Liu and Key, 2003), allowing additional heat and moisture fluxes into the troposphere (Andreas, 1980).
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In cloud-free conditions, the temperature of the ice-ocean surface, as opposed to the near-surface air-temperature, can be determined by infrared radiometers on polar-orbiting satellites. The long-time series of the AVHRR on the NOAA satellites has two channels in the thermal infrared which can be combined to provide a correction for the effects of the intervening atmosphere, and therefore obtain clear-sky measurements of ice and water surface temperatures (Key and Haefliger, 1992; May et al., 1998). AVHRR’s approximately 1 km2 resolution at the surface is very well suited to regional polynya sampling. While the measurements of microwave radiometers on spacecraft are largely unaffected by clouds, they suffer from relatively poor spatial resolution. The delineation of larger polynyas can be achieved with high frequency channels (85 GHz on SSM/I, 89 GHz on AMSR-E) and analytical techniques to provide ground resolution of less than 10 km (Markus and Burns, 1995). The measurement of surface temperature requires information from the lower frequency bands at 6.9 GHz (AMSR-E) that have correspondingly poor surface resolution and contamination by emission from land through the antenna side-lobes. This renders them unsuited to provide useful measurements of surface temperature in polynyas. 2.3
Surface Humidity
Humidity is a quantity not often measured at the surface in polar regions, due in part to the low saturation levels with respect to water and uncertainties associated with common measuring techniques. With the exception of certain specialized sites, such as Halley Station (75◦ 35 S, 26◦ 25 W) where a frostpoint hygrometer records relative humidity with respect to both ice and water (King and Anderson, 1999), most coastal weather stations near polynyas derive humidity from dewpoint and drybulb temperatures, which decrease in accuracy with decreasing surface air temperatures (King and Turner, 1997). Other sensor types which may be used on shipboard and aircraft surveys of polynyas to detect relative humidities must be cold temperature-calibrated (Anderson, 1994), and yet may still be unable to measure accurately humidities below the ice point. Vertical profiles of humidity are monitored through radiosonde launches at upper air stations and from ships as well as satellite sounding units (e.g., TOVS, Francis and Schweiger, 2000; AMSU-B, SSM/T2, Heygster et al., 2003). Aside from the systematic low bias of rawinsonde humidities (Cullather et al., 1998; Miloshevich et al., 2001; Miloshevich et al., 2004) and uncertainties in the calibration of TOVS (Overland et al., 2002), the spatial distribution of humidity profiles (less than 5 surface observations per 2.5 pixel per month at high latitudes, Kistler et al., 2001, or 100 × 100 km2 pixels, Francis, 2002) does not adequately sample rapid fluctuations, the boundary layer, or downwind polynya influences on moisture. Consequently, model and re-analysis products tend to overestimate low level humidity (Rogers et al., 2001), leading to a positive bias in longwave radiative forcing and surface air temperatures (Overland et al., 1997). However, due to the general lack of available relative humidity measurements, most studies use NWP output for climatological analyses (e.g., Barber et al., 2001), model initialization (e.g., Häkkinen and Geiger, 2000; Armstrong et al., 2003), and parameterization of cloud cover (e.g., Overland et al., 1997; Hall, 2004). The few in situ time series collected within polynyas have focused on the turbulent transfer of humidity into the upper boundary layer (Pinto et al., 1999; Curry et al., 2000) and the consequent effect on cloud formation downwind of the open water area (Burk et al., 1997; Key et al., 2004). Large air–sea temperature differences across the polynya interface, coupled with wind forcing, generate a significant sensible heat and evaporative flux into the atmosphere (Burk and Thompson, 1995; Popov, 2001). The moisture entering the
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atmospheric surface layer is vertically transported by turbulent eddies, resulting in an increasing relative humidity profile peaking at the top of the boundary layer. These convective plumes, which may measure from 600 to 1000 m in height, are comprised mainly of supercooled water while still coupled to the relatively warm polynya atmosphere but can become mixed phase clouds as they are advected downwind beyond the polynya boundary and over ice (Pease, 1987; Mailhot et al., 2002; Zulauf and Krueger, 2003). Acting as a source of moisture and CCN, the plumes seed cloud formation, both over the polynya and downwind, increasing cloud incidence and fraction (Fett et al., 1997; Key et al., 2004). Relative humidities measured over polynyas generally exceed upwind coastal observations by 20% (Key, 2004), and also exhibit greater variability at a range of temporal scales, fluctuating between 45 and 100% depending on the temperature-induced changes in the saturation vapor pressure, proximity of the polar vortex and synoptic mesoscale weather systems (Overland et al., 1997). 2.4
Profiles
As in other regions, most of our knowledge of atmospheric profiles of temperature and humidity at high latitudes has been obtained from radiosonde ascents. Radiosondes are routinely launched from Arctic upper air observing stations, which tend to be clustered at the coast or inland settlements. The relatively few launches made offshore, either over ice or open water, are often restricted to ships and large floating ice stations by the operational requirements of sounding units (e.g., available helium, power, antenna line-of-sight, and sheltered sonde preparation areas). Satellite-derived profiles augment this sparse in situ data distribution, though at a much-reduced spatial, vertical, and temporal resolution. Large pixel sizes of spaceborne soundings, such as the TOVS 100 km product, often sample over varied ocean, ice, and landscapes, merging the atmospheric characteristics of a marine, icecovered and coastal region into a single profile. Despite complications presented by differing scenes and minimal vertical resolution, TOVS temperature profiles collocated with radiosondes launched over the Beaufort Flaw Lead and ARM NSA site show good agreement above the boundary layer (Figure 5; Francis, 2002; Key et al., unpublished). At the surface, air temperature values reflect whether snow, ice, ocean, or bare soil are present, introducing discrepancies between the various profiles equivalent to several degrees at the lower boundary. Relative humidity retrievals, on the other hand, are quite disparate, describing a more localized environment, both in terms of cloudiness and moisture flux in the boundary layer. Profiles collected at locations a minimal distance apart but over different surfaces are as dissimilar as the spaceborne profile is from either of the in situ soundings. Furthermore, a known dry bias in radiosonde relative humidity among the Vaisala RS-80 series (Miloshevich et al., 2001) is surpassed by the remotely-sensed humidity profile, as well as by NWP products, indicating a tendency for these large-pixel profiles to bias towards drier land-based environments. Accurate retrievals of relative humidity, at least from the high resolution radiosonde soundings and lidar profiles, are useful for identifying laminar stratus (Herman and Goody, 1976) and multilayered clouds, especially during overcast and foggy (Curry et al., 1995a) conditions. With improvements to the radiosonde calibration unit for recent radiosondes and profiles derived from spectra collected in the rotational water vapor window (19–25 μm) by the extended-range AERI (AERI-ER, Tobin et al., 1999), evolving turbulent moisture fluxes can be detected within and near polynyas.
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Figure 5: Beaufort Flaw Lead (BFL). Blue temperature and cyan humidity curves from an independent radiosonde launch within the BFL are shown in reference to radiosondes launched from the NSA-AAO ARM site (top left) and the Barrow NWS station (top right), as well as MODIS retrievals (bottom left) and the NCEP Final Analysis one-degree product (bottom right). In all cases, the independent sounding was interpolated to match the vertical resolution of the comparable dataset. Spatio-temporal distances between the soundings are given for reference in each panel.
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Figure 6: Analysis of surface-based cloud observations for field expeditions to Arctic Polynyas during the sunlit seasons over several years. The bar graph gives cloud amount for each observational data-set: NEW92—Northeast Water Polynya in 1992; NEW93—Northeast Water Polynya in 1993; CH98—Cape Hershel in 1998; NOW98—North Water Polynya in 1998; SLIP99—St. Lawrence Island Polynya in 1999; NOW99—North Water Polynya in 1999; BFL00—Barrow Flaw Lead in 2000. These results indicate that the Arctic is predominantly cloudy. The data set with the highest incidence of clear skies (CH98) is the only land-fast site, located on the coast of Ellesmere Island at the northern edge of the North Water Polynya. From Key et al. (2004). 2.5
Clouds
Cloudiness is frequent and extensive in the Arctic, and particularly over polynyas, where clouds are reported in 70–85% of daylight measurements (Figure 6). As mentioned in Section 2.3, enhanced moisture and CCN fluxes across the air–sea boundary in polynyas contribute to this elevated cloud occurrence (Bromwich et al., 2001), which influences not only the polynya but also areas downwind (Figure 7; Adolphs and Wendler, 1995). In situ measurements over both Arctic (Key et al., 2004) and Antarctic (Arbetter et al., 2004) polynyas reveal a shift in cloud distribution towards multiple cloud types at several atmospheric levels and an elevated occurrence of cumuliform cloud types, compared with observations over sea and landfast ice (Figure 8; Key et al., 2004). Internal cloud properties, such as optical depth, cloud liquid water content, cloud droplet phase, and particle effective radius determine the radiative character of the cloud, as well as the nature and magnitude of its effect on the boundary layer and underlying surface. Studies conducted with a polar-optimized radiative transfer model (Streamer: Key and Schweiger, 1998) indicate that downwelling radiation is more sensitive to changes in the cloud particle effective radius than variations in the cloud liquid water content (Key, 2004), especially at smaller solar zenith angles. This susceptibility to cloud microphysical content thus figures significantly into cloud radiative forcing and cloud-albedo feedback (Curry et al.,
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(a)
(b) Figure 7: An atmospheric plume emanating from the polynya southeast of Bennett Island in the Eastern Siberian Sea, as imaged by the NOAA-6 satellite (top). From Dethleff (1994). The lower panel shows simulated mean total vertical turbulent temperature flux (K; m s−1 ) from a 200 m lead, orientated along the wind direction, from simulations using a cloud-resolving model (scaled by 103 ). From Zulauf and Krueger (2003).
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Figure 8: Spatial distribution of cloud forms over three polynyas and one flaw lead. Each pair of images consists of a top panel showing east–west distribution of stratiform (blue), cumuliform (red), cirriform (yellow), multiple cloud types (green), and clear sky (black) across each open water area. The bottom panel of each pair depicts north–south gradients in observed cloud form for, from left to right, (a) the St. Lawrence Island Polynya, Beaufort Flaw Lead, (b) North Water (summer), North Water (fall), and Northeast Water. From Key et al. (2004).
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(b)
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1993, 1995b; Key, 2004). As these effects play a substantial role in the maintenance or modification of sea ice patterns, including those critical to polynya formation such as the stability of ice bridges and grounded ice tongues and the rate of ice melt at the polynya boundaries (Shine and Crane, 1984), additional measurements for model testing and parameterization improvement are necessary. Aircraft surveys, such as BASE (Pinto and Curry, 1995) and FIRE-ACE (Curry et al., 2000) provide comprehensive, though brief, time series of cloud and radiation properties during daylit months. However, long-term measurements of cloud and radiative components germane to calculations of the forcing are very limited over polynyas and largely confined to those derived from satellite measurements (e.g., Schweiger et al., 1999; Francis, 2002; Spangenberg et al., 2004). In fact, consistent measurement of any cloud parameter within the Arctic has generally relied on the rapid repeat of polar-orbiting satellites for adequate coverage. While today and in the near future (Section 5), a number of space-borne sensors will supply Arctic cloud products, most existing cloud histories are based on radiances retrieved from the extensive record of the NOAA AVHRR series. Two climatological products, from the International Satellite Cloud Climatology Project (ISCCP: Schiffer and Rossow, 1983) and the Cloud And Surface Parameter Retrieval software (CASPR: Key, 2002) estimate cloud amount and microphysics using AVHRR as a baseline dataset. Additional information from the TOVS sensors and gridded meteorological fields help constrain the algorithms which generate the final cloud fields. In the case of ISCCP, however, the measurement of cloud cover remains hampered by the lack of thermal and visible contrast between sea ice and cloud cover and the effect of illumination within a complex ice-ocean scene. When collocated with in situ assessments of cloud cover, the ISCCP diurnal cycle overestimates cloud cover in low illumination and underestimates during the rest of the day by as much as 50% (Key et al., 2004). Despite these discrepancies, remote sensing imagery is the preferred data source for regional analyses of cloud cover. 2.6
Aerosols
The proximity of the Arctic to large sources of North American and Eurasian pollution aerosols and the exchange of air masses with mid-latitudes during the migration of the polar front introduce a large concentration of sulfuric aerosol to high northern latitudes during winter and early spring (Figure 9, Shaw, 1982). These anthropogenic aerosols, which are mostly composed of sulfur dioxide and soot particles (Hara et al., 2003), are quickly scavenged by cloud formation in the boundary layer in April and May (Bigg and Leck, 2001; Dong and Mace, 2003). Those sulfur dioxide particles, which are vertically mixed above 100 m (Bigg and Leck, 2001), may be bolstered by additional inputs from Eurasia in winter and spring and remain aloft for days to weeks, during which time they catalyze ozone destruction through the activation of atmospheric halogenic species (Barrie et al., 1994). Over Alaska, the Beaufort Sea, and the Canadian Archipelago, thin horizontal (O(100 km)) layers of industrial pollution form within 2–4 km of the ocean–ice surface (Shaw and Khalil, 1989) in winter and spring. These seasonal Arctic Haze layers may increase the aerosol optical depth over the area to as high as 0.5 (Stone, 1997); act as ice forming nuclei (Bigg and Leck, 2001); enhance the reflectivity of the cloud; increase the longwave emissivity (Garrett et al., 2002); and alter the cloud’s radiative forcing properties. After this peak in primarily anthropogenic aerosol is depleted in late spring, a second surge of aerosol occurs in May and June, associated with phytoplankton production. These inputs are derived primarily from polynyas and leads, where gas exchange, nutrient and
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Figure 9: Five-day aerosol trajectories showing the transpolar influx of Eurasian aerosol to the western Arctic (from Shaw, 1982). The trajectories indicate pathways from the industrial regions of Europe over the Laptev Seas and East Siberian Sea and into the Western Arctic. The day of year in 1982 on which the trajectories reached central Alaska is shown by the numbers, and the tick-marks are 24 h intervals. Solid lines designate hazy conditions, and dashed lines clear periods. “M” indicates trajectories of marine aerosols. light levels encourage phytoplankton blooms; however, at certain Arctic locales, long-range transport of North Atlantic biogenic aerosol has also been documented (Xie et al., 1999). The biogenic aerosol present in largest concentration is dimethylsulfide (DMS), which is oxidized into a number of sulfur-based compounds that serve as cloud condensation nuclei. Other organic by-products, including NH3 and amino acids (L-methionine), and marine detritus,
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bacteria, diatom frustules (Leck et al., unpublished), make effective ice formation nuclei and are injected into the atmosphere through bubble bursting at the ocean surface and frost flower formation on frazil ice. These physical processes also introduce significant amounts of sea salt into the boundary layer especially over the freezing polynya surface. Through convective and updraft mechanisms, salt is lifted to height and may undergo deliquescence in clouds to form numerous ionic species. Suspended sea salt has been detected in remote sensing imagery to be a large component of aerosols in the so-called “roaring forties” latitudinal band of the Southern Ocean. Nearer Antarctica, however, katabatic winds and steeply sloping topography prevent much of this wind-generated sea salt aerosol from penetrating further southward over the continent. In fact, the aerosol concentrations over Antarctica are an order of magnitude less than those measured over the Arctic, even during the productive summer months (Bason, 2000). So, while sea salt concentrations may be used in the Arctic as a proxy for percent open water when studying ice cores, it is not an accurate representation in the Antarctic (Wagenbach et al., 1998). Those aerosols which do settle out of the atmosphere over Antarctica, usually descend from stratospheric levels to become part of the firn and may include black carbon particulates transported over long distances from tropical biomass burning areas (Wolff and Cachier, 1998) and nitrate compounds precipitated from polar stratospheric clouds (Kottmeier and Fay, 1998). In addition to these nucleation and accumulation mode particles are naturally-occurring atmospheric ice features which perturb the radiative profile. The formation of diamond dust and ice fog is contingent upon a number of factors, and is most likely to occur under mixed phase conditions, when large ice particles and abundant water droplets coexist. These prerequisites are often met in areas downwind of polynyas and leads (Rogers et al., 2001), where water vapor and biogenic ice nuclei from the open ocean surface are advected over the relatively cold, supersaturated sea ice periphery (Girard and Blanchet, 2001). In diamond dust events, the ice crystals that form are few but relatively large (30–60 μm; Witte, 1968; Hobbs and Rangno, 1998), may remain suspended for minutes to hours, have low emissivities (Shupe and Intrieri, 2004), and tend towards radiative cooling of the surrounding environment. Ice fog, on the other hand, is composed of numerous spherical ice crystals of 10–30 μm diameter, which do not aggregate efficiently and sediment slowly, allowing the fog to linger for hours to days and enhance surface warming through longwave emission (Girard and Blanchet, 2001). Ice fog is often found under surface inversions near highly sloping topography, accounting for high percentages of surface observations in the North Water Polynya (15%, September; Key et al., 2004), Northeast Water Polynya (40%, August; Minnett, 1995), and flaw leads near Alert (40%, Spring; Girard and Blanchet, 2001). While both diamond dust and ice fog occur year-round, their radiative impact is of greater importance in the winter, when longwave effects dominate; however, it has been suggested that radiative effects ascribed to wintertime diamond dust actually have their provenance in thin liquid layers (Shupe and Intrieri, 2004). The low effective emissivity and sparse nature of diamond dust, especially in times of low illumination, make it a difficult target for remote sensing or in situ observation, possibly leading to an underestimation of its occurrence and importance to radiative forcing. 2.7
Radiation
The electromagnetic radiation that propagates through the atmosphere is generally divided into two components—shortwave (SW) with wavelengths of about 0.35 to 3 μm, and longwave (LW) covering 5 to 50 μm. This distinction reflects not only their origins (SW from the
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sun; LW from the atmospheric constituents, including clouds, and the surface, as well as the sun), but also their interactions with the atmosphere and the surface, and the technologies used to measure their intensities. In propagating through the atmosphere, the SW radiation is primarily scattered whereas the LW is absorbed and re-emitted. While wavelengths shorter than 0.35 μm (ultraviolet) are strongly absorbed by ozone in the upper atmosphere, and some SW wavelengths are absorbed by water vapor, the clear-sky atmospheric transmissivity (measured incident SW, both direct and diffuse, divided by the insolation at the top of the atmosphere, and normalized by the secant of the solar zenith angle) shows very little variation (Minnett, 1999; Hanafin and Minnett, 2001). Scattering occurs across the whole spectrum, but has spectral dependences determined by the ratio of the wavelength of the light and the size of the scatterers. On the other hand, gaseous absorption and re-emission are very strongly wavelength dependent with distinct spectral lines. In some parts of the spectrum molecular absorption is sufficiently strong that the atmosphere is essentially opaque over spectral intervals several micrometers wide (e.g. absorption and emission by CO2 molecules around 15 μm wavelength). In spectral intervals where the atmospheric gases exhibit relatively few absorption lines, the propagation of the LW radiation can be quite efficient and heat can escape from the surface to space. These spectral intervals are referred to as “atmospheric windows” and their clear transmissivity is a strong function of the atmospheric water vapor distribution. Since the water vapor content of polar atmospheres is generally much less than at lower latitudes, these windows are particularly clear. Insertion of clouds and aerosols which absorb and re-emit in a spectral continuum given by Planck’s Function scaled by the spectral emissivity, has a much more pronounced effect on the radiative coupling between the surface and the atmosphere than at lower latitudes. Hence, cloud radiative forcing is an especially important characteristic of the polar regions.
3 NWP Models Most meteorological and radiative processes occurring within polynyas are of too fine a scale to be adequately represented in numerical weather prediction (NWP) models, which range in resolution between 12 km and 275 km and are output every 6 to 24 hours. Surface measurements are also too few and too unevenly distributed to provide a comparable validation dataset at model resolution. The compromise, which advances both the model-derived and in situ measurements, is an assessment of model physics using independent in situ observations (Curry et al., 2002) and a move towards regional or nested grids targeted towards certain areas or atmospheric features. Those surface and upper air observations that are available in polar regions are generally assimilated into large-scale analyses operated by NCEP (National Centers for Environmental Predictions) and ECMWF (European Centre for Medium-range Weather Forecasts), and later refined into re-analysis products—NCEP-R and ERA-40, respectively. While other models, such as the Polar MM5, ETA, NGM, and GEM also produce gridded meteorological features at various levels throughout the atmosphere at equal or higher spatial resolution, NCEP and ECMWF output is global, long-term, and widely used in process studies, validation exercises, and as initialization for climate models. Polynya-specific analyses of NCEP and ECMWF fields have identified regional and climatological characteristics of the open water region, while also pinpointing dynamical and scale deficiencies in the models. ECMWF and ERA-40, which have relatively high spatial resolutions, assimilate varied satellite products, and better represent ice feedbacks (Bromwich et al., 2001), are generally regarded as more
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accurate than NCEP and NCEP-R; although, the ECMWF topography appears to be insufficiently precise (Cullather et al., 1998) and may account for the offset of atmospheric features near Antarctica and the Cosmonaut Sea Polynya (Arbetter et al., 2004). Basic meteorological fields from models, such as surface air temperature and wind, which are used to estimate the air–sea fluxes and ice production within the polynyas, have provided insight in some cases as to the interaction between the polynya and the surrounding environment. Over the Weddell Sea, Moore et al. (2002) identified an extreme year in the surface meteorology of a local polynya, which NCEP-R portrayed as 20 K warmer and 50% cloudier than regional climatological values. Air–sea fluxes from NCEP-R in the Arctic suggested significant dense water production due to enhanced heat loss over the Northwestern Polynya in the Sea of Okhotsk (Martin et al., 1998). However, both NCEP and ECMWF display a cold bias in surface air temperature (Bromwich et al., 2001; Armstrong et al., 2003), which may have underestimated the climatological extreme in the Weddell Sea and overestimated the negative buoyancy flux in the Northwestern Polynya. Winds, too, are biased in the ECMWF and NCEP-R products. Overestimation of wind speeds, possibly through a northward displacement, intensification, or zonal distortion of the jet stream in the Arctic leads to exaggerated westerly and northerly components (Francis, 2002). In areas of katabatic influence, important to polynya formation and maintenance, NCEP is too coarse to resolve either the drainage current or the interaction of these winds with the synoptic flow (Samelson unpublished), while ECMWF surface winds resolve the katabatic flow but shifted from its true location (Arbetter et al., 2004). From concerns regarding the resolution of basic fields, new regional and nested models have been developed. The Polar MM5, which is utilized by the Antarctic Mesoscale Prediction System (AMPS), includes a 10-km domain in the western Ross Sea, where a recurring polynya forms under persistent southerly flow. While synoptic and boundary layer features are generally well-resolved by this model, increased cloudiness associated with the open water areas is underestimated (Monaghan et al., 2005). The ETA model, too, under-estimates cloud cover below 5 km height, due to its imposition of a strong inversion layer, which suppresses turbulent exchange to the upper boundary layer (Greenberg et al., unpublished). And while the NGM (Nested Grid Model), operated by the National Meteorological Center, captures the east–west gradient in mean surface air temperature over the North Water Polynya, for example, smoothing associated with its 190 km grid spacing likely dampens the magnitude of temperature difference between Ellesmere Island and Greenland (Barber et al., 2001).
4 Surface Interactions Interactions between the surface and overlying atmosphere are commonly divided into two groups—radiative and turbulent. Radiative fluxes are considered as having two components, one that originates at the sun (SW) and the other in the thermal infrared (LW), which comprises the emission from the surface and constituents of the atmosphere. The SW and LW interactions with the surface are markedly different. Whereas the absorptivity and emissivity in the LW is relatively consistent for snow, ice and open water, the surface reflectivity in the SW is highly responsive to surface composition. Dry snow on ice reflects more than 80% of the incident insolation in the visible (Hanesiak et al., 2001), whereas open water reflects less than 10% (Payne, 1972). This reflectivity, or albedo, effect is also sensitive to the solar zenith and azimuth angles. Taken together, the albedo and sun angle effects account for the pronounced
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difference in upwelling shortwave radiation over fresh snow and open water surfaces. Due to the rapidly varying albedo within polynyas during the daylit season, surface albedo effects significantly influence the local radiative profile and, thus, net cloud radiative forcing. Turbulent exchanges are so-called because of the efficiency with which properties can be transported through the atmospheric boundary layer by turbulent eddies. Heat, gas (including water vapor) and momentum exchange across the air–sea boundary is moderated by the stability of the lower boundary layer of the atmosphere, which in turn is influenced by the air– surface temperature difference. Atmospheric temperature inversions limit the efficiency of vertical transport, while unstable boundary layers associated with large air–sea temperature differences, such as those found in the vicinity of polynyas, promote turbulent transfer. The resulting influx of heat and moisture into the atmosphere above polynyas most visibly affects cloud and plume formation both over and downwind of the open water area. 4.1
Radiative Fluxes
Cloud radiative forcing (CRF) was defined in Ramanathan et al. (1989) as the sum of the short-wave (CSW ) and the long-wave (CLW ) contributions: CRF = CLW + CSW ,
(1)
where CLW = LW(c) − LW(c=0) ,
(2)
CSW = SW(c) − SW(c=0) .
(3)
LW and SW are, respectively, the longwave and shortwave fluxes incident at the surface in all (c) and clear sky (c = 0) conditions. When only the differences in downwelling, short- and longwave radiation incident at the surface under clear and all-sky conditions are considered, the measure is one of surface CRF—i.e., the influence of cloud cover on the underlying surface. However, in many analyses, the response of the surface to changing insolation is included in the forcing calculation, comprising a net cloud radiative forcing. This latter calculation can be performed at the surface or the top of the atmosphere, using in situ and remotely-sensed data, respectively. The sign and magnitude of cloud radiative forcing indicates whether a cloud preferentially scatters insolation (negative), slowing ice melt or promoting ice re-freeze, or absorbs and re-emits radiation in the longwave (positive), furthering ice melt or hindering re-freeze. During Arctic daylight conditions, in situ and satellite-based studies (Schweiger and Key, 1994) demonstrate a negative incident (Tsay et al., 1989) and net cloud radiative forcing (Walsh and Chapman, 1998; Intrieri et al., 2002) over the Arctic in general and over polynyas in particular (Minnett, 1999; Hanafin and Minnett, 2001). The main factors influencing the incident SW at the surface are the transmittance of the cloud-free atmosphere, the amount and types of clouds present (Figures 10 and 11), and whether the sun is obscured by clouds (Minnett, 1999; Hanafin and Minnett, 2001). The broad-band clear sky atmospheric transmittance has been found to be quite invariant in several data sets taken in different polynyas, having a value of 0.89 ± 0.02 (Minnett, 1999; Hanafin and Minnett, 2001; Key, 2004) whereas the cloud effects have been found to be more variable, reflecting the different local conditions that influence the types and properties of lower clouds in particular (Minnett, 1999; Hanafin and Minnett, 2001; Key, 2004; Key et al., 2004). The effects of the surface conditions have been found also to influence the incident SW CRF, in that contemporaneous measurements
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Figure 10: Time series from the Northeast Water Polynya in the summer of 1993 of measured surface insolation, SW (top), and observed cloud amount and type (center). The smooth line in the bottom panel represents the calculated top-of-atmosphere insolation. The vertical colored lines in the bottom panel indicate the energy removed from the insolation by the clouds and clear-sky effects—that is, the lower edges of the colors are the measured SW (as in the top panel). Zero values indicate missing data. After Minnett (1999). taken at a coastal station and from a research icebreaker in the nearby North Water Polynya show significant differences in clouds and CRF, that can be attributed to radiative interactions between the surface albedo and the clouds themselves (Key, 2004). Incorporating the surface albedo into the calculation to derive net SW CRF leads to a strong dependency on the surface conditions, in particular whether there is snow on the ice, whether the snow is dry or moist, whether the surface includes melt ponds, or whether the surface is ice-free water (Figure 12). The range of surface albedo for these conditions is very large, especially during the melt season going from greater than 80% to less than 10% (e.g. Hanesiak et al., 2001; Perovich et al., 2002a, 2002b; Eicken et al., 2004), with a consequent effect on the net CRF (Walsh and Chapman, 1998). Uncertainties in the correct specification of the surface albedo can potentially lead to a change in sign during many of the sunlit months (Intrieri et al., 2002). Additional radiative contributions from cloud–cloud interactions, both within layers of the same cloud and between proximate clouds, are unknown over polynyas and beyond the scope of most box or superparameterization models available for radiative calculation. Complex ray paths of insolation filtering through mixed phase cloud and laminar stratus decks may enhance or diminish forward scattering, affecting the amount of radiation measured at the surface, and thus, the surface cloud radiative forcing over a given area (Rouse, 1987; Jin and Barber, 2005). Arctic aerosols, especially those locally generated, such as sea salt and biogenic sulfates (DMS; Leck et al., 2004) may also influence the surface radiative forcing. Generally, the effects are more pronounced during the sunlit part of the year as the aerosols scatter more of the incident insolation back to space, leading to a negative radiative forcing. However,
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Figure 11: Incident cloud forcing plotted against cosine solar zenith angle (θ) for clouds of type: (a) stratiform, (b) cumuliform, (c) cirriform and (d) multi-level. Colors represent cloud fraction in octals as shown in the central panel. The black lines represent the average value of net cloud forcing as a function of cosine solar zenith angle calculated in bins of 0.05. From Hanafin and Minnett (2001).
because the polar atmospheres have low integrated water vapor amounts, the atmospheric infrared spectral windows are very clear, and so the incident LW is susceptible to enhancement by the presence of aerosols. Such effects may be even greater in the Arctic than at lower latitudes, where aerosol LW forcing can reach approximately 10 Wm−2 (Vogelmann et al., 2003). 4.2
Turbulent Fluxes
While radiative fluxes dominate the surface heat budget in polar regions, turbulent exchange of heat between the ocean and the atmosphere can be significant within polynyas. High wind speeds, large areas of open-water, and unstable lower atmospheric boundary layers all contribute to enhanced turbulent flux over polynyas, though the measurement of these fluxes and controlling parameters are difficult and infrequent.
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Figure 12: Time series of daily averaged net surface cloud radiative forcing for a range of surface albedos, given by the colors. The incident radiative forcing is derived from measurements taken at the Cape Hershel Ice Camp on the northern edge of the North Water Polynya during 1998. Air–sea temperature difference is an important parameter in determining the lower boundary layer stability which in turn has a large influence on the ocean-atmosphere fluxes. An unstable, or neutrally stable, boundary layer allows for efficient transport of heat, gases and moisture vertically through turbulent diffusion, whereas a stable boundary layer decouples the surface from the troposphere, inhibiting vertical transport. Over much of the world’s oceans the air–sea temperature difference is small, and negative (cool air over warmer water) leading to a neutrally stable or unstable boundary layer, but over polynyas and leads, in situations of off-ice air flow, very large temperature differences can occur (Walter, 1989; Massom et al., 1998), leading to a very unstable boundary layer and large sensible (400 Wm−2 ) and latent heat (130 Wm−2 ) fluxes (Andreas et al., 1979). More recently, Pinto et al. (2003) measured sensible and latent heat values of 100 and 30 Wm−2 from a lead near SHEBA in the Beaufort Sea. Similar values characterize air–sea fluxes over Antarctic polynyas (Massom et al., 1998; Roberts et al., 2001) where observations indicate that polynya sensible and latent heat transfer is two orders of magnitude greater than that over sea ice (Worby and Allison, 1991). Summing over the contribution of all polynyas and leads to air–sea heat exchange underscores the dominance of these open water areas to heat flux in polar regions despite the relatively small areal fraction they occupy (Maykut, 1978). The extreme conditions prevalent downwind of the ice edge and over open water render questionable the use of standard exchange coefficients in bulk aerodynamic formulae. As the internal atmospheric boundary develops over the first few hundred meters of open water, convection is both forced and free leading to a value of the sensible heat transfer coefficient of approximately 1.8×10−3 compared to approximately 1.0 ×10−3 determined in mid-latitude conditions that are not fetch-limited (Andreas and Cash, 1999). The situation becomes more complex within polynyas and leads during conditions when frazil ice is forming and being advected downstream, such that the heat fluxes develop a strong dependence on fetch (Alam and Curry, 1998). For most polynyas, however, the downwind dimension is greater than a few hundred meters, and so the open ocean coefficients can be used; however, it is noteworthy that the largest ocean to atmosphere fluxes occur close to the upwind ice edge. The injection of moisture into the atmospheric boundary layer leads to enhanced plume and cloud formation (Dethleff, 1994; Mailhot et al., 2002; Zulauf and Krueger, 2003; Arbetter et al., 2004), which is apparent in satellite imagery (Fett et al., 1997; Figure 7), and which
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can significantly modify the surface radiation budget, with increases in the LW component of ∼70 Wm−2 modeled downwind (Pinto and Curry, 1995). This increase both in cloudiness and longwave emission to the surface constitutes a positive radiative feedback towards enhanced ice melt in the vicinity of leads and polynyas.
5 Outlook for the Future The International Polar Year (2007–2009) refocuses international attention on changes occurring in the high latitudes. General data deficits and lack of long-term monitoring stations, especially within polynyas and the maritime Arctic, are expected to be addressed by multidisciplinary and multi-national research cruises, stations, buoys, and modeling efforts. New technology that can withstand the rigors of polar deployment have been developed and are being tested in projects leading up to the IPY. The autonomous profiling CTD, moored in locations along the Alaskan Chukchi shelf during the Shelf-Basin Interaction study (Grebmeier et al., 2005), has already demonstrated a unique sampling strategy which increased hydrographic resolution in this area over 1000-fold in a matter of months. The atmospheric analog of this instrument, the long-used radiosonde, has undergone a transformation in recent years to include GPS-derived wind profiles and better-calibrated relative humidity measurements. To extend these atmospheric measurements both in horizontal and vertical space over the Arctic ice pack, instrumentation has been integrated into a small, unmanned light craft—e.g. the aerosonde (Inoue and Curry, 2004)—which has a 500 mile and 24-hour duration. Typical flight patterns otherwise carried out by piloted aircraft can be programmed into the aerosonde flight plan and measurements can be transmitted back to a shore-based receiving station in real time. Since the payload on these aircraft is only in the range of 2.5 kg, sensors must be miniaturized for deployment. Similar AUV’s have been developed for oceanographic surveys (Erikson and Rhines, unpublished), though, as of writing, they are restricted to open water areas. Combined measurements from the aerosonde and AUV for integrated air–sea sampling over polynyas has not yet been attempted but represents one future of polynya science. Shipboard measurement of polynyas will continue, utilizing science-directed research icebreakers, e.g. the USCGC Healy and CCGS Amundsen. Support from coastal stations will be increased as new stations are founded in undersampled areas, such as the Eastern Siberian Seas and the Canadian Archipelago. Measurements at these sites will add to the scant microphysical literature, though they remain characterizations of isolated, land-based locations and generally are unrepresentative of polynya conditions. Merging these varied data types with recently-launched, high resolution remote sensing retrievals, such as those from space-borne lidar (e.g., CALIPSO, PARASOL, ICESat) and cloud radar (CloudSat) will better identify the effects of polynyas on surface meteorology, boundary layer dynamics and downwind cloud optical and radiative properties.
6 Summary The complex interplay between polynyas and the overlying polar atmosphere tests the boundaries of measurement and modeling efforts alike. Intense, localized meteorological forcing may melt or fracture and advect ice downwind, thereby allowing unhindered turbulent and radiative heat exchange across the air–sea boundary. Cloud and plume formation over and in the vicinity of the polynya is fostered by the exchange, which further alters the radiative
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profile and possibly nearby ice and snow cover. The ranges of temporal and spatial scales on which these feedbacks and interactions take place are regulated by atmospheric boundary layer dynamics, which are poorly sampled over polynyas. Proximate coastal measurements of meteorological and radiative fields, which are assimilated by models and gridded analyses, often represent a land-based microclimate influenced by topography, surface cover, and office flow. Remotely-sensed retrievals within these regions provide an alternative data source, though coarse spectral and spatial resolution of many spaceborne sensors limits their applicability to polynyas. Future satellite missions targeted towards active quantification of clouds and aerosols with lidar and radar will forward understanding of cloud formation processes in dry polar atmospheres while adding much-needed data to the polynya archive. Continued in situ sampling from research icebreakers, aircraft, ice camps, and novel autonomous technology will provide useful time series, process and validation data for improving models and satellite retrievals. These combined approaches to studying polynya meteorology will enhance our understanding of the role of polynyas in physical systems and influence on global climate.
Appendix A: Acronyms AERI-ER AMAP AMPS AMSR-E AMSU-B ARM BASE CALIPSO CASES CASPR CCN CloudSat CRF ECMWF ERA-40 ETA FIRE-ACE
Atmospheric Emitted Radiance Interferometer-Extended Range, http:// www.arm.gov/science/research/R00029.stm Arctic Monitoring and Assessment Programme, http://www.amap.no/ Antarctic Mesoscale Prediction System, http://www.mmm.ucar.edu/rt/ mm5/amps/ Advanced Microwave Scanning Radiometer—EOS, http://www.ghcc. msfc.nasa.gov/AMSR/ Advanced Microwave Sounding Unit-B, http://www2.ncdc.noaa.gov/docs/ klm/html/c3/sec3-4.htm Atmospheric Radiation Measurement Program, http://www.arm.gov/ Beaufort and Arctic Storms Experiment, http://gcss-dime.giss.nasa.gov/ base/base.html Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, http: //www-calipso.larc.nasa.gov/ Canadian Arctic Shelf Exchange Study, http://www.cases.quebec-ocean. ulaval.ca/ Cloud And Surface Parameter Retrieval system, http://stratus.ssec.wisc. edu/caspr/caspr.html Cloud Condensation Nuclei Cloud Satellite, http://cloudsat.atmos.colostate.edu/ Cloud Radiative Forcing European Centre for Medium-range Weather Forecasts, http://www. ecmwf.int/ ECMWF 40-year Re-Analysis, http://www.ecmwf.int/products/data/ archive/descriptions/e4/ ETA (η) coordinate system model, http://meted.ucar.edu/nwp/pcu2/etintro. htm First ISCCP Regional Experiment—Arctic Cloud Experiment, http:// eosweb.larc.nasa.gov/ACEDOCS/
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P.J. Minnett and E.L. Key GEM IAPP ICESat IPY ISCCP LW MM5 NCAR NCEP NCEP-R NEW NGM NMC NSA-AAO NWP PARASOL ROPEX SBI SHEBA SSM/I SSM/T2 SW TIROS TOVS
Global Environmental Multiscale model, http://weatheroffice.ec.gc.ca/ model_forecast/index_e.html International Arctic Polynya Programme, http://www.aosb.org/IAPP.html Ice, Cloud, and land Elevation Satellite, http://icesat.gsfc.nasa.gov/ International Polar Year, http://www.ipy.org/ International Satellite Cloud Climatology Project, http://isccp.giss.nasa. gov/ Longwave component of the electromagnetic spectrum (λ = ∼5 to ∼50 μm) PSU (Penn Sate University)/ NCAR mesoscale model, http://www.mmm. ucar.edu/mm5/ National Center for Atmospheric Research, http://www.ncar.ucar.edu/ National Centers for Environmental Predictions, http://www.ncep.noaa. gov/ NCEP Reanalysis, http://dss.ucar.edu/pub/reanalysis/ Northeast Water Polynya, http://www.emi.dtu.dk/research/DCRS/seaice/ new.html Nested Grid Model National Meteorological Center North Slope of Alaska—Adjacent Arctic Ocean, http://www.arm.gov/sites/ nsa.stm Numerical Weather Prediction Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar, http://smsc.cnes.fr/PARASOL/ Ronne Polynya Experiment, http://www.esr.org/ropex/ronice.html Shelf-Basin Interaction study, http://sbi.utk.edu/ Surface Heat Budget of the Arctic Ocean, http://sheba.apl.washington.edu/ Special Sensor Microwave Imager, http://dmsp.ngdc.noaa.gov/html/ sensors/doc_ssmi.html Special Sensor Microwave Water Vapor Profiler, http://www2.ncdc.noaa. gov/docs/klm/html/c3/sec3-4.htm Shortwave component of the electromagnetic spectrum (λ = ∼0.35 to ∼3 μm) Television Infrared Observation Satellite, http://www.earth.nasa.gov/ history/tiros/tiros.html TIROS Operational Vertical Sounder, http://www2.ncdc.noaa.gov/docs/ podug/html/c4/sec4-0.htm
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Chapter 5
Gas Fluxes and Dynamics in Polynyas L.A. Miller1 and G.R. DiTullio2 1 Institute of Ocean Sciences, Fisheries and Oceans Canada, 9860 West Saanich Road, Sidney, BC V8L 4B2,
Canada; e-mail:
[email protected] 2 Department of Biology, College of Charleston, 205 Fort Johnson, Charleston, SC 29412, USA
Abstract High biological productivity and active ice formation make polynyas prime air–sea exchange sites for a number of radiatively- and biologically-active gases, such as carbon dioxide and dimethylsulfide (DMS). Limited observations, to date, have shown that polynyas are generally sources to the atmosphere for biogenic gases (e.g., DMS, oxygen, methylhalides) but sinks of CO2 , which is drawn down both by primary production coupled with organic carbon export and by high solubility in the cold, high-salinity waters typical of polynyas. However, this simple summary belies large data gaps, and our conceptual models of gas dynamics in polynyas are riddled with untested assumptions. The most important needs for additional research and information are in wintertime and transition-period processes, ice biogeochemistry and permeability, and climate change feedback processes.
1 Introduction Polynyas are clearly critical to ice formation and heat exchange in polar oceans (Barber and Massom, 2007; Minnett and Key, 2007; Williams et al., 2007), and a number of recent studies have indicated that polynyas are probably also important sites for air–sea gas transfer. However, most of these studies (Table 1) have been short, seasonal expeditions, and as a result, our ideas about gas dynamics in polynya environments are largely extrapolations and conjecture based on what is known of open water or fully ice-covered seas. Whether a polynya is a source or a sink of a gas depends not only on the biogeochemistry of the gas and the season (as in temperate waters) but also on ice cover timing and polynya formation dynamics. In turn, polynya dynamics are controlled by local climatological forcing and likely by global scale atmosphere-ocean coupling (e.g., El Niño-Southern Oscillation) via long-range teleconnections (Savage et al., 1988; Gloerson and Mernicky, 1998; Arrigo and van Dijken, 2004a). Because polynyas are often biologically rich areas (Tremblay and Smith, 2007), they are potentially effective sources of biogenic gases, such as dimethylsulfide (DMS; Section 4 below) and alkylhalides (e.g., methylbromide; Section 5). Conversely, highly productive polynyas may be net sinks of CO2 , depending on the balance of Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74005-3
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Table 1: Gas studies in polynyas Polynya
Gases
Years
Season
References
Weddell Ross Sea
CO2 CFCsa , O2 , CO2
1981 1984
Spring Summer
Cape Bathurst Ross Sea
CFCs, CO2 DMS
1989–1995 1992
Summer Summer
Northeast Water
CO2 , O2 , CFCs
1992
Summer
North Water
CO2 , DMS
1993–1994
Spring–fall
Ross Sea
CO2 , DMS
1994–1996
Summer
Ross Sea
CO2 , DMS
1996–1997
Spring–fall
Mertz Mertz
O2 CO2 , CFCs, CH3 Xb
1999 2000–2001
Winter Summer
Chen and Poisson (1984) Takahashi et al. (1985) Trumbore et al. (1991) McLaughlin et al. (2002) DiTullio et al. (2003) DiTullio and Smith (1995) Yager et al. (1995) Wallace et al. (1995) Top et al. (1997) Miller et al. (2002) Bouillon et al. (2002) Bates et al. (1998) DiTullio et al. (2003) Arrigo et al. (1999) Sweeney et al. (2000b) DiTullio et al. (2003) Williams and Bindoff (2003) Sutherland et al. (2002a, 2002b) Yvon-Lewis et al. (2004)
Cape Bathurst St. Lawrence Is.
CO2 , O2 , CH4 , CO DMS
2002–2004 2001
All year Winter
a Chlorofluorocarbons. b Methylhalides: CH Br and CH Cl. 3 3
primary production, remineralization, and vertical organic matter export in relation to the timing of ice cover and wind events (Section 3). Sustained ice formation also encourages formation and export of cold, dense shelf waters (Williams et al., 2007) in which gases readily dissolve (Section 2), and polynyas where these processes occur could provide another significant sink for atmospheric greenhouse gases, such as carbon dioxide and chlorofluorocarbons (see also Hoppema and Anderson, 2007). Finally, as extremely variable phenomena, polynyas are likely sensitive to climate variations, with potential for both positive and negative feedbacks. While our understanding of gas dynamics in polynyas may be limited, research in this field has become more intensive over recent years (Table 1), and ideas about the role polynyas play in air–sea gas exchange are changing rapidly. Therefore, we attempt here to review the data currently available, while also emphasizing that those data are still open to interpretation and highlighting what we think are some particularly promising directions for future research.
2 Gases in Cold, Salty Water Gas partitioning (KH in atm M−1 ) between water and air is defined by Henry’s Law KH =
PA , [A(aq) ]
(1)
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Figure 1: Equilibrium oxygen (O2 ) concentration in seawater as a function of temperature and salinity, µmol kg−1 . Darkened region is below the freezing point. Table 2: Examples of equilibrium gas concentrations (mol kg−1 ) in fresh and seawater at 0 and 25◦ C under typical atmospheric partial pressures, PA Gas
O2 a N2 a Ara CO2 b CFC-11d DMSe
Fresh water (S = 0)
Seawater (S = 35)
0◦ C
25◦ C
0◦ C
25◦ C
0.46 × 10−3 0.82 × 10−3 22 × 10−6 1.9 × 10−3 0.10 × 10−6 3.2 × 10−12
0.26 × 10−3 0.49 × 10−3 13 × 10−6 1.9 × 10−3 0.026 × 10−6 1.1 × 10−12
0.35 × 10−3 0.62 × 10−3 17 × 10−6 2.2 × 10−3 0.068 × 10−6 2.8 × 10−12
0.21 × 10−3 0.38 × 10−3 10 × 10−6 2.0 × 10−3 0.018 × 10−6 0.95 × 10−12
PA (atm)
0.21 0.78 0.0093 0.38 × 10−3 c 0.26 × 10−9 c 2 × 10−12 c,f
a Weiss (1970). b Total inorganic carbon-carrying capacity, assuming that alkalinity is 1900 µeq kg−1 in freshwater and 2300 in seawater; Lewis and Wallace (1998). c Atmospheric concentrations of these gases are variable; the tabulated values are typical values used in calculating the equilibrium aqueous concentrations. d Trichlorofluoromethane, CCl F; Warner and Weiss (1985). 3 e Dacey et al. (1984). f Barnard et al. (1982).
which describes the equilibrium relationship between the concentration of a gas in solution ([A(aq) ] in mol l−1 ) and its partial pressure in the overlying atmosphere (PA in atm). The Henry’s Law constant, KH , is a function of temperature as well as salinity, and for most gases, solubility in seawater increases with both decreasing temperature and salinity (Figure 1, Table 2). The salinity dependence is due to the “salting out” effect, whereby the total load of solutes in the solution inhibits further dissolution. When describing the potential for air–sea exchange, the partial pressure of the gas in the water (pA ; defined as the partial pressure in a small volume of gas overlying and in equilibrium with the solution) is used more commonly than KH . By definition, if pA is the same as PA , there will be no net gas exchange, whereas if pA is less than PA , there will be a net flux into the water, and vice versa.
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The equilibrium state described by thermodynamics (Eq. (1)) is not the only factor affecting how much gas moves across the air–sea interface. The flux, F , of a gas into or out of the ocean is directly related to both the gradient in partial pressures across the air–sea interface (p, the thermodynamic control) and to the gas transfer velocity, k (the kinetic control), F = kp.
(2)
The gas transfer velocity is usually expressed as a function of the gas solubility and wind speed (e.g., Liss and Merlivat, 1986; Wanninkhof and McGillis, 1999) but is also dependent on other factors which are much more difficult to parameterize (Liss et al., 1997). Nonetheless, field studies most commonly estimate gas fluxes by determining p analytically and calculating k from either observed or modelled winds. This approach has an uncertainty of up to 100%, mainly due to poor parameterizations of k, but also because short time-scale variations in both winds and p (which are very difficult to identify with the snap-shot, station-based sampling typical of oceanographic field work) heavily influence the total flux (Boutin and Etcheto, 1991; Bates and Merlivat, 2001; Fransson et al., 2004). For some gases, air–sea fluxes can be measured directly using a number of eddy techniques, and the methods for shipboard application are now reaching maturity (McGillis et al., 2001; Huebert et al., 2004), showing great promise for reducing the uncertainties in air–sea gas exchange rates. In Table 2 we have given the carbon dioxide solubility as the carbon-carrying capacity or the total dissolved inorganic carbon (DIC) concentration in equilibrium with the atmosphere. When dissolved in water, CO2 hydrolyzes, forming carbonic acid (H2 CO3 ), bicarbonate 2− (HCO− 3 ), and carbonate (CO3 ), 2− + CO2 + H2 O H2 CO3 H+ + HCO− 3 2H + CO3
(3)
generating a large, rapidly-exchanging pool of dissolved inorganic carbon. Analogous reactions occur in aqueous solutions of SO2 and NO2 , which also have quite high solubilities. Although the equilibrium concentration of the dissolved CO2 gas molecule in seawater decreases with increasing salinity (Figure 2a) like other gases, the carbon-carrying capacity of seawater increases with salinity (Figure 2b). The very high alkalinity of seawater (an increase in salinity is almost always accompanied by an increase in alkalinity) is mainly set by the carbonate system and is closely related to the buffering capacity of seawater, i.e., the amount of acid (such as additional CO2 ) that can be added before the pH starts to drop, inhibiting further CO2 absorption. Thus, as salinity increases, Eq. (3) shifts further toward the right, increasing dissociation of the carbonic acid, dramatically increasing the carbon-carrying capacity, decreasing pCO2 , and thereby encouraging CO2 dissolution. A comprehensive discussion of the seawater carbonate system is presented in Zeebe and Wolf-Gladrow (2001).
3 Carbon The primary motivation for studying air–sea carbon dioxide exchange is to understand the role the ocean plays in the global carbon cycle and controlling atmospheric CO2 levels, which in turn impact global climate through the greenhouse effect (Arrhenius, 1896). These studies have particular urgency at this time, because anthropogenic CO2 emissions to the atmosphere appear to be significantly altering the natural global carbon cycle and warming the planet’s surface (Folland et al., 2001). Numerical models have indicated that this warming
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Figure 2: (a) Equilibrium concentration (µmol kg−1 ) of aqueous CO2 gas (H2 CO∗3 , the sum of CO2(aq) and H2 CO3 , which cannot be distinguished analytically) in seawater as a function of temperature and salinity. (b) Seawater carbon-carrying capacity (µmol kg−1 total DIC) as a function of temperature and salinity. For both figures pCO2 = 380 µatm, and alkalinity varies linearly from 1900–2300 eq kg−1 between salinities of 0 and 35 psu. Darkened regions are below the freezing point. Stippled area in (b) gives characteristics typically observed in polynya surface waters. is likely to appear soonest and most dramatically in the polar regions, particularly in the Arctic (Cubasch et al., 2001), and some data are consistent with that prediction, although regional trends, interannual and seasonal variability, and other factors complicate the analysis (Polyakov et al., 2002; Comiso et al., 2003; Curran et al., 2003). An accelerated rate of change in polar regions, coupled with the high variability in polynya opening, closing, and productivity, could mean that gas exchange in polynyas will produce disproportionately large climate change feedbacks, relative to the small total area covered by polynyas. Polynyas potentially contribute to global oceanic carbon export through both the solubility and biological pumps (e.g., Volk and Hoffert, 1985). The solubility pump generally refers to the process through which carbon is removed from surface waters with formation of deep and bottom waters by convection in the Greenland, Labrador, and Weddell Seas (Anderson et al., 2000; Tait et al., 2000; Hoppema, 2004). However, these are not the only places where cold, high-salinity waters with high carbon-carrying capacity (Figure 2b) sink away from the surface. The intermediate waters (often also called shelf waters) formed under coastal leads and polynyas, where ice is formed and removed by wind or currents (e.g., Foster, 1972; Cavalieri and Martin, 1985, 1994; Haarpaintner et al., 2001), may also draw down atmospheric CO2 (Figure 3). A recent study of the inorganic carbon system during sea-ice formation in the Storfjorden polynya (Svalbard, Figure 4; Anderson et al., 2004) saw high DIC concentrations below 50 metres (m) that were associated with brines, which presumably had been produced at the surface when sea ice was forming. However, intermediate water formation is difficult to study, and this phenomenon has not yet been directly observed in any other polynya. Polynya contributions to the biological pump, by which photosynthetic production and then vertical export of organic matter removes carbon from the surface into deep waters and sediments, are even less straight-forward. While polynyas often have very high rates of both primary and secondary biological production (Tremblay and Smith, 2007; Ducklow and
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Figure 3: CO2 drawdown in ice-manufacturing polynyas.
Figure 4: Locations of some Arctic polynyas discussed in the text.
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Figure 5: The seasonal rectification hypothesis. Modified from Yager et al. (1995). CT denotes total dissolved inorganic carbon (DIC), OC denotes total organic carbon. Yager, 2007; Deibel and Daly, 2007), a high photosynthetic rate does not necessarily imply net annual CO2 consumption, and it is not clear to what extent polynyas are net exporting versus recycling systems. Particularly in shallow coastal polynyas where surface mixing can extend to the bottom, even deposition to the sediments does not necessarily isolate carbon from the atmosphere for long time periods. The timing of polynya opening and closing, in relation to the seasonal primary production cycle, is probably critical to the net annual biogenic CO2 flux. This concept was first presented by Yager et al. (1995) as the seasonal rectification hypothesis (Figure 5), attempting to describe the annual CO2 cycle in a seasonal polynya that opens in early spring and closes again in late fall. In such a polynya (or in any seasonally ice-covered sea), respiratory carbon remineralization during winter would cause supersaturation in the surface waters under the ice. By the time the ice has melted sufficiently for patches of open water to appear in spring, ice algae would have already depleted surface DIC concentrations to undersaturated levels, and any air–sea CO2 exchange would be directed into the water. Throughout the open water season, primary production would keep the surface waters undersaturated, but low winds could limit atmospheric CO2 drawdown. Early autumn storms, however, could
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facilitate equilibration between the atmosphere and surface waters, but by the time the surface waters become supersaturated due to excess respiration over production, the surface would be covered with ice, again preventing outgassing. A cycle of this sort would make the polynya a significant net annual CO2 sink, but that sink would be dependent on the timing of polynya formation and closing. If the polynya opens during the winter, particularly during a storm, enough CO2 could outgas in a short period to completely counteract the summer and autumn drawdown. In addition, high winds during the open water season would not only encourage air–sea exchange, but would also mix carbon-rich subsurface waters into the surface, where they would inhibit atmospheric CO2 drawdown. The seasonal build-up and remineralization of labile dissolved organic carbon (DOC) will also contribute to the biogenic CO2 source/sink balance in polynyas. The DOC pool in seawater, including the refractory fraction, is also subject to photochemical oxidation, which can either degrade the organic matter (facilitating biological uptake or directly producing inorganic carbon) or polymerize it, creating larger, more refractory molecules (Carlson, 2002). In polar regions light in the surface waters is limited both seasonally and by ice cover. As a consequence, photochemical cycling of dissolved organic matter would be accelerated in polynyas, relative to the surrounding, ice-covered waters. Higher incidence of ultra-violet (UV) radiation due to stratospheric ozone depletion in polar regions could further promote photochemical DOC degradation in polynyas (e.g., Kieber and Mopper, 1994). Photochemical oxidation, as well as primary production, may also be significant within the thin ice that often covers polynyas (Belzile et al., 2000). A paucity of data limits our understanding of the role calcium carbonate plays in polynya CO2 cycling. Changes in alkalinity associated with CaCO3 precipitation and dissolution dramatically influence pCO2 through the bicarbonate buffering system: Ca2+ + 2HCO− 3 CaCO3 + CO2 + H2 O.
(4)
In the past calcite producing coccolithophorids (e.g., Emiliania huxleyi) were not considered to be ecologically or biogeochemically important in polynya dynamics, because of known temperature constraints on their growth rates. However, E. huxleyi blooms have been observed at high latitudes (albeit perhaps due to climate-induced sea surface warming; Sukhanova and Flint, 1998), and other organisms (e.g., pteropods) may also significantly influence alkalinity in polynyas. Rivers are important sources of alkalinity to the surface waters of the Arctic Ocean (Anderson et al., 1983), and therefore, alkalinity effects are probably particularly important for polynyas there. Methane (CH4 ) is a stronger greenhouse gas than CO2 and is generally thought to be released from sediments (Macdonald, 1976; Tilbrook and Karl, 1994) and may accumulate under ice in polar waters (Kvenvolden et al., 1993). Data on methane distributions or dynamics specifically in polynyas have not yet been reported, but the timing of ice cover in relation to rates of microbial methane cycling processes in polynyas could be relevant to the net oceanic source-versus-sink balance of atmospheric methane. 3.1
The Ross Sea
The Ross Sea, Antarctica, contains two important, recurring polynyas (Figure 6), one in Terra Nova Bay and another off the Ross Ice shelf (the Ross Sea polynya, the largest polynya in the Antarctic at 396,000 square kilometres (km2 )), which have been sites of numerous polynya studies, starting with Priestley’s descriptions of the Terra Nova Bay polynya during the winter of 1912 (Priestly, 1962; Bromwich and Kurtz, 1982). That polynya forms
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Figure 6: Ross Sea bathymetry. Dark grey lines are semi-permanent ice features (ice shelves and glacial tongues); light grey shaded regions give the approximate locations of the Terra Nova Bay polynya, to the west, and the larger Ross Sea polynya. Depth contours are every 200 m through 1000 m and every 1000 m thereafter. consistently during the austral winter, as katabatic winds descend off Victoria Land and push the ice offshore, while the Drygalski Ice Tongue prevents advection of more ice into the area (Bromwich and Kurtz, 1984; Van Woert, 1999). Katabatic winds off the Ross Ice Shelf also appear to play the major role in keeping the Ross Sea polynya open, although warm atmospheric temperatures, particularly in association with La Niña events in the Pacific (Bromwich et al., 1998; Van Woert et al., 2003) may contribute at times. The first surface pCO2 data from the Ross Sea were collected by Takahashi et al. (1985) for late January and early February of 1984. Although they did not report the ice conditions, Takahashi et al. found that pCO2 was much lower in areas where the two polynyas form than in the rest of the Ross Sea. These observations have been confirmed by later studies, including those extending into both the spring and fall (Figure 7; Bates et al., 1998; Sweeney et al., 2000a; see also the on-line surface water pCO2 data repository at the Lamont-Doherty Earth Observatory). The lowest surface pCO2 values observed in the Ross Sea to date have been about 130 µatm (Table 3). Bates et al. (1998) and Sweeney et al. (2000b) both calculated small (less than 10 mmol m−2 d−1 ) air-to-sea fluxes during spring and summer, but their use of long-term averaged wind speeds could have significantly underestimated the true fluxes, and Sweeney et al. saw evidence for larger fluxes (up to 70 mmol m−2 d−1 ) during an autumn cruise.
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Figure 7: Surface pCO2 observations in the Ross Sea. Grey scale ranges from 110 (light) to 370 (dark) µatm. Triangles: Takahashi et al. (1985), circles: Sweeney et al. (2000a). pCO2 was measured directly and corrected to in situ temperature. Table 3: Carbon dioxide partial pressures, pCO2 (µatm), in polynya surface waters Polynya
Minimuma
Month
Maximuma
Month
References
Ross Sea
130
January/ February
360
November
North Water Northeast Water
130 170
June July/August
500 280
April July/August
Sweeney (2003) Takahashi et al. (1985) Bates et al. (1998) Miller et al. (2002) Yager et al. (1995)
a Observed minima and maxima, not necessarily true annual extremes.
Since the mid-1990s, a number of interdisciplinary programs have targeted the carbon system of the Ross Sea, producing a substantial, albeit disjointed, data set. Although large seasonal biogenic carbon drawdowns are consistently observed in the Ross Sea (Bates et al., 1998; Arrigo et al., 2000; Gordon et al., 2000; Sweeney et al., 2000b), Carlson and Hansell (2003) reported that relatively small quantities of DOC are produced during the summer. What DOC does accumulate is labile on time scales of only two months (the role of photochemistry versus direct heterotrophy in that lability has not been investigated), suggesting
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that in the Ross Sea DOC export is relatively unimportant to carbon sequestration, although it may still be playing a role in the annual cycle of air–sea CO2 exchange. Similarly, Asper and Smith (2003) estimated that about 60% of the POC in the surface waters is respired directly back to CO2 during the summer, with only about 13% exported below 200 m. Sweeney (2003) synthesized data from a number of cruises to compile a model annual cycle which confirmed the hypothesis (Figure 5) that seasonal sea ice creates a net annual carbon dioxide sink by preventing winter outgassing. Arrigo et al. (2000) suggested that due to light effects, Phaeocystis antarctica usually dominated over diatoms (which were less effective at either consuming DIC or producing DOC) in the more deeply mixed Ross Sea polynya, while diatom blooms were relatively more important in the more stratified Terra Nova Bay polynya. Thus, a climatic warming that increases stratification could favor diatoms, thereby reducing carbon drawdown efficiency (Arrigo et al., 1999). In addition, large icebergs calving off ice sheets in Antarctica have recently disrupted circulation and increased ice cover sufficiently to reduce seasonal primary productivity by as much as 90% (Arrigo and van Dijken, 2003a). Finally, iron released by melting sea ice appears to contribute to the high spring productivity observed in the Ross Sea (Sedwick and DiTullio, 1997), and loss of the seasonal sea ice could lead to lower productivity rates sustained over longer periods, with accompanying implications for lower carbon export. Gordon et al. (2000) also showed that alkalinity does not vary much during the summer season, contributing evidence that calcifying organisms are unimportant in the Ross Sea carbon cycle under the current climate regime. Dense shelf water under the Ross Sea was first reported by Jacobs et al. (1970), although it is uncertain how much forms in the polynyas versus under the ice shelf (Williams et al., 2007). In 1984, Trumbore et al. (1991) saw elevated levels of CFCs at depth along the shelf slope, as well as in the High Salinity Shelf Water in the western region of the Ross Sea, and they suggested at least part of those high CFC concentrations must have entered the water through polynyas and leads during winter. Takahashi et al. (1985) report inorganic carbon (DIC and pCO2 ) and oxygen data from that same cruise, and the waters which were high in CFC-12 may also have been more saturated with CO2 when last at the surface (implying additional, wintertime equilibration before convection) than the low-CFC-12 waters. However, the difference was not significant, relative to the uncertainty in the ratio of dissolved oxygen consumption to organic carbon remineralization. Later, Sweeney (2003) confirmed that CO2 export with shelf water formation is probably insignificant, based on measured DIC concentrations in deep water masses and estimates of brine rejection in the Ross Sea. 3.2
The North Water
First described in 1616, the North Water, in northern Baffin Bay (Figure 8), is among the most extensively studied of all recurrent polynyas (Dunbar and Dunbar, 1972). The North Water is a coastal polynya with complex circulation (Melling et al., 2001) and forms when an ice bridge in the narrow straits to the north blocks ice flowing into the area (Ingram et al., 2002). Air-borne infrared radiometry studies during 1978/79 and 1980/81 showed that even during winter, the North Water has only a very thin ice cover (Steffen, 1986). Sampling completed during 1997–99 extended into early spring and late fall (Barber et al., 2001; Deming et al., 2002), providing a relatively comprehensive understanding of the annual carbon cycle in the North Water. A synthesis of inorganic carbon data from 1998 and 1999 (Miller et al., 2002) confirmed that although the surface waters were supersaturated in CO2 under the sea ice in early spring, by the time the ice cleared, pCO2 had dropped below atmospheric values, and the water remained undersaturated until the ice began to form
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Figure 8: The North Water. Shaded region gives the approximate location of the polynya. Depth contours are every 100 m through 1000 m. again in the autumn (Figure 9). Ice algal activity was extremely high in the spring (Michel et al., 2002), consistent with the observed pCO2 trend, but colored dissolved organic matter (CDOM) observations in the ice and in the surface waters indicated that photochemistry, as well as respiration, may have placed at least some limits on the photosynthetic CO2 sink (Belzile et al., 2000; Scully and Miller, 2000). Belzile et al. (2000) found that the ice not only contained high levels of photoreactive CDOM, but was also more transparent to ultraviolet than to photosynthetically active radiation, implying that within the ice, photochemical degradation may have been more efficient than photosynthesis. Unfortunately, Belzile et al. were unable to either confirm or refute that possibility with direct photooxidation measurements. Scully and Miller (2000) also saw high concentrations of CDOM in surface waters during the spring and early summer, with maxima at the surface, likely derived from ice melt. Because of photobleaching, CDOM maxima in other oceanic areas are more commonly observed below the surface (Miller, 1998), and therefore, photochemical degradation to inorganic carbon may have been sluggish in the North Water. Miller et al. (2002) found that total organic carbon varied dramatically in the surface waters throughout the study, at least partially attributable to rapid turnover of labile DOC, although contamination problems were also noted. The net result of the competing biological and photochemical processes occurring in the North Water during the summer was very low surface water pCO2 values (Figure 9; Table 3; Miller et al., 2002). The Miller et al. study did not directly measure air–sea CO2 fluxes but did generate a rough estimate of 0.3 Tmol for the net seasonal atmospheric CO2 drawdown from spring to fall. With the assumption that ice prevents outgassing during the winter, this
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Figure 9: Seasonal average pCO2 and ice cover observed in the North Water during 1998 and 1999 (Miller et al., 2002). pCO2 was calculated from measured DIC and total alkalinity; error bars are standard deviations of all measurements during each time period, indicating regional variability. Horizontal line gives the approximate atmospheric pCO2 during the study (365 µatm). value could be taken as an estimate of the annual CO2 drawdown by the North Water. For perspective, 35,000 km2 of open water (about half of the entire North Water region) could outgas that same amount of carbon during winter (assuming supersaturation levels observed in April) in about 20 days under wind speeds of 25 m s−1 (storm conditions). Bourke and Paquette (1991) saw some evidence of intermediate and deep water formation in North Water during the 1980s. However, Miller et al. (2002) were unable to conclusively identify an associated solubility-mediated CO2 drawdown, mainly because of no wintertime sampling and very little dissolved oxygen data. 3.3
The Northeast Water
The Northeast Water (NEW), off the northeastern coast of Greenland at about 78–80◦ N (Figure 10), was the subject of an interdisciplinary study during the summers of 1992 and 1993 (Hirche and Deming, 1997). The polynya formed because a land-fast ice barrier to the south, over Belgica Trough, prevented ice in the East Greenland Current from entering the area via an anticyclonic eddy around Belgica Bank (Schneider and Budéus, 1994). Although ice- and snowmelt runoff from Greenland appear to lower the salinity near the coast, the ice barrier that blocked ice from flowing into the Northeast Water also blocked melt waters, and therefore, the surface waters in the central NEW had relatively high salinities (Schneider and Budéus, 1994), as well as very low temperatures (Budéus et al., 1996), facilitating CO2 absorption (see Section 2, above). Schneider and Budéus speculated that the Northeast Water could have been an ice factory in winter, exporting shelf water, but no clear evidence for shelf or deep water formation was found (Budéus and Schneider, 1995;
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Figure 10: The Northeast Water. Shaded region gives the approximate location of the polynya. Depth contours are at 200 m, 500 m, 1000 m, and every 1000 m thereafter. Top et al., 1997). Consistent with the low temperatures, Yager et al. (1995) saw very low pCO2 values in the surface waters (Table 3) during the summer of 1992. Despite that undersaturation Yager et al. determined that calm weather limited atmospheric CO2 absorption, leading directly to development of the seasonal rectification hypothesis (Figure 5). In a later reanalysis that included data from 1993, Falck (1999) estimated somewhat higher shortterm air-to-sea gas exchange rates at some stations. Falck also estimated that overall, the net effects of calcium carbonate precipitation and dissolution were insignificant, although the alkalinity data presented in Yager et al. showed some rather dramatic localized variations indicating that either calcifying organisms or local or remote terrestrial runoff had some important (or at least interesting) effects. Surface DOC concentrations measured during the same study were already higher in the polynya than under the ice in early spring, although much of that material appeared to be terrestrial and refractory (Skoog et al., 2005). The DOC concentrations then decreased from spring to summer, concomitant with an increase in DON (Skoog et al., 2001). Among the possible explanations were microbial degradation of the organic matter within the polynya, which could have released CO2 , and export to the East Greenland Current. Wallace et al. (1995) and Falck (1999) derived very high rates of primary production from observed net
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Figure 11: Approximate locations of some Antarctic polynyas discussed in the text. enrichments in dissolved oxygen and concluded that the bulk of the resulting organic matter must have been exported from the polynya, either by vertical sinking or horizontal advection, rather than being remineralized within the polynya. 3.4
Other Polynyas
A significant component of Antarctic Bottom Water appears to be formed in a polynya off the Mertz Glacier Tongue (Figure 11) on the Adélie Coast, south of Australia and New Zealand (Williams and Bindoff, 2003; Marsland et al., 2004; Williams, 2004). Although a number of cruises have attempted to study the carbon dioxide system in the polynya, due to extremely difficult conditions, very few have been successful. A transect along 145◦ E during late summer of 1993 showed that pCO2 dropped dramatically near the coast (Metzl et al., 1999). Sutherland et al. (2002a, 2002b) also report surface pCO2 data from the area during the 2000–2001 summer, but they show no clear relationship with the polynya. A three-year study of the Mertz Polynya, including a detailed winter expedition, investigated deep water formation under the polynya and found that the oxygen levels in the High Salinity Shelf Water (HSSW) were higher than those seen within the (undersaturated) surface waters of the eastern part of the polynya (Williams and Bindoff, 2003). Williams and Bindoff concluded that the HSSW must have formed from surface waters on the western side of the polynya, after the westward current along the coast had carried the water through the polynya, providing more time for equilibration with the atmosphere. Additional analysis of the oxygen fluxes is underway to further test this hypothesis (G. Williams, personal communication).
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Polynyas in the Weddell Sea (Figure 11) have long been invoked as potential sources for Antarctic Bottom Water (Foster, 1972; Gordon, 1978; Comiso and Gordon, 1998), but early interpretations of inorganic carbon (Chen and Poisson, 1984; Anderson and Jones, 1991) and CFC (Mensch et al., 1996) data from the Weddell Sea were consistent with the idea that the deep waters in that region form mainly under ice shelves rather than in polynyas. However, total inorganic carbon in Weddell Sea Bottom Water increased significantly between 1993 and 1998 (Hoppema et al., 1998), implying a direct connection with increasing atmospheric CO2 concentrations, possibly through coastal polynyas. Holland (2001) showed that the Weddell Polynya, which forms intermittently at Maud Rise (Figure 11), could produce convection as deep as 1 km, and in 1998 Hoppema (2004) found a large, sub-surface water mass with high inorganic carbon concentrations, extending to depths of about 1 km in that same area. Hoppema explained his observations as resulting from sub-surface remineralization, without discussing the polynya, but the close to proximity to the polynya certainly warrants further investigation into how biogenic and physical processes may interact in this potentially deep-convecting polynya. Although the Cape Bathurst polynya (Figure 4) is one of the largest recurring polynyas in the Arctic, only sparse information is available on its physical forcing (Arrigo and van Dijken, 2004b; Barber and Hanesiak, 2004) or biogeochemistry. McLaughlin et al. (2002) measured CFC distributions in the area but related them to large-scale circulation of the central Arctic basin, rather than to local shelf and polynya processes. Data from recently completed studies that included carbon dioxide, methane, and carbon monoxide distributions and dynamics are expected to dramatically expand our understanding of carbon cycling in this polynya.
4 Sulfur Over the last three decades research into the marine sulfur cycle has become increasingly important, beginning with the seminal paper by Charlson et al. (1987), documenting the importance of dimethylsulfide [DMS; (CH3 )2 S] fluxes to the atmosphere, the role of DMS in forming cloud condensation nuclei, and its potential negative feedback on global climate. Although direct measurements of DMS fluxes are rare, with methods only recently becoming available (e.g., Huebert et al., 2004), at least 70% of the sulfur flux from the oceans to the atmosphere is thought to be in the form of DMS, accounting for as much as 50% of all natural (including terrestrial and volcanic) atmospheric sulfur sources (Schlesinger, 1991). Although the oceanic DMS cycle is complex (Malin et al., 1992; Andreae and Crutzen, 1997), the oceanic DMS flux to the atmosphere ultimately depends on the algal production rate of the DMS precursor [dimethylsulfoniopropionate, DMSP; (CH3 )2 SCH2 CH2 COOH], the DMSP degradation pathway (Kiene, 1996b; Kiene et al., 1999) and rates (Kiene and Bates, 1990; Kiene, 1996a), as well as various physicochemical factors (e.g., diffusivity). Phytoplankton species composition and physiological state are the two most important biological factors determining DMSP production rates; in general, dinoflagellates and haptophytes (e.g., Phaeocystis) have the highest DMSP cellular quotas, relative to other algal classes (Keller and Korjeff-Bellows, 1996). Algal DMSP lyase activity (DLA) moderates the conversion of DMSP to DMS (Cantoni and Anderson, 1956), but the presence of the enzyme is species-specific and not well documented (e.g., Stefels et al., 1995). Harada et al. (2004) recently showed that DLA is higher at low than at high nutrient concentrations (consistent with the hypothesis that DLA is involved with oxidative stress protection; Sunda et al., 2002).
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Significant DLA can also result in diel DMS levels varying by more than an order of magnitude in systems dominated by vertically migrating dinoflagellates (Merzouk et al., 2004). In addition, trophodynamic factors such as grazing and food-web structure also directly influence DMS production and release. For instance, krill grazing on pelagic or sea-ice algae and copepod grazing on diatoms have been shown to be significant mechanisms of DMS release to the water column in both Antarctic and Arctic polynya regions (Daly and DiTullio, 1993; Lee et al., 2003; Kasamatsu et al., 2004). Finally, DMSP can be used by bacterioplankton as an osmolyte (Kiene et al., 2000), possibly helping explain the high DMSP levels found in sea-ice algae (see below). The polar regions are a disproportionately large source of marine DMS (Kettle et al., 1999) due to haptophyte dominance in these areas. However, polynya regions are undersampled with respect to DMS and DMSP, and of 37 coastal polynya systems identified in the Antarctic region (Arrigo and van Dijken, 2003b), sulfur measurements have been made in only a few, mainly the Ross Sea (Figure 6) and Prydz Bay (Figure 11). Due to the massive blooms of the colonial haptophyte, Phaeocystis antarctica, DMS concentrations in the Ross Sea can exceed 300 nM in surface waters during austral summers (DiTullio et al., 2003). These values are 100 times higher than the average concentrations observed in the world’s oceans (Kettle et al., 1999). During the peak colonial P. antarctica bloom in the Ross Sea (November; DiTullio et al., 2000), integrated DMS values (reaching 25 mmol m−2 ) in the upper 50 m of the water column were also orders of magnitude higher than the average observed in Southern Ocean waters (Curran et al., 1998). During late summer (February) of 1992, DMS concentrations were lower (up to 2 mmol m−2 ) than during the colonial bloom period in spring (DiTullio and Smith, 1995), but still high compared with observations in temperate waters. At present, we do not clearly understand the important physiological and life-history factors affecting DMSP production in P. antarctica blooms, preventing reasonable estimations of the true DMS sea-to-air fluxes in the Ross Sea. Although DMSP production is high in the Ross Sea, both DMSP export to depth with sinking biodetritus (DiTullio et al., 2000) and DMS consumption rates (DiTullio et al., 2003) within the water column may also be high. Elevated DMS concentrations have also been observed during a colonial P. antarctica bloom in the Prydz Bay polynya region (Figure 11; Gibson et al., 1990). Similar to the Antarctic, measurements of biogenic sulfur production have been made in only a couple of the many polynyas in the Arctic (Winsor and Björk, 2000). Although the North Water (Figure 8) is one of the most productive polynyas in the Arctic, a recent study found relatively low DMS and DMSP concentrations in the region (Bouillon et al., 2002), especially in comparison to the Ross Sea polynya. This difference was attributed to the dominance of large, DMSP-poor centric diatoms (Thalassiosira spp. and Porosira glacialis) in the North Water and DMSP-rich Phaeocystis antarctica in the Ross Sea. Bouillon et al. suggested that the two polynyas are at opposite ends of a DMS production spectrum. As noted above, algal species composition and physiological state are undoubtedly the important parameters responsible for these differences. While surface water DMS concentrations may be low (less than 10 nM) during winter (DiTullio and Smith, 1992), deep mixing events in shallow coastal polynyas can directly link the benthic DMS and DMSP cycles with the atmosphere. Recent work in the St. Lawrence Island polynya (Figure 4) promises to provide insight into the importance of this phenomenon. Sea ice from both the Antarctic and Arctic often contains unusually high concentrations of DMSP (Kirst et al., 1991; Levasseur et al., 1994; DiTullio et al., 1998; Bouillon et al.,
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2002). For example, DMSP concentrations in ice algae from the Weddell Sea (Figure 11) were orders of magnitude higher than in phytoplankton from nearby open waters (Kirst et al., 1991). Not only haptophytes (Kirst et al., 1991), but also diatoms (Levasseur et al., 1994; DiTullio et al., 1998; Trevena et al., 2000, 2003) are important producers of DMSP in sea ice. Sea-ice algal DMSP production will strongly influence DMS release in a polynya region itself, as well as affecting nearby marginal ice zones through sea ice and algae transport out of the polynya. Quantifying the sea-to-air DMS flux associated with sea-ice algal DMSP production from polynya regions, however, will be a challenge for future research due to the spatial and temporal patchiness of biogenic sulfur production in both polynya waters and in the ice (DiTullio et al., 1998; Trevena et al., 2000). The majority of these studies reported DMS and DMSP standing stocks and did not measure cycling rates. Before accurate conceptual or numerical models of sulfur cycling and air–sea exchange in polynyas can be developed, many more comprehensive measurements of pelagic and sea-ice DMS cycling rates in polynya regions, as well as benthic remineralization rates, are needed.
5 Methylhalides Almost no data are available from polynyas on methyl halides, which may contribute to tropospheric ozone depletion (Barrie et al., 1988). However, Sturges et al. (1992, 1993) have found that ice-algal communities in both the Antarctic and the Arctic can produce bromoalkanes. Yvon-Lewis et al. (2004) determined CH3 Br and CH3 Cl along the transect between Hobart, Tasmania and the Mertz Glacier, and they found extremely low concentrations at the southern end of the transect, which they interpreted as indicating strong vertical mixing, although they did not make a direct connection with possible convection in the Mertz polynya. Incubations of various phytoplankton species have shown that Phaeocystis produce more CH3 Br than other phytoplanktonic species (Scarratt and Moore, 1996; Sæmundsdóttir and Matrai, 1998), and Baker et al. (1999) noted a positive correlation between CH3 Br and DMSP concentrations in North Atlantic waters dominated by Phaeocystis spp. Since Phaeocystis spp. are such an important component of phytoplankton communities in both Arctic (Smith et al., 1991) and Antarctic waters (El-Sayed et al., 1983; Gibson et al., 1990), it is logical to predict that production in polynyas is a significant source of atmospheric methylhalides.
6 The Future for Air–Sea Gas Exchange in Polynyas We are still learning what questions to ask about gas dynamics in polynyas; we have in hand only intriguing snippets of data showing that every polynya is different. Methods for determining air–sea gas exchange from satellites are only now being conceived, and therefore, a comprehensive understanding of the role polynyas play in global gas cycles is many months of ship time and many sample analyses away. The following gives our personal, and certainly biased, list of the outstanding questions that need to be addressed. • What happens in fall and winter? For obvious reasons, most of the chemical data from polynyas has been collected in summer or spring seasons representing less than half the year. We certainly cannot construct a complete picture of a polynya’s annual cycle from
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such limited data, and assumptions that gas fluxes are not significant in ice and that wintertime biological activity is limited to slow respiration are no longer acceptable. Short-term high wind events strongly dominate air–sea gas fluxes (Bates and Merlivat, 2001), and therefore open water during winter storms will be important to net annual exchanges in polynya areas, particularly for gases like carbon dioxide and methane that tend to accumulate under sea ice and for shallow, coastal polynyas where wind-driven mixing can directly connect the benthos with the surface. Leads within pack ice could also be significant sites of winter outgassing, especially considering the areal extent of these features. In addition, if we want to confirm whether intermediate and deep water formation in polynyas is an important mechanism for CO2 sequestration, we will need to sample when convection is occurring, not in the summer, when the surface waters are stratified by meltwater and warming. Of course, autumn and winter (and even early spring) field work in polar regions is extremely difficult and requires careful planning, as well as substantial financial and logistical resources. However, we have learned a lot over the last decade about how to conduct such work (Gibson and Trull, 1999; Perovich et al., 1999; Rosenberg et al., 2001; Barber et al., 2004), and we have now entered an era in which it is quite reasonable to attempt extensive winter sampling. • How much does gas move in ice? While it is probably true that sea ice inhibits direct air–sea gas exchange, recent studies on first-year ice have shown that it is not simply a passive barrier. Eddy correlation measurements have found very large vertical CO2 fluxes above first year sea ice, sometimes contrary to the air–sea gradient (Papakyriakou et al., 2004; Semiletov et al., 2004). In addition, both the inorganic and organic carbon contents of sea ice can be very high (Giannelli et al., 2001; Krembs et al., 2002; Delille et al., 2004). We have known since at least the mid-1970s that sea ice is permeable to gas transfer (Gosink et al., 1976), and these new studies imply that sea ice may be acting almost as a capacitor, storing carbon and controlling the air–sea exchange. The implications for gas fluxes in polynyas and leads, which are often covered by thin, new ice, could be significant. • What are the potential climate change feedbacks in polynyas? Perhaps the most critical question is how much gas fluxes really can be changed by temperature and seasonal ice cover. Based on past studies, polynyas appear to be net sinks for atmospheric carbon dioxide but sources of DMS, implying that polynyas could mitigate anthropogenic greenhouse warming. However, we do not know how robust those source-versus-sink balances are. Without comprehensive seasonal data and rate constants for the important elemental cycling processes, we do not even know whether the net sources and sinks we have hypothesized are true, much less whether they are as critically dependent on the timing of the ice cover and primary production cycles as they appear to be. Nonetheless, we can speculate about some examples of possible, although probably simplistic, feedbacks. If seasonal polynyas are indeed net annual CO2 sinks, warming could easily erode the sink by extending open water into fall, winter, or early spring periods when the surface waters are supersaturated. Although this positive feedback could be significant, it would probably be only short lived. Currently, carbon held under the ice during winter is still in the surface waters and therefore is protected from outgassing only until that water mass advects into more temperate areas where ice does not form in the winter; i.e., seasonal rectification can only provide carbon storage through the inter-annual to decadal time scale. Long-term carbon storage is effected through deep- and intermediate-water
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L.A. Miller and G.R. DiTullio formation, as well as through particulate organic carbon export, and a systematic global warming that simply melts ice, resulting in less ice cover at any given time of the year, would probably increase surface stratification and reduce any net carbon export through shelf water formation. On the other hand, if despite a decrease in spring and summer ice, substantial areas of ice still form in winter (Folland et al., 2001), simply increasing the areas of seasonal ice cover and polynyas, the carbon sink could be enhanced. While a decrease in ice cover might be expected to increase primary production, the accompanying increase in exposure to UV radiation would have the opposite effect, with possible decreases in emissions of biogenic gases, such as DMS. The net effect of UV on DMS fluxes in polynyas, however, will certainly hinge upon the differential UV effects on DMSP production in diatoms and Phaeocystis spp. versus the numerous factors affecting DMS cycling and photooxidation rates. Increasing UV exposure also increases DOC cycling, although whether that would be manifested as an increase in CO2 regeneration or as polymerization to more refractory forms of DOC is uncertain. In addition, concentrations of both DMSP and DOC are higher in ice than in seawater, and therefore, a loss of ice may further decrease biogenic gas releases, as well as organic matter recycling. Finally, species shifts induced by either UV or sea surface temperatures (i.e., Phaeocystis to diatoms, not to mention coccolithophorids) could have dramatic effects. To say the least, the balance between all these competing effects is difficult to predict or model at this time.
Throughout this chapter, we have presented many speculations and little concrete information, and the question of climate change feedbacks takes that discrepancy to the extreme. Yes, it is a scientific cliché to claim that too little is understood and that more research is required, but the cliché is applicable here. Nonetheless, we now have the capacity to adequately address the important questions concerning gas distributions and exchanges in polynyas and to estimate their role in regional and global elemental cycles.
Acknowledgements We thank S. Johannessen, K. Johnson, R. Macdonald, C. McNeil, and W. Williams for helpful conversations and T. Takahashi for providing the data from the 1984 Polar Sea expedition in the Ross Sea. P. Olsen was of critical assistance in digging up some rather obscure, as well as misquoted, references. The thorough comments of three anonymous reviewers were also very helpful.
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Sweeney, C., 2003. The annual cycle of surface water CO2 and O2 in the Ross Sea: A model for gas exchange on the continental shelves of Antarctica. In: DiTullio, G., Dunbar, R. (Eds.), Biogeochemistry of the Ross Sea. In: Antarctic Research Series, vol. 78. American Geophysical Union, Washington, DC, pp. 295–312. Sweeney, C., et al., 2000a. Biogeochemical regimes, net community production and carbon export in the Ross Sea, Antarctica. Deep-Sea Research II 47, 3369–3394. Sweeney, C., et al., 2000b. Nutrient and carbon removal ratios and fluxes in the Ross Sea, Antarctica. Deep-Sea Research II 47, 3395–3421. Sæmundsdóttir, S., Matrai, P.A., 1998. Biological production of methyl bromide by cultures of marine phytoplankton. Limnology and Oceanography 43, 81–87. Tait, V.K., Gershey, R.M., Jones, E.P., 2000. Inorganic carbon in the Labrador Sea: Estimation of the anthropogenic component. Deep-Sea Research I 47, 295–308. Takahashi, T., et al., 1985. Assessment of carbon dioxide sink/source in the oceanic areas: the results of 1982–84 investigation. Final Technical Report to U.S. Department of Energy, Contract DE-ACO2-81-ER60000A and B, Lamont-Doherty Geological Observatory, Palisades. Tilbrook, B.D., Karl, D.M., 1994. Dissolved methane distributions, sources, and sinks in the western Bransfield Strait, Antarctica. Journal of Geophysical Research 99C, 16383– 16393. Top, Z., Bignami, F., Hopkins, T., 1997. Tritium-3 He ages of deep waters in the NEW Polynya. Journal of Marine Systems 10, 175–184. Tremblay, J.-E., Smith, W.O., 2007. Primary production and nutrient dynamics in polynyas. In: Smith, W.O. Jr., Barber, D.G. (Eds.), Polynyas: Windows to the World. Elsevier, Amsterdam. Trevena, A.J., Jones, G.B., Wright, S.W., van den Enden, R.L., 2000. DMSP, algal pigments, nutrients and salinity profiles in pack ice from eastern Antarctica. Journal of Sea Research 43, 265–273. Trevena, A.J., Jones, G.B., Wright, S.W., van den Enden, R.L., 2003. Profiles of dimethylsulphoniopropionate (DMSP), algal pigments, nutrients, and salinity in the fast ice of Prydz Bay, Antarctica. Journal of Geophysical Research 108C, 3145, doi:10.1029/2002JC001369. Trumbore, S.E., Jacobs, S.S., Smethie, W.M., 1991. Chlorofluorocarbon evidence for rapid ventilation of the Ross Sea. Deep-Sea Research I 38, 845–870. Van Woert, M.L., 1999. Wintertime dynamics of the Terra Nova Bay polynya. Journal of Geophysical Research 104C, 7753–7769. Van Woert, M.L., et al., 2003. The Ross Sea circulation during the 1990s. In: DiTullio, G., Dunbar, R. (Eds.), Biogeochemistry of the Ross Sea. In: Antarctic Research Series, vol. 78. American Geophysical Union, Washington, DC, pp. 5–34. Volk, T., Hoffert, M.I., 1985. Ocean Carbon Pumps. In: Sundquist, E.T., Broecker, W.S. (Eds.), The Carbon Cycle and Atmospheric CO2 . In: Geophysical Monogr. Ser., vol. 32. Amercian Geophysical Union, Washington, DC, pp. 99–110. Wallace, D.W.R., Minnett, P.J., Hopkins, T.S., 1995. Nutrients, oxygen, and inferred new production in the Northeast Water Polynya, 1992. Journal of Geophysical Research 100C, 43230–43240. Wanninkhof, R., McGillis, W.R., 1999. A cubic relationship between air–sea CO2 exchange and wind speed. Geophysical Research Letters 26, 1889–1892. Warner, M.J., Weiss, R.F., 1985. Solubilities of chlorofluorocarbons 11 and 12 in water and seawater. Deep-Sea Research 32A, 1485–1497.
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Weiss, R.F., 1970. The solubility of nitrogen, oxygen and argon in water and seawater. DeepSea Research 17, 721–735. Williams, G., 2004. Adélie Land Bottom Water Formation. Ph.D. Thesis, Univ. Tasmania, 221 pp. Williams, G.D., Bindoff, N.L., 2003. Wintertime oceanography of the Adélie Depression. Deep-Sea Research II 50, 1373–1392. Williams, W.J., Carmack, E.C., Ingram, R.G., 2007. Physical oceanography of polynyas. In: Smith, W.O. Jr., Barber, D.G. (Eds.), Polynyas: Windows to the World. Elsevier, Amsterdam. Winsor, P., Björk, G., 2000. Polynya activity in the Arctic Ocean from 1958 to 1997. Journal of Geophysical Research 105C, 8789–8803. Yager, P.L., Wallace, D.W.R., Johnson, K.M., Smith, W.O. Jr., Minnett, P.J., Deming, J.W., 1995. The Northeast Water Polynya as an atmospheric CO2 sink: A seasonal rectification hypothesis. Journal of Geophysical Research 100C, 4389–4398. Yvon-Lewis, S.A., King, D.B., Tokarczyk, R., Goodwin, K.D., Saltzman, E.S., Butler, J.H., 2004. Methyl bromide and methyl chloride in the Southern Ocean. Journal of Geophysical Research 109, C02008, doi:10.1029/2003JC001809. Zeebe, R.E., Wolf-Gladrow, D., 2001. CO2 in Seawater: Equilibrium, Kinetics, Isotopes. Elsevier, San Francisco, 346 p.
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Chapter 6
Biogeochemistry of Polynyas and Their Role in Sequestration of Anthropogenic Constituents M. Hoppema1 and L.G. Anderson2 1 Alfred Wegener Institute for Polar and Marine Research, D-27515 Bremerhaven, Germany 2 Göteborg University, Department of Chemistry, S-421 96 Göteborg, Sweden
Abstract Polynyas are common occurrences all around the Arctic and Antarctic. Coastal polynyas are generally highly productive, which can lead to substantial CO2 drawdown. Consequently, they are important sink regions for atmospheric CO2 . Depending on the surface area, the timing, duration and other factors, large differences exist as to the importance of polynyas in a biogeochemical sense. In the Arctic, the North Water Polynya seems to be the most important one, while in the Antarctic the most important is the Ross Sea Polynya. Polynyas in the Arctic have been better investigated and therefore the important polynyas are described with some confidence as to accuracy and completeness. For the Antarctic, this only holds for the Ross Sea Polynya. For many other Antarctic polynyas, only incomplete information is available. This is true even for the large, well known Weddell Polynya of the 1970s, which represents one of the few open-ocean polynyas. Here its biogeochemical role is semiquantitatively assessed by combining the physical data from the 1970s with the known distributions of biogeochemical properties from recent years. It is deduced that the Weddell Polynya was a significant one-time sink for anthropogenic CO2 and CFCs, with ensuing deep-sea sequestration. Notably, some coastal polynyas are instrumental in transferring anthropogenic CO2 from the ice-free shelves to the abyssal oceans.
1 Introduction Polar regions are extreme regions, characterized by extreme conditions. They are cold, stormy, foggy, scantily-inhabited and poorly-investigated. One of the most characteristic properties of these regions is the perennial ice coverage, which necessarily has a large seasonal variation superimposed on it. Ice coverage has a large impact on the physical, chemical, geological and biological features. The ice itself is the matrix for organisms, particulate matter and chemical species, but it also influences the distributions of properties in the ocean underneath and the atmosphere above it. Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74006-5
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Under the pack ice of the Antarctic Ocean, significant undersaturations of oxygen and anthropogenic chlorofluorocarbons (CFCs) have been observed (e.g., Weiss et al., 1979; Klatt et al., 2002). These are gases with a relatively short equilibration time with the atmosphere, which implies that the ice cover exerts a strong influence. For species with considerably longer equilibration times, like CO2 , the influence of the ice cover is likely to be even greater, because they would need a longer time of ice-free water to achieve equilibrium. Several studies document the importance of ice cover with regard to the CO2 distribution (e.g., Bates et al., 1998; Gibson and Trull, 1999; Sweeney, 2003). Moreover, Stephens and Keeling (2000) contend that the Antarctic sea ice was instrumental in causing the large differences between atmospheric CO2 levels in the glacial and interglacial periods. The surface waters of the Arctic Ocean are supplied in a different way than in the Antarctic Ocean, namely, through advection from both the Pacific and Atlantic Oceans. These waters are cooled on their way to the Arctic and hence the solubility of gases increases, resulting in undersaturation, as time is too short to reach equilibrium before the water reaches the ice cover. For CO2 this is reinforced by substantial primary production over the shelves prior to entrance to the central ice-covered Arctic Ocean (e.g., Kaltin and Anderson, 2005). If the ice pack represents such a major factor, we expect that the absence of sea ice in regions which are usually ice covered will give rise to major changes and anomalies in the oceanic and atmospheric conditions. This holds for chemical species as well as for biogeochemical processes. Hence, we expect and use it as our working hypothesis that polynyas are characterized by anomalous spatial and temporal property distributions, which may exert a basin-wide influence. Our aim is to qualitatively and, if possible, quantitatively estimate the added value that polynyas possess for the cycling of anthropogenic and non-anthropogenic species in ice-covered regions. A scenario for anomalous conditions within a polynya according to Lizotte (2003) could be as follows. Many coastal, wind-driven polynyas are “ice factories”, implying that sea ice is produced while the brine is rejected and transported to depth. The ice is subsequently blown away from the shore. Thus dense shelf water is produced which will be detrained off the shelf. This draws water onto the shelf to replace it, which in the Antarctic is mostly Modified (warm, saline) Circumpolar Deep Water (MCDW)—this in turn contributes heat to keep the polynya open. Brine production tends to deepen the surface mixed layer on the shelf, which is accompanied of a redistribution of material, both in dissolved and particulate phase. Also, the MCDW that mixes on the shelf influences the property concentrations. This sets the initial conditions for the spring biogeochemical processes on the shelf. Subsequently, during spring and early summer the polynya changes into an ice-melting area. This tends to stabilize the upper surface mixed-layer, which is beneficial for phytoplankton growth, and thus CO2 and nutrient drawdown. In the Arctic Ocean the same type of “ice factories” are present in the wind-driven polynyas, but the produced brine-enriched water will be replaced by shelf water originating from adjacent oceans and from river runoff. Different formation mechanisms exist for different polynyas. In this regard they are commonly assigned either as sensible-heat or latent-heat polynyas. The former are generated by sensible heat from the ocean, i.e., mostly relatively warm water that upwells into the cold polynya area, thus inducing ice melt or impeding ice growth. These polynyas may be associated either with convective overturning of the water column or entrainment of deep water in the mixed layer. In latent-heat polynyas, sea ice is continually formed and removed by winds and/or currents. The cold polynya loses heat to the even colder atmosphere; this heat is delivered by the latent heat of fusion of the sea ice. Although this is an often used and convenient classification, in reality most polynyas are a mixture of these types. Another subdivision
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could be into coastal and open-ocean polynyas. The majority of polynyas occurs along the coasts of the continents or islands. However, open-ocean polynyas, especially when they develop to large-scale features like the Weddell Polynya of the 1970s, may instantly have impact on vast regions and even on the global overturning circulation. This warrants major consideration of such features. Due to the contrasting hydrographic backgrounds, differences exist between Arctic and Antarctic polynyas and their formation. In the Arctic the water column features a relatively high stability and thus vertical mixing across the stable pycnocline is hardly feasible. Hence, in Arctic polynyas the sensible-heat component of polynyas is not well developed—albeit some particular sensible-heat polynyas do occur (Morales Maqueda et al., 2004). In contrast, the Antarctic water column possesses a low stability, which allows irregular convective overturning. Besides that, upwelling of warm deep water is common in the Southern Ocean, which also plays a role in polynya formation. It is manifest that such differences in occurrence and formation history also exert impact on biogeochemical processes in Arctic and Antarctic polynyas.
2 2.1
Antarctic Polynyas Weddell Polynya in the 1970s
The Weddell Polynya can be definitely considered the most impressive polynya of the Antarctic Ocean. Its spatial extent was about 350 000 km2 (Carsey, 1980). Many theoretical studies were dedicated to this unique phenomenon (e.g., Beckmann et al., 2001; Holland, 2001), which occurred each winter during the three consecutive years 1974–1976. It has not occurred to this extent ever since. Gordon (1982) speculated that a polynya may have occurred at least once before the 1970s. The polynya as such has been observed only by microwave satellite imagery (Carsey, 1980), but Gordon (1978, 1982) and Foldvik et al. (1985) discovered features in the Weddell Sea that were thought to be oceanographic remnants of it. In particular, during the Islas Orcadas cruises in the austral summers of 1976 and 1977, Gordon (1978) found a chimney with a radius of 14 km extending to 4000 m depth west of Maud Rise. A similar feature was observed nearby by Foldvik et al. (1985). The chimney was characterized by extremely cold, low-salinity and high-oxygen water as to when compared to the surrounding deep water. Gordon (1978) suggested that, provided the chimney derived from the Weddell Polynya, there must have been many more of such features. The observations of Foldvik et al. (1985) seem to support this. In addition, Gordon (1982) described a “cold spot” in 1978 near 20◦ W—in its third year the position of the polynya, which had drifted with the general flow of the Weddell Gyre—with a temperature maximum between 0.2 and less than −0.2◦ C. The spot was characterized by cold water (relative to the surrounding water) down to 2700 metres (m). During the Weddell Polynya the cold, relatively low-saline winter surface waters were transferred directly to the deep ocean layers by local convective processes. Gordon (1982) assessed the impact of polynya convection on the ventilation of the deep Weddell Sea. He considered distributions of potential temperature and salinity of a cruise just before (1973) and a cruise right after (1977) the occurrence of the polynya—since the Weddell Sea supports a regime with low energy, the effects of convection are expected to persist for a relatively long time. The volume of deep water involved in polynya processes (with deep-water cooling) was estimated to be 1.5 × 1015 m3 . An analysis of the mixing diagram suggested that 10–20%
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of the modified deep-water must have been derived from winter surface water. Thus the total volume of surface water convected into the deep Weddell Sea amounted to 1.5–3 × 1014 m3 , which equals 1.6–3.2 Sv (1 Sv = 106 m3 s−1 ) during the entire period of three years (1974– 1976). This is a substantial contribution to the total ventilation by the Weddell Sea/Atlantic sector of the Southern Ocean (Orsi et al., 2002) and more than the recent ventilation through Weddell Sea Bottom Water production (Fahrbach et al., 2001). In situ observations and modeling have shown that the Weddell Polynya had a major impact on the oceanographic conditions of the Weddell region. But did the polynya also have impact on the ocean-atmosphere exchange of gases, such as oxygen, CO2 and CFCs, or other biogeochemical properties? Gordon (1978, 1982) measured dissolved oxygen during his polynya cruises, but no data on CO2 or CFCs are available. We assess the effects of the polynya on the oceanic CO2 , its anthropogenic component and the nutrients, using as a basis the analysis of Gordon (1982). In 1977 in the cold chimney, O2 concentrations greater than 5.6 ml/l were observed, where normally in the temperature maximum/O2 minimum layer less than 5.0 ml/l are found (Gordon, 1978; units as used by the author instead of the more usual µmol kg−1 ). Also in 1978 the O2 minimum (less than 4.8 ml l−1 ) was interrupted by O2 values greater than 5.2 ml/l (Gordon, 1982). This proves that surface water, which has a high O2 concentration, was transported down through the water column. Thus the convective processes associated with the polynya resulted in elevated ventilation of the deep Weddell Sea as compared to the same region without a polynya. Since surface water has a relatively low Dissolved Inorganic Carbon (DIC) concentration—DIC is the sum of all CO2 species— as compared to deeper water (Figure 1), we expect that the processes that caused a high-O2 state simultaneously caused a low-DIC state in the deep water. This also would be true for the major nutrients (nitrate and phosphate), which in their vertical distributions have many features in common with DIC. We estimate, using typical DIC concentrations in the surface layer and the deep water (Figure 1), that a cold spot as observed by Gordon (1978) would probably contain 5–10 µmol kg−1 less DIC than the surrounding water. The DIC difference caused by the polynya is much smaller than the corresponding O2 difference (greater than 25 µmol kg−1 ), because of the much stronger O2 gradients within the water column. Although convective processes associated with the polynya cause a low-DIC patch in the deep-water realm, simultaneously they transfer anthropogenic CO2 into the deep water. The surface water has been in contact with an atmosphere that also contains fossil-fuel CO2 and thus, depending on the degree of equilibration, it is “contaminated” with a certain portion of it. The degree of equilibration in the Weddell Sea should be relatively high (Anderson et al., 1991) because the elevated level of CO2 in the atmosphere merely inhibits outgassing of CO2 of upwelled deep-water with a high partial pressure of CO2 (pCO2 ). Hence, the elevated CO2 in the surface layer is of anthropogenic origin. Poisson and Chen (1987) estimated that in 1981 the Weddell Sea surface layer contained 28 µmol kg−1 of anthropogenic CO2 . If we take the value for the mid-1970s to be 25 µmol kg−1 , we calculate (using 1.5–3 × 1014 m3 of convected surface-water as estimated by Gordon, 1982) that the total uptake during the period of the three polynya years amounts to 4–8 × 1012 mol C. This equals 1.5–3×1013 g C yr−1 , which is significantly larger than the amount (8×1012 g C yr−1 ) estimated by Anderson et al. (1991) for the sequestration of anthropogenic CO2 in 1989 through dense bottom-water formation in the southern Weddell Sea. Clearly, an open-ocean polynya event contributes to enhanced ventilation and uptake of anthropogenic CO2 , but since the processes associated with polynya formation distort the extant dynamics and circulation of the region, these should also be taken into account. Enhanced ventilation and the corresponding oceanic uptake of anthropogenic species rely on
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Figure 1: Section cutting the Weddell Gyre (as displayed in the chart of the Atlantic sector of the Southern Ocean) contoured for DIC (µmol kg−1 ) in the upper 2 km of the water column. Data from FS Polarstern cruise ANT XV/4, April–May 1998. the fact that surface water is becoming isolated from the atmosphere. However, the surface water must be replaced by upwelled subsurface water. In the Weddell Sea the intermediateand deep-waters have low concentrations of O2 and CFCs and high concentrations of DIC and nutrients, which are derived from the Circumpolar Deep Water of the Antarctic Circumpolar Current to the north. As these upwelled O2 and CFC undersaturated waters reach the surface, they will in time equilibrate with the atmosphere, which implies that the polynya event will eventually have led to a net uptake of atmospheric O2 and CFCs. For CO2 it is more complicated. Contrary to O2 and CFCs, the upwelled subsurface water is oversaturated in CO2 , which after introduction in the surface layer, will tend to release CO2 to the atmosphere. The relevant question is whether the Weddell Polynya eventually has led to more CO2 uptake or not—starting from the fact that the contemporary Weddell Sea is a sink for atmospheric CO2 (Hoppema et al., 1999). The answer is probably yes. The Weddell circulation creates a highly efficient biogeochemical–physical coupling between the surface and subsurface layers. Organic material produced in the surface layer is almost completely degraded at shallow depths beneath the surface layer (Usbeck et al., 2002). The CO2 -charged intermediate water is partly upwelled into the surface layer, but more importantly, about 80% of it is isopycnally transferred to greater depths within the Weddell Gyre, and from there to the abyssal Antarctic Circumpolar Current north of it (Hoppema et al., 2002). This unique mechanism provides the sequestration of natural CO2 in the abyssal oceans, where the Weddell Sea contributes a significant part of the ocean-wide deep-sea sequestration (Hoppema, 2004). If, due to the polynya event, increased upwelling of CO2 -charged subsurface water occurs, then as a consequence less CO2 will be available for deep-sea sequestration. This would
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tend to increase the atmospheric CO2 and thus counteract the uptake of anthropogenic CO2 . It is not difficult to see that if surface water with DIC in the range of 2200–2215 µmol kg−1 is replaced by subsurface water containing 2265 µmol kg−1 (see Figure 1) and the same alkalinity, this would lead to a net release of CO2 to the atmosphere. However, this is not all. The potential CO2 release from the surface layer is to a certain extent counteracted by biological activity fixing CO2 in organic material, which in turn is remineralized below the surface layer. This does not necessarily have to take place during the occurrence of the polynya, which is during winter when biological activity is at its minimum. Also CO2 drawdown during the ensuing spring and summer tends to counteract the high CO2 concentration due to upwelling, because undersaturation due to biological drawdown of CO2 induces the uptake of the previously released CO2 . Compensation of the increasing trend, caused by the upwelling of CO2 -rich deep water, is only feasible if the extent of biological drawdown of CO2 is enhanced beyond the usual level in the Weddell Sea. For this it is important to know which factor is limiting for primary production, and whether the upwelled water might increase its level. Currently primary production in the Southern Ocean is thought to be limited by the micronutrient iron (Boyd et al., 2000; Tremblay and Smith, 2007). The upwelled water tends to increase the iron content of the surface layer (De Baar and De Jong, 2001), and thus the usual level of primary production in the Weddell Sea will be enhanced due to the additional iron-charged upwelled deep-water. The elevated production will lead to elevated downward transport of organic material, which will be remineralized and isopycnally transported into the abyssal oceans. In summary, there are two competing processes involved in CO2 uptake or release due to the occurrence of the Weddell Polynya. Convective transport of anthropogenic CO2 -enriched surface water into the deep Weddell Sea tends to increase the CO2 concentration of the Weddell Sea. Upwelling of natural-CO2 enriched subsurface water, which would otherwise be available for abyssal sequestration, tends to decrease it. We suspect that the latter effect will to a large extent be counteracted by elevated biological activity in the surface layer, which through the biological carbon pump transfers CO2 to the intermediate-water layers again. Thus, the Weddell Polynya was instrumental in the uptake of an additional amount of anthropogenic CO2 in the order of 1013 g C. Although this is not a large share of the total oceanic uptake of fossil-fuel CO2 of order 2×1015 g C, it is a substantial part of the deep-sea sequestration of anthropogenic CO2 , which is important on time scales of hundreds of years. For the major nutrients the net effect of the Weddell Polynya is relatively insignificant. Since the nutrients are largely independent on anthropogenic activity and they cannot be exchanged with the atmosphere, their distributions are subject only to the major redistribution process associated with the polynya (i.e., the replacement of high-nutrient deep-water by low-nutrient surface-water). By the ensuing biogeochemical processes, the nutrient concentrations in the surface and deep layers are largely returned to their pre-polynya levels. An intriguing hypothesis was presented by Gordon (1982). He contended that conditions favorable for open-ocean convection, and thus polynya formation, may coincide with conditions unfavorable for dense-water formation on the shelves. The factor that could link these regions is the variable divergence of the system. Stronger divergence transfers more ice from the shelves to the open ocean, inducing additional ice formation over the shelves, thus promoting dense water formation. In contrast, ice advected to the open ocean is melted there, thus inhibiting convection. With less divergence the opposite of these processes occurs. If this remote coupling exists, it would strongly diminish the relevance of open-ocean polynyas like the Weddell Polynya in effecting elevated ventilation and uptake of anthropogenic species. We have emphasized the surplus that the Weddell Polynya was able to deliver with regard to
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ventilation and uptake of CO2 . Of course, the shelves are vital regions for deep- and bottomwater formation as well, and thus also for the uptake of anthropogenic species. If the scenario proposed by Gordon (1982) were true, the Weddell Polynya was just a different vehicle for achieving a similar magnitude of ventilation and uptake as ever. However, we suspect that the factors determining the extent of bottom-water formation on the shelves and deep ventilation in open-ocean polynyas act on sufficiently different spatio-temporal scales to allow only a weak negative correlation between the two mechanisms. While the absolute concentration of anthropogenic CO2 in the surface water of the Weddell Sea has continuously increased because of the increasing atmospheric CO2 level, if in the future the Weddell Polynya were to form, its effect on the oceanic CO2 uptake will increase. On the other hand, the DIC anomaly that it will cause in the deep-water will become smaller and smaller because the DIC concentration difference between the surface-water and the deep-water would decrease. Since O2 and the major nutrients are in a quasi steady state and independent of anthropogenic changes, their distributions due to convective processes in a future polynya will be similar to those in the 1970s. However, the latter conclusion would not hold true if the magnitude of future biological activity in the Southern Ocean would be changed through anthropogenic causes. 2.2
Recurrent Offshore Polynyas
After more than four decades of remotely sensed ice observations, the Weddell Polynya has been shown to be virtually a one-time event. Other open-ocean polynyas have been observed, although with different characteristics. Well-known examples of recurring polynyas are the Cosmonaut and the Maud Rise polynyas (Comiso and Gordon 1987, 1996), the occurrence of which has been documented in most years. Their lifetimes are between days and weeks. In some years the polynyas reoccurred several times during one winter. Their size is highly variable, but generally they are much smaller than the Weddell Polynya. As its name suggests, the Maud Rise polynya is found near the sea mount with the same name in the southern Weddell Sea just east of the prime meridian. Note that also the Weddell Polynya of the 1970s is thought to be associated with circulation-topography interactions at Maud Rise (Holland, 2001; Muench et al., 2001). The Cosmonaut polynya takes its name from the Cosmonaut Sea near the eastern edge of the Weddell Gyre circulation. Comiso and Gordon (1996) argued that the Cosmonaut polynya may form through locally enhanced upwelling of warm subsurface water (i.e., without convective overturning). Ushio et al. (1999), using oxygen data, demonstrated that convection in a shelf-break polynya in the Cosmonaut Sea entrains significantly more deep-water into the surface layer than that which takes place under the Weddell Sea pack ice. Several hypotheses exist as to the formation of (transient) open-ocean polynyas (Morales Maqueda et al., 2004), where also convection may play a role. Convective-like features in the Weddell Sea can also originate from the deeply mixed coastal current (Bersch, 1988), and these may subsequently be advected into the open ocean. In the formation of such features, (coastal) polynyas are likely to be involved. Wakatsuchi et al. (1994) described the formation of polynyas within the Antarctic Divergence between 70–120◦ E which come into existence by the combined action of upwelling and advection of dense coastal waters. Despite uncertainty as to its formation, downward transport of cold surface water may well be associated with open-ocean polynya generation. Unfortunately, there are no in situ oceanographic measurements to test this. However, remnants of convective processes may be discerned in vertical property distributions using non-polynya open-ocean data. For this
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Figure 2: Scatter diagrams of salinity (A), oxygen (B), silicate (C), and DIC (D) versus potential temperature for the central Weddell Sea. Data from FS Polarstern cruise ANT XIII/4, April 1996. purpose, plots of potential temperature (θ) versus salinity (S), oxygen (O2 ), DIC and silicate are displayed for the Weddell Sea (Figure 2). Seizing a suggestion by Gordon (1991), nonlinear features in the mixing plots between the Circumpolar Deep Water (CDW; θ > 0◦ C) and the Weddell Sea Bottom Water (WSBW; θ < –0.7◦ C) could be due to a third source water mass in the deep Weddell Sea, namely, deep-water ventilated through open-ocean convection. As described above, during and immediately after the Weddell Polynya this water mass was strongly present as a low-salinity portion centered at θ = −0.2◦ C (Gordon, 1991). In the 1990s, the mixing lines for salinity and O2 appear to be slightly non-linear and suggest lower salinity and higher O2 (Figure 2). This in turn suggests a surface-water component in the deep-water. However, the deviations from linear two end-member mixing seem to envelope a larger portion of the deep-water column (i.e., −0.2 < θ < −0.6◦ C). The reason for this is that, apart from the WSBW and CDW and the ventilated deep water through convective processes, a fourth end-member of mixing exists in the deep Weddell Sea. This is deep-water that originates to the east of the Weddell Gyre, most likely in Prydz Bay (Nunes
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Vaz and Lennon, 1996; Orsi et al., 1999; see also Section 2.5). In the Weddell Sea it is centered at θ of about −0.5◦ C. This water mass is characterized by a distinct CFC maximum, high salinity, high O2 , and low DIC and nutrient concentrations (Hoppema et al., 2001a). Notwithstanding the multi end-member mixing within the deep Weddell Sea, the anomaly at θ = −0.2◦ C is estimated to be about 0.002 for S and 2 µmol kg−1 for O2 . In the θ –DIC plot no anomaly can be distinguished near this temperature. Using typical O2 concentrations and salinities of the deep-water and the surface-water in winter, we estimate the fraction of surface water transported down into the deep-water to be of the order of 1%. It is evident that with such a small fraction and the generally much smaller water column gradients in DIC, a DIC anomaly would not be discernable. Note that a DIC anomaly due to the fourth end-member is evident from a non-linearity near −0.5/−0.6◦ C (Figure 2D). The ventilation of the deep-water of the Weddell Sea by convective processes in transient polynyas seems to be minor as compared to the Weddell Polynya, where the fraction was 10–20%. Because we do not know the time scale over which this anomaly is generated, it is impossible to accurately assess its importance. The estimates of the deep water residence time range from 3 years for the upper layers (Hoppema et al., 2002) to 35 years for the entire water column (Rutgers van der Loeff and Berger, 1993). If we take the short residence time estimate, transient polynyas ventilate only 10% of the ventilation of the Weddell Polynya. But in contrast to the Weddell Polynya, the transient polynyas are annually recurring features, which regularly contribute to the ventilation of the deep-water of the Weddell Sea and the sequestration of anthropogenic species. The polynya formation mechanism proposed by Wakatsuchi et al. (1994)—involving eddies in the Antarctic Divergence with deep-water upwelling combined with dense shelf water advection/downwelling—is potentially quantitatively significant for the transfer of ventilated, anthropogenic CO2 enriched shelf water into the intermediate-water layers, because of the high frequency of eddies there. The depth of sequestration depends on the density of the shelf water and the degree of underway mixing with adjacent, less dense water. Intermediateand deep-waters are subject to ventilation by this process and not the bottom waters formed around the Antarctic. The mechanism for the Cosmonaut polynya as postulated by Comiso and Gordon (1996) may be a special case of this process associated with the Antarctic Divergence, and even the Maud Rise polynya may have something to do with it (Enomoto and Ohmura, 1990). 2.3
Coastal Polynyas in the Weddell Sea
There are several polynyas bordering the Weddell embayment (here defined from the Antarctic Peninsula to 20◦ E), but their characteristics are very different and thus it is not appropriate to treat the coastal Weddell polynyas as a coherent group. Most information about the physical and biological signature of the polynyas was collected from the comprehensive studies by Arrigo and Van Dijken (2003) and Barber and Massom (2007). In the southern Weddell Sea, one of the largest polynyas around the Antarctic occurs off the very wide Ronne Ice Shelf; that is, in summer, but in winter the polynya is only moderately large. On the eastern side of the Antarctic Peninsula with the Larsen Ice Shelf, a vast field of perennial ice is found (Comiso and Gordon, 1998). Because of the strong winds and the strong ice transport from the south, the Larsen Ice Shelf polynya is small and does not open every year. Often the winter area of the polynya is larger than the summer area, because of the abating winds, which blow the ice off the coast. Off the southeastern ice shelves, which border a relatively narrow continental shelf, some small to moderately large polynyas usually occur, while in the Lazarev Sea east of the prime meridian two small polynyas are found.
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According to Arrigo and Van Dijken (2003), the Ronne polynya is as productive as the mean Southern Ocean south of 50◦ S—indicating that per unit area it is much more productive, and definitely more productive than the sea-ice covered ocean. All other Weddell Sea polynyas are less productive. This obeys the correlation between the width of the continental shelf and the extent of primary production as found by the above authors. An indication for the primary production potential of the southern and southeastern polynyas is found in Smetacek et al. (1992). They observed a superbloom associated with ice platelets in late winter/early spring. Within this, admittedly thin, layer of platelet ice (of order 1 m), the nutrients were exhausted to undetectable levels. The superbloom was most likely driven by passive accumulation of cells via floating platelet ice. These results are highly relevant for the polynya region in the southern Weddell Sea for two reasons. First, the level of solar irradiance as early as October is sufficient to support intensive phytoplankton blooms. And secondly, primary production in this region is apparently not limited by iron like in most other Antarctic regions. These conditions enable a long period of active biological production in the coastal polynya, which is eventually only limited by the macronutrients (probably nitrate) like in other oceanic provinces. Smetacek et al. (1992) cite also other superblooms off the southern Weddell Sea coast. They also contend that these superblooms act to significantly enhance the productivity of the entire North West (NW) Weddell Sea. Off the Filchner-Ronne Ice Shelf, one of the most important Antarctic regions for bottom water formation is found (Foldvik et al., 2004). The wide shelf and polynya are pivotal factors for salinization of the shelf water, which together with modified Warm Deep Water comprises the main ingredient of the bottom-water. This region may not be the most efficient for the uptake of anthropogenic CO2 (see also Section 2.5), but merely due to the quantity of bottom water production it must be significant for deep-sea sequestration. Figure 3 illustrates that the bottom water on the continental slope originating from the Filchner-Ronne shelf contains significant anthropogenic CO2 . Because of the adverse ice conditions in this part of the Weddell Sea, Larsen Ice Shelf polynya is normally not accessible for ships. However, in 1993 the ice conditions were extremely different from other years (Comiso and Gordon, 1998) and a large polynya had opened; fortuitously the ice breaker FS Polarstern happened to be there. Over the Larsen shelf and the continental slope pCO2 and nutrients (Figure 4) were found to be much lower than in the offshore Weddell Sea. Depletions of DIC and nutrients in the surface layer, which were caused by very rapid biological uptake, appeared to be several times higher than in other Weddell Sea areas (Hoppema et al., 2000). Carbon consumptions were comparable to those in the hyperproductive Ross Sea Polynya (Smith and Gordon, 1997; Bates et al., 1998). This is surprising because most of this region hardly ever experiences open water conditions (apart from irregular leads). It is suspected that the local phytoplankton population is extremely well adapted to a low irradiance regime, which enables rapid growth during a short period of time (Hoppema et al., 2000). Additionally, the grazer population, which can be an important factor in keeping Antarctic phytoplankton stocks low (Smetacek et al., 1990), is expected not to be well developed in such a region without a large phytoplankton biomass. At the location closest to the Larsen Ice Shelf, which may usually be situated in the coastal polynya, the nutrient and DIC depletion was the highest (Figure 4C). Although the nutrient concentrations did not reach limiting levels, the large potential of the coastal polynya in CO2 drawdown has been demonstrated. Smetacek et al. (1992) speculated that superblooms may also occur off the Larsen Ice Shelf under the influence of platelet ice. We expect that analogous to the Ross Sea Polynya, the Larsen Ice Shelf polynya, when it forms, is a sink of CO2 due to the high biological carbon drawdown.
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Figure 3: Anthropogenic CO2 concentration in four sections over the continental slope of the Weddell Sea. Data from Anderson et al. (1991). There are indications that off Larsen Ice Shelf, deep- and bottom-water formation takes place (e.g., Fahrbach et al., 1995; Weppernig et al., 1996). The local polynya is likely to play a role in this. The intriguing observations of anthropogenic CO2 are highly relevant for occurrences near Larsen Ice Shelf (Anderson et al., 1991; see Figure 3). In the southern Weddell Sea, anthropogenic CO2 is confined to the surface and bottom layers only, whereas off the tip of the Antarctic Peninsula the large deep-water volume of Weddell Sea Deep Water contains significant quantities of it. This implies that there must be input of anthropogenic CO2 into the western Weddell Sea, most likely from the Larsen Ice Shelf polynya. Note that tracer studies happen to suggest that off Larsen Ice Shelf predominantly deep-water is produced, and not bottom-water (Weppernig et al., 1996). Thus although the Larsen Ice Shelf polynya is relatively small, transient and does not occur every year, it must be highly efficient in the transfer of anthropogenic CO2 into the ocean. In the coastal polynya in the Lazarev Sea, Naqvi (1986) observed relatively high O2 and low nitrate concentrations in the upper 100 m, but the nutrients certainly were not limiting phytoplankton growth. In addition, the author did not find indications for shelf water that may participate in dense bottom-water production. Although bottom-water cannot be produced there, Orsi et al. (1993) reported that ventilated deep-water, which is not dense enough to reach the bottom, may originate from the coast of the eastern Weddell Gyre. It should be appreciated that for the entire Antarctic, such deep-water ventilation is approximately as important as ventilation by bottom-water production (Orsi et al., 2002). Thus, the
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Figure 4: Distribution of pCO2 (A), nitrate (B), DIC depletion (C) and salinity (D) at the sea surface off the Larsen Ice Shelf in the polynya (except for the DIC depletion, which is for the entire surface layer of about 100 m depth). SB is shelf break. Data from FS Polarstern cruise ANT X/7 of January 1993; see also Hoppema et al. (2000). small polynyas of the Lazarev Sea that are situated over a narrow continental shelf may still be active sites of ventilation and biological activity. 2.4
Ross Sea and Terra Nova Bay Polynyas
The Ross Sea Polynya is the largest (both in winter and summer) and best investigated annually recurring polynya of the Antarctic. Both synoptic winds and katabatic surges contribute to its formation, but also intrusion of deep-water on the shelf has its share in opening the ice (Jacobs and Comiso, 1989). A comparably small but influential feature off the western coast of the Ross Sea is the Terra Nova Bay polynya. It is highly persistent and thought to be producing as much as 10% of the annual sea ice of the Ross Sea and about 1 Sv of High Salinity Shelf Water (Kurtz and Bromwich, 1985). Thanks to many predominantly U.S. and Italian expeditions to this region, a solid baseline of in situ biogeochemical data exists. In
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contrast to most other Antarctic coastal regions, the biogeochemistry of which is only known from “snapshots”, seasonal cycles of several properties are known for the Ross Sea Polynya. Here we focus on the budget and net fluxes of inorganic carbon and nutrients. Additional material can be found in Miller and DiTullio (2007). Hyperproductivity is the term that has been used to describe austral spring phytoplankton growth in the Ross Sea Polynya (Smith and Gordon, 1997)—this is the more surprising as this is the southernmost region of the Antarctic Ocean. There is an enormous consumption of nutrients and CO2 in the spring and summer (Bates et al., 1998; Gordon et al., 2000), which are primarily fixed in particulate organic matter (Smith and Asper, 2000). However, the nutrient concentrations are never fully depleted and thus plankton growth appears not to be limited by the major nutrients. In the Terra Nova Bay polynya nutrients occasionally approach detection limits (Saggiomo et al., 1998). In late winter, DIC concentration in the surface layer was about 2200–2230 µmol kg−1 (Bates et al., 1998). There exists uncertainty as to the pCO2 in this time of the year; while Bates et al. (1998) find values close to those in the atmosphere, Sweeney (2003) reports significantly higher ones around 425 μatm. The lower values may have been due to early phytoplankton growth (Smith and Gordon, 1997). By late January the pCO2 has fallen to only about 130 µatm, while the DIC reduction is as high as 150 µmol kg−1 (Sweeney, 2003). The corresponding nitrate reduction is about 25 µmol kg−1 . Spatial distributions of pCO2 clearly reveal the polynya region by much lower values than those in adjacent areas (Sweeney et al., 2000; Barbini et al., 2003). Also in Terra Nova Bay, pCO2 values under 200 µatm were measured in summer (Barbini et al., 2003). Since alkalinity appears to be conservative, the CO2 reductions are predominantly caused by photosynthesis of non-calcifying organisms (Bates et al., 1998), which is supported by plankton data indeed. Bates et al. (1998) assessed that the Ross Sea Polynya is a net CO2 sink, where the CO2 uptake from the atmosphere (1–3.8 Tg C until mid January) roughly matches the export production at 250 m (Nelson et al., 1996). Smith and Asper (2000) showed that the nitrogen budget is strongly dominated by particulate matter in spring and that later in the season also regeneration, export and dissolved organic nitrogen contribute. Sweeney (2003) deduced net O2 uptake from the atmosphere and transport off the Ross Sea shelf. For CO2 he calculates a biologically-mediated uptake amounting to 1.3–1.8 mol m−2 during the summer and a negligible release during the ice-covered winter. Note that the polynya does not seem to play a great role in his computations. In contrast to O2 , no CO2 enrichment in the off-shelf flowing water could be found. We expect the water flowing off the Ross Sea shelf to be enriched in CO2 , namely in anthropogenic CO2 , which accompanies Ross Sea Bottom Water formation. North of the Ross Sea, this bottom-water is found to be contaminated with anthropogenic CO2 (Sabine et al., 2002). In the Ross Sea Polynya and in the Terra Nova Bay polynya, High Salinity Shelf Water is produced by brine rejection, which is the basis for the densest bottom-water mass generated in the Antarctic (Jacobs et al., 1985; Gouretski, 1999). Especially the Terra Nova Bay polynya appears to contribute much (Budillon et al., 2003). Since this polynya is persistently open, the extent of re-equilibration of CO2 and other gases should be relatively high, implying that the uptake of anthropogenic CO2 must be high as well. In aggregate, the Ross Sea Polynya appears to be more productive than the abutting regions and drawdown of nutrients and CO2 is substantially enhanced. Furthermore, the Ross Sea and Terra Nova Bay polynyas are the sites of High Salinity Shelf Water formation, which is accompanied by uptake of anthropogenic CO2 and CFCs. These in turn are sequestered in the Ross Sea Bottom Water in the abyssal oceans north of the Ross Sea. However, compared
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to the Weddell Sea and Atlantic sector of the Southern Ocean, the rate of bottom-water formation in the Ross region is relatively small (Orsi et al., 1999), and hence the absolute contribution of the Ross Sea Polynya to the sequestration of anthropogenic species must be relatively small as well. 2.5
East Antarctic Coastal Polynyas
Studies by Massom et al. (1998), Arrigo and Van Dijken (2003) and Barber and Massom (2007) using SSM/I satellite data have highlighted East Antarctica (20–160◦ E) as a region with many coastal polynyas. Most of them are relatively small as compared to the Ross Sea and Ronne Ice Shelf polynyas. The local bathymetry appears to be the major factor for their occurrence (Massom et al., 1998). As the water flow and sea-ice drift along the coast is westward, any protruding features like north-south headlands, glacier tongues, floating ice shelves or grounded icebergs cause sea-ice reduction in the lee of it. Additionally, severe katabatic winds remove the pack ice from the coast. Some polynyas are semi-recurrent, i.e., they do not occur every year. These are generally the smallest polynyas. The largest polynyas in this region are the Cape Darnley (68–69◦ E), the Prydz Bay (78◦ E), the Davis Sea/Shackleton Ice Shelf (93–95◦ E) and the Mertz Glacier (144–145◦ E) polynyas. Knowledge about most of these polynyas has been acquired by remote sensing techniques, because wintertime access to the region is difficult due to hostile conditions (but see Williams and Bindoff, 2003). This means that our knowledge of the biogeochemical conditions in these polynyas is scanty. Arrigo and Van Dijken (2003), using SeaWiFS satellite data, report that the East Antarctic polynyas, with the exception of the Prydz Bay polynya, are less productive than the large polynyas in the Ross and Weddell Seas, and on an annual basis even less productive than average in the Southern Ocean south of 50◦ S—however, per unit area they are more productive. The reason for this is thought to be the (small) width of the continental shelves in East Antarctica—note that the Prydz Bay shelf is wider indeed. High productivity in the Prydz Bay polynya measured in situ was reported by Zilin et al. (2001). This was also found by Gibson and Trull (1999) who presented unique data covering a full annual cycle in the inner Prydz Bay and found the region to be a strong CO2 sink, the oceanic pCO2 never reaching supersaturation. Nutrient depletions were much higher than usually observed in Antarctic waters, and nitrate even reached values where it could be limiting to phytoplankton growth. In contrast, Sambrotto et al. (2003) reported nutrient consumptions in the Mertz polynya region which are of the same order of magnitude as those in other Antarctic regions. Generally pCO2 undersaturations were found in the Mertz area, the highest close to the coast, but definitely not to the same extent as in Prydz Bay. Ishii et al. (1998) presented DIC data along the East Antarctic coast for the austral summer, and observed distinct longitudinal DIC minima at the locations of wintertime polynyas, including the Lützow-Holm Bay, Casey Bay, Prydz Bay and West Ice Shelf polynyas. In keeping with the Gibson and Trull (1999) data, the largest CO2 drawdown was observed in Prydz Bay. The few in situ data do not appear contradictory to the satellite data (Arrigo and Van Dijken, 2003). Sambrotto et al. (2003) presented a good correlation between the SeaWiFS and in situ chlorophyll for the Mertz region indeed. The few CO2 data for the polynya regions point to pCO2 undersaturation and thus uptake of atmospheric CO2 , largely caused by CO2 drawdown by phytoplankton during the spring and summer period. The data of Gibson and Trull (1999), albeit only of regional nature, suggest that also in winter the polynyas be CO2 sinks, or only slight sources at most. Prydz Bay may be exceptional (see above), but we believe that similar processes occur in
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other polynyas but only at a somewhat lower level. We think that the coastal polynyas reinforce the CO2 uptake of the waters south of the Polar Front. We base this on the valuable data of Gibson and Trull (1999); polynyas offer longer ice-free conditions, which allow a longer equilibration of undersaturated waters with the atmosphere and thus elevated CO2 uptake. Moreover, they lengthen the productive season, which leads to elevated CO2 drawdown. Some East Antarctic polynyas play a role in the formation of dense bottom water. Therefore, they are instrumental in sequestering anthropogenic CO2 and CFCs in the deep ocean. Rintoul (1998) underscored the importance of this bottom-water, called Adélie Land Bottom Water, as it contributes about 25% to the total Antarctic Bottom Water volume. It is certain that the Mertz polynya and the nearby Adélie Depression are necessary for producing dense shelf water through brine rejection during sea-ice formation (Gordon and Tchernia, 1972; Fukamachi et al., 2000) which constitutes a vital ingredient of bottom-water. An additional factor are intrusions of saline deep-water onto the shelf (Rintoul, 1998), which by the way may also contribute to keeping the polynya open (sensible heat)—but latent heat is still more important for the Mertz polynya (Williams and Bindoff, 2003). Vaillancourt et al. (2003) observed (remnants of) mesoscale deep convective features reaching up to 1400 m depth near the boundary of the polynya. Also within other East Antarctic polynyas dense shelf water is formed and thus multiple sources of deep- and bottom-water off East Antarctica are possible (Gordon and Tchernia, 1972). In fact, Bindoff et al. (2000) document deep-water formation near 104◦ E. However, the Mertz region is likely to be the main source (Rintoul, 1998). In the Antarctic Bottom Water of the Indian sector of the Southern Ocean, which at least partly derives from the East Antarctic coast (Rintoul, 1998), significant storage of anthropogenic CO2 was found (Sabine et al. 1999, 2002; McNeil et al., 2001). Additionally, the CFC concentrations of Antarctic Bottom Water in this region are among the highest of the Antarctic (Orsi et al., 1999). In contrast, the Antarctic Bottom Water in the Weddell Sea region was found to be relatively poor in anthropogenic CO2 (Poisson and Chen, 1987; Hoppema et al., 2001b). This indicates that over the East Antarctic shelves, the equilibration of surface waters with the atmosphere must occur to a larger degree as compared to other regions, allowing more anthropogenic CO2 to enter the nascent bottom-water. This is in agreement with the significantly higher saturation of CFCs (but still below 100%) in the High Salinity Shelf Water of the Mertz region than that of the other bottom water formation regions (Orsi et al., 2002). This may well be related to the high persistence of open water in the Mertz polynya (Arrigo and Van Dijken, 2003) even in winter. More to the west, a significant singular source of dense bottom water is found in Prydz Bay (Nunes Vaz and Lennon, 1996; Wong et al., 1998; Orsi et al., 1999). This probably stems from saline shelf water of the Prydz Bay continental shelf (Wong et al., 1998). It is evident that the Prydz Bay polynya plays a prominent role in the salinization of this dense shelf water. After leaving the bay, the Prydz Bay Bottom Water flows westwards (Orsi et al., 1999) within the coastal current due to the Coriolis force, towards the Weddell Sea where it interleaves with the local Weddell Sea Deep Water at about 3500 m depth. Its ventilation strength is estimated to be of the same order of magnitude as that of the local Weddell Sea ventilation by means of bottom-water formation (Hoppema et al., 2001a), namely 2.7 ± 0.9 Sv (a recent analysis suggests that the more likely figure should be near the lower boundary of this estimate; O. Klatt, 2004, personal communication). This is a significant contribution to the total ventilation by the Southern Ocean, which amounts to 14 Sv (Orsi et al., 2002). Since we do not know the concentration of anthropogenic CO2 in the shelf water source of Prydz Bay Bottom Water, it is impossible to estimate its share to CO2 sequestration. However, we
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expect that this ventilated deep water with Prydz Bay origin is a more than proportional contributor to the sequestration of anthropogenic CO2 . This we base on the saturation of CFCs on the Prydz Bay shelf, which is relatively high as compared to that at other bottom water production sites (Orsi et al., 2002), thereby presuming some kind of positive correlation between the air–sea equilibration of CFCs and anthropogenic CO2 . In the end, this is likely to be caused by the relatively large winter surface area of the polynya (Arrigo and Van Dijken, 2003). 2.6
Summary and Concluding Remarks for Antarctic Polynyas
All around the Antarctic continent, coastal polynyas occur. Generally, these are characterized by high productivity with accordingly high drawdown of carbon and nutrients. The Ross Sea Polynya is even assigned hyperproductive (Smith and Gordon, 1997). Elevated levels of iron over the continental shelves (De Baar and De Jong, 2001) and prolonged ice-free conditions (i.e., more light availability for photosynthesis) are pivotal factors in this. They are considered to be sinks for CO2 , also for its anthropogenic portion, and CFCs. In some polynyas also processes take place which precondition the shelf waters for deep and bottom water formation. As the dense shelf water, produced through cooling and addition of brine from ice formation in the polynya, is incorporated in the nascent deep and bottom waters, it carries with it contaminations in the form of anthropogenic CO2 and CFCs. Obviously, since uptake of anthropogenic species is impeded by ice cover, polynyas tend to relieve this impediment. Another factor that may be important for the uptake of anthropogenic CO2 is, somewhat counter intuitively, upwelling and intrusions of CO2 -charged deep water onto the shelf. Because of the ever increasing level of CO2 in the atmosphere due to fossil-fuel burning, the outgassing of CO2 in the polynya is strongly diminished due to the reduced pCO2 difference between ocean and atmosphere. In fact, this is equivalent to uptake of anthropogenic CO2 . This may be a more efficient way for anthropogenic CO2 to enter the shelf water than through slow air–sea exchange after cooling of the shelf water. Thus, polynyas with upwelling of CO2 -rich warm deep water may be particularly efficient in the uptake of anthropogenic CO2 . Stated differently, the larger the sensible-heat component of the polynya, probably the more efficient it is for uptake of anthropogenic CO2 . Open-ocean polynyas are relatively uncommon. Oceanographic factors involved may be convective overturning or entrainment of warm subsurface water. Their biogeochemical role is largely unknown, because in the last 30 years only transient occurrences have been documented. If we assume that non-linear features in the θ-property relationships for the Weddell Gyre are a consequence of convective processes in open-ocean polynyas, we must conclude that their contribution to deep ventilation and uptake of anthropogenic species is minor. There is one exception to this, namely, the large Weddell Polynya in the 1970s. Although no contemporaneous CO2 measurements exist, we estimated, based on an analysis by Gordon (1982), the uptake of anthropogenic CO2 through convective processes in the polynya to be in the order of 1013 g C. This is significant as compared to the CO2 sequestration in the abyssal oceans. The Weddell Polynya caused larger changes in the spatial distributions of biologically-mediated properties and biogeochemical processes. In particular, subsurface water enriched in biologically-mediated CO2 was transferred into the surface layer. However, enhanced biological activity is likely to have reduced this higher level of CO2 in the surface layer. In the end this resulted only in a relocation of properties within the Weddell Gyre.
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Polynyas in the Arctic Ocean
Polynyas are found more or less all around the Arctic Ocean (see Barber and Massom, 2007, for locations), at least during some time of the year. In winter many polynyas are formed by the wind, which blows the sea ice away, while land or land-fast ice prevents new sea ice to drift into the area. Examples of the former type are the Storfjorden and St. Lawrence Island polynyas (SLIP), and of the latter type the Laptev Sea polynya. Often polynyas open up during winter along the Alaskan North coast (e.g., Winsor and Chapman, 2002) and also over the Siberian shelf (e.g., Dokken et al., 2002), when strong wind blows off the coast. Most of these polynyas disappear in the summer, simply for the reason that the area is ice-free during this season. Except for the SLIP, these polynyas are not present during the biologically productive times, even if biogeochemical processes during the spring–summer season condition the waters that are present during the polynya period. These polynyas are efficient sea-ice factories and thus also produce high-salinity, brine-enriched waters, which are important in ventilating the deeper layers of the surrounding seas. In the coupled system of summer productivity and winter remineralization, these high-salinity waters are also a route for transporting chemical constituents. In the Arctic Ocean shelf-slope plumes are an intimate part of the processes that determine the properties of the mid-depth and deep waters of the Arctic Ocean, including their chemical constituents (e.g., Rudels et al., 1994; Jones et al., 1995; Schauer et al., 1997; Anderson et al., 1999). Polynyas that open in the spring and that are present at least partly into the summer season are the Northeast Water polynya (NEW) (northeast coast of Greenland), the North Water polynya (NOW) (between Ellesmere Island and Greenland) and the Cape Bathurst polynya (Mackenzie Shelf). All of these polynyas are biologically productive regions, with the NOW being exceptionally productive. Normally these polynyas are not present during winter. They open up before the surrounding waters early in the spring through the specific flow of waters that take away the sea ice, while ice barriers prevent new ice from upstream to enter the area. Consequently, these polynyas are not such efficient sea-ice factories, but through biological processes they are important in transforming the chemical signature of the waters present in them. Some biogeochemical aspects of these polynyas can also be found in Miller and DiTullio (2007). 3.1
Storfjorden Polynya
Storfjorden is situated in the eastern Barents Sea, between the islands of Spitsbergen, Barentsøya and Edgeøya in the Svalbard Archipelago. It is about 160 km long, with a sill depth of 120 m and a maximum depth of about 180 m. Water of Atlantic origin enters the fjord from the Norwegian Sea, but the characteristics of the water in Storfjorden are significantly modified by sea-ice formation in winter. Over the shallow areas along the eastern part of the fjord, offshore winds transport the ice out of the fjord, thereby forming a polynya that remains ice-free until late winter, thus enhancing ice production (Haarpaintner et al., 2001a). Most of the ice formed in the winter season is exported, but some remains, melts in the summer and (together with meltwater from the Barents Sea) contributes to a lower salinity of the surface water in Storfjorden (e.g., Haarpaintner et al., 2001b). The Storfjorden polynya is not of any quantitative importance with regard to biogeochemical transformation or transport of chemical constituents, but it is a very suitable laboratory where especially the winter processes can be studied under relatively accessible conditions.
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Ice formation in winter produces brine, resulting in dense water that flows off the fjord shelves to fill the deeper regions of the fjord (Haarpaintner et al., 2001a). This flow carries with it near-surface constituents, including gases that enter the surface water from the atmosphere. Furthermore, the high-salinity bottom water produced in winter is enriched in chemical constituents from organic matter decaying at the sediment surface. Anderson et al. (1988) evaluated the chemical modification by the decay of organic matter in the highsalinity bottom water of Storfjorden from data collected in summer 1986. The regeneration rates of phosphate, O2 and total dissolved inorganic carbon (DIC) followed the classical P : O2 : C ratios 1 : −135 : 106 of Redfield et al. (1963), with the rate during the winter of 1986/87 corresponding to 3.7 mmol C m−2 d−1 over a six-month period, or about 8 g C m−2 yr−1 . Provided no build-up of organic matter occurs in the fjord, this would also represent the export production. The dense water formed in the deeper layers of the fjord will eventually overflow the sill and subsequently sink down into the deeper regions of Fram Strait (Quadfasel et al., 1988; Schauer and Fahrbach, 1999) and transport with it the decay products. The volume of cold, brine-enriched water found in 1987 (produced during the winter season of 1986/87) was 385 × 109 m3 , while its mean salinity was 35.25. For producing this excess salt, a mean sea-ice production of 1.5 m over the whole fjord is needed (Anderson et al., 1988). The mean DIC excess in the cold, brine-enriched water was 12 µmol kg−1 (Anderson et al., 1988), resulting in a total transport of 55 × 109 g C into the deeper waters of Fram Strait (if all bottom water would exit Storfjorden). In the spring of 2002 exceptionally high salinity waters were found in Storfjorden (Rudels et al., 2005), with elevated DIC concentration but without corresponding signals in the nutrient and O2 concentrations (Anderson et al., 2004). As the surface water pCO2 was well below atmospheric levels (286 µatm relative to 376 µatm), the excess DIC was attributed to uptake from the atmosphere. Anderson et al. (2004) suggested that sea-ice formation enhances the air–sea exchange of CO2 , and hypothesizes it to be a result of an efficient exchange across the surface film during the ice-crystal formation, increased solubility in the low-temperature brine, and the rapid transport of the brine-enriched, high-salinity water to deeper waters. 3.2
Northeast Water Polynya
The Northeast Water polynya (NEW) is formed from the combined effects of a fast-ice barrier (the Norske Øer Ice Shelf), that extends from the Greenland coast to bridge a trough system, and a northward flowing coastal current (e.g., Böhm et al., 1997). The NEW opens up in April/May and increases in size throughout the summer, is reduced in size at the end of summer and is normally closed by the end of September. It was extensively studied in the early 1990s in a multidisciplinary international program. The nutrient and O2 distributions reveal that primary productivity had already started before the polynya opens in spring and it continues all through the summer. New nutrients are continuously supplied by the northward flowing coastal current underneath the ice barrier south of the polynya (e.g., Wallace et al., 1995), which supports primary production throughout the productive season. The nutrient concentrations decrease along the flow path within the polynya, and nitrate concentrations often drop below the detection limit in the downstream area of the polynya, while phosphate still is found at about 0.6 µM (Wallace et al., 1995). This is caused by the low nitrate concentration (approximately 4 µM) relative to that of phosphate (approximately 1.1 µM) in the surface mixed-layer of the East Greenland Shelf
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Water (Kattner and Budéus, 1997), which is the source of the surface layer of the NEW. The low nitrate concentration relative to that of phosphate points to a Pacific origin (Wallace et al., 1995; Jones et al., 1998). Primary production also depletes DIC in the summer surface waters by up to 2 mol C m−2 (for 1992; Yager et al., 1995). The associated decrease in pCO2 (average: 218 ± 34 µatm) can be compensated for by a flux of CO2 from the atmosphere if a stormy period of a few weeks occurs before the freeze-up in September. The authors hypothesize a rectification scenario: The decrease in pCO2 caused by primary production induces a CO2 flux from the atmosphere before the polynya is covered by sea ice in the fall, while the subsequent sea-ice cover prevents outgassing of the CO2 that is produced by organic matter decay in the fall and the winter. Extensive blooms of ice algae will decrease pCO2 before the sea-ice cover melts in spring and the resulting melt water creates a thin, highly stratified surface water that initially hampers air–sea exchange (Yager et al., 1995). The rectification hypothesis may be amplified by off-shelf flow of high-pCO2 water. Dissolved organic carbon concentrations observed within the NEW surface water exceed those of the surrounding surface waters (110 µM versus 96 µM), which is attributed to melting sea ice and ice algae (Skoog et al., 2001). These authors also found that the dissolved organic carbon concentrations in polynya surface water decreased from 110 to 105 µM as the productive season progressed, while dissolved organic nitrogen concentrations increased from 5.6 to 6.1 µM. This resulted in a decrease in the C : N ratios of dissolved organic matter from spring to summer from 20 to 17. Furthermore, they found a significant decrease in the dissolved organic matter C : N ratio in all water masses within the polynya area as the productive season progressed. Observations of non-Redfield behavior have also been reported by Daly et al. (1999) in the dissolved and particulate pools as well as in the rates of transformation among them. 3.3
North Water Polynya
The North Water polynya (NOW) is located between Ellesmere Island and Greenland and opens up in spring by dominating southward wind and currents. Sea ice is prevented from drifting into the polynya region from the north by an ‘ice bridge’ of conglomerate thick sea ice formed in Nares Strait (e.g., Ingram et al., 2002). NOW is one of the largest polynyas in the Arctic and also one of the most biologically productive ones. It was extensively studied from 1997 to 1999 within the International North Water Polynya Study. Water from the Arctic enters the NOW region from the north through Nares Strait, and from the south a branch of the West Greenland Current supplies water (e.g., Bâcle et al., 2002). These two water sources with different chemical and physical characteristics affect the biogeochemical conditions of the polynya (e.g., Mei et al., 2002). The water from the Arctic has high silicate (and phosphate) concentrations and is referred to as silicate-rich Arctic Water (SRAW) relative to that entering from Baffin Bay (referred to as Baffin Bay Water; BBW) (Tremblay et al., 2002a). In the surface, SRAW dominates in the northwest, while BBW dominates in the southeast, consistent with the current regime (Ingram et al., 2002). The surface mixed-layer (SML) depth starts to get shallow (less than 20 m) in April in the BBW, but in the SRAW not until June (Tremblay et al., 2002a). Hence, depletion of nutrients begins in late April and late May in the BBW and SRAW regimes, respectively (Mei et al., 2002; Tremblay et al., 2002a). The nitrate concentration in April is about the same (about 10 µM) all over the polynya and is more or less depleted due to primary production by May in the BBW and in July
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Figure 5: Mean concentrations of DIC, normalized to S = 33 (filled symbols) and nitrate (open symbols) in the surface mixed-layer of SRAW (stars) and BBW (circles) in 1998. Note that the SRAW nitrate values have been offset by +15 µM and the two scales are set to a C : N ratio of 106 : 16. DIC data from Miller et al. (2002) and nitrate from Tremblay et al. (2002b). in the SRAW (Tremblay et al., 2002b). Nutrients are added to the surface water during this period by horizontal advection and vertical diffusion. Primary production consumes CO2 and it has been shown that the surface water pCO2 in the NOW decreases from oversaturation (approximately 450 µatm) in April to below 200 µatm in June (Miller et al., 2002). The observed DIC concentration (normalized to a salinity of 33) decreases from April to July (in 1998), but not as expected from the nitrate change considering the canonical C : N ratio of 106 : 16—this holds especially for the BBW (Figure 5). There are several possible reasons for the non-Redfield C : N change in the BBW. One likely reason is wind-driven vertical replenishment of nitrate over half of the new production during the first 7 weeks of production (Tremblay et al., 2002b), which has a larger relative impact on the nitrate supply than on that of DIC. Advective supply from the south can have a similar effect. Another cause that could explain the rapid decrease of DIC in May–June is that plankton utilizes remineralized nitrogen when the nitrate concentration gets low, while still taking up new CO2 if the carbon still remains in the organic form. The increase in DIC from June to July is likely a result of a flux of CO2 from the atmosphere into the surface water driven by the strong undersaturation during this time (Miller et al., 2002). The total decrease in DIC and nitrate in the SRAW is close to the canonical C : N ratio, but the May and June data deviate somewhat (Figure 5). The latter can be due to the method how the mean concentration was computed and/or to advective input from the Arctic coupled to biochemical processes upstream. Within the upper 150 m of the BBW, the new production was computed from the disappearance rates of nitrate and DIC during the bloom period (24 April to 18 June) as 15.9 ± 2.1 mmol N m−2 d−1 (equivalent to 114 ± 16 mmol C m−2 d−1 when applying the observed Particulate Organic Carbon (POC) : Particulate Organic Nitrogen (PON) ratio of 7.17 ± 0.15) and 105 ± 16 mmol C m−2 d−1 , respectively (Tremblay et al., 2002b). About 80% of this consumed nitrate and DIC was found in the particulate matter in the top 150 m during the bloom, while the rest supposedly had been exported to deeper layers. The total
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consumption of DIC during the bloom period (55 days) equals 6 mol C m−2 or 72 g C m−2 of which at least 14 g C m−2 is lost to the sediments during this time. Computations of Miller et al. (2002) for the BBW (eastern part of polynya area) during the same time period show that 2.35 mol C m−2 or 28 g C m−2 is taken up from the atmosphere. It is likely that a large fraction of the POC that is found in the top 150 m during the bloom period also is lost from the surface water, either by sedimentation or by advection, before it decays, making this region a very efficient carbon sink. The high productivity of the NOW results in a marked increase of nutrient concentrations towards the bottom (below 450 m), most pronounced for silicate and in the southern part of the polynya (Michel et al., 2002; Tremblay et al., 2002b). This silicate enrichment is driven by dissolution of biogenic silica as a result of diatom production in the surface and subsequent sedimentation. The high productivity in the NOW causes a transport of nutrients from surface layer to depth, where it influences the local and downstream nutrient signatures, very significantly for silicate (Tremblay et al., 2002b). Highly productive regions also produce sulfur gases, the most important being dimethylsulfide (DMS) which is a component of a biogeochemical cycle including dimethylsulfoniopropionate (DMSP) and dimethylsulfoxide (DMSO) (e.g., Bouillon et al., 2002). The volatile DMS, while highly supersaturated, is lost to the atmosphere where it may form cloud condensation nuclei, which are important in regulating the radiative balance of the atmosphere. Surprisingly low concentrations of DMS were detected in the upper water column (0–25 m) of the NOW in the spring of 1998, with mean values starting at 0.17 nmol l−1 in April, increasing to 0.65 and 1.08 nmol l−1 in May and June, respectively (Bouillon et al., 2002). This is significantly lower than the 12 nmol l−1 which was found in August 1991 in the open water west of Svalbard and lower than the average 3.0 ± 0.6 nmol l−1 observed in the iceedge zone (Leck and Persson, 1996). Also significantly higher DMS concentrations (mean 11.1 nmol l−1 ) have been reported from the Barents Sea (Matrai and Vernet, 1997). It is not possible from this single investigation to reject polynyas as important DMS production regions (see also Miller and DiTullio, 2007), and certainly more work is needed to determine the dynamics of DMS production in these areas. 3.4
Cape Bathurst Polynya
The Cape Bathurst polynya is formed in the outer Amundsen Gulf when easterly winds dominate the area at a time when open water or reduced ice-cover are present, mainly in the form of large fractures or leads along the coast. The pack ice of the central Arctic Ocean is the offshore boundary of the polynya. Observations made using satellite imagery have shown that the Cape Bathurst polynya varies considerably on an interannual basis with regard to seaice retreat and formation (Arrigo and Van Dijken, 2004). During the five years (1998–2002) these authors studied the polynya, it typically opened rapidly in June and began to freeze in October, except for 1998 when the opening started two months earlier and the freezing one month later, making this a year with an exceptionally long period of open water. The variable ice conditions also result in variable primary production, both in its timing and strength as evaluated from SeaWiFS satellite data (Arrigo and Van Dijken, 2004). It varied by a factor two over the five years of study, from 90 g C m−2 yr−1 in 2000 to 175 g C m−2 yr−1 in 1998. This makes the Cape Bathurst polynya about as productive as the NOW. The Cape Bathurst polynya was not much studied before the year 2000, when the Canadian Arctic Shelf Exchange Study (CASES) started. When the results of this study become available, a greater understanding of the biogeochemical processes in the Cape Bathurst polynya will be possible.
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The St. Lawrence Island polynya (SLIP) is one of several polynyas on the shallow continental shelf of the Bering Sea, which are all formed in the winter south of all major islands and peninsulas, as prevailing northerly winds force sea ice away from the sheltering landmasses. SLIP is a winter polynya, as open water normally exists in the Bering Sea from May to November. However, because light conditions are advantageous before May within the polynya, the pelagic production is favored here. Furthermore, the very shallow bottom depth (30–70 m) results in an extensive supply of organic matter to the sediment surface, which supports a rich macrobenthic community (e.g., Grebmeier and Cooper, 1995). This rich benthic community is the basis for a large population of marine mammals and birds. The production of sea ice in the polynya results in brine-enriched bottom water that is enriched in nutrients due to mineralization of organic matter at the sediment surface, as was observed in April 1999 and March 2001 (Clement et al., 2004). Nevertheless, the SLIP does not have any essential impact on the biogeochemistry of the Bering Sea water masses, as it only constitutes a small part of a high-productive region, even though it plays an important role for the ecosystem of the region. 3.6
Laptev Sea Polynya
Within the Laptev Sea a flaw lead exists in winter along the fast-ice boundary being up to more than 2000 kilometres (km) long and more than 10 km wide (Zakharov, 1966). The flaw lead is maintained and controlled by the wind regime and in early winter it lies close to the coast at 5–10 m water depth, but during the progression of winter it shifts over 500 km offshore to a bottom depth of 20–30 m (Dethleff, 1995). Within this polynya, extensive sea ice is produced which entrains sediments by convectively induced resuspension, because of the shallow bottom depth. The majority of the suspended matter is of terrigenous origin but also marine organic matter is produced within the Laptev Sea. Most of the sea ice produced within the Laptev Sea is exported to the deep Arctic Ocean within the Eurasian branch of the transpolar drift. In fact, about 60% of the sea ice exported from Arctic shelf seas originates in the Laptev Sea, bringing with it in the order of 10 Tg of suspended particulate matter (SPM) per year, which is more than 70% of the SPM exported with sea ice from Arctic shelf seas (Eiken, 2004). Consequently, processes in these flaw leads are important in transporting SPM from the shelves (much of terrigenous origin) out into the deep central Arctic Ocean. The flaw lead region is also reflected in the composition of benthic species, such as Ostracods (Stepanova et al., 2003). It likely also has an effect on the biogeochemical signature of the bottom waters of the region, even if a distinct signal has not been reported yet. Furthermore, the brine released during sea-ice production could be a conduit for transport of chemical constituents from the shallow shelves to intermediate waters of the Arctic Ocean. 3.7
Comparison of the Different Arctic Polynyas
Flaw leads are present in the lee of land around most of the Arctic Ocean during the freezing season, being important sea-ice factories. However, as these “polynyas” disappear into open water in the spring, they mainly have an indirect affect on the biogeochemistry of Arctic water masses. From a biogeochemical viewpoint, the North Water polynya seems to be the most important, followed by the Cape Bathurst polynya and the Northeast Water polynya. New findings in future investigations, however, might change this view.
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There is no question that several Arctic polynyas are important sink regions for atmospheric CO2 , but their relation to the anthropogenic issue is more difficult to accurately assess. The increase of atmospheric pCO2 will strengthen the oceanic uptake by increasing the difference in partial pressure between the atmosphere and sea surface. This is valid for all oceanic regions, but in polynyas additional aspects have to be considered. These are mainly coupled to the magnitude of sea-ice production, which likely will change (and already might have changed) in a climate warming scenario. Possible feedbacks to this are: • More polynya area results in more sea-ice production with resulting brine volume and an increase in transport into deeper water masses; • A larger polynya area in the spring could enhance primary production; • Changes in the supply of nutrients, either vertically or horizontally, affect primary production; and • The ice barriers that maintain some polynyas, e.g. the NEW and NOW, might “collapse”, thus decreasing their importance. There are several other possible feedback processes of variable importance, but the above are likely the most relevant. However, without better knowledge of the sensitivity of the individual processes to the forcing, it is not possible to make any quantitative assessments. Polynyas are regions of tight coupling between physical and biogeochemical processes that have important impact on several levels. Sea-ice production is the starting link of ventilation of subsurface waters with transport of chemical constituents by them. The highproductivity polynyas are the basis of a rich ecosystem supporting most trophic levels up to birds, seals and polar bears (e.g., Stirling, 1997). This rich ecosystem results in a transformation of chemical constituents and flux of particulate matter to subsurface layers. Consequently, polynyas effect the biogeochemical environment in several different ways.
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Chapter 7
Physical Control of Primary Productivity in Arctic and Antarctic Polynyas K.R. Arrigo1 1 Department of Geophysics, Stanford University, Stanford, California, 94305-2215, USA
Abstract The unique physical characteristics of Arctic and Antarctic polynyas often make them highly productive marine environments. Here I focus on four major polynyas (the NEW and NOW polynyas in the Arctic and the Ross Sea and Mertz Glacier polynyas in the Antarctic) and compare and contrast the major physicochemical features that control rates of phytoplankton growth and primary production in each. Included in this analysis are the effects of temperature, solar radiation (including ultraviolet), and nutrient delivery. Also discussed is the positive feedback that exists between cloud cover and polynya size as well as the importance of the timing of polynya formation on ecosystem structure.
1 Introduction Rates of phytoplankton growth and primary production in the upper ocean are controlled by ambient temperature and the availability of light and nutrients. These physicochemical aspects of the marine environment are determined by both physical and biological processes. Physical processes controlling the temperature of surface waters include transmission of incident shortwave radiation, emitted long wave radiation, the flux of latent heat due to freezing or melting of sea ice, and the horizontal and vertical movement of sensible heat. However, the amount of shortwave radiation (e.g. light) absorbed by the water column and the ice can be modified by the presence of high concentrations of biogenic material, including algal cells and their constituent pigments. Similarly, nutrient concentrations in surface waters are replenished by the physical processes of convective- and wind-driven mixing with nutrientrich waters from below or by exchange with the atmosphere, via aeolian deposition. Nutrient concentrations are modified, however by the biological processes of nutrient assimilation, nitrogen-fixation, denitrification, and organic matter remineralization. Nowhere is the importance of these physical and biological processes more obvious, or their interactions more complex, than in the polynyas that form within the seasonal ice pack in the polar regions of both the Arctic and the Antarctic. Because of the unique physical attributes of polynyas, they represent some of the most biologically productive marine Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74007-7
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ecosystems on the planet (Smith, 1995; Smith and Gordon, 1997; Tremblay et al., 2002a; Arrigo and van Dijken, 2004). While the dynamics of the sea ice play a critical role in structuring the polynya ecosystem and controlling its rate of biological productivity, other factors, such as ocean circulation patterns and meteorological conditions, play important roles as well. The objective here is to evaluate how physical forcing mechanisms that impact the dynamics of polynya formation and expansion also help determine the degree of biological productivity within waters associated with the polynya. This will be achieved by focusing on four of the most well-studied and biologically significant polynyas from both the Arctic and the Antarctic regions. The major Arctic polynyas to be focused on here include the Northeast Water polynya (NEW) and the North Water polynya (NOW). Analysis of Antarctic polynyas will be based on recent research on the Ross Sea polynya (RSP) and the Mertz Glacier polynya (MGP).
2 Formation of the Four Major Polynyas As described in previous chapters, polynyas can be described as being either latent heat polynyas, sensible heat polynyas, or some combination of both. Because the mode of formation determines the physicochemical characteristics of the polynya, which in turn will impact rates of biological activity, it is important to understand how each polynya forms. One generalization that can be made concerning the four polynyas that will be discussed here is that they are all considered to be either latent-heat polynyas or have characteristics of both latent heat and sensible-heat polynyas. None of the four is strictly a sensible heat polynya, which are actually quite rare and have not been that well studied. 2.1
The NEW polynya (Arctic)
The NEW polynya, located along the northeastern coast of Greenland (Figure 1A), remains open from approximately the beginning of May to the end of September (Bohm et al., 1997). Its formation is due to the presence of several ice shelf barriers that extend perpendicular
Figure 1: Location and approximate size of the major (A) Arctic and (B) Antarctic polynyas discussed in the text. NOW = North Water polynya, NEW = Northeast Water polynya, RSP = Ross Sea polynya, and MGP = Mertz Glacier polynya.
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to the prevailing currents and winds and cause a divergence of the sea ice field in their lee. In summer, winds are weak and the Norske Oer Shelf Ice restricts the motion of sea ice associated with the northward flowing coastal current, representing the dominant forcing of the southern part of the NEW polynya. During this season, the polynya gradually increases in size towards the north since the air–sea heat budget is positive and no new ice-formation takes place (Schneider and Budeus, 1995, 1997). Melting of glacial ice and sea ice induces vertical stability in certain parts of the polynya, giving rise to enhanced primary production (Schneider and Budeus, 1995). 2.2
The NOW Polynya (Arctic)
The NOW polynya, located along the northwest corner of Greenland (Figure 1A), is formed between November and March as a result of the annual appearance of an ice bridge in Smith Sound, along with a southward flowing surface current (Barber et al., 2001). When ice jams stop the inflow of ice from the north, the continued drift of ice southward below the blockage creates a large polynya without melting ice by sensible-heat input (Ingram et al., 2002). However, upwelling near the Greenland coast can bring relatively warm water to the base of the turbulent surface layer where it is entrained via convection driven by brine drainage from the ice. The resulting flux of sensible heat supplies about one-third of the heat loss at the surface of the NOW polynya and slows further sea ice growth (Melling et al., 2001; Mundy and Barber, 2001). The formation and duration of the initial ice bridge is highly variable and appears to have formed later and broke up earlier in the 1990s than in the 1980s. The average sea-ice formation and decay dates closely follow the mean temperature spatial pattern, illustrating a strong atmosphere-surface coupling (Barber et al., 2001). 2.3
The RSP Polynya (Antarctic)
Despite the presence of strong winds throughout the winter months, sea ice concentrations in the RSP (located north of the Ross Ice Shelf in the southwestern Ross Sea, Figure 1B) remain generally high, although small areas of open water occasionally form near the ice shelf (Gloersen et al., 1992). This is because cold winter sea ice has a low brine volume and sufficiently high tensile strength to resist break-up by offshore katabatic winds. Only when temperatures rise and the brine volume of the sea ice increases and the sea ice begins to melt, can ice breakup and polynya expansion begin. The precise timing of breakup in the spring apparently is controlled in part by the thickness of the sea ice, which is a function of cumulative degree days (Maykut, 1986), with colder years exhibiting a delay in polynya formation. Arrigo et al. (1998) found that the timing of RSP formation was more strongly correlated with air temperatures in fall and winter than with the magnitude of the wind stress in spring, reflecting the importance of sea ice integrity in determining the timing of initial sea ice breakup. Upwelling of relatively warm upper circumpolar deep water (UCDW) has been proposed as a mechanism for weakening the integrity of the ice pack, facilitating early polynya expansion (Jacobs and Comiso, 1989; Dinniman et al., 2003; Arrigo and van Dijken, 2004). Soon after expansion, however, shortwave radiation increases the temperature of surface waters by as much as 3–4◦ C, accelerating the increase in the size of the RSP (Arrigo et al., 2000) and enhancing biological activity (Arrigo and McClain, 1994). 2.4
The MGP Polynya (Antarctic)
As described by Maslanik et al. (2001), the MGP, located near the coast of Adelie Land almost directly south of Tasmania (Figure 1B), is formed in the lee of the Mertz Glacier
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tongue, which extends northeastward from the Antarctic continent, and whose impact is enhanced by the close proximity of numerous small grounded icebergs and the large iceberg B-9B. As the coastal East Wind Drift current drives the flow of sea ice to the west, the Mertz Glacier tongue and nearby iceberg field forces the bulk of the ice pack to remain offshore, creating an inshore divergence in the moving sea ice field (Massom et al., 2001). This results in the formation of a semi-constant compact barrier of thick broken-out fast-ice and other large floes from the east that extends westwards from north of the Mertz Glacier terminus. This barrier reduces the flow of sea ice from within the polynya that would normally result from the intense northward flowing katabatic winds. An annual fast-ice promontory, which forms to the west of the Mertz Glacier, further narrows the outlet path for sea ice. As a result of this and high sea ice-production rates, the MGP is somewhat unusual in that it periodically fills in with ice, significantly reducing the open-water area until a synoptic storm event can clear the polynya region of its newly-formed ice.
3
Role of Physicochemical Properties of Polynyas
Because of their complex and varying modes of formation, polynyas can differ greatly with respect to their physicochemical characteristics, which can influence both the taxonomic composition of the phytoplankton population and their rates of growth and primary production. The relationship between phytoplankton taxonomic composition and aspects of the physical and chemical environment is not well understood in either Arctic or Antarctic polynyas. For example, although the NEW polynya exhibits distinct regional differences in the physical and chemical characteristics of the water column, few corresponding biological differences are discerned (Booth and Smith, 1997). In general, the relationship between phytoplankton community structure and physicochemical properties of polynyas are restricted to relatively gross taxonomic divisions. For instance, in the RSP, the largest phytoplankton blooms are dominated by the prymnesiophyte Phaeocystis antarctica and are located in the weakly stratified waters north of the Ross Ice Shelf (Arrigo et al., 1998; Goffart et al., 2000). This bloom develops early, probably beginning in late October or early November, at a time when sea ice is still abundant in the region. A second type of bloom is dominated by a number of different diatom species and develops later in the year, beginning in December and January after the sea ice has largely disappeared. Diatom blooms are much smaller but more numerous than the P. antarctica bloom, and are associated with more strongly stratified surface waters near the marginal ice zone (MIZ). There appears to be little spatial or temporal overlap between these two bloom types, suggesting that the difference in community composition does not represent ecological succession (Arrigo et al., 1998; Goffart et al., 2000). Similarly, data from the NOW polynya in the Arctic showed that two major ecological regions existed, each dominated by a different phytoplankton community. The eastern NOW polynya region is characterized by warm saline surface waters and is dominated by picophytoplankton. The northwestern region is colder and less saline and is dominated by nanophytoplanktonic diatoms (Mostajir et al., 2001). Although more detailed taxonomic information are not yet available, work is continuing in order to better understand the relationship between phytoplankton community structure and characteristics of the physicochemical environment of polynyas. This is particularly important in the face of the ongoing environmental changes that have been documented in both polar regions, as well as changes that are predicted for the near future. Anticipated changes in temperature, precipitation, nutrient inventories, and circulation patterns are expected to
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impact phytoplankton community structure to varying degrees (Nehring, 1998; Arrigo et al., 1999; Peperzak, 2003). How these changes will alter ocean biogeochemistry and pelagic food webs needs to be further addressed. Fortunately, physicochemical characteristics of the water column are much more easily related to patterns of phytoplankton growth and primary production than they are to phytoplankton community structure. This is because these relationships are more easily measured and quantified, and hence, much more data are currently available. In particular, sea surface temperature, the ambient light field (including both visible and UV wavelength ranges, as well as its modulation by ice cover, wind-mixing, and stratification via melting of sea ice and solar insolation), and nutrient concentrations (including macronutrients and trace metals) have received considerable attention in recent years, particularly with respect to their importance in controlling phytoplankton dynamics within Arctic and Antarctic polynyas. 3.1
Effect of Temperature
Temperature has been shown to modulate rates of growth and production in polar waters via its regulation of metabolic activity (Eppley, 1972). Many metabolic activities of polar microalgae, such as carbon fixation and nutrient assimilation, exhibit Q10 values in excess of two (Priscu et al., 1989), meaning that for every 10◦ C increase in temperature, metabolic activity increases by a factor of two (or more for higher values of Q10). This increase with temperature is most apparent when resources such as light and nutrients are in ample supply. In general, rates of production by psychrophilic (cold-loving) algae will increase with increasing temperature, until a threshold is reached, beyond which rates will level off or even decline (Figure 2). Almost all polar algae grow at ambient temperatures that are a few degrees below their optimum (Li, 1980; Palmisano et al., 1987; Arrigo and Sullivan, 1992) so that seasonal warming in the summer generally results in enhanced growth and production rates. This is not always the case, however, as microbial activity and substrate utilization in the NEW polynya exhibited various responses to short-term warming, responding to temperature significantly at just 50% of the stations sampled (Yager and Deming, 1999). Nevertheless,
Figure 2: Changes in growth rate with temperature measured for psychrophilic marine phytoplankton acclimated to a temperature of −1.1◦ C. Redrawn from data in Li (1985).
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processes that increase the temperature of surface waters in polynyas will generally result in higher rates of growth and primary production, at least until resource limitation sets in. There are two primary sources of heat to polynya surface waters, solar radiation and upwelling of warm, saline deeper waters. Solar radiation is probably the most reliable and effective mechanism for heating surface waters as the reduced sea ice cover and increasing solar elevation during spring and summer result in a rapid increase in shortwave radiant flux to surface waters of polynyas in both polar regions. The importance of surface temperature in controlling phytoplankton bloom dynamics was particularly well demonstrated during a study of the NOW polynya. Mei et al. (2002) showed that during April and May, high phytoplankton biomass was consistently greatest in the warm waters along the Greenland side of the NOW polynya. There, the phytoplankton bloom began two months earlier than in the cooler waters found along the Canadian side (Odate et al., 2002). Similarly, high phytoplankton biomass was observed again in September, associated with the warmer and saline water in the southeastern part of the study area (Odate et al., 2002). Both the sensible heat due to deep warm water entrainment into the mixed layer and the biological heating effect via phytoplankton light absorption appeared to contribute to the pattern of phytoplankton distribution in the NOW polynya (Mei et al., 2002). However, given the generally small change in sea surface temperature measured over a typical seasonal cycle, temperature is not likely to be as important as other factors, such as resource limitation, in controlling rates of primary production within polynyas. For example, a 5◦ C increase in sea surface temperature, a reasonable upper limit to the seasonal temperature change experienced by polynyas, would increase metabolic rate only by about 40%, assuming a Q10 of 2.0. Because light availability and nutrient concentration both have much wider dynamic range than temperature (both can vary by orders of magnitude), these resources have a much greater potential to influence rates of primary production in polar waters, particularly in highly productive polynyas. 3.2
Effect of Light and Nutrients
The productivity of polynyas in both Arctic and Antarctic regions is determined most strongly by a balance between the availability of light and nutrients. In general, light availability is a function of season, cloudiness, sea ice cover, and the intensity of surface stratification, often described in terms of the mixed layer depth. Stratification can result from a freshwater flux from melting sea ice and from solar heating of surface waters. Nutrients are supplied to the ice-free surface waters of polynyas either via advection of high nutrient water from less productive regions adjacent to the polynya, from upwelling of nutrient rich waters from below the pycnocline, or via aeolian deposition. The importance of these various processes in supplying vital resources to the phytoplankton community differs with the specific physical characteristics of a particular polynya. In the NEW polynya, surface waters are characterized by their low nitrate and high silicate concentrations, being derived from East Greenland Shelf Water (Kattner and Budeus, 1997). The anti-cyclonic surface circulation in the NEW polynya follows the topography of the trough system and continuously supplies nutrients to the surface throughout the year. The northern boundary of this tongue of relatively nutrient-rich water is controlled by the uptake of nutrients by phytoplankton during the summer months when light levels are high (Kattner and Budeus, 1997). Ultimately, nutrients are exhausted, and the phytoplankton bloom begins to decline in intensity. However, a second, albeit short-lived, phytoplankton bloom has been observed to form during autumn in the NEW polynya, likely the result of vertical diffusion of
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nitrate associated with increased wind mixing (Touratier et al., 2000). Therefore, processes that can replenish nutrients to surface waters while light is still in ample supply can result in enhanced rates of annual production. That is not to say that light is not an important factor governing primary production in the NEW polynya. A simple non-linear relationship was found to exist among the attenuation of solar radiation by sea ice, the percentage of open water, sea ice thickness, and phytoplankton primary production (Smith, 1995), suggesting that light indeed plays an important role there. In fact, the NEW polynya is characterized by the presence of multiple ecological regimes, defined primarily by the degree of stratification, which controls the ambient radiation field. A heavily ice-covered regime (more than 50% ice cover) is characterized by low levels of phytoplankton biomass and primary production that is dominated by small size classes. In the open water regime, surface stratification is strong, nutrients are abundant, and biomass and production are high and dominated by large phytoplankton. In the mixed ice regime, where the surface layer is strongly influenced by melt water and the pycnocline is relatively deep, production is intermediate and partitioned between small and large phytoplankton size classes (Pesant et al., 1996). Clearly, both irradiance and nutrient fields exert strong influences on phytoplankton productivity and ultimately result in a mosaic of biomass within the polynya (Smith, 1995). Nutrients may play a larger role within the NOW polynya, particularly during late spring when low-salinity Arctic water enters northern Smith Sound and mixes with Baffin Bay water. The Arctic water originates from the Bering Sea and contains high concentrations of phosphate and silicate. Baffin Bay water dominates in the southeast NOW region and is associated with relatively shallow upper mixed layers and weak horizontal advection. Phytoplankton depletion of macronutrients in Baffin Bay water begins in April and continues until nitrate is exhausted from the upper mixed layer in early June (Tremblay et al., 2002b). Primary production then shifts to recycled nitrogen sources as the community composition moves to one dominated by dinoflagellates and ciliates (Lovejoy et al., 2002). Over half of the new production during this period can be attributed to wind-driven replenishment of nitrate in the euphotic zone (Tremblay et al., 2002a). The NOW polynya appears to act as a silicate trap in which the biota differentially transports surface silicate to depth, as well as playing a key role in reducing biogenic silica dissolution, thereby influencing local and downstream nutrient signatures (Michel et al., 2002). Collectively, the results from NOW imply that the timing and magnitude of blooms are controlled by a succession of oceanic and meteorological forcing, including early advection of nutrient-rich surface waters into the polynya and later wind-driven upwelling of high nitrate waters from below the thermocline (Tremblay et al., 2002a). In Antarctic waters, macronutrients like nitrate and phosphate are generally in ample supply, so that nutrient limitation is more likely to be the result of an inadequate abundance of trace metals, such as iron. This limitation has been demonstrated most convincingly in offshore waters north of the continental shelf (Boyd et al., 2000; Gervais et al., 2002; Coale et al., 2004), but evidence suggests that iron limitation is important in highly productive Antarctic polynyas as well (Arrigo et al., 2003a, Coale et al., 2004). Strong convection replenishes iron in surface waters where it reaches concentrations of about 0.3 nM prior to the initiation of the phytoplankton bloom (Sedwick and DiTullio, 1997). However, by late December, iron in surface waters of the RSP are nearly undetectable, leading to the decline of the phytoplankton bloom at a time when one third of the macronutrients have not yet been exhausted (Fiztwater et al., 2000; Arrigo et al., 2003a). Macronutrients are also still well above growth-limiting concentrations when diatoms begin to decline in the western
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MIZ of the expanding RSP (Arrigo et al., 2000), with the exception of those regions where iron released from melting sea ice raise iron concentrations in the upper 30 m of the water column above 2 nM (Sedwick and DiTullio, 1997). Therefore, even in the RSP, which is located on the productive Antarctic continental shelf, experimental results have shown that iron availability controls the rate of annual primary production. In the MGP the combination of light and nutrient availability is important in defining the strength of the phytoplankton bloom and the intensity of primary production. Two distinct deep mixing features were observed in the MGP, associated to varying degrees with high phytoplankton abundance and high photosynthetic competency (Vaillancourt et al., 2003). One was observed along the Adelie Land coast and was characterized by a deep mixed layer with chlorophyll a concentrations of 386 mg m−2 and elevated efficiency of photosystem II (Fv/Fm > 0.5). A second feature was located at the eastern end of the study area where a bloom of Phaeocystis antarctica formed within a shallow (24 m) mixed layer, with surface chlorophyll a concentrations of 8 mg m−3 and elevated Fv/Fm. Within this feature, the silicic acid to nitrogen utilization ratio explained over 30% of the variability in the distribution of surface water pCO2 , suggesting that a strong link exists in these waters between iron availability and phytoplankton primary production. Possible iron sources include aeolian deposition, release from melting sea ice, coastal sediments, and the transport of UCDW (Sambrotto et al., 2003). The spatial coherence of phytoplankton biomass, photocompetency, high salinity, and deep-mixing suggests that blooms of Phaeocystis and diatoms form in this region after physical disturbances result in mixing of nutrient-rich subsurface waters into the euphotic zone (Vaillancourt et al., 2003). 3.3
Effect of UV Radiation
Because polynya surface waters are exposed to solar radiation much earlier than adjacent ice-covered waters, they can receive relatively high doses of ultraviolet radiation (UVR), particularly in the Antarctic where reduced stratospheric ozone concentrations (the ozone hole) can persist into the spring (Frederick and Snell, 1988). Recently, the Arctic has also experienced a recurrent springtime thinning of the stratospheric ozone layer (Manney et al., 2003), resulting in increased exposure of surface waters to UVB radiation (UVB, 280 to 320 nm). However, little is known about the biological effect of such UVB enhancement on the Arctic marine ecosystem. Understanding the impact of enhanced UVB in Arctic waters is complicated by the fact, that unlike the Antarctic, a large amount of dissolved organic carbon (DOC) is deposited near the coast from the many rivers that flow into it. A significant fraction of this DOC consists of colored dissolved organic matter (CDOM) that absorbs very strongly in the UVB range and may help screen out the additional UVB radiation resulting from diminishing ozone concentrations (Vasseur et al., 2003). Arrigo and Brown (1996) showed primary production was enhanced in the upper approximately 30 m of the water column by the presence of even low concentrations of CDOM, where increases in production due to the removal of damaging ultraviolet radiation more than offset any reduction resulting from a decrease in water clarity. Unfortunately, measurements of the effects of UVB radiation on primary production in polynyas of either polar region are relatively rare. In the NOW polynya, Belzile et al. (2000) measured vertical profiles of UVR and photosynthetically available radiation (PAR) beneath five large sea ice floes. They found that 2–13% of the incident UVB radiation was transmitted through the snow (0.01–0.09 m thick) and ice (0.5–1.3 m thick) to the waters below. The relatively high UVR transparency found in their study coincided with the seasonal maximum of incident UV irradiance. Hence, they concluded that the resulting high UVR : PAR
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ratio may have a negative effect on phytoplankton communities in surface waters, as well as those within the sea ice (Belzile et al., 2000), although these effects were not actually measured. However, in the St. Lawrence Island polynya (Arctic), measurements of underwater PAR and UVR, together with an analysis of DNA damage using dosimeters, indicated that phytoplankton probably were not being negatively influenced by ambient levels of UVR, at least in late winter and early spring (Cooper et al., 2002). Even in Antarctic waters below the ozone hole, the impact of enhanced UVB radiation on rates of primary production is predicted to be very small (Arrigo, 1994; Arrigo et al., 2003b). When integrated to the 0.1% light depth, model results show that the loss of primary production resulting from enhanced fluxes of UVB due to severe ozone depletion are less than 0.25%. The loss of primary production is minimized by the strong attenuation of UVR within the water column and by sea ice which is at its peak extent at the time of the most severe ozone depletion (Arrigo et al., 2003b). Coastal polynyas in the Antarctic are most greatly affected by decreased ozone concentrations during the month of November (a 1– 3% loss of primary production), when ozone levels are still low and UVR fluxes are high. However, the phytoplankton blooms in most coastal polynyas, including the RSP and the MGP, have not yet reached their peak biomass before ozone levels increase to normal levels (Arrigo and van Dijken, 2003), further minimizing the impact of enhanced UVB on annual rates of production (Arrigo et al., 2003b).
4
Cloud Reduction over Polynyas with High Latent Heat Flux
Because rates of primary production early in the phytoplankton bloom are strongly controlled by light availability, Arrigo and van Dijken (2004) conducted a satellite-based study of the RSP to determine whether there were significant inter-annual differences in cloud cover (which can reduce incident irradiance by more than 50%) and whether differences in the degree of cloud cover are translated into changes in rates of primary production within the polynya. They found that the fraction of RSP surface waters covered by clouds exhibited a distinct seasonal pattern. During the month of October, polynya surface waters were rarely exposed to sunny skies, a reflection of the greater cloudiness during this time of year and the extremely small size of the RSP during October. Cloud-free open water area increased in November, the same time that the RSP started to increase in size, with a maximum of about 20% of the ice-free ocean surface experiencing sunny skies for some portion of the day. The amount of RSP surface waters exposed to sunny skies increased dramatically in December, reaching as high as 80% on some days. Cloudiness generally increased again in January, when the fraction of RSP surface waters covered by clouds always exceeded 0.8. The mean fraction of open water area covered by clouds during spring and summer varied relatively little from year to year, ranging from 0.90 to 0.94. Although these interannual differences in mean cloud-free open water area appear to be small, to the extent that sunny days are responsible for a disproportionate fraction of primary production, years when the cloud-free open water area is more variable might be expected to be more productive. Surprisingly, the interannual difference in the fraction of RSP surface waters covered by clouds was able to explain 98% of the variability in annual primary production in the RSP (Figure 3C), with cloudy years exhibiting the lowest annual production rates. It is important to note, however, that cloud cover in the RSP is highly negatively correlated with polynya size (Figure 3A), making it impossible to statistically separate the effects
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Figure 3: Relationship between (A) the annual mean open water area and the annual mean fraction of open water covered by clouds, (B) the annual mean open water area and annual pelagic primary production, and (C) the annual mean fraction of open water covered by clouds and annual pelagic primary production for the RSP.
Figure 4: Possible causes for the high correlation between polynya size and cloud cover. Additional solar insolation associated with light cloud cover (A) will melt more ice, increasing polynya size. Alternatively, enhanced heat flux from the open water within the polynya (B) may increase cloud dissipation. Evidence from the RSP suggests that (B) is more likely. of sea ice cover (Figure 3B) from effects of clouds in controlling annual production (Figure 3C). This high correlation implies that a physical connection exists between the amount of open water and the degree of cloudiness in the southwestern Ross Sea (Maslanik et al., 2001). This connection could be the result of enhanced solar insolation during the spring and summer of years when cloud abundance is low (Figure 4A), causing increased radiative heating of the sea surface and enhanced sea ice melting (Minnett, 1999; Wendler et al., 2000; Hanafin and Minnett, 2001). Alternatively, an oceanic heat source could accelerate sea ice melt (Jacobs and Comiso, 1989), and the resulting increase in open water could facilitate a higher oceanic heat flux to the atmosphere (Figure 4B), resulting in the dissipation of clouds (Wang and Wang, 2001). The second possibility is the more plausible for the RSP because in 2000–01, the unusually heavy sea ice cover, and hence the reduced area of open water, were clearly controlled by the presence of the B-15 iceberg fragments (Arrigo et al., 2002) and not by decreased solar insolation. This indicates that in 2000–01, the amount of open water was controlling
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the cloud cover, and not vice versa, a relationship that the high correlation between these two variables suggests applies to other years as well (Arrigo and van Dijken, 2004). The oceanic heat flux required both to melt the sea ice and for cloud dissipation in the RSP is probably derived from the movement of warm UCDW onto the continental shelf, the heat content of which represents a potential flux of greater than 200 W m−2 to the atmosphere (Jacobs and Comiso, 1989). This value is consistent with the estimated oceanic heat flux of 125 W m−2 that would be required for cloud dissipation (Sass, 2001). Jacobs and Comiso (1989) also noted that the breakout of sea ice in the Ross Sea is too rapid to be explained by winds or by solar radiative heating and must instead be the result of a deep water heat source. Considering the strong relationship that exists between the Multivariate ENSO Index (Wolter, 1987; Wolter and Timlin, 1993) and sea ice cover in the RSP (Arrigo and van Dijken, 2004), if in fact the movement of warm UCDW onto the continental shelf is an important factor controlling distributions of sea ice (and clouds) in the RSP, then there might be a connection between ENSO, the volume of UCDW that moves onto the shelf each year, and annual primary production (Arrigo and van Dijken, 2004).
5 Timing of Polynya Expansion There is evidence to suggest that the timing of polynya formation in both polar regions can impact not only the initiation of the phytoplankton bloom (often in non-intuitive ways), but the nature of the associated food web as well. Arrigo et al. (1998) reported that early expansion of the RSP could actually lead to a later development of the phytoplankton bloom. This is because the start of the phytoplankton blooms in the RSP are controlled by the interaction between seasonal changes in the frequency and intensity of katabatic winds and yearly differences in the timing of polynya formation. For example, higher than average winter temperatures over the Ross Sea result in a earlier polynya formation, presumably due to a thinner and weaker sea ice cover. Because the wind strength and the frequency of katabatic surges declines substantially between October and December, the earlier the polynya forms, the more likely that newly exposed surface waters will experience high winds associated with katabatic surges that can extend approximately 250 km beyond the coastal slope break (Bromwich and Kurtz, 1984; Bromwich, 1989; Bromwich et al., 1992). These strong katabatic winds may mix the phytoplankton out of the surface layer and increase the advective component of surface waters (with their associated biota) northward, forcing phytoplankton beneath the consolidated ice pack. The continuous removal of phytoplankton into regions unfavorable for growth would delay bloom development, particularly if the initiation of the bloom were dependent upon seeding by algal assemblages released from melting sea ice. Conversely, in years when the Ross Sea polynya forms later, katabatic events at the time of polynya formation will be less frequent and winds will be relatively weaker. The prolonged presence of sea ice in this case would buffer surface waters within the polynya from wind stress, which would decrease both vertical mixing and the advection of surface waters, allowing more rapid phytoplankton growth and earlier bloom formation once the polynya became ice-free. Timing of sea ice retreat may be an important mechanism controlling primary production and food web interactions in Arctic polynyas as well. Based on data collected from the Arctic, Hunt et al. (2002) proposed the Oscillating Control Hypothesis, which predicts that ecosystems experiencing seasonal ice retreat will alternate between bottom-up control in cold regimes and top-down control in warm regimes. When sea ice persists into the
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spring, phytoplankton bloom in cold, partially ice-covered water, whereas when ice retreats early (before mid-March), the increased solar radiation incident in the sea surface allows phytoplankton to bloom in warm water. In years when sea ice is persistent, low temperatures reduce both zooplankton production and juvenile fish survival, resulting in bottom-up control, a simple food web, and decreased populations of piscivorous (fish-eating) fish. Alternatively, when sea ice retreats early, phytoplankton bloom in warm water and zooplankton populations grow rapidly, providing plentiful prey for juvenile forage fish, and ultimately piscivorous fish. Marine birds and pinnipeds that feed on these forage fish also may fare better in cold regimes due to reduced competition from large piscivorous fish. Concepts such as the Oscillating Control Hypothesis exemplify the importance of interactions between physical aspects of the polynya system and the productivity of the biological populations that reside there. This is because at their most basic level, polynyas expose surface waters of polar oceans to atmospheric influences. The nature and intensity of these influences change over time so that the timing of polynya formation and its subsequent expansion is an important consideration when evaluating the physical influences acting on polynya dynamics. Once the polynya has formed, physical and chemical factors continue to influence biology through advection of nutrients into the polynya, mixing of nutrients to the sea surface, and radiative and meltwater stratification of the mixed layer. The production of a particular polynya will reflect the interplay between all of these physical factors, which ultimately, help structure food webs in these biologically rich polar waters.
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Odate, T., Hirawake, T., Kudoh, S., Klein, B., LeBlanc, B., Fukuchi, M., 2002. Temporal and spatial patterns in the surface-water biomass of phytoplankton in the North Water. Deep-Sea Research II 49, 4947–4958. Palmisano, A.C., SooHoo, J.B., Sullivan, C.W., 1987. Effects of four environmental variables on photosynthesis-irradiance relationships in Antarctic sea-ice microalgae. Marine Biology 94, 299–306. Peperzak, L., 2003. Climate change and harmful algal blooms in the North Sea. Acta Oecologica—International Journal of Ecology 24, S139–S144. Pesant, S., Legendre, L., Gosselin, M., Smith, R.E.H., Kattner, G., Ramseier, R.O., 1996. Size-differential regimes of phytoplankton production in the northeast water Polynya (77◦ –81◦ N). Marine Ecology Progress Series 142, 75–86. Priscu, J.C., Palmisano, A.C., Priscu, L.R., Sullivan, C.W., 1989. Temperature dependence of inorganic nitrogen uptake and assimilation in Antarctic sea-ice microalgae. Polar Biology 9, 443–446. Sambrotto, R.N., Matsuda, A., Vaillancourt, R., Brown, M., Langdon, C., Jacobs, S.S., Measures, C., 2003. Summer plankton production and nutrient consumption patterns in the Mertz Glacier Region of East Antarctica. Deep-Sea Research II 50, 1393–1414. Sass, B.H., 2001. Modelling of the time evolution of low tropospheric clouds capped by a stable layer. HIRLAM Tech. Rep. Norrköping (http://www.knmi.nl/hirlam/TechReports/). Schneider, W., Budeus, G., 1995. On the generation of the Northeast Water Polynya. Journal of Geophysical Research 100, 4269–4286. Schneider, W., Budeus, G., 1997. Summary of the Northeast Water Polynya formation and development (Greenland Sea). Journal of Marine Systems 10, 107–122. Sedwick, P.N., DiTullio, G.R., 1997. Regulation of algal blooms in Antarctic shelf waters by the release of iron from melting sea ice. Geophysical Research Letters 24, 2515–2518. Smith, W.O. Jr., 1995. Primary productivity and new production in the Northeast Water (Greenland) polynya during summer-1992. Journal of Geophysical Research 100, 4357– 4370. Smith, W.O. Jr., Gordon, L.I., 1997. Hyperproductivity of the Ross Sea (Antarctica) polynya during austral spring. Geophysical Research Letters 24, 233–236. Touratier, F., Legendre, L., Vezina, A., 2000. Northeast Water Polynya 1993: construction and modelling of a time series representative of the summer anticyclonic gyre pelagic ecosystem. Journal of Marine Systems 27, 53–93. Tremblay, J.-E., Gratton, Y., Fauchot, J., Price, N.M., 2002a. Climatic and oceanic forcing of new, net, and diatom production in the North Water. Deep-Sea Research II 49, 4927–4946. Tremblay, J.-E., Gratton, Y., Carmack, E.C., Payne, C.D., Price, N.M., 2002b. Impact of the large-scale Arctic circulation and the North Water Polynya on nutrient inventories in Baffin Bay. Journal of Geophysical Research 107, 3112. Vaillancourt, R.D., Sambrotto, R.N., Green, S., Matsuda, A., 2003. Phytoplankton biomass and photosynthetic competency in the summertime Mertz Glacier Region of East Antarctica. Deep-Sea Research II 50, 1415–1440. Vasseur, C., et al., 2003. Effects of bio-optical factors on the attenuation of ultraviolet and photosynthetically available radiation in the North Water Polynya, northern Baffin Bay: ecological implications. Marine Ecology Progress Series 252, 1–13. Wang, S., Wang, Q., 2001. Surface fluxes and stratocumulus clouds in DECS: a modeling study. In: Fourth Conference on Coastal Atmospheric and Oceanic Prediction, St. Petersburg, FL.
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Wendler, G., Moore, B., Dissing, D., Kelley, J., 2000. On the radiation characteristics of Antarctic sea ice. Atmosphere-Ocean 38, 349–366. Wolter, K., 1987. The Southern Oscillation in surface circulation and climate over the tropical Atlantic, Eastern Pacific, and Indian Oceans as captured by cluster analysis. Journal of Climate and Applied Meteorology 26, 540–558. Wolter, K., Timlin, M.S., 1993. Monitoring ENSO in COADS with a seasonally adjusted principal component index. In: Proc. 17th Climate Diagnostics Workshop. Norman, OK, NOAA/N MC/CAC, NSSL, Univ. Oklahoma, pp. 52–57. Yager, P.L., Deming, J.W., 1999. Pelagic microbial activity in an arctic polynya: testing for temperature and substrate interactions using a kinetic approach. Limnology and Oceanography 44, 1882–1893.
Chapter 8
Primary Production and Nutrient Dynamics in Polynyas J.-E. Tremblay1 and W.O. Smith Jr.2 1 Département de Biologie, Université Laval, Québec, QC G1K 7P4, Canada 2 Virginia Institute of Marine Sciences, College of William and Mary, Gloucester Pt., VA 23062, USA
Abstract Phytoplankton assemblages in polynyas are strongly impacted by the unique environment of those systems, and their growth and accumulation is always greater within a polynya than under heavy ice. The extent of this enhancement is dependent on the physical conditions of the polynya—the duration of the polynya’s existence, the distribution of ice and snow, and the physical circulation within it. We review the polynyas in both Arctic and Antarctic waters that have been intensively studied and compare them with respect to biomass, daily productivity, chemical and physical constraints, annual productivity, export, and effects on food webs and the local biogeochemical cycles. We conclude that the most productive polynyas (the North Water polynya and the Ross Sea polynya) have remarkably similar short-term productivity, and the annual productivity and seasonal timing of both are also similar. However, the two have strong dissimilarities in modes of control and export. The ecological consequences of enhanced production within a polynya are also investigated, and appear to vary among polynyas. We suggest that the differences among polynyas within polar systems reflect the differences in large-scale physical forcing that exist across the Arctic and Antarctic, and that generalizations among polynyas need to encompass this variability.
1 Introduction Polynyas are areas of reduced ice cover within regions of extensive consolidated ice, and are physically generated features. Regardless of whether they are latent-heat polynyas, sensibleheat polynyas, or mixed-mode polynyas (Muench, 1990), these singularities have reduced concentrations of ice and snow, which in turn implies that more solar radiation penetrates into the water column. Because irradiance in polar regions is the primary control of phytoplankton growth (Smith and Sakshaug, 1990), the growth of phytoplankton in polynyas is enhanced relative to the surrounding regions. And while this has been repeatedly shown to be true, there has been no detailed, bi-polar comparison of field-based phytoplankton investigations across polynyas. Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74008-9
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Polynyas occur throughout both the Arctic and Antarctic (Figure 1). In the Arctic they occur in the lee of islands (e.g., the St. Lawrence Island polynya) and downstream of ice shelves (e.g., the Northeast Water polynya off the coast of northeastern Greenland) or ice bridges (e.g., the North Water polynya between Greenland and Ellesmere Island). In the Antarctic they largely occur off ice shelves (e.g., the Ross Sea polynya off the Ross Ice Shelf) and the coast (e.g., the East Lazarov Sea polynya off the coast of Queen Maude Land; Arrigo and van Dijken, 2003). Some polynyas have been observed in deep water (such as the Maud Rise polynya in the Weddell Sea), but nearly all polynyas occur on the continental shelves of both poles. This is because many polynyas are generated by the action of wind; furthermore, in those systems where heat input from below is important, this heat input is often driven by the interaction of currents with the continental shelf itself. As a result, these regions are those that are most impacted by polynyas (Arrigo and van Dijken, 2003). Given that the shelves are already much more productive than the offshore waters, the question then becomes “What is the biological significance of polynyas?” Is annual productivity increased by their presence, or is the timing of the productivity pulse simply earlier, with yearly production being equal? A remote-sensing investigation of the Antarctic suggests that mean, annual primary production in several polynyas is possibly lower than the mean for the Southern Ocean (Arrigo and van Dijken, 2003). A definite answer is difficult to obtain, however, as it is very difficult to find a region of similar size and bathymetry that is not a polynya with which to compare it. Only by in-depth comparison to broad patterns of growth and accumulation can the significance of polynyas be understood. Arctic and Antarctic polynyas are in many ways very different (Table 1). For example, in the Arctic macronutrients are initially modest and usually depleted to the detection limit during phytoplankton growth. Nitrate initially ranges from less than 5 (e.g., Northeast Water) to 12 μM (North Water) in the upper mixed layer, and is dependent on mixing regime and the location of polynyas relative to the incursions of Pacific- and Atlantic-derived waters. In the Antarctic macronutrients are always elevated (NO3 initially about 30 μM; Jones et al., 1990; Smith et al., 2003), and are seldom depleted during the short, polar growing season. Recent results on investigations of the productivity of Antarctic continental shelf systems have shown the importance of iron in limiting growth, biomass and phytoplankton assemblage composition (Martin et al., 1990; Sedwick and DiTullio, 1997; Olson et al., 2000; Coale et al., 2003). Similarities also exist between Arctic and Antarctic polynyas, with many of the physical characteristics and temporal patterns of phytoplankton growth and accumulation being alike (Table 1). Given the variety of chemical and physical features, a continuum of productivity within polynyas would be expected. As polynyas are expected to have altered regimes of productivity (either in magnitude or in the timing of the productivity pulse), it is expected that they also would be the sites of extensive benthic and higher trophic level (e.g., birds, whales, seals) productivity (see Arrigo and van Dijken, 2003; Karnovsky et al., 2007). Certainly many polynyas are the locations of extensive accumulations of birds and marine mammals, but that enhancement may also be driven by factors independent of productivity (such as access to nesting and breeding sites, refugia from predation or breathing for marine mammals). In polynyas the cryobenthic coupling is altered by the removal or thinning of ice, and the benthic response to this change may be different than the increased pulse of the enhanced water column production. Finally, because many of the higher trophic levels integrate different spatial scales than do phytoplankton (by foraging over different scales of time and space), it may be difficult to ascribe a food web enhancement to increased polynya productivity alone. Indeed, it is expected that
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Figure 1: Distribution of polynyas in May 1998 as derived from SeaWiFS satellite imagery. (A) Northern hemisphere (1: Cape Bathurst; 2: North Water; 3: Northeast Water, 4: Whaler’s Bay; 5: Laptev polynya and flaw lead; 6: Wrangel Island), and (B) Southern hemisphere (1: Ross Sea; 2: Terra-Nova Bay; 3: Mertz Glacier). The estimated pigment concentration is derived from the NASA OC4 (version 4) algorithm.
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Table 1: Comparison of the physical, chemical and physiographic properties of five different polynyas. ND = no data Variable
Polynya Northeast Water
Ross Sea
Mertz Glacier
Terra Nova Bay
Location
7◦ –79◦ 30 N, 71◦ –79◦ W
77◦ –81◦ N, 5–15◦ W
76◦ 30 –77◦ 30 S, 170–178◦ W
66◦ 30 S, 145◦ E
75◦ S, 165◦ E
Mean depth and range (m) Winter water temperature (◦ C) Winter salinity (psu) Summer water temperature (◦ C) Summer salinity (psu) Winter [NO3 ] (μM) Minimum [NO3 ] (μM) Winter [Si(OH)4 ] (μM) Summer [Si(OH)4 ] (μM) Max. chlorophyll concentration (μg l−1 ) Max. daily productivity (mg C m−2 d−1 ) Annual productivity (g C m−2 y−1 )
300 (170–600) −1.86 33.6 ∼1.0 (max 2) ∼31.0 ∼11.014 <0.0514 1214 ∼1.014 19.812 527012 25412
300 (150–700) −1.75 33.0 ∼1.0 30.5 ∼5.0 <0.05 6 ∼1.0 915 173313 6612
600 (300 – 900) −1.86 34.80 ∼0.16 33.76 ∼30 87 ∼806 ∼576 158 ∼60008 60–2008,9
7001 (100–1000) −1.902 34.632 −0.803 34.124 ∼30 ∼244 ∼743 ∼563 ∼1.5 11304 ND
600(100–1100) −1.86 34.825 0.16 (max > 2) 33.66 ∼30 ∼76 ∼806 ∼296 ∼1010 ∼200011 66–12511
1 Porter-Smith (2003), 2 Williams and Bindoff (2003), 3 Vaillancourt et al. (2003), 4 derived from Sambrotto et al. (2003), 5 Picco et al. (1999), 6 Catalano et al. (1999), 7 Gordon et al. (2000), 8 Smith and Gordon (1997), 9 Arrigo et al. (2000), 10 Innamorati et al. (1999), 11 Lazzara et al. (1999), 12 Klein et al. (2002), 13 Pesant et al. (1996), 14 Tremblay et al. (2002b), 15 Smith et al. (1997).
J.-E. Tremblay and W.O. Smith Jr.
East North Water
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there will be a gradient in effects that is a function of polynya size, productivity and depth; however, the nature of this relationship is presently unclear. In this chapter we provide a cross-system comparison to elucidate both the differences and the similarities among Arctic and Antarctic polynyas. Major polynyas are reviewed with respect to nutrient supply, temporal patterns of known biomass development and assemblage composition, the fate of primary production and implications for biogeochemical flux and ecosystem productivity.
2 Phytoplankton Characteristics of Individual Polynyas 2.1
North Water Polynya
The North Water is the most spectacular polynya of the Arctic by virtue of its biological richness and size (max ca. 80,000 km2 ; Barber and Massom, 2007; Figure 1). It is a mixedmode polynya whose existence is contingent on the formation of an ice bridge at 79◦ N. Once established, the bridge prevents Arctic ice from descending into Smith Sound, where winds and sensible heat combine to create open water conditions (Ingram et al., 2002). The supply of oceanic heat is confined to the east (Greenland side) and is provided by upwelling and the vertical mixing of Atlantic waters from the West Greenland Current. Latent heat dominates in the west where the inflow of cold, Pacific-derived Arctic water transits rapidly through Smith Sound. Because water masses and the contribution of latent and sensible heat are segregated laterally in the North Water, we will treat it as two distinct systems, recognizing that a continuum of conditions exists in the transition zone. 2.1.1
Temporal Patterns and Assemblage Composition
In the east the supply of sensible heat does not induce outright melting in early spring, but is sufficient to dampen ice growth and vertical mixing (Melling et al., 2001). Shallow mixed-layers (less than 20 m) are common, and the favorable irradiance regime can trigger a precocious phytoplankton bloom as early as late April (Tremblay et al., 2002a, 2006a; Figure 2A). Despite low initial chlorophyll a (less than 0.3 μg l−1 ), phytoplankton biomass is initially dominated by large solitary (Coscinodiscus sp., Actinocyclus sp.) and chain-forming (Thalassiosira spp., Porosira glacialis) centric diatoms (Lovejoy et al., 2002). The latter strongly dominate at the bloom’s apogee. During bloom development, episodic storms can disrupt the weak density stratification and supply nutrients until restratification occurs in late May as a result of insolation and ice melt (Tremblay et al., 2002b). The upper mixed layer is then isolated from the deep nutrient reservoir and the bloom culminates with the exhaustion of nitrate. In 1998 chlorophyll a reached a maximum of ca. 19 μg l−1 in early June, which is more than expected from initial nitrate concentrations (about 12 μM) owing to mixing during May. Following the abrupt collapse of the bloom in late June, chlorophyll a continued at slightly higher concentrations than pre-bloom levels due to the persistence of Chaetoceros socialis in the nitracline (Booth et al., 2002). This species is largely responsible for a secondary bloom in September. Autotrophic dinoflagellates and haptophytes (e.g., Phaeocystis sp.) remain uniformly scarce from April to September. There are no field data on interanual variability, but the remote-sensing study of Bélanger (2001) implies that the timing of the bloom may vary by at least a month. In the northwest net phytoplankton growth is delayed by partial ice cover and the deep mixed layers associated with convection and wind mixing (Tremblay et al., 2002b). Notable
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Figure 2: The temporal pattern of clear-sky incident irradiance, nitrate, silicate, and chlorophyll in the upper mixed layer of the North Water Polynya. (A) Eastern sector, and (B) northwestern sector. Modified from Klein et al. (2002) and (Tremblay et al., 2002b). Time was also given in days since equinox to facilitate comparison with the Ross Sea (Figure 4). accumulations of chlorophyll a occur only in July after the collapse of the ice bridge, ice melt and the establishment of strong, seasonal stratification (Figure 2B). Winds are weak at this time and the upward supply of nitrate presumably small. The diatom bloom occurs slightly earlier than in adjacent, seasonally ice-covered Barrow Strait (Anning, 1989; Fortier et al., 2002). During May high phytoplankton growth proceeds along the main axis of flow into
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the polynya, as the cells take advantage of increasing irradiance away from the ice bridge (Tremblay et al., 2002b). Dominance of the bloom assemblage by chain-forming diatoms is weaker than in the east, with increased contributions of C. socialis, pennate diatoms, and enhanced abundances of flagellates and grazers, such as ciliates and tintinids (Lovejoy et al., 2002). By the time Arctic waters reach the southwest they harbor moderate chlorophyll a concentrations (5 μg l−1 ). 2.1.2
Magnitude and Fate of Primary Production
High overall productivity and diatom dominance in the North Water is favored by an unusual combination of factors. Firstly, the Polar Surface Water (PSW) flowing southward into Smith Sound is relatively rich in nitrate (11 μM) and silicate (25 μM) (surface concentrations in PSW above the Arctic halocline are typically a third of those). The anomaly suggests a direct supply of Bering Sea Water or erosion of the upper halocline by vertical-mixing processes upstream of the northernmost stations (Tremblay et al., 2002a). The second scenario is consistent with recent reports of wind-driven, mixing events in the high Arctic (Yang et al., 2004). Secondly, the eastern North Water is located in the Atlantic sector of Baffin Bay, where winter nitrate concentration is much higher than the norm for PSW. Despite its Atlantic origin, the silicate:nitrate ratio (ca. 1.0) of Baffin Bay Water is high enough to support substantial diatom growth. Finally, episodic mixing events during bloom development can double the availability of nitrate in the east. The fact that deposition rates of organic carbon in sediments have been stable over the past few centuries (Hamel et al., 2002) suggests that the unique combination of factors leading to high productivity in the North Water is persistent on long time scales. In the east primary production is 377 g C m−2 y−1 , of which 254 g occurs in particulate form (hereafter “phytoplankton production”) (Klein et al., 2002). The latter ranges from 5.4 to 5270 mg C m−2 d−1 at individual locations. Roughly 45% of the total production occurs during the 43-day build-up of the main diatom bloom. The associated net deficit of nitrate (890 mmol m−2 ) is equivalent to a cumulative new production of 80 g C m−2 (molar C : N ratio of 7.5; Tremblay et al., 2002b). An independent estimate based on the product of phytoplankton production and the f -ratio (in vitro nitrate uptake/uptake of nitrate + ammonium = 0.69) yields 86 g C m−2 . Uptake data are thus adequate for estimating new production beyond the point of net nitrate loss. Using data from 1998 (Tremblay et al., 2006a) and 1999 (Garneau, pers. comm.), annual new phytoplankton production is in the order of 100 g C m−2 and the overall f -ratio (that for the entire growing season) equals 0.39. Based on the silicate deficit and the build up of particulate organic carbon (POC) and biogenic silica (BioSi), roughly 80% of the new production was mediated by diatoms (Tremblay et al., 2002b). The highest vertical flux of biogenic particles out of the upper 50 m occurs soon after bloom’s apogee, with the maximum for biogenic silica slightly preceding the maximum for POC (Figure 3A). At the time of peak sedimentation (early June) most of the sinking material consists of intact diatoms (Caron et al., 2004). The contribution of fecal pellets increases later in the season but does not account for a large part of the total flux. The cumulative flux during the open-water period amounts to 27% of the production of phytoplankton. Bottommoored particle-interceptor traps deployed at 200 m (Hargrave et al., 2002) record much lower fluxes, with annual values ranging from 2.6 to 6.7 g C m−2 or at most 3% of annual phytoplankton production (Figure 3B). The 200 m POC flux shows a bi-modal distribution in which the main peak lags a month behind the maximum flux at 50 m and the secondary
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Figure 3: The temporal pattern of vertical flux in the eastern sector of the North Water Polynya during 1998–99. (A) Fluxes of particulate organic carbon (POC) and biogenic silica (BioSi) at 50 m (short-term, drifting traps), and (B) POC flux and the POC : Chl ratio of settling particles at 200 m (bottom-moored traps). Modified from Tremblay et al. (2006b) and Hargrave et al. (2002). Also plotted is the chlorophyll a from Figure 2A.
peak coincides with the fall bloom. Reduced POC : pigment ratios of the sinking material during the two events indicate an elevated contribution of algal material. The modest overall vertical export in the eastern North Water has been attributed to low settling velocities (Mei et al., 2003; Caron et al., 2004), zooplankton grazing and DOC production (Tremblay et al., 2006b). Most of the POC synthesized by phytoplankton is transformed and retained in the upper 50 m, and 77% of the small quantity that escapes this layer is remineralized before it reaches 200 m. This observation is consistent with the low rates of organic C accumulation in sediments (range 1.1–1.5 g C m−2 y−1 ; Hamel et al., 2002), which represents at most 1% of phytoplankton production in surface waters. In the west annual phytoplankton production (76 g C m−2 y−1 ) and new production (55 g C m−2 y−1 ) are moderate (Klein et al., 2002; Tremblay et al., 2002b). A substantial
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part of this production occurs in May and early June when Arctic waters transit southward through Smith Sound. Some of the production is presumably advected under consolidated ice in the southwest and not available for grazers and export to deep waters within the polynya. Nevertheless, traps moored at 260 m recorded the highest vertical fluxes (mean 9.9 g C m−2 y−1 ; Hargrave et al., 2002) in the North Water (not shown), corresponding to 13 and 18% of total and new production. Elevated sinking export relative to the east is probably caused by the interception of particles produced upstream, possibly as far as Kane Basin, where late-summer blooms occur after the North Water ceases to be a polynya strictu senso (Bélanger, 2001). Since the mean southward velocity of Arctic waters entering Smith sound is generally in excess of 20 km d−1 , sinking particles can travel long horizontal distances before reaching 260 m. 2.2
Northeast Water Polynya
The Northeast Water was a mid-size, latent-heat polynya (max ca. 44,000 km2 ) located off the northeastern coast of Greenland. Reduced ice cover was made possible by the sheltering effect of ice shelves that prevented ice from entering the polynya (Barber and Massom, 2007). During winter southeasterly winds forced forming ice away from the Ob’ Bank ice shelf (if present) and generated open waters in the North (Minnett et al., 1997). In May spring insolation slowed ice formation, the prevalent anticyclonic gyre carried existing ice away from the Norske Øer ice shelf, and the polynya started growing from the South in early June. The highest incidence of open waters occurred in late July when solar radiation has melted residual ice. The polynya closed again in September. As the ice bridge disappeared late in the 1990’s, the Northeast Water polynya no longer forms. 2.2.1
Temporal Patterns and Assemblage Composition
The complex circulation and ice field in the Northeast Water region makes it difficult to establish a definitive time series. Nevertheless, initial chlorophyll a levels were low and did not increase above 1 μg l−1 before mid-June, when the upper water column begins to strongly stratify. Maximum chlorophyll a generally did not exceed 2 μg l−1 , but unusual values of up to 7–9 μg l−1 have been reported (Smith et al., 1997). Given the low initial concentrations of nitrate (ca. 5 μM), these anomalous standing stocks were possibly linked to localized events of upward nitrate diffusion (Touratier et al., 2000). After mid-July nitrate was generally exhausted in the upper mixed layer and chlorophyll a concentrations and chlorophyll-specific productivity decreased progressively (Smith, 1995). At high chlorophyll a concentrations the biomass of autotrophs was strongly dominated by C. socialis, with secondary contributions by Fragilariopsis spp. and Thaliassosira spp. (Booth and Smith, 1997). Autotrophic flagellates on average made up 23% of the autotrophic carbon, and haptophytes (e.g., Phaeocystis pouchetii) made substantial contributions to flagellate biomass away from the East Greenland Current. 2.2.2
Magnitude and Fate of Primary Production
There are no definitive estimates of annual phytoplankton production for the Northeast Water, but an approximation can be made from available measurements. Mean primary production was ca. 0.21 g C m−2 d−1 in 1992 (18 July–18 August; Smith, 1995) and the median value for 1993 was 0.22 (23 May–17 August; Pesant et al., 1997, 2000). Daily rates ranged from 10 to 1733 mg C m−2 d−1 at individual stations. Extrapolating the 1993 median
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to the open-water period (ca. 120 days) yields an annual phytoplankton production of ca. 66 g C m−2 y−1 (see Klein et al., 2002) and a new production of ca. 43 g C m−2 y−1 using a mean f -ratio of 0.65 (Smith et al., 1997). Bottom-moored traps located at 130 m recorded an annual POC flux of 1.03 g C m−2 y−1 (site “F” in Bauerfeind et al., 1997), or ca. 2 and 3% of total and new phytoplankton production, respectively. Although elevated vertical fluxes were recorded during the polynya’s opening in 1993, most of the annual flux occurred in August and September 1992. At this time, the bulk of the flux was mediated by appendicularian houses and fecal pellets, with diatoms representing only 10% of the total. Although the contribution of diatoms was higher at the time of opening, it consisted mostly of Melosira arctica, a filament-forming species released from the ice (and whose production is largely unaccounted for in the above estimates). During June and July, episodic sedimentation of pelagic diatoms was associated with weak wind pulses over the polynya (Pesant et al., 2002). A budget for phytoplankton >5 μm (mostly diatoms) in surface waters suggests that during the productive period (July– August), 32 to 67% of the production either sank or was advected east in the anticyclonic gyre (Pesant et al., 1997). Given the low sinking flux within the polynya, the main export pathway was presumably lateral transport and subsequent sinking east of the polynya. The remaining production was consumed in situ by copepods, appendicularians and heterotrophic dinoflagellates. Grazing by appendicularians seems to have led to significant vertical export, whereas that by dinoflagellates and copepods promoted recycling and remineralization within the surface layer. 2.3
Cape Bathurst Polynya
The Cape Bathurst Polynya is relatively small, with a maximum extent of open waters of ca. 24,000 km2 (Barber and Massom, 2007). Remotely-sensed ice cover and chlorophyll a for the period 1998–2002 shows substantial interannual variability (Arrigo and van Dijken, 2004a). The open water period and maximum chlorophyll a concentration (derived from SeaWiFs imagery) generally ranges from 3 to 5 months and ca. 3–5 μg l−1 , respectively, and blooms occur in June and in September (Arrigo and van Dijken, 2004a). The latter is generally the strongest, possibly due to enhanced mixing supply of nutrients combined with reduced grazing. Estimated annual phytoplankton production ranges from 90 to 175 g C m−2 y−1 . In 1998 an unusually early forming polynya was associated with a warmweather anomaly that presumably induced early ice melt and vertical stratification of the water column, and kept the waters ice-free for seven months (Arrigo and van Dijken, 2004a). This anomaly resulted in the generation of high chlorophyll a concentrations (8 μg l−1 ) in early May. 2.4
Other Arctic Polynyas
The Arctic Ocean is home to several other polynyas that are maintained by sensible heat (Whaler’s Bay Polynya off Svalbard, several small polynyas in the Canadian Archipelago) and latent heat (Laptev Sea polynya and flaw lead system; Wrangel Island polynya). Published information on phytoplankton is too scant to establish clear patterns, but satellite imagery suggests that some of those are sites of enhanced phytoplankton production (e.g. Laptev Sea; Figure 1). In the Bering Sea wind-driven polynyas commonly form in the lee of coastlines. Limited information on phytoplankton is available for the shallow (60 m) St.-Lawrence Island Polynya (Stringer and Groves, 1991). The polynya is dynamic and
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ephemeral; its size, shape and location vary as a function of wind, and hence it can be located to the north of the island initially, but switch to the south of the island in a matter of days. The extent of the polynya is typically small from January to April (2000–4000 km2 ) and can reach a maximum of ca. 16,000 km2 in May. Integrated chlorophyll a concentrations in open waters range from 10–50 mg m−2 in April to 100–1000 mg m−2 in May–June (Cooper et al., 2002). Considering all sampling periods and stations, the mean concentration of chlorophyll a is only 0.60 ± 0.34 μg l−1 (max of 2.47 μg l−1 ), despite the high nutrient concentrations supplied by the Anadyr Current (Grebmeier and Cooper, 1995). It is likely that the local pelagic production is transported laterally before substantial biomass can accumulate within the polynya. Proxy indicators of sediment deposition and the stable isotopic signature of fresh organic deposits suggest that ice algae, rather than phytoplankton, sustain the local benthic production (Cooper et al., 2002). 2.5 2.5.1
Ross Sea Polynya Temporal Patterns and Assemblage Composition
The Ross Sea polynya, centered at ca. 77◦ 30 S, 174◦ E, is the Antarctic’s most thoroughly studied polynya, and the phytoplankton dynamics of the polynya are well characterized. It has characteristics of both latent and sensible heat polynyas, in that winds advect ice away from the ice shelf, but heat is also added through the movement of Modified Circumpolar Deep Water onto the shelf (Jacobs and Comiso, 1989). The growing season begins early, in late October or early November (Smith and Gordon, 1997), approximately six weeks earlier than the annual bloom in the Polar Front region some 2000 km to the north. During this time a thin layer of ice and relatively little snow, allowing sufficient irradiance to penetrate and drive photosynthesis, often covers the region. Initial phytoplankton biomass is low (less than 0.05 μg l−1 ; Smith et al., 2000b). The polynya is often initially dominated by the haptophyte Phaeocystis antarctica, which appears to be able to utilize low irradiances for its growth (Moisan and Mitchell, 1999), although some diatoms and autotrophic dinoflagellates grow as well. Few losses to the assemblage apparently occur during spring, as the chlorophyll concentrations continue to increase through mid to late December (DiTullio et al. (2000) found evidence of deep transport of active populations to depth, but the mechanism and quantitative significance of these events was not shown). Chlorophyll concentrations reach up to 15 μg l−1 , but more typically reach ca. 8 μg l−1 (Figure 4). During this period nitrate is reduced from 30 to around 15 μM, and silicic acid from 80 to 72 μM. Growth normally proceeds much more slowly thereafter, and it appears that the reason for this decrease is the onset of iron limitation (Sedwick and DiTullio, 1997; Olson et al., 2000; Sedwick et al., 2000; Arrigo et al., 2003). At the same time losses of P. antarctica from the euphotic zone become more rapid and are likely due to the aggregation of colonies (enhanced by the high biomass conditions) and more rapid sinking of these larger particles (Asper and Smith, 2003). As a result, the chlorophyll decreases rapidly in January (Arrigo and McClain, 1994; Smith and Gordon, 1997; Smith and Asper, 2001; Figure 4), so that by the end of the month chlorophyll concentrations are 1.0 μg l−1 or less (Smith et al., 2000a). Little accumulation normally occurs in February, and by the end of the month ice forms, vertical mixing increases and the water column returns to winter conditions. This transition likely takes a number of months, but phytoplankton standing stocks are less than 0.1 μg l−1 by the beginning of May (Smith et al., 2000b). Smith et al. (2000b) generated a seasonal composite by merging data from two field seasons, and the patterns found appeared to explain the temporal progression well. However,
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Figure 4: The temporal patterns of clear-sky incident irradiance (E0 ), nitrate (NO3 ), silicic acid (Si(OH)4 ), and chlorophyll (chl) within the Ross Sea polynya. Based on the climatology of Smith et al. (2003) 77◦ S, 173◦ E. estimates of inter-annual variability have since been made by remote-sensing (Arrigo and van Dijken, 2004b) and by collecting data on net seasonal production and the temporal patterns of phytoplankton biomass and composition. Variations among years are substantial and were partly related to the impact of El Niño and an exceptionally large iceberg on ice dynamics (Arrigo and van Dijken, 2004b). Variations in the composition of the phytoplankton were also observed, as well as the temporal patterns of assemblage change (Smith et al., 2006). For example, in 2001–2 the production was less than the climatological mean (Smith et al., 2003), but in 2003–4 it was greater. In the former year the initial growth was dominated by Phaeocystis antarctica, as it was during the studies of Arrigo et al. (1999) and Smith et al. (2000a). However, in 2003–4 diatoms dominated the assemblage throughout the year. Thus the assemblage composition is not as predictable as often believed. Furthermore, Smith et al. (2006) suggest that the factors limiting growth also vary among years, and that no iron limitation occurred during 2003–4. The release from iron limitation during this year was, however, unexplained, although it may be related to the extent (both in space and time) of inputs from modified Circumpolar Deep Water (Hiscock et al., in press). 2.5.2
The Role of Iron
In January after the “normal” bloom of P. antarctica, diatom growth continues, with the rate likely controlled by iron availability. Diatoms are often found in mixed layers that are shallower than those in which P. antarctica assemblages are found (Arrigo et al., 1999; Smith
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Figure 5: The temporal patterns of in vivo fluorescence (an index of chlorophyll) from one location in the Ross Sea polynya (77◦ S, 173◦ E). Lines represent a 50-point running average for three depths (19, 25 and 41 m). The initial maximum is caused by the early growth of Phaeocystis antarctica within the polynya, and the second maxima is caused by an intense diatom bloom (Smith et al., 2006). and Asper, 2001), although there is no significant difference between the photosynthetic capacity of the two groups (van Hilst and Smith, 2002). They are thus exposed to higher daily irradiance and have greatly restricted iron inputs from below. Olson et al. (2000) found that photosynthesis in all species tested (a number of diatoms and P. antarctica) were equally limited by iron during late January. In other years a bloom of diatoms followed that of P. antarctica; furthermore, this secondary bloom can be nearly of the same magnitude as the P. antarctica bloom (Figure 5), which suggests that iron limitation was not strong during that particular year. Coale et al. (2003) found that colonial P. antarctica had higher iron demands than did diatoms and all other species tested, which is consistent with the early bloom of P. antarctica relative to diatoms. In addition, in January the mixed layer continues to become shallower, and when P. antarctica biomass declines, more radiation penetrates and is available to diatoms. It should be noted that the only report of macronutrient depletion in the Ross Sea was from a region near the Victoria Land coast (Smith and Nelson, 1985) in waters that had extremely strong stratification and were potentially influenced by glacial run-off. Thus the waters potentially were enriched with iron, and the strong stratification and high irradiances allowed the diatoms to accumulate and deplete both nitrate and phosphate, and reduced silicic acid to ca. 4 μM. The variations in iron inputs (as a function of melt water input either from land or ice) may play a role in the ultimate contribution of diatoms to the Ross Sea phytoplankton. Iron
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can be supplied from aerosols, ice, deeper water and sediments, but to date no clear understanding of the relative role of each is available. Atmospheric deposition is known to be extremely low in the Antarctic, particularly farther south (Fung et al., 2000), and so aerosols as a source of iron are likely insignificant relative to inputs from the water column. However, the degree of deposition on ice at low rates throughout winter and its subsequent release into the water upon melting is uncertain, and may depend on latitude. Sedimentary sources of iron would be dependent on depth and vertical circulation, because many polynyas have extensive stratification induced by melt-water, which is not easily broken down by winds, and in a similar manner, deep-water supply of iron to the surface may be restricted in summer months. Hence, given the restricted inputs to the surface, iron can and does become limiting to phytoplankton growth in the summer in some Antarctic polynyas. In the Southern Ocean iron input from the atmosphere is extremely low (Fung et al., 2000), as most of the continent is ice-covered. Iron concentrations in the Ross Sea polynya are initially elevated and above saturation concentrations (Fitzwater et al., 2000; Coale et al., 2003), but fall to levels low enough to limit phytoplankton growth. Hiscock et al. (in press) suggest that deep-water intrusions of modified Antarctic Deep Water onto the shelf result in increased levels of iron at the surface, and therefore stimulate both the magnitude and duration of phytoplankton growth in the region. Data from other cruises support this hypothesis (Smith et al., unpublished). It is interesting that off-shelf intrusions of modified ACC water stimulate diatom growth on the continental shelf of the west Antarctic Peninsula as well (although P. antarctica exists, it is largely confined to regions close to the coast; Prézelin et al., 2000), and we suggest that this is due to iron stimulation of diatom production. A novel mechanism for the control of phytoplankton assemblage composition has been proposed by Tortell et al. (2002), and this mechanism may be operative in the Ross Sea. They found in shipboard enrichments in tropical waters that under high iron, high pCO2 conditions, Phaeocystis spp. dominated the growth of the phytoplankton, whereas under reduced iron and pCO2 conditions, diatoms grew and dominated. In the Ross Sea Fe and pCO2 covary; that is, early in the spring, the water column is characterized by high iron and pCO2 , where the opposite conditions occur in summer (when diatoms are more commonly encountered). It is possible that this type of Fe–CO2 interaction occurs in the Ross Sea and influences assemblage composition, but experimental proof is unavailable. 2.5.3
Magnitude and Fate of Primary Production
The annual productivity of the Ross Sea polynya has been estimated using a number of techniques. Arrigo and van Dijken (2003) used pigment data derived from satellite imagery along with a simple productivity model and estimated that the annual productivity of the polynya (1997–2002) was 151 ± 21 g C m−2 y−1 . Nelson et al. (1996), based on temporal patterns of phytoplankton accumulation, suggested that productivity was ca. 112 g C m−2 y−1 , and Smith and Gordon (1997) used data collected in the austral spring to update the estimate of Nelson et al. to about 190 g C m−2 y−1 . Arrigo and van Dijken (2003) used a bio-optical model and concluded that the polynya’s annual productivity was 149 g C m−2 y−1 . Regardless of the estimate, the Ross Sea polynya is the most productive location within the entire Antarctic. Using nutrient deficits derived from the nitrate climatology of Smith et al. (2003), net community production for the polynya was estimated to be 1.21 mmol N m−2 d−1 over the entire growing season (Table 2), which is equivalent to 1.50 g C m−2 d−1 . Nitrate is the dominant nitrogen source during the hyperproductive periods, and ammonium is utilized more
Month
NO3 (mol m−2 )
October November December January February
6.23 6.04 5.38 4.90 5.02
Si(OH)4 (mol m−2 )
15.8 15.7 15.4 14.5 14.4
NO3 a (mol m−2 )
Si(OH)4 a (mol m−2 )
– 0.19 ± 0.01 0.85 ± 0.11 1.33 ± 0.13 1.21 ± 0.08
– 0.16 ± 0.07 0.46 ± 0.03 1.32 ± 0.68 1.43 ± 0.27
N-based primary productivityb (g C m−2 d−1 ) – 0.51 ± 0.02 2.20 ± 0.40 2.38 ± 0.35 0.02 ± 0.04c
Diatom primary productivityb (g C m−2 d−1 ) – 0.10 ± 0.04 0.19 ± 0.03 0.56 ± 0.43 0.16 ± 0.18c
Percentage of productivity by diatoms (%) – 20.1 8.8 23.4 1068
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Table 2: Climatological estimates of nutrient deficits from the Ross Sea polynya. To estimate primary production from nitrate disappearance, a C/N molar ratio of 6.2 was used (Shields et al., unpublished), and an f -ratio of 0.9, 0.75, 0.5 and 0.5 for October, November, December and January, respectively. To estimate diatom production, a BioSi/C ratio of 0.62 was used (Nelson and Smith, 1986)
a Cumulative removal calculated from October. b Monthly means calculated from nutrient removal over 30 days. c Negative values excluded from means.
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during January and February. Using those climatological estimates, primary productivity equals 153 g C m−2 y−1 (Table 2). These estimates are similar to those of Arrigo and van Dijken (2003), who estimated January and annual productivity to be 1.46 g C m−2 d−1 and 151 g C m−2 y−1 (in contrast to our estimates of 2.20 g C m−2 d−1 and 153 g C m−2 y−1 ). The slightly lower short-term estimates of Arrigo and van Dijken (2003) likely result from the inability of the remote-sensing approach to account for biological losses of pigments within the water column, but the annual estimates are nearly equal due to the longer growing season observed during the year analyzed by Arrigo and van Dijken (2003). In a similar fashion, the productivity of diatoms can be estimated from silicic acid uptake (Table 2). Diatom carbon productivity (converted by the ratio measured by Nelson and Smith (1986) during a nearly monospecific diatom bloom off the coast of Victoria Land) was a relatively small fraction of the total productivity (19.8% on an annual basis). This is an underestimate, as the nitrate concentrations increased slightly during February (likely due to the increased frequency of storms and vertical-mixing), although silicic acid concentrations decreased. This suggests that Si uptake was in fact greater than the nutrient budgets would indicate, but regardless, diatom contribution to the annual production is relatively minor. It also should be noted that in February the relative productivity of diatoms to the total is extreme (more than 1000%), but this is likely a result of the modification of the Si : N uptake ratio under iron limitation as well as the small and variable magnitude of nitrogen uptake. The temporal nature of nutrient removal shows that in the Ross Sea polynya normally there is a temporal separation of the growth of diatoms and P. antarctica (Figure 4). Phaeocystis growth begins in spring, and its biomass reaches a maximum in December; indeed, a majority of the productivity during this time can be attributed to P. antarctica (up to 94% in December). However, its biomass and productivity declines rapidly thereafter (most likely due to iron limitation), and by mid-February it has little net effect on nitrate removal. In contrast, diatoms continue to grow and remove silica (and nitrogen), and in February nearly all of the productivity can be attributed to diatoms (Table 2). During 2003–4 a detailed analysis of fluorescence at a single location within the polynya confirms this pattern of spring P. antarctica growth—a primary biomass maximum in December, a decline in pigments, and a secondary maximum that results from diatomaceous growth (Figure 5; Smith et al., 2006). The patterns of vertical export from the surface layer of the Ross Sea have been investigated through the use of time-series sediment traps. Because the number of traps that can be deployed during any one season is limited, flux data are often limited in space. However, the flux from the Ross Sea polynya can be characterized from the data of Dunbar et al. (1998), Collier et al. (2000), Accornero and Gowing (2003), and Langone et al. (2003). Silica fluxes are maximal in late summer, which appear related to the initiation of ice production and increased sinking rates of metabolically stressed diatoms, as well as the grazing on diatoms by mesozooplankton (Figure 6). Carbon fluxes, however, are greatly delayed in time, with the maximum occurring in April–May, well into the austral winter. This peak is due to organic flux mediated by pteropods. While the biology of these groups (Limacina sp. and Cliona sp.) has not been well studied in the Antarctic, pteropods contribute to the fluxes in nearly all sediment trap measurements. It is controversial as to whether these should be included as a “real” flux or a migratory contribution, but in the Ross Sea Collier et al. (2000) argued that the pteropods were largely undergoing disintegration, suggesting that their collection was a result of death within the water column and subsequent sinking. Pteropods are also frequently found in other traps in the Ross Sea, so the event reported by Collier et al. (2000)
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Figure 6: The temporal patterns of vertical flux of biogenic silica (BSi) and particulate organic carbon (POC) from within the Ross Sea polynya. Data taken from Collier et al. (2000) and Langone et al. (2003) and a simple arithmetic mean generated for this one season (1996–7). Also plotted is the climatological chlorophyll a (chl) concentration from Smith et al. (2003). is not simply an isolated occurrence, but likely is a feature of the biological dynamics of the polynya. Removal of the pteropod fraction results in a four-fold reduction in flux (Collier et al., 2000). Both biogenic fluxes are temporally decoupled from surface production. The annual POC flux at 200 m in the central Ross Sea, including the contribution of pteropod remains in the traps, is in the order of 6 g C m−2 y−1 (Collier et al., 2000), which is ca. 4% of the annual surface production. 2.6
Mertz Glacier Polynya
The Mertz Glacier polynya is not as well characterized as the Ross Sea Polynya, but is apparently quite different with regard to many of its biological features. It is also characteristic of a large number of polynyas in this area of the Antarctic; it has been estimated that there are 28 recurrent polynyas along the east Antarctic coast, covering an area of 169,000 km2 during winter (Massom and Comiso, 1994). The region apparently is opened largely by the strong katabatic winds flowing off the coast of East Antarctica and funneled by the shore’s topography, but also may have some heat brought into the region by the upwelling of Modified Circumpolar Deep Water from off the shelf (Sambrotto et al., 2003). Macronutrients were always elevated ([NO3 ] > 22 μM, [Si(OH)4 ] > 58 μM) and above saturation levels, and iron concentrations ranged from 0.1–0.2 nM (Sambrotto et al., 2003), with the lowest levels coinciding with regions of the greatest nitrate removal. Chlorophyll concentrations in the polynya generally were 2.5 μg l−1 or below, and diatoms dominated these areas. Studies on the physiological state of phytoplankton have also been conducted in this polynya (Vaillancourt et al., 2003). Observed Fv /Fm ratios (proxy for the maximum quantum yield of photosystem II) of ca. 0.45 indicated that the polynya’s phytoplankton were not limited by nutrients and were
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physiologically robust, although the surface assemblage did exhibit strong photoinhibition that could be relieved by short (hours) periods of reduced light. Unfortunately, the taxonomic dominance of the assemblage was not determined at this time. 2.7
Terra Nova Bay Polynya
The Terra Nova Bay polynya is a rather small polynya, but its significance lies in the fact that it exhibits distinct responses that are different from those of the Ross Sea. Furthermore, it has a deep-water connection (ca. 1000 m) that apparently can act as a conduit for Antarctic Deep Water release from the continental shelf into the deeper waters of the Pacific Sector. It is forced by strong katabatic winds that blow from the west, and maintain a polynya of variable size through much of the year (van Woert, 1999). Its nutrient structure is similar to that of the Ross Sea, with summer nitrate concentrations around 15 μM (Catalano et al., 1999). Arrigo and McClain (1994) used surface chlorophyll a concentrations estimated from the Coastal Zone Color Scanner (CZCS) to show the marked difference in temporal bloom dynamics between the Ross Sea and Terra Nova Bay polynya (TNB). They found that the phytoplankton bloom in the TNB was at least a month later than that in the RS polynya, and suggested that this was due to deeper vertical mixing driven by strong katabatic winds. However, Nuccio et al. (1999) and Fonda Umani et al. (2002) compiled cell count data from TNB and showed that phytoplankton abundance was maximal in late December (dominated by P. antarctica), declined thereafter, but exhibited a secondary peak in mid-February. The latter feature may have been similar to that observed by Arrigo and McClain (1994), but no satellite images of the region earlier in the year were available to observe the primary cell abundance maximum. Based on a single survey, Arrigo et al. (1999) suggested that TNB was also the site of large diatom blooms. While it no doubt is the site of frequent diatom accumulations (the sediments there are diatomaceous oozes), other species have been observed, including Phaeocystis antarctica, dinoflagellates, and other phytoflagellates (Innamorati et al., 1992; Nuccio et al., 1999; DiTullio et al., 2000; Fonda Umani et al., 2002).
3
Ecological Consequences of Polynya Production
In the far northern and southern polynyas, it might be expected that the enhanced production would generate a cascade of increased production throughout the entire food web, terminating with increased biomass of higher trophic levels, as has been proposed for large oceanic regions (e.g., Ryther, 1969; Nixon, 1982; Pauly and Christensen, 1995). In the Antarctic Arrigo and van Dijken (2003) found a positive relationship between penguin colony size and the productivity of polynyas. For other animals, however, the relationship may not always be positive, as some large bodies of water fail to show an increase in higher trophic level productivity with increased productivity as a result of changing composition of various trophic levels. In addition, polynyas may have a higher trophic level fauna that is restricted in time and space due to their life histories, and many species of zooplankton may be unable to exploit very early and fast-growing blooms. Hence any increased productivity may not be reflected in the yield of higher trophic levels. In this case, a large fraction of the primary production is expected to sink to deep waters, which would result in a relatively efficient biological CO2 pump and benthic–pelagic coupling. Given the well-defined spatial relationships of productivity within some polynyas, the available data provide some insights into this fundamental oceanographic relationship.
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North Water Polynya
The congruence of high primary production and abundant planktivorous birds and marine mammals in the North Water Polynya has long been recognized by explorers and, more recently, confirmed by the scientific community (Brown and Nettleship, 1981; Stirling et al., 1981; Stirling, 1997). While this observation led to the hypothesis of a causal link between high primary production and the apparent success of the herbivorous food web (Dunbar, 1981), the possibility exists that mammals and birds utilize the polynya also to gain access to open waters. The high incidence of accessible water facilitates breathing and foraging and the greater productivity perhaps is only a fringe benefit. Ice entrapment is a significant cause of mortality for cetaceans in Baffin Bay (Heide-Joergensen et al., 2002) and they may use the North Water as a refuge. Satellite-tracking investigations show that many belugas overwinter in the polynya, but move south and west during summer to feed in seasonally ice-free bays (Richard et al., 2001), as presumably do walruses, narwals and bowhead whales (Richard et al., 1998). Although this pattern questions the causal link between enhanced productivity and the aggregation of marine mammals in the North Water, the ability to forage early in the year within the polynya possibly has a positive impact on fitness and survival. Moreover, the linear enrichment of 15 N across trophic levels is consistent with a full-fledged herbivorous food web based on local phytoplankton production (Hobson et al., 2002). In contrast, key herbivorous copepods and planktivorous birds are clearly synchronized with the early phytoplankton outburst. The recruitment of the herbivore Calanus hyperboreus tracks phytoplankton blooms in the Arctic and occurs two month earlier in the North Water than in adjacent seasonally ice-covered regions (Ringuette et al., 2002). Such flexibility is possible because the females spawn at depth during winter and the new cohort can react opportunistically to a bloom. In other smaller species (e.g. Oithona similis), egg development strongly depends on temperature and is delayed so that first-feeding nauplii may not take advantage of the main bloom (Ringuette et al., 2002; Deibel and Daly, 2007). The relatively low biomass of zooplankton relative to maximum chlorophyll a concentrations in the North Water (Saunders et al., 2003) suggests that planktonic carnivores and birds regulate their biomass (Tremblay et al., 2006b). The most abundant planktivorous bird (dovekie; Alle alle) breeds on the steep slopes of the adjacent Greenland Coast (Boertman and Mosbech, 1998) and migrates south for the winter (Stenhouse and Montevecchi, 1996). During May 1998, the vast majority of foraging dovekies (>30 million birds) were observed in the eastern sector when the diatom bloom was well underway (Karnovsky and Hunt, 2002). At this time very few birds were seen in the west over waters with low chlorophyll a. The highest numbers of foraging birds in the east coincided with the maximum abundance of their primary prey, the copepods C. hyperboreus and C. glacialis. Feeding forays eventually encompass the northern and western North Water, tracking productive waters after the demise of the bloom in the east. 3.2
St. Lawrence Island Polynya
It appears that much of the production in the St. Lawrence Island polynya is partitioned to the benthos, although substantial bird populations do exist in the region. Maps of the distributions of benthic organisms and biomass, however, do not show a clear relationship to the average position (and hence production) of the polynya (Grebmeier and Cooper, 1995), despite the fact that benthic biota often show a positive relationship to primary productivity (Grebmeier and Barry, 1991). From this discrepancy we suggest that the production pulse is too short and not spatially constrained to produce a significant impact within the polynya’s
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benthos and/or that the pulse is not great enough (relative to the large seasonal production of the Bering shelf) to be of ecological impact. Additionally, the birds may forage over distances that are greater than those of the polynya’s extent, thereby obscuring a relationship of polynya production and higher trophic level yield. 3.3
Ross Sea Polynya
As previously noted, the Ross Sea polynya is the locus for the Antarctic’s largest primary productivity in the Southern Ocean, with an annual production of 150 to 200 g C m−2 . Much of this production occurs in November and December and is concentrated in the south-central region (Arrigo et al., 1999). The distribution of planktivorous birds (Ainley and Jacobs, 1981) was shown to be a maximum at the continental shelf break and north of Ross Island. Interestingly, the minimum bird biomass (≤0.004 g wet weight m−2 ) was observed north of the Ross Ice Shelf and roughly parallel to the spatial dimension of the Ross Sea polynya in early December. The relationship between production (as determined by the climatological net community production averaged over the same spatial scales of the higher trophic level biomass—a 2◦ grid resolution) and bird biomass was negative, and the relationship with whale biomass was insignificant (the trend was also negative). When a similar analysis was completed for whale biomass and penguin biomass, a similar negative relationship was observed. While we do not suggest that this is a causal relationship, it does seem to indicate that the enhanced production is not transferred into the food web. There might be multiple reasons for this negative relationship. The first might be that the polynya’s large production is dominated by the colonial form of Phaeocystis antarctica, and mesozooplankton may not be capable of directly ingesting particles of this size and quality. Hence the biomass of P. antarctica may be partitioned between the water column (i.e., in situ microbial regeneration) and the benthos, where it is utilized after microbial colonization and degradation. The ecosystem model of Tagliabue and Arrigo (2003), which explicitly models interactions between phytoplankton and zooplankton, indicates that zooplankton may be unable to track the high growth rates of P. antarctica. Kemp et al. (2001) used a generic model of a plankton system and found that trophic efficiency (a measure that is related to yield at the highest trophic level) decreased with increasing nutrient loading, and suggested this was due to a saturation of the ability of zooplankton to utilize high standing stocks of phytoplankton. A further effect of this inability was to shunt much of the phytoplankton into the microbial food web where the material was oxidized (Kemp et al., 2001). A second potential explanation for the lack of a positive relationship between production and bird biomass is that the birds depend more of ice-related production than on that of the water column. Ice-algal production supports a separate and largely independent faunal community that is available to pelagic birds, and the regions with high bird biomass experience greater periods of ice cover than in the polynya. A third explanation might be that the continental shelf break is the sight of a major oceanographic front whose circulation influences the mesoscale distribution of prey for birds and whales. Additionally, whales forage over long distances, and so the “instantaneous” biomass distribution of such large animals may not reflect seasonal or shorter-term variations in phytoplankton. Finally, it should be noted that the bird distributions were assessed in January–February, and the distributions may reflect recent oceanographic and ice conditions rather than the influence of seasonal production. Whether a similar relationship would be found earlier in the season remains uncertain.
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4 Comparison of Arctic and Antarctic Polynyas As with many comparisons of biological processes between the Arctic and Antarctic, the observed differences often appear to be the result of large-scale differences in the physical forcing, and in the case of polynyas, this again seems to be the case. Antarctic polynyas are always characterized by elevated macronutrient concentrations (generated by the large-scale Antarctic divergence around the continent) that are rarely depleted to levels that might limit phytoplankton growth. In contrast, Arctic polynyas are much more heterogeneous with regard to nutrient levels, which can range from high in those influenced by Atlantic and Bering Sea waters to low levels in those influenced by Polar Surface Water. Seasonal productivity in these latter polynyas must remain low in the absence of wind-driven upwelling or erosion of the upper halocline. Antarctic polynyas also may exhibit signs of iron limitation (e.g., Ross Sea polynya; Sedwick and DiTullio, 1997; Olson et al., 2000), but Arctic polynyas would not be expected to do so, largely based on the shallow nature of the continental shelf (and hence to a source of iron from sediments) and the rates of atmospheric inputs (Duce and Tindale, 1991; Fung et al., 2000). The depth of mixing is also different between the two systems, as mixed layer depths (and nutrient supply) during winter are much deeper in the Antarctic than in the Arctic, where mixing is usually restricted by the presence of a permanent halocline (Muench, 1990). Riverine inflow in the Arctic is far greater than in the Antarctic, where the amount of glacial iron input is low, except in localized situations. Primary productivity, both on a daily and seasonal basis, appears to be greatest in the North Water and Ross Sea polynyas. However, the factors that are responsible for the large productivity are quite different. The North Water polynya is highly productive due to the influence of nutrient-replete waters, episodic vertical mixing and the restriction of ice cover by the ice barrier at the inflow of Smith Sound. In contrast, the productivity in the Ross Sea polynya is greater than in other regions largely because of the duration of the growing season, which allows biomass to accumulate. Few losses due to herbivorous ingestion occur, and growth proceeds until limitation by iron takes place. Net seasonal production in both is similar. The temporal pattern of the North Water polynya is more similar to the temperate North Atlantic, with spring and autumn blooms, whereas a unimodal pulse of production normally characterizes that of the Ross Sea (Figure 4), although some years exhibit biomodal peaks (Figure 5). Our analysis also suggests that the supply of sensible heat leads to an exceptionally early growth season. The phytoplankton bloom in the eastern North Water peaked two months after the spring equinox, whereas the delay was three months in the Ross Sea and four months in the western North Water and Northeast Water polynyas. The Ross Sea and the North Water are also quite different with respect to temporal patterns of vertical flux (Figure 7) and food web structure. In the Ross Sea polynya vertical flux is a function of two independent processes. The first is the sinking of biogenic matter (diatoms and/or organic matter associated with P. antarctica) that had aggregated into larger, more rapidly sinking particles (Dunbar et al., 1998; Asper and Smith, 2003), which occurs at the end of the growing season (Figure 6). This signal is reflected in a maximum flux of biogenic silica. The second process is the large carbon flux associated with the demise of the pteropod populations, an event which occurs much later in the year and quantitatively dominates the annual flux. In the North Water, peak fluxes of carbon and biogenic silica occur simultaneously soon after the main diatom outburst and the secondary fall bloom (Figure 3). The contribution of zooplankton detritus to the carbon flux increases during summer, but its importance is much less than in the Ross Sea and a long-delayed flux maximum is not
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Figure 7: Comparison of vertical particulate organic carbon (POC) fluxes between the Ross Sea and the North Water. The time series begins on the 10th day of summer, following the demise of the phytoplankton bloom. observed. Nevertheless, the two polynyas share a similar flux pattern for biogenic silica, which is driven by the passive sinking of diatoms and aggregates. Food web utilization of biogenic matter in the two polynyas seems to vary markedly. In the North Water zooplankton feed on the diatom-based phytoplankton, and in turn are fed upon by upper trophic levels. The end result appears to be a rather linear food chain, with the enhanced productivity in the polynya being efficiently transferred and resulting in enhanced trophic level biomass. In contrast, the substantial standing stocks of pelagic birds, penguins, whales and seals found in the Ross Sea may not be directly utilizing the polynya-derived primary productivity, as the correlation between upper trophic level biomass and polynya production is extremely poor. Unfortunately, few polynyas other than the North Water and Ross Sea polynyas have been studied in enough detail to make meaningful comparisons among their food webs, temporal patterns, and controls of export. Due to increased availability of light, phytoplankton are clearly more productive in polynyas than adjacent waters as long as the seasonal ice cover persists. But do annual productivity and the pathways of organic carbon flow differ in polynyas? Once seasonally ice-covered water open up, they become potentially as productive as nearby polynyas have been earlier in the year, provided they share similar loadings of macronutrients and iron. Although the definitive test is not available, the key elements of physical forcing and timing discussed in this book and elsewhere imply that polynyas confer high productivity to a region. Firstly, it has been shown that a longer open water period leads to greater productivity in the Arctic Ocean (Rysgaard et al., 1999) because phytoplankton deplete the initial stock of nutrients and then make prolonged use of those supplied by recycling and added by episodic
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inputs (e.g., diffusion, tidal mixing). A similar case can be made for the accumulation of biomass in the Ross Sea and its dependence on iron supply. Secondly, early-opening waters are generally subjected to relatively strong wind-driven mixing during late-winter and early spring, which can favor nutrient renewal without bloom suppression when buoyancyrestoring forces (e.g. sensible heat) keep phytoplankton above the critical depth. This process is especially important in the Arctic where nutrients otherwise are largely restricted to depths below the permanent halocline. At the other extreme, summer blooms associated with seasonal ice melt typically experience limited nutrient renewal due to strong stratification. A late opening date also limits the time during which phytoplankton benefit from nutrient renewal via diffusion and tidal mixing. One exception to this pattern occurs along productive, retreating ice edges, of which polynyas are considered a special case. The amount of primary production transferred toward upper trophic levels depends on the ability of pelagic herbivores to graze the phytoplankton. This ability depends on the palatability of the dominant species and/or the temporal match between primary and secondary producers. In the Ross Sea the strong contribution of large Phaeocystis colonies to the bloom may impede grazing; conversely, an uncoupling between phytoplankton and grazers may be caused by the fast growth rates of Phaeocystis (Tagliabue and Arrigo, 2003) or the low abundance of poised grazers. By virtue of their weak stratification and deep mixed layers, early-opening latent-heat polynyas should favor dominance by Phaeocystis since it may photosynthesize more efficiently than diatoms under low light. In this view, Phaeocystis should bloom first in all polynyas. Observations in the North Water do not support this notion because substantial departures from background biomass are mediated by diatoms despite large east-west differences in the initial depth of the mixed layer. These diatoms sustain the local herbivorous food web owing to the presence of winter-spawning copepods that are able to exploit the bloom despite its precocious development.
5 Summary and Conclusions Despite the large data suite gathered from a number of polynyas, much remains to be understood. It is clear that Arctic and Antarctic polynyas are different, but it is far from clear why these differences exist. Examples include food web structure, patterns of vertical flux, and the timing and variations of the pulses of production. Polynyas are convenient regions to study, as they are by definition physically bounded systems, but in situ studies are often difficult due to the difficulty of gaining access to these ice-surrounded regions. Hence, further studies of polynyas are warranted in order to build a better understanding of their biological dynamics, their variability over longer time scales (years to decades) and the food webs within them. These efforts will allow to further refine proficient ecosystem models (e.g. Arrigo et al., 2003; Worthen and Arrigo, 2003). Polynyas, by virtue of their physical attributes, may be sensitive indicators of regional and/or global climate change. In a warming scenario, established recurring polynyas may open earlier or expand, while new ones may appear elsewhere as the heat balance shifts or the ice fragments. In view of our analysis it is likely that this would lead to greater primary production, especially if late-winter storms erode the Arctic halocline and inject nutrients into surface waters. At the same time, however, the peripheral ice margins retreat and polynyas may eventually loose their integrity earlier in the year. Other polynyas whose existence is contingent on the formation of an ice bridge (e.g. the North Water) may no longer exist. Rapid changes in sea ice have been observed in some regions of the Arctic during the past 25
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years (Johannessen et al., 1999; ACIA, 2005) as well as in the Antarctica peninsula region (Larson ice shelf; Kwok and Comiso, 2002). Indeed, changes in ice concentration appear to be extensive over the entire Arctic Ocean, and hence it can reasonably be assumed that individual polynyas will exist strictu senso for shorter periods despite a longer overall period of open water. The net result of these processes is difficult to predict, but possible consequences include shifting productivity, assemblage composition and vertical flux patterns (both in time and space). Arctic regions where polynyas occur may in fact become more temperate than polar in nature. Unfortunately, models do not yet address the physical-biological interactions under conditions of large-scale climate change. In the Antarctic only one area (the Antarctic Peninsula) is experiencing rapid change, as evidenced by the marked decreased in ice concentrations (Kwok and Comiso, 2002) and the dramatic break-up of ice shelves (e.g., the Larson Ice Shelf). This is not necessarily an indication of global climate change, as other parts of the Antarctic (such as the Ross Sea sector) presently are increasing in ice concentrations (Kwok and Comiso, 2002). However, any small, localized polynya in the Peninsula region would presumably be impacted in a manner similar to that of the Arctic: shortened period of integrity, decreased polynya-related production, and a shift to more open water conditions. In the Ross Sea it might be expected that the increased ice cover will restrict the spatial extent of polynya growth, but unless substantial reductions in the seasonal ice coverage occur, will not greatly restrict the annual production. However, because the stratification in the Ross Sea might be reduced under colder conditions (e.g., a mixed layer depth of 30 vs. 20 m), the phytoplankton may become dominated by Phaeocystis rather than diatoms (Arrigo et al., 1999; Smith et al., 2000b). Over longer time periods when stratification is expected to increase (Sarmiento et al., 1998), the assemblage composition may shift from Phaeocystis to diatoms. Changes such as these (Zmix depths) are quite subtle, and cannot be replicated within regional or large-scale models accurately, which in turn implies that many of the biological effects of regional or global climate change may not be predictable using today’s present models. Indeed, modeling the intra-Antarctic climate changes is challenging. Emerging paradigms on the ecology of the different bloom-forming taxa need to be refined, as they do not fully reconcile major observations. Based on reports that Phaeocystis photosynthesize very efficiently at low irradiance in the Arctic (Cota et al., 1994) and the Antarctic (Moisan and Mitchell, 1999), one might expect systematic, initial blooms of the former in early-opening polynyas. So why do diatoms dominate all bloom assemblages in Arctic polynyas studied so far despite large differences in their physical generation and mixing regimes? Why did Phaeocystis not bloom in the Ross Sea during 2003–4? These questions are crucial given the potential importance of species composition on trophic transfers and vertical fluxes. It could be that large changes in iron supply, top-down effects (e.g. differential grazing of microheterotrophs on solitary Phaeocystis cells), supply of seed stock from the ice, or nutrient ratios supersede the role of photosynthetic adaptations in influencing the success of different phytoplankton species. Another puzzling aspect of the carbon dynamics of polynyas is that despite large differences in physical forcing, taxonomic composition and temporal patterns, the efficiency of their biological pump is very low and similar. Estimates of the proportion of the annual particulate primary production intercepted by traps at 200 m are less than or equal to 3%. Even if the sinking fluxes were somewhat underestimated by advective losses or trapping biases, it is clear that most of the phytoplankton production is transformed and retained in surface waters, and that polynyas are not efficient vectors of particulate matter transfer to deep waters. These findings suggest that the positive relationship between pulsed diatom production
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at high latitudes and export efficiency (Buesseler, 1998; Ducklow et al., 2001) may not apply to polynyas. How the export efficiency of polynyas and polar systems in general respond to climate variability and change is presently unknown. Phytoplankton processes of polynyas could be monitored as indicators of large-scale change at the ecosystem level. Successful monitoring can be achieved with remote sensing in conjunction with in situ process studies and automated observatories. Pigments can be monitored from space, and so a remote and rapid tool for change is already available. Various aspects of phytoplankton ecology and their fate in the ecosystem can be monitored using moorings (e.g., physiological state, assemblage composition, nutrient concentrations, vertical distributions, sinking fluxes, etc.), and the data potentially relayed to a land-based location for analysis; hence, the “state of the polynya” can be remotely assessed and monitored over long time periods, thereby providing a record by which change can be measured. Comprehensive process studies, although limited for logistic reasons, will cement the different pieces together and lead to predictive models. Understanding these important systems is crucial as an oceanographic window into change, and it is a tremendous challenge to synthesize our knowledge and derive a means by which further large-scale changes can be recognized, quantified and predicted.
Acknowledgements This work was supported by a NCE grant (Arcticmet) to JET and NSF grants OPP-0087401 and OPP-0337247 to WOS. We thank Michael Dinniman for his help with the Ross Sea climatology, Jill Peloquin with Figure 1, and David Ainley for discussions of the fate of production in the Ross Sea polynya.
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Richard, P.R., Heide-Joergensen, M.P., Orr, J.R., Dietz, R., Smith, T.G., 2001. Summer and autumn movements and habitat use by belugas in the Canadian high arctic and adjacent areas. Arctic 54, 207–222. Ringuette, M., et al., 2002. Advanced recruitment and accelerated population development in Arctic calanoid copepods of the North Water. Deep-Sea Research II 49, 5081–5099. Rysgaard, S., Nielsen, T.G., Hansen, B.W., 1999. Seasonal variation in nutrients, pelagic primary production and grazing in a high-Arctic coastal marine ecosystem, Young Sound, northeast Greenland. Marine Ecology Progress Series 179, 13–25. Ryther, J.H., 1969. Photosynthesis and fish production in the sea. Science 166, 72–76. Sambrotto, R.N., et al., 2003. Summer plankton production and nutrient consumption patterns in the Mertz Glacier Region of East Antarctica. Deep-Sea Research II 50, 1393– 1414. Sarmiento, J.L., Hughes, T.M.C., Stouffer, R.J., Manabe, S., 1998. Simulated response of the ocean carbon cycle to anthropogenic climate warming. Nature 393, 245–249. Saunders, P., Deibel, D., Stevens, C., Rivkin, R., Lee, S., Klein, B., 2003. Copepod herbivory rate in a large Arctic polynya and its relationship to seasonal and spatial variation in copepod and phytoplankton biomass. Marine Ecology Progress Series 261, 183–199. Sedwick, P.N., DiTullio, G.R., 1997. Regulation of algal blooms in Antarctic shelf waters by the release of iron from melting sea ice. Geophysical Research Letters 24, 2515–2518. Sedwick, P.N., DiTullio, G.R., Mackey, D.J., 2000. Iron and manganese in the Ross Sea, Antarctica: Seasonal iron limitation in Antarctic shelf waters. Journal of Geophysical Research 105, 11321–11336. Smith, W.O. Jr., 1995. Primary productivity and new production in the Northeast Water (Greenland) Polynya during summer 1992. Journal of Geophysical Research 100, 4357– 4370. Smith, W.O. Jr., Nelson, D.M., 1985. Phytoplankton bloom produced by a receding ice edge in the Ross Sea: spatial coherence with the density field. Science 227, 163–166. Smith, W.O. Jr., Gordon, L.I., 1997. Hyperproductivity of the Ross Sea (Antarctica) polynya during austral spring. Geophysical Research Letters 24, 233–236. Smith, W.O. Jr., Sakshaug, E., 1990. Autotrophic processes in polar regions. In: Smith, W.O. Jr. (Ed.), Polar Oceanography, Part B. Academic Press, San Diego, pp. 477–525. Smith, W.O. Jr., Asper, V.L., 2001. The influence of phytoplankton assemblage composition on biogeochemical characteristics and cycles in the southern Ross Sea. Antarctica. DeepSea Research II 48, 137–161. Smith, W.O. Jr., Gosselin, M., Legendre, L., Wallace, D., Daly, K., Kattner, G., 1997. New production in the Northeast Water Polynya: 1993. Journal of Marine Systems 10, 199– 209. Smith, W.O. Jr., Anderson, R.F., Moore, J.K., Codispoti, L.A., Morrison, J.M., 2000a. The US Southern Ocean Joint Global Ocean Flux Study: an introduction to AESOPS. DeepSea Research II 47, 15–16. Smith, W.O. Jr., Marra, J., Hiscock, M.R., Barber, R.T., 2000b. The seasonal cycle of phytoplankton biomass and primary productivity in the Ross Sea, Antarctica. Deep-Sea Research II 47, 3119–3140. Smith, W.O. Jr., Dinniman, M.S., Klinck, J.M., Hoffman, E., 2003. Biogeochemical climatologies in the Ross Sea, Antarctica: seasonal patterns of nutrients and biomass. Deep-Sea Research II 50, 3083–3101. Smith, W.O. Jr., Catalano, G., Shields, A.R., Peloquin, J.A., Tozzi, S., Dinniman, M., Asper, V., 2006. Biogeochemical budgets in the Ross Sea: variations among years. Deep-Sea Research II 53, 815–833.
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Stenhouse, I.J., Montevecchi, W.A., 1996. Winter distribution and wrecks of Little Auks (Dovekies) Alle a. Alle in the Northwest Atlantic. Sula 10, 219–228. Stirling, I., 1997. The importance of polynyas, ice edges, and leads to marine mammals and birds. Journal of Marine Systems 10, 9–21. Stirling, I., Cleator, H., Smith, T.G., 1981. Marine mammals. In: Stirling, I., Cleator, H. (Eds.), Polynyas in the Canadian Arctic. Occasional paper 45, Ottawa, Canadian Wildlife Service, pp. 45–58. Stringer, W.J., Groves, J.E., 1991. Location and areal extent of polynyas in the Bering and Chukchi Seas. Arctic 44, 164–171. Tagliabue, A., Arrigo, K.R., 2003. Anomalously low zooplankton abundance in the Ross Sea: an alternative explanation. Limnology and Oceanography 48, 686–699. Tortell, P.D., DiTullio, G.R., Sigman, D.D., Morel, F.M.M., 2002. CO2 effects on taxonomic composition and nutrient uptake in an Equatorial Pacific phytoplankton assemblage. Marine Ecology Progress Series 236, 37–43. Touratier, F., Legendre, L., Vezina, A., 2000. Northeast Water Polynya 1993: Construction and modelling of a time series representative of the summer anticyclonic gyre pelagic ecosystem. Journal of Marine Systems 27, 1–3. Tremblay, J.E., Gratton, Y., Carmack, E.C., Payne, C.D., Price, N.M., 2002a. Impact of the large-scale Arctic circulation and the North Water Polynya on nutrient inventories in Baffin Bay. Journal of Geophysical Research 107, doi:10.1029/2000JC000595. Tremblay, J.E., Gratton, Y., Fauchot, J., Price, N.M., 2002b. Climatic and oceanic forcing of new, net and diatom production in the North Water Polynya. Deep-Sea Research II 49, 4927–4946. Tremblay, J.E., Michel, C., Hobson, K.A., Gosselin, M.G., Price, N.M., 2006a. Bloom dynamics in early-opening waters of the Arctic Ocean. Limnology and Oceanography 51, 900–912. Tremblay, J.E., Hattori, H., Michel, C., Ringuette, M., Mei, Z.-P., Lovejoy, C., Fortier, L., Hobson, K.A., Amiel, D., Cochran, K., 2006b. Trophic structure and pathways of biogenic carbon flow in the eastern North Water Polynya. Progress in Oceanography 71, 402–425. Vaillancourt, R.D., Sambrotto, R.N., Green, S., Matsuda, A., 2003. Phytoplankton biomass and photosynthetic competency in the summertime Mertz Glacier Region of East Antarctica. Deep-Sea Research II 50, 1415–1440. van Hilst, C.M., Smith, W.O. Jr., 2002. Photosynthesis/irradiance relationships in the Ross Sea, Antarctica and their control by phytoplankton assemblage composition and environmental factors. Marine Ecology Progress Series 226, 1–12. van Woert, M.L., 1999. Wintertime dynamics of the Terra Nova Bay polynya. Journal of Geophysical Research 104, 7753–7769. Williams, G.D., Bindoff, N.L., 2003. Wintertime oceanography of the Adelie Depression. Deep-Sea Research II 50, 1373–1392. Worthen, D.L., Arrigo, K.R., 2003. A coupled ocean-ecosystem model of the Ross Sea. Part 1: Interannual variability of primary production and phytoplankton community structure. In: DiTullio, G.R., Dunbar, R.B. (Eds.), Biogeochemistry of the Ross Sea. In: Antarctic Research Series, vol. 78. Washington, DC, pp. 93–105. Yang, J., Comiso, J., Walsh, D., Krishfield, R., Honjo, S., 2004. Storm-driven mixing and potential impact on the Arctic Ocean. Journal of Geophysical Research 109, doi:10.1029/2001JC001248.
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Chapter 9
Zooplankton Processes in Arctic and Antarctic Polynyas D. Deibel1 and K.L. Daly2 1 Ocean Sciences Centre and Department of Biology, Memorial University, St. John’s,
Newfoundland and Labrador, A1C 5S7 Canada 2 College of Marine Science, University of South Florida, 140 Seventh Ave. S., St. Petersburg, Florida, 33701 USA
Abstract There are various similarities and differences in zooplankton processes between Arctic Ocean (AO) and Southern Ocean (SO) polynyas, many of which are due to fundamental differences in their respective ecosystem properties. The composition of zooplankton communities in AO and SO polynyas is largely dependent upon advection from local, ice-covered waters, with little evidence of an endemic, polynya zooplankton fauna. While copepods are common in both systems, a major difference is the predominance of euphausiids in the SO and appendicularian tunicates in the AO. The same genera of small copepods occur in both the AO and SO and appear to derive little benefit from the higher primary productivity and extended growing season of polynyas. In contrast, larger calanoid copepods appear to derive recruitment and life cycle benefits from the diatom production and heat in polynyas, with higher egg production rates and shorter generation times. Most large calanoid copepods overwinter in diapause in AO polynyas, while some proportion of SO populations remain in surface waters. Grazing impact by copepods in AO polynyas accounts for about 20% of primary productivity d−1 , with appendicularian tunicates accounting for another 20% d−1 . The few estimates of community impact in the SO are variable. In both regions, individual zooplankton feeding rates are high and equivalent to boreal ocean values; thus, grazing impact depends primarily on the biomass of zooplankton and phytoplankton. SO zooplankton contribute to the vertical particulate flux through faecal pellets from euphausiids, copepods and pteropods, while the contribution in AO polynyas is primarily through appendicularian tunicate faecal pellets and shed houses and copepod faeces. Maximum pellet flux in both the AO and SO occurs at times of high biomass of diatoms. The primary benefits of polar polynyas to zooplankton processes results from the greater production of diatoms and extended productive period, with few differences in individual daily rations or food web transfer efficiencies relative to temperate and boreal systems.
Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74009-0
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1 Introduction Polynyas are areas of open water in the midst of ice-covered seas. However, the physical, chemical and biological dynamics of individual polynyas vary. They differ in their interannual persistence, in the time of opening and closing, and in the maximum area of open water (Barber and Massom, 2007). Polynyas are strongly controlled by physical processes, with steep temporal and spatial gradients of chemical and biological properties. There are several ways in which reduced ice cover can force variability in biological responses, including changing levels of irradiance and rates of upwelling of heat and nutrients, and changes in the timing and intensity of mixing and stratification (Ingram et al., 2002). Polynyas occur within a continuum, from those existing for long periods of time to briefly-open polynyas that are difficult to distinguish from surrounding ice-covered seas. The four most studied polynyas in the Arctic Ocean (AO) span a three-fold range in days open per year, from more than 300 days (d) for the St. Lawrence Island Polynya (SLIP), to ca. 100 d for the Northeast Water Polynya (NEW), only slightly longer than the 45–90 d of open water in the nearby Barents Sea (Hirche and Kosobokova, 2003). In general, shelf areas within the Canadian archipelago are ice free for ca. 40 d each year (Ringuette et al., 2002). In the Southern Ocean (SO) the largest, deep-ocean polynya, the Weddell Sea polynya, results from deep convective warming of surface waters over a seamount, while another has been detected in the Cosmonaut Sea (Comiso and Gordon, 1987). Arrigo and van Dijken (2003) detected 52 coastal, winter polynyas using satellite images (SSM/I) of sea ice distribution between June 1997 and May 2002. Some of these consisted of adjacent polynyas that formed a single larger polynya during spring, yielding a total of 37 polynyas that persisted into spring or summer. Twenty-eight of the 37 polynyas were observed during each year of the study.
2 Zooplankton in Arctic Ocean Polynyas 2.1
Species Composition and Abundance
A complete species list of mesozooplankton from AO polynyas is unavailable. This makes regional comparisons of the mesozooplankton community of polynyas and their surrounding, ice-covered waters difficult. Nevertheless, we know that the species composition of the zooplankton community of various Arctic polynyas shares similarities, but that there are also important differences that are related to advection of fauna from nearby water masses. The most complete species list for any Arctic polynya has been compiled for the Northwater Polynya (NOW), consisting of 20 species of crustaceans (Ringuette et al., 2002). The most abundant zooplankton in the NOW are Oithona similis, Metridia longa, Oncaea borealis, Pseudocalanus spp., Microcalanus pygmaeus, Calanus hyperboreus, C. glacialis, Oikopleura spp., and C. finmarchicus (Table 1). This community composition reflects the dual source waters of the NOW (Atlantic waters of the West Greenland Current containing C. finmarchicus, and Arctic water originating from the Nansen and Amundsen basins of the AO). C. glacialis and Fritillaria borealis are considered to be endemic to the Arctic, while Pseudocalanus minutus is characteristic of polar coastal waters (Richter, 1994). The NOW and the Greenland Sea share 4 of the 5 most abundant zooplankton species, while the Arctic, neritic species C. glacialis is abundant in the NOW but not reported from the Greenland Sea. The NOW also shares zooplankton with surface and mid-depth waters of the AO, where Polar Surface Water (PSW) is typified by O. similis, M. pygmaeus and P. minutus (Auel
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Table 1: Abundance of zooplankton species (# of animals m−2 ) in the North Water Polynya (NOW) and regional seas. NOW East = eastern stations along the Greenland coast, NOW West = western stations along the coast of Ellesmere Island. Single values are means or medians, either directly from the published paper or taken by eye from published figures. Rare species are not included. NR = species not reported Species & taxa Copepoda Calanus finmarchicus C. glacialis C. hyperboreus Chiridius obtusifrons Paraeuchaeta spp. Gaidius spp. Heterohabdus spp. Metridia longa Microcalanus pygmaeus Pseudocalanus elongatus P. minutus Pseudocalanus spp. Scaphocalanus brevicornis Scolecithricella minor Spinocalanus spp. Temorites brevis Xanthocalanus borealis Oithona similis Oncaea borealis Harpacticoida Tunicata Oikopleura vanhoeffeni Oikopleura spp.
NOW East1 793 2112 3523 20 119 266 152 8990 4941 NR NR 5203 79 42 94 NR NR 14,188 7981 39 NR 40– 11,0003
NOW West1
Barrow Strait1
1170 NR 3143 1005–2394 4399 601–1210 54 0–249 180 10–25 97 NR 39 NR 5940 1251–1485 3532 3201–3518 NR NR NR NR 5165 12,124–700,0004 38 NR 18 0–15 107 NR 3 NR 9 NR 7395 6642–7811 2885 5240–10,005 85 0–117 NR 40– 11,0003
NR 1000
Nansen Basin
Greenland Sea2
25005 , 18526 , 36007 3005 2005 , 11007 NR NR NR NR 10005 , 25007 12007 13007 NR 21,000–12,50008 NR NR NR NR NR 15007 NR NR
2000–6800 NR 3000–18,000 NR NR NR NR 3000–9000 NR NR 7000–30,000 NR NR NR NR NR NR 80,000–480,000 35,000–110,000 NR
6007 NR
NR NR
1 Ringuette et al. (2002). 2 Richter (1994). 3 Acuña et al. (2002). 4 Hattori and Saito (1997). 5 Mumm et al. (1998). 6 Auel and Hagen (2002). 7 Mumm (1993). 8 Hanssen (1997).
and Hagen, 2002), and where the large C. hyperboreus, C. glacialis and M. longa dominate biomass. Barrow Strait (BS) is a second ice-covered region close to the NOW, which is isolated from direct contact with the Atlantic Ocean, being most affected by outflow from the Amundsen and Canada Basins. The BS is ice-covered for most of the year, having an ice-free period of less than 70 d (Ringuette et al., 2002). Calanus finmarchicus is not reported here (Table 1), in accordance with the view that this species does not reproduce in the AO (Conover and Huntley, 1991; Hirche and Kosobokova, 2003). However, the numerically dominant species in the BS are similar to those in the NOW (Table 1). The primary difference is the predominance of Pseudocalanus spp. and relatively low abundance of M. longa in the BS. This is likely due to the adaptation by Pseudocalanus spp. to feed on epontic algae before ice melt (Conover et al., 1986) and to the relative shallowness of the BS, providing insufficient deepwater habitat for M. longa. In addition, the BS appears to support higher abundances of the
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Table 2: Abundance of zooplankton species (# of animals m−2 ) in the Northeast Water Polynya (NEW) in comparison to regional seas. NEW North = the northern polynya including the Westwind and Northern troughs, NEW Central = the central polynya with open water, NEW South = southern polynya including the ice-covered, Southern Trough. For comparison to the upstream Nansen Basin of the Arctic Ocean see Table 1. Rare species not included. NR = species not reported Species & taxa
NEW South1
NEW Central1 NEW North1
East Greenland Current2
Copepoda Calanus finmarchicus C. glacialis C. hyperboreus Metridia longa Microcalanus pygmaeus Pseudocalanus spp. Scolecithricella minor Oithona similis Oncaea borealis Harpacticoids
500–11,000 120–2900 300–3100 600–1800 1000–3000 180–4000 10–1600 22,000–75,000 100–13,000 50–500
0–180 0–950 0–800 50–3100 400–4100 20–850 10–500 0–25,000 1500–10,000 0–120
3555–12,373 30–554 260–2064 NR NR NR NR NR NR NR
Tunicata Appendicularians Fritillaria borealis Oikopleura spp. Chaetognatha Ostracoda Pteropoda Polychaeta
1000–20,000 950–19,000est 50–1000est 150–1000 50–100 0–2000 0–500
0–2300 1000–16,000 200–7900 0–2800 50–3000 620–6800 0–400 1000–35,000 500–8000 0–1100
1000–6000 950–5700est 50–300est
5000–250,000 47,500–237,000est 2500–13,000est
NR NR NR
50–1300 10–5000 510–200 0–50
100–1300 10–1000 50–5200 0–15,000
NR NR NR NR
1 Ashjian et al. (1995, 1997). 2 Hirche et al. (1991). est Values estimated, see text.
appendicularian Oikopleura vanhoeffeni than does the NOW (Table 1). In summary, the zooplankton community of the NOW is most like that of the Nansen Basin (Table 1), reflecting the importance of the inflow of Arctic water (Tremblay et al., 2002). In general, the NOW supports higher abundances of zooplankton than regional, ice-covered waters, particularly C. glacialis, C. hyperboreus and Fritillaria borealis. The Northeast Water Polynya (NEW) has a more ’Atlantic’ zooplankton community. The most abundant species are Fritillaria borealis, Oithona similis, Calanus glacialis, polychaete larvae, Oncaea borealis, Calanus hyperboreus and pteropods (Table 2). Microcalanus pygmaeus, C. finmarchicus and Oikopleura spp. are also abundant. All taxa are relatively rare in the central NEW, but are more abundant downstream. M. longa is the dominant copepod species in the central NEW, and C. glacialis is dominant in the north. C. finmarchicus is most abundant in the ice-covered, southern NEW, which is most heavily influenced by water of Atlantic origin. Thus, the central NEW supports significantly higher densities of C. glacialis, and lower densities of C. finmarchicus, than do regional, ice-covered waters. This is generally similar to the case of the NOW. As was true in the NOW, the density of the cyclopoid copepod O. borealis does not seem to be affected by the presence of the NEW. Understanding of appendicularian ecology in polynyas is limited by taxonomic uncertainties. For example, Ashjian et al. (1997) presented total appendicularian numbers for the NEW, which at several stations were very high (250,000 animals m−2 ). Recounting of some
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Table 3: Abundance of zooplankton species (# of animals m−2 ) in the Laptev Sea Polynya and upstream Barents Sea. Rare species not included. NR = abundance of species not reported in primary citations Species & taxa
Laptev Sea
Barents Sea
Copepoda Calanus finmarchicus C. glacialis C. hyperboreus Drepanopus bungei Pseudocalanus spp. Oithona similis Harpacticoids
NR 0–150,0003 NR 250–55004 2000–31,0005 NR NR
2417–13,3681 , 10,000–100,0002 138–12621 , 0–80002 645–12,1291 , 10–9002 NR NR 400,000–600,0006 3000–10,0006
Tunicata Fritillaria borealis Oikopleura spp.
1–13005 3–9605
0–130,0006 NR
1 Hirche et al. (1991). 2 Hirche and Kosobokova (2003). 3 Kosobokova and Hirche (2001). 4 Abramova (1999). 5 Hanssen (1997). 6 Arashkevich et al. (2002).
samples indicated that is greater than 99% of the appendicularians were small Fritillaria borealis (Deibel and Lee, unpubl.). This is similar to other reports from the AO, which indicate that >90% of appendicularians are generally fritillarids (Kosobokova and Hirche, 2001). In Table 2 we estimate the abundance of F. borealis and Oikopleura spp. by assuming that 95% of the counts of Ashjian et al. (1997) were fritillarids. The Laptev Sea Polynya (LSP) is a flaw polynya created just west and north of the New Siberian Islands, which is influenced by runoff from the Lena River (Kosobokova et al., 1998). The zooplankton community is more neritic in character than that of the NOW or NEW. Copepods are the most abundant group in spring and summer, with chaetognaths ranking second (Table 3). Drepanopus bungei, Acartia spp., Calanus glacialis, C. finmarchicus and Pseudocalanus spp. dominate at shelf stations (Abramova, 1999), while slope and deep stations are dominated by C. glacialis, C. finmarchicus, C. hyperboreus and Metridia longa (Kosobokova et al., 1998). The presence of C. finmarchicus indicates the influence of Atlantic Water from the Barents Sea. However, it is unlikely that C. finmarchicus reproduces in the Nansen Basin or in the LSP (Kosobokova and Hirche, 2001). Fritillaria borealis and Oikopleura spp. are found at all stations on the Laptev Shelf, regardless of depth (Table 3), suggesting that they may be broadly distributed in the AO. The LSP supports orders of magnitude greater densities of Calanus glacialis than the surrounding waters of the Barents Sea and Nansen Basin (Table 3). As a result, it has been suggested that Eurasian polynyas generally, and the LSP specifically, serve as nursery areas for C. glacialis (Kosobokova and Hirche, 2001). Although the NEW, NOW and LSP are vastly different in bathymetry and hydrography, they all have high densities of C. glacialis. The St. Lawrence Island Polynya (SLIP) is fundamentally different from the other polynyas, as it is surrounded by ice only 2–3 months of the year. A branch of the Anadyr Current flows into the SLIP bringing high levels of inorganic nutrients onto the shallow continental shelf south of St. Lawrence Island (Springer et al., 1989). As a result, the rates of primary production in the SLIP are 5–10 times higher than in surrounding waters.
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Table 4: Mean areal abundance (# m−2 ) of zooplankton in the St. Lawrence Island Polynya (SLIP) and regional seas. For some taxa range is in parentheses. Stratified vertical tows to near bottom with 100 μm mesh (this paper). n = 13 stations. NR = species not reported. Species occurring in low numbers are not shown Species & taxa
SLIP
Bering Slope
Bering Shelf
Anadyr Strait
Shpanberg Strait1
Copepoda Calanus marshallae Neocalanus plumchrus N. cristatus Eucalanus bungeii Metridia pacifica M. norvegica Pseudocalanus spp. Microcalanus pygmaeus Acartia longiremis Scolecithricella minor C. abdominalis Oithona similis O. conifera Microsetella norvegica
13,251 356 8 70 16,073 1189 245,348 6810 8893 74 20 141,601 29,972 NR
30,953 2136
11,253
95,090 NR 170,452 39,450 1404 NR NR 237,965 162,033 3476
551 NR 282,190 592 6041 NR NR 128,139 3920 785
9859, 10001 40001 151 30001 30001 NR 275,180, 30,0001 27 16,598, 10001 NR 10001 102,351, 20,0001 2674 1179
15,000 NR NR NR 1000 NR 40,000 NR 10,000 NR NR 5000 NR NR
Tunicata Total appendicularia Oikopleura vanhoeffeni O. labradoriensis Fritillaria borealis
NR 48,195 30 90
NR 227 99 154
NR 36,002 NR 1841
35001 83,595 NR 66
500 NR NR NR
7323 (530–29,825)
NR
NR
NR
NR
755
12,647
8246, 2001
60
173
6058
5001
80
NR NR
NR NR
NR NR 375
NR NR 19,303
5001 NR 20001 NR NR 63,605
50 NR 50 NR NR NR
NR
227
14,297
NR
1254
2003
16,997
NR
Chaetognatha Total chaetognaths Parasagitta elegans Cnidaria Euphausidae Amphipoda Pteropoda Ostracoda Decapoda Trochophora Balanidae Polychaeta
3398 (0–28,004) 89 (0–1391) 75 (0–268) 48 (0–285) 16 (0–175) 160 (0–1189) 31,889 (0–136,297) 9750 (0–38,238) 8862 (646–30,701)
1 Springer et al. (1989).
The zooplankton community is dominated by copepods, followed by appendicularians and trochophore larvae (Table 4). The coastal nature of the SLIP area is characterized by the dominance of Pseudocalanus and Acartia, and its cold, productive nature is character-
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ized by the predominance of O. vanhoeffeni, at abundances greater than any of the other polynyas reviewed here. Composition of the zooplankton community in the SLIP is most like that of the Bering Sea shelf and Anadyr water (Table 4). For example, the three most abundant species had the same rank order in the Bering shelf, Anadyr Current and SLIP stations (Pseudocalanus spp., Oithona similis and O. vanhoeffeni). 2.2
Individual and Community Biomass
Individual dry weights (DW) of copepods in the central NEW are generally lower than is typical of the same species in regional waters in mid-summer, while DW’s to the north and south of the main polynya area are generally higher (Ashjian et al., 1995, Table 5). It is not known whether these differences reflect variations in the reproductive status of populations inside and outside of the polynya, and/or poor nutritional conditions within the polynya (Daly, 1997). Although the central NEW supports high densities of Metridia longa, body size of the animals is not exceptionally large in a regional context. In the NOW the DW’s of Calanus hyperboreus copepodite V’s range from 2.4 to 3.6 mg (Table 5). C. glacialis CV (1.0–1.4 mg) and Metridia longa females (0.4–0.5 mg) weigh considerably less (Stevens et al., 2004a). There is a spatial pattern in DW, with the two calanoids generally larger in the east, and M. longa larger in Arctic-influenced waters (Table 5). The DW of CV C. hyperboreus in the NOW is similar to that in the southern NEW but 2–3 times greater than in the central and northern NEW; additional evidence of the unfavorable food environment for this large copepod in the NEW generally. Appendicularians are abundant in the NOW, showing large spatial variability in body size, abundance and biomass. Oikopleurid appendicularians (primarily O. vanhoeffeni) range in size from less than 0.5–4.8 mm, with an inverse relationship between body size and abundance (Acuña et al., 2002). The population is dominated by small animals above the pycnocline (mean length = 0.26 mm) and by larger animals below (mean = 2.52 mm) (Deibel et al., unpubl.). There is considerable plasticity in body size of appendicularians throughout an annual cycle, making comparison among studies difficult. In Newfoundland waters, size-at-maturity of adult O. vanhoeffeni is greater in the spring when food is abundant, suggesting they may respond to phytoplankton production during the spring bloom (Choe and Deibel, unpubl.). The mean trunk length of O. vanhoeffeni in the NOW during summer is 0.94 mm, similar to the summer mean in Newfoundland of 0.6 mm. This indicates that the NOW (75◦ N) provides as suitable a habitat for O. vanhoeffeni as does the Labrador Current off Newfoundland (47◦ N). The favorable food environment provided by the SLIP is evident in the mean body size of O. vanhoeffeni (3.9 mm), similar to the mean in Newfoundland waters during the spring bloom (3.5 mm). Of the polynyas reviewed here, the SLIP has the highest zooplankton biomass and the LSP the lowest (Figure 1). Total zooplankton biomass in the SLIP ranges from 1.5– 7.4 g C m−2 , with Oikopleura spp. forming 88% of the total. Pseudocalanus spp. dominates the copepod fraction, followed by Calanus marshallae and Acartia spp. Surrounding waters support higher biomass of copepods (1–4 g C m−2 ) but much lower biomass of appendicularians (Shiga and Deibel, unpubl.). The zooplankton biomass in the SLIP is similar to that of the Nansen Basin and central Arctic Ocean, but is 50% less than that of the open waters of the Greenland and Barents seas (Figure 1). Zooplankton biomass in the NOW ranges from 0.1–7.0 mg C m−2 , with higher values in waters influenced by the West Greenland Current (Figure 1). Biomass is dominated in the east by Calanus finmarchicus and C. glacialis, and in the west by C. glacialis and C. hyperboreus (Ringuette et al., 2002). Total biomass in the eastern NOW is much higher than in
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Table 5: Dry weight and relative total lipid content of mesozooplankton in Arctic polynyas and surrounding ice-covered waters. Modified and amended from S. Smith and SchnackSchiel (1990). Only those stages appear in this table which have matching data from at least one of the polynyas reviewed here. ‘?’ indicates that data were not available in the original reference Species and stage
Location
Month
Dry weight (μg)
Lipid (% DW)
Reference
Calanus glacialis adult female
NEW south NEW central NEW central NEW north Greenland Barrow Strait Arctic T-3 Arctic T-3 Fram Strait Barents Sea Barents Sea Barents Sea Barrow Strait NOW east NOW west Barrow Strait Barents Sea Barents Sea NEW south NEW south NEW NEW NEW north NOW NOW Greenland Jones Sound Barrow Strait Fram Strait Fram Strait Barents Sea ? Arctic T-3 Arctic T-3 NEW south NEW north NOW east NOW west Jones Sound Barrow Strait Fram Strait Fram Strait Barents Sea
July–Aug July–Aug Aug July–Aug May–Aug April–Sept ? ? summer ? June–July ? April–Sept Aug–Sept Aug–Sept April–Sept ? June–July July–Aug Aug July–Aug Aug July–Aug April July May–Aug July–Aug April–Sept summer August ? ? June August July–Aug July–Aug Aug–Sept Aug–Sept July–Aug April–Sept summer August ?
1146–1152 770 680–980 1009–1252 810 851 440 714 533 810 960 600 711 1040–1190 1120–1390 547 600 618 4143–5416 3730–3830 2007 1190–2650 3083–6018 ? ? 1800 4067 (max) 3769 3168 3600–4300 2350 1893 1380 (min) 2300 (max) 3111 1563–1776 2380–3570 2930–3050 2736 (max) 1361 1920 2000 1000
37–43 36 ? 42–46 ? ? 50 56 35 ? 24 ? ? 51–58 32–51 ? ? 48 53–57 ? 44 ? 51–60 19 43 ? 71 ? 34 36–46 ? 26 29 74 57 57–60 46–70 50–61 64 ? 38 41–45 ?
Ashjian et al. (1995) Ashjian et al. (1995) Daly (1997) Ashjian et al. (1995) Daly (1997) Conover and Huntley (1991) Lee et al. (1971) Lee (1975) S. Smith (1990) Hirche and Kosobokova (2003) Tande and Henderson (1988) Hirche and Kosobokova (2003) Conover and Huntley (1991) Stevens et al. (2004a) Stevens et al. (2004a) Conover and Huntley (1991) Hirche and Kosobokova (2003) Tande and Henderson (1988) Ashjian et al. (1995) Daly (1997) Ashjian et al. (1995) Daly (1997) Ashjian et al. (1995) Fisk et al. (2001) Fisk et al. (2001) Daly (1997) Head and Harris (1985) Conover and Huntley (1991) S. Smith (1990) Auel et al. (2003) Hirche and Kosobokova (2003) S. Smith (1990) Lee (1974) Lee (1974) Ashjian et al. (1995) Ashjian et al. (1995) Stevens et al. (2004a) Stevens et al. (2004a) Head and Harris (1985) Conover and Huntley (1991) S. Smith (1990) Auel et al. (2003) Hirche and Kosobokova (2003)
male male CV CV CV CV CV C. hyperboreus adult female
CV CV CV CV CV CV CV CV CV
(Continued on next page)
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Table 5: (Continued) Species and stage Metridia longa adult female
Location
Month
Dry weight (μg)
Lipid (% DW)
Reference
NEW NEW north NOW east NOW west Barrow Strait Arctic T-3 Arctic T-3 Norway Norway
July–Aug July–Aug Aug–Sept Aug–Sept April–Sept ? Oct Oct March
368 387 370–450 460–520 324 176 ? ? ?
32 30 11–30 33–35 ? 34 57 84 (max) 27 (min)
Ashjian et al. (1995) Ashjian et al. (1995) Stevens et al. (2004a) Stevens et al. (2004a) Conover and Huntley (1991) Lee (1974) Falk-Petersen et al. (1987) Falk-Petersen et al. (1987) Falk-Petersen et al. (1987)
Figure 1: Total zooplankton biomass in three arctic polynyas relative to surrounding icecovered waters. Original units in cited papers were often dry weight. DW was converted to units of carbon by multiplying by 0.5. Black dots indicate maximum values of biomass. Data from Conover and Huntley (1991) (Barrow Strait), Kosobokova et al. (1998) (Laptev Sea Polynya), Saunders et al. (2003) (NOW west, NOW east), Mumm et al. (1998) (Central Arctic Ocean Upper, Greenland Sea Upper), Ashjian et al. (1995) (NEW north, NEW central, NEW south), Hopkins (1969), S. Smith (1988) and Hirche et al. (1991) (East Greenland Current), Mumm (1993) (Nansen Basin Upper), Shiga and Deibel (unpubl.) (SLIP), Kosobokova and Hirche (2001) (Central Arctic Ocean), Arashkevich et al. (2002), Sato et al. (2002) and Hirche and Kosobokova (2003) (Barents Sea), Richter (1994) (Greenland Sea). Barrow Strait (3.2 vs. 0.3 g C m−2 , Figure 1). Although mean biomass in the eastern NOW is nearly equal to that in the SLIP (Figure 1), the composition of the community is different, with appendicularians dominant in the SLIP and copepods dominant in the eastern NOW.
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Zooplankton biomass in the NEW is spatially variable, with highest values in the southern NEW, dominated by Calanus finmarchicus (3.3 g C m−2 ) and lower values in the central and northern NEW (1.3 g C m−2 ) (Figure 1). Biomass in the central NEW is dominated by Metridia longa and in the northern NEW by C. glacialis (Ashjian et al., 1995). The NEW does not support unusually high zooplankton biomass in a regional context (Figure 1). If one assumes that ca. 65,000 m−2 of the appendicularians in the NEW were F. borealis weighing 1–2 μg C animal−1 , and that the remaining animals were juvenile Oikopleura spp. of the small size found in other Arctic polynyas and weighing 3.8 μg C animal−1 (Deibel et al., 2005), appendicularian biomass is ca. 0.09 g C m−2 , or about 3% of copepod community biomass. This is within the range of estimates of oikopleurid appendicularian biomass from Young Sound, eastern Greenland, of 0.04–0.10 g C m−2 (Rysgaard et al., 1999). The same assumptions applied to the central and southern NEW result in biomass estimates of 0.01– 0.02 g C m−2 , ca. 0.5% of copepod biomass. This compares with appendicularian biomass estimates made by Pesant et al. (1998, 2000) of 5–10 g C m−2 , based upon a misinterpretation of the data presented by Ashjian et al. (1995). Total copepod biomass in the LSP ranges from 0.05–0.75 g C m−2 , much lower than either the NEW or the eastern NOW, but similar to the Barrow Strait and Arctic-influenced NOW (Figure 1). Biomass is dominated by Drepanopus bungeii and Pseudocalanus on the shelf, and by Calanus hyperboreus and C. glacialis beyond the shelf break. Although the LSP is influenced by inflow from the Barents Sea, it supports much less biomass (0.2–10 g C m−2 for the Barents Sea). 2.3 2.3.1
Individual Feeding Rates, Diet and Community Grazing Individual Feeding Rates
There has been considerable work done on individual feeding rates of copepods and appendicularians in Arctic polynyas, which are as high as those in boreal and temperate latitudes, meaning that population grazing impact is primarily a function of mesozooplankton biomass (Saunders et al., 2003). In the NEW, gut chlorophyll content (a proxy of ingestion rate) of oikopleurid appendicularians increases with increasing body size and decreases both with increasing total chlorophyll and with increasing proportions of large phytoplankton (Acuña et al., 1999). 43% of the total variability in ingestion rate is accounted for by body size and the proportion of large phytoplankton. The authors assume the negative relationship with the biomass of large phytoplankton was due to clogging of the inlet filters of the house by the large cells. The mean ingestion rate is 23 μg C d−1 and the carbon-specific daily ration is 117% d−1 . Highest faecal pellet production rates (directly related to ingestion rate) for Calanus hyperboreus are in May in the southern NEW, and lowest rates are in July in the northern NEW (Daly, 1997). Carbon-specific egestion rates of faecal pellets are ca. 7% d−1 for C. hyperboreus and C. glacialis and ca. 25% d−1 for M. longa. Assuming a carbon assimilation efficiency of 80% gives carbon-specific daily rations of 9% d−1 for the two calanoids and 30% d−1 for M. longa. Carbon-specific egestion of faeces is positively correlated with POC in the chlorophyll maximum (Daly, 1997), contrasting with the inverse relationship between ingestion rate and phytoplankton biomass for Oikopleura (Acuña et al., 1999). There is a phytoplankton bloom in the eastern NOW during May and June. Copepod biomass tracks phytoplankton abundance, with a maximum in the southeast in May, and in the northwest in June. Monthly carbon-specific herbivory rate tracks phytoplankton and copepod biomass, with maxima in the southeast in May–June and in the northwest in June
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Figure 2: Weight-specific ingestion rate of phytoplankton by copepods in the North Water Polynya as a function of initial chlorophyll a concentrations in feeding experiments. BBW is the eastern NOW and SRAW + MIX is the colder, western NOW. Reprinted from Saunders et al. (2003). (Saunders et al., 2003.) Carbon-specific herbivory rate increases linearly with increasing chlorophyll, with no sign of a saturation functional response (Figure 2). Carbon-specific herbivory rates in the NOW are similar to those in the NEW (ca. 10% d−1 for Calanus spp. and M. longa). Oikopleura vanhoeffeni in the NOW has a five-fold increase in gut chlorophyll content over a 5-fold range in chlorophyll concentration (Acuña et al., 2002). Above a chlorophyll concentration of 250 mg m−2 , gut chlorophyll content decreases 30–40%, presumably due to clogging of the inlet filters and increased frequency of back flushing. Gut chlorophyll content also increases as a 1.57 power of body size (Acuña et al., 2002), somewhat lower than the 2.14 power in the NEW (Acuña et al., 1999). This likely reflects the generally larger cell size of phytoplankton in the NOW relative to the NEW. Much work has been done on the metabolism and feeding of Calanus glacialis in the boreal North Atlantic Ocean. The carbon-specific daily ration is 12–14% for stage CIV and CV (Tande and Båmstedt, 1985; Head, 1986). These carbon-specific ingestion rates are close to those of C. hyperboreus and C. glacialis in the NEW and the NOW, of ca. 5–9% (Daly, 1997; Saunders et al., 2003), suggesting that polynyas do not confer an energetic advantage via the daily ingestion rates of calanoid copepods. 2.3.2
Diet
Fatty acid (FA) biomarkers have revealed spatial variability in the diet and trophic linkages among mesozooplankton and their prey in Arctic polynyas. Thus, they have provided crucial tests of hypotheses concerning whether polynyas serve as oases of biological productivity in the midst of ice-covered seas. Furthermore, several phytoplankton FA’s are essential for zooplankton growth and reproduction, and examination of how these FA’s are acquired by
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zooplankton is crucial for understanding recruitment dynamics of polynya zooplankton populations. For example, in the northern NEW, Calanus hyperboreus and C. finmarchicus show decreasing levels of diatom FA’s and increasing levels of dinoflagellate FA’s as a function of distance from the East Greenland Current (Graeve, 1993), suggesting that these two species ingest significant quantities of phytoplankton during the summer in the northern NEW. The fatty acid compositions of Calanus hyperboreus, C. glacialis and Metridia longa, particularly the high levels of 16:1(n-7) and 20:5(n-3) (EPA, an essential FA), indicate that copepods are actively feeding and that diatoms are an important source of food as late as September in the NOW polynya (Stevens et al., 2004b). Diatom production is prolonged in the NOW, with a dense Chaetoceros socialis population persisting well into autumn (Booth et al., 2002). This extended diatom production may be the primary benefit of the NOW to calanoid copepods, resulting in a delay of diapause, reduced generation times and increased annual recruitment (Stevens et al., 2004b). In addition, bacteria form part of the diet of all copepods investigated in the NOW, but to varying extents (Stevens et al., 2004b). It is assumed that copepods do not ingest free-living bacteria, but rather marine snow, bacterivorous microzooplankton, or other particles associated with bacteria. Calanoid copepods have been shown to ingest marine snow both in situ (Dagg, 1993) and in vitro (Dilling et al., 1998) and their ingestion of protozoans is well documented (Froneman et al., 1996; Levinson et al., 2000). In Calanus hyperboreus and Metridia longa, ingestion of bacteria occurs only at southeastern stations where bacterial biomass is highest. C. glacialis, on the other hand, appears to ingest bacteria in both the southeastern and northwestern NOW, often in association with diatom-based herbivory. Compared to the other copepods, C. glacialis may be more likely to eat dying cells or marine snow, where bacterial and diatom biomarkers presumably co-occur (Stevens et al., 2004b). Appendicularians feed non-selectively using a series of fine-mesh, mucous filters (Deibel, 1998). Inlet filters on the outside of the house can prohibit the ingestion of large cells. The pore size of the inlet filters is both species and body size dependent (Deibel, 1998). The diet of appendicularians has been investigated only in the NOW, where HPLC analyses of gut pigments indicate a relatively greater ingestion of chlorophyll b at southeastern stations relative to diatom-rich waters to the northwest (Acuña et al., 2002). This suggests significant ingestion of non-diatom prey at southeastern stations. Microscopic analyses support the pigment data, showing that more than 92% of gut content volume is diatoms at the northwestern stations, while less than 77% of gut content volume is diatoms at two southeastern stations. 2.3.3
Community Grazing Rates
Estimates of the grazing impact of large calanoid copepods in the NEW polynya are seasonally dependent. Soon after the polynya opens in the spring, copepods may ingest as much as 41% of primary production (PP) d−1 (Daly et al., 1999), whereas during late June Hirche et al. (1991) estimate that the copepod community removes 25–33% d−1 , due to a low abundance of animals in the central polynya. In August-September the copepod grazing impact is 15% of daily PP within the central polynya, 500% in the southern NEW and 100% in the northern NEW (Ashjian et al., 1995). Even though copepod grazing was greater than 100% of PP in the northern and southern NEW, there was no indication that copepods were food limited in these two areas. The authors suggest that the copepods may have been feeding on dense patches of phytoplankton or omnivorously in deep troughs. The copepods in the NOW have a weight-specific herbivory rate ranging from 0– 24% d−1 from April through July (Saunders et al., 2003). Given a community biomass
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of 20–3200 mg C m−2 (Figure 1), this is equivalent to a community herbivory range of 0–800 mg C m−2 d−1 . During the spring bloom, copepod community grazing impact is generally less than 10% of daily PP, but following the bloom at southeastern stations, grazing impact ranges from 15–55% of daily PP (Saunders et al., 2003). The carbon-specific herbivory rate of individuals explains neither the timing nor magnitude of community herbivory, which is accounted for only by the biomass of phytoplankton and copepods (Saunders et al., 2003). Saunders et al. (2003) conclude that the generally low grazing impact of copepods in AO polynyas is due to high rates of PP and relatively low standing stocks of grazers. Oikopleurid appendicularian population ingestion rates of chlorophyll in the NOW range from 0.01–2.08 mg C m−2 d−1 (Acuña et al., 2002). Since animals less than 1 mm in size dominate abundance and biomass at most stations, appendicularians from 0.5–1.5 mm long are the primary contributors to population ingestion. These ingestion rates translate into herbivory rates of 0.33–104 mg C m−2 d−1 , or 0.1–33% of PP d−1 in July (Acuña et al., 2002). Given that appendicularians generally remove about twice as much phytoplankton from suspension as is ingested (Gorsky et al., 1984), on average appendicularians may account for 20% of daily PP in the NOW, equivalent to the community ingestion rate of copepods. Herbivorous ingestion rates of appendicularians in the SLIP range from 150–1000 mg C m−2 d−1 , apparently causing depletion of autotrophic nanoplankton across the St. Lawrence Island shelf (Deibel et al., 2005). This is several times higher than estimates of the ingestion rate of copepods in the SLIP (30–50 mg C m−2 d−1 ). However, because PP rates are so high, the grazing impact of appendicularians remains 10–20% of PP, essentially the same as in the NEW. This grazing impact is similar to literature values from elsewhere in the Arctic Ocean, suggesting a general trend of grazing impact of copepods and appendicularians of ca. 20% of PP d−1 for each group. This leaves about 60% of daily PP to be ingested by fritillarid appendicularians or herbivorous protists, or to be exported to the benthos. 2.4
Faecal Pellet Production and Vertical Flux
In the NEW annual biogenic flux is dominated by ice algae and appendicularian houses and faeces (Bauerfeind et al., 1997). Appendicularian carbon flux is 0.2–5.3 mg m−2 d−1 in August and September, equivalent to 10–37% of daily POC flux (Bauerfeind et al., 1997). The integrated, annual flux of appendicularian houses and faeces is 975 mg C m−2 , 14% of POC flux, while copepod faecal pellets contribute less than 5% of the annual POC flux (Bauerfeind et al., 1997), perhaps because of their reingestion by Metridia and Oithona at intermediate depths (Daly, 1997). Thus, appendicularians make at least a 3-fold greater contribution to the total annual flux of POC than do copepods. In the NOW, more than 99% of the copepod faecal pellets produced in the epipelagic zone do not reach 200 m (Sampei et al., 2004). Faecal flux is highest in July/August (20– 33 mg C m−2 d−1 ) when annual maxima of total vertical flux occurs (13–102 mg C m−2 d−1 ). The contribution of phytoplankton to the total flux ranges from 0–16%, with maxima in spring. Faecal pellets contribute from 1–63% to the total flux (mean = 20%), with maxima in the July/August. This annual mean value is similar to the contribution of copepod pellets and appendicularian pellets and houses to the annual vertical flux in the NEW (i.e., = 19% y−1 ). The potential vertical flux of oikopleurid appendicularian faeces in the NOW in July is 8 mg C m−2 d−1 (Acuña et al., 2002). This is more than an order of magnitude larger than estimates of zooplankton faecal flux from sediment traps deployed a month earlier, of 0.5 mg C m−2 d−1 (Sampei et al., 2002), perhaps due to a seasonal increase in the abundance of appendicularians which reach their peak in the NOW in August/September (Acuña et al.,
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2002). The potential flux in the NOW is similar to that in NEW in August (5.3 mg C m−2 d−1 , Bauerfeind et al., 1997). Appendicularian faeces account for ca. 8% of the maximum vertical export of POC from the NOW of ca. 100 mg C m−2 d−1 in July (Sampei et al., 2004). The inclusion of discarded filter houses may raise this flux to ca. 80% of POC flux (Bauerfeind et al., 1997). Thus, the ratio of export of copepod:appendicularian faeces is ca. 2.5 : 1 in the NOW. 2.5
Seasonal Energy Storage and Egg Production Rates
Calanoid copepods of the AO are generally large and store energy in the form of lipids, primarily wax esters (WE) (Lee et al., 1971). For example, the lipid content of adult female Calanus hyperboreus in the AO ranges from a maximum of 74% of dry weight (DW) in August, to a minimum of 29% in June, following spawning (Table 5). AO copepods use lipid stores for overwintering at depth and in some cases, for making eggs the following spring. Under starvation C. hyperboreus adults lose 0.3% of body mass d−1 , 87% of which is combusted lipid (Conover, 1965). In the NOW in September, Calanus hyperboreus stores more lipid (up to 70% of DW) and has a greater proportion of wax esters (84–93%) than do C. glacialis or Metridia longa (Table 5, Stevens et al., 2004b). This interspecies difference in lipid storage seems to occur throughout the AO (Conover and Huntley, 1991). There is little variability within the NOW in either the total lipid or WE content of C. hyperboreus or C. glacialis, while M. longa at southeastern stations has lower lipid levels that do animals from northwestern stations. Stevens et al. (2004b) proposed that these spatial differences in lipid storage by M. longa are due to the dominance of a microbial food web at southeastern stations and a diatom-based one at northwestern stations. Open waters of the NEW do not seem to promote lipid storage by calanoid copepods, with lipid levels of adult female and CV C. hyperboreus and adult C. glacialis lower inside than outside the polynya (Table 5). Unlike in the NOW (Stevens et al., 2004a), there is no spatial trend in the lipid content of M. longa in the NEW (Ashjian et al., 1995), suggesting that this deep-living copepod spawns continuously in the NEW, independently of ice cover. Even though rates of primary productivity are higher in the eastern NOW, followed by the western NOW and the NEW (Klein et al., 2002), levels of lipid storage by calanoid copepods are similar in both (Table 5). Maximum values for adult female Metridia longa are ca. 35% in all regions of both polynyas. Comparative data in Table 5 indicate that earlier and higher levels of primary productivity in Arctic polynyas do not result in higher levels of lipid storage by copepods than in surrounding ice-covered waters. Egg production rates (EPR) of C. glacialis in the NEW range from 40 to 100 female−1 d−1 , among the highest ever reported for this species (Ashjian et al., 1995). However, EPR’s are variable, as Hirche et al. (1991) found EPR’s of 15–45 female−1 d−1 in the central NEW in June and 3–7 female−1 d−1 in the ice-covered East Greenland Current. These values from the NEW are much higher than published values from the field (i.e., 22–42 d−1 in the Fram Strait) or laboratory (i.e., 10–20 d−1 ) and correspond to 3–6% of body C d−1 . However, carbon-specific EPR’s are highest in the polynya because females there mature at a smaller body size (Ashjian et al., 1995), and since egg production appears to depend upon feeding (Hirche and Kosobokova, 2003), it seems that C. glacialis does not rely on lipid stores for egg production in the NEW. Unresolved are the relatively high EPR’s both inside and outside of the NEW polynya, along with relatively low standing stocks of copepods. It is enigmatic that the abundance of
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copepod nauplii is four-times greater in the ice-covered NEW than in the ice-free central polynya. The relatively high stocks of the omnivorous Metridia longa in the central polynya may limit the population of C. glacialis by preying upon eggs and nauplii. The Laptev Sea Polynya is an area of active spawning of Calanus glacialis. In July EPR is highest between 50 and 200 m in open water or near the ice edge. EPR’s decrease from July (20–60 eggs female−1 d−1 ) to September (0–8 female−1 d−1 , Kosobokova and Hirche, 2001). Unlike the southern NEW, there is no egg production by C. glacialis when ice cover exceeds 70%. In July there is no correlation between EPR and chlorophyll, suggesting that eggs are produced from lipid stores, while in September egg production depends upon contemporaneous feeding. This may explain the apparently contradictory results in the NEW, where eggs are produced from lipid stores early in the productive season and depend upon active feeding later. The authors conclude that C. glacialis is no more abundant in the LSP than in other Arctic shelf seas and that spawning variability depends upon the dynamics of ice, with copepod production occurring after the ice melts. Clutch size of Calanus hyperboreus in the NOW decreases with time, from ca. 160 eggs female−1 d−1 early in the spring to 100 eggs female−1 d−1 in late spring (Ringuette et al., 2002). Egg laying begins 1–9 weeks after the final molt to the adult stage, and spent females may loose 90% of their body weight during vitellogenesis and spawning (Conover, 1967). The maximum clutch size of C. hyperboreus in the Fram Strait is 57 eggs female−1 (S. Smith, 1990). Thus, the NOW appears to support much higher EPR’s of C. hyperboreus than surrounding ice-covered waters. 2.6
Secondary Production and Generation Time
Ringuette et al. (2002) use the proportion of CI-CIII as a measure of the ’recruitment success’ of copepod species in the NOW. They find success to increase with increasing chlorophyll for Calanus hyperboreus, with increasing chlorophyll and temperature for C. glacialis and with increasing temperature for Pseudocalanus spp. Thus, they conclude that C. hyperboreus is food limited in the NOW, C. glacialis is co-limited by both food concentration and temperature and Pseudocalanus is primarily temperature limited. Therefore, some polynyas can be sources not only of food, but also of heat, which should accelerate copepod development times relative to surrounding ice-covered waters (Ringuette et al., 2002). The NOW has a productive season of ca. 6 months, apparently long enough to permit early reproduction and reduced generation times of large, calanoid copepods, in some cases removing an entire year from the life cycle (Ringuette et al., 2002). For example, recruitment of copepodite stage I of Calanus hyperboreus, C. glacialis and Pseudocalanus spp. starts in May–June, 1.5–3 months earlier than CI recruitment in the Barrow Strait (Figure 3). CI of Metridia longa recruits in June, one month earlier than in the BS. Thus, for the 4 calanoids which depend upon the diatom bloom, the eastern polynya gives a 2–4 week advantage in spawning time over the western area and a 2–6 week advantage over the BS (Ringuette et al., 2002). Copepods captured in sediment traps indicate that young-of-the-year Calanus hyperboreus start to molt to the adult stage in September in the eastern NOW and in December in the western NOW. This demonstrates the life cycle advantage conferred upon large copepod species by some Arctic polynyas and specifically shows the temporal advancement of the production cycle in the eastern NOW. Life cycle advantages of developing in the NOW do not extend to all copepod species, however, as there is no difference in recruitment time of copepodites of Microcalanus spp.,
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Figure 3: Calendar week of recruitment of CI of three species of copepods in the Northwater Polynya vs. the calendar week of the first onset of the annual phytoplankton bloom. Statistics are given for least squares linear regression. Reprinted from Ringuette et al. (2002). Oithona similis and Oncaea borealis between the NOW and the BS (Ringuette et al., 2002). These species may reproduce year-round independently of ice cover, perhaps depending primarily on the microbial food web for prey (Ringuette et al., 2002). In fact, nauplii of O. borealis are found in all months in which samples are taken. In ice-covered waters of the AO, Calanus hyperboreus has a three-year life cycle (Dawson, 1978), but in Norwegian fjords and the Greenland Sea it is one-year, spawning between January and March (Matthews et al., 1978; S. Smith, 1990). These differences can be accounted for by temperature-dependent development times. C. hyperboreus has been found to require 114 d to develop from egg to CIII at 4–6◦ C, and 250 d from egg to adult at 2–4◦ C (Conover, 1965). Based upon an embryonic development time of 8 d at −2◦ C, S. Smith (1990) estimates a life span of 201–350 d in the AO. C. hyperboreus should be capable of a 1-year life cycle outside of the AO (and perhaps in Arctic polynyas), but requires 2–3 years in the high Arctic. Calanus glacialis in the NEW spawns in July/August, 4–6 weeks behind the eastern NOW and at about the same time as in the ice-covered BS. However, C. glacialis generally has a 2-year life cycle in ice-covered waters, maturing in April–May. Spawning starts when algae begin to sluff off the ice in May, and generally is over by mid-July, when the watercolumn phytoplankton bloom is maximal. Although the NEW is not as advantageous as the NOW for reproduction of C. glacialis, it still provides a one-year life cycle in comparison to the two-year cycle of ice-covered waters.
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3 Zooplankton of the Southern Ocean 3.1
Species Composition and Abundance
Hempel (1985) describes three large-scale, pelagic ecological provinces related to latitude, different water masses, the seasonal variability in sea ice cover, and bathymetry. The two provinces relevant to polynyas are the seasonal sea-ice zone and the pack-ice zone. The seasonal sea-ice zone is ice covered during winter and largely ice-free during summer. This zone may extend as far north as the Polar Front and south over the continental shelf in most sectors of the SO. The euphausiid, Euphausia superba, often dominates zooplankton abundance and biomass. When krill are scarce, four copepod species, Calanoides acutus, Calanus propinquus, Rhincalanus gigas and Metridia gerlachei, dominate the biomass throughout much of the sea-ice region (Voronina, 1998). The most southerly zone (the permanent pack-ice zone) includes regions over the continental shelf (e.g., the southwestern Weddell Sea and southern Ross Sea) and over deep water (e.g., sections of the Ross, Weddell, Admundsen, and Bellingshausen seas). In coastal waters the euphausiid, E. crystallorophias, replaces or co-exists with E. superba. Depending on the location, zooplankton biomass and production may be low in the permanent pack-ice zone and the seasonal pulse of primary production from phytoplankton and ice algae probably sinks out to support a rich epibenthic community (Hempel, 1985). In the coastal zone where most Antarctic polynyas occur, Eicken (1992) proposes an additional four subzones: the ice shelf, fast ice (sea ice that is anchored to land or ice shelves), coastal polynya, and the pack-ice boundary. Ice shelves are a floating extension of the Antarctic ice sheets and a source of fresh melt-water that acts to shoal and stabilize the mixed layer. The coastal polynyas also provide access to open water for predators, such as minke and killer whales, crabeater and leopard seals, and emperor and Adélie penguins (Gill and Thiele, 1997), in addition to being significant regions of primary production. The pack ice boundary, including the edge of the perennial pack ice and along the edges of polynyas, is usually characterized by highly deformed ice with many ridges, which provides a favorable habitat for Euphausia superba and E. crystallorophias, as well as other metazoans which graze on sea ice algae (Daly and Macaulay, 1988; Marschall, 1988; Stretch et al., 1988; Schnack-Schiel et al., 1998). In some areas where freshwater flows from beneath ice shelves, platelet ice forms in the water column and then floats up under fast ice and pack ice. Its open matrix permits biological production that may be up to 10 times higher (i.e. “superblooms”) than surrounding habitats (El-Sayed, 1971; Smetacek et al., 1992). Thus, different types of ice play important roles in the ecology of Antarctic marine zooplankton and their predators. 3.1.1
Zooplankton Abundance near the Weddell Sea Polynya
Data on winter zooplankton abundance are limited, but some information exists for the Weddell Sea polynya, which occurs near the Maud Rise. Even when the Weddell Sea Polynya is not open, a large number of leads and thin sea ice are observed in this region. West of Maud Rise chlorophyll concentrations are elevated during all seasons and 0.1–0.15 mg chl m−3 persists in open water into austral winter (June) (Spiridonov et al., 1996). Densities of Euphausia superba larvae (∼2000 ind 1000 m−3 and copepodite stages CIII–CV of Calanus propinquus (∼20,000 ind 1000 m−3 , Calanoides acutus (∼8000 ind 1000 m−3 ), and Rhincalanus gigas (∼2400 ind 1000 m−3 ) also are elevated in the upper 1000 m. In addition, the copepods, Microcalanus pygmaeus and Ctenocalanus citer, pteropods (Limacina spp.), and appendicularians are common. During late winter/early spring, zooplankton densities
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D. Deibel and K.L. Daly
continue to be elevated near Maud Rise, but C. acutus has the highest abundances (2000– 5450 ind 1000 m−3 ), C. propinquus is intermediate (<500–5000 ind 1000 m−3 ), and R. gigas is lowest (up to 300 ind 1000 m−3 ; Bathmann et al., 1993). In contrast, deep under the perennial pack ice in the Weddell Gyre abundances are much lower, with C. acutus averaging 1269 ind 1000 m−3 , Metridia gerlachei 1370 ind 1000 m−3 , and C. propinquus and R. gigas are scarce (Voronina et al., 2001). The mean total abundance of summer zooplankton is 32,200 ind 1000 m−3 , with the primary contributors being C. propinquus, M. gerlachei, R. gigas, Oncaea spp., Paraeuchaeta spp., Tomopteris spp., ostracods, and chaetognaths (Pakhomov et al., 2002). 3.1.2
Zooplankton Abundance in the Vicinity of Coastal Polynyas
Weddell Sea The continental shelf in the southern Weddell Sea lies south of the Antarctic Circle, and is relatively narrow on the northeastern end and much wider offshore of the Filchner-Rønne Ice Shelf (∼90–500 km) and northward along the east side of the Antarctic Peninsula. Arrigo and van Dijken (2003) identify at least four winter polynyas along the southeastern coast: one off the Rønne Ice Shelf, one near Halley Bay, and two others further east. Smetacek et al. (1992) also report a series of spring polynyas (October to December), that may be more than 20 km wide along the shelf. Sea ice recedes periodically as far as the Filchner-Rønne Ice Shelf and may remain open for several weeks. During June, young copepodites of large calanoid copepods are the dominant zooplankton on the shelf to the east of Kapp Norvegia, where densities are elevated compared to copepod densities offshelf (Spiridonov et al., 1996). Calanus propinquus has the highest abundance (∼20,000 ind 1000 m−3 ) similar to that over Maud Rise, followed by Calanoides acutus (∼5000 ind 1000 m−3 ), Euphausia superba larvae (∼2000 ind 1000 m−3 ), and Rhincalanus gigas (scarce). Numerous exoskeletons in net samples indicate that relatively high densities of adult E. superba also are present. These densities are similar to those observed on the Kapp Norvegia shelf during winter (Schnack-Schiel and Hagen, 1995), but higher than abundances from summer studies (Tables 6a, 6b). The winter investigations, however, used smaller mesh nets (100 μm) than the summer study, which may retain higher numbers of copepodites. Zooplankton abundances are higher over the northeastern end of the southern shelf (i.e., Kapp Norvegia), than over the southern end (i.e., Halley Bay and Filchner-Rønne Ice Shelf) or in the oceanic community off-shelf (Tables 6a, 6b). Offshore of Halley Bay, the southwesterly flowing current at the shelf edge is deflected to the northwest, advecting zooplankton into the western Weddell Gyre. South of Halley Bay, a narrow coastal current transports zooplankton southward along the shelf. Larval E. crystallorophias and Metridia gerlachei dominate the less diverse southern shelf community, where they are retained in a cyclonic gyre over the Filchner Depression. The small copepods, Oncaea spp. and Oithona spp., are primarily found in oceanic waters. Euphausiid distribution is spatially variable along the southern Weddell shelf and between years. During austral summer 1983, adult and juvenile Euphausia superba were present at most stations on the shelf in moderate numbers, with highest densities over the shelf slope on the northeastern end, especially near Kapp Norvegia (Piatkowski, 1989), while larvae were scarce. Fevolden (1980), however, found larvae of E. superba (16–30,000 ind 1000 m−3 ), E. crystallorophias (200–12,400 ind 1000 m−3 ), and T. macrura (200–1300 ind 1000 m−3 ) along the shelf between December 1978–March 1979. Two years later, Hempel and Hempel (1982) collected E. crystallorophias larvae along the entire shelf in January. E. superba calyptopis did not appear until February and were concentrated off Kapp
Zooplankton taxa
Southern Weddell Sea, summer1
Southern Weddell Sea, Kapp Norvegia shelf, summer1
Southern Weddell Sea, Halley Bay shelf, summer1
Weddell Sea, oceanic summer1
Enderby Land coast, late winter– spring2
Prydz Bay, on shelf, spring3
Prydz Bay, offshelf, spring3
Prydz Bay, on shelf, summer3
Prydz Bay, offshelf, summer3
Total abundance Copepoda Calanoides acutus Calanus propinquus Ctenocalanus citer Ctenocalanus spp. Metridia gerlachei Microcalanus pygmaeus Oithona frigida Oithona similis Oithona spp. Oncaea antarctica Oncaea curvata Oncaea spp. Paraeuchaeta spp. Rhincalanus gigas Stephos longipes Copepodites Ostracoda
20,170 16,059 1314 676
31,107
9247
22,968
+ +
+ +
+ +
249 125 17 1
1908 3 2 <1
608 441 339 43
271 26 21 4
71 39 9 13
797
+
+
+
4052
+
+
+
4
<1
17
1
1
775
+
+
+
837 75 59 154 6183 423
+ +
+ + + + +
+ + +
4 99
<1 <1
1 40
<1 <1
2 14
+ +
Zooplankton Processes in Arctic and Antarctic Polynyas
Table 6a: Mean abundance (ind 1000 m−3 ) of zooplankton from different Antarctic regions where polynyas occur. Only zooplankton species that occurred in more than one region and ≥5% of a taxa subcategory are included below. Species presence shown by (+). AP = Antarctic Peninsula; MB = Marguerite Bay
+ 1 (Continued on next page)
289
290
Table 6a: (Continued) Zooplankton taxa
Southern Weddell Sea, Kapp Norvegia shelf, summer1
Southern Weddell Sea, Halley Bay shelf, summer1
Weddell Sea, oceanic summer1
+
+
36 191
+
87 122
+
2208 1981
Enderby Land coast, late winter– spring2
Prydz Bay, on shelf, spring3
Prydz Bay, offshelf, spring3
Prydz Bay, on shelf, summer3
Prydz Bay, offshelf, summer3
+
30 <1∗
1799 938
152 5
241 8
20 <1
+ +
+ +
<1∗ 28
859 2
82 65
168 28
3 16
+
+
10 10 <1 1 1
2
1
2
3
1
2
1
1
1 68 12 56 3
<1 <1 104
7 2 5 3
<1 <1 2
3
104
3
1
228 586 10
+
+
+
499
+
+
+
4
1
<1
405
41
41
+
+
+
1 Boysen-Ennen and Piatkowski (1988). 2 Hosie and Stolp (1989). 3 Hosie (1994). ∗ Large densities observed on the under-surface of sea ice by divers.
1 3
1 1
D. Deibel and K.L. Daly
Euphausiacea Euphausia crystallorophias Euphausia superba Thysanoessa macrura Amphipoda Coelenterata Ctenophora Polychaeta Mollusca Clione limacina Clione sp. Limacina helicina Thecate pteropods Chaetognatha Eukrohnia hamata Sagitta gazellae Tunicata Fritillaria borealis Oikopleura sp. Salpa thompsoni
Southern Weddell Sea, summer1
Zooplankton taxa
Eastern Antarctica, summer4
Ross Sea, McMurdo Sound, spring5
AP, Croker Passage, early autumn6
AP, MB inner shelf, autumn7
Total abundance Copepoda Calanoides acutus Calanus propinquus Ctenocalanus citer Ctenocalanus spp. Metridia gerlachei Microcalanus pygmaeus Oithona frigida Oithona similis Oithona spp. Oncaea antarctica Oncaea curvata Oncaea spp. Paraeuchaeta spp. Rhincalanus gigas Stephos longipes Copepodites Ostracoda Euphausiacea Euphausia crystallorophias Euphausia superba Thysanoessa macrura
133 (1926)† 43 9 13
75,979 75,394 273 31 14,650
119,460 111,565 1010 50 180
77,723 66,063 32,402 657
318,116 61,096 2372 5240
48,630 19,664 219 1018
2970
19,060 11,480
930 26,269
8543 36,923
1474
5
14
135 4454 295
3071 1296 770
2501 246 7920
2125 3940
781 6588 2851 1786
1562 1460 120,073
3939ψ 1ψ 80
1065 762 49
118,650 1422 140
5
3 13
48,579
1020 4500
7943
13,500 56,940
7 941
12 (1926)† 7 (777)† <1 (1149)† 4 8
164 23 23
9
430 40 2530
AP, MB outer shelf, autumn7
AP, MB offshelf, autumn7
AP, MB inner shelf, winter8
AP, MB outer shelf, winter8
AP, MB offshelf, winter8
8065 6495
22,766 18,050
8752 7228
5372 758 13,751
587 826
1060 675
599 193
13,591 146 59
27
36
9
291
(Continued on next page)
Zooplankton Processes in Arctic and Antarctic Polynyas
Table 6b:
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Table 6b: (Continued) Zooplankton taxa
2
10 6 4 58
Ross Sea, McMurdo Sound, spring5
AP, Croker Passage, early autumn6
13 3 201 132 62
280 1 1160 31
70
<1
7
28
175 160 <10 93 40
AP, MB inner shelf, autumn7
2084
134,599
AP, MB offshelf, autumn7
13,298
AP, MB inner shelf, winter8
AP, MB outer shelf, winter8
AP, MB offshelf, winter8
59 47
246 459 1605
225 107
22
8
1
2084
134,599
13,298
25
1597
106
61
748
1098
17
317
389
317
28 58
AP, MB outer shelf, autumn7
63ψ
314 3
4 Hosie et al. (2000). 5 Foster (1987). 6 Hopkins (1985). 7 Daly, A. Timonin, and T. Semenova (unpubl.) 8 Ashjian et al. (2004). † Abundances from targeted tows. ψ Lancraft et al. (2004).
D. Deibel and K.L. Daly
Amphipoda Coelenterata Ctenophora Polychaeta Mollusca Clione limacina Clione sp. Limacina helicina Thecate pteropods Chaetognatha Eukrohnia hamata Sagitta gazellae Tunicata Fritillaria borealis Oikopleura sp. Salpa thompsoni
Eastern Antarctica, summer4
Zooplankton Processes in Arctic and Antarctic Polynyas
293
Norvegia. Similarly, Hubold et al. (1988) reported high densities (21,430 ind 1000 m−3 ) of E. superba calyptopis larvae primarily off Vestkapp at coastal and slope stations during February 1985, whereas E. crystallorophias larvae (various stages) occurred all along the shelf, but were 10-fold more abundant (15,000 ind 1000 m−3 ) in the south. In addition, Gutt and Siegel (1994), using an ROV during January 1988, observed relatively high densities (several 10’s to more than 230 krill m−3 ) of large E. superba near bottom at the shelf break off Halley Bay, which is the southern end of their reported range. Large E. superba also were observed feeding in the platelet ice layer of a “super-bloom” (more than 100 mg chl m−3 ) between Vestkapp and Halley Bay (Smetacek et al., 1992). Advection and latitudinal differences in the timing of the seasonal increase in primary production and zooplankton reproduction explains some of the variability of zooplankton abundance and composition along the Weddell Shelf (Boysen-Ennen and Piatkowski, 1988; Hubold et al., 1988). Polynya size and primary production in sea ice, platelet ice, and in the water column occur earlier over the northeastern shelf than further to the south (Smetacek et al., 1992). Euphausia crystallorophias calyptopis larvae are present in January, when larval E. superba are virtually absent. By February, E. superba calyptopis are abundant over the shelf and slope, indicating later reproduction compared to E. crystallorophias. Both E. superba and E. crystallorophias typically have a higher abundance of older stages in the north and younger stages further south along the shelf. Thus, reproduction may occur primarily along the northeastern shelf, after which larvae are transported southward along the coast. Lazarev Sea and Lützow-Holm Bay The shelf in the Lazarev Sea is narrow (26–95 km) and has the smallest polynyas in the SO (Arrigo and van Dijken, 2003). The concentrations of Euphausia crystallorophias during austral summer are similar to those in other regions (Tables 6a, 6b), with adults, juveniles, and larvae ranging from 2–683, 4–86, and 27–1689 ind 1000 m−3 , respectively (Pakhomov et al., 1998). In the Cosmonaut Sea, polynyas are relatively small and form intermittently (Arrigo and van Dijken, 2003). E. crystallorophias occurs in relatively low densities (on average 3.7–49.3 ind 1000 m−3 ) during summer at stations 500 m deep and just offshelf at stations 3000 m deep (Pakhomov and Perissinotto, 1996). The densest concentrations of larvae and adults are found in the vicinity of the summer polynyas. Tanimura et al. (1986) investigated the seasonal abundance and composition of zooplankton under fast-ice near Syowa Station in Lützow-Holm Bay, which has a relatively small polynya that forms aperiodically during spring–summer (Arrigo and van Dijken, 2003). Chlorophyll concentrations range up to 11.3 mg m−3 (Fukuchi et al., 1985a). Total zooplankton abundance is highest in austral winter (June: 5,000,000 ind 1000 m−3 ) and austral summer (January: 3,000,000 ind 1000 m−3 ) and lowest in March (700 ind 1000 m−3 ). Copepods (including nauplii) are the dominant component (85%), followed by larvae of benthic polychaetes. Paralabidocera antarctica, an ice-associated copepod, is the most abundant species during January, while Oithona similis and Oncaea curvata are the dominant species during the rest of the year. Appendicularians (Fritillaria borealis) also are abundant between January and April. Chaetognaths, ostracods, and low abundances of larval and adult Euphausia superba and E. crystallorophias also are present in the vicinity (Fukuchi et al., 1985b). Further to the east off Enderby Land zooplankton abundances are among the lowest in all sectors (Tables 6a, 6b). Prydz Bay The Prydz Bay region has a relatively wide continental shelf (179–228 km) and one of the largest polynyas. Hosie (1994) summarizes the zooplankton community ecology derived from surveys conducted during austral spring and summer between 1981 and
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D. Deibel and K.L. Daly
1987 (Tables 6a, 6b). Although there were inter-annual differences, in general near-shore communities dominate south of 67◦ S and are characterized by Euphausia crystallorophias, amphipods, and sometimes the mysid, Antarctomysis maxima. E. crystallorophias, with densities ranging from 3.6–847 ind 1000 m−3 , also occur in this region (Pakhomov and Perissinotto, 1996). The pteropod, Limacina helicina, and the salp, Ihlea racovitzai, are present both near shore and in oceanic assemblages. Zooplankton on the outer shelf and offshelf vary, with oceanic species, such as Euphausia superba, Thysanoessa macrura, and Salpa thompsoni dominating at different stations and years. Chaetognaths and the copepods, Calanoides acutus, Calanus propinquus, Metridia gerlachei, and Rhincalanus gigas, are common, especially offshore. Appendicularians also are prevalent in some years. Latitudinal zonation (51–68%), seawater temperature (28–38%), and chlorophyll (27–38%) explain much of the variability in community composition during November and January/February surveys, but not during March (Hosie, 1994). Ice recession also is an important variable (60%) during periods of active ice ablation. Based on these surveys, Hosie (1994) concludes that E. superba is the most important species in terms of biomass and as food for upper trophic level predators. This euphausiid is most abundant near the continental shelf break in a transition zone between neritic and oceanic communities. Overall, zooplankton densities are lower in Prydz Bay compared with abundances in other regions (Tables 6a, 6b). Eastern Antarctica Eastern Antarctica (east of Prydz Bay to east of Adélie Land), between 80–150◦ E, has a relatively narrow continental shelf (100–200 km) with no large embayments. Summer phytoplankton biomass is highest in inshore regions of the Mertz Glacier Polynya, but the dominant bloom species is Phaeocystis (Sambrotto, 2003), which may not be ingested by many zooplankton. Summer sampling on the outer shelf and offshore suggests that zooplankton densities are relatively low in this region (Tables 6a, 6b). The neritic zone and the Mertz Glacier Polynya are dominated by Euphausia crystallorophias (Hosie et al., 2000; Nicol et al., 2004). The highest densities of E. superba occur near the shelf break between 80–115◦ E in the vicinity of greater winter sea ice extent and higher summer chlorophyll concentrations (Nicol et al., 2000). Targeted tows on aggregations detected by acoustics yield much higher densities of E. superba and E. crystallorophias than are collected in the routine net samples; therefore, total biomass may be underestimated for this region (Tables 6a, 6b). Ross Sea The Ross Sea has a wide continental shelf (150–715 km). The Ross Sea polynya occurs near the ice shelf in late winter due to katabatic winds coming from the continent and increases markedly in size during spring. The seasonal bloom is one of the largest in the Antarctic and is predominately composed of Phaeocystis antarctica in the south-central sector of the polynya and diatoms on the western side (W. Smith et al., 1996; Arrigo and van Dijken, 2003). Small coastal polynyas also occur over the narrow shelf to the east along Marie Byrd Land. Other locations may remain ice-covered year round. Little is known about the abundance and distribution of zooplankton throughout the Ross Sea, because most biological investigations have focused on other trophic levels. Zooplankton sampling has primarily occurred in McMurdo Sound or Terra Nova Bay during summer. Copepods (i.e., Calanoides acutus, Paraeuchaeta antarctica, Metridia gerlachei, Oithona similis), euphausiids (Euphausia crystallorophias, E. superba), and pteropods (Limacina helicina) generally dominate the community in terms of abundance and biomass (Hopkins, 1987; Foster, 1987; Azzali and Kalinowski, 2000; Sala et al., 2002; Tables 6a, 6b). Oithona is the most abundant copepod species in McMurdo Sound (Hopkins, 1987; Foster, 1987),
Zooplankton Processes in Arctic and Antarctic Polynyas
295
Figure 4: Acoustic echogram of a dense layer of Euphausia crystallorophias in the upper 100 m over the shelf break in the eastern Ross Sea. SV is volume backscattering strength in dB; blue is low density and black is high density (K. Daly, unpubl.). whereas Metridia is more common in Terra Nova Bay (up to 138,000 ind 1000 m−3 ; Carli et al., 2000). Euphausiids have a patchy distribution, and therefore it is likely that they are not wellrepresented in the studies described above. In the Ross Sea, Euphausia crystallorophias and E. superba are spatially separated. E. crystallorophias occur over the shelf, primarily south of 74◦ S in the vicinity of the Ross Ice Shelf (Bottino, 1974; Foster, 1987; Hopkins, 1987; Azzali and Kalinowski, 2000), while E. superba larvae are primarily north of the shelf break. Although E. superba occur over the northern part of the Ross Sea shelf, the highest abundances (average: 1000 ind 1000 m−3 ; maximum: 1918 ind 1000 m−3 ) are located near the shelf break (Sala et al., 2002). E. crystallorophias has lower average densities (19 ind 1000 m−3 ) over the shelf than E. superba, but higher maximum densities (6148 ind 1000 m−3 ). The narrow continental shelf (approximately 100 km) off of Marie Byrd Land and in the Amundsen Sea has a number of small coastal polynyas (Arrigo and van Dijken, 2003). A summer study in the eastern Ross Sea indicates that Euphausia superba occur throughout this region, along with Thysanoessa macrura (primarily offshelf) and E. crystallorophias (onshelf) (Ackley et al., 2003). Hydroacoustic data show that acoustic targets are 5 to 15 times higher in coastal leads over the shelf than offshelf. Most acoustic targets detected in the upper 200 m of the water column occur above 60 m throughout the study area (Daly, unpubl.). Based on net tows, the acoustic targets (Figure 4) over the shelf and near the shelf break are mainly attributed to dense aggregations of E. crystallorophias and juvenile fish (Pleuragramma antarcticum). This study concludes that coastal polynyas of the eastern Ross
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D. Deibel and K.L. Daly
Sea are an important habitat for seals and penguins and provide predators with open water access to prey. Antarctic Peninsula The shelf west of the Antarctic Peninsula is relatively broad (150– 300 km) and the Peninsula extends further north than other Antarctic coastal regions. The continental shelf on the lower part of the Antarctic Peninsula is typically covered by sea ice in winter, except for a polynya in Marguerite Bay. Winter chlorophyll concentrations within the Marguerite Bay polynya average 0.02 mg m−3 , and by early September low levels of primary production occur in both sea ice and in surface waters (Daly, 2004). The polynya in Marguerite Bay is highly productive and remains intact during spring (Arrigo and van Dijken, 2003). The abundance and biomass of zooplankton on the western Antarctic Peninsula shelf is higher than other shelf regions (Tables 6a, 6b). It also is one of the few areas for which there is information on the seasonal abundance and biomass of the zooplankton community. In Croker Passage, which is on the inner shelf of the upper section of the Peninsula, the zooplankton community during autumn 1983 was composed of 106 species <17 mm long (Hopkins, 1985). Lancraft et al. (2004) reports macrozooplankton concentrations from those samples. The copepods, Metridia gerlachei, Microcalanus pygmaeus, Oncaea antarctica, and Oncaea curvata, are the most numerous species, with the small O. curvata comprising almost half of the total zooplankton abundance. In Marguerite Bay, on the lower part of the Peninsula, total zooplankton abundance (Tables 6a, 6b) is somewhat lower than that in Croker Passage, primarily owing to the lack of small Oithona and Oncaea. Large copepod species, however, are more abundant in Marguerite Bay. Dense concentrations of large E. superba occur over the inner shelf in bays and fjords during autumn and winter, which are in the vicinity of the polynya (Tables 6a, 6b). Larval E. superba occur throughout the region in autumn and winter, with highest densities on the outer shelf and near the shelf break (341,615 ind 1000 m−3 ) and lowest densities near-shore (5835 ind 1000 m−3 ; Daly, unpublished). In addition, the ctenophore, C. antarctica, appears to be episodically important during summer, autumn, and winter along the Antarctic Peninsula (Kaufmann et al., 2003; Scolardi et al., 2006). 3.2
Individual and Community Biomass
Both copepod and euphausiid species have highly variable dry weight (DW) and lipid (% DW) concentrations during all seasons (Figures 5–7). Additional data are needed, particularly for winter, before valid seasonal and regional comparisons can be made. Based on limited data, DW and lipids appear to be higher in Calanoides acutus, Calanus propinquus, and Metridia gerlachei CV and females collected over shelves in comparison to individuals collected in deep water (Figure 5). The geometric mean lipid content is higher in C. acutus (36%) and C. propinquus (34%) than in Metridia (19%) during summer and lipid declines in all three copepod species during winter. For Euphausia crystallorophias, average lipid content is highest in adults (34% DW) and juveniles (31% DW) during austral summer (Figure 6). In contrast, E. superba has a lower average lipid accumulation (adults, 23% DW; juveniles, 21% DW; Figure 7). Lipid content is highest in E. superba in late summer and autumn, then declines through winter. Larval stages have even lower lipid storage (average 16.6% DW) during summer and autumn, which declines to 9.6% DW by November. The seasonal decline in lipids suggests that both copepods and euphausiids use lipids to some extent as fuel for metabolism during winter.
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Figure 5: Seasonal dry weight and lipid content for Calanoides acutus, Calanus propinquus, and Metridia gerlachei copepodite stage 5 and adult females. Symbols are individuals collected over continental shelves (Q) versus individuals collected offshelf (1). Data from Hagen (1988), Schnack (1985), Schnack-Schiel et al. (1991), Schnack-Schiel and Hagen (1995), Falk-Petersen et al. (1999), and K. Daly (unpubl.). Regional and seasonal differences in body size, weight, and body carbon (C) and nitrogen (N) in krill larvae are correlated with food concentration. E. superba larvae collected onshelf west of the Antarctic Peninsula during late summer have a larger size and weight concomitant with higher chlorophyll values than individuals collected offshore in the Drake Passage or in the Lazarev Sea with much lower chlorophyll (Huntley and Brinton, 1991; Meyer et al., 2002b; Meyer et al., 2003; Daly, 2004). Furthermore, larval body DW and C and N decrease between autumn and winter in the vicinity of the Marguerite Bay polynya as
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D. Deibel and K.L. Daly
Figure 6: Seasonal dry weight and lipid content for Euphausia crystallorophias larvae (0–13 mm), juveniles (13–20 mm), immature adults (21–25 mm), mature adults (26–34 mm), and female life history stages. Data from Hagen (1988), Ikeda and Kirkwood (1989), Kattner and Hagen (1998), Falk-Petersen et al. (1999), Pakhomov et al. (1998), Ju and Harvey (2004), Nicol et al. (2004), and K. Daly (unpubl.). a result of food limitation (Daly, 2004). Despite their less robust winter condition, starved larvae can survive for up to a month using about 1% of body C and N d−1 . These results suggest that combusting body protein and lipid to support metabolism may be a viable shortterm overwintering behavior. Seasonal differences in body C and N of dominant copepods also occur. Calanoides acutus (46% C, 9.4% N of DW), C. propinquus (49% C, 13% N), and Metridia gerlachei (47% C, 12% N) have a similar body content for CV and adult stages during spring and summer (Ikeda and Mitchell, 1982; Schnack, 1985). Body C and N values, however, during winter
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Figure 7: Seasonal dry weight and lipid content for Euphausia superba larvae (0–15 mm), juveniles (15–30 mm), and adults (>30 mm). Symbols are calyptopis larvae (F), furcilia stages I–III (2), furcilia stages IV–VI (X), furcilia stage unknown (∗), juveniles (Q), and adults (—). Data from Hagen (1988), Kawaguchi et al. (1986), Ikeda and Kirkwood (1989), Daly (1990), Huntley and Brinton (1991), Huntley and Nordhausen (1995), Daly (1998), Hagen et al. (2001), Atkinson et al. (2002), Meyer et al. (2002a), Meyer et al. (2002b), Stübing and Hagen (2003), Meyer et al. (2003), Stübing et al. (2003), Daly (2004), Ju and Harvey (2004), and K. Daly (unpubl.). from the Antarctic Peninsula are variable. Huntley and Nordhausen (1995) report relatively high values for C. acutus (54% C, 6.2% N) and M. gerlachei (47% C, 6.2% N), whereas C. propinquus (24% C and 3.8% N) appear to be significantly lower (Daly unpubl.), likely because C. acutus overwinters in diapause below 250 m, while C. propinquus and M. gerlachei are omnivores and remain in surface waters (Hopkins and Torres, 1989; Schnack-Schiel and Hagen, 1995). Overall, the shelf west of the Antarctic Peninsula has the highest zooplankton community biomass of all Antarctic shelves, even if Antarctic krill are excluded from the total biomass estimate (Tables 7a, 7b). Community biomass is an order of magnitude lower in waters over the Ross Sea and Weddell Sea shelves and another order of magnitude lower over all other shelves. Since the zooplankton community is not well sampled in the Ross Sea, biomass may be under-estimated. Alternatively, mortality in the Ross Sea may be significantly higher compared with other regions owing to the large number of upper trophic level predators (Ainley, 1985). In the southern Weddell Sea the maximum summer biomass (11.2 mg DW m−3 ) is near Kapp Norvegia, where juvenile and adult Euphausia crystallorophias contribute about 30% of the total. Dry weight biomass decreases with latitude, with the lowest biomass (4 mg m−3 ) occurring near Halley Bay and over the southern shelf (Boysen-Ennen et al., 1991). In this
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Table 7a: Mean biomass (mg DW 1000 m−3 of zooplankton from different Antarctic regions where polynyas occur. Only zooplankton species that occurred in more than one region and ≥5% of a taxa subcategory are included below. DW is dry weight; total biomass is integrated 0–1000 m. AP = Antarctic Peninsula; MB = Marguerite Bay Zooplankton taxa
Southern Weddell Sea, KnappNorvegia shelf, summer1
Southern Weddell Sea, Weddell Sea, oceanic, Halley Bay summer1 shelf, summer1
Ross Sea, AP, Croker McMurdo Passage, Sound, early autumn3 2 summer
AP, MB, inner shelf, autumn4
Total biomass (g DW/m2 ) Copepoda Calanoides acutus Calanus propinquus Metridia gerlachei Paraeuchaeta antarctica Rhincalanus gigas Calanidae copepodites Ostracoda Euphausiacea Euphausia crystallorophias Euphausia superba Thysanoessa macrura Amphipoda Cyllopus lucasi/ magellanicus Epimeriella macronyx Eusirus propeper dentatus Hyperiella dilatata Primno macropa Vibilia propinqua Mysidacea Decopoda Acanthephyra pelagica Coelenterata Dimophyes arctica Diphyes antarctica Pyrostephos vanhoeffeni Hydromedusae Polychaeta Mollusca Limacina helicina Limacina sp. Marseniopsis sp. Chaetognatha Eukrohnia hamata Sagitta gazellae Unid. chaetognaths
11.3 6301 1608 1088 620 297
3.96 730 224 130 213 80.5
9.20 4544 1051 987 944 287
2.68 1633 1196 8.87 116 111
295 43,074
9.3 2648 4.4 4121 3667
1.8 63.2 20.3 927 910
267 773 62.2 1168 91.8
402 51.5 6.6
8.5 7.9 162 19.7
885 191 103 10.6
1.3
29.8 99.9
244 2681 607 27.6 1230 467 54.5
32.2 172 172
4.28
159 239,120
763 236,966
239,000 120 32.1
1658
18.4
0.7 2.8
2.1
11.1 61.9 14.3
4.28
5.1
10.6
67.2 0.8 47.1 18.1
250 158 76.6 14.8
58.9 56.8 833 521 211 37.9
0.612
20.6 50.5 0.1
8.9 806 666
108 165 10
130 707 704
43.2 208 11.4 59.2 137
108 177 4.3 104 68.1
38.8 582 30.5 123 428
0.918 0.306
9.6
18.4
7368
27.4 14.4 8.5 4.5
2757
2757 123 202
111 <1
201 4.90 4.90
25.8 21.6
2103
(Continued on next page)
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Table 7a: (Continued) Zooplankton taxa
Southern Weddell Sea, KnappNorvegia shelf, summer1
Southern Weddell Sea, Halley Bay shelf, summer1
Weddell Sea, oceanic, summer1
Ross Sea, McMurdo Sound, summer2
AP, Croker Passage, early autumn3
Tunicata Fritillaria sp. appendicularians Salpa thompsoni Salps
507
877
1631
1.53 1.53
1730
507
877
1631
AP, MB, inner shelf, autumn4
1730
1 Boysen-Ennen et al. (1991). 2 Hopkins (1987). 3 Hopkins (1985). 4 Ashjian et al. (2004).
region E. crystallorophias and to a lesser extent the pteropod, Limacina helicina, dominate the biomass. Assuming a wet to dry weight conversion of 20%, the average biomass of E. crystallorophias is even lower in the Lazarev Sea (0.5 mg m−3 ), the Cosmonaut Sea (0.3 mg m−3 ), and Prydz Bay (2.6 mg m−3 ; Pakhomov and Perissinotto, 1996). In Lützow-Holm Bay total zooplankton wet weight biomass under sea ice between May and December varies, with highest levels in winter (25.5 mg m−3 ) and spring (15.2 mg m−3 ; Fukuchi et al., 1985b). Copepods, chaetognaths, and ostracods are the dominant taxa. In Prydz Bay dry weight biomass (11.9 mg m−3 ; Hosie et al., 1988) is comparable to the maximum values over the Weddell shelf. Hopkins (1987) reported that for the Ross Sea biomass ranges from 1.8–4.25 mg DW m−3 for all zooplankton <20 mm in length in McMurdo Sound, which is lower than the copepod biomass (9.32–11.4 mg DW m−3 ) in nearby Terra Nova Bay (Sertorio et al., 2000). Euphausia crystallorophias furcilia contribute significantly to the biomass in McMurdo Sound (Hopkins, 1987). Adult E. crystallorophias occur in high densities close to the Ross Ice Shelf, where biomass ranges from 0.08–474 mg DW m−3 , while E. superba has a high biomass on the outer shelf adjacent to Victoria Land (677– 1511 mg DW m−3 ; Sala et al., 2002). Along the Antarctic Peninsula the euphausiid, Euphausia superba, dominates the community in terms of biomass, followed by the large copepods, Metridia gerlachei, Calanoides acutus and Paraeuchaeta antarctica. In addition, the ctenophore, Callianira antarctica, and the mysid, Antarctomysis ohlinii, are important contributors to neritic biomass. 3.3
Individual Feeding Rates and Diet
Ingestion rate experiments conducted near the Antarctic Peninsula for Calanoides acutus, Calanus propinquus, Rhincalanus gigas, Metridia gerlachei, and Euphausia superba indicate that all species efficiently ingest the most abundant food items (diatoms, dinoflagellates, and microflagellates; 5–175 μm in length) with log-linear feeding rates up to the maximum food concentration offered (189 μg C l−1 ; Schnack, 1985, Schnack et al., 1985). Copepods are most active on the shelf, resulting in higher filtration, ingestion, assimilation efficiency, and respiration rates (Schnack et al., 1985), with M. gerlachei having the highest weightspecific ingestion rate (0.36 μg C μg ind−1 d−1 ; Schnack, 1985). Daily rations for C. acutus, C. propinquus, and M. gerlachei are 5–16% body C, while R. gigas is lower (2–5% body C). Grazing impact by copepods ranges from 3.8 to 50% of primary production on Antarctic shelves (Schnack et al., 1985; Li et al., 2001; Cabal et al., 2002).
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Table 7b: Zooplankton taxa
AP, MB, outer shelf, autumn4
AP, MB, offshelf, autumn4
AP, MB, inner shelf winter4
AP, MB, outer shelf, winter4
AP, MB, off shelf winter4
Total biomass (g DW/m2 ) Copepoda Calanoides acutus Calanus propinquus Metridia gerlachei Paraeuchaeta antarctica Rhincalanus gigas Calanidae copepodites Ostracoda Euphausiacea Euphausia crystallorophias Euphausia superba Thysanoessa macrura Amphipoda Cyllopus lucasi/ magellanicus Epimeriella macronyx Eusirus propeper dentatus Hyperiella dilatata Primno macropa Vibilia propinqua Mysidacea Decopoda Acanthephyra pelagica Coelenterata Dimophyes arctica Diphyes antarctica Pyrostephos vanhoeffeni Hydromedusae Polychaeta Mollusca Limacina helicina Limacina sp. Marseniopsis sp. Chaetognatha Eukrohnia hamata Sagitta gazellae Unid. chaetognaths Tunicata Fritillaria sp. appendicularians Salpa thompsoni Salps
85.2 38,626
19.0 4110
259 12,064
32.9 21,442
9.06 3565
169 39,210
131 11,213
599 235,110
459 5317
196 2492
150
21
2119
262
38
5
14
6339
3
18
524
1119
2143
340
174
524 4109 773
1119 26 69
2143 26 36
340 304 501
174 67 41
731
34
36
425
23
1510
2266
412
4291
2470
98
79
7 91
12 12
79
present
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Seasonal feeding patterns were investigated for Calanoides acutus and Calanus propinquus in the south east (SE) Weddell Sea using gut contents and faecal pellet size in the posterior gut (Pasternak and Schnack-Schiel, 2001). Only 5.4% of C. acutus and 18% of C. propinquus contained food out of >3000 individuals examined. In general, C. acutus females contain food only during late spring near surface. At stations over the shelf, the percentage of all C. acutus stages with food is low in spring and summer, but increases to a maximum in autumn. Males only occur in late spring and contain little food. Females feed throughout the water column in late winter/early spring, then primarily in deeper water in late spring and summer, after which they appear to stop feeding. A comparison of gut contents with ambient microplankton concentrations suggests that all stages of C. acutus prefer small diatoms, dinoflagellates, and protozoans. C. propinquus CII and CIII also prefer small diatoms and dinoflagellates, while later stages prefer large diatoms and protozoans. Based on winter metabolism and behavior, Paraeuchaeta antarctica, Metridia gerlachei, Rhincalanus gigas, Thysanoessa macrura, and Euphausia superba remain active during winter (Huntley and Nordhausen, 1995), and thus can take advantage of early primary production in polynyas. Feeding behavior and overwintering strategies also vary with life history stages of Euphausia superba. Laboratory experiments show that first-feeding larvae may not survive delayed food or when only small flagellates are available (Ikeda, 1984; Ross and Quetin, 1989). Early primary production in polynyas provides an important food source for young larvae. During summer in Marguerite Bay, calyptopis (C) III and furcilia (F) I and II ingest 28, 25, and 15% body C, respectively, while feeding in dense phytoplankton blooms (Meyer et al., 2003). CIII and FI larvae are most efficient at feeding on small diatoms, and all larvae preferentially feed on larger protozoans. Estimated C assimilation efficiencies range from 74–92% and gross growth efficiencies from 73–82%. The critical food concentration to maintain growth is about 3 mg chl m−3 , a level rarely observed in offshore waters. In contrast, feeding experiments for FIII in the Lazarev Sea during autumn using natural assemblages at in situ concentrations (80–98% diatoms) did not indicate any feeding selectivity for items 12–220 μm in size (Meyer et al., 2002b). The daily ingestion rate is equivalent to 0.4–1.3% body C d−1 , which does not offset the 2.5% body C d−1 loss to metabolism. Alternative food sources or concentrated layers of microplankton may be needed to support their metabolic demand. Sea ice biota is an alternative food source for older Euphausia superba furcilia (Hamner et al., 1989; Daly 1990, 2004), juveniles (Daly and Macaulay, 1991; Daly, 1998), and adults (O’Brien, 1987; Marschall, 1988). Since larvae must eat during winter, juvenile recruitment in spring may depend on the timing and extent of sea ice and associated biota. Indeed, several studies show a correlation between recruitment indices of krill and sea ice extent for the Antarctic Peninsula (Kawaguchi and Satake, 1994; Siegel and Loeb, 1995). Despite this, the amount of sea ice biota at the ice-water interface where krill feed remains largely unknown. Furthermore, given that a large percentage of the continental shelf is above the Antarctic Circle, light limitation may strongly constrain the availability of ice algae. For example, a winter study in Marguerite Bay yielded very low concentrations of sea ice biota on the undersurface of sea ice (0.05 and 0.07 mg chl m−3 in 2001 and 2002, respectively) (Daly, 2004). In this region, larval krill growth rates are near zero until September when irradiance, sea ice chlorophyll (up to 0.35 mg m−3 ), and larval growth rates increase. Gut and faecal pellet contents indicate that larvae are opportunistic scavengers on molt exuviae, detritus, and microzooplankton during winter. Juvenile and adult Euphausia superba have a greater flexibility to survive low food conditions than larval stages. Juvenile and adult krill under fast ice in Lützow-Holm Bay between
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autumn and spring (May–October) have a reduced metabolism in winter and some portion of the population feeds on benthic detritus (Kawaguchi et al., 1986). By spring juveniles feeding under sea ice have ingestion rates (51–69 μmol C ind−1 d−1 ) that meet metabolic demands and support growth rates (6.4% increase mol t−1 ) similar to those during summer (Daly, 1998). Highest growth rates of juvenile E. superba west of the Antarctic Peninsula are related to diatom blooms, whereas the lowest rates occur when prymnesiophytes or cryptophytes dominate (Ross et al., 2000). Ingestion rates based on gut fluorescence range from 0.06–3.65 μg pigment ind−1 d−1 for krill <30 mm and 0.3–96.9 for krill >30 mm (Daly, 1998; Pakhomov et al., 2002). Estimated daily rations for individuals 16–55 mm range from 1–17% of body C or 1–81% of primary production. Euphausia crystallorophias also is an opportunistic feeder, foraging over a wide range of depths. Individuals may feed primarily on micro- and mesozooplankton in spring and on phytoplankton during summer (Hopkins, 1987) and they may feed on the undersurface of sea ice (O’Brien, 1987). Faecal pellets from individuals below sea ice off Enderby Land substantiate feeding on both phytoplankton and zooplankton (O’Brien, 1987). In addition E. crystallorophias may feed on phyto-detritus at the sea floor (200 m) based on ROV observations in the Ross Sea during summer (V. Asper, pers. comm.). Summer chlorophyll ingestion rates in shelf waters of the Lazarev Sea are 2.5–25.2 ng chl ind−1 h−1 for CIII/FI stages and 52–471 ng chl ind−1 h−1 for adults (Pakhomov et al., 1998). Daily rations are estimated to range from 14% body C for larvae to 2.1% body C for adults (Pakhomov et al., 1998). The population grazing impact is relatively low (∼ 1% of primary production), except in dense swarms where it reaches 97% (Pakhomov and Perissinotto, 1996). During winter in the Mertz Glacier Polynya, juvenile and adult E. crystallorophias gut contents show little evidence of feeding, and fatty acid biomarkers indicate a reliance on carnivory (Nicol et al., 2004). In addition, growth rates and molting rates decrease compared to spring adult values (0.11% body length d−1 ) or juvenile summer rates (0.35% body length d−1 ). There is no published information on E. crystallorophias in relation to Phaeocystis. Suh and Nemoto (1988) suggest that based on feeding appendage morphology, E. crystallorophias may not efficiently ingest flagellates. Thus, extensive Phaeocystis blooms in the Ross Sea and Prydz Bay may occur even in regions where E. crystallorophias are abundant. The role of salps, and in particular Salpa thompsoni, as grazers and their relationship to Antarctic krill is unresolved. Due to their fast growth rates and reproduction, salps may dominate plankton abundance and biomass (wet weight) and exclude other zooplankton (Huntley et al., 1989; Perissinotto and Pakhomov, 1998). Furthermore, salps may be competitors of krill and perhaps predators on larval krill. Additional evidence for competition is reported for the Antarctic Peninsula (Loeb et al., 1997) and the Lazarev Sea (Perissinotto and Pakhomov, 1998). More recent studies, however, indicate that salps typically inhabit different water masses than krill and therefore, are usually spatially segregated (Kawaguchi et al., 1998; Pakhomov et al., 2002). Salp abundances may be elevated near the western continental shelf of the Antarctic Peninsula and in eastern Antarctica, primarily in association with warmer water of the ACC that comes near the shelf in these regions. Consequently, they may not be abundant in most polynyas. 3.4
Faecal Pellet Production and Vertical Flux
Weddell Sea Krill faecal pellets, with copepod pellets present in low numbers, dominate sediment trap material on the SE Weddell shelf (Bodungen et al., 1988). Other small, round, or triangular pellets (36% of pellets by volume) are attributed to microzooplankton
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(Nöthig and Bodungen, 1989). Highest flux rates (POC equivalent to 43–65% of the daily primary production) occur when krill pellets are present in traps. Elsewhere, the vertical flux is from 1–15% of production. Most pellets appear to contain diatoms. In Halley Bay during summer, chlorophyll concentrations (up to 1.9 mg m−3 ) and abundance of euphausiid faecal pellets (420 mm3 m−2 ) are higher in the vicinity of the shelf polynya than offshore (González et al., 1994). The vertical distribution of pellets is relatively constant through 250 m and both Euphausia superba and E. crystallorophias contribute to the pellet flux. The abundance of cyclopoid copepods (known to consume pellets) and euphausiid faeces show an inverse relationship, with cyclopoids being abundant offshelf and krill faecal material higher onshelf. Euphausiid pellets in this region typically contain fragments of diatoms and some remains of crustaceans. A third summer study off of Kapp Norvegia (Bathmann et al., 1991) detected three sedimentation events (>2 g DW m−2 d−1 ). A small early January flux was composed of copepod and oval pellets, while the first large flux event was dominated by large krill pellets (64% of total C) containing ice algae. Other events resulted from the flux of densely packed round pellets and diatoms. Lützow-Holm Bay The particulate flux in ice-covered waters during summer is primarily composed of faecal pellets, 200–1000 μm long (Fukuchi and Sasaki, 1981). Presumably these pellets are produced by copepods, as they are the most abundant zooplankton, followed by foraminifera, chaetognaths and appendicularians. Total zooplankton abundance is 12– 60 ind m−3 , with chlorophyll biomass averaging 0.02 mg m−3 under the sea ice. The total particulate flux is calculated to be 21, 103, and 27 mg C m−2 d−1 at 50, 100, and 150 m, respectively. Prydz Bay region Faecal pellet fluxes in Ellis Fjord, east of Prydz Bay, during January range from 86 to 6.1 × 104 pellets m−2 d−1 , while chlorophyll ranges from 2–16 mg m−3 (Beaumont et al., 2001). Pellet types include: (1) mini-spherical pellets, 5–100 μm in diameter, that are possibly produced by protozoans, (2) irregularly-shaped pellets, ca. 30 μm in diameter, typical of heterotrophic dinoflagellates, (3) cylindrical pellets, 50 × 300 μm, produced by large copepods, (4) cylindrical pellet strings, about 70 × 200 μm, produced by euphausiids, and (5) oval pellets, 100 × 130 μm, that are attributed to small copepods. Oval pellets make up 38–77% of the total pellets collected and are likely produced by Oncaea curvata, which dominate zooplankton abundance. Pellets primarily contain diatoms, with some silicoflagellates, flagellates, and dinoflagellate cysts. The pellet flux at 40 m is only 12% of that collected at 5 m, suggesting that many pellets are being recycled near surface. Based on zooplankton densities and pellet flux, a mean pellet production rate of 8 pellets ind−1 d−1 is estimated which is lower than that reported for most copepods globally (Mauchline, 1998). Ross Sea Although phytoplankton blooms occur each year over the central and western shelf, the particulate vertical flux is highly variable in time and space. A few studies report that zooplankton faecal pellets are a significant component of the particulate flux in these regions. Mesozooplankton faecal pellets, similar to those produced by euphausiids, account for up to 70% of the mass flux north of Ross Island (Dunbar et al., 1998). In contrast, faecal pellets are not a dominant component of the flux where Phaeocystis blooms reside, suggesting that this phytoplankton species is a less desirable food. A year-long investigation of pellet fluxes in the south-central region near the Ross Sea ice shelf shows a seasonal pattern with a peak flux (5370 pellets m−2 d−1 ) in early February and relatively high fluxes (1949
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pellets m−2 d−1 ) into April (Accornero and Gowing, 2003). Winter fluxes range from 0–4 pellets m−2 d−1 . The types of pellets are similar to those described for Ellis Fjord. Oval pellets are most abundant, whereas cylindrical pellets are important in terms of volume. Faecal pellets account for up to 49% of the total DW flux and contribute up to 59% of the total organic carbon flux. The annual pellet flux, 31 × 104 m−2 , is more than three times higher than that in the northern Weddell Sea (Fischer et al., 1988). Oval pellets may be produced by the abundant cyclopoid copepods, athecate dinoflagellates, or pteropods. Indeed, the disappearance of one type of oval pellet coincided with a mass sedimentation of pteropods in April. Diatoms are the dominant component of most faecal pellets. In addition, oval pellets and ellipsoidal pellets are significantly correlated with biogenic silica and organic carbon fluxes. Gowing et al. (2001) examine short-term (∼2 days) particle fluxes at five sites on the shelf, including the central and western Ross Sea polynyas. Carbon cycling and flux dynamics of Phaeocystis and diatoms vary owing to differences in size, composition, and grazing by zooplankton. While it is clear that Antarctic zooplankton graze on diatoms, feeding on Phaeocystis, especially large colonies, is uncertain. Phaeocystis and diatom carbon typically dominate sediment trap material (20–40% of organic carbon flux), but heterotrophic dinoflagellates, ciliates, and radiolarians also may be important. Faecal pellets contribute 5–30% of the carbon flux in the Ross Sea polynya and 30–48% in the western polynya. Gowing et al. (2001) conclude that mesozooplankton grazing is higher in the western polynya. Pellets from microzooplankton grazers exceed those for mesozooplankton, suggesting that microzooplankton influence the POC flux through grazing and sedimentation. Another study in the Terra Nova Bay polynya indicates that the particulate flux is primarily due to diatoms, silicoflagellates, faecal pellets, and shelled pteropods (Accornero et al., 2003). Faecal pellet fluxes are higher at 180 m than at 90 m with maximum fluxes during late summer/early autumn. Annual fluxes are 3983 and 1151 × 103 pellets m−2 y−1 in 1996 and 1997, respectively. In each year, this is equivalent to 11 and 7.6% of the total mass flux and 53 and 34% of the organic carbon flux. Oval pellets again account for a large percentage (60–91%) of the flux in summer, while cylindrical pellets typical of euphausiids make up most of the flux during winter. Pteropods also are the dominant organism in trap samples during summer. Antarctic Peninsula Little is known about vertical fluxes associated with polynyas along the Peninsula. Daly (1998) reports that the average faecal pellet mass produced by juvenile Euphausia superba from in situ feeding during November is 0.26 mg DW krill−1 h−1 and pellet size is 1–4 mm long and 140 μm in diameter. The particle flux in coastal bays, which is dominated by krill pellets, is much higher than expected based on primary production (D. Karl, pers. comm.). Since juveniles primarily feed on the undersurface of sea ice, sea ice production likely fuels the flux. Krill pellets (300–1200 μm long, 160 μm diameter) also dominate the particulate flux near Bransfield Strait during spring and summer (Bodungen, 1986; Wefer et al., 1988). Based on sediment trap results, Bodungen (1986) estimates that krill grazing is equivalent to 45% of the primary production in this region. Hence, krill faecal pellets are expected to contribute to the vertical flux in polynyas, especially during diatom blooms.
Zooplankton Processes in Arctic and Antarctic Polynyas 3.5
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Seasonal Life History, Energy Storage and Egg Production Rates
The important environmental processes influencing zooplankton reproduction in polar oceans are low temperatures and the pronounced seasonal variability in irradiance, sea ice cover, and primary production. Life history strategies of copepods that ameliorate low food conditions during winter include vertical migration into deep water and diapause, remaining at the surface and feeding omnivorously, and lipid storage. Much of the lipid sequestered by individuals is retained throughout winter and used to support egg production prior to, or coinciding with, the spring bloom (Hagen, 1999). The gonads of all major copepod species develop during winter and maximum feeding and reproduction occurs during summer (Schnack-Schiel, 2001). By autumn ovaries in Calanoides acutus, Calanus propinquus, and Metridia gerlachei usually are spent. Reproductively active female M. gerlachei and Ctenocalanus citer, however, are observed near Marguerite Bay along the Antarctic Peninsula during March, indicating that the reproductive period of these species may extend from summer into autumn when sufficient food is available (Niehoff et al., 2002). Seasonal differences in Calanoides acutus sex ratios are pronounced, with the highest male/female ratio occurring in winter (Schnack-Schiel, 2001). C. acutus may be one of the few copepod species in the Antarctic that undergoes ontogenetic vertical migration and diapause during winter. Females occur throughout the year, but males are often rare or absent. Spawning in C. acutus is primarily restricted to periods of high primary production (Atkinson, 1998). Egg production rates along the Antarctic Peninsula range from 4–46 eggs female−1 d−1 during spring when chlorophyll values are relatively high (1.1– 19 mg chl m−3 ; Schnack-Schiel, 2001). This is generally higher than egg production rates (0.2–15 eggs female−1 d−1 ) in offshelf regions with lower chlorophyll concentrations. The mean egg production rate of 4% carbon d−1 is estimated to require a daily carbon intake of 12% of body carbon (Atkinson, 1998). Calanus propinquus, Metridia gerlachei, Oithona similis, and likely Microcalanus pygmaeus employ a strategy of extending their growth season by feeding omnivorously and/or on detritus with less dependence on diapause (Atkinson, 1998). Oithona similis may remain near surface year round. Some percentage of Calanus simillimus, C. propinquus, and Rhincalanus gigas populations are observed in surface waters during winter, while the remainder are in diapause in deep water. The proportion of surface dwelling individuals may be dependent on location. For example, R. gigas collected in surface waters on shelf during winter are reported to have food in their guts (Marin and Schnack-Schiel, 1993), but individuals collected offshelf, in deeper water, have empty guts, and are in diapause (Hopkins et al., 1993). In contrast, Metridia gerlachei tend to inhabit deeper waters year-round and may not have a significant seasonal migration or enter diapause (Huntley and Escritor, 1992; Schnack-Schiel and Hagen, 1995). Along the Antarctic Peninsula average egg production rates for Calanus propinquus range from 1–42 eggs female−1 d−1 and from 0–6 eggs female−1 d−1 for Metridia gerlachei (Schnack-Schiel, 2001). During spring and summer Metridia has the highest egg production rate in 0◦ C water (Calbet and Irigoien, 1997). Although egg production is significantly correlated with chlorophyll concentration, experiments do not reveal a functional relationship with food density. The daily carbon produced as eggs is 0.8–1.8 μg C female−1 d−1 , which is estimated to require a carbon intake of 2.4–5.4 μg C female−1 d−1 . Egg production in Calanoides acutus (2.1–4.0% body C d−1 ), C. propinquus (2.4–2.9% body C d−1 ), and M. gerlachei (1.6% body C d−1 ) relative to body carbon compares favorably with that of Arctic copepods (1.4–5% body C d−1 ) (Kurbjeweit, 1997).
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There is insufficient information to determine whether copepod reproduction is higher in the vicinity of polynyas relative to other Antarctic locations. An analysis of global fecundity patterns in copepods suggests that reproduction is positively correlated with temperature and body weight and that fecundity is positively correlated with chlorophyll for broadcast spawners, but not sac-spawners, probably because these are omnivores (Bunker and Hirst, 2004). In general, egg production of broadcaster spawners appears to be more dependent on food than temperature, except possibly at cold temperatures. These findings further indicate that food limitation is more important for fecundity than for juvenile weight-specific growth rates. The underlying factors controlling zooplankton fecundity in the Antarctic warrant further investigation. Euphausia superba store triacylglycerols and to a lesser extent phosphatidylcholine as energy reserves (Hagen et al., 1996). Large E. crystallorophias and Thysanoessa macrura store wax esters as the primary lipid reserve, but they also accumulate phosphatidylcholine, which may be more easily mobilized than neutral lipids. In addition, small E. crystallorophias store triacylglycerols (Falk-Petersen et al., 1999); therefore, this species may have more flexibility in accumulating different lipid classes. Autumn lipid levels for adult T. macrura average 47% DW and decrease to 16% by October–December (Hagen and Kattner, 1998). A similar reduction is observed for adult E. crystallorophias (autumn: 44% to spring: 21%; Kattner and Hagen, 1998) (Figure 6). Both T. macrura and E. crystallorophias use lipid to overwinter, but a significant portion is used to support gonad maturation and egg production during early spring. It is unclear to what extent Euphausia superba spawn over Antarctic shelves and whether polynyas influence reproduction. Reproduction in E. superba can extend from November to April, with the highest activity during January and February. The onset of reproduction appears to be related to the timing of ice retreat and formation of ice edge blooms in late spring and therefore reproduction is delayed in more southerly regions (Cuzin-Roudy and Labat, 1992). Based on larval stage composition, Spiridonov (1995) proposes five environmental regions to account for the observed variation in timing and extent of krill reproduction. For example, early spawning with a long duration (3.5 mos) occurs near the southern boundary of the ACC, a variable onset of reproduction (±1.5 mos) with a relatively long duration occurs on the shelf west of the Antarctic Peninsula, and areas with a delayed onset and a short duration (ca. 1.5 mos) occur near the Ross Sea slope, in the Lazarev Sea, and in the SE Weddell Sea. E. superba is capable of producing multiple broods, but estimates of fecundity remain poorly known and are being debated (Cuzin-Roudy, 1993). Egg production rates range from 310–15,000 eggs brood−1 (Harrington and Ikeda, 1986). During the peak season ovaries may contain four batches of oocytes in different stages (Cuzin-Roudy, 1993). The timing of the physiological phases of egg development is flexible, and they may overlap or alternate depending on external food resources. Cuzin-Roudy (2000) shows that body size is positively correlated with egg batch size and predicts that an average sized female can produce 3779 eggs per batch and up to 7500 eggs y−1 . Nicol et al. (1995) estimates that multiple spawning may require >0.5 μg chl l−1 and krill filtration rates >10 l h−1 . The onset of spawning in Euphausia crystallorophias also is interannually variable (Pakhomov and Perissinotto, 1997), but may start as early as October and extend until January (Fevolden, 1980; Kirkwood, 1996). Typically this species spawns before E. superba in the same region. Gravid females have been observed under 100% ice in early November (Harrington and Thomas, 1987); thus, female reproduction may depend on internal lipid sources and not on the timing of spring blooms. Peak spawning, however, is correlated with the development of coastal polynyas (Pakhomov and Perissinotto, 1996), which occur prior
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to the formation of ice edge blooms that E. superba may need for reproduction. E. crystallorophias eggs are also neutrally buoyant (Ikeda, 1986) due to a large perivitelline space (Harrington and Thomas, 1987). This may be characteristic of eggs of neritic euphausiids and allow eggs to hatch near surface or at shallow depths. Based on laboratory observations, 95–217 eggs may be released per brood, with up to three successive broods 6–10 days apart (Harrington and Thomas, 1987). 3.6
Secondary Production and Generation Time
Generation time and life cycles of most Antarctic zooplankton are still uncertain and variations probably occur between different environmental habitats and latitudes. Calanoides acutus, Calanus propinquus, Metridia gerlachei, and Rhincalanus gigas are believed to have a one-year life cycle, but some data suggest that all but C. propinquus may live for up to two years in more southerly locations such as the Weddell Sea (Atkinson, 1998; SchnackSchiel, 2001). Under optimal condition, Euphausia superba can develop from an egg to a mature adult within three years (Ikeda, 1987) and may survive for up to seven years, whereas E. crystallorophias may mature in their second year and survive for 4–5 years (Siegel, 2000). E. crystallorophias growth rates tend to be slower than that of E. superba. The time from egg to complete larval development is about 240 days (Kirkwood, 1996). On the Antarctic Peninsula even though E. crystallorophias spawns earlier, the larvae of both species overwinter as late furcilia stages (F4–6) of similar size and recruit to the juvenile stage the following spring (Daly and Zimmerman, 2004). Since winter growth rates of E. superba larvae in the vicinity of the Marguerite Bay polynya are near zero and development and molting rates reduced until ice algae began to accumulate in September (Daly, 2004), the role of SO polynyas in krill production is unclear. Spawning in some copepod and euphausiid species may start earlier in lower latitudes than over higher latitude shelves due to the seasonal timing of ice melt and irradiance (Spiridonov, 1995; Atkinson, 1998). In Gerlache Strait the onset of spawning in Calanoides acutus is as early as October and ceases by mid-January (Huntley and Escritor, 1992), whereas in the SE Weddell Sea reproduction typically occurs between November and March (Fevolden, 1980; Hubold et al., 1988; Atkinson, 1998). If polynyas are not present at higher latitudes, spawning may be delayed even further in the year. In contrast, the timing of spawning in species not dependent on spring blooms appears to be similar among different locations (e.g., Weddell Sea, East Antarctic, and Antarctic Peninsula; Siegel, 2000). In addition, enhanced summer shelf production results in higher growth rates compared with rates in offshelf regions. For instance, Calanoides acutus copepodite stage duration (26 d) was somewhat shorter (and gut pigment higher) in the Gerlache Strait compared to stage duration (30 d) in the nearby Drake Passage (Huntley and Brinton, 1991). Overwintering stages offshelf in the Drake Passage descend to depth during late March, while those in the productive Gerlache Strait are delayed. Larval stages of Euphausia superba also are larger in size and weight and have higher growth and development rates in coastal waters of the Antarctic Peninsula during late summer and autumn, where chlorophyll concentrations are higher, than offshore in the Drake Passage (Meyer et al., 2003; Daly, 2004) or in the SE Weddell Sea (Meyer et al., 2002b). Long-term data sets of physical and biological parameters are needed to obtain a mechanistic understanding of the role of coastal polynyas and shelf regions in the spatial and temporal variability of Antarctic zooplankton secondary production.
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4 Conclusions The AO has broad, shallow continental shelves with multi-year ice cover in many regions, while the SO has deeper continental shelves often covered by annual sea ice. These and other differences in physical processes have well-documented effects on phytoplankton species composition and productivity, and consequently, on zooplankton processes. In spite of these differences, there are similarities in zooplankton processes in polynyas of the two oceans. Advection from local waters is important in setting the initial conditions for polynya zooplankton community composition and abundance, with little evidence of an endemic zooplankton fauna in either northern or southern polynyas. Copepods are predominant in both AO and SO polynyas, with many of the same genera. Sac-spawning species include Oithona spp., Oncaea spp. and Microcalanus pygmaeus. Among the broadcast spawning calanoids, there are ecologically equivalent species in both the AO and SO, including Calanus hyperboreus/Calanoides acutus, C. glacialis/C. propinquus, and Metridia longa/M. gerlachii, respectively. Because broadcast spawners depend upon diatom production for recruitment, they obtain benefits from the higher primary productivity and extended productive season of diatoms in polynyas. Microcalanus, Oithona and Oncaea appear to derive little benefit from the presence of polynyas. While AO and SO polynyas have typical dominant species, specific areas and polynyas in both the north and south have characteristic compositions. The dominant zooplankton over all shelves of the SO, except the Ross Sea, are Euphausia superba, E. crystallorophias, Calanoides acutus, Calanus propinquus and Metridia gerlachei. The Ross Sea contains predominantly E. crystallorophias, Oithona and Metridia. Also, pteropods are frequently abundant throughout the SO. Salps are seldom abundant on Antarctic shelves and not likely associated with polynyas. A major difference in the species composition of polynyas is that appendicularians are predominant in the Arctic, while euphausiids are predominant in the Antarctic. Typical zooplankton in AO polynyas are Calanus glacialis, C. hyperboreus, C. finmarchicus, Pseudocalanus spp., Metridia longa, Oithona similis, Oncaea borealis, Microcalanus pygmaeus, Oikopleura vanhoeffeni and Fritillaria borealis. In general, C. glacialis and P. minutus are typical of AO polynyas, but not of the surrounding boreal Atlantic Ocean or the Atlantic-influenced, eastern AO. C. finmarchicus is typical of the boreal and Atlanticinfluenced AO, but does not reproduce in the AO or in AO polynyas. Eurasian polynyas seem to be sites of recruitment of C. glacialis. Most copepod species have a one-year life cycle in polar polynyas, 1–2 years shorter than in local, ice-covered waters. Polynyas are sources of food and heat, both of which can increase recruitment rates and decrease generation times. In both the SO and AO, polynya primary production appears to extend feeding and reproduction in copepods and euphausiids into late summer and early autumn. Diatom production results in high copepod egg production rates in both the north and south, but while seasonal energy storage and winter diapause are common in many copepod species of AO polynyas, only Calanoides acutus demonstrates true diapause in the SO. All other copepods species in the SO have some portion of their population in near-surface waters during winter, especially over continental shelves. These individuals must feed during winter to survive. These life cycle adaptations reflect the strong seasonality of diatom production and use of alternative overwintering strategies by some SO copepods. Zooplankton biomass tracks the trend in primary productivity among AO polynyas, but not as clearly in SO polynyas, possibly due to the effects of blooms of Phaeocystis. Individual, zooplankton ingestion rates are similar among AO polynyas and between AO and
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SO polynyas, and while higher than rates in local, ice-covered waters, they are similar to regional, boreal rates. Thus, the grazing impact of polynya zooplankton communities on prey is largely accounted for by the biomass of grazers, and secondarily by the concentration of prey. Copepod community grazing impact as a proportion of daily primary productivity in the AO (including polynyas and non-polynya open waters) clusters around 20% d−1 . Grazing rates of the appendicularian community also cluster around 20% d−1 , indicating that they should not be ignored. Although there is less information available for SO polynyas, grazing impact by copepods and euphausiids can account for a significant proportion of daily primary production. There are qualitative and quantitative differences in the mesozooplankton contribution to the vertical flux in polar polynyas. In the SO, faecal pellets of euphausiids, copepods and pteropods may constitute from 4–90% of the particulate organic carbon flux, while in AO polynyas appendicularian faecal pellets and houses often dominate the vertical flux, with most copepod pellets recycled in the upper mixed layer. Periods of high pellet flux in both polar regions is primarily associated with grazing during diatom blooms. In summary, the primary benefit of polar polynyas to zooplankton appears to be higher rates of diatom productivity and an extended productive season, both of which lead to increased recruitment with little change in individual daily rations or food-chain transfer efficiency. Thus, zooplankton production in AO polynyas should be predictable given existing knowledge of relationships between primary and secondary production in boreal and polar oceans generally.
Acknowledgements This paper is a contribution from the Northeast Water and Northwater Polynya programs and the Southern Ocean GLOBEC Program (Contribution No. 269). The authors thank L. Fortier and M. Fortier for collection of zooplankton, P. Saunders, N. Shiga, N. Choe and M. Ringuette for sharing unpublished data, M. Marrari and S. Gilbert for help with acoustic analyses and T. Connelly for her editing and refreshing jelly bean breaks. This paper was partially supported by an NSERC Discovery Grant to D.D. and NSF grants OPP-9910610 and OPP-0196489 to K.D.
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Chapter 10
Pelagic Bacterial Processes in Polynyas H.W. Ducklow1 and P.L. Yager2 1 Virginia Institute of Marine Science, The College of Williams and Mary, Gloucester Point, VA 23062, USA 2 Department of Marine Sciences, The University of Georgia, Athens, GA 30602, USA
Abstract Polynyas are considered model ecosystems for understanding high-latitude carbon cycling, especially with regards to climate-sensitivity of the biological pump. Explanations for highly efficient carbon export from polynyas and other marginal ice zones often focus on the balance of autotrophy and heterotrophy in these perennially cold ecosystems. The remineralization of algal production is controlled, at least in part, by the activities of pelagic heterotrophic bacteria. Here, we review these activities in both Arctic and Antarctic polynya ecosystems and include a discussion of commonly used methods. Recent research findings from the Northeast Water (NEW), North Water (NOW), and Ross Sea Polynya (RSP) programs are summarized and compared. Overall the pelagic bacteria of these ecosystems respond quickly to spring and summertime algal blooms, similar to their temperate counterparts. We find little evidence for growth rate limitation by low temperature, at least during the phytoplankton growing season. Despite sometimes significant rates of bacterivory and viral lysis, bacterial growth is fast enough for stocks to accumulate to levels similar to those observed in temperate oceans. Despite apparent differences in DOM cycling and availability, Arctic and Antarctic polynya bacteria are more similar than dissimilar in their seasonal activities. High-latitude food web structure, leading bacteria to a focus on hydrolysis and solubilization of particulate matter may partly explain this finding. We speculate about the impacts of global warming on these ecosystems and envision a scenario in which hemispheric differences in polynya microbial ecology and biogeochemical function will be amplified.
1 Introduction One of the regions where global climate changes are expected to have the greatest impact is the high latitudes (Hansen et al., 1984; Cubasch et al., 2001). Both the Arctic as a whole, and the Antarctic Peninsula are experiencing the most rapid rates of regional warming on the planet (R. Smith et al., 1996; Shindell et al., 1999). Significant reductions have occurred during the past 30 years in both the areal extent (Johannessen et al., 1999; Parkinson and Cavalieri, 2002) and thickness (Yu et al., 2004) of arctic sea ice cover. High-latitude oceans are also unique and critical to the global carbon cycle for several reasons, including strongly seasonal, short-term but high rates of biological productivity and Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74010-7
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the formation of deep water (Knox and McElroy, 1984; Sarmiento and Toggweiler, 1984; Siegenthaler and Wenk, 1984). The geochemical models outlined in these papers show how the “efficiency of the biological pump” (which depends inversely on the extent of remineralization) in high-latitude seas significantly determines the degree to which carbon is sequestered in the global deep sea, therefore influencing the long-term atmospheric CO2 concentration. Coastal polar ecosystems are among the most productive in the world, with spatial and temporal heterogeneity as common characteristics. Seasonally ice-covered regions such as marginal ice zones (Treguer and Jacques, 1992) and polynyas are sites where the strong influences of light, nutrients, and temperature merge to create short-term but dramatic blooms which can occur rapidly (Sullivan et al., 1988; W. Smith and Gordon, 1997). In an environment characterized by strong localized pulses of primary production, the response of microorganisms within the euphotic zone may determine the rate of carbon, nitrogen, and phosphate remineralization and also the fraction of total production that is exported from that zone (Wiebe and Pomeroy, 1991). High-latitude oceans account for approximately 10–20% of the global oceanic carbon production (Behrenfeld and Falkowski, 1997) and 4–11% of the global carbon export to the deep (Laws et al., 2000). They are sites where the f-ratio (new/total production) or e-ratio (export/total production) is found to be quite high relative to temperate and tropical latitudes, this effect is thought to be due to the combination of low temperature and high production (Laws et al., 2000). While this outcome has often been attributed to the disabling of bacteria by low temperatures, we now know that high rates of bacterial growth can be observed in polar waters (Rivkin et al., 1996; Rich et al., 1997; Carlson et al., 1998), and that both bacterial and viral communities have been observed to respond dynamically to small increases in available substrate; i.e. they exhibit pulseresponsiveness to patchy resource supply (Yager and Deming, 1999) and springtime algal blooms (Yager et al., 2001). Microbial communities in the polar oceans are therefore dynamic and appear not to be perpetually limited by temperature (Pomeroy and Wiebe, 2001). The explanation for high export ratios remains focused on the strongly seasonal and sometimes extremely high rates of biological production that occur at high latitudes. Interestingly, the efficiencies of the northern and southern high-latitude biological pumps (Table 1; defined as the effectiveness of reducing surface nutrients relative to subsurface values, Sarmiento et al., 2004) seem to differ significantly, with the northern marginal ice zones showing greater efficiencies, greater particle export, and somewhat higher particle export ratios, on average. The structure of high-latitude food webs and the role of coldadapted bacteria may be a critical part of understanding what determines the different high Table 1: Biological carbon pump efficiency and particle export in marginal ice zones (after Sarmiento et al., 2004)
Biological Pump Efficiency Particle Export Rate Particle Export Ratio
Northern Hemisphere (NH) marginal ice zones
Southern Hemisphere (SH) marginal ice zones
0.45–50 (low end for NH) 22 µmol C m−2 d−1 (highest) 0.4 (highest for NH)
0.14–0.17 (lowest for SH) 3–5 µmol C m−2 d−1 0.3 (highest for SH)
Data from figures in Sarmiento et al. (2004); NH includes data from the Northeast Water Polynya; SH includes data from the Ross, Weddell, and Bellinghausen Seas (J. Dunne, personal communication).
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export fractions. Significant insights to these processes have come from recent microbiological studies in Arctic and Antarctic polynyas, which we review here. Polynyas are considered model environments for seasonally ice-covered continental shelves with potentially climate sensitive shelf-slope-basin interactions (NEWater Investigators, 1993). The proposed biological pump for the Northeast Water Polynya (NEW) region (Cochran et al., 1995; Bauerfeind et al., 1997; Daly et al., 1999), for example, identifies carbon shunts to both the local shelf benthos and the nearby slope and Greenland Sea basin. The timing of biological activity with respect to sea-ice coverage is quite important, however, in controlling the magnitude and direction of carbon flux in seasonally ice-covered oceans like a polynya (Yager et al., 1995; Miller et al., 2002). Biological and biogeochemical processes in polynyas are therefore coupled to climate through their dependence on sea ice distribution and timing. The high-latitude pump efficiency most likely depends on the fate of the particulate organic matter produced. High particulate organic matter (POM) concentrations are associated with high primary production in the Antarctic, where there are no terrestrial POM inputs. The Arctic receives significant inputs of terrestrial organic matter (Opsahl et al., 1999; Benner et al., 2005), but most of the material is dissolved and the particulate material is mostly deposited in the delta or on the shelf (Macdonald et al., 1998). Thus, POM in the Arctic is also often strongly correlated to chlorophyll (e.g., Guo et al., 2004), even on the shelves (Hodges et al., 2005). In contrast, DOM concentrations in the Arctic tend to be high (see Benner et al., 2005 and references therein), but do not correlate well with other bloom indices (Wheeler et al., 1997; Hodges et al., 2005), perhaps because terrestrial inputs of DOM are so large, production mechanisms (e.g., Jumars et al., 1989) are slow, or bacterial uptake is rapid (e.g., Yager and Deming, 1999). DOM appears not to accumulate beyond the seasonal scale in the Antarctic, and is not an important part of the export production (Carlson et al., 1998, 2000). In addition to being an effective link between high-latitude pelagic and benthic food webs (Petersen and Curtis, 1980), particles are an important source of organic matter to bacterioplankton in arctic seas during summertime production (Ritzrau, 1997; Yager et al., 2001). Increased particle-associated bacterial activity, depletion of nitrogen in POM, and increased extracellular protease activity observed in the summertime NEW and North Water (NOW) polynyas suggest that particle-associated bacteria respond to POM (Vetter and Deming, 1994; Huston and Deming, 2002). Particle-associated bacteria may therefore be responsible for observable changes in bacterial abundance and community structure during times of high particle production in summertime Arctic and Antarctic seas (Putt et al., 1994; Yager et al., 2001; Hodges et al., 2005). In coastal polar regions, where carbon export may not follow typical steady-state assumptions, the efficiency of the biological pump may also depend on the role of the microbial loop (Legendre and Fèvre, 1995); i.e., whether microbes operate as a carbon link between dissolved organic matter (DOM) and higher trophic levels or simply respire the available organic carbon, returning primary production to the inorganic pool, and allowing little carbon transfer to higher trophic levels or direct export. The behavior of the microbial loop in high-latitude regions is of particular interest (Karl, 1993; Pomeroy and Wiebe, 1993) because sub-zero in situ temperatures may constrain some marine bacteria to utilizing organic matter only when it is available in high concentrations (Pomeroy and Deibel, 1986; Pomeroy et al., 1990). Seasonally ice-covered seas at high latitudes may be potential one-way sinks for atmospheric carbon, driven by a unique linkage between strongly seasonal biological productivity and sea-ice formation (Yager et al., 1995). This scenario is sensitive to climate change
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because of the predicted increase in the areal extent of these regions due to global warming (Ingram et al., 1989) and provides a negative feedback to increasing anthropogenic CO2 . The rectification scenario, however, also depends on low pelagic respiration rates during summer, which allow primary production to draw down inorganic carbon in the surface waters, and the exact phasing of biological activity and the cycle of sea ice formation and ablation (Carrillo et al., 2004). The scenario further assumes that biological activity would remain the same if high-latitude oceans warm. This is not a reasonable expectation since many of the marine biota in these regions have adapted to a narrow range in temperature and may show especially strong sensitivity to warming (Baross and Morita, 1978). Warming of a few degrees would likely increase overall rates of activity, but the relative rates of increase in production, respiration, and hydrolytic activity, as well as any resulting changes in community structure, are largely unknown.
2 Overview: Microbial Food Webs in Polar Seas 2.1
Methods and Terminology
The methods and terminology used to describe microbial ecology may not be familiar to all readers so we provide a brief summary here. Interested readers can consult Kirchman (2000) for further details. The generic term “microbial” refers to any organism so small a microscope is needed to see it. In practical terms, most all plankton organisms except krill and the larger copepods are included in this grouping. Nonetheless the term is often used as a synonym for “bacteria”. Here we try to restrict its use to the more inclusive meaning that refers to the total plankton assemblage that can be caught in a Niskin bottle, including viruses, bacteria, phytoplankton, protozoan and microzooplankton grazers. “Bacteria” is incorrectly used as a catchall term (e.g., Ducklow, 2001) for members of two of the three principal Domains of Life, the Archaea and the Bacteria. Both groups are very small (usually <1 µm long in seawater), unicellular prokaryotes that look superficially alike but are distinct groups, differing fundamentally at the most basic levels of genomic and molecular structure. Domain Bacteria includes both heterotrophic and autotrophic groups, and very likely mixotrophic organisms as well. The autotrophic bacteria are members of the cyanobacteria, but they are rare in cold waters and probably absent in any significant functional way from the Arctic and Antarctic marine ecosystems (Azam et al., 1991; Robineau et al., 1999). Archaea are genetically about as distant from Bacteria as each group is from H. sapiens. Both groups are well represented in Antarctic (Murray et al., 1999; Church et al., 2003) and Arctic (Bano and Hollibaugh, 2002; Bano et al., 2004) seas. Before the recognition of the Archaea as a Domain by Woese and colleagues in the 1980s (Woese et al., 1990) microbiologists considered “archaebacteria” as primitive true bacteria. Since they are indistinguishable under light microscopes, most reports of “bacterial” standing stocks still contain an unrecognized fraction of Archaea. Here, when we use the term bacteria without italics it should be understood that it may include members of both groups. Most Archaea (and even most Bacteria) still cannot be cultured in the lab; hence our understanding of their roles in nature is very limited, save for a few special groups like the Methanogens. The standing stocks of “bacteria” are estimated using epifluorescence microscopy (Hobbie et al., 1977) or more recently, flow cytometry (Brown and Landry, 2001). These methods utilize fluorochrome dyes that bind to nucleic acids in cells and enhance detection
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of particles with the same dimensions as the wavelength of visible light. There are generally 108 –109 cells l−1 in seawater, with higher numbers near the surface and much lower numbers below a few 100 meters. These methods do not by themselves distinguish individual bacterial species (or Domains) unless coupled with molecular probes specific for individual groups such as fluorescent in situ (meaning inside intact cells) hybridization or FISH (DeLong, 1993). FISH investigation of bacterial community composition is just beginning and it is not yet well known if there are blooms of individual bacterial species mirroring diatom and other phytoplankton blooms. Bacteria utilize organic matter but are not able to incorporate particles or dissolved molecular forms greater than about 500 Da directly. Water samples are incubated with individual, radioisotopically-labelled organic compounds like glucose or acetate or mixtures of compounds (e.g., amino acids; see discussion in Yager and Deming, 1999) to determine uptake rates. When coupled with chemical analysis of the ambient concentrations, the turnover times can be calculated for these same compounds in the original sample. When multiple incubations are run with a range of substrate concentrations, uptake kinetic parameters (e.g., maximum specific utilization rates and specific affinity; Button et al., 2004) can be assessed. These kinetic parameters give an indication of the capabilities of the microbial community (including permease abundance and properties), independent of potentially dynamic substrate concentrations. Unfortunately, there is a myriad of potentially usable compounds dissolved in seawater and the exact composition is still very poorly known. This makes very difficult the detection of the individual substances bacteria are really using at any given time. An alternative approach is to look instead at the bacterial production (BP) rate, which is the metabolic result of uptake of whatever substances were used over some previous time interval. Bacterial (secondary) production is the synthesis of biomass from preformed organic precursors, in contrast to primary production at the expense of sunlight and inorganic nutrients. It is estimated most often from incorporation of 3 H-labelled thymidine or 3 H-leucine, biosynthetic precursors of DNA and protein respectively (Ducklow, 2000). The approach is based on the fundamental argument that actively growing cells are necessarily making protein and DNA, thus the rates of cell production can be extrapolated from these processes. In practice extrapolation is uncertain because the relationship between precursor incorporation, macromolecular synthesis and production varies as a largely unknown function of environmental conditions and physiological state of the cells. Bacterial production rates tend to mirror the distributions of bacterial stocks in space and time, and average 5–20% of the simultaneous rates of primary production (PP). Below, we review several extensive studies of bacterial stocks and production in Arctic and Antarctic polynyas. An important concept linking bacterial utilization of organic substances or bacterial carbon demand (BCD) and bacterial production (BP) rates is the conversion or growth efficiency (BGE = BP/BCD) of the cells (del Giorgio and Cole, 1998). Various metabolic costs and requirements impose a tax on utilization of resources such that the rate of biomass production is somewhat less than the original rate of uptake. The difference is the respiration, which can be measured independently but the techniques are painstaking and so respiration estimates are much less common than production estimates. For convenience, if not accuracy, the total consumption rates are often back-calculated from production estimates and some assumed value of the conversion efficiency. BGE averages 10–30% in most ocean systems. Thus, if BP/PP is ∼10% and BGE 20%, bacteria are using an amount of carbon equivalent to about half the primary production (0.1/0.2 = 0.5). In other words, over some appropriate time and space scales trophodynamic processes in plankton food webs route about half the primary production through bacterial metabolism. The rest accumulates, sinks or is metabolized by
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animals. An alternative approach, if radioisotopically-labelled substrates are used to measure cell uptake, determines the fraction respired to CO2 by capturing the 14 CO2 or 3 H2 O produced by the cells after incubation long enough to assume steady state. Adding this respiration component (R) together with the substrate incorporated into biomass (I) gives total utilization (U). Respiration efficiency (RE = R/U) or incorporation efficiency (IE = I/U) can then be calculated for a specific substrate or known mix of substrates (see for example Griffiths et al., 1978). Incorporation efficiencies measured in this way are usually higher than BGE, which is usually based on the incorporation of bulk DOM rather than a specific (and usually highly labile) model substrate. The effect of cold temperature on the partitioning of primary production through particulate and dissolved pools is a major research topic in polar microbiology (see below). 2.2
Food Web Structure and Function
Microbial food webs consist of autotrophic primary producers and protozoan grazers in addition to bacteria. Together these microscopic (less than 20 µm) plankton form food webs functionally and structurally analogous to, and integrated with, the food web of larger plankton (Miller, 2004). Bacterioplankton are important in marine food webs and biogeochemical cycles because they are the principal agents of dissolved organic matter (DOM) utilization and oxidation in the sea (Ducklow, 2001; Carlson, 2002). All organisms liberate DOM through a variety of physiological processes, and additional DOM is released when zooplankton fecal pellets and other forms of organic detritus dissolve and decay (Nagata, 2000). By recovering the released DOM, which would otherwise accumulate, bacterioplankton initiate the microbial loop, a complicated suite of organisms and processes based on the flow of detrital-based energy through the food web (Pomeroy, 1974; Azam et al., 1983). The flows of energy and materials through the microbial loop can rival or surpass those flows passing through traditional phytoplankton-grazer-based food webs. Bacteria also play a role in decomposition and mineralization of particulate matter through production of extracellular hydrolytic enzymes (D.C. Smith et al., 1992; Christian and Karl, 1995; Vetter et al., 1998). Although attached and particle-associated bacteria are usually much less abundant than free-living cells, they are important members of the plankton community during the terminal stages of blooms (Putt et al., 1994), in marine snow (Azam and Long, 2001) and in the vertical flux of sinking particles (Karl et al., 1988). Marine planktonic Archaea are abundant but play as-yet unknown ecological roles in microbial food webs (Karner et al., 2001). Whether or not the structure and functioning of microbial food webs in polar seas are the same as in lower latitudes remains controversial. After it was recognized that energy and material flows through plankton food webs in the temperate, tropical and even subpolar seas (Miller, 1993) were often dominated by microbial processes, Antarctic food webs were thought to be the last bastion the “classic” diatom–krill–predator food chain. This idea was attractive because of the well-known high productivity at upper levels of Antarctic food webs (whales, seals, birds, etc.) presumably sustained by short food chains of larger plankton (ElSayed, 1988; Azam et al., 1991). But even Antarctic coastal regions under the pervasive influence of sea ice were often found to be dominated by nanoplankton, <20 µm diatoms and microzooplankton grazers (Hewes et al., 1985; Weber and El-Sayed, 1987), leaving open the question of how long food webs built of nanoplankton, protozoans and bacteria could support the whales. The importance and role of bacteria in polar oceans remains a contentious issue. A major question concerns not the roles but rather the controls and dynamics of bacterial growth in polar seas and how the total bacterial assemblage or individual groups are
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“coupled” to the ultimate source of their nutrition, primary production by phytoplankton. In a widely cited paper Cole et al. (1988) showed in an early synthesis of data from lakes and oceans that bacterial production (BP) and abundance were correlated with both primary production (PP) and chlorophyll, with a mean BP : PP ratio of about 30%. This global scale conclusion demonstrated the ultimate dependence of bacterioplankton on phytoplanktonic primary producers. 30% now appears to be too high a proportion of the PP, at least for habitats not receiving subsidies of allochthonous organic matter, with about 10% suggested as a more realistic value for the open sea (Ducklow, 1999). Characteristic levels of BP : PP may be a fundamental distinction between the Arctic, which receives terrigenous inputs of organic matter (Hansell et al., 2004) and has high levels of BP : PP (Rich et al., 1997) and the Antarctic, which doesn’t have either. The coupling problem is complicated by lack of agreement on the meaning of the term coupling, which can be thought of as having two components: phase and intensity. Bacteria can be considered well- or tightly-coupled to phytoplankton if relevant properties (biomass, production) in various samples in a time series of measurements are correlated with phytoplankton (phasing) and if the bacterial production is a significant fraction of the primary production (intensity); or as Bird and Karl (1999) put it, “. . .it should be shown that bacterial community production is not just correlated with primary production, but is a substantial fraction of it. . .” In the RACER (Research on Coastal Antarctic Ecosystem Rates) Project Karl and colleagues carried out intensive seasonal (summer, Dec.–March 1987; spring, Nov. 1989) investigations of microbial processes in the West Antarctic Peninsula and Drake Passage. During the spring bloom in the Gerlache Strait, bacteria were not correlated with chlorophyll levels, with no apparent response of increased abundance at Chl greater than 2.5 µg l−1 (Bird and Karl, 1999). Bacterial biomass was less than 2% of the total plankton biomass and BP was approximately 3% of the co-occurring primary production. They ascribed the lack of response to intense bacterivory by heterotrophic nanoplankton (HNAN) populations that suppressed the bacterial response as the phytoplankton bloomed, and kept BP : PP low (see below), i.e., to top-down control. Bird and Karl concluded that at least in their study area and during the spring bloom period, the microbial loop was uncoupled from primary producers, but they added that the uncoupling was not necessarily more widespread in space and time, and could be expressed more strongly in other seasons. Moran et al. (2002) defined phytoplankton-bacterial coupling by focusing specifically on the release of recently synthesized DOC from active phytoplankton (14% of total PP). They showed that the released DOC met the metabolic requirement of bacteria in the Austral summer in the Bransfield Strait region of the West Antarctic Peninsula and concluded that bacteria and phytoplankton were strongly coupled. They also concluded that BP was a very low fraction (mean 1.5 ± 0.4%) of the total particulate plus dissolved production but termed the coupling “strong” nonetheless. Judging the degree of coupling is complicated by lags in response of bacterial consumers to phytoplankton production. In the RACER summer study, bacterial abundance, glutamate and thymidine incorporation rates peaked in January following the phytoplankton peak in December, though a careful analysis of spatial statistics failed to reveal strong correlations between the bacterial and phytoplankton properties within either month (Bird and Karl, 1991; Karl et al., 1991). A regression model incorporating lags showed that glutamate incorporation was best predicted by total plankton biomass and total phytoplankton pigment, indicating a direct flow of resources from the producers to microheterotroph consumers, expressed over a longer period than a single cruise. But Karl et al. explained the lack
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of direct coupling by concluding that either the glutamate and thymidine activity were due to phytoplankton uptake or that a large portion of the bacterial assemblage was inactive. Polynyas often experience reduced ice cover or ice-free conditions earlier in the growing season, and under lower irradiance conditions than other marginal ice zone habitats. Therefore growth conditions for phytoplankton and bacterioplankton in these habitats may differ significantly from the surrounding marginal ice zones. The related issues of controls and coupling of plankton populations in polynyas are considered below. 2.3
Bacterial Growth in Cold Water
Polar microbiology dates almost from the beginning of modern marine microbiology. Levin (1899) reported almost no bacterial cells in water samples from Spitsbergen, but his methods were suspected to be faulty since he also failed to detect any microorganisms in bird feces. Ekelöf (1907) first noted the presence of viable bacteria in waters near Ross Island, Antarctica. More modern studies date from the early work of John Sieburth at McMurdo Station in the late 1950s (Sieburth, 1963) and from the Soviet expeditions throughout the world oceans in the 1960s (Kriss et al., 1967), before more intensive investigations flowered in the 1980s (Fuhrman and Azam, 1980). The key factor facing polar bacteria is, of course, persistent cold temperature near the freezing point of seawater at −1.8◦ C. Unlike in the temperate or tropical surface ocean, pulse-responsive bacteria in polar oceans must also be cold-adapted (i.e. able to respond quickly to a food source while living at sub-zero temperatures). Prevailing wisdom suggests that most organisms operate very slowly when they are cold. Yet, slow organisms, designed for a steady food supply, seem ill-suited for the dynamic polar environment. This apparent contradiction has been one of the underlying themes of recent research, directing the focus toward those organisms that seem to do best, by some measure, at low temperature. Psychrophilic (cold-loving) bacteria have been studied in the laboratory for decades and, while we do not review that literature here (see for example reviews by Russell, 1990; Karl, 1993) and references therein, especially earlier reviews of Baross and Morita (1978) and Morita (1966), there are indications that strategies for survival and definitions of optimality may be fundamentally different from those we consider standard in the temperate environment. The same case can be made for organisms living in especially low-nutrient environments, or oligotrophs (see reviews by Morgan and Dow, 1986). These two types of “extreme” environments, and the constraints on organisms associated with them, probably come together in polar areas, much like they do in the deep sea (Deming, 1986, who suggested a link between oligotrophy and barophily). While there are times in the polar spring and summer when phytoplankton bloom and dissolved organics may become readily available (either directly or via grazing), the most common condition in these permanently cold environments is probably low concentrations of organic nutrients. As a consequence, the most competitive organism might have a rapid response to high nutrient pulses, but overall a greater tendency for storage (Amy et al., 1983) and low endogenous metabolism (e.g., more active and/or efficient respiratory chain, lower energy of maintenance, and lower minimal growth rate; Morgan and Dow, 1986). Transport systems that capture the ephemeral food supply must be constitutive, i.e., always ready (Koch, 1979), but catabolic enzymes that utilize specific substrates should be sensitive to available substrate concentrations (Novitsky and Morita, 1977; Harder and Dijkhuizen, 1983). There also appear to be advantages in being a generalist (Button, 1994). The link between temperature and substrate concentration lies at the center of the polar microbial activity debate. At issue is whether bacteria in permanently cold (less than 5◦ C)
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waters respond differently to pulses of substrates than do bacteria in permanently or seasonally warm water. The initial results of Pomeroy and co-workers (Pomeroy and Deibel, 1986; Pomeroy et al., 1990) suggested that the microbial community in northern seas require relatively high concentrations of organic matter at low temperatures. These greater food requirements might result in an under-utilization of primary production and thus provide a greater chance for carbon export (Pomeroy and Wiebe, 1988). Yager and Deming (1999) examined temperature and substrate effects on bacterial utilization and incorporation efficiency, directly testing the Pomeroy hypothesis for its application in the NEW Polynya, and found that only some stations fit the Pomeroy prediction (see below). In their recent review, Pomeroy and Wiebe (2001) concluded, “inhibitory effects are not sufficient to alter overall ecosystem function.” Bacteria in low-nutrient or pulsed-nutrient environments can increase their potential uptake of substrate in two ways: by increasing the number of binding proteins on the cell surface (thereby increasing their maximum specific uptake rate, Vmax ), or by synthesizing a binding protein with a greater affinity for substrate (thereby lowering the half-saturation constant, Km ; Matin and Veldkamp, 1978). ANT-300, a psychrophilic marine Vibrio from Antarctic waters, has at least two transport systems with different affinities for arginine (Geesey and Morita, 1979). Some bacteria from low nutrient environments have more affine amino acid transport systems than their copiotrophic counterparts from nutrient-rich habitats (Ishida et al., 1982). Whether these organisms studied in lab culture are typical of the vast majority of non-cultured organisms is not yet known. New genomic studies linking gene sequences to gene expression in situ may answer this key question (Peck et al., 2005). In an environment where the food supply is often scarce, it also makes sense for organisms to use the energy wisely. Deming and Yager (1992) suggested that polar deep-sea sediment bacteria show uniquely high incorporation efficiencies on certain organic substrates. The metabolic pathway of dissolved organic carbon taken up by heterotrophic bacteria can vary according to the quantity and type of compounds available. Griffiths et al. (1978) report respiration efficiencies for the Beaufort Sea that depend on substrate type, temperature, and also on the season. Growth efficiency in protozoa (e.g., bacteriovores) can depend on the physiological state of the cell (Fenchel and Finlay, 1983) and increases with lower temperature in some psychrophilic organisms (Choi and Peters, 1992), perhaps providing a means for surviving dark polar winters with low food availability. The fate of bacterial carbon may therefore differ fundamentally in low temperature environments and be sensitive to warming. The adaptations of bacteria for utilization of naturally occurring substrates in persistent cold are illustrated by the observation that semilabile DOC produced during phytoplankton blooms is entirely consumed within the growth season (Carlson et al., 1998) in contrast to subtropical habitats (Carlson et al., 2002). Polynyas are ideal sites for studying bacterial processes in persistently cold water because they afford access to high-latitude, ice-free areas during periods when the surrounding ocean is ice-covered, and because they exhibit high productivity, making possible tests of the substrate-temperature relationship that governs bacterial growth in perpetually cold waters. Here we examine bacterial processes in the three best-studied polynya systems, the Ross Sea polynya (Antarctica) and the North and Northeast Water polynyas in the Arctic (Figure 1).
3
Bacterial Processes in the Ross Sea Polynya (RSP)
The Ross Sea polynya, the largest in the Antarctic at 400,000 square kilometres (km2 ), is formed and maintained near the edge of the Ross Ice Shelf at 76–79◦ S, 180◦ W by sensible
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Figure 1: Polar projections of the southern (left) and northern (right) hemispheres showing the distribution of polynyas (in black). Typical wintertime sea ice coverage is also shown (dark gray). The Antarctic figure is based on Arrigo and van Dijken (2003) and the Arctic map is modified from an original figure prepared by Martin Fortier (available on the International Arctic Polynya Program website: http://www.aosb.org/IAPP.html) based on the distributions given in Smith and Rigby (1981). heat flux and katabatic winds blowing off the ice shelf and advecting the sea ice to the north. Remotely sensed ice images reveal an area of reduced to absent ice cover throughout the year (Gloersen et al., 1992). Early season access (October–November) to the southern Ross Sea was achieved in 1994 by the US Research Icebreaker Nathaniel B Palmer, allowing detailed studies of water column biogeochemistry, ecology and microbiology in the RSP (W. Smith and Gordon, 1997; W. Smith et al., 2000b; Ducklow et al., 2001). There are no extensive, systematic observations of microbial processes in other principal Antarctic polynyas such as those in the Weddell Sea and Marguerite Bay. In contrast to other studies (e.g. RACER; Huntley et al., 1991), observations were carried out in the RSP above the Antarctic Circle over a wide range of irradiance and day length conditions and over a large segment of the annual cycle in a single year. Comprehensive studies of the plankton ecosystem in the RSP were carried out during the Ross Sea Polynya Project cruises in Nov.–Dec. 1994 and Dec. 1995–Jan. 1996 (W. Smith and Gordon, 1997; W. Smith et al., 1999); Research on Ocean–Atmosphere Variability and Ecosystem Response in the Ross Sea (ROAVERRS, 1996–98; Arrigo et al., 1999) and the US JGOFS Antarctic Environment and Southern Ocean Process Study (AESOPS, Oct. 1996–Dec. 1997; W. Smith et al., 2000a). Much of the sampling in the latter two programs was carried out in the vicinity of the polynya along latitude 76◦ 30 S. The most conspicuous and important feature of plankton ecology in the RSP is dominance of the phytoplankton bloom by the colonial haptophyte Phaeocystis antarctica (W. Smith et al., 2003), which exerts strong influence over the ecology and biogeochemistry of the region. In particular, the colonial morphology of P. antarctica may be an adaptation protecting the cells against grazing by micro- and macro-zooplankton (W. Smith et al., 2003). Possibly as a consequence there is a paucity of krill in the central and southern
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Figure 2: A: Bacteria in the upper 150 meters and B: primary production in the Ross Sea polynya in the 1996–97 Austral summer (closed triangles). C: ratio of 150 m bacterial production to primary production. Open symbols are cruises in 1994–95. Circle-dots are Nov. 1997. Ross Sea (Atkinson et al., 2004). Zooplankton play an important role in releasing DOM from algal cells through sloppy feeding and their own metabolic processes (Nagata, 2000; Carlson, 2002); thus the presence or absence of Phaeocystis may indirectly influence bacterial stocks and dynamics by modulating the supply of grazer-induced DOM release. The JGOFS program provided observations in the RSP during a single annual growth cycle, extending from 80–100% ice cover in October, 1996 through ice retreat in November and the Phaeocystis bloom to the end of the solar irradiated Austral daytime in April, 1997. The polynya was nearly 100% ice covered in October, 1996, with a physically homogeneous (well-mixed, −1.85◦ C) water column to about 100 m with 31 µM NO3 and low chlorophyll, indicating the bloom had not been initiated. Observations in Nov.–Dec. 1994 and 1996 show the physical oceanography of the water column was nearly the same. Ice cover still approximately 80% but it was thin, permitting an extensive under-ice bloom of Phaeocystis to commence. The extent of nitrate drawdown indicated the Phaeocystis bloom was also at the same stage although there was somewhat higher Chl a in 1994. Primary productivity was very high in both years, exceeding 200 mmol C m-2 d−1 (W. Smith and Gordon, 1997; W. Smith et al., 2000b) (Figure 2B). The bloom peaked in Dec. and was declining by January (W. Smith et al., 2000b) but showed the strongest nitrate depletion at that time. By late April, deep mixing had greatly diluted the remaining stock of phytoplankton in the water column and recharged the nitrate under conditions of intermittent ice formation. Primary production was very strongly light limited (PAR greater than 10 E m−2 d−1 ) and was less than
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Figure 3: Growth of bacteria in the Ross Sea polynya. A: Abundance, averaged over the upper 50 m and over successive 10-day intervals. B: biomass over 0–50 meters. The regression line has a slope of 0.03 d−1 (r 2 = 0.63, n = 145, not including the April cruise). 2 mmol C m−2 d−1 at that time (W. Smith et al., 2000b). In comparison to the diatom bloom in the Palmer LTER region of the marginal ice zone (not a polynya) offshore of the West Antarctic Peninsula (R. Smith et al., 1998a), the bloom in the RSP starts a month earlier, reaches higher levels of biomass and is dominated by Phaeocystis. Bacterioplankton grew steadily and dramatically in the RSP between mid-October and mid-February (Figure 3). In October, prior to the Phaeocystis bloom, the bacterial cell abundance was low, about 1×108 cells l−1 , equivalent to the lowest surface water levels observed in the world oceans, while by February they reached 2 × 109 cells l−1 (Figure 3A), which is nearly the highest abundance observed in the open sea (Ducklow, 1999). Thus the RSP exhibits the full range of bacterioplankton variability observed in oceanic systems studied to date. Bacterial biomass (some of which is probably Archaeal) ranges over two orders of magnitude, form 2–200 mmol C m−2 in the upper 50 m (Figure 3B), growing exponentially with a doubling time of about 20 d. This is the net rate of increase reflecting the slight excess of growth over removal by predation, adsorption on sinking particles, viral lysis and other processes. The mean intrinsic growth rate of the cells must be greater than the 0.03 d−1 net observed rate of increase suggested here (slope in Figure 3B). There is large spatial and interannual variability in the upper 100 m, but bacterial stocks were relatively uniform and constant between 100–200 m. By April, the bacterial abundance declines as a result of removal and deep mixing (Ducklow et al., 2001). Bacterial production rates also increased dramatically, from 0.1–1 mmol C m−2 d−1 in October–November to over 10 mmol C m−2 d−1 in January (Ducklow et al., 2001). This bacterial production was equivalent to about 5–15% (mean 4%) of the simultaneous primary production (14 C estimates) in October–November, with substantial spatial and temporal variability (Figures 2A, 2C). The fraction then increased, at some stations, to 40–50% by January with a mean of 11%, as bacteria decomposed organic matter in the now declining Phaeocystis bloom, and as primary production rates declined by a factor of 2–4 (W. Smith et al., 2000b). Specific growth rates derived from conservative bacterial production estimates (Ducklow et al., 1999, 2000) and abundance data (i.e., P/B ratios) were <0.1 d−1 in October and increased to 0.25–0.5 d−1 in November–December 1994, but stayed less than 0.25 d−1 in
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November–December, 1997. Rates up to 0.25 d−1 , suggesting potential doubling times of approximately 3 days (d), are common in the upper 50 m. These potential rates of increase were suppressed to a mean net rate of 0.03 d−1 by removal processes but still permitted doubling of the stock in more than 20 days. To understand further the bacterial role in the carbon cycle of the RSP, Carlson et al. (1999) conducted a series of incubation experiments in gas-tight plastic bags to monitor bacterial growth, DOC consumption and DIC production. In this way, complete carbon budgets were constructed for bacterial conversion of ambient DOC into biomass and respired CO2 , and conversion efficiencies (growth efficiencies, BGE) were evaluated. The BGE in the RSP had a mean value over the October–January period of 25% but it increased from 11% in October to 43% in late January. To some extent the increase in BGE offsets the lower BP : PP ratios (Figure 2C) observed early in the season. In October, when BGE was 11% and BP : PP averaged 0.04, the bacteria metabolized 0.04/0.11 or 36% of the contemporaneous primary production. In January, when BP : PP averaged 0.11 and BGE was 43%, the bacteria metabolized 26% of the primary production. In a closer analysis of the bacterial carbon budget Ducklow (2003) integrated the euphotic zone bacterial and primary production rates and followed the standing stock of DOC over two time intervals, Oct. 18–Feb. 02 and Feb. 02–April 28, which denoted the period of active phytoplankton growth until the peak of the bloom, and the period of decline from the peak of the bloom to the end of the sampling period, respectively. Over the full growth season, the 14 C-estimated particulate net primary production was 14 mol C. DOC production was estimated by calculating the amount of labile carbon metabolized by the bacteria (the BCD) during the October–February period when semilabile, bulk DOC was accumulating. Gross DOC excretion rates were not measured and some, but probably not all of the DOC production could have been at the expense of the particulate biomass as it was consumed and metabolized by grazers. As a result it is not possible to calculate the true ratio of DOC production to total primary production. However, we note that nearly all (93%) of the DOC produced over the course of the season was consumed by the bacteria, and this amounted to 20% of the total seasonal primary production. Large-volume (20-l) seawater cultures showed that bacterial growth in the RSP was not temperature- or substrate-limited. In control (whole-water) experiments incubated at ambient temperatures (−1.8 to 0◦ C) with no added substrates, bacteria grew at 0.15–0.3 d−1 in the presence of bacteriovores with brief lag periods following enclosure and setup (0–2 d; W. Smith et al., 1998). Bacterial abundance reached 1–2 × 109 cells l−1 , about the same as attained in the RSP a few weeks later in the growth season. Experimental treatments included temperature manipulations (+2–3◦ C above ambient; i.e., growth at 0 to 3◦ C) and substrate additions (60 µM carbon as glucose or 10–15 µM natural plankton lysates; Ducklow et al., 1999). Some samples were gently filtered through 0.8 µm membranes to reduce grazer abundance and increase growth yields. In these treatments, DOC was increased by 10–15 µM over the ambient concentration of 43 µM, due to breakage of Phaeocystis cells. In these samples with reduced grazing and enhanced organic matter, bacterial growth rates were no different from control treatments. Bacterial growth rates were also unaffected by increased temperature (which was limited to the seasonal range for the RSP). In general these results suggest that bacteria in the RSP were poised for rapid growth even early in the season (November) when temperatures were at the annual minimum. Growth was not inhibited by temperature, or alternatively, there was already sufficient organic matter present in the water to release the bacteria from any temperature limitation (see above). In addition, bacterial growth did not respond to increased substrate concentrations, indicating either that nutrient limitation was
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Figure 4: Bacteria–chlorophyll relationships in the Ross Sea polynya (upper 50 meters; after Cole et al., 1988). A: Abundance vs. Chl. B: Bacterial production vs. Chl. See text for explanation of lines. imposed by other elements or compounds, or that bacteria were growing at near-maximum rates. Finally, we ask, how does bacterial ecology inside the polynya differ from other regimes? The best-studied region outside the Ross Sea is the West Antarctic Peninsula (WAP; see Figure 1), previously studied by Karl and colleagues in the RACER Program (Karl et al., 1991) and by the Palmer, Antarctica Long-Term Ecological Research Program (Ross et al., 1996a). The WAP differs from the Ross Sea principally in phytoplankton composition. Seasonal blooms are dominated by diatoms and start about a month later than in the RSP (R. Smith et al., 1998a, 1998b). As described previously, Karl and colleagues concluded that bacterial production was poorly coupled to phytoplankton processes during the bloom. They ascribed the lack of coupling to efficient suppression of the bacterial response by intensive bacterivory. As we showed above, bacteria respond conspicuously to the Phaeocystis bloom in the RSP: the amplitude of the bacterial bloom is as large as any in the global ocean. The response, or coupling between bacteria and phytoplankton can be demonstrated by plotting bacterial abundance or production against chlorophyll for a time series during the growth season (Figure 4). Bacterial abundance did not respond to increasing Chl within cruise periods, except for November–December, suggesting that bacteria were not sustained directly from DOM release from the phytoplankton in October and January– February. Rather, they consumed semilabile DOC during those periods (Ducklow, 2003). However coupling is evident over the entire October to February period (Figure 4A). Bacteria in the RSP are lower in abundance for a given level of Chl than elsewhere in the oceans, as indicated by the position of the observations below the regression lines from the global syntheses of Cole et al. (1988) and Bird and Kalff (1984). This may be due to intensive grazing as in the WAP or just because the bacteria start at such a low abundance in October. By the peak of the bloom in January–February, the bacteria “catch up” to the global relationship, attaining the levels of abundance specified by the regressions. A slightly different pattern is manifested in bacterial production: it is somewhat low per unit chlorophyll in the early season, but an order of magnitude greater than the Cole et al. (1988) regression
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Figure 5: Bacteria–bacteriovore (heterotrophic nanoflagellate, HNAN) relationships in the Ross Sea polynya and Gerlache Strait (after Bird and Karl, 1999). The solid line is the Model II regression and the lower and upper dotted lines are the ratios 10,000 : 1 and 100 : 1 bacterial cells per HNAN cell, respectively.
would predict in January–February (Figure 4B). Overall, bacteria are clearly coupled to phytoplankton processes during the Phaeocystis bloom in the RSP, in contrast to a lack of clear coupling during the diatom bloom in the WAP. Bacterial abundance and production rates are suppressed at low chlorophyll levels but attain high levels at peak chlorophyll concentrations during the peak of the bloom. The extent to which this behavior is a response to specific properties of the Phaeocystis cycle, or to other features of the polynya, is not known. Bird and Karl (1999) ascribed the lack of coupling in the Gerlache Strait (WAP) to intensive removal by heterotrophic nanoplankton bacteriovores (HNAN). They diagnosed this condition by computing the ratio of bacterial cells per individual HNAN for samples taken at various times of the bloom cycle. Figure 5 reproduces the observations of Bird and Karl (1999) along with observations from the RSP. There were only about 100 bacteria per HNAN in the WAP, and an order magnitude more in the RSP over the full growth season. There were consistently fewer HNAN available to graze on bacterial cells in the RSP than in the WAP system studied by Bird and Karl (1999). The bacteria : HNAN ratio approached 10,000 in some samples in the RSP. The reasons for the disparity are not known but may reflect differing degrees of top-down control by krill in the two systems. Krill are scarce in the interior Ross Sea (Atkinson et al., 2004) perhaps due to unpalatibility of Phaeocystis (W. Smith et al., 2003). In contrast krill are abundant and serve as the principal, dominant herbivores in the WAP (Ross et al., 1996b). If there is an intermediate trophic level between krill and bacteriovores, the resulting trophic cascade could explain the contrasting relationship of bacteria and the predators in the two systems.
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Bacterial Processes in Greenland Polynyas (NEW and NOW) The Northeast Water (NEW) Polynya
The NEW Polynya is a seasonally recurrent opening in the permanent ice of the coastal Arctic situated over the continental shelf of northeastern Greenland (77–81◦ N, 6–17◦ W, Figure 1). It was the study area for an intensive, international, multi-disciplinary research project investigating biogeochemical cycling in high-latitude oceans in 1992 and 1993 (NEWater Investigators, 1993). The summer polynya typically starts to open in May, and closes by late September to mid-October with a maximum areal extent that ranged from 59,000 (in 1992) to 120,000 km2 in 1985 (observations from 1978 to 1994, Bohm et al., 1997). Its formation involves several seasonally dependent mechanisms (Minnett et al., 1997): (1) new ice formation is reduced during the spring with the increase in solar insolation; (2) ice is exported from the region by an anticyclonic surface current (5–20 cm s−1 ) that follows the topographic contours around Belgica Bank (Johnson and Niebauer, 1995); (3) the resupply of ice to the area was limited by the presence of two semi-permanent ice shelves (Norske Øer to the south, and Ob Bank to the northeast; Schneider and Budeus, 1995; we note that these ice shelves have recently melted and the continued existence of the NEW is now a matter of debate); and (4) small areas of open water, once present, reduce the albedo of the surface water, allowing greater absorption of solar insolation, increasing the rate of ice melt and some surface water warming (up to 3◦ C in the top meter; Minnett, 1995). Wind events can easily redistribute the remaining sea ice, however, leading to a great deal of temporal and spatial heterogeneity in ice cover throughout the region (Gudmandsen et al., 1995; see for example, the AVHRR image of the region provided by Minnett et al., 1997). As with most of the Arctic, surface water is colder (approximately −1.8◦ C in the upper 100 m) than the deeper Atlantic-source waters (approximately +2◦ C) and there is a strong halocline between them. Nutrient concentrations in the East Greenland Shelf Water flowing into the NEW region are characterized by low nitrate (less than 4 µM; with a low N : P) and high silicate (10– 14 µM; Kattner and Budeus, 1997) characteristic of Pacific-origin Arctic surface water. New nutrients are supplied to the polynya as a tongue of cold water flowing northward from under the Norske Ør ice shelf (Wallace et al., 1995). These nutrients are depleted by phytoplankton as the anticyclonic current carries them through the open water region, setting up a kind of “chemostat” or bloom gradient along the arc of the gyre between Norske Ør and the Westwind Trough. The spatial gradients of inorganic carbon and nitrogen are consistent with biological processes following the Redfield ratio (Wallace et al., 1995). Additional nutrients may be supplied by the East Greenland Current or the Upper Halocline Arctic Water that flows over Ob Bank, triggering local hotspots of primary production and additional export near the shelf break. During the 24-h sunlight of the boreal summer days, reduced snow and ice cover allow deeper light penetration, enabling ice-algae and phytoplankton blooms. Ice algal blooms dominate primary production early in the season, while phytoplankton blooms (mostly diatoms) develop later. Because of the heterogeneity in the summertime ice coverage, however, all stages of this succession may be present in the polynya region at any one time. The average euphotic zone (0.1% I0 ) depth in 1992 was 46±18 m. Primary production in the NEW is nitrogen and light limited and was therefore modest in 1992 (22.5 ± 23.3 mmol C m−2 d−1 , maximum at 95 mmol C m−2 d−1 , W. Smith, 1995) and only about two-fold higher on average in 1993 (Pesant et al., 1996; W. Smith et al., 1997). New production was a large fraction of the total production, with f -ratios averaging 0.65 overall (W. Smith et al., 1997), and dropping to 0.39 when nitrate concentrations dropped below 0.5 µM.
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The northern region of the polynya is dominated by copepod species of arctic origin, particularly the small omnivorous Metridia longa (Ashjian et al., 1995). The generally low abundance of large herbivorous Calanus species increases the potential for export of unconsumed primary production (Ashjian et al., 1995) while at the same time reducing one of the main pathways (Jumars et al., 1989) by which dissolved organic matter is generated from particulate primary production and made available to pelagic bacteria. Estimates for the grazing impact on primary production based on copepod egg production were less than 30% at 90% of the stations sampled in 1993 (Hirche and Kwasniewski, 1997). Yet, potential estimates based on particulate carbon and nitrogen production suggest a greater impact (averaging 45%; Daly, 1997), with the C : N ratio of fecal pellets reflecting nitrogen deficiency. Copepod pellet export was not considered a major pathway for carbon loss from the system (Bauerfeind et al., 1997), with contents of sediment traps consisting primarily of diatoms (averaging 10%; but up to 81% ice algae in June) and the houses and feces of appendicularians (up to 40% in August). These findings concur with the result that particulate matter in the polynya was primarily diatomaceous, with unusually elevated C : N ratios (typically greater than 10) apparently reflecting nitrogen limitation (W. Smith et al., 1995) at the surface. Particulate organic carbon concentrations in the polynya were generally high overall (surface values for all stations measured between July 18–August 18, 1992 averaged 211 ± 181 mg m−3 ; W. Smith et al., 1995) and exhibited a great deal of heterogeneity among stations that showed no association with ice cover. Dissolved organic carbon (DOC) concentrations in the NEW start the summer season relatively high (100 µM for the sub-euphotic surface mixed layer; Daly et al., 1999; 110 µM for surface waters in June; Skoog et al., 2001), but the C : N ratio of the dissolved organic pool is also quite high (ca. 20) compared to nearby regions. Unlike other arctic regions, the NEW receives no local riverine inputs that could explain the high DOC. The East Greenland Current carries levels of 75.8 µM (±10.2; Amon et al., 2003). One explanation for the early season high could be DOC production by ice algae and ice melt prior to the opening of the polynya (Skoog et al., 2001). According to one analysis based on depth gradients, a small buildup (23 µM) of DOC was observed in the NEW polynya over the early summer of 1993 (Daly et al., 1999), with no coincident buildup of DON, despite potentially significant DON excretion by zooplankton (Daly et al., 1999). In a contradictory analysis based on water masses (Skoog et al., 2001), DOC decreased slightly (from 110 to 105 µM) while DON increased slightly (from 5.6 to 6.1 µM) in the polynya surface waters over the summer of 1993. A corresponding build up of DON in intermediate waters (from 4.83 to 5.97 µM; Skoog et al., 2001) requires solubilization of sinking particles as the explanation. Bacterial abundance in the surface waters of the NEW was generally low with an average of 0.7 (±0.45; n = 28 stations) × 108 cells l−1 for the upper 50 m in 1992 (with individual samples ranging from 0.12 to 5.3 × 108 l−1 ; n = 281) and 1.6 (±1.1; n = 37) × 108 cells l−1 in 1993 (with individual samples ranging from 0.20 to 7.9 × 108 l−1 ; n = 353). Abundance generally increased toward the subsurface near 25 m and then decreased into deeper waters. While attempts were made during both field seasons to collect time series data by returning multiple times to the same geographic position, variability at the “time series station” (80.4◦ N, 13.3◦ W) was extremely high and no consistent temporal changes were observed. Instead, several authors have used spatial variation along the principal axis of circulation in the NEW as a proxy for time (e.g. Wallace et al., 1995; Touratier et al., 2000). If we apply those same assumptions (Figure 6), an increase in average bacterial abundance (based on integrating through the upper 50 m) by about an order
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Figure 6: Average bacterial cell abundance (calculated by integrating over the top 50 m) in the Northeast Water during (a) 1992 and (b) 1993, plotted against the time each parcel of surface water has traveled through the open waters of the polynya. The time calculation was based on the distance from NEW 92 Station 42 (80◦ N, 16◦ W) and a current velocity of 10 cm s−1 . Net population growth rates of 0.05 (±0.02; 95% ci) d−1 for 1992 and 0.039 (±0.035) d−1 for 1993 are calculated from the slope of a Model 1 regression of the log(abundance) data against the time traveled. of magnitude can be seen to occur during the approximately two weeks that it takes water to move out from under the Norske Ør ice shelf, along the Norske Trough and into the Westwind Trough region. Net accumulation rates of 0.05 and 0.04 per day can be calculated for 1992 (Figure 6A) and 1993 (Figure 6B), respectively. These growth rates are as high or higher than those measured in the RSP. The peak biomass (greater than 109 l−1 ) observed in the RSP during January is not attained in the NEW, however, perhaps because of the much shorter time available for the bloom to progress or because of nitrogen limitation of the entire system. Bacterial abundance correlated significantly with particulate organic carbon in 1992 (W. Smith et al., 1995). Bacterial abundance also correlated with chlorophyll concentrations (Figure 7). Using the appropriate Model II geometric mean regression with correction factors (see Cole et al., 1988), the two years of data from the polynya have the same slope (0.47) but initial bacterial abundances are higher by about a factor of five (y-intercept of log–log plot increases from 4.85 to 5.38). The significantly different intercept suggests that interannual variability, perhaps the timing of snow and ice melt and the extent of the ice algal bloom, may influence the initial conditions and thus the potential response of what is very likely to be a complex polynya ecosystem. Both years fall significantly below both the slope and intercept of the published relationship for all oceans (Cole et al., 1988) and significantly below the relationship observed in the RSP. The NEWP relationship is positive and significant, though, so the bacteria seem to be “coupled” to the algal bloom (unlike the lack of coupling observed in the Gerlache Strait; Bird and Karl, 1999). Yet, their response does not result in as great an increase in abundance as seen elsewhere. In the absence of any other data, one might interpret the shallower slope as temperature limitation, but the growth rates described above (along with other data discussed below) would suggest otherwise. As mentioned above
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Figure 7: Bacterial abundance (cells per ml) plotted against chlorophyll a concentration (µg kg−1 ; data courtesy of Walker O. Smith Jr.) for all samples collected from the Northeast Water region in 1992 (closed diamonds) and 1993 (open diamonds). Samples were analyzed using standard methods (Smith et al., 1995). Regression lines (short dashed for 1992 and long dashed for 1993) are calculated using Model II regression. The solid line uses the Model II regression slope (but Model I intercept and correction factor) from Cole et al. (1988). for the RSP, the reduced slope may indicate that the bacteria are under significant grazing pressure. This and other alternative hypotheses will be discussed below. Bacterial biovolumes tended to be large, averaging 0.126 (±0.003; n = 3500 cells measured) µm3 per cell throughout the euphotic zone of the time series station during the two Polar Sea expeditions (Yager and Deming, 1999). Cell size tended to increase with depth to 25 metres (m). These biovolumes are larger than those observed in the RSP, although they are based on a much less extensive data set and do not include deeper samples. If we use 26.6 fg C cell−1 , appropriate for cells of 0.126 µm3 (Simon and Azam, 1989), across all the euphotic zone samples, total integrated biomass in the upper 50 m ranged from 3 to 60 mmol C m−2 (average 17.6 ± 11.9; n = 37 stations). So, despite their much lower abundance, the larger pelagic bacteria in NEW may constitute about the same amount of biomass as found in the RSP during all but the peak season (Jan.–Feb.). Bacterial production was estimated from 3 H-leucine incorporation (Kirchman, 1993) at selected depths at or near the fluorescence maximum for seventeen stations (Yager, 1996). Values ranged from 0.08 to 23.4 mg C m−3 d−1 (with the median rate at 1.2 mg C m−3 d−1 for 1992 stations and 6.1 mg C m−3 d−1 for 1993). These rates are comparable (using the same conversion factors) to all but the late season rates seen in the RSP. If we group all the data from both years (Figure 8A), no significant correlation (r = 0.14; n = 20) exists between chlorophyll concentration and bacterial production. This result is very likely due to the nature of the data set (a small range in chlorophyll values). Nevertheless, the data scatter well around the Cole et al. (1988) regression, suggesting that even though bacterial numbers did not increase as expected with chlorophyll, bacterial production levels were generally as high as we might predict. Notably, the 1993 data, with overall greater abundance, matched the Cole et al. (1988) prediction better than the data from 1992.
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Figure 8: Bacterial production (BP; measured using 14 C-leucine uptake, Kirchman, 1993) plotted against (A) chlorophyll a concentrations, and (B) primary production (PP; measured using 14 C-bicarbonate uptake; data courtesy of Walker O. Smith Jr.) for NEW 1992 (closed diamonds) and NEW 1993 (open diamonds). Except where labeled, these data are from fluorescence maximum depths (15–38 m) only. No significant correlation was found for BP versus chlorophyll, but the Cole et al. (1988) relationship is shown for reference (solid line). Combining the BP versus PP data from both years gives a significant correlation and regression line (dotted line) not significantly different from the Cole et al. (1988) relationship (solid line). Using that same small data set from Yager (1996) to compare bacterial production to primary production (14 C method; W. Smith, 1995; W. Smith et al., 1997), a significant relationship is observed (Figure 8B; r = 0.46; n = 19). The Model II slope (0.80 ± 0.36) is not significantly different from that of Cole et al. (1988). The ratio of bacterial to simultaneous primary production ranged greatly (from 1.07 to 954%) with a median value of 18%. The highest value was observed on Belgica Bank where primary production was very low, but measurable POM suggested advection from other regions. If we remove four values over 260% (three from the time series station about 8 days from Station 42), and plot the ratio along the axis of circulation (Figure 9; as with bacterial abundance above), there is a significant increase in BP : PP with time since entering the polynya (r = 0.52; n = 15). As with the bloom progression in biomass, the bacterial production in the NEW ramps up similarly to that in the RSP, except for the late season peak. Notably, the greatest variability (including three of the highest values greater than 260%, but also the three lowest values less than 5%) was observed at the time series station with no apparent temporal trend, illustrating the system’s heterogeneity. One experiment at the time series station in mid-August 1993 (Yager, 1996) compared BP at in situ temperature (−1◦ C) and at 0◦ C, observing a 39% reduction in the rate with warming. While a single experiment does not provide enough evidence to generalize confidently, it at least supports the possibility that psychrophilic bacterial populations sometimes dominate the polynya ecosystem (further supported by kinetic experiments discussed below) or suggests that conversion factors are sensitive to temperature.
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Figure 9: The ratio of bacterial production (BP; measured using 14 C-leucine uptake, Kirchman, 1993) to primary production (PP; measured using 14 C-bicarbonate uptake; data courtesy of Walker O. Smith Jr.) for NEW 1992 (closed diamonds) and NEW 1993 (open diamonds) plotted against the time each parcel of surface water has traveled through the open waters of the polynya (as in Figure 6). Three data points with ratios greater than 270% (from the time series station, about 8 days from Station 42) are not included in the plot. A significant linear increase (r 2 = 0.27) over time (solid line) was observed. Bacterial utilization of mixed amino acids throughout the water column (14 C-labeled, using a single substrate concentration, 18 nM; Ritzrau, 1997) was highly correlated to POC concentration, pigment concentrations, and bacteria abundance. Rates were highest in the cold surface waters, compared to the warmer deep waters, with estimated amino acid turnover times ranging from 7 to 500 days. Bacterial utilization of dissolved substrate increased dramatically just above the seafloor where sedimenting bloom events combine with resuspension to create bottom boundary (or nepheloid) layers with very high rates of microbial activity (Ritzrau, 1996; Ritzrau and Thomsen, 1997). While the boundary-layer activity is clearly particle-associated, utilization rates there did not correlate with POC concentrations, suggesting that a great deal of solubilization had already occurred by the time the material reached the bottom. A kinetic approach, where a range of mixed amino acid concentrations were presented to bacteria at in situ temperatures (Yager and Deming, 1999), revealed that NEW bacteria were always ready and able to respond to small increases in their available food supply, even at subzero temperatures. Specific affinities observed for mixed amino acids are among the highest ever reported. Maximum utilization rates (or the rate achieved at saturating levels of substrate, also called “heterotrophic potential” in the literature) are similar to other temperate and polar seas, confirming that rates are controlled by substrate availability and not some inherent characteristic of the bacterial population. Additional experiments incubating replicate samples at different temperatures (from −1 to +5◦ C; a range reflecting NEW surface water variability) showed a great deal of heterogeneity in their response to short term warming (with Q10 for Vmax and specific affinity ranging from 0.25 to 13 and 0.23 to 5,
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respectively; Yager and Deming, 1999), with the more psychrophilic responses (Q10 < 1) coming from stations with greater amino acid concentrations or lower POC : PON ratios. Community structure, or the balance between psychrophilic and psychrotolerant bacteria, may therefore be controlled somewhat by nitrogen availability. For 10 nM substrate additions incubated at in situ temperature over a total of eight stations in 1993, the uniformity of incorporation efficiency (IE) as a function of substrate type was remarkably high (Yager, 1996). In general, leucine was used extremely efficiently, ranging from 90 to 100% (data not shown). Mixed amino acids were used slightly less efficiently, averaging 82 ± 5%. The efficiency on glucose averaged 68 ± 4%, while glutamic acid, which may go directly into the respiratory cycle, averaged 41 ± 5%. These incorporation efficiencies are greater than or equal to values typically reported for other marine systems (e.g. Williams, 1970). Incorporation efficiencies in the NEW tended to drop with higher substrate concentrations (Yager, 1996), perhaps indicating luxury consumption or lower tendency for storage. They also tended to be higher at low in situ temperatures, decreasing with short term warming. Under conditions of nitrogen limitation, bacteria store carbon energy in the form of long-side-chain polyhydroxyalkanoate (PHA; Ramsay et al., 1992). Lower temperature (Huijberts et al., 1992; Alvarez et al., 1997) and unsteady food supply (Pagni et al., 1992) can also increase PHA production. Carbon storage due to these or other factors may explain observations of high incorporation efficiency. There is some discussion in the literature about the validity of using individual, specified radiolabeled substrates to measure growth efficiency (see for example, the review by Pomeroy and Wiebe, 1993). Bacterial incorporation of 14 C-labeled substrates appears to be more efficient than that observed on a more “natural” mix of dissolved organic matter (e.g., Linley and Newell, 1984) or than that determined by the ratio of net biomass production relative to the sum of respiration (CO2 ) and net biomass production (e.g., Bjørnsen, 1986; Carlson and Ducklow, 1996; Carlson et al., 1999). Bacteria gain more benefit from taking up an amino acid than a less labile organic molecule, and may therefore utilize it differently (e.g., more efficiently). Since bacteria are known to be capable of diauxic growth (as originally proposed by Monod, 1942), they can be expected to regulate their uptake processes so as to utilize the better substrate first when confronted with resources of diverse quality; i.e., as NEW polynya bacteria encounter amino acids, they will prefer them to other less labile material. By following a mixture of amino acids that mimics the composition of phytoplankton or zooplankton exudates, the NEW efficiencies may reflect “reality” better than a single substrate. Nevertheless, if the bacteria in the polynya are not regularly exposed to such a labile food supply, these efficiencies reflect their behavioral responses to high quality food pulses and not some “average” condition. Bacteria use extracellular enzymes to hydrolyze particulate organic material to a dissolved form they can use directly (D.C. Smith et al., 1992). In the NEW polynya water column, potential hydrolysis rates scaled per bacterium are slower than reported for other more temperate pelagic environments, potentially favoring the export of POM (Vetter and Deming, 1994; although rates may have been underestimated since saturating levels of substrate may not have been used; see discussion below). When scaled to sample dry weight, however, pelagic rates, especially those measured on live floating sediment trap samples, were greater than sediment hydrolysis rates, suggesting that water column processes are nevertheless important to the fate of carbon in the Arctic. This later result complements the high benthic boundary layer activity rates mentioned above. Hydrolysis rates generally increase with incubation temperature but peptidase activity in particular showed psychrophilic behavior (no increase in rate with short term warming) in several samples.
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Figure 10: (A) Grazing rate (d−1 ; estimated by the slope) by microzooplankton as a function of bacterial growth in the absence of grazers (μ, d−1 ; estimated by the y-intercept) determined from the dilution versus bacterial growth relationship (see Tremaine and Mills, 1987; data from Yager, 1996). A solid line shows the 1 : 1 line where the two rates would be equal; (B) Average bacterivory (black column) and bacterial growth (white column) for the Polarstern NEW 1993 stations subdivided into Type I (diatom dominated; n = 5) and Type V (autotrophic flagellate dominated; n = 4) ecosystems (Pesant et al., 1996). Bacterivory estimated using the dilution technique (Yager, 1996) ranged from 0 to 2.9 d−1 and in general kept pace with bacterial specific growth rates (Figure 10A). When Polarstern cruise stations are subdivided into the two ecosystem types suggested by Pesant et al. (1996; using the size-fractionated phytoplankton biomass and production to divide the polynya stations into those ecosystems dominated by large diatoms, Type I, and those dominated by small autotrophic flagellates, Type V), bacterivory tends to equal or exceed bacterial growth at Type V stations (n = 5) but fall short of growth at Type I stations (n = 4; Figure 10B). According to Legendre and LeFèvre (1995), these ecosystem types indicate the local importance of the microbial loop, with Type I reflecting an herbivorous food web and Type V reflecting a microbial food web or loop. Our data confirm this idea in that a stronger link between bacterial growth and bacterivory occurs where autotrophic flagellates dominate. In an herbivorous food web, bacteria are thought to be active, but apparently not as strongly controlled by microzooplankton grazers.
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Taken together, this suite of results points to the conclusion that bacteria in the NEW polynya were taking up dissolved organics as they became available, using them efficiently with minimal respiration, and passing the carbon and energy to higher trophic levels. Bacterial biomass does not accumulate as fast as it might, however, because the cells are living under intense grazing pressure. An obvious question arises: given that bacteria have minimized respiration in this and perhaps other polar environments, do the bacteriovores play a more dominant role than their prey in carbon and nitrogen remineralization? The only study of the growth efficiency of polar heterotrophic flagellates is that by Choi and Peters (1992). In this study using cultured organisms, IE was high at subzero temperatures (60–70%) and decreased with warming. If these flagellate results can be extrapolated to other grazers in polar waters, then highly efficient trophic links may continue up the food web, providing an ecosystem response that contributes as a whole to an efficient biological pump. 4.2
The North Water (NOW) Polynya
The NOW polynya (75–79◦ N, 68–80◦ W) is the largest (approximately 80,000 km2 ) and most biologically productive polynya in the Arctic (Deming et al., 2002; Figure 1). A historical debate concerns whether latent heat or sensible heat contributes most to the formation of the NOW, as the outcome will likely influence the mode of primary production. The most recent data from the region suggests that, in a fashion similar to the NEW, the NOW polynya forms because of wind and ocean advection of ice (latent heat) southward from Nares Strait, which is typically blocked by an ice bridge preventing the flow of ice from the Lincoln Sea into Baffin Bay (Ingram et al., 2002). Sensible heat comes into play later in the season, but is not enough to open the polynya alone (Melling et al., 2001). Two major currents move water through the area: the cold, fresher, silicate-rich Baffin Current from the north and the warmer, saltier West Greenland Current (WGC) from the south (Bacle et al., 2002). The phytoplankton bloom starts in the southeast during April when nutrient rich WGC waters combine with moderate vertical stratification and partial ice cover; this bloom spreads north and westwards during May and June (Tremblay et al., 2002). In the northern reaches of the NOW, the bloom starts later in June once reduced ice cover allows sufficient light penetration. The nutrient-rich Baffin Current creates a bloom gradient (or “chemostat”) similar to that observed in the NEW. Maximum mean phytoplankton production (particulate plus dissolved; Klein et al., 2002) in the east occurs during May 1998 (5.3 g C m−2 d−1 ), and in the north during June 1998 (3.3 g C m−2 d−1 ). Nitrogen-limited new production (averaging 1.1 g C m−2 d−1 ; Tremblay et al., 2002) is 3 to 8 times higher than in the NEW. One reason for this may be the intermittent spring and summer storm activity that helped mix nutrients up from below. The steep canyon walls around the NOW contribute to much greater wind speeds than were ever experienced in the NEW. Less than half of the measured new production was based on pre-season nitrogen inventories; the rest came from deep mixing (Tremblay et al., 2002). Diatoms dominate the algal bloom (Lovejoy et al., 2002) and lead to high potential export of particulate organic carbon (up to 155 g C m−2 y−1 ; Klein et al., 2002) with fratios ranging from 0.4 to 0.7. Dinoflagellates bloom later in the season, particularly on the eastern side after nutrient levels decrease; ciliates also contributed significantly to the bloom in some areas (Lovejoy et al., 2002). Most notable is that the growing season is much longer (April through September; Booth et al., 2002) than typically observed in high latitudes. This extended season is primarily due to the remarkable life history (including resting spores) and low-nutrient tolerance of the colonial diatom, Chaetoceros socialis (Booth et al., 2002).
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Although they recruited earlier than their counterparts in nearby Barrow Strait (Ringuette et al., 2002), copepods never attained the biomass expected for such high levels of primary production (Saunders et al., 2003). Large appendicularian tunicates such as Oikopleura vanhoeffini were important, especially late in the season, and were large and abundant enough to graze on the relatively small C. socialis, consume about 10% of primary production, and account for 4% of biogenic carbon export (Acuna et al., 2002). The diet of these filter feeders includes microflagellates and ciliates, as well as bacteria. Dissolved organic carbon concentrations in the NOW (Miller et al., 2002) are generally high in the spring, decrease during June, and then increase again in the late summer. They reach maximum concentrations (182 µM; over twice as high as in the RSP) in the east during May. The lowest values (40–50 µM, reflecting deep ocean levels) are observed in the north and west during June. By contrast, particulate carbon shows a single peak (40–60 µM, up from 8–10 µM in April; Miller et al., 2002), usually in June. Dissolved organic carbon in the NOW is also made more available to bacteria by exposure to ultraviolet light (PinetteMatthews, 2003). Annual fluxes of particulate matter from the NOW polynya (1–14 g C m−2 y−1 ) are the highest ever reported for ice-covered arctic regions (Hargrave et al., 2002; measured by moored sediment traps at ∼200 m), underscoring the importance of understanding not only free-living but also particle-attached bacterial activity. Two peaks in export occurred, a small one in the spring and a larger second peak in late summer. During the late summer peak, mucous-rich mats of C. socialis dominated the trap contents. These carbon-rich mats may be the remains of directly deposited colonial diatoms (Booth et al., 2002) or the discarded houses of appendicularians (Acuna et al., 2002). Except during times of peak sedimentation, the POC : N ratio of trap contents increased with depth (Hargrave et al., 2002) and between April and June (Huston and Deming, 2002), providing evidence for bacterial solubilization of particulate nitrogen. Bacterial abundance in the North Water during July 1998 ranged from 0.4–1.6 × 9 10 cells l−1 (Middelboe et al., 2002), generally higher than in the NEW. A somewhat smaller range was observed during August 1997 (averaging 1.06–5.59×108 cells l−1 in the top 50 m. Corresponding chlorophyll data for the 1997 data set (0.32–3.7 µg Chl a l−1 averaged over the top 50 m) sets the NOW data squarely between the NEW92 and NEW93 regression lines in Figure 8, also falling below the Cole et al. (1988) regression. Integrated chlorophyll values for July 1998 were similar to August 1997 values (Klein et al., 2002), so the higher abundances in the July 1998 data may correspond better with the January RSP data and the Cole et al. (1988) prediction. Bacterial growth rates during August 1997 (measured by time dependent changes in cell abundance in diluted incubations; Delaney, 2005; Rivkin, unpublished data) ranged from 0.11–0.24 d−1 . These rates are on the low end of those measured in the NEW using a similar technique, but they are much faster than NEW or RSP estimates of net population growth in situ, as expected when removal processes are operating. Using bacterial abundance data and assuming 20 fg C cell−1 , integrated bacterial carbon production ranged from 17–123 mg C m−2 d−1 over the top 50 m (0.332 to 2.46 mg C m−3 d−1 ; Delaney, 2005; Rivkin, unpublished data). These values also fit squarely within the range observed in the NEW and fall just below the Cole regression (Figure 8A) when plotted versus their reported average chlorophyll concentrations. Bacterial growth rates in July 1998 (based on bacterial production measurements using 3 H-thymidine and experimentally determined conversion factors of 1.8 × 1018 cells per mole thymidine incorporated; Middelboe et al., 2002) are about the same or faster (0.11 to
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0.40 d−1 ) than those reported for 1997. Using conversion factors from nearby regions for cell carbon (29 fg C cell−1 ) and growth yield (0.25), Middelboe et al. (2002) report the bacterial carbon demand measured in the NOW surface waters ranging from 6–7 µg C l−1 d−1 in the far north to 15–63 µg C l−1 d−1 in the south. Depth integrated bacterial carbon production ranged from 0.10–0.15 g C m−2 d−1 in the north to 0.27 to 0.45 g C m−2 d−1 in the south. Using average primary production data for the same regions and dates (includes particulate and dissolved production; Klein et al., 2002), the calculated BP : PP ratio ranges from 11– 16% in the north to 100–167% in the south. If BGE was 25%, we calculate that the bacteria metabolized 44–64% of the primary production in the north and between 400 and 600% of the coincident primary production in the south. Values greater than 100% indicate either that the two rate processes are uncoupled in time and space (so averages are therefore deceiving), or that the NOW receives allochthonous organic matter inputs that pelagic bacteria can use. That the system is net heterotrophic, however, seems unlikely given that, between June and July 1998 in the northern stations, DOC increased from 52 to 120 µmol kg−1 and DIC decreased from 2120 to 2000 µmol kg−1 (Miller et al., 2002). Interestingly, the DIC stayed about the same in the south between June and July of 1998 (unfortunately, there are no DOC data available) despite the modest primary production. Bacterial growth rates during July 1998 were enhanced by additions of 5 µM glucose and warming by 5◦ C, but not by additions of inorganic nitrogen (10 µM NH4 Cl) or phosphate (3 µM Na2 HPO4 ), suggesting that they were carbon limited (Middelboe et al., 2002). The effects were more pronounced in the north compared to the central polynya region. Bacterial activity on the sinking particles, as measured by extracellular enzyme activity and the percent of actively respiring cells (CTC+; Huston and Deming, 2002), was higher than in the surrounding seawater. Cell-specific hydrolysis rates in the subzero temperatures of the NOW are as high as those measured in temperate environments. Interestingly, Huston and Deming (2002) comment that the much lower hydrolysis rates measured in the NEW polynya (Vetter and Deming, 1994) may have been due to methodological problems rather than environmental differences; according to experiments done in the NOW, the single level of substrate added to the NEW experiments (10 µM versus 200 µM) was probably below saturation and would have therefore underestimated actual hydrolysis rates. Rates of hydrolysis correlated well with seasonal changes in the C : N ratios of the sinking particles, suggesting that the bacteria were responding to food quality. The observed enhancement of leucine-aminopeptidase activity relative to other extracellular enzyme activities measured (chitobiase and beta-glucosidase) would have preferentially solubilized nitrogen, consistent with the observed seasonal increase in the C : N ratio of particles (Huston and Deming, 2002). These results further support the idea that arctic polynya bacteria are particularly focused on the availability of dissolved organic nitrogen. Grazing by microzooplankton on bacteria was estimated in August 1997 (Bussey, 2003) using the dilution technique. Samples were from the subsurface chlorophyll maximum. The fraction of bacterial biomass ingested ranged from 18% in the east to 51% in the west. The fraction of bacterial production ingested ranged from 41% in the east to 77% in the west. Clearly, grazers were having a significant impact on bacteria in the NOW polynya. In the first study of viral impacts on polynya bacteria, Middelboe et al. (2002) found another agent of mortality for bacterial production. They determined (using both the frequency of visibly infected cells and viral production rates in batch cultures) that 6–28% of bacterial production was lost to viruses (Middelboe et al., 2002). Viral abundance (1.4–5.6 × 109 l−1 ) correlated with bacterial abundance and bacterial production rates.
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5 Other Polynyas The Cape Bathurst (CAB) Polynya and the St. Lawrence Island (SLIP) polynyas are other Arctic polynyas (Figure 1) where particle rich pelagic systems have been identified. The SLIP is primarily a winter polynya, with potentially significant particle flux to the benthos during brine formation (Cooper et al., 2002). As far as we know, however, no pelagic bacterial activities have been monitored there. The Cape Bathurst polynya on the Canadian Beaufort Shelf is tightly coupled to the dynamics of the Mackenzie River (Arrigo and van Dijken, 2004). Arrigo and van Dijken (2003) identified 37 Antarctic polynyas ranging in size from the Lazarev Sea polynya (1000 km2 ) to 400,000 km2 for the RSP (Figure 1). Most of these have not been studied at all, and very few have been sampled for bacterial properties. Data from Prydz Bay suggest that the phasing of bacterial and phytoplankton blooms might vary interannually. Billen and Becquevort (1991) attributed a one-month lag between the diatom and bacterial blooms in 1986–87 to delayed production and utilization of macromolecular, polymeric DOC. Lancelot et al. (1991) hypothesized a similar scenario for a Phaeocystis bloom in Prydz Bay. In the Prydz Bay polynya the maximum abundances of bacteria and bacteriovores were 8.6 × 108 cells l−1 and 4.5 × 106 cells l−1 with a prey:predator ratio of 80–300 (Leakey et al., 1996). However the bacteriovores only removed 10–36% of the daily bacterial production, allowing a small bloom (from 2 to 8 × 108 cells l−1 ). Simon et al. (1999), in one of the few studies focusing on species composition of Antarctic microbial communities, observed that the bacterial community was dominated by members of the Cytophaga-Flavobacterium group following a Phaeocystis bloom in the Lazarev Sea.
6 Summary and Prospects One theme of this chapter is that many of our recent insights about bacterial growth in cold, polar waters come from observations and experiments performed in both Arctic and Antarctic polynyas. Although few of the 37 Antarctic polynyas and just a few Arctic polynyas have been examined in much detail, we can make some important observations about bacterial growth in these systems: • Bacteria grow at rates identical to warmer, temperate habitats. There is little evidence for growth rate limitation or inhibition by low temperature, at least during the phytoplankton growing season. • Bacterial physiology responded quickly to additions of organic matter in the Arctic, whereas in the Antarctic, no growth response was seen because growth was apparently already at near-maximal rates. The experimental approaches are not strictly comparable, but we conclude that bacteria in both environments are adapted to respond quickly to organic matter additions resulting from phytoplankton blooms. • Bacterial growth is in excess of removal by bacteriovores and viruses during or directly following local phytoplankton blooms, enabling bacterial stocks to accumulate to levels similar to those observed in temperate oceans. • Bacterial growth efficiencies and incorporation efficiencies are similar to or higher than lower latitudes and bacterial production reaches similar ratios of BP : PP as in temperate regimes (5–15%; with some higher exceptions, especially in the Arctic).
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The question remains—are bacterial ecology and population dynamics different in polynyas when compared to the surrounding marginal ice zones? Somewhat surprisingly, we are unable to answer this question because similar comprehensive seasonal studies have not been accomplished outside of polynyas. Another question is, are bacteria different in the Arctic and Antarctic? From the work discussed here, the answer at this time is, not very. Yet, the Arctic receives large inputs of river runoff, containing terrestrial organic matter. These inputs are entirely lacking in the Antarctic. With global warming leading to melting permafrost and tundra, river input to the Arctic will increase—it has increased 7% since 1936 (Peterson et al., 2002). Currently both the Antarctic and Arctic are experiencing the most rapid rates of warming on the planet, but the warming is regionally variable (Doran et al., 2002). Polynyas themselves are climate sensitive environments, which may increase or decrease in size, or may disappear entirely (as would seem to be the case with the NEW) with future warming. Depending on their location in either polar region, polynyas may experience different rates of warming (or cooling). Polynyas in the arctic may experience different rates of terrestrial input for the same reasons. As noted above, polynyas currently behave as rectified (one-way) sinks for atmospheric CO2 , but this behavior is sensitive to the precise phasing of the seasonal cycles of ice formation and retreat, phytoplankton blooming, and microbial respiration. With additional terrestrial DOC and warming, bacterial respiration in Arctic polynyas may increase, tipping the CO2 balance in favor of respiration and outgassing. In the Antarctic, warming may lead to melting of ice shelves (Rignot and Jacobs, 2002; Shepherd et al., 2003), increased glacial runoff and stratification. Phytoplankton blooms may decrease in frequency and amplitude with the change in mixing regime. Increased warming will likely lead to changes in bacterial communities and their performance. Thus we envision a scenario in which differential warming may lead to increased differentiation of biogeochemical function and microbial ecology in polynyas.
Acknowledgements Many of the analyses and interpretations of data presented here arose from countless discussions with Jody Deming during the early 1990’s; PLY is grateful for her mentorship. Shelly Carpenter and Jan Gaylord (from the Deming laboratory) carefully generated the bacterial abundance data from the Polar Sea 1992 and 1993 expeditions to the NEW. Chlorophyll a and primary production data from the NEW were provided by W. O. Smith Jr. Yager’s research was originally supported by NSF-OPP-9113960 (to J. Deming) and a Department of Energy Global Change Fellowship (1991–1996). Preparation of this manuscript was partially supported by NSF-OPP-0217282 to HWD.
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Chapter 11
Benthic Processes in Polynyas J.M. Grebmeier1 and J.P. Barry2 1 Marine Biogeochemistry and Ecology Group, Department of Ecology and Evolutionary Biology,
The University of Tennessee, Knoxville, Tennessee, 37932, USA 2 Monterey Bay Aquarium Research Institute, Moss Landing, California, 95039, USA
Abstract Polynyas are areas of open water in ice-covered seas, and are important sites for enhanced water column and benthic production in both the Arctic and Antarctic. Research on polynyas during the last twenty years has allowed multi-disciplinary evaluation of processes in various polynyas, including the Northeast Water (NEW) polynya, North Water (NOW) polynya, St. Lawrence Island polynya (SLIP), and the Bathurst (BATH) polynya in the Arctic, and the Ross Sea polynya (RSP), McMurdo Sound polynya (MSP), and Terra Nova Bay (TNB) polynya in the Antarctic. Several other coastal polynyas around Antarctica have received limited study. Studies of benthic patterns and processes in polynyas indicate that faunal biomass, productivity, and carbon cycling are dependent on depth, season, ice cover, carbon supply, and hydrographic forcing. Polynyas generally have high primary productivity, which typically supports rich benthic communities through enhanced vertical carbon flux. Short and long-term indicators can provide a “foot print” of water column processes within polynyas, such as sediment community oxygen consumption and benthic biomass. Although zooplankton production directly influences the retentive nature of carbon in the euphotic zone, depth is the critical factor for the impact of net carbon export to the underlying benthos. We use a simple modeling exercise of two polynya case studies, the SLIP in the Arctic and RSP in the Antarctic, to evaluate export efficiency and benthic carbon cycling. Depth ultimately influences whether a polynya is a “retentive” versus “export” ecosystem, impacting the underlying benthic populations and associated carbon cycling. Defining polynyas and marginal ice zones as retentive versus export systems is potentially a powerful tool for understanding polar shelf processes, particularly when primary production is not measured simultaneously with benthic parameters.
1 Introduction Polynyas are areas of open water in seas that are usually ice-covered, and often form during winter or early spring in response to intense katabatic winds (e.g. Kurtz and Bromwich, 1985), latent heat, or both (Zwally et al., 1985). Because sea ice cover in polynyas is low or absent, they are much more accessible than non-polynya areas. As such, they have historically been the focus or route of expeditions for both exploration and research. Indeed, most Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74011-9
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polar explorers used polynyas to sail closer to the poles before launching their polar treks. Both Scott and Amundsen’s 1911 expeditions to the South Pole landed along the Antarctic coast in the southernmost areas of polynyas in the Ross Sea. Likewise, Peary’s 1909 expedition to the North Pole very likely sailed through the Smith Sound polynya in northwest Baffin Bay (more recently called the North Water polynya) to reach as far north as possible near Ellsemere Island, where he launched his bid for the pole by dogsled. Since these early explorations, polar research cruises have commonly focused on accessible waters in and near polynyas. Owing to their early seasonal occurrence, the upper water column in polynyas receives considerably greater solar radiation than nearby ice-covered areas, typically promoting early, intense phytoplankton productivity and, subsequently, high fluxes of sinking organic debris that fuels benthic productivity. Coastal polynyas on the Antarctic continental shelf vary in both the timing and magnitude of primary production (approximately 18 to 151 g C m−2 y−1 ), but on average (118 g C m−2 y−1 ) generally greatly exceed estimates of productivity in non-polynya shelf areas (34 g C m−2 y−1 ), and are equal to or greater than annual production in the open Southern Ocean (100 g C m−2 y−1 ; Arrigo and van Dijken, 2003; Tremblay and Smith, 2007). Arctic polynyas also vary considerably in formation, persistence, and productivity, but can experience even higher productivity than estimated for Antarctic polynyas. Rates of primary production can reach >250 g C m−2 y−1 in the North Water polynya (Klein et al., 2002), presently considered to be the most productive Arctic polynya. Annual estimates within the St. Lawrence Island polynya are 100–200 g C m−2 y−1 (Springer et al., 1996). By comparison, the high suspended inorganic sediment load of riverine inflow into the Laptev Sea polynya limits light penetration, thereby reducing primary production even when the polynya is open, resulting in low annual production (40 g C m−2 y−1 ; Sakshaug, 2004). Low light penetration, coupled with enhanced winddriven vertical mixing within the polynya, reduces stratification and inhibits the initiation of a phytoplankton bloom. Consequently, light limitation reduces water column productivity, and subsequent export production. These same limitations characterize the Northwest Cape polynya off northwest Alaska during its winter formation. Primary productivity in coastal Antarctic polynyas is highly variable, ranging from 20 to 160 g C m−2 y−1 (Arrigo and van Dijken, 2003; Tremblay and Smith, 2007). Such variation in primary production will no doubt lead to similar variation in the supply of useable carbon at the seabed, influencing benthic faunal patterns and energy turnover. Because the Antarctic continental shelf (about 600 m) is much deeper than that in the Arctic (∼60 m), retention and recycling of material in the water column may play a larger role in regulating south polar benthos than in the Arctic. Under-ice production in the northern Bering Sea is a limited but important source of early organic carbon to the ecosystem before ice melt and thermal stratification catalyzes the major open water bloom during spring. Because low temperatures limit rates of zooplankton growth and reproduction during winter and early spring, new production during the onset of spring is not heavily grazed, and settles rapidly to the underlying sediments. These dual phytodetrital sources (ice and water column production) ultimately support a rich benthic community (clams, amphipods, polychaetes and brittle stars) that in turn support populations of diving sea ducks, gray whales, walruses and bearded seals (Grebmeier and Cooper, 1995; Grebmeier and Dunton, 2000; Cooper et al., 2002). The recent phenomenon of early spring ice retreat has increased downwelling irradiance early in the season. The cause of this early season ice retreat is not well understood, but may result from a shift in the dominant wind field from cold northerlies to warmer southerlies, and the northward transport of relatively
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warm seawater. As mentioned previously, the timing of sea ice retreat plays an important role in the development of the phytoplankton bloom and associated food web dynamics. Thus, there is a critical need to understand how climatic changes influence wind patterns, and thus ice extent, in the context of polar ecosystem structure and dynamics. Polynyas provide a window for biological activity prior to partial or full retreat of marginal ice zones. Seasonality in polar seas is severe, with primary production occurring during spring and summer, particularly in polynyas, often overwhelming the grazing potential of zooplankton communities, resulting in rapid and large fluxes of organic carbon to the seafloor until temperature-controlled zooplankton populations can graze down seasonal production (Grebmeier and Barry, 1991). Although the degree of coupling between benthic and pelagic systems varies seasonally, studies have shown that benthic biomass or rates of benthic carbon remineralization or both are coupled to the sinking flux of organic carbon reaching the seabed (Grebmeier et al., 1989; Gage and Tyler, 1991; C. Smith, 1997; Levin et al., 2001; K. Smith et al., 2002; Barry et al., 2003), which in turn is linked to patterns of primary productivity. Benthic oxygen demand beneath polynyas should also be regulated largely by the magnitude of organic carbon input. The large contrast in primary production between open-water polynyas and areas under sea ice is mirrored to some extent by the benthos, allowing a “footprint” of overlying water column production export to the benthos, presumably due to the scaling of vertical carbon flux from primary production in the euphotic zone. Export of particulate organic carbon (POC) from the upper ocean to the benthos is controlled by the magnitude of primary production, as well as the degree of carbon recycling in the upper ocean (Wassman et al., 2004). If the seasonal phytoplankton blooms develop slowly, zooplankton populations may have sufficient time to respond numerically and graze a significant fraction of the production (Coyle and Pinchuk, 2002). Such communities with strong coupling between primary production and zooplankton grazers are termed “retentive systems” (Wassmann, 1998), in which the fraction of primary production exported (e) is low (e.g., e less than 0.2). In these systems the timing of production and subsequent micro- and meso-zooplankton growth, along with microbial transformations, can reduce the fraction of primary production exported to the seabed, thereby limiting benthic populations and carbon cycling. In contrast, if a strong bloom/bust pattern of primary productivity is more typical, such as in polynyas, the zooplankton assemblage is coupled weakly to changes in production, and recycling in the water column will be low and the fraction of primary production exported higher (e.g., e greater than 0.4). In these “export” systems much of the organic matter will sink to the benthos, allowing for enhanced benthic processing of carbon. Studies in the Arctic and Antarctic reveal some general patterns and processes typical of polynyas. These include sea ice formation and brine release, ice edge production, retention and export processes influenced by zooplankton production levels, and ultimately the response of the underlying benthos to seasonal pulses of organic carbon that would occur later and in lower quantities in non-polynya regions. The timing of water column productivity, advective processes, and the depth of the system affect the “footprint” of polynya production in the underlying benthos. Key hydrographic and benthic processes identified by past polynya studies may provide insight concerning the influence of ongoing environmental change in polar regions on pelagic-benthic coupling and benthic carbon cycling. Benthic signatures, such as sediment respiration (an indicator of carbon supply to the benthos), sediment chlorophyll content, and short-term radioisotopic signatures respond over short time scales (days to weeks; Cooper et al., 2002). By comparison, other benthic signatures, such as total sediment
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Figure 1: Location of large, recurring polynyas in the: (A) Arctic and (B) Antarctic. The key polynyas with multidisciplinary studies in the Arctic are: BATH = Bathurst polynya, NEW = Northeast Water polynya, NOW = North Water polynya, and SLIP = St. Lawrence Island polynya. Although mentioned in the text, the Laptev Sea polynya (LAPTEV) and Northwest Cape polynya (NWC) have relatively few biological measurements. In the Antarctic, the key polynyas with multidisciplinary studies include: MSP = McMurdo Sound polynya, RSP = Ross Sea polynya, and TNB = Terra Nova Bay polynya. Coastal Antarctic polynyas are shown as gray polygons, which approximate the late winter position of polynyas.
organic carbon and the structure and biomass of benthic faunal populations, develop over longer time-scales in response to the patterns of organic carbon deposition on the seabed (Grebmeier and Dunton, 2000; Barry et al., 2003). Although these processes occur in marginal ice zones and polar shelves in general, annual, recurrent polynyas provide a localized, often earlier, seasonal time frame for these processes that is comparable over wide spatial scales. Multidisciplinary studies of Arctic polynyas have focused on the Northeast Water (NEW) polynya off the eastern coast of Greenland (W. Smith et al., 1997), the North Water (NOW) polynya in Baffin Bay west of Greenland (Deming et al., 2002), the St. Lawrence Island polynya (SLIP) south of St. Lawrence Island (Grebmeier and Cooper, 1995), and the Bathurst (BATH) polynya in the Canadian Arctic Archipelago (Arrigo and van Dijken, 2004b, Figure 1A). In the Antarctic multidisciplinary studies of polynyas have focused on the Ross Sea polynya (RSP; Barry et al., 2003), the McMurdo Sound polynya (MSP; Barry, 1988; Barry and Dayton, 1988), and the Terra Nova Bay (TNB) polynya (Povero et al., 2001; Figure 1B). Measurements within most of these polynyas included hydrography, bathymetry, primary production, zooplankton production, export production, sediment carbon cycling and benthic faunal structure and biomass. Together these factors interact to influence the patterns and dynamics of the benthos underlying each polynya.
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2 General Polynya Patterns and Processes In comparing the pattern of ice cover, upper ocean productivity, the biological pump (defined as the sum of a suite of biologically-mediated processes that transport carbon from the surface euphotic zone to the ocean’s interior), and benthic patterns among these polynyas (Table 1), several generalities are evident: 1. Arctic polynyas have higher rates of primary production than those observed in the Antarctic. Among Arctic polynyas, the most productive water column occurs in the NOW polynya (rates up to 254 g C m−2 y−1 ), followed by the SLIP (100–200 g C m−2 y−1 ), both influenced by the nutrient-rich Pacific water inflow. The lowest annual production was found in the NEW polynya (average of 66 g C m−2 y−1 ), considered to be an oligotrophic polynya. Estimates of primary productivity in Antarctic polynyas derived from satellite observations (Arrigo and van Dijken, 2003) indicate a range of productivity from 21 to 160 g C m−2 y−1 . The most productive large Antarctic polynyas include those in the Ross Sea (151 g C m−2 y−1 ), adjacent to the Ronne Ice Shelf (103 g C m−2 y−1 ), Prydz Bay (104 g C m−2 y−1 ), and the Amundsen Sea (161 g C m−2 y−1 ). 2. The rich spring summer zooplankton populations in the NOW polynya and to a relatively lesser extent the NEW polynya consume much of the primary production before it reaches the benthos, and thus can be considered “retentive systems” as defined by Wassmann (1998). By comparison, the shallow SLIP area in the northern Bering Sea is open during the late winter, with cold seawater temperatures and seasonal development timing limiting zooplankton populations and microbial transformations in the spring (Coyle and Pinchuk, 2002; Lovvorn et al., 2005), allowing the ice-edge and spring bloom to be exported efficiently to the shallow shelf, resulting in rich benthic populations. Satellite and shipboard observations indicate maximum primary production in late April/May (Cooper et al., 2002), with little zooplankton growth until later in the season (Lovvorn et al., 2005). 3. Although it is deeper, the RSP has similar ice and water column dynamics, including reduced zooplankton populations. Recent modeling studies indicate that the timing of ice retreat influences species composition of phytoplankton, which along with low zooplankton growth, produces an “export” type polynya system (Tagliabue and Arrigo, 2003). Low grazing, however, does not always lead to rapid export, due to the low sinking rates of Phaeocystis-dominated assemblages during rapid growth conditions (Smith and Asper, 2000). This results in a time lag in the deposition of organic matter to the seabed, which can influence water column chlorophyll, carbon flux, and benthic populations observed under the polynya (Dunbar et al., 1998; Barry et al., 2003; Grebmeier et al., 2003). In contrast, the TNB is a system where productivity appears to develop slower providing zooplankton the time to track more readily changes in production (Tagliabue and Arrigo, 2003). Thus, the TNB may, at least during some years, tend to higher retention than polynyas with rapidly pulsed patterns of primary production. 4. Highest POC export at the 100–200 m depth occurred in the NOW polynya (1–15 g C m−2 y−1 ), then BATH polynya (3–6 g C m−2 y−1 ) and lowest in the NEW polynya (0.4–4 g C m−2 y−1 ). No POC export has been determined directly in the SLIP. By comparison, the POC export in the RSP ranges from 1–10 g C m−2 y−1 . 5. Highest average sediment community oxygen consumption (SCOC) occurs in the shallow SLIP region (up to 40 mM O2 m−2 d−1 ) and deeper BATH polynya (Franklin Bay)
368 Table 1: Summary of carbon production, export and benthic rates and tracers in selected polynyas in the Arctic and Antarctic (NEW = Northeast Water polynya, NOW = North Water polynya, SLIP = St. Lawrence Island polynya, BATH = Bathurst polynya, RSP = Ross Sea polynya). TOC = total organic carbon in sediments, C/N = carbon/nitrogen ratio in sediments, Sed chl = sediment chlorophyll, CPE = chlorophyll plus phaeopigments in surface sediments, nd = no data Polynya
Depth (m)
Annual production (g C m−2 y−1 )
POC export (g C m−2 y−1 )
Macro-faunal benthic biomass (g wet wt m−2 )
Sediment community oxygen consumption (mM O2 m−2 d−1 )
TOC (%)
NEW NOW SLIP BATH RSP
150−515 247−680 20−70 200 300−800
66 76−254 100−200 90−175 151
0.4−4 1−15 nd 3−6 1−10
38−756 nd 41−2239 nd 62
0.7–4.3 1.7–4.1 0.6–39.7 1.8–21.0 0.0–8.4
0.1 0.5–3.2 0.5–1.8 nd 0.3–1.2
C/N
9–15 5–7 nd 5–9
Sed. Chl (mg m−2 )
CPE (Chl + Phaeo); (mg m−2 )
Citation
<1−3 0−5 10−30 1 0−20
9–46 4–42 nd 1–11 nd
1–5 6–8 9–12 13–15 16, 17
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References: 1: Bodungen et al. (1995), 2: Ambrose and Renauld (1995), 3: Ambrose and Renauld (1997), 4: W. Smith et al. (1997), 5: Schnack (1998), 6: Grant et al. (2002), 7: Klein et al. (2002), 8: Hargrave et al. (2002), 9: Grebmeier and Cooper (1995), 10: Cooper et al. (2002), 11: Clement et al. (2004), 12: Springer et al. (1996), 13: Arrigo and Van Dijken (2004a), 14: Renaud et al. (2006), 15: Makoto et al., unpublished, 16: Grebmeier et al. (2003), 17: Barry et al. (2003).
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region (up to 21 mM O2 m−2 d−1 ), with the lowest values in the deeper NEW and NOW polynyas (1–4 mM O2 m−2 d−1 ) where upper water column processes dominated during the spring and summer bloom periods. SCOC values were lower in the RSP (0– 8 mM O2 m−2 d−1 ), but had higher values than either the NEW or NOW polynyas. Benthic biomass was only measured in some of the polynyas, with values ranging from highs in the SLIP region (41–2239 g wet wt m−2 ) and NEW region (38–756 g wet wt m−2 ) to lowest in the Antarctic RSP (62 g wet wt m−2 ). However, the Ross Sea is primarily dominated in biomass by epifaunal organisms that were not included in the macrofaunal data presented. Two cases studies, SLIP and RSP, are used in the following evaluation to elucidate patterns of polynya benthic production under “export” systems compared to their nature under “retentive” upper water column process-dominated polynyas. 2.1
St. Lawrence Island Polynya (SLIP)
In the Bering Sea south of St. Lawrence Island, the transport of Pacific-derived water is predominantly from south to north (Coachman et al., 1975; Stabeno et al., 1999). During ice formation in the winter, cold saline water (greater than 34 psu and less than −1.5◦ C) is produced over the northern shelf and flows northward through Bering Strait (Figure 2A). One consequence of the northward transport is that a supply of nutrient-rich water to the northern shelf upwells near St. Lawrence Island, thereby stimulating primary production when light regimes are adequate in the spring (Nihoul et al., 1993; Cooper et al., 2002; Figure 2B). Water-column primary production and the final location of carbon deposition to the benthos are related to ice extent during late winter-early spring, ice melt, and the overall northward set of the particulate matter-rich water in the region. In the ice-covered winter/early spring period, the seasonal SLIP develops south of St. Lawrence Island as prevailing northerly winds force sea ice away from wind-sheltering land-masses (“latent heat” polynyas; Morales Maqueda et al., 2004). The SLIP extends 20– 40 km (sometimes further) south over a shelf 30–70 m deep (Schumacher et al., 1983). This brine injection can develop into periodic baroclinic currents that transport water and any entrained organic matter to the south and west as geostrophic balance is reached (Schumacher et al., 1983). Recent studies indicate, however, that most brine water produced in the SLIP during winter in the late 1990s was advected eastward (Weingartner et al., unpublished). The original hypothesis of periodic baroclinic currents transporting water and organic matter to the south and then west within a “cold pool” that is maintained throughout the summer (Grebmeier and Cooper, 1995) may need revision, although this recurrent cold pool occurs as a persistent hydrographic feature of the system. Once spring primary production and export occurs, stratification during summer apparently “caps” the underlying cold pool with its entrained high carbon load until the water becomes mixed in the winter (Schumacher et al., 1983; Cooper et al., 2002; Grebmeier et al., 2006b). Both high oxygen uptake (an indicator of carbon supply to the benthos) and high benthic biomass (an interannual integrator of overlying water column processes) occur at this site, indicating that there is a persistent “footprint” of carbon deposition and benthic productivity under this cold pool. Total organic carbon content and C/N ratios of surface sediment also indicate deposition of high quality organic carbon southwest of St. Lawrence Island (Grebmeier and Cooper, 1995). Pelagic–benthic coupling can be studied via underlying sediment processes on various time scales. Sediment metabolism can be an indicator of weekly-to-seasonal carbon
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Figure 2: (A) Ice cover MODIS image of the St. Lawrence Island polynya (SLIP) region on April 22, 2002, showing nearshore opening of the SLIP south of St. Lawrence Island. Station locations for cruises from 1988–2002 are contained in the black box. Additional black boxes to the northwest in the East Siberian Sea (ESS) and northeast in the Chukchi Sea slope area (Slope) also indicate the location of stations used in the carbon export analysis. The SLIP expands during the spring, producing an ice-free northern Bering Sea in late spring/summer. Ice image courtesy of NASA/Visible Earth website (http://visibleearth.nasa.gov/). (B) Chlorophyll-a concentrations (mg m−3 ) as measured by the SeaWiFS platform in April (top panel), May (middle panel), and June (bottom panel) in 2004 (courtesy of NASA: http://oceancolor.gsfc.nasa.gov/SeaWiFS/). Black areas represent land areas, sea ice cover, or cloud cover and the white box indicates the location of SLIP stations used in the analysis. Note that after the breakup of sea ice in April, chlorophyll-a concentrations peak in May and again subside in June. Warmer colors (orange–red) indicate higher chlorophyll content (10–30 mg m−3 ) and cooler colors (blue–green) indicate lower chlorophyll content (0.1–1 mg m−3 ).
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Figure 3: Major benthic faunal taxon groups by: (top panel) biomass (g wet wt m−2 ) and (bottom panel) abundance (# m−2 ) in the St. Lawrence Island polynya (SLIP) region and the Ross Sea polynya (RSP) region. SE = standard error. depositional processes, while benthic faunal populations can act as multi-year, long-term integrators of several marine processes. The SLIP region is dominated by bivalves (Mollusca), polychaetes (Annelida), brittle stars (Echinodermata) and amphipods (Arthropoda, Figure 3; Grebmeier and Cooper, 1995; Simpkins et al., 2003). There are three major cluster assemblages in the SLIP region: (1) the “western assemblage” southwest of St. Lawrence Island, which is considered the most productive community, dominated by nuculanid, nuculid and tellinid bivalves, and orbiniid polychaetes (mean biomass = 542 g wet wt m−2 ), (2) the offshore “southern assemblage” dominated by nuculanid and nuculid bivalves and leuconid cumaceans (biomass = 298 g wet wt m−2 ), and (3) the “northern assemblage”, closest to St. Lawrence Island, dominated by nuculid and tellinid bivalves, leuconid cumaceans and
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phoxocephalid amphipods (biomass = 150 g wet wt m−2 ; Simpkins et al., 2003). Benthic productivity is linked directly to higher trophic levels through feeding on bivalves by diving spectacled eiders (Lovvorn et al., 2003) and marine mammals (walruses and bearded seals; Simpkins et al., 2003). 2.2
Ross Sea Polynya (RSP)
The Ross Sea is a high polar marine system located along the southernmost bight of the Antarctic continent (Figure 1B). The continental shelf is deep and broad, with an average depth near 600 metres (m) due to isostatic depression caused by the weight of the continental ice (Drewry et al., 1983). The rugged Antarctic coastline along the western border rises steeply through the Victoria Land Ranges to the polar ice cap. The Ross Ice Shelf forms the southern boundary of the sea, with a fairly large subshelf cavity that is ventilated from flow through the Ross Sea. The bathymetry of the continental shelf under the Ross Sea is complex. Three major banks with depths as shallow as 200 m are found near the northern shelf margin, while deep basins occur in the southern and western Ross Sea. The major circulation pattern in the Ross Sea derives from the westward flow of the Antarctic Circumpolar Current as a large clockwise eddy centered on the Ross Sea. Flow in the upper water column is principally southward in the eastern Ross Sea, westward along the Ross Ice Shelf front, and northward along the Victoria land coast (Pillsbury and Jacobs, 1985). Several smaller scale features persist in the Ross Sea, including divergence of flow near the shallower banks (Jacobs et al., 1989), along-slope flow near the continental shelf break (Dinniman et al., 2003; van Woert et al., 2003), and a warm core penetrating the Ross Ice Shelf cavity which melts the bottom of the ice shelf (Jacobs et al., 1970; Pillsbury and Jacobs, 1985). Several polynyas, including the large RSP, the MSP and the TNB, form in the south west Ross Sea (Figure 4, left panel). Like many continental shelf polynyas, the MSP and TNB are thought to be “latent heat” polynyas, which arise through the transport of ice by divergence currents or wind stress (or both) from the polynya (Morales Maqueda et al., 2004). However, the RSP is likely a “mixed” polynya in that it is generated and maintained by both winds and a subsurface heat source (Jacobs and Comiso, 1989). Ice transport and northward advection in the RSP are driven by strong winds produced by the synoptic pressure field (Zwally et al., 1985), or by downslope katabatic winds flowing from the polar ice cap and northward across the Ross Ice Shelf barrier (Kurtz and Bromwich, 1983; Bromwich et al., 1993). Intrusions of warm (about 1◦ C) Modified Circumpolar Deep Water from the Antarctic Circumpolar Current onto the shelf occur episodically, and provide heat that can enhance ice ablation. The MS polynya forms near Ross Island in northern McMurdo Sound, where divergence in ice cover is thought to be generated from either upwelling or katabatic wind surges or both. The TNB is opened by strong katabatic winds (Kurtz and Bromwich, 1985). Continual removal of ice from these polynyas can result in large ice production rates. Ice production in the TNB is estimated to be 60 m y−1 (Kurtz and Bromwich, 1985), which increases the salinity of the water column and contributes to the formation of high salinity shelf water. The areal extent of these winter polynyas expands during spring and summer to form a single large “post-polynya”, which is the largest polynya of the Antarctic continental shelf (Arrigo and van Dijken, 2003). Primary production in the RSP is high, and commences in early spring as solar irradiance increases. Satellite observations (Comiso et al., 1993; Arrigo and McClain, 1994; Arrigo and van Dijken, 2003, 2004a, 2004b) provide synoptic views of the spatial and temporal scales of polynya productivity, with high production for the Ross Sea, particularly in the
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Figure 4: (Left panel) Map of the Ross Sea indicating positions of the Terra Nova Bay (TNB) polynya, McMurdo Sound (MS) polynya, and Ross Sea polynya (RSP). Image indicates ice cover on November 20, 1998, showing opening of polynyas during spring. These polynyas typically expand during summer, producing a nearly ice-free Ross Sea. Station locations for cruises in 1996–1998 are indicated by yellow circles. A large portion of the RSP is obscured by cloud cover. (Right panel) Composite SeaWiFS image of the RSP during spring (courtesy of NASA: http://oceancolor.gsfc.nasa.gov/SeaWiFS/). Gray indicates Antarctic continent and white indicates sea ice. Warmer colors indicate higher chlorophyll content. High phytoplankton productivity preceding the regional bloom is typical in the RSP. TNB polynya and the central RSP (Figure 4, right panel). Shipboard observations in and around these polynyas have identified spatial variation in phytoplankton community patterns that are coupled closely to mixed layer depth, with diatom-dominated communities in the North West polynya near Terra Nova Bay where mixed layer depth is shallow compared to the Phaeocystis-dominated regions in the central Ross Sea (Arrigo et al., 2000). Primary productivity in the RSP ranges from 27 to 53 Tg C m−2 y−1 (∼75 to 151 g C m−2 y−1 ; Arrigo and van Dijken, 2003; Tremblay and Smith, 2007). Changes in productivity among years are linked strongly to climatic controls on sea ice cover. The Antarctic Circumpolar Wave, a cyclic pattern of ice-cover and wind-stress (White and Peterson, 1996) is now thought to be linked to ENSO cycles (Turner, 2004), and may play a large role in controlling the productivity of the upper ocean in the Ross Sea. Observations of the areal extent of the RSP since satellite observations became available in 1978 indicate that the size of the polynya varies by a factor of at least 20 (Dunbar et al., 1998). Sedimentation of organic debris from the upper ocean to the seafloor is spatially variable, with roughly a one month lag period following the intense phytoplankton blooms of spring and summer. Fluxes of organic carbon measured in sediment traps moored in the RSP region vary considerably, with peak fluxes occurring from late January to May, depending upon location and year (Dunbar et al., 1998; Collier et al., 2000). The export fraction estimated from sediment traps is also quite variable. Export is quite high (e = 0.5 to 0.65) immediately north of the Ross Sea, and decreases to 0.15 nearer the Subantarctic front (Buesseler et al., 2003). Within the RSP, however, export estimated in at least one study (Collier et al., 2000)
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was very low (e about 0.02 to 100 m), far lower than observed north of the polynya. Fluxes in the southwest Ross Sea are dominated by diatoms and the prymnesiophyte Phaeocystis (Dunbar et al., 1998), or the pteropod Limacina helicina (Collier et al., 2000). Organic tracers in seafloor sediments largely reflect the composition of the phytoplankton assemblage. Chlorophyll in surficial sediments are highest during spring and summer directly beneath the large bloom areas in the Ross Sea, matching the spatial distribution of phytoplankton blooms (Grebmeier et al., 2003). Stable carbon isotopes in sediments also reflect those in the surface water, with heaviest (enriched in 13 C) carbon beneath areas of higher ice cover (Villinski et al., 2000). The distribution of phytoplankton pigments also reflected the upper water column planktonic assemblage, with fucoxanthin (a predominately diatom pigment) most common in the western Ross Sea (Grebmeier et al., 2003). The benthos in the Ross Sea can be divided into distinct assemblages that are associated more closely with seabed features than either the position of the overlying polynyas or productivity (Barry et al., 2003). Megafauna on shallow, current-swept banks are dominated by suspension-feeders such as Porifera (sponges) and Ectoprocta (bryozoans; Figure 3). These groups are particularly rich along the crests of banks where intensification of nearbottom currents increases the resuspension and flux of particulate organic debris. Deeper on the shelf/slope, the “Mixed-Slope Assemblage” is found, with elements of the shallower suspension-feeding assemblage, as well as holothurians, ophiuroids, annelid worms, and other deposit feeders. Slightly deeper, in muddy settings where phytodetritus is deposited on the seabed, a deposit feeding, ophiuroid-worm assemblage dominates the seabed. In the deepest basins of the shelf, the “Deep-Basin Assemblage” is distinct from other assemblages, with lower densities. This assemblage includes a suite of deposit-feeders, comprised mainly of ophiuroids, maldanid polychaete worms, and small burrows occupied by an unknown taxon, and erratic glacially-deposited boulders or cobbles occupied by sponges and bryozoans. Total faunal density in this assemblage is less than half that observed in other groups, and twenty times less than the richest assemblages on shallow banks. Macrofauna inhabiting the soft sediments are dominated numerically by annelids (mainly polychaete worms) and bivalve mollusks. Excluding the Porifera and Ectoprocta, which occur in sediment samples but are generally considered megafauna, the Annelida and Echinodermata (mainly small ophiuroids) also dominate the biomass of macrofauna throughout the Ross Sea (Figure 3). Productivity in the Ross Sea polynya has long been considered to be important to the energetics of the benthos in McMurdo Sound. Dayton and Oliver (1977) hypothesized that the sudden arrival of a rich planktonic community under the fast ice in McMurdo Sound was derived from the Ross Sea polynya to the northeast. Barry and Dayton (1988) showed that current patterns in McMurdo Sound were consistent with the advection of the spring bloom from the Ross Sea polynya to McMurdo Sound.
3 Means to Assess Interpolynya Differences Benthic abundance and biomass were assessed using small van Veen grabs (Grebmeier et al., 1989), box corer samples (Gambi et al., 1994; Knox and Cameron, 1998; Gambi and Bussotti, 1999), or seafloor camera systems (Bullivant and Dearborn, 1967; Gutt and Starmans, 1998; Starmans et al., 1999; Barry et al., 2003). The infauna was then sorted, identified and weighed. Sediment community oxygen consumption was measured using the methods of Grebmeier and McRoy (1989) and Grebmeier et al. (2003), where enclosed sediment cores were maintained in the dark at in-situ bottom temperatures for 12–24 hours, with oxygen
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concentrations determined at the start and end of the experiment and oxygen flux measurements adjusted to a daily areal flux. Models of polynya primary production, export fluxes, and benthic carbon cycling can be used to estimate rates of benthic carbon remineralization in a variety of Antarctic polynyas. We used Christensen’s (2000) model, which estimates deep-sea benthic oxygen demand (BOD) from primary production in the upper ocean, and Arrigo and Dijken’s (2003) estimates of upper ocean primary productivity for 37 coastal Antarctic polynyas, to estimate patterns of BOD among polynyas. Although Christensen’s model has not yet been applied to polar benthos, error introduced due to colder polar temperatures should be small considering the relatively small difference in bottom water temperatures between the Antarctic and the deep-sea of major ocean basins. Estimates of benthic carbon demand provide a basis for comparing patterns among benthic systems beneath Arctic and Antarctic polynyas (Figures 1A, 1B). Christensen’s model estimates BOD as BOD = 19.6537 × Z −0.936411 × PP1.139487
g C m−2 y−1 ,
(1)
where PP is primary productivity and Z equals depth (m). We assumed the oxygen content of polar bottom water and sediments supported that benthic oxygen demand. Christensen (2000) suggests that seabed fluxes greater than 9 g C m−2 y−1 would have increasing percentages of anaerobic carbon remineralization, but that estimates of much higher BOD (about 30 g C m−2 y−1 ) would only err by about 20%. In addition, studies indicate that regions on shallow polar shelves with high benthic macrofaunal biomass are driven primarily by benthic respiration (Grebmeier and McRoy, 1989; Moran et al., 2005). Since our polynya case studies fall within 600 m depth, we expect a conversion of sediment community oxygen consumption (SCOC; mM O2 m−2 d−1 ) to benthic oxygen demand (BOD; g C m−2 y−1 sensu Christensen, 2000) provides a reasonable estimate of carbon supply for these productive regions. The average depth of Antarctic polynyas was determined from the ETOPO2 database, using the areal extent of “post-polynyas” (the summertime expansion of winter polynyas) described by Arrigo and van Dijken (2003). The depth of the SLIP was based on in situ measurements. Flux to the seabed in each polynya was calculated using the Martin et al. (1987) equation: Fluxz = PP × e × (z /100 )0.858
(2)
to estimate the fraction of primary production (PP) reaching the seabed. The export fraction (e) is the proportion of primary production reaching 100 m, and z is depth in meters. We then adjusted the export fraction for Antarctic polynyas so that the estimated Martin flux matched the BOD (estimated from the Christensen model), leading to a value for e of 0.445. We also plotted BOD estimates (from direct SCOC measurements) for the RSP and SLIP (Table 2, Figure 5), to compare them with estimates from other polynyas.
4 Rate Processes and Their Controls 4.1
Benthic Oxygen Demand
Estimates of BOD for coastal Antarctic polynyas averaged 11.0 ± 10.3 g C m−2 y−1 , similar to numerous measurements throughout the RSP (mean = 8.8 ± 6.0 g C m−2 y−1 ; Table 2, Grebmeier et al., 2003). BOD in the SLIP (Grebmeier and Cooper, 1995; Grebmeier and Dunton, 2000; Cooper et al., 2002) is generally much higher (mean = 43.8 g C m−2 y−1 ),
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Table 2: Estimated benthic oxygen demand (BOD), carbon flux, and estimated primary production (PPest ) for Antarctic polynyas, the Ross Sea polynya (RSP), and the St. Lawrence Island polynya (SLIP) region. For Case 1 (e = 0.286), PPest (151.3 g C m−2 y−1 ) was assumed to equal that estimated by Arrigo and van Dijken (2003), then export flux was adjusted so that the flux to depth balanced the BOD measured directly on the seafloor in the RSP (Grebmeier et al., 2003). For Case 2, export flux (e = 0.445) was adjusted so that carbon flux (based on Arrigo and van Dijken, 2003) equaled the estimated BOD for Antarctic polynyas (Christensen model). Case 1: carbon flux (g C m−2 y−1 ) e = 0.286
Sites
Depth (m)
BOD (g C m−2 y−1 )
Antarctic polynyas RSP SLIP
400
11.0
7.1
600 60
8.8 43.8
5.7 25.5
Case 1: PPest (g C m−2 y−1 ) e = 0.286
Case 2: carbon flux (g C m−2 y−1 ) e = 0.445
Case 2: PPest (g C m−2 y−1 ) e = 0.445
65.7
11.0
65.7
151.3 107.4
8.8 39.7
97.2 69.1
likely due to both the shallow depth (approximately 26–85 m) and the higher rates of annual primary production. Comparisons between benthic patterns and processes among similar depths in the Arctic and Antarctic are limited. For the few inshore coastal Antarctic polynyas that overlap in depth with those from the Arctic, estimated rates of BOD are comparable (Table 2). Measurements of BOD on the Arctic continental slope (Grebmeier et al., 2006a) either overlapped or were somewhat higher than those observed for the Antarctic (Figure 5— pink dots). High Arctic BOD values between 600 and 1000 m depth were measured in or near Barrow Canyon where anomalously high carbon flux has also been observed (Moran et al., 2005; Grebmeier et al., 2006a). Benthic hot spots, such as Barrow Canyon, likely exist in other Arctic and Antarctic locations, but may be more common on Arctic shelves due to the potential for rapid trans-shelf and down-canyon transport of organic material. We expect similar processes on Antarctic shelves to be dampened by carbon remineralization in the water column prior to its arrival at the seabed. The patterns of biomass or abundance for benthic fauna in the SLIP region and East Siberian Sea (Grebmeier and Cooper, 1995, and unpublished data, Simpkins et al., 2003; Dunton et al., 2005), and SW Ross Sea (Barry et al., 2003; Barry, unpublished) mimicked those for benthic oxygen demand (Table 1). Biomass and faunal abundance were highest on the Arctic shelf, particularly in the SLIP region (mean = 396 g wet wt m−2 or 2296 ind m−2 ). Benthic biomass outside the SLIP region on the shelf (196 g wet wt m−2 ) and abundance (1063 ind m−2 ) were significantly lower (t-test, p < 0.01) than the nearby SLIP, but higher (p < 0.01) than in the Ross Sea polynya. As expected with the lower food available at deeper depths, biomass and abundance were considerably lower in the RSP benthos than for either of the Arctic continental shelf sites (Table 1, Figure 3). Mean biomass and abundance for the Ross Sea shelf benthic fauna, where the mean depth is 608 m, were only 62 g wet wt m−2 , and 946 ind m−2 , respectively. 4.2
Retention Versus Export of Carbon to the Benthos
We evaluated the role of retention or export of carbon from the upper ocean to the seafloor using a modified Martin model (Equation (2); Martin et al., 1987). In examining the average
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Figure 5: Benthic oxygen demand (BOD) versus depth for Arctic and Antarctic polynyas. Green dots indicate estimated BOD for 37 coastal polynyas (see text). Light blue dots indicate BOD for 58 stations in the Ross Sea polynya (RSP) during 1996–1998 (Grebmeier et al., 2003). BOD in the St. Lawrence Island polynya (SLIP; 135 stations, red dots) and under sea ice in the East Siberian Sea (ESS; 5 stations, dark blue dots) illustrates the higher BOD of the shallower arctic shelf. Purple dots indicate the Arctic continental slope (Arctic Slope; 34 stations), where high BOD was observed in and near Barrow Canyon (Moran et al., 2005; Grebmeier et al., 2006a). Curves indicate estimates of carbon flux, based on a modified Martin et al. (1987) function. The export fraction in the Martin curve is adjusted to 0.45 in the green line, which matches the mean estimated production of Antarctic polynyas. Estimated carbon flux rates for the RSP using both high retention of primary production (red line) or high export (green line) are also shown. Note that observations for the Ross Sea and coastal Antarctic polynyas fall between these extremes, as do many of the shallow sites in the SLIP region. pattern of carbon flux from all Antarctic polynyas, we assumed a mean PP among Antarctic polynyas of 66 g C m−2 y−1 (Arrigo and van Dijken, 2003), and solved Equation (2) for e, so that the average C flux to the seafloor (mean depth = 400 m) among polynyas equaled the mean BOD value for polynyas estimated using the Christensen (2000) model. Assuming that BOD is balanced by carbon input to the seabed, the export fraction (e) required to balance carbon flux and BOD provides an estimate of the level of retention of carbon in the upper ocean; our estimated e value was 0.445 for the average polynya (Table 2—Case 2). We also
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examined the range of export fractions among coastal Antarctic polynyas by calculating e for each polynya based on its depth, PP, and BOD. Export fractions ranged from 0.35 to 0.50 among polynyas, with a mean e of 0.42 (SD = 0.03). These results suggest that Antarctic polynyas generally have moderate rates of carbon export from the euphotic zone to the seabed compared to highly retentive systems with high planktonic carbon recycling (e ∼ 0.1 to 0.2; see Wassmann, 1998). However, seasonal and spatial variation in the degree of retention clearly plays an important role in these highly dynamic polar systems. The highly pulsed nature of polar phytoplankton blooms generally overwhelms the ability of grazers to crop primary production, especially early in the spring. Carbon export can, however, be low even under low grazing rates, owing to slow sinking speeds (Asper and Smith, 1999). During bloom senescence, phytoplankton sinking rates and zooplankton grazing rates are expected to be higher, leading to much greater retention. Likewise, differences in sinking rates among phytoplankton assemblages, coupled with dynamic upper ocean physics may also influence greatly rates of sinking from the upper water column. Together, these factors contribute to the general transition of phytoplankton blooms from potential dominance by export to more retentive systems. Our model provides a cursory examination of variation in estimates of export or retention among polynyas with varying depth and ranges of primary production. Carbon flux curves for both highly retentive (e = 0.1, low C flux) and high export (e = 0.9, high C flux) systems are shown in Figure 5 to assess the distribution of observations for Antarctic polynyas (green circles) and Ross Sea BOD measurements (light blue dots). Primary productivity was set to 100 g C m−2 y−1 to match that reported by Arrigo et al. (2000) for the Ross Sea during 1996, and the BOD values in Figure 5 were measured by Grebmeier et al. (2003). These high and low export fractions are similar to Wassman’s (1998) estimate for the range of export fractions in pelagic systems. The distribution of measured BOD (or C flux) values in the Ross Sea fall mostly within these boundaries, suggesting that the Ross Sea, like most other coastal Antarctic polynyas, is a neither highly retentive nor high export system, but can seasonally have high export ratios. With a PP of 100 g C m−2 y−1 , an export fraction of 0.42 is required to support the carbon demand (8.9 g C m−2 y−1 ) measured on the seafloor in the Ross Sea. Direct measurements of sedimentation and carbon flux in the Ross Sea indicate that export is low from the euphotic zone, particularly during early spring, at least where the prymesiophyte Phaeocystis dominates, due to its very slow sinking speed. Estimates of export flux in the Ross Sea were extremely low (about 1% to 200 m), suggesting that recycling may be very high in the water column (Collier et al., 2000). Low export recorded in sediment traps and slow Phaeocystis flux is contrary to some observations or indications of rapid flux in this area (DiTullio et al., 2000) and high export in the Ross Sea where the zooplankton community is poorly developed (Tagliabue and Arrigo, 2003). In addition, the export fraction has been observed to increase from near 0.2 in the Subantarctic to as much as 0.5 to 0.65 just north of the Ross Sea (Buesseler et al., 2003). More extensive seasonal studies of primary production, export, and remineralization in the Ross Sea polynya (Asper and Smith, 1999, 2003; Smith and Asper, 2000) indicate that grazing and production are largely decoupled during early spring as primary productivity and phytoplankton standing stocks increase. Grazing lags production until summer, and export increases somewhat later than the decline in productivity, in part due to the time scales for aggregation and sinking of Phaeocystis. Although these studies provide a clearer view of the dynamics of production and export from the Ross Sea polynya, the role of recycling and the efficiency of the biological pump in polar polynyas appear quite variable, and influenced strongly by local oceanographic processes.
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Results of our simple model may also be influenced by our assumption that the exponent (0.858) in the power function for estimating carbon flux was constant. Estimates of b throughout the world ocean indicate that b varies considerably (Berelson, 2001), with important effects on flux estimation. In summary, it remains unclear whether the polynyas of the Ross Sea region and other coastal Antarctic polynyas can be characterized as either highly retentive or having high rates of carbon export. More extensive efforts to quantify the timing and magnitude of production and export to the seabed are required to understand factors influencing retention and export in polar polynyas. It is likely that the chronology of spring–summer events observed in the Ross Sea is a good template for the general seasonal cadence of production, remineralization, senescence and export from highly productive polynyas. Variation in the timing, coupling between production and grazing, and rates of export, will likely respond to forcing by location and oceanographic variability. Productivity in Arctic seas is highly variable, with lowest rates in the deep, central Arctic Ocean (5–30 g C m−2 y−1 ), moderate levels on Arctic Shelves (about 30 g C m−2 y−1 ), and highest levels in the Bering Sea (greater than 50–400 g C m−2 y−1 ; Sakshaug, 2004). The SLIP region, located in the Bering Sea immediately south of St. Lawrence Island, benefits from the characteristically high primary production of the Bering Sea region. Using an estimate of 250 g C m−2 y−1 for primary productivity, and assuming that measured rates of BOD (mean = 43.8 g C m−2 y−1 ) reflect carbon flux to the seabed, the estimated export fraction (e) is only 0.11, suggesting that retention of phytoplankton productivity in the SLIP is high. This type of low export value might be explained in summer when zooplankton and upper trophic level foraging water column feeders are common in this area. Appendicularians are an abundant component of the SLIP region zooplankton in summer (when the region is ice free) and are capable of high grazing rates on phytoplankton biomass (Shiga et al., 1998; Deibel and Daly, 2007). Planktivorous and piscivorous seabirds are also extremely abundant on St. Lawrence Island (Springer et al., 1987; Piatt and Springer, 2003), and feed in the region surrounding the island in summer, including the SLIP region. However, there is little zooplankton production in the late winter/early spring (Clement et al., 2004; Lovvorn et al., 2005), and as we discuss below, the SLIP is likely an export system as the ice retreats in the spring. A potential uncertainty in our calculations is that, although historically high rates of primary production have been observed on the northern Bering Sea shelf north of St. Lawrence Island (up to 300–400 g C m−2 yr−1 ; Springer et al., 1996), coincident with higher benthic biomass (Grebmeier and Cooper, 1995), there are no direct primary production measurements in the SLIP. It is notable that only one sediment trap study has occurred north of St. Lawrence Island which measured sinking fluxes from the euphotic zone of 0.5 g C m−2 d−1 in the 1980s (Fukuchi et al., 1993) in areas where rates of benthic carbon remineralization (estimated from SCOC) were 0.48 g C m−2 d−1 (Grebmeier and McRoy, 1989). However, resuspension may have biased fluxes measured in shallow or near-bottom sediment traps. With a 120 day growing season and an average 0.5 g C m−2 d−1 flux we estimate an average export production of approximately 60 g C m−2 y−1 , which is approximately 30– 60% of the original primary production, yielding an export ratio of 0.3–0.6. Considering the similar range of SCOC south of St. Lawrence Island, we would expect export during the period of high water column production to be higher than estimated in our modeling exercise since SCOC is relatively high in the late winter/spring (Cooper et al., 2002). Even with high grazing rates in the water column in summer, the shallow depth of the shelf and high primary production results in high carbon fluxes to the seabed, supporting a
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rich benthos and supporting high benthic oxygen demand (Grebmeier and Cooper, 1995). Studies in the late winter/early spring (March/April) indicate no water column production, but SCOC was still significant (1–17 mM O2 m−2 d−1 ; Cooper et al., 2002; Clement et al., 2004), suggesting an alternate carbon source, such as carbon production from the previous winter being incorporated in the sediments to support benthic processes, or microbial reprocessing of a previous bloom phytodetritus providing an alternate carbon source to benthic processes as suggested by Lovvorn et al. (2005). In addition, ice edge production that occurs further south in the Bering Sea in March may be advected northward to provide an additional carbon source for these benthic communities before the spring bloom in May in the SLIP region. These alternate carbon sources essentially decouple the SLIP from overlying production in the early spring, complicating the retention versus export description. Once the May spring bloom occurs carbon is exported rapidly to the benthos (Grebmeier and Cooper, 1995; Cooper et al., 2002; Lovvorn et al., 2005), before the zooplankton populations can develop, thus supporting benthic populations and benthic predators (walruses, gray whales, bearded seals). Once June arrives and ice retreats from the region stratification limits production and zooplankton populations (copepods and appendicularians) can expand with warmer surface temperatures, thus developing a more retentive system. Since the SLIP data used in our modeling exercise ranged from late winter/early spring (March–April) through September (fall), it encompasses pre-bloom (SLIP), bloom (post SLIP) and post-bloom (open water) export conditions (Figure 2B) that complicate the export versus retentive description of the SLIP region (Figure 5). In summary, we expect the SLIP to be an export system in spring with rapid carbon export to the benthos as ice cover retreats, changing to a retentive system during summer and fall as the SLIP expands to the broader open sea with the regional ice retreat. This discussion emphasizes the importance of time series data to evaluate seasonal and annual carbon processing in polar systems. 4.3
Depth as the Major Factor for Variance in Benthic Carbon Cycling
Depth of the continental shelf is the most important determinant of differences between benthic patterns and processes on Arctic and Antarctic shelves, as Hargrave (1973) showed as a general pattern for all deep ocean systems. The large differences in benthic faunal abundance, biomass, and benthic oxygen demand between Arctic and Antarctic polynyas and the high ice-covered shelf regions of Antarctica and the East Siberian Sea in the Arctic (Figure 5) are likely due to the long-term response of the benthos to average levels of carbon input from the upper ocean. The great depth of the Antarctic continental shelf, due to the isostatic depression of the Antarctic continent, allows increased remineralization time of organic debris in the water column and reduces the flux of organic carbon to the seabed. Although there was a large difference observed in BOD between the RSP and SLIP, the few BOD measurements from shallow Antarctic waters with depths similar to the Arctic shelf overlapped the range of BOD measured in the SLIP (Figure 5). Primary productivity is highly variable in both the Arctic and Antarctic, and is also expected to affect carbon delivery to the seabed, as is variation in the recycling of organic material in the water column. Primary productivity in the Southern Ocean fringing the Antarctic continent is high, with variable levels of production within polynyas. By comparison, primary production beneath sea-ice is much lower. Based on Arrigo and van Dijken’s (2003) estimates and assuming that the Antarctic Peninsula region (with high marginal ice zones and open water) is about 25% of the total non-polynya Antarctic continental shelf, primary production in non-polynya, non-Peninsula shelf areas (i.e. ice-covered shelves) is approximately 26.5 g C m−2 y−1 . Compared to the BOD in Antarctic polynyas (mean = 11.0 g
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C m−2 y−1 ), BOD under ice-covered, non-Antarctic Peninsula regions of the shelf (estimated using the Christensen model and assuming PP = 26.5 g C m−2 y−1 , Z = 608 m) is near 2.0 g C m−2 y−1 . This is close to the estimate for the least productive Antarctic polynya (BOD = 1.6 g C m−2 y−1 ), but far lower than either the mean (11.0 g C m−2 y−1 ) or maximally (43.8 g C m−2 y−1 ) productive SLIP region (Table 2). Thus, ice-covered regions of the Antarctic shelves are expected to have the lowest rates of benthic oxygen demand and biomass, even compared to the ice-covered regions of the East Siberian Sea (Figure 5). When we compare graphically the estimated BOD for Antarctic polynyas (Arrigo et al., 1998) with measurements from the RSP and SLIP (Figure 5), the expected decrease in BOD with increasing depth is clearly apparent. The field data (red, blue) in Figure 5 show large differences by depth, and the polynya estimates (green) overlap the field observations, though they are somewhat shallower because Arrigo et al. (1998) excluded stations with depths greater than 1000 m. The estimated BOD for the Ross Sea (15.2 g C m−2 y−1 ) is among the green dots, and is higher than the measured averages for the RSP (8.9 g C m−2 y−1 ; Grebmeier et al., 2003), which may be related to the expected increase of SCOC later in the season as flux to the seafloor increases (Table 2). As described earlier, PPest (estimated primary production) is based on results from Arrigo et al. (1998), with the assumption that the average BOD is balanced by carbon flux to the seabed at that depth. From this we converted each BOD estimate to a PP based on the Martin curve (Equation (2)). Export flux (e-ratio) was adjusted in Table 2 for Case 1 to match the flux estimated by Arrigo et al. (1998) for the Ross Sea polynya. With those assumptions, e = 0.286. In the second case we used e = 0.445 (the estimated export fraction for Antarctic polynyas based on the Christensen model), and the estimated primary productivity for the Ross Sea was only 97 g C m−2 y−1 compared to the literature value of 150 g C m−2 y−1 (Table 2). However, this may be more reasonable, considering that SCOC (thus BOD) should increase in the late summer as carbon availability increases near the seabed. In comparison, the original estimate of PP for the SLIP (107 g C m−2 y−1 ; Table 2) is low compared to other estimates of spring/summer primary production for the SLIP region (100–200 g C m−2 y−1 ; Springer et al., 1996). Assuming the same relationship for the Arctic, PP also decreases when assuming an export fraction of 0.445 (from 107 to 69 g C m−2 y−1 ). However, the PP estimates from Springer et al. (1996) are based only on summer measurements, so the PPest does not include the spring bloom, which is the maximum productivity event (Cooper et al., 2002, Figure 2B). These modeling exercises indicate the benthos on shallow Arctic shelves likely receive higher carbon inputs to support carbon consumption than Antarctic shelves. Barry (1988) measured oxygen concentrations of approximately 7 ml l−1 O2 (312 µM O2 ) during early summer in McMurdo Sound. Oxygen levels over Arctic shelves are known to be much higher in the surface waters (450 µM O2 ) during the phytoplankton bloom (Codispoti et al., 2005). Furthermore, Barry et al. (1988) found only minor O2 gradients with depth in the far SW Ross Sea, suggesting sufficient oxygen available for carbon consumption with depth and shallow Arctic seas can experience bottom water O2 values about 300 µM as the benthos rapidly consumes the oxygen and recycles nutrients to the water column for further production (Codispoti et al., 2005). Although Christensen indicates that a flux of 9 g C m−2 y−1 is near the maximum sustainable for aerobic oxidation in sediments, his model of O2 diffusion into the sediment is affected by the initial O2 concentration (200 µM O2 was used as an initial level). Much higher values (greater than 300 µM) have been observed in the Arctic and Antarctic. A strong relationship between high sediment respiration and benthic biomass occurs on the productive Arctic shelves, suggesting that consumption of carbon is largely aerobic in the shelf areas of
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the northern Bering and Chukchi Seas. Converting SCOC from the SLIP region into carbon units (based on the assumption of macrofaunal aerobic respiration driving SCOC) yields an average export flux ranging from 20–40 g C m−2 y−1 , similar in magnitude to values measured north of St. Lawrence Island by Grebmeier and McRoy (1989) and Fukuchi et al. (1993). Thus, Christensens’ carbon flux value of 9 g C m−2 y−1 is the low end for productive Arctic shelf systems, and carbon flux is likely much higher for highly oxygenated polar shelves, such as the SLIP. 4.4
Comparison of SLIP and RSP to other Polynyas
Defining polynyas and marginal ice systems as retentive vs. export is potentially a powerful tool for understanding polar benthic processes, particularly when primary production and benthic parameters are not measured concurrently. Export production can be translated into benthic biomass and BOD, and thus, can represent indirectly the variability in overlying PP from measurements of short-term scales via BOD (days to weeks) and long-term scales via benthic biomass (months-years). Inherent in this determination is depth to the surface sediments, which is the largest driving factor for differences seen in both BOD and benthic biomass. Figure 6 is a generalized relationship between primary production, benthic oxygen demand, and variability in export production, derived from both actual and modeled data. The lowest PP and BOD occurs in the ice-covered Antarctic, with little relationship among the factors controlling e ratios. The NEW polynya has low production and export potential, resulting in limited benthic production and carbon cycling. By comparison, the shallower nature of the East Siberian Sea, even when under ice cover, can export more carbon to the benthos when summer production occurs, although notably the eastern part of this system is still under the Pacific-influence, thus higher production potential than the icecovered Antarctic waters. Antarctic polynyas have variable primary production, and changes in e influence the BOD in the underlying sediments. By comparison, the Arctic slope region near Barrow Canyon, a major conduit for carbon, has a higher e potential, resulting in high BOD in the underlying sediments. When we compare the NOW polynya, the most productive polynya in the Arctic, with the SLIP and RSP, we see that all three polynya regions can be retentive systems, with e about 0.2 and similar BOD values. However, whereas NOW appears to maintain its retentive nature over the seasonal cycle, the SLIP and RSP have the potential to change from retentive to export systems, depending on the season, spatial location, and depth. Depth is the dominant factor influencing e in our study, and is likely the main factor driving the differences seen in carbon flux values and resultant BOD. For example, NOW, SLIP and RSP may experience the same primary production (e.g. 150 g C m−2 y−1 ), but they can have variable benthic oxygen demand depending on depth and water column processes influencing carbon flux to the benthos. Thus, in polar systems defined as export regions, BOD and benthic biomass will be greatest in shallow polynyas and marginal ice zones, like the SLIP and northern Bering/Chukchi seas. This same relationship holds in the shallower regions of the RSP that have high seasonal ice edge production and export to depth, supporting the huge benthic epifaunal biomass known for the region as well as highest BOD for the RSP. For polynyas and marginal ice zones in deeper shelf systems, such as the NOW region and Barents Sea, the amount of advection and water column consumers (e.g. zooplankton and higher trophic levels) make for a more retentive system, and the water column is more tightly coupled to zooplankton populations. These deeper systems also provide a longer time period for surface carbon to be consumed and recycled.
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Figure 6: Schematic of primary production, export flux (e) and benthic oxygen demand (BOD) in retentive vs. export marine systems. Key: Ant ice = nonpolynya, nonpeninsula, high ice Antarctic sites; Ant polynyas = Antarctic polynyas; Arc slope = Arctic slope/Barrow Canyon; ESS = East Siberian Sea; NEW = Northeast Water polynya; NOW = North Water polynya; RSP = Ross Sea polynya; and SLIP = St. Lawrence Island polynya region. 4.5
Comparison of Polynyas to Retreating Marginal Ice Zones and Climate Change
Because polynyas are open areas in ice-covered seas, one might ask whether they differ from seasonal ice zones in their ecological responses to a changing ice cover. Polynyas may well be an extreme sort of marginal ice zone (MIZ), in which their latitudinal position is a key factor affecting ecosystem response versus normal ice edge dynamics per se. Since polynyas tend to open further poleward than most marginal ice zones, they may play a larger role in epipelagic production and carbon input for the benthos relative to lower latitude marginal ice zones. If so, one might expect the poleward retreat of the MIZ to be a negative impact on overall ecosystem productivity. Alternatively, if primary production will be enhanced by ice retreat in general and an enhanced light environment for nutrient-rich waters, polar benthos may experience higher rates of carbon flux following long-term ice retreat, with consequences on rates of benthic production as well as patterns and dynamics of faunal assemblages.
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Polar benthos generally have highly seasonal carbon inputs, and greater amplitudes and variability in carbon flux than non-polynya sites, due to their intimate connection to atmosphere-ocean-ice interactions. The responses of biological systems within polynyas to climate warming could range from an increase in microbial carbon remineralization to a wider range of trophic changes, including higher energy allocation to growth versus reproduction beneath polynyas. Higher carbon flux could alter the structure, distribution, and dynamics of benthic communities beneath polynyas. Long-term responses of benthic populations will likely depend on the magnitude, variability, and stability of carbon flux over both long- and short-temporal scales. High variation in carbon flux may favor simpler trophic webs with increases in microbial remineralization. Over longer time scales, stable increases in carbon flux could support the development of more complex food webs, with increased trophic structure. Although polynyas are clearly hot spots for benthic production in polar regions, the largest benthic populations in the Arctic are found not directly beneath polynyas, but rather downstream from coastal polynyas and on continental shelves underlying seasonal ice zones. The highest benthic biomass in the Bering/Chukchi shelf complex occurs north of Bering Strait in the southern Chukchi sea, downstream of the SLIP and productive Chirikov basin north of St. Lawrence Island, and at the end point of the Bering Sea “Green Belt” region (Springer et al., 1996). In addition, benthic biomass decreases further northward until one reaches the head of Barrow Canyon, where the carbon-rich Bering Sea Anadyr water sweeps the Chukchi shelf production down slope, both eastward and offshore into the Canada Basin (Grebmeier and Cooper, 1994). By comparison, some of the highest epifaunal benthic biomass in the Ross Sea occurs on the shallow banks under the RSP, although ice cover variability and timing of the ice edge retreat are intimately connected to overlying production. In most cases, advection of primary production from polynyas and marginal ice zones supports these rich communities. The advective nature of both the RSP and the SLIP/continental shelf regions is likely an important aspect of the productivity of these systems, and would likely change with the timing and extent of the seasonal ice edge retreat with climate change. Increased poleward heat flux expected with long term warming should increase zonal winds, with increased effects on ice cover and wind-driven currents. In south polar regions, transport in the Antarctic Circumpolar Current may increase, with influences on the Ross Sea and perhaps most Antarctic polynyas. Although the effects of long term warming on primary productivity remain unclear, increased wind stress and reduced ice cover should amplify advection over polar shelves, perhaps redistributing the sinking flux of organic material over wider areas. Overall, warming effects on primary production and advection may have large influences on polar benthos.
5 Conclusions Polynyas can be sensitive indicators of ice variability and climate change thus comparison of select polynyas between north and south polar regions is expected to be a useful focus for projecting the trajectory of change associated with long term warming in polar systems. Defining polynyas and marginal ice systems as retentive versus export is potentially a powerful tool for understanding benthic processes on continental shelves. Time-series are essential to understand the physical–biochemical–biological links within these specialized ecosystems. Long term warming may change the character of production in polar regions from
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pulsed, high amplitude systems to more protracted, low amplitude productivity events. What does that mean to the ultimate benthic community structure, especially if warming allows subpolar species to move poleward? Less predictable food supply in the marginal ice zone may counteract the projected increased productivity with warming. Thus, polynyas may become less important overall, even if spatially they have enhanced productivity for limited periods of time. In addition to atmospheric and seawater warming, the ocean itself is becoming more acidic—0.1 unit pH drop already (Sabine et al., 2004)—and it has been estimated that within the next 200 years the ocean could be approximately 0.8 units more acidic in its surface waters (Caldeira and Wickett, 2003). Benthic fauna reliant on calcification, such as bivalves, echinoderms and crustaceans, may experience difficulties in CaCO3 deposition. These organisms dominate on polar shelves, and although this change may be slower to occur in the colder polar regions, the impacts on commercial crustacean fisheries (shrimp, crab) and mollusk fisheries (bivalves, gastropods) may be of concern. As ice conditions change with global climate change, carbon production, export and benthic processes will likely be affected. A high priority for polar research is to increase our understanding of the types of changes expected during the evolution from ice-covered or ice-influenced, to open water ecosystems in polar regions, and in particular, changes in trophic linkages between upper ocean and benthic consumer groups.
Acknowledgements Financial support for this review was provided to J.M. Grebmeier by NSF grant #OPP9910319 and NOAA funding via CIFAR Subaward No. UAF 04-0048 and to J.P. Barry by NSF grant #OPP-9420680 and the Monterey Bay Aquarium Research Institute. We thank Kevin Arrigo for kindly providing data concerning Antarctic polynyas, Chirk Chu for Arctic data analyses, Karen Frey for Arctic chlorophyll maps, as well as our students, technicians, Captains and crews for project support during data collection and laboratory analyses over the years.
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Chapter 12
The Impact and Importance of Production in Polynyas to Top-Trophic Predators: Three Case Histories N. Karnovsky1 , D.G. Ainley2 and P. Lee3 1 Department of Biology, Pomona College, Claremont, CA 91711, USA 2 H.T. Harvey and Associates, 3150 Almaden Expressway, Suite 145, San Jose, CA 95118, USA 3 Hollings Marine Laboratory, College of Charleston, 331 Fort Johnson Rd, Charleston, SC 29412, USA
Abstract Polynyas in both the Arctic and Antarctic are known to be sites of substantial accumulations of toptrophic predators. Three polynyas (the Ross Sea polynya, the North Water polynya, and the Northeast Water polynya) are described in detail with regard to the distribution and activities of birds and marine mammals. The comparison shows that a substantial variation occurs both spatially and temporally among polynyas, but confirms that these regions are critical and active sites of material and energy transfer in polar systems.
1 Introduction Areas of predictable or persistent open water within the regional sea-ice cover, called polynyas (from a Russian word), are important to the top-trophic, air-breathing species that inhabit the polar regions. Certain species of top predators (some more than others), are so dependent upon polynyas that, in areas of persistent sea ice, the quest to access or remain associated with these ice-free waters drives many of these species’ life-history patterns (see summary in Ainley, 2002a; Ainley et al., 2003a; also Stirling, 1980; Brown and Nettleship, 1981; Dunbar, 1981; Massom, 1988). In some areas, especially near the coast where strong, near-continual winds sweep new ice clear, along with the heat expelled from continual freezing of surface seawater, ‘latent heat’ polynyas are formed. Elsewhere in coastal areas, such as in much of the Arctic Basin or Hudson Bay, offshore winds alternate with onshore winds to create (and sometimes close) open leads parallel to the shoreline (‘flaw’ leads). In still other areas, warmer subsurface waters are brought to the surface, usually as a function of currents rising over ridges and rising shelf topography or through upwelling. This situation results in surface waters that Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74012-0
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remain above freezing. These open waters are called ‘sensible-heat’ polynyas. Recurring polynyas, either latent or sensible, are those that remain open throughout the winter, or open at the same time each spring at the same location. While birds and mammals may use any polynya opportunistically, recurring polynyas are of the greatest biological importance because over-wintering or migrating species depend on them for predictable access to the ocean (see further comments in Ainley, 2002a, 2002b; Ainley et al., 2003a). In the Antarctic, the importance of polynyas to upper-trophic level species has only recently become appreciated, in spite of the fact that there are many latent-heat polynyas generated along the coast by fierce katabatic winds (e.g. Massom et al., 1998). Only along the northwestern coast of the Antarctic Peninsula are polynyas of little importance to birds and mammals (see contrasting views in Fraser and Trivelpiece, 1996; Ainley, 2002a). North of Marguerite Bay (Chapman et al., 2004), the development of sea ice is at best transient, both seasonally and on a longer time scale, thus precluding the development of significant polynyas (Massom et al., 1998; Arrigo and van Dijken, 2003). Most of the Antarctic coast is far enough south (well below the Antarctic Circle), and has appreciable sea ice, that almost all Antarctic polynyas are in total darkness from late fall to early spring. For that reason, they are then of little if any interest to the majority of birds and mammals, most of which need daylight to locate and capture prey. Only the locations of winter and earlyspring breeding emperor penguins Aptenodytes forsteri (Kirkwood and Robertson, 1997; Massom et al., 1998), and perhaps Weddell seals Leptonychotes weddellii, are affected by winter/spring polynyas, as they provide access to open water for the penguin or areas of persistent fast ice for the seal. These two species, capable of exploiting prey at great depths, are not dependent on bright light for feeding (Kooyman, 1989). On the other hand, these polynyas continue to persist as open areas in the ice pack well into the late spring (beyond the freezing period) and, therefore, known as ‘post-polynyas,’ they then affect the breeding location of other species, (for example, the Adélie penguin Pygoscelis adeliae; (Ainley, 2002a; Arrigo and van Dijken, 2003). In the Arctic, the situation is different. There, polynyas, many of which occur well to the south of the Arctic Circle (within sunlight all of the year), are the major feature affecting life-history patterns of top-trophic species (Brown and Nettleship, 1981; Stirling, 1997). The distribution of most bird and mammal colonies in the Arctic, and in turn the villages (especially prehistoric ones) of the hunter-gatherer native peoples Homo sapiens, is strongly related to the presence of polynyas (Schledermann, 1980; Henshaw, 2003). Major physical-biological investigations have taken place to deduce the processes that enhance their value, both in providing access to open water and in stimulating biological productivity. For example, the North Water of Baffin Bay provides valuable winter habitat for belugas Delphinapterus leucas, bowhead whales Balaena mysticetus, bearded seals Erignathus barbatus, and walruses Odobenus rosmarus (Richard et al., 1998; Stirling, 1997). Studies of other polynyas, such as the Northeast Water Polynya (Falk et al., 1997), the Cape Bathhurst Polynya (Harwood and Stirling, 1992), the major polynyas south of the Chukchi Peninsula, St. Lawrence and St. Matthew islands in the Bering Sea (Niebauer et al., 1999; Petersen et al., 1999), and polynyas of the Belcher Islands in southeast Hudson Bay (Gilchrist and Robertson, 2000), all testify to the biological importance of these persistent open-water areas. So important are polynyas to air-breathing animals in the Arctic that large die-offs occur when, in rare cases, a previously predictable polynya freezes for an extended period (Stirling et al., 1981). The hunting success of polar bears Ursus maritimus and humans increases as the amount of open water decreases (thus, confining prey) making
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the hunted seals more vulnerable to predation. These major mortalities can alter population growth of affected species for decades. Herein, we discuss some examples of polynyas that occur in both the Antarctic and Arctic in terms of their effects on the occurrence and biology of top-trophic predators. Previous reviews of the importance of polynyas for upper trophic levels have focused mainly on the way that the physical presence of open water provides access to food for these species. In this chapter, we focus on the role of primary production within polynyas that enhance feeding opportunities for upper trophic levels. We highlight the particular processes within certain polynyas that allow them to support large populations of upper-trophic predators. In recent decades, much has been learned about why certain polynyas are more important than others for feeding and breeding top predators and why certain areas within productive polynyas are more utilized than others. In these respects, the best-known polynya in Antarctica is the Ross Sea Polynya and that in the Arctic is the North Water. As a contrast to these highly productive examples, we also examine the Northeast Water Polynya in the Greenland Sea.
2 Ross Sea Polynya 2.1
Physical Characteristics
The Ross Sea Polynya (RSP) is the largest and best-known polynya in the Antarctic, and is in fact the largest on Earth (see map in Barber and Massom, 2007). Three other polynyas also exist in the Ross Sea region (Terra Nova Bay, Ross Passage and Pennell Bank; Jacobs and Comiso, 1989; Jacobs and Giulivi, 1998; Figure 1). The RSP, overlying the southernmost continental shelf, is essentially a latent heat polynya generated by persistent, offshore katabatic winds blowing across the wide extent of the Ross Ice Shelf (Bromwich et al., 1992, 1995, 1998). It also has elements of a sensible heat polynya owing to the upwelling of warmer Circumpolar Deep Water (CDW) at banks and along the ice front, a process also driven by winds and circulation (Jacobs and Comiso, 1989; Jacobs and Giulivi, 1999; Jacobs et al., 2002; Arrigo and van Dijken, 2004). The polynya is prevalent along the ice front over a distance of a several hundred kilometers for most of the winter, but grows substantially outward during August and September owing to a seasonal increase in strength and persistence of winds. The open water continues to expand northward throughout the austral spring until, finally, it finally joins the open water of the Ross Passage polynya in the northwestern Ross Sea by the beginning of summer (December). This phenomenon has facilitated the early-season passage of ships into the Ross Sea, as first discovered by James Clarke Ross’ expeditions in 1841–42 (Ross, 1847). On an inter-annual basis, as a direct function of variation in annual average wind strength, the RSP is smallest during El Niño and largest during La Niña (Arrigo and van Dijken, 2004). On a longer-term basis, the RSP on average grew larger during the 1980s (Parkinson and Cavalieri, 2002) compared to the previous decade, a result of stronger winds (Ainley et al., 2005). In addition, those winds increased sea-ice production in the polynya, resulting in an increase in overall sea-ice extent in the region. During the 1990s, the pattern may have reversed or at least leveled off (Parkinson and Cavalieri, 2002), a change coincident with a lessening of winds (Ainley et al., 2005). 2.2
Organic Production
Among Antarctic polynyas, total production (the product of the open water area and the spatial mean daily primary production rate) varies by two orders of magnitude, from 0.03 to
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Figure 1: Distribution of bird biomass in the Ross Sea during December; the four major ‘post-polynyas’ are shown. Cells are 0.5 latitude × 2.5 longitude degrees; cell effort occurred in any cell outlined (see Ainley et al., 1984 for actual cruise tracks). Biomass in the northern Ross Sea from the King Edward VII Peninsula toward the northwest is dominated by the Antarctic Petrel Thalassoica antarctica and Snow Petrel Pagodroma nivea; along the western Ross Sea/Victoria Land coast, it is dominated by the Adélie and emperor penguins. 48 Tg C yr−1 . Four polynyas, the Ross Sea, Ronne Ice Shelf, Prydz Bay, and Amundsen Sea polynyas, are responsible for more than 75% of total polynya production in the Southern Ocean. The Ross Sea, as is the case for a number of areas of the Southern Ocean, is dominated by two major phytoplankton groups, Phaeocystis antarctica and diatoms (Arrigo et al., 1999, 2000; Smith et al., 2000). Due largely to the RSP, the Ross Sea is also the most productive stretch of water of its size south of the Polar Front (Arrigo and van Dijken, 2003, 2004). Estimates of annual production range from 100 to 200 g C m−2 yr−1 , two-thirds of which is contributed by blooms of P. antarctica, with the remainder coming from contributed by diatoms. A fair amount is known about the relative dominance of the factors that control the distributions of P. antarctica and diatoms in the Ross Sea. Iron, light, CO2 , and grazing have all been implicated in structuring these phytoplankton communities (Arrigo et al., 1999, 2000; Caron et al., 2000; Sedwick et al., 2000; Tortell et al., 2002; Tagliabue and Arrigo, 2003). In general, P. antarctica dominates the open waters of the south-central Ross Sea from late October to mid-December where sea ice is continually blown away (i.e., polynya
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formation is occurring as a result of the latent heat mechanism). These waters are poorly stratified and initially have relatively high levels of iron that eventually become depleted as the bloom progresses (Sedwick et al., 2000). Diatom blooms typically dominate in areas of loose pack ice, (i.e., the marginal ice zone or (MIZ), surrounding the polynya. In these locations, the water column becomes strongly stratified as the pack ice melts in mid to late summer (Arrigo et al., 1999, 2000; Goffart et al., 2000). There is also additional input of iron from the ice pack as the ice pack breaks up (Sedwick et al., 2000). It is worth noting that while Phaeocystis and diatom blooms tend to dominate the phytoplankton community at different times during the austral summer and at different locations in the Ross Sea, mixed species blooms are not uncommon (Garrison et al., 2003). Because the Ross Sea Polynya regularly supports phytoplankton biomass exceeding 10 mg Chl a m−3 , it is distinct from other regions of the Southern Ocean where chronically low iron concentrations that persist year-round preclude such intense phytoplankton blooms. Interestingly, distributions of Phaeocystis and diatom blooms in the Ross Sea are spatially and temporally disjunct due to a taxon-specific response to irradiation. Phaeocystis dominates the more deeply mixed open waters of the polynya due to an ability to grow faster at variable irradiance levels; diatoms are superior in well-lit waters owing to a higher nonlimited growth rate and a resistance to photo-inhibition at the irradiance levels typical of the Ross Sea later in the summer. 2.3
Middle and Upper Trophic Levels
The Ross Sea is a neritic ecosystem, lying as it does over the largest continental shelf of Antarctica. The middle trophic level, as judged by the markedly overlapping diet among all upper-trophic level predators, is dominated by two species: crystal krill Euphausia crystallorophias and Antarctic silverfish Pleuragramma antarcticum (summarized in CCAMLR, 2002; see also Biggs, 1982; Ainley et al., 2003b). Therefore, the Ross Sea shelf ecosystem is not directly part of what has been called the ‘Antarctic marine ecosystem,’ where supposedly just one major forage species occurs: Antarctic krill E. superba (e.g., Laws, 1977; May et al., 1979; Atkinson et al., 2004). As part of that ecosystem, and judging from the diet of top-trophic predators, the waters of the Ross Sea continental slope are rich with Antarctic krill, although myctophid fish are important forage there as well (Ainley et al., 1984; Ichii et al., 1998). Owing to water column–benthic coupling, the benthic communities of the neritic Ross Sea are also exceedingly rich, including those underlying both the Phaeocystis and diatom-dominated portions (Grebmeier and Barry, 1991; Hecq et al., 2000; Barry et al., 2003). Considering the abundance of marine birds and mammals at the large scale, few, if any ocean areas south of the Polar Front can compare with the Ross Sea. Although it borders less than 10% of the Antarctic coastline, the Ross Sea supports 38% and 26%, respectively, of the world population of Adélie and emperor penguins. While the global population of the Weddell seal has not been estimated, the Ross Sea contributes 45% of the population in the Pacific sector, a third of the Antarctic circumference (150◦ E to 55◦ W). Large numbers of leopard Hydrurga leptonyx, crabeater Lobodon carcinophagus seals, along with minke Balaenoptera bonaerensis and killer Orcinus orca whales are found in waters over the continental shelf and slope. An abundance of the large and voracious Antarctic toothfish Dissostichus mawsoni, which occurs over the shelf, completes the suite of upper-trophic level species (CCAMLR, 2002, 2004). The enhanced productivity of the RSP plays a role in the large numbers of top-trophic predators present. Yet ironically, very few frequent the waters dominated by the Phaeocystis
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Figure 2: The distribution of cetacean biomass in the Ross Sea during December; the four major ‘post-polynyas’ are shown. Two species contribute most of the biomass, minke whale and killer whale, with the minke whale dominating by far. Cells are 0.5 latitude × 2.5 longitude degrees; survey effort occurred in any cell outlined (see Ainley et al., 1984 for actual cruise tracks). bloom, which contributes the most to Ross Sea primary production. In fact, in terms of bird and mammal abundance, the south-central waters of the Ross Sea are a virtual ‘desert.’ Almost all the top predators are concentrated in the MIZ ringing the polynya (Ainley et al., 1984; Ainley, 1985; Figures 1 and 2), although complicating the source of productivity in the MIZ of the northern Ross Sea is the shelf-break front, where CDW is upwelled (Ainley and Jacobs, 1981). Nevertheless, the concentration of birds and cetaceans in the diatom bloom of the western Ross Sea confirms that processes in the MIZ, independent of other factors, are critical. Interestingly, the Phaeocystis bloom in the RSP produces large amounts of dimethylsulfide (DMS; DiTullio and Smith, 1995), which has been implicated as a foraging cue for Antarctic procellariiform seabirds such as petrels, shearwaters and albatrosses (Nevitt et al., 1995; Nevitt, 2000). However, very few of these seabirds are seen in the south-central Ross Sea. Petrels, in particular, tend to be found in the MIZ to the northeast of the RSP (Ainley et al., 1984; Figure 1). This finding suggests that either the concentration of DMS in the surface waters of the MIZ (and its transfer to the atmosphere) are greater in the MIZ than the open
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waters of the RSP due to grazing by krill and copepods, or the petrels use other, perhaps visual, foraging cues to locate their prey in the MIZ. The distribution of top-trophic predators indicates a great deal about where the forage species are concentrated, (i.e., in the MIZ that borders the RSP). Except in constricted areas (e.g., Azzali and Kalinowski, 2000; Ackley et al., 2003), no broad-scale, direct surveys of middle-trophic level species have been made in the Ross Sea. Acoustic krill surveys by Azzali and Kalinowski (2000), although mostly confined to the edge of the MIZ in the western Ross Sea, do show a lessening of acoustic biomass toward the center of the RSP. Diatoms, which typically dominate the phytoplankton community in the MIZ, are a major food source for krill and the growth and reproductive success of adult krill are linked to diatom blooms in the MIZ (Quetin and Ross, 1985, 1991). Goffart et al. (2000) found that copepods and the pteropod Limacina helecina were important predators of diatoms in the MIZ and based on the high levels of phaeophorbides present, a critical and sustained part of the diatom production was transferred to higher trophic levels. In the MIZ to the north of the polynya, they found increased grazing pressure, even in mixed communities, with very large amounts of krill present. There is conflicting evidence as to whether zooplankton grazers are capable of grazing Phaeocystis sp. (see also references cited by Breton et al., 1999; Haberman et al., 2003a; Tagliabue and Arrigo, 2003). Metz (1998) found that in the Bellingshausen Sea females of the copepod Oncaea curvata preferred non-motile prey such as Phaeocystis sp. (consuming up to 300% of their body weight per day) over flagellates and other copepods. Similarly, Breton et al. (1999) observed that the copepod Temora longicornis did not feed on Phaeocystis in flagellated form. Cotonnec et al. (2001) found in the English Channel that calanoid copepods selectively grazed Phaeocystis, non-selectively grazed diatoms and avoided dinoflagellates. Haberman et al. (2003a) reported that krill off the Antarctic Peninsula could effectively graze small Phaeocystis colonies but not larger colonies or single cells, and did so at a rate comparable to the diatom Thalassiosira antarctica. However, in an experiment using mixed phytoplankton communities, Haberman et al. (2003b) determined that krill preferentially selected diatoms even when the Phaeocystis spp. colonies were similar in size to diatoms. Phaeocystis antarcticas spp. colonies in the RSP do not appear to be heavily grazed by predators in either the microbial or classic food webs (Dunbar et al., 1998; Caron et al., 2000; Goffart et al., 2000). In the western Ross Sea near Terra Nova Bay, zooplankton fecal pellets comprised 70% of the total mass flux to the sediments as compared to just 38% in the RSP (Dunbar et al., 1998). Moreover, early and rapid (1–2 days) sedimentation of Phaeocystis spp. blooms to deep water and sediments has been reported by DiTullio et al. (2000). Modeling studies by Tagliabue and Arrigo (2003) suggest that the high growth rates observed for P. antarctica in the Ross Sea simply allow P. antarctica to outgrow the zooplankton grazers. Consequently, export of phytoplankton biomass by this mechanism appears to prevent populations of mid-trophic level predators from exerting top-down control on the Phaeocystis blooms. Arrigo and van Dijken (2003), in order to confirm the biological importance of Antarctic polynyas in general and the RSP in particular, correlated Adélie penguin colony size around the continent to the size and productivity of 37 post-polynyas (Adélie penguins nest only after the ice-freezing season has passed). Both they and Ainley (2002a) found that 90% of all Adélie penguin colonies are associated with polynyas. Arrigo and van Dijken (2003), in addition, found that the magnitude of annual production in polynyas explained 65% of the variance in penguin colony size. However, annual production is related mostly to polynya
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size and to the width of the continental shelf over which the various post-polynyas exist. Given the apparent lack of interest among top-trophic predators, including Adélie penguins, for the central RSP, as opposed to the MIZ ringing it, further investigation of the relationship between top-trophic predators, polynyas and their MIZs in the Antarctic should be a scientifically rewarding enterprise.
3 North Water Polynya 3.1
Physical Characteristics
The North Water Polynya (NOW) is the largest recurring polynya in the northern hemisphere (Stirling, 1981, 1997; see map in Barber and Massom, 2007). While it has been recognized for some time as potentially one of the most productive regions in the Arctic, recent findings show that the levels of primary productivity associated with the ice-free waters are the highest found in the Arctic (Klein et al., 2002). Its existence has long been known to the Inuit who inhabit the region, with European explorers documenting its presence as early as 1616 (Dunbar and Dunbar, 1972; Schledermann, 1980). Both latent and sensible heat mechanisms are implicated in the formation and persistence of the NOW (Mundy and Barber, 2001). Formation of the North Water Polynya is initiated by three smaller recurring polynyas in Smith Sound, Lady Ann Strait and Lancaster Sound that merge in early spring to form one contiguous polynya (Steffen, 1985). Initially, an ice bridge forms during winter (between November and May) in Kane Basin that prevents ice from entering from the Arctic and allows newly formed ice to be pushed away by a combination of strong northerly winds and southerly currents (latent heat mechanism). The ice bridge is also the northern extent of the polynya. Relatively deep mixing and strong convection occur along the western (Canadian) side of the polynya as a result of the latent heat mechanism (Melling et al., 2001). On the eastern (Greenland) side of the polynya, air temperatures are several degrees warmer than temperatures on the Canadian side from heat input from the atmosphere (Barber et al., 2001). As a consequence, ice formation is delayed during freeze-up resulting in thinner ice cover during winter, which in turn leads to earlier ablation in spring (Mundy and Barber, 2001). A branch of the West Greenland Current brings relatively warm water close to the sea surface. However, this input of sensible heat contributes to the persistence of the NOW, not its formation (Melling et al., 2001; Mundy and Barber, 2001; Bâcle et al., 2002). Land-fast ice along the coasts of Ellesmere Island and Greenland defines the western and eastern boundaries of the NOW. As a result, there is essentially no marginal ice zone along these borders. The southern edge of the polynya is defined by the pack ice in Baffin Bay. By late May, it is found between 76◦ 30 N and 75◦ 30 N. By late June, it is located between 75◦ N and 74◦ N. The NOW typically attains its maximum size during July, at which time the ice tongue that separates the polynya from Baffin Bay melts (Dunbar, 1969). 3.2
Organic Production
The NOW is the biologically most active region in the Arctic. Chl a concentrations can reach levels of approximately 20 µg l−1 at the height of the bloom with average annual production on the order of 152 g C m−2 yr−1 (Lewis et al., 1996; Klein et al., 2002; Mei et al., 2002). During April, phytoplankton biomass and productivity are low. Initiation of the spring bloom begins in the warmer, more stratified waters of the Greenland (eastern) portion of the NOW and are associated with partial ice cover (Mei et al.,
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2002; Tremblay et al., 2002a, 2002b). Diatoms such as Thalassiosira spp. and Fragilariopsis spp. dominate the phytoplankton community in this part of the polynya while autotrophic flagellates such as single cell Phaeocystis spp. dominate the poorly stratified waters along the Canadian (eastern) side (Booth et al., 2002; Lovejoy et al., 2002a; Vidussi et al., 2004). During May and June, the diatom bloom spreads north- and westward to encompass the entire polynya, with Chaetoceros spp. becoming the dominant member of the phytoplankton community (Klein et al., 2002; Mei et al., 2002; Booth et al., 2002; Lovejoy et al., 2002a). More favorable conditions for diatoms along with ciliate grazing pressure possibly prevent autotrophic flagellates from dominating the phytoplankton community (Lovejoy et al., 2002b; Vidussi et al., 2004). At the height of the bloom in mid-May in 1998, maximum total phytoplankton production reached 377 g C m−2 yr−1 in the eastern portion of the polynya, with the total annual production of particulate organic carbon reaching 234 g C m−2 yr−1 (Klein et al., 2002). In June, nitrate became the limiting nutrient (Tremblay et al., 2002b) and by July, dinoflagellates replaced diatoms (Lovejoy et al., 2002a). 3.3
Middle and Upper Trophic Levels
Arctic phytoplankton blooms support many types of herbivores including copepods, amphipods, larval fish, appendicularians, etc. Critical links in the transfer of energy from phytoplankton to upper-trophic predators in the NOW are copepods such as Calanus hyperboreus and C. glacialis, the predatory pelagic amphipod Themisto libellula and Arctic cod Boreogadus saida (Welch et al., 1992). Themisto libellula and Arctic cod consume smaller zooplankton and therefore represent an extra trophic level in-between phytoplankton and upper-trophic organisms. Upper-trophic predators, such as seabirds that breed in the High Arctic, are dependent on the early availability of food and the subsequent seasonal development of the pelagic food web so that they can feed themselves and their offspring throughout the breeding season. The NOW opens in March when most areas at the same latitude are still covered in ice. The extended period of open water (6–7 months) and phenomenal levels of primary productivity in the NOW (Klein et al., 2002; Mei et al., 2002) support a tremendous biomass of zooplankton (Saunders et al., 2003) and fish. A large proportion of the primary productivity in the NOW is transferred to higher trophic levels through metazoan grazers. Mei et al. (2002) calculated that of the total organic carbon production (1.7 g C m−2 d−1 ), 65% was attributable to particulate organic carbon of which production by large (greater than 5 µm) phytoplankton species accounted for 81%. A significant proportion (18%) of the production by large phytoplankton was then transferred to the herbivorous food web. These middle trophic level taxa, in turn, support large populations of seabirds, seals and whales (Stirling, 1980; McLaren, 1982; Nettleship and Evans, 1985; Holst and Stirling, 1999; Karnovsky and Hunt, 2002). The most abundant top-trophic predator in the North Water is a copepod specialist, the dovekie (Alle alle) (Renaud et al., 1982; Boertman and Mosbech, 1998; Kampp et al., 2000; Egevang et al., 2003). An estimated 30 million pairs of these small seabirds migrate to the NOW to feed in its productive waters (Karnovsky and Hunt, 2002; Egevang et al., 2003). While there, they breed along the Northwest coast of Greenland (Salmonsen, 1981). Despite their large numbers, the dovekies concentrate their feeding activities in certain areas of the polynya. Like the RSP, there are vast areas that, at certain times, are not utilized by uppertrophic predators; other places become “hot spots” of feeding activity. Examination of the foraging patterns of dovekies highlights the processes that are critical for energy to flow to upper-trophic predators. During winter when the polynya freezes over,
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the dovekies that breed adjacent to the NOW are off the northeast coast of North America (Stenhouse and Montevecchi, 1996). In fact, only one seabird remains in the polynya during winter, the Black Guillemot (Cephus grylle), which survives in small coastal leads during winter (Renaud and Bradstreet, 1980). However, the NOW is an important wintering area for marine mammals such as beluga and Narwhal (e.g. Heide-Jorgensen et al., 2003). Dovekies arrive in the NOW in May, long after the polynya opens (usually in late March) (Karnovsky and Hunt, 2002). Given that seabirds must initiate breeding early so that they can fledge their chicks before the polynya closes, their delay in arrival seems counter-intuitive until one examines the levels of primary production in early spring. As indicated earlier, levels of primary production in the polynya are very low throughout April. Dovekie arrival in May coincides with the development of the spring bloom on the eastern side of the polynya along the west coast of Greenland (Klein et al., 2002; Mei et al., 2002; Vidussi et al., 2004). During May, dovekies are found there, almost exclusively, at densities exceeding 1000 birds per square kilometer (km2 ) (Karnovsky and Hunt, 2002). There is a tight correlation between where densities of dovekies are highest and where chlorophyll levels are highest (Figure 3). This association is due to the timing of the vertical migration of large-sized calanoid copepods from their wintering depths to the surface to feed on the phytoplankton bloom (Ringuette et al., 2002). During summer (June and July) when the phytoplankton blooms occur on both the eastern and western sides of the polynya, large-sized calanoid copepods can be found throughout the polynya (Saunders et al., 2003) and dovekies expand their foraging range to include the entire polynya (Figure 4). The eastern side of the polynya appears to be important for other species as well. Teilmann et al. (1999) found that ringed seals Phoca hispida equipped with satellite transmitters remained primarily on the eastern side of the polynya (Teilmann et al., 1999). Lighter ice conditions may explain why ringed seals spend 90% of their time on the eastern side (Born et al., 2004). Diets of immature ringed seals collected on the eastern (Qaanaaq, Greenland) and western (Grise Fjord, Nunavut) side of the polynya differed (Holst et al., 2001). Immature seals on the eastern side fed primarily on Arctic cod whereas immature seals on the western side fed primarily on zooplankton (T. libellula). Female ringed seals on the eastern side had thinner blubber and lower ovulation rates but reached sexual maturity earlier (Holst and Stirling, 2002). Stable nitrogen values of seabirds breeding on the eastern side (Haklyut Island) revealed they fed more often on lower trophic level prey (zooplankton) than those on the western side (Coburg Island) that consume more Arctic cod (Hobson et al., 2002), a pattern that contrasts with that found for immature ringed seal diets. The implications of the east-west differences in the polynya vary for different species. The early opening of the eastern portion of the polynya and the early appearance of abundant zooplankton there makes that area important for zooplanktivores but may increase foraging costs for piscivores. Thick-billed murres (Uria lomvia), which feed fish to their young, worked harder to find food on the eastern (Greenland) side of the polynya than on the western side (Coburg Island) but managed to raise more chicks than their Coburg Island counterparts (Falk et al., 2002). The east-west response of dovekies is dramatic and clearly in response to the distribution of their primary prey, copepods. The east-west gradients for other species are complicated and highlight the need for greater understanding of the demography, distribution and abundance of Arctic cod and T. libellula. Furthermore, the western side of the polynya has a strong north–south gradient in productivity. The low prey availability on the northwestern side of the polynya likely contributed to the demise of Adolphus Washington Greely’s expedition on Pim Island (Weslawski and Legezynska, 2002), whereas one of the largest seabird colonies in the world (Coburg Island) is on the southwestern side.
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Figure 3: Distribution of chlorophyll a and foraging dovekies in the North Water Polynya during May 1998. Each dot represents number of dovekies sitting on the water per 3 nm transect. The open circles represent where no birds were seen. SeaWifs photo taken by Simon Belanger and Pierre LaRouche.
4 Northeast Water Polynya Like RSP, the NOW bloom of diatoms is critical for supporting large numbers of uppertrophic predators. In contrast to the NOW, the Northeast Water Polynya (NEW) located off the east coast of Greenland (see map in Barber and Massom, 2007), is a latent heat polynya that does not experience early stratification of the upper water column (Schneider and Budéus, 1997) and does not have an extensive spring diatom bloom (Lara et al., 1994). The NEW is open for shorter periods (four months) and is characterized by low levels of phytoplankton biomass and primary productivity. While maximum chlorophyll a levels (9.9 mg m−3 ; Smith et al., 1997) are half of those observed in the NOW (19.8 mg m−3 ; Klein et al., 2002; Mei et al., 2002), maximum concentrations integrated over the water column are less than one tenth (27.5 mg m−2 for the NEW versus 300 mg m−2 for the NOW). Mean values of particulate phytoplankton production in the NEW (0.5 g C m−2 d−1 ) are higher than observed in the NOW before the spring bloom (0.2 g C m−2 d−1 ) but are less than those observed during the bloom (1.7 g C m−2 d−1 ; Smith, 1995; Pesant et al., 1996; Smith et al., 1997; Klein et al., 2002; Mei et al., 2002). Similarly, maximum rates of par-
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Figure 4: Seasonal changes in foraging patterns in dovekies in the North Water Polynya. Each dot represents number of dovekies sitting on the water per 3 nm transect. The open circles represent where no birds were seen.
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ticulate phytoplankton production are higher in the NOW than the NEW (4.4 g C m−2 d−1 and 1.7 g C m−2 d−1 , respectively; Smith, 1995; Pesant et al., 1996; Smith et al., 1997; Klein et al., 2002; Mei et al., 2002). Consequently, there are much lower levels of secondary production in terms of both copepod biomass and Arctic cod production (Ashjian et al., 1995; Fortier et al., 1994; Hirche et al., 1994; Michaud et al., 1996) and relatively small numbers of upper-trophic predators (Falk et al., 1997). A glaring difference between the seabird communities in the NEW and the NOW is the virtual absence of subsurface feeders such as dovekies and thick-billed murres in the NEW (Falk et al., 1997; Joiris et al., 1997). These are species that number in the millions in the NOW. Subsurface feeders like dovekies and thick-billed murres need high densities of zooplankton prey in order to be able to forage profitably. The dominant seabird species in the NEW are the northern fulmar Fulmarus glacialis and black-legged kittiwake Rissa tridactyla (Falk and Møller, 1995a; Falk et al., 1997). These birds can fly much longer distances than can the alcids (murre, dovekie) to find food and can successfully feed on patchy prey resources (Falk and Møller, 1995b). Carbon flux to seabirds in the NOW is 3000 times greater then in the NEW (Karnovsky and Hunt, 2002). The MIZ in the RSP appears to be critical habitat for upper-trophic predators. Likewise, ice edges in the NOW specifically, and in the Arctic in general, are heavily attended by upper-trophic predators (Bradstreet and Cross, 1982; Stirling, 1997). The ice edge provides physical access to prey that are found under the ice such as T. libellula, Arctic cod and epontic amphipods such as Apherusa glacialis. The role of ice algae production in energy flux to upper-trophic predators needs to be explored further as well as the role of ice edge production in the overall carbon budget of polynyas.
5 Conclusion While it has long been known that polynyas are important for upper-trophic predators, we now understand there is tremendous heterogeneity amongst polynyas in the number and type of upper-trophic predators that they support. There is also temporal and spatial variation within polynyas in terms of where and when there is significant carbon flux to upper-trophic predators. The occurrence of the RSP and the NOW is largely due to latent heat processes but they both have areas that become stratified and experience large-scale diatom blooms (the MIZ in the RSP and the eastern side of the NOW). These places are “hot spots” of feeding activity for upper-trophic predators. Our understanding of the role of polynyas for top trophics has greatly expanded in the last decade. We now are beginning to understand the physical and biological processes within polynyas that make them important places for top predators.
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Chapter 13
Polynyas and Climate Change: A View to the Future W.O. Smith, Jr.1 and D.G. Barber2 1 Virginia Institute of Marine Sciences, College of William and Mary, Gloucester Point, VA 23062, USA 2 Centre for Earth Observation Science, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada
Abstract There is no longer doubt that the climate of polar systems is changing, but the changes are far from uniform in time and space. Similarly, the changes of climate give rise to direct and indirect alterations of processes within polynyas. Arctic systems are undergoing the largest, most rapid change, and it is expected that polynyas within the Arctic will largely respond by a decrease in the duration of existence each season. This in turn will increase the maritime nature of Arctic polynyas, and simultaneously reduce their polar features. Antarctic polynyas might be expected to have a larger gradient of responses because the direction and magnitude of climate change is not uniform. It is suggested that changes in hemispheric characteristics of deep water will result in substantial changes in the duration of periods of polynya existence, as well as possibly major alterations of the biogeochemical cycles and food webs of the polynyas. Arctic polynyas will likely respond to polar climate change based on the type of polynya they represent. The flaw lead polynya system will likely become larger and exist for a longer duration over the annual cycle; ice-edge polynyas have already begun to change (e.g., North East Water) and in the future we may see these types of polynyas become more like marginal ice zones than true polynyas. Polynyas, by nature of their largely ephemeral nature, are indeed “indicator” regions for large-scale change, and by monitoring changes within these sensitive areas, we suggest that polynyas can act as a model system for changes in polar marine environments.
1 Introduction Polynyas, as this volume demonstrates, are highly complex environments that are generated by a variety of physical mechanisms, and initiate a series of interactions that often, but not always, give rise to a productive ecosystem. Yet polynyas also exist within a gradient, and many are ephemeral and have little apparent impact on the local food webs, air–sea–ice interactions, or biogeochemistry. Others are the major sites of physical, chemical and biological exchanges in the entire region, and hence are critical oceanographic features. Despite this Elsevier Oceanography Series 74 Edited by W.O. Smith, Jr. and D.G. Barber ISSN: 0422-9894 DOI: 10.1016/S0422-9894(06)74013-2
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widespread appreciation, surprisingly few interdisciplinary studies have completely characterized the physics, meteorology, biology and chemistry on similar spatial and temporal scales, and no interhemispheric comparison exists. Polar regions, like much of the rest of the world, are changing due to anthropogenic influences, and have been greatly modified in the past two centuries. For example, Antarctic whale populations (the top predators of the system) have been decimated since the start of whaling in the early 1900’s, and Arctic whale populations have undergone a similar, earlier destruction (Knox, 1994). Given that this change occurred before the advent of modern polar oceanography, an understanding of the pristine (i.e., pre-humankind intervention) polar ocean is lacking and can only be surmised (Jackson et al., 2001). Furthermore, numerical models of air-sea-ice interactions repeatedly confirm that polar systems will be among the first to exhibit the effects of increased atmospheric greenhouse gas concentrations and surface temperature changes (e.g., Washington and Meehl, 1989; Sarmiento et al., 1998); indeed, many environmental and ecological components in the Arctic already appear to be changing (Sturm et al., 2001, 2005). Climate models converge on a single feature; they predict the first and strongest signals of global scale climate change to occur in the high latitudes of our planet. These models predict a reduction in sea ice extent over the next several decades resulting in a seasonally icefree Arctic as early as 2050 (Johannessen et al., 2004; Lindsay and Zhang, 2005; Overpeck et al., 2005). Observational studies, based on the passive microwave satellite record, confirm these predictions for both rates of reduction and, to a certain extent, geographic location. The northern hemisphere loses, on average, 65,000 to 75,000 square kilometres (km2 ) of sea ice extent each year and has lost over 2,000,000 km2 since 1979 (Rothrock et al., 1999; Comiso, 2002). The record of sea ice areal minima has become rather common in the instrumental record (Figure 1), with the minimum on record being that of 2005. It is also important to note that this reduction represents a switch from perennial ice (i.e., multiyear ice) to annual ice (i.e., first-year ice), and hence is a reduction in ice volume and mass, as well as areal extent. Studies of ice thickness (volume), although more difficult, also suggest an overall reduction in the thickness distribution of sea ice (Yu et al., 2004). Recent results provide compelling evidence for an overall northern hemisphere volume decrease of 32%, most of which resulted from a reduction in thickness of ice over two m (i.e., multiyear). This coincided with an increase in the extent of open water and young ice of between 20 and 30%. It is interesting, though not statistically appropriate, to extrapolate the regression line in Figure 1; the zero intercept occurs near 2050. While changes in the Arctic have been rapid and have apparently occurred over the entire region (albeit with inter-annual variations in space), similar changes have not as yet been observed in the Antarctic as a whole (Figure 2). One region, the West Antarctic Peninsula, has had as rapid atmospheric temperature changes of any region on earth—ca. 1.2◦ C decade−1 since 1950. These air temperature increases have coincided with dramatic decreases in ice cover and extent (ca. −7.0% decade−1 ; Kwok and Comiso, 2002; minimum ice extent has decreased even more so, by 29.0% decade−1 ; Figure 2a) and an attendant alteration of ecosystem processes (Fraser and Hofmann, 2003; Ainley et al., 2005). However, another region in the Antarctic—the Ross Sea—has been changing in the opposite direction; that is, ice concentrations have been increasing (+5.6% decade−1 ; Kwok and Comiso, 2002) and minimum ice extent has increased by 16% decade−1 (Figure 2b), and some components of the ecosystem also have changed, perhaps in response to the change in ice (e.g., Wilson et al., 2001). Based on satellite imagery, net, pan-Antarctic changes in ice concentrations since 1979 have been insignificant (Kwok and Comiso, 2002), in marked contrast to the Arctic;
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Figure 1: Minimum sea ice concentration (SIC) defined as <15% concentration, and computed for the end of September of each year, using the SMMR and SSM/I passive microwave record (National Snow and Ice Data Center, NSIDC).
Figure 2: Minimum extent of sea ice concentration, defined as <15% concentration, and computed for the end of February of each year, using the SMMR and SSM/I passive microwave record for (a) the Bellingshausen-Amundsen Sector, and (b) the Pacific/Ross sector. Figure provided by J. Comiso and R. Gersten, NASA. indeed, ice extent in the Antarctic has increased by 1.2% decade−1 during the period from 1979–2005 (Comiso and Gersten, pers. comm.). Similar meta-analyses for polynyas are much more difficult due to their smaller size and transient nature. Parkinson (2002) found that sea ice season length (the length of time that a region is covered by pack ice) is generally decreasing outward from the coast “except in regions of coastal polynyas”. The Ross Sea, which is dominated by polynya processes, is undergoing a marked increase in sea-ice season (i.e., it is covered for longer time periods). While ice season length in the Peninsula region is decreasing, polynyas in that region are quite restricted, so those changes to polynyas are likely to be fewer, less dramatic and confined to winter (a critical period for krill recruitment and survival; Hofmann et al., 2004). The major Arctic polynyas (such as NOW; Barber and Massom, 2007) may be influenced in
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parallel to the overall Arctic ice cover, but at least one (NEW) no longer exists in the form that it did in the early 1990’s, as the ice bridge that allowed for restricted ice movement has disappeared. The northern hemisphere circumpolar flaw lead (CFL) series of polynyas is directly affected by the transposition of perennial ice for annual ice in the central arctic. The central pack ice pulls away from the fast ice forming the Siberian shelf polynyas (Dmitrenko et al., 2005) and the northern American equivalents off the coasts of Alaska and Canada Recent evidence also shows that the reversal of the Beaufort Sea Gyre is increasing in frequency and occurring for longer throughout the annual cycle (Lukovich and Barber, 2005). The continued reduction of perennial ice in the central basin has significant ramifications on hemispheric climate, of both the ocean and atmosphere, and it also affects the timing and extent of the CFL polynya system. While recent changes in polynya ice-cover have been noted and are expected to continue, a more complex question is “What physical, biological, meteorological and chemical changes can be expected in the future?” The answer to this question forms the core of each of the chapters in this volume. These changes are non-uniform and vary based on the physical setting of the individual polynya, and are also by no means absolute, but are discussed as potential alterations that may occur in the near future.
2 Polynyas and Climate Change: The Arctic Polynyas in the Arctic will be dramatically influenced by the overall increase in surface temperature, closer connection between the ocean surface and atmosphere (more open water) and the translation of multiyear sea ice to first year forms. The reduction in the central pack will directly affect the flaw lead system and may have associated effects on coastal polynyas and polynyas with the Arctic islands of Canada, Northern Europe and Russia. Specifically, many will completely disappear during summer, and with warmer air temperatures, many of the systems will become more temperate-like. Polynya locations, if they are not linked to bathymetric and geographic features, may migrate northward, but those polynyas that are tightly coupled to specific physical features (e.g., the ice bridge that generates the North Water polynya) will likely decrease in size and duration. Indeed, if the ice bridge is weakened or altered, the NOW polynya may be greatly reduced in duration, with significant but unpredictable biological consequences. Other polynyas, like the St. Lawrence polynya, will simply disappear, and it will become much more like the shallow Bering Sea shelf that is unaffected by short-term openings during winter. These changes will induce other changes that provide feed-backs, both positive and negative, to larger areas. For example, with a longer ice-free season, it would be expected that evaporation would be greater, and therefore more clouds and precipitation would be likely. Similarly, the reduced ice concentrations will decrease the local albedo, and hence allow for more heat to be absorbed by the ocean surface layer, thus feeding back into a later ice formation the next fall. Less ice cover also increases the amount of irradiance that penetrates into the ocean, and therefore potentially increases production; however, the increased heat and ice melt will also increase stratification and reduce nutrient flux to the surface, which ultimately will decrease summer production. The magnitude of the net change for many of the polynya-associated biological processes remains uncertain. The most intensive study conducted in the Arctic is that of the NOW polynya. Results from this study point towards particular feedbacks and processes which make the response of the polynya to climate change rather complex. The NOW polynya was found to be among
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the most productive ecosystems in the Arctic due to the availability of nutrients (Tremblay et al., 2002). The phytoplankton bloom begins in April on the east side of the polynya, peaking in May–July, and spreading westward until October with oncoming fall freeze-up. The longlasting bloom (6 months) was found to be largely controlled by the availability of nutrients that corresponded to changes in water-mass circulation; in particular the large flux of water from the Arctic Basin through NARES strait. Net carbon production was found to be an order of magnitude higher in the polynya compared to adjacent waters (Tremblay et al., 2002). Changing the stability of the ice bridge, or changing the sensible heat flux from the atmosphere (or ocean) to the surface could cause significant changes in the primary production of the NOW polynya. There is a critical balance between solar (thermal) stratification, freshwater stratification from sea ice melt, and the role which winds play (relative to ice concentration) in mixing the ocean surface for nutrient replenishment (Mei et al., 2002). This is due to the fact that the NOW polynya is not generally nutrient limited and when light is made available early in the season, very strong and sustained production occurs (Tremblay et al., 2002) so long as the ice concentrations are low. Without the ice bridge you would expect a sustained flux of sea ice into the polynya region from the Lincoln Sea. This would tend to decrease the PAR flux into the surface layer because of increase ice advection into the polynya. Wind mixing would also be expected to decrease but whether this would result in a reduction of nutrients to the surface layer is unknown. Stratification of the water column would also be much different with no ice bridge in the NOW polynya. Early in the year thermal stratification would be lower than expected due to transfer of latent heat from sea ice to the ocean surface and an increase in surface albedo. Lat in the season melt water stratification would likely increase due to more freshwater from the melting sea ice within the polynya. We believe that under these conditions the NOW polynya would act more like a marginal ice zone that a true polynya (Barber et al., 2001). Conditions for other polynyas in the Arctic will be different (than NOW) since the local conditions which create polynyas respond to different hemispheric to local scale forcing. For example the Cape Bathurst Polynya is part of the Circumpolar flaw lead system (Barber and Massom, 2007) and as such it responds to hemispheric forcing of sea ice motion of the Beaufort Gyre and transpolar drift. The flaw lead appears to be a place where atmospheric forcing creates conditions which are conducive to upwelling at the shelf-slope break (Lukovich and Barber, 2005) which can create optimal conditions for nutrient renewal into the polynyas so long as sea ice is present to assist with this upwelling (Carmack and Chapman, 2003). In this work the authors show the importance of the position of sea ice relative to the shelf break in stimulating wind driven upwelling.
3 Polynyas and Climate Change: The Antarctic Generalizations concerning changes in polynyas in the Antarctic are difficult, simply because the nature and direction of change varies throughout the region. Polynyas in the West Antarctic Peninsula region will likely change in a manner similar to those in the Arctic, with the largest effect being that they will be reduced in duration before disappearing completely during austral summer. However, because some of these polynyas (for example, Marguerite Bay) are influenced by waters of the Antarctic Circumpolar Water (ACC), the changes may not be as simple as a might be thought. For example, comparison of data from 1990 and 1999 from the Drake Passage show that waters above the 27.85 neutral density surface have become dramatically warmer and saltier (T > 1◦ C and S > 0.1 on the 27.62 surface;
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Heywood, personal communication). Perhaps even more dramatically, silicic acid concentrations increased by nearly 10 µM, whereas oxygen concentrations have decreased by more than 45 µmol kg−1 . Thus, inputs onto the shelf potentially will be substantially different, with altered nutrient concentrations and lower oxygen levels, which in turn may influence the surface layer biogeochemical processes. The Ross Sea polynya is a mixed-mode polynya and is influenced by Modified Circumpolar Deep Water advected onto the shelf (Jacobs and Giulivi, 1998). Hence, it is intimately linked to the large-scale circulation of the currents that flow along the continental shelf break. Changes in the deeper waters of the ACC have been documented by Gille (2002), who showed that waters at 900 m throughout the Antarctic have warmed up to 1◦ C. Warming in the southern ACC is ca. 0.025◦ C per decade. Hence, potentially the waters being advected onto the Ross Sea continental shelf might have more heat than previously. If this heat were lost to the surface, it would mean that more ice would be melted in the polynya, and potentially lengthen the duration of the polynya by increasing the size earlier in the season. By melting more ice on the shelf, it also might increase surface stratification and result in a change of phytoplankton functional groups (Arrigo et al., 1999), thereby dramatically altering both the shelf biogeochemical cycles and the local food web. The frequency of intrusions of MCDW onto the Ross Sea continental shelf is poorly constrained, but models of circulation suggest that these intrusions occur frequently (on the order of weeks) and can be quite energetic (velocities of ca. 12 cm s−1 ; Dinniman et al., 2003). The flows are linked to the larger flow of the Ross Sea gyre and the ACC. It is uncertain if the long-term transport and physical forcing of the ACC is increasing; however, we do know that during positive periods of the Southern Annular Mode (SAM, an index of barometric pressure between Antarctica and lower latitudes; Thompson and Solomon, 2002; Marshall et al., 2004) that ACC transport is predicted to increase (Hall and Visbeck, 2002). Coincident with this change, the return flow at depth would be expected to increase, and ultimately the strength (frequency and velocity) of the intrusions onto the shelf would also increase. Testing this hypothesis, and the impacts on polynya processes, will be a challenge to be addressed in the coming years. If the polynya were to open earlier due to increased heat input, it would also likely generate an opening to the Pacific Ocean earlier as well. While this will not have any impact on most organisms, it would allow whales to enter earlier, and thus potentially have a greater impact through their feeding than is present today. Similarly, opening the passage to the continental shelf would also make it easier for the extant fisheries industry to expand into the region, which has largely been unaffected by fishing pressure previously. Removal of large top predators from the system will undoubtedly generate significant, but unpredictable, changes in food-web structure. Aoki et al. (2005) demonstrated a long-term trend in the salinity of bottom water off Adelie Land (near 140◦ E), and Jacobs (2004) showed a similar freshening of the waters on the shelf in the same region. As the deeper waters of this region are influenced by deepwater formation on the Ross Sea continental shelf, and because these deep waters have been shown to have a decreased salinity in recent years (Jacobs et al., 2002), it possibly suggests that polynya processes are contributing to this large-scale change by a decreased production of ice during winter. This contradicts the trends in ice concentrations, however, and may be impacted more by changes in glacial ice melt or surface precipitation. Regardless, these changes can potentially influence the character of the numerous coastal polynyas in the IndoPacific sector of the Antarctic that are impacted by offshore water advection (e.g., Prydz Bay; Arrigo and van Dijken, 2003).
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4 Conclusions Given that change is occurring in both the Arctic and Antarctic (Figures 1 and 2), even if at non-uniform rates, these changes will strongly impact polynyas. However, the changes will not be identical in all polynyas, as the character of change is heterogeneous. Because of the complexities of these changes and their driving forces, models may be the most robust means to assess the degree and significance of such changes. Long-term measurements of key variables within polynyas would also provide a record of change that presently is unavailable (except when remotely sensed). Polynyas may be the first regions in polar systems that will display an integrated response to anthropogenic changes, and this makes them invaluable areas of study to assess the responses at all levels and scales to large-scale anthropogenic alterations. As such, polynyas indeed are “windows to the world’s oceans”.
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Author Index Alverson, K., see Moore, G.W.K. 147 Alverson, K., see Moore, K. 68 Amann, R., see Simon, M. 349 AMAP, 134 Ambrose Jr., W.G. 368 Amiel, D., see Booth, B.C. 243, 282, 346, 347, 399 Amiel, D., see Miller, L.A. 325, 347, 348 Amiel, D., see Tremblay, J.E. 246, 257 Amon, R.M.W. 339 Amon, R.M.W., see Benner, R. 325 Amon, R.M.W., see Opsahl, S. 325 Amos, A.F., see Jacobs, S.S. 71, 173, 372 Amy, P.S. 330 Anderson, D.G., see Cantoni, G.L. 178 Anderson, L.G. 167, 170, 178, 196, 203, 209, 210 Anderson, L.G., see Fransson, A. 166 Anderson, L.G., see Hoppema, M. 164 Anderson, L.G., see Jones, E.P. 209, 211 Anderson, L.G., see Kaltin, S. 194 Anderson, L.G., see Rudels, B. 68, 209 Anderson, M.R., see Rivkin, R.B. 324 Anderson, P.S. 135, 136 Anderson, P.S., see King, J.C. 136 Anderson, R.F., see Smith Jr., W.O. 249, 250, 332 Anderson, R.G., see den Hartog, G. 66 Anderson, R.J., see Smith, S.D. 66 Anderson, R.J., see Topham, D.R. 66 Andreae, M.O. 178 Andreae, M.O., see Barnard, W.R. 165 Andreae, M.O., see Charlson, R.J. 178 Andreas, E.L. 57, 127, 134, 135, 151 Andreas, E.L., see Semiletov, I. 181 Andreasen, C., see Falk, K. 392, 403 Anning, J.L. 244 Anselme, B., see Pfirman, S.L. 59 Aoki, S. 416 Aragno, M., see Pagni, M. 344 Arashkevich, E. 275, 279 Arbetter, T.E. 33, 34, 127, 139, 147, 151 Arbetter, T.E., see Bailey, D.A. 33, 34, 121 Arbetter, T.E., see Curry, J.A. 146 Arbetter, T.E., see Stroeve, J.C. 2 Archer, A., see van Woert, M. 28 Archer, S.D., see Leakey, R.J.G. 349 Arctic Climate Impact Assessment (ACIA), 68, 74, 75 Arctic Climatology Project, 128
Aagaard, K. 17, 20, 64, 68, 70, 73 Aagaard, K., see Coachman, L.K. 369 Aagaard, K., see Schumacher, J.D. 59, 369 Aagaard, K., see Weingartner, T.J. 5 Abildhauge Thomsen, M., see Vidussi, F. 399, 400 Abramova, E.N. 275 Accornero, A. 254, 306 Achman, D., see Cochran, J.K. 325 ACIA, 262 Ackley, S.F. 3, 28, 295, 397 Ackley, S.F., see Andreas, E.L. 127 Ackley, S.F., see Hunke, E.C. 28, 119 Acquarone, M., see Born, E.W. 400 Acquarone, M., see Teilmann, J. 400 Acuña, J.-L. 273, 277, 280–284, 347 Acuña, J.-L., see Deibel, D. 280, 283 Adam, P., see Nihoul, J.C.J. 369 Adams, J.M., see Overland, J.E. 136, 137 Adams, N., see Massom, R.A. 39, 40, 42 Adolphs, U. 21, 61, 134, 139 Ahlnas, K., see Wendler, G. 41 Ainley, D.G. 258, 299, 391–397, 412 Ainley, D.G., see Arrigo, K.R. 30, 232 Ainley, D.G., see Karnovsky, N.J. 240 Ainley, D.G., see Wilson, P.R. 412 Akasofu, S.-I., see Semiletov, I. 181 Akeeagok, S., see Heide-Joergensen, M.P. 257 Akitomo, K. 64 Alam, A. 57, 92, 151 Alam, A., see Curry, J.A. 146 Alam, A., see Pinto, J.O. 151 Alexenina, M.G., see Rogachev, K.A. 65, 66, 75 Alford, M.E., see Carmack, E.C. 67 Alfultis, M.A. 61, 66 Alison, I., see Massom, R. 61 Allan, R.J., see White, W.B. 3 Alldredge, A.L., see Dilling, L. 282 Alldredge, A.L., see Smith, D.C. 328, 344 Allison, I., see Bindoff, N.L. 6, 21, 37 Allison, I., see Heil, P.I. 40 Allison, I., see Lytle, V.I. 37 Allison, I., see Massom, R.A. 5, 6, 21, 37, 39, 40, 42, 226 Allison, I., see Roberts, A. 6, 37, 129, 151 Allison, I., see Worby, A.P. 151 Allison, I., see Wu, X. 6 Alvarez, H.M. 344 421
422 Armstrong, A.E. 136, 147 Armstrong, R.A., see Sarmiento, J.L. 324 Arntz, W.E., see Starmans, A. 374 Arrhenius, S. 166 Arrigo, K., see Ainley, D.G. 393, 412 Arrigo, K.R. 17, 22, 25–28, 30, 31, 37, 163, 164, 172, 173, 178, 179, 201, 202, 206–208, 213, 224–227, 229–233, 240, 242, 248–250, 252, 254, 256, 258, 261, 262, 272, 288, 293–296, 332, 349, 364, 366–368, 372, 373, 375–378, 380, 381, 392–395, 397, 416 Arrigo, K.R., see DiTullio, G.R. 397 Arrigo, K.R., see Tagliabue, A. 258, 261, 367, 378, 394, 397 Arrigo, K.R., see Worthen, D.L. 261 Artegiani, A., see Sertorio, T.Z. 301 Ashjian, C.J. 274, 275, 277–280, 282, 284, 292, 301, 339, 403 Askne, J., see Dokken, S.T. 209 Asper, V.L. 173, 249, 259, 378 Asper, V.L., see Smith Jr., W.O. 205, 249–251, 254, 367, 378 Asselin, R. 114 Atkinson, A. 299, 307, 309, 333, 337, 395 Atkinson, A., see Meyer, B. 297, 299, 303, 309 Atkinson, B.W., see Dare, R.A. 4, 6 Auel, H. 272, 273, 278 Auel, H., see Mumm, N. 273, 279 Austin, L.M.C., see Biggs, N.R.T. 104, 105 Awaji, T., see Akitomo, K. 64 Azam, F. 326, 328 Azam, F., see Ducklow, H.W. 334 Azam, F., see Fuhrman, J.A. 330 Azam, F., see Simon, M. 341 Azam, F., see Smith, D.C. 328, 344 Azzali, M. 294, 295, 397 Azzali, M., see Sala, A. 294, 295, 301 Backhaus, J.O., see Kämpf, J. 33 Bâcle, J. 63, 211, 346, 398 Bâcle, J., see Ingram, R.G. 34, 61, 63, 130, 173, 211, 225, 243, 272, 346 Bâcle, J., see Lovejoy, C. 243, 245, 346, 399 Bâcle, J., see Miller, L.A. 325, 347, 348 Bailey, D.A. 33, 34, 121 Bailey, D.A., see Arbetter, T.E. 33, 34, 127, 139, 147, 151 Bailey, D.A., see Lynch, A.H. 117, 131 Baines, P.G. 70–73 Baker, J.M. 180 Baker, K.S., see Ross, R.M. 304 Baker, K.S., see Smith, R.C. 323, 334, 336 Bakke, L.W., see Matthews, J.B.L. 286 Baldwin, R.J., see Smith Jr., K.L. 365 Ball, F.K. 37
Author Index Ballard, G., see Wilson, P.R. 412 Båmstedt, U., see Tande, K.S. 281 Bano, N. 326 Bano, N., see Hodges, L.R. 325 Bano, N., see Yager, P.L. 324, 325 Barber, D.G. 2, 5, 7, 8, 10, 34, 35, 55, 61, 63, 68, 70, 129, 136, 147, 163,173, 178, 181, 201, 206, 209, 225, 243, 247, 248, 272, 393, 398, 401, 413, 415 Barber, D.G., see Hanesiak, J.M. 147, 149 Barber, D.G., see Ingram, R.G. 34, 61, 63, 130, 173, 211, 225, 243, 272, 346 Barber, D.G., see Jin, X. 149 Barber, D.G, see Lukovich, J. 414, 415 Barber, D.G., see Mundy, C.J. 34, 225, 398 Barber, D.G., see Wilson, K. 61 Barber, D.G., see Wilson, K.J. 7, 34, 36 Barber, D.G., see Yackel, J.J. 36 Barber, R.T., see Buesseler, K.O. 373, 378 Barber, R.T., see Hiscock, M.R. 250, 252 Barber, R.T., see Smith Jr., W.O. 249, 262, 332–334, 394 Barbini, R. 205 Barnard, W.R. 165 Barnes, C., see Cochran, J.K. 325 Barnes, P.W., see Kvenvolden, K.A. 170 Barnes, P.W., see Reimnitz, E. 59 Barnett, A.M., see Lee, R.F. 278, 284 Barnett, D. 59 Baross, J.A. 326, 330 Baross, J.A., see Jumars, P.A. 325, 339 Barrie, L.A. 143, 180 Barrie, L.A., see Xie, Y. 144 Barry, J.P. 365–368, 374, 376, 381, 395 Barry, J.P., see DiTullio, G.R. 397 Barry, J.P., see Grebmeier, J.M. 257, 365, 367, 368, 374–378, 381, 395 Barry, R.G., see Serreze, M.C. 2 Barton, K.J., see Ainley, D.G. 412 Barton, K.J., see Wilson, P.R. 412 Bason, F. 145 Bates, N.R. 164, 166, 171, 172, 181, 194, 202, 205 Bates, N.R., see Carlson, C.A. 335, 344 Bates, N.R., see Hansell, D.A. 329 Bates, T.S., see Huebert, B.J. 166, 178 Bates, T.S., see Kiene, R.P. 178 Bathmann, U. 305 Bathmann, U., see Meyer, B. 299 Bathmann, U.V. 288 Bathmann, U.V., see Atkinson, A. 299 Bathmann, U.V., see González, H.E. 305 Bathmann, U.V., see Meyer, B. 297, 299, 303, 309 Bauch, H.A., see Stepanova, A. 214 Bauer, J.E. 92 Bauer, J.E., see Yager, P.L. 324, 325 Bauerfeind, E. 248, 283, 284, 325, 339 Bauerfeind, E., see Pesant, S. 248
Author Index Baumann, M.E.M., see Hirche, H.-J. 274, 275, 279, 282, 284 Bayer, R., see Fahrbach, E. 6 Bayer, R., see Mensch, M. 178 Bayre, R.C., see Garrison, D.L. 395 Beaumont, K.L. 305 Beckmann, A. 121, 195 Becquevort, S., see Billen, G. 349 Becquevort, S., see Lancelot, C. 349 Beddington, J.R., see May, R.M. 395 Beffa, T., see Pagni, M. 344 Behrenfeld, M.J. 324 Bélair, S., see Mailhot, J. 137, 151 Bélanger, S. 243, 247 Bélanger, S., see Booth, B.C. 243, 282, 346, 347, 399 Bélanger, S., see Klein, B. 346–348 Bélanger, S., see Ringuette, M. 347 Bellerby, R.G.J., see Hoppema, M. 197, 201, 207 Belzile, C. 170, 174, 230, 231 Bengston, J.L., see Simpkins, M.A. 371, 372, 376 Benner, R. 325 Benner, R., see Opsahl, S. 325 Benvenuti, S., see Falk, K. 400 Berelson, W.M. 379 Beret, R., see Klein, B. 346–348 Berger, G.W., see Rutgers van der Loeff, M.M. 201 Bergmann, M.A., see Welch, H.E. 399 Bermamasco, A., see Picco, P. 242 Bersch, M. 199 Bertoia, C., see van Woert, M. 28 Bigg, E.K. 143 Bigg, K., see Leck, C. 149 Biggs, D.C. 395 Biggs, D.C., see El-Sayed, S.Z. 180 Biggs, N.R.T. 59, 74, 92, 93, 95–97, 101, 102, 104, 105, 107 Biggs, N.R.T., see Morales Maqueda, M.A. 4, 7, 8, 27, 31–33, 43, 57, 61, 64, 70, 74, 87, 88, 90, 195, 199, 369, 372 Biggs, N.R.T., see Tear, S. 102–106 Bignami, F., see Ashjian, C.J. 274, 275 Bignami, F., see Minnett, P.J. 11, 338 Bignami, F., see Top, Z. 164, 176 Billen, G. 349 Billen, G., see Lancelot, C. 349 Bindoff, N.L. 6, 21, 37, 40, 207 Bindoff, N.L., see Aoki, S. 416 Bindoff, N.L., see Marsland, S.J. 6, 37, 119, 177 Bindoff, N.L., see Massom, R.A. 39, 40, 42 Bindoff, N.L., see Williams, G.D. 37, 164, 177, 206, 207, 242 Bindoff, N.L., see Wong, A.P.S. 28, 207 Bingemer, H., see Barnard, W.R. 165 Bird, D.F. 329, 336, 337, 340 Bird, D.F., see Karl, D.M. 329, 336
423 Bishop, J.K.B., see Fung, I.Y. 252, 259 Björk, G., see Dokken, S.T. 209 Björk, G., see Rudels, B. 210 Björk, G., see Winsor, P. 5, 59–61, 71, 74, 92, 179 Bjørnsen, P.K. 344 Bjornsen, P.K., see Middelboe, M. 347, 348 Blanchet, J.-P., see Girard, E. 145 Blomquist, B.W., see Huebert, B.J. 166, 178 Blume, B., see Meyer, B. 297, 299, 303, 309 Bochdansky, A.B., see Acuña, J.-L. 280, 281 Bochdansky, A.B., see Deibel, D. 280, 283 Bodungen, B.v. 304, 306, 368 Bodungen, B.v., see Nöthig, E.-M. 305 Bodungen, B.v., see Schnack, S.B. 301 Boekelheide, R.J., see Ainley, D.G. 394–396 Boer, G.J., see Cubasch, U. 323 Boer, G.J., see Flato, G. 2 Boer, G.J., see Fyfe, J.C. 3 Boertmann, D. 257, 399 Boertmann, D., see Egevang, C. 399 Boetius, A., see Smith Jr., K.L. 365 Böhm, E. 210, 224, 338 Böhm, E., see Minnett, P.J. 11, 338 Bond, N.A., see Hunt, G.L. 233 Bond, N.A., see Niebauer, H.J. 392 Bond, N.A., see Overland, J.E. 136, 137 Booth, B.C. 226, 243, 247, 282, 346, 347, 399 Booth, B.C., see Acuna, J.L. 347 Booth, B.C., see Smith Jr., W.O. 339–341 Booth, B.C., see Vidussi, F. 399, 400 Born, E.W. 400 Born, E.W., see Teilmann, J. 400 Bottenheim, J.W., see Barrie, L.A. 180 Bottino, N.R. 295 Bouillon, R.-C. 164, 179, 213 Bourke, R.H. 175 Boutin, J. 166 Boyd, P.W. 198, 229 Boysen-Ennen, E. 290, 293, 299, 301 Bradstreet, M.S.W. 403 Bradstreet, M.S.W., see Renaud, W.E. 400 Brasseur, P., see Nihoul, J.C.J. 369 Breton, E. 397 Brichta, M., see Niehoff, B. 307 Brinton, E., see Huntley, M. 297, 299, 309 Brodeur, R.D., see Hunt, G.L. 233 Broecker, W.S. 6, 26 Broenkow, W.W., see Martin, J.H. 375–377 Bromwich, D.H. 6, 21, 26, 28, 61, 130, 132, 139, 146, 147, 170, 171, 233, 372, 393 Bromwich, D.H., see Cullather, R.I. 136, 147 Bromwich, D.H., see Kurtz, D.D. 28, 61, 130, 204, 363, 372 Bromwich, D.H., see Monaghan, A.J. 147 Bromwich, D.H., see Parish, T.R. 5, 20, 24, 129 Bromwich, D.H., see Rogers, A.N. 136, 145
424 Brooks, M.L., see Lovvorn, J.R. 367, 379, 380 Brooks, M.T., see Kemp, W.M. 258 Brown, C.W., see Arrigo, K.R. 230 Brown, M., see Sambrotto, R.N. 230 Brown, R.D., see Goodison, B.E. 6 Brown, R.G.B. 257, 391, 392 Brown, S.L. 326 Bruchhausen, P.M., see Jacobs, S.S. 173, 372 Bruckhausen, P.M., see Jacobs, S.S. 71 Brunet, C., see Cotonnec, G. 397 Bruton, J.A., see Kiene, R.P. 178, 179 Brylinski, J.-M., see Breton, E. 397 Buch, K.A.J. 131 Buchholz, F., see Meyer, B. 299 Buckley, P.T., see Sturges, W.T. 180 Budd, W.F., see Marsland, S.J. 37, 119, 177 Budd, W.F., see Wu, X. 3, 6 Budéus, G. 63, 175 Budéus, G., see Amon, R.M.W. 339 Budéus, G., see Kattner, G. 211, 228, 338 Budéus, G., see Minnett, P.J. 11, 338 Budéus, G., see Pesant, S. 248 Budeus, G., see Robineau, B. 326 Budéus, G., see Schneider, W. 63, 75, 129, 175, 225, 338, 401 Budillon, G. 6, 26, 205 Budillon, G., see Povero, P. 366 Buesseler, K.O. 263, 373, 378 Buesseler, K.O., see Ducklow, H.W. 263 Buffoni, G. 43 Bullister, J., see Mensch, M. 178 Bullister, J.B., see Orsi, A.H. 71 Bullister, J.L., see Orsi, A.H. 6, 196, 201, 203, 206–208 Bullivant, J.S. 374 Bump, J.K., see Lovvorn, J.R. 367, 379, 380 Bunker, A.J. 308 Burk, S.D. 136 Burk, S.D., see Fett, R.W. 137, 151 Burns, B.A., see Markus, T. 8, 9, 30, 31, 59, 130, 136 Burton, H.R., see Curran, M.A.J. 179 Burton, H.R., see Gibson, J.A.E. 179, 180 Businger, J.A., see Andreas, E.L. 151 Bussey, H.J. 348 Bussotti, S., see Gambi, M.C. 374 Butler, J.H., see Yvon-Lewis, S.A. 164, 180 Button, D.K. 327, 330 Buzov, A.Y. 59 Byers, M.L., see Smith, R.C. 334, 336 Cabal, J.A. 301 Cachier, H., see Wolff, E.W. 145 Cai, X., see Yu, Y. 5 Calbet, A. 307
Author Index Caldeira, K. 385 Cameron, D.B., see Knox, G.A. 374 Campbell, W.J., see Gloersen, P. 2, 3, 57, 61, 225, 332 Campbell, W.J., see Parkinson, C.L. 63 Cantoni, G.L. 178 Cappelletti, A., see Buffoni, G. 43 Carleton, A.M. 3 Carleton, J.H., see Hamner, W.M. 303 Carli, A. 295 Carlson, C.A. 170, 172, 324, 325, 328, 331, 333, 335, 344 Carlson, C.A., see Bates, N.R. 164, 171, 172 Carlson, C.A., see Ducklow, H.W. 332, 334, 335 Carlson, C.A., see Smith Jr., W.O. 335 Carlson, G.A., see Bates, N.R. 194, 202, 205 Carmack, E., see Aagaard, K. 17 Carmack, E., see McLaughlin, F. 164, 178 Carmack, E.C. 29, 64, 67, 71, 73, 74, 415 Carmack, E.C., see Aagaard, K. 64, 70, 73 Carmack, E.C., see Bâcle, J. 63, 211, 346, 398 Carmack, E.C., see Foster, T.D. 70, 71, 73 Carmack, E.C., see Grebmeier, J.M. 369 Carmack, E.C., see Hamblin, P.F. 67 Carmack, E.C., see Lovejoy, C. 399 Carmack, E.C., see Macdonald, R.W. 59 Carmack, E.C., see McLaughlin, F.A. 66 Carmack, E.C., see Omstedt, A. 64 Carmack, E.C., see Rogachev, K.A. 65, 66, 70, 75 Carmack, E.C., see Tremblay, J.-É. 211, 229, 243, 245, 399 Carmack, E.C., see Williams, W.J. 73, 163, 164, 173 Caron, D.A. 394, 397 Caron, D.A., see Smith Jr., W.O. 332, 337 Caron, G. 245, 246 Carrada, G.C., see Saggiomo, V. 205 Carrasco, J.F., see Bromwich, D.H. 21, 26, 233, 372, 393 Carrillo, C.J. 326 Carsey, F.D. 32, 64, 195 Cash, B.A, see Andreas, E.L. 57 Cash, B.A., see Andreas, E.L. 134, 151 Catalano, G. 242, 256 Catalano, G., see Goffart, A. 226, 395, 397 Catalano, G., see Hecq, J.H. 395 Catalano, G., see Smith Jr., W.O. 250, 251, 254 Cattaneo-Vietti, R., see Povero, P. 366 Cavalieri, D., see Martin, S. 5 Cavalieri, D.J. 5, 6, 17, 37, 43, 167 Cavalieri, D.J., see Gloersen, P. 2, 3, 57, 61, 225, 332 Cavalieri, D.J., see Martin, S. 5, 60, 61, 71 Cavalieri, D.J., see Parkinson, C.L. 2, 63, 323, 393 Cavalieri, D.J., see Signorini, S.R. 5 Cavalieri, D.J., see Vinnikov, K.Y. 2 Cavalieri, D.J., see Weingartner, T.J. 5
Author Index Cavalieri, D.J., see Zwally, H.J. 3 Caya, D., see Saucier, F.J. 117 CCAMLR, 395 Chamberlain, V.A., see Carmack, E.C. 67 Chan, W., see Barber, D.G. 7, 8, 10, 34, 35, 61, 63, 129, 136, 147, 225, 398, 415 Chao, S.-Y. 132 Chapman, D.C. 59, 72–74 Chapman, D.C., see Carmack, E. 415 Chapman, D.C., see Carmack, E.C. 73 Chapman, D.C., see Gawarkiewicz, G. 64, 72 Chapman, D.C., see Winsor, P. 5, 209 Chapman, E.W. 392 Chapman, W.L., see Lynch, A.H. 117, 131 Chapman, W.L., see Walsh, J.E. 148, 149 Charlson, R.J. 178 Chekalyuk, A.M., see Olson, R.J. 240, 249, 251, 259 Chen, C.-T.A. 164, 178 Chen, C.-T.A., see Poisson, A. 196, 207 Chenggang, L., see Zilin, L. 206 Chiantore, M., see Povero, P. 366 Chierici, M., see Anderson, L.G. 167 Chierici, M., see Fransson, A. 166 Chiuchiolo, A., see Renaud, P.E. 368 Choi, J.W. 331, 346 Christensen, J.P. 375, 377 Christensen, K.D., see Falk, K. 392, 403 Christensen, V., see Pauly, D. 256 Christian, J.R. 328 Church, J., see Marsland, S.J. 6 Church, J., see Rintoul, S.R. 6 Church, J.A., see Aoki, S. 416 Church, M.J. 326 Church, M.J., see Ducklow, H.W. 332, 334 Churun, V., see Dmitrenko, I. 17 Cimbelli, A., see Frezzotti, M. 29, 41 Clark, M.S., see Peck, L.S. 331 Clarke, A., see Peck, L.S. 331 Clarke, C.W., see May, R.M. 395 Clarke, E.D., see Ainley, D.G. 393, 412 Cleator, H., see Stirling, I. 60, 66, 257, 392 Clement, J.L. 61, 214, 368, 379, 380 Clow, G.D., see Doran, P.T. 350 Coachman, L.K. 369 Coachman, L.K., see Aagaard, K. 17, 64, 70 Coale, K.H. 229, 240, 251, 252 Coale, K.H., see Fitzwater, S.E. 252 Coale, K.H., see Fiztwater, S.E. 229 Cochran, J.K. 325 Cochran, J.K., see Klein, B. 346–348 Cochran, J.K., see Miller, L.A. 325, 347, 348 Cochran, J.K., see Tremblay, J.E. 246, 257 Cochran, T.G., see Hosie, G.W. 292, 294 Cockell, C.S., see Peck, L.S. 331 Codispoti, L.A. 381
425 Codispoti, L.A., see Gordon, L.I. 172, 173, 205, 242 Codispoti, L.A., see Smith Jr., W.O. 249, 250, 332 Cole, J.J. 329, 336, 340–342, 347 Cole, J.J., see del Giorgio, P.A. 327 Collier, R. 254, 255, 373, 374, 378 Colony, R.L., see Pfirman, S.L. 74 Colony, R.L., see Rigor, I.G. 2 Comiso, J.C. 2, 3, 29, 31, 33, 34, 61, 65, 66, 68, 130, 167, 178, 199, 201, 202, 272, 372, 412 Comiso, J.C., see Ackley, S.F. 3, 28 Comiso, J.C., see Gloersen, P. 2, 3, 57, 61, 225, 332 Comiso, J.C., see Gordon, A.L. 2, 3, 4, 33, 66 Comiso, J.C., see Jacobs, S.S. 26, 28, 37, 67, 204, 225, 232, 233, 249, 372, 393 Comiso, J.C., see Kwok, R. 4, 28, 262, 412 Comiso, J.C., see Martin, S. 5 Comiso, J.C., see Massom, R.A. 255 Comiso, J.C., see Parkinson, C.L. 2, 63 Comiso, J.C., see Sullivan, C.W. 324 Comiso, J.C., see Wilson, P.R. 412 Comiso, J.C., see Yang, J. 245 Comiso, J.C., see Zwally, H.J. 3, 5, 29, 32, 61, 89, 363, 372 Condie, S.A. 72 Condie, S.A., see Baines, P.G. 70–73 Connelly, T.L., see Yager, P.L. 324, 325 Connolley, W.M., see King, J.C. 3 Connolley, W.M., see Marshall, G.J. 416 Connolley, W.M., see Vaughan, D.G. 3 Conover, R.J. 273, 278, 279, 284–286 Conover, R.J., see Welch, H.E. 399 Convey, P., see Peck, L.S. 331 Cook, J., see Stretch, J.J. 287 Cooper, B.A., see Springer, A.M. 379 Cooper, L.W. 231, 249, 349, 364, 365, 367–369, 375, 379–381 Cooper, L.W., see Clement, J. 368, 379, 380 Cooper, L.W., see Clement, J.L. 61, 214 Cooper, L.W., see Grebmeier, J.M. 214, 249, 257, 364, 366–369, 371, 374–381, 384 Cooper, L.W., see Lovvorn, J.R. 367, 372, 379, 380 Cornils, A., see Niehoff, B. 307 Costa, D.P., see Hofmann, E.E 413 Cota, G.F. 262 Cota, G.F., see Sturges, W.T. 180 Cotonnec, G. 397 Coyle, K.O. 365, 367 Craig, H., see Weiss, R.F. 194 Crane, R.G., see Goodison, B.E. 6 Crane, R.G., see Shine, K.P. 143 Cranston, R.E., see Macdonald, R.W. 325 Crawford, R.E., see Welch, H.E. 399 Cross, W.E., see Bradstreet, M.S.W. 403 Crutzen, P.J., see Andreae, M.O. 178 Crutzen, P.J., see Barrie, L.A. 180 Cubasch, U. 167, 323
426 Cullather, R.I. 136, 147 Cullather, R.I., see Rogers, A.N. 136, 145 Cunningham, G.F., see Kwok, R. 3 Curran, M.A.J. 3, 167, 179 Curry, J.A. 2, 136, 137, 143, 146 Curry, J.A., see Alam, A. 57, 92, 151 Curry, J.A., see Inoue, J. 152 Curry, J.A., see Liu, J. 4 Curry, J.A., see Pinto, J.O. 136, 143, 151, 152 Curry, J.A., see Stamnes, K.R.G.E. 128 Curtin, T.B., see Steele, M. 68 Curtis, J., see Wendler, G. 21, 37 Curtis, M.A., see Petersen, G.H. 325 Curtis, M.F., see Welch, H.E. 399 Cuzin-Roudy, J. 308 Dacey, J.W.H. 165 Dagg, M. 282 Dahms, H.-U., see Schnack-Schiel, S.B. 287 Daley, R.J., see Hobbie, J.E. 326 Dall’Antonia, L., see Falk, K. 400 Daly, K.L. 179, 211, 277, 278, 280–283, 287, 296–299, 303, 304, 306, 309, 325, 339 Daly, K.L., see Deibel, D. 169, 257, 379 Daly, K.L., see Scolardi, K.M. 296 Daly, K.L., see Smith Jr., W.O. 242, 247, 248, 338, 342, 366, 368, 401, 403 Darby, M.S. 6, 74, 92, 96–98 Darby, M.S., see Willmott, A.J. 91, 98–100, 129 Dare, R.A. 4, 6 Davenport, S.R., see Nicol, S. 294, 298, 304 Dawson, J.K. 286 Dayton, P.K. 374 Dayton, P.K., see Barry, J.P. 366, 374 De Abreu, R.A., see Hanesiak, J.M. 147, 149 De Baar, H.J.W. 198, 208 De Baar, H.J.W., see Hoppema, M. 178, 197, 201, 207 De Jong, J.T.M., see De Baar, H.J.W. 198, 208 de la Mare, W.K. 3 de la Mare, W.K., see Kawaguchi, S. 304 de la Mare, W.K., see Nicol, S. 308 de Mora, S.J., see Bouillon, R.-C. 164, 179, 213 de Mora, S.J., see Lee, P.A. 179 De Ruyck, C.C., see Lovvorn, J.R. 367, 379, 380 De Vernal, A., see Hamel, D. 245, 246 de Waard, P., see Huijberts, G.N. 344 Dearborn, J.H., see Bullivant, J.S. 374 DeFelice, T.P. 130 Deibel, D. 169, 257, 280, 282, 283, 379 Deibel, D., see Acuña, J.-L. 280, 281, 347 Deibel, D., see Lee, P.A. 179 Deibel, D., see Pomeroy, L.R. 325, 331 Deibel, D., see Saunders, P. 257 Deibel, D., see Saunders, P.A. 279–283, 347, 399, 400
Author Index Deibel, D., see Stevens, C.J. 277–279, 282, 284 del Giorgio, P.A. 327 Delaney, M.P. 347 Deleersnijder, E., see Nihoul, J.C.J. 369 Delille, B. 181 DeLong, E.F. 327 DeLong, E.F., see Church, M.J. 326 DeLong, E.F., see Karner, M.B. 328 DeLong, E.F., see Murray, A.E. 326 DeMaster, D.J., see Nelson, D.M. 205, 252 Demers, S., see Belzile, C. 170, 174, 230, 231 Demicheli, L., see Picco, P. 242 Deming, J.W. 173, 330, 331, 346, 366 Deming, J.W., see Hirche, H.-J. 175 Deming, J.W., see Huston, A.L. 325, 347, 348 Deming, J.W., see Krembs, C. 181 Deming, J.W., see Smith Jr., W.O. 339–341 Deming, J.W., see Vetter, Y.-A. 325, 328, 344, 348 Deming, J.W., see Yager, P.L. 164, 169, 172, 176, 211, 227, 324, 325, 327, 331, 341, 343, 344 den Hartog, G. 66 den Hartog, G., see Smith, S.D. 66 den Hartog, G., see Topham, D.R. 66 Dennett, M.R., see Caron, D.A. 394, 397 Dennett, M.R., see Smith Jr., W.O. 332, 337 Dethleff, D. 5, 140, 151, 214 Dethleff, D., see Reimnitz, E. 59, 60 Detrich, H.W., see Peck, L.S. 331 Dickey, W.W. 134 Dickson, M.L., see Buesseler, K.O. 373, 378 Dickson, M.L., see Ducklow, H.W. 334 Dieckmann, G.S. 3 Dieckmann, G.S., see Giannelli, V. 181 Dietz, R., see Heide-Jorgensen, M.P. 400 Dietz, R., see Richard, P.R. 257, 392 Dijkhuizen, L., see Harder, W. 330 Dijkhuizen, L., see Stefels, J. 178 Dilling, L. 282 Dinniman, M.S. 225, 372, 416 Dinniman, M.S., see Hiscock, M.R. 250, 252 Dinniman, M.S., see Smith Jr., W.O. 240, 250–252, 254, 255 Dissing, D., see Wendler, G. 232 DiTullio, G.R. 164, 179, 180, 249, 256, 378, 396, 397 DiTullio, G.R., see Arrigo, K.R. 332, 373, 378, 394, 395, 416 DiTullio, G.R., see Daly, K.L. 179 DiTullio, G.R., see Grebmeier, J.M. 367, 368, 374–378, 381 DiTullio, G.R., see Miller, L.A. 205, 209, 213 DiTullio, G.R., see Sedwick, P.N. 173, 229, 230, 240, 249, 259, 394, 395 DiTullio, G.R., see Smith Jr., W.O. 294 DiTullio, G.R., see Tortell, P.D. 252, 394 Dix, M., see Cubasch, U. 323
Author Index Djenidi, S., see Nihoul, J.C.J. 369 Dmitrenko, I.A. 17, 414 Dokken, S.T. 209 Doney, S.C., see Fung, I.Y. 252, 259 Dong, X. 143 Donnelly, J., see Hopkins, T.L. 307 Doran, P.T. 350 Douglas, D.C., see Petersen, M.R. 392 Dow, C.S., see Morgan, P. 330 Drewry, D.J. 372 Drinkwater, M.R., see Martin, S. 5 Drobot, S., see Serreze, M.C. 2 Drobot, S.D. 2 Drucker, R. 10, 60, 61, 70 Drucker, R., see Martin, S. 5, 60, 61, 66, 113, 119, 130, 147 Druker, R., see Martin, S. 60, 66 Du, Y., see Bromwich, D.H. 132, 393 Duce, R.A. 259 Ducklow, H.W. 167, 263, 326–329, 332, 334–336 Ducklow, H.W., see Carlson, C.A. 324, 325, 331, 335, 344 Ducklow, H.W., see Church, M.J. 326 Ducklow, H.W., see Laws, E.A. 324 Ducklow, H.W., see Smith Jr., W.O. 335 Dueck, L., see Richard, P.R. 257, 392 Dukowicz, J.K., see Hunke, E.C. 112 Dunbar, M.J. 61, 173, 257, 391, 398 Dunbar, R.B. 254, 259, 305, 367, 373, 374, 397 Dunbar, R.B., see Arrigo, K.R. 332, 373, 378, 394, 395, 416 Dunbar, R.B., see Barry, J.P. 365–368, 374, 376, 395 Dunbar, R.B., see Collier, R. 254, 255, 373, 374, 378 Dunbar, R.B., see DiTullio, G.R. 397 Dunbar, R.B., see Langone, L. 254, 255 Dunbar, R.B., see Nelson, D.M. 205, 252 Dunbar, R.B., see Villinski, J.C. 374 Dunne, J., see Sarmiento, J.L. 324 Dunphy, J., see Pomeroy, L.R. 325, 331 Dunton, K.H. 376 Dunton, K.H., see Grebmeier, J.M. 364, 366, 375 Durran, D.R. 115 Dymond, J., see Collier, R. 254, 255, 373, 374, 378 Dyrssen, D.W., see Anderson, L.G. 170 Ebert, E.E., see Curry, J.A. 2, 143 Egevang, C. 399 Eggink, G., see Huijberts, G.N. 344 Egorov, V.G., see Cooper, L.W. 349 Eicken, H. 149, 287 Eicken, H., see Dmitrenko, I.A. 414 Eicken, H., see Krembs, C. 181 Eicken, H., see Pfirman, S.L. 74 Eiken, H. 214
427 Ekelöf, E. 330 El-Sayed, S.Z. 180, 287, 328 El-Sayed, S.Z., see Weber, L.H. 328 Elander, M., see Falk, K. 392, 403 Endoh, T., see Ohshima, K.I. 74 Engelbart, D., see Kottmeier, C. 4, 5, 29, 127, 135 Englebretson, R.E., see Burk, S.D. 136 Englebretson, R.E., see Fett, R.W. 137, 151 Enomoto, H. 201 Enomoto, H., see Takizawa, T. 33 Eppley, R.W. 227 Erickson, K.A., see Miller, L.A. 325, 347, 348 Ericson, M., see Falk, K. 392, 403 Ermold, W., see Steele, M. 88 Escritor, E., see Huntley, M. 307, 309 Esposito, F., see Accornero, A. 306 Estrada, M., see Moran, X.A.G. 329 Etcheto, J., see Boutin, J. 166 Evans, P.G.H., see Nettleship, D.N. 399 Fahnestock, M.A., see Arrigo, K.R. 30, 232 Fahrbach, E. 6, 196, 203 Fahrbach, E., see Broecker, W.S. 6, 26 Fahrbach, E., see Hoppema, M. 178, 197, 201, 202, 204 Fahrbach, E., see Markus, T. 6, 29, 61 Fahrbach, E., see Rintoul, S.R. 6 Fahrbach, E., see Schauer, U. 71, 210 Fairall, C.W., see Huebert, B.J. 166, 178 Fairbanks, R.G., see Jacobs, S.S. 27, 205 Fairbanks, R.G., see Weppernig, R. 203 Falck, E. 176 Falck, E., see Anderson, L.G. 167, 210 Falck, E., see Daly, K.L. 325, 339 Falk, K. 392, 400, 403 Falk, K., see Hobson, K.A. 400 Falk, K., see Kampp, K. 399 Falk-Petersen, S. 68, 279, 297, 298, 308 Falkowski, P.G., see Behrenfeld, M.J. 324 Falkowski, P.G., see Laws, E.A. 324 Farley, E.V., see Grebmeier, J.M. 369 Farmer, D.M. 64 Fauchot, J., see Tremblay, J.-É. 74, 212, 213, 224, 229, 242–246, 274, 346, 399, 415 Fay, B., see Kottmeier, C. 145 Feder, H.M., see Grebmeier, J.M. 365, 374, 376, 377 Fenchel, T. 331 Fenchel, T., see Azam, F. 328 Fer, I. 71 Ferrigno, J.G., see Frezzotti, M. 29, 41 Fett, R.W. 137, 151 Fett, R.W., see Burk, S.D. 136 Fetterer, F., see Serreze, M.C. 2 Fetterer, F., see Stroeve, J.C. 2 Fevolden, S.E. 288, 308, 309
428 Fichefet, T. 21, 119 Fichefet, T., see Goosse, H. 33, 64, 131 Field, A., see Gordon, A.L. 6, 29 Field, J.G., see Azam, F. 328 Figurkin, A., see Gladyshev, S. 5 Findlay, S., see Cole, J.J. 329, 336, 340–342, 347 Finlay, B.J., see Fenchel, T. 331 Fischer, G. 306 Fischer, G., see Wefer, G. 306 Fischer, V., see Bathmann, U. 305 Fisher, E.C., see Kaufmann, R.S. 296 Fisher, N.S., see Gorsky, G. 283 Fisk, A.T. 278 Fisk, A.T., see Hobson, K.A. 257 Fitzwater, S.E. 252 Fitzwater, S.E., see Martin, J.H. 240 Fiztwater, S.E. 229 Flagg, C., see Codispoti, L.A. 381 Flatau, P.J., see Vogelmann, A.M. 150 Flato, G.M. 2, 112 Flato, G.M., see Fyfe, J.C. 3 Flint, M.V., see Springer, A.M. 364, 368, 379, 381, 384 Flint, M.V., see Sukhanova, I.N. 170 Fogelqvist, E., see Anderson, L.G. 167 Foldvik, A. 29, 195, 202 Foldvik, A., see Aagaard, K. 20, 68, 70 Folland, C.K. 166, 182 Fonda Umani, S. 256 Forbes, A., see Wong, A.P.S. 28, 207 Fortier, L. 403 Fortier, L., see Deming, J. 173 Fortier, L., see Deming, J.W. 346, 366 Fortier, L., see Fortier, M. 244 Fortier, L., see Michaud, J. 403 Fortier, L., see Ringuette, M. 347 Fortier, L., see Tremblay, J.E. 246, 257 Fortier, M. 244 Fortier, M., see Hobson, K.A. 257 Fortier, M., see Ringuette, M. 347 Foster, B.A. 292, 294, 295 Foster, T.D. 70, 71, 73, 167, 178 Foster, T.D., see Carmack, E.C. 29 Fountain, A.G., see Doran, P.T. 350 Fowler, C., see Massom, R.A. 39, 40, 42 Fowler, C., see Serreze, M.C. 2 Fowler, C.W., see Maslanik, J.A. 225, 232 Fowler, S.W., see Gorsky, G. 283 Francis, J.A. 136, 137, 143, 147 Francis, J.A., see Schweiger, A.J. 143 Francois, R., see Collier, R. 254, 255, 373, 374, 378 Fransson, A. 166 Fraser, K.P.P., see Peck, L.S. 331 Fraser, W.R. 392, 412 Fraser, W.R., see Ainley, D.G. 393, 412
Author Index Fraser, W.R., see Chapman, E.W. 392 Frederick, J.E. 230 Frey, K.E., see Eicken, H. 149 Frey, K.E., see Grebmeier, J.M. 369 Frezzotti, M. 29, 41 Fritsen, C.H., see Doran, P.T. 350 Froidevaux, L., see Manney, G.L. 230 Froneman, P.W. 282 Froneman, P.W., see Pakhomov, E.A. 288, 293, 298, 304 Frost, B.W., see Jumars, P.A. 325, 339 Füetterer, D., see Wefer, G. 306 Fuhrman, J.A. 330 Fukamachi, Y. 71, 207 Fukuchi, M. 293, 301, 305, 379, 382 Fukuchi, M., see Deming, J. 173 Fukuchi, M., see Deming, J.W. 346, 366 Fukuchi, M., see Kasamatsu, N. 179 Fukuchi, M., see Odate, T. 228 Fukuchi, M., see Ohshima, K.I. 74 Fukuchi, M., see Sampei, M. 283, 284 Fukuchi, M., see Sato, M. 279 Fukuchi, M., see Tanimura, A. 293 Fung, I.Y. 252, 259 Fung, I.Y., see Hansen, J.E. 323 Fusco, G., see Budillon, G. 6, 26 Fütterer, D., see Fischer, G. 306 Fyfe, J.C. 3 Gachon, P., see Saucier, F.J. 117 Gage, J.D. 365 Gagnon, J.-M., see Hobson, K.A. 257 Galbraith, P.S., see Minnett, P.J. 11, 338 Gallée, H. 5 Gallee, H. 119 Gambi, M.C. 374 Gambi, M.C., see Accornero, A. 306 Gammelgaard, M., see Vidussi, F. 399, 400 Gammelsrød, T., see Foldvik, A. 29, 195 Garcia, M., see Rintoul, S.R. 6 Garneau, M.E., see Klein, B. 346–348 Garneau, M.E., see Miller, L.A. 325, 347, 348 Garrett, D., see Vinnikov, K.Y. 2 Garrett, T.J. 143 Garrick, R.C., see Gibson, J.A.E. 179, 180 Garrison, D.L. 395 Garrison, D.L., see DiTullio, G.R. 179, 180 Garrison, D.L., see Gowing, M.M. 306 Garrity, C., see Bauerfeind, E. 248, 283, 284, 325, 339 Garwood, R.W., see Jiang, L. 72 Gascard, J.-C., see Haarpainter, J. 88 Gascard, J.-C., see Haarpaintner, J. 17, 71, 167, 209, 210 Gasol, J.M., see Moran, X.A.G. 329 Gawarkiewicz, G. 64, 72
Author Index Gawarkiewicz, G., see Chapman, D.C. 72, 73 Geer, G.K., see Bromwich, D.H. 28 Geesey, G.G. 331 Geesey, M.E., see DiTullio, G.R. 164, 179 Geiger, C.A., see Ackley, S.F. 28 Geiger, C.A., see Häkkinen, S. 136 Genhai, Z., see Zilin, L. 206 Georgii, H.-W., see Barnard, W.R. 165 Gerdes, D., see Bathmann, U. 305 Gershey, R.M., see Tait, V.K. 167 Gersonde, R., see Fischer, G. 306 Gersonde, R., see Wefer, G. 306 Gertsen, R.A., see Comiso, J. 68 Gervais, F. 229 Giannelli, V. 181 Gibb, S.W., see Baker, J.M. 180 Gibson, A., see Garrison, D.L. 395 Gibson, J.A.E. 179–181, 194, 206, 207 Gieskes, W.W.C., see Stefels, J. 178 Gilchrist, G., see Falk, K. 400 Gilchrist, G., see Hobson, K.A. 400 Gilchrist, H.G. 392 Gill, A.E. 29, 70, 73 Gill, P.C. 287 Gill, W.H., see Kaufmann, R.S. 296 Gille, S.T. 416 Gillett, N.P. 3 Gilmore, D., see Wendler, G. 21, 37 Giovannoni, S.J., see Carlson, C.A. 331 Girard, E. 145 Giulivi, C.F., see Jacobs, S.S. 26, 27, 393, 416 Gladyshev, S. 5, 71 Glockner, R.O., see Simon, M. 349 Gloersen, P. 2, 3, 57, 61, 225, 332 Gloersen, P., see Parkinson, C.L. 2, 63 Gloersen, P., see Zwally, H.J. 3, 32 Gloerson, P. 163 Glueck, M.F., see Lynch, A.H. 117 Gluek, M.F., see Lynch, A.H. 131 Gobeil, C., see Macdonald, R.W. 325 Goeyens, L., see Hoppema, M. 202, 204 Goffart, A. 226, 395, 397 Goffart, A., see Hecq, J.H. 395 Goldberg, S.J., see Carlson, C.A. 331 Golovin, P.N. 5 Gong, D.Y. 3 González, H.E. 305 Gonzalez, J., see Kiene, R.P. 178 Goodal, J.L., see Dunton, K.H. 376 Goodison, B.E. 6 Goodwin, K.D., see Yvon-Lewis, S.A. 164, 180 Goody, R., see Herman, G. 137 Goosse, H. 33, 64, 131 Goosse, H., see Fichefet, T. 21, 119 Goosse, H., see Hecq, J.H. 395 Gorbunov, M.Y., see Gervais, F. 229
429 Gordienko, P.A. 128 Gordon, A.L. 3, 4, 6, 29, 33, 66, 178, 195, 196, 198–200, 207, 208 Gordon, A.L., see Comiso, J.C. 29, 31, 33, 34, 61, 65, 66, 130, 178, 199, 201, 202, 272 Gordon, A.L., see Jacobs, S.S. 372 Gordon, A.L., see Martinson, D.G. 33, 64, 130 Gordon, A.L., see Rintoul, S.R. 6 Gordon, A.L., see Zwally, H.J. 5, 29, 32, 61, 89, 363, 372 Gordon, L.I. 172, 173, 205, 242 Gordon, L.I., see Bates, N.R. 164, 171, 172, 194, 202, 205 Gordon, L.I., see Smith Jr., W.O. 202, 205, 208, 224, 242, 249, 252, 324, 332, 333 Gordon, R.M., see Fitzwater, S.E. 252 Gordon, R.M., see Fiztwater, S.E. 229 Gordon, R.M., see Martin, J.H. 240 Gorsky, G. 283 Gosink, T.A. 181 Gosselin, M.G., see Belzile, C. 170, 174, 230, 231 Gosselin, M.G., see Caron, G. 245, 246 Gosselin, M.G., see Daly, K.L. 325, 339 Gosselin, M.G., see Hamel, D. 245, 246 Gosselin, M.G., see Levasseur, M. 179, 180 Gosselin, M.G., see Mei, Z.-P. 415 Gosselin, M.G., see Merzouk, A. 179 Gosselin, M.G., see Michel, C. 174, 213, 229 Gosselin, M.G., see Miller, L.A. 325, 347, 348 Gosselin, M.G., see Pesant, S. 229, 242, 248, 338, 345, 401, 403 Gosselin, M.G., see Renaud, P.E. 368 Gosselin, M.G., see Rich, J. 324, 329 Gosselin, M.G., see Smith Jr., W.O. 242, 247, 248, 338, 342, 366, 368, 401, 403 Gosselin, M.G., see Tremblay, J.-E. 243, 245 Gouretski, V. 205 Gowing, M.M. 306 Gowing, M.M., see Accornero, A. 254, 306 Gowing, M.M., see Garrison, D.L. 395 Gradinger, R., see Hirche, H.-J. 274, 275, 279, 282, 284 Graeve, M. 282 Grant, J. 368 Gratton, Y., see Ingram, R.G. 34, 61, 63, 130, 173, 211, 225, 243, 272, 346 Gratton, Y., see Klein, B. 346–348 Gratton, Y., see Mei, Z.-P. 415 Gratton, Y., see Melling, H. 8, 10, 34, 61, 63, 74, 173, 225, 243, 346 Gratton, Y., see Melling, H.M. 398 Gratton, Y., see Tremblay, J.-É. 74, 211–213, 224, 229, 242–246, 274, 346, 399, 415 Gray, J.S., see Azam, F. 328 Grebmeier, J.M. 152, 214, 249, 257, 364–369, 371, 374–382, 384, 395
430 Grebmeier, J.M., see Barry, J.P. 365–368, 374, 376, 395 Grebmeier, J.M., see Clement, J. 368, 379, 380 Grebmeier, J.M., see Clement, J.L. 61, 214 Grebmeier, J.M., see Cooper, L.W. 349 Grebmeier, J.M., see DiTullio, G.R. 397 Grebmeier, J.M., see Dunton, K.H. 376 Grebmeier, J.M., see Lovvorn, J.R. 367, 372, 379, 380 Grebmeier, J.M., see Simpkins, M.A. 371, 372, 376 Green, S., see Vaillancourt, R.D. 207, 230, 242, 255 Grenfell, T.C., see Eicken, H. 149 Grenfell, T.C., see Perovich, D.K. 149 Grey, J., see Leakey, R.J.G. 349 Griffiths, R.P. 328, 331 Grigg, S.B. 6 Grigioni, P., see van Woert, M. 28 Groves, J.E., see Stringer, W.J. 60, 248 Gudmandsen, P. 338 Gudmandsen, P., see Minnett, P.J. 11, 338 Guest, P., see Curry, J.A. 146 Guglielmo, L., see Hecq, J.H. 395 Gultepe, I., see Mailhot, J. 137, 151 Guo, L.D. 325 Gustafson, E., see Button, D.K. 327 Gutt, J. 293, 374 Gutt, J., see Starmans, A. 374 Haarpainter, J. 88 Haarpaintner, J. 17, 71, 167, 209, 210 Haarpaintner, J., see Skogseth, R. 71 Haarpaintner, J., see Zyranov, D. 117 Haas, C., see Giannelli, V. 181 Haas, C., see Schnack-Schiel, S.B. 287 Haberman, K.L. 397 Haefliger, M., see Key, J. 136 Hagen, W. 297–299, 307, 308 Hagen, W., see Atkinson, A. 299 Hagen, W., see Auel, H. 272, 273 Hagen, W., see Boysen-Ennen, E. 299, 301 Hagen, W., see Hirche, H.J. 403 Hagen, W., see Kattner, G. 298, 308 Hagen, W., see Meyer, B. 297, 299, 303, 309 Hagen, W., see Mumm, N. 273, 279 Hagen, W., see Schnack-Schiel, S.B. 288, 297, 299, 307 Hagen, W., see Stübing, D. 299 Hakkinen, S. 73 Häkkinen, S. 136 Hall, A. 3, 136, 416 Hamblin, P.F. 67 Hamel, D. 245, 246 Hamner, P.P., see Hamner, W.M. 303
Author Index Hamner, P.P., see Stretch, J.J. 287 Hamner, W.M. 303 Hamner, W.M., see Stretch, J.J. 287 Hanafin, J.A. 146, 148, 150, 232 Hanesiak, J.M. 147, 149 Hanesiak, J.M., see Barber, D.G. 2, 5, 7, 8, 10, 34, 35, 61, 63, 129, 136, 147, 178, 225, 398, 415 Hansell, D.A. 329 Hansell, D.A., see Bates, N.R. 164, 171, 172, 194, 202, 205 Hansell, D.A., see Carlson, C.A. 172, 324, 325, 331, 335, 344 Hansell, D.A., see Smith Jr., W.O. 335 Hansen, B.W., see Levinson, H. 282 Hansen, B.W., see Rysgaard, S. 260, 280 Hansen, J.E. 323 Hansing, R.L., see Wheeler, P.A. 325 Hanssen, H. 273, 275 Hanssen, H., see Kosobokova, K.N. 275, 279 Hanssen, H., see Mumm, N. 273, 279 Hara, K. 143 Harada, H. 178 Haran, T.M., see Serreze, M.C. 2 Harder, W. 330 Hare, J.E., see Huebert, B.J. 166, 178 Hargrave, B., see Grant, J. 368 Hargrave, B.T. 245–247, 347, 368, 380 Hargrave, B.T., see Sampei, M. 283, 284 Harms, S., see Fahrbach, E. 196 Harrington, S.A. 308, 309 Harris, L.R., see Conover, R.J. 273 Harris, L.R., see Head, E.J.H. 278 Harris, P.T., see Massom, R. 61 Harris, P.T., see Massom, R.A. 5, 19, 23, 24, 28, 42, 130, 151, 206, 392 Harvey, H.R., see Ju, S.-J. 298, 299 Harvey, R., see Grebmeier, J.M. 152 Harwood, L.A. 392 Hatfield, E., see Acuña, J.-L. 280, 281 Hatfield, E., see Acuna, J.L. 347 Hatton, A.D., see Baker, J.M. 180 Hattori, H. 273 Hattori, H., see Sampei, M. 283, 284 Hattori, H., see Tremblay, J.E. 246, 257 Haugan, P., see Haarpaintner, J. 17 Haugan, P.M., see Fer, I. 71 Haugan, P.M., see Haarpainter, J. 88 Haugan, P.M., see Haarpaintner, J. 71, 167, 209, 210 Haugan, P.M., see Skogseth, R. 71 Haus, J., see Nihoul, J.C.J. 369 Hayasaka, S.S., see Griffiths, R.P. 328, 331 Head, E.J.H. 278, 281 Hecq, J.H. 395 Hecq, J.H., see Goffart, A. 226, 395, 397 Heide-Joergensen, M.P. 257 Heide-Joergensen, M.P., see Richard, P.R. 257
Author Index Heide-Jorgensen, M.P. 400 Heidt, L.E., see Sturges, W.T.C. 180 Heil, P.I. 40 Heinemann, G. 132 Heinze, C., see Broecker, W.S. 6, 26 Helle, J.H., see Grebmeier, J.M. 369 Hellmer, H.H., see Dieckmann, G. 3 Hellmer, H.H., see Gordon, A.L. 6, 29 Hempel, G. 287 Hempel, G., see Hempel, I. 288 Hempel, I. 288 Hempel, I., see Hubold, G. 293, 309 Henderson, R.J., see Tande, K.S. 278 Henshaw, A. 392 Herman, A.W., see Conover, R.J. 273 Herman, G. 137 Hestad, L., see Matthews, J.B.L. 286 Hewes, C. 328 Heygster, G. 136 Heymsfield, A.J., see Miloshevich, L.M. 136, 137 Hibler III, W.D. 112 Hibler III, W.D., see Lemke, P. 107 Hibler, W.D., see Flato, G.M. 112 Hill, K.L., see Massom, R.A. 5, 6, 21, 37, 39, 40, 42, 61, 226 Hillaire-Marcel, C., see Hamel, D. 245, 246 Hillman, S.R., see Aagaard, K. 20, 68, 70 Hines, K.M., see Bromwich, D.H. 132 Hirawake, T., see Odate, T. 228 Hirche, H.-J. 175, 272–275, 278, 279, 282, 284 Hirche, H.-J., see Budéus, G. 175 Hirche, H.-J., see Kosobokova, K.N. 275, 279, 285 Hirche, H.-J., see Mumm, N. 273, 279 Hirche, H.J. 339, 403 Hirche, H.J., see Lara, R.J. 401 Hirota, J., see Lee, R.F. 278, 284 Hirschberg, D., see Cochran, J.K. 325 Hirschberg, D.J., see Miller, L.A. 325, 347, 348 Hirst, A.G., see Bunker, A.J. 308 Hiruki-Raring, L.S., see Simpkins, M.A. 371, 372, 376 Hiscock, M.R. 250, 252 Hiscock, M.R., see Buesseler, K.O. 373, 378 Hiscock, M.R., see Smith Jr., W.O. 249, 262, 332–334, 394 Hishida, M., see Wakatsuchi, M. 199, 201 Hjort, C., see Falk, K. 392, 403 Hobbie, J.E. 326 Hobbs, P.V. 145 Hobbs, P.V., see Garrett, T.J. 143 Hobbs, P.V., see Perovich, D.K. 149 Hobson, K.A. 257, 400 Hobson, K.A., see Fisk, A.T. 278 Hobson, K.A., see Holst, M. 400 Hobson, K.A., see Tremblay, J.E. 243, 245, 246, 257
431 Hodges, L.R. 325 Hodgson, D.A., see Vaughan, D.G. 3 Hoelemann, J.A., see Dmitrenko, I. 17 Hoffert, M.I., see Volk, T. 167 Hofmann, E.E. 413 Hofmann, E.E., see Fraser, W.R. 412 Hofmann, E.E., see Prézelin, B.B. 252 Hofmann, E.E., see Ross, R.M. 336 Hoffman, E.E., see Smith Jr., W.O. 240, 250, 252, 255 Hohmann, R., see Muench, R.D. 33 Holbrook, N.J., see Grigg, S.B. 6 Holby, O., see Anderson, L.G. 196, 203 Holemann, J.A., see Dmitrenko, I.A. 414 Holland, D.M. 33, 63, 64, 87, 111, 113, 117, 118, 120, 121, 130, 178, 195, 199 Holland, D.M., see Lindsay, R.W. 121 Holland, D.M., see Willmott, A.J. 5, 43, 59 Hollibaugh, J.T., see Azam, F. 326, 328 Hollibaugh, J.T., see Bano, N. 326 Hollibaugh, J.T., see Hodges, L.R. 325 Hollibaugh, J.T., see Yager, P.L. 324, 325 Holloway, G. 2 Holm-Hansen, O., see Arrigo, K.R. 231 Holm-Hansen, O., see El-Sayed, S.Z. 180 Holm-Hansen, O., see Hewes, C. 328 Holm-Hansen, O., see Huntley, M.E. 332 Holm-Hansen, O., see Karl, D.M. 329, 336 Holmes, R.M., see Peterson, B.J. 350 Holst, M. 399, 400 Holst, M., see Hobson, K.A. 257 Holt, B., see Martin, S. 5, 60, 66 Holt, S.J., see May, R.M. 395 Honjo, S., see Collier, R. 254, 255, 373, 374, 378 Honjo, S., see Comiso, J.C. 167 Honjo, S., see Fischer, G. 306 Honjo, S., see Yang, J. 245 Hood, R.R., see Kemp, W.M. 258 Hop, H., see Welch, H.E. 399 Hopke, P.K., see Xie, Y. 144 Hopkins, T., see Top, Z. 164, 176 Hopkins, T.L. 279, 292, 294–296, 299, 301, 304, 307 Hopkins, T.L., see Ashjian, C.J. 274, 275 Hopkins, T.L., see Lancraft, T.M. 292, 296 Hopkins, T.S., see Böhm, E. 210, 224, 338 Hopkins, T.S., see Minnett, P.J. 11, 338 Hopkins, T.S., see Wallace, D.W.R. 164, 176, 210, 211, 338, 339 Hoppema, M. 164, 167, 178, 197, 201, 202, 204, 207 Hoppema, M., see Usbeck, R. 197 Horibe, Y., see Jacobs, S.S. 27, 205 Hoshiai, T., see Tanimura, A. 293 Hosie, G.W. 290, 292–294, 301 Hosie, G.W., see Beaumont, K.L. 305
432 Howes, B.L., see Dacey, J.W.H. 165 Huber, B.A., see Gordon, A.L. 3, 6, 29 Huber, B.A., see Muench, R.D. 33 Hubold, G. 293, 309 Hubold, G., see Boysen-Ennen, E. 299, 301 Huebert, B.J. 166, 178 Hughes, C., see Rintoul, S.R. 6 Hughes, T.M.C., see Sarmiento, J.L. 262, 412 Huijberts, G.N. 344 Huisman, G.W., see Huijberts, G.N. 344 Hunke, E.C. 28, 112, 119 Hunke, E.C., see Ackley, S.F. 28 Hunt, G.L. 233 Hunt Jr., G.L., see Karnovsky, N.J. 257, 399, 400, 403 Huntley, M.E. 297, 299, 303, 304, 307, 309, 332 Huntley, M.E., see Conover, R.J. 273, 278, 279, 284 Huntsman, S., see Sunda, W. 178 Hurrell, J.W. 3 Huston, A.L. 325, 347, 348
Ianuzzi, R.A., see Martinson, D.G. 33 Ichii, T. 395 Ichii, T., see Kawaguchi, S. 304 Ikeda, T. 298, 299, 303, 309 Ikeda, T., see Harrington, S.A. 308 Ikeda, T., see Hosie, G.W. 301 Imai, I., see Ishida, Y. 331 Imasato, N., see Akitomo, K. 64 Ingram, R.G. 34, 61, 63, 130, 173, 211, 225, 243, 272, 346 Ingram, R.G., see Bâcle, J. 63, 211, 346, 398 Ingram, R.G., see Holland, D.M. 63, 117, 118 Ingram, R.G., see McLaughlin, F.A. 66 Ingram, R.G., see Melling, H. 8, 10, 34, 61, 63, 74, 173, 225, 243, 346 Ingram, R.G., see Melling, H.M. 398 Ingram, R.G., see Minnett, P.J. 11, 338 Ingram, R.G., see Williams, W.J. 73, 163, 164, 173 Ingram, W.J. 326 Innamorati, M. 242, 256 Innamorati, M., see Nuccio, C. 256 Inoue, H.Y., see Ishii, M. 206 Inoue, J. 152 Intrieri, J.M. 148, 149 Intrieri, J.M., see Shupe, M.D. 145 Irigoien, X., see Calbet, A. 307 Isaac, G.A., see Mailhot, J. 137, 151 Isch, C., see Pagni, M. 344 Ishida, Y. 331 Ishii, M. 206 Ishikawa, S., see Kawaguchi, K. 299, 304 Ishikawa, T. 5, 34
Author Index Jaccard, P., see Fer, I. 71 Jacka, K., see Massom, R.A. 39, 40, 42 Jacka, T.H., see Wu, X. 3 Jackson, J. 412 Jacobs, S.S. 6, 26–29, 37, 67, 71, 173, 204, 205, 225, 232, 233, 249, 372, 393, 416 Jacobs, S.S., see Ainley, D.G. 258, 396 Jacobs, S.S., see Pillsbury, R.D. 26, 67, 372 Jacobs, S.S., see Rignot, E. 350 Jacobs, S.S., see Sambrotto, R.N. 230 Jacobs, S.S., see Trumbore, S.E. 164, 173 Jacobs, S.S., see Wilson, P.R. 412 Jacques, G., see Treguer, P. 324 Jankowski, E.J., see Drewry, D.J. 372 Jasper, S., see Carmack, E.C. 74 Jasper, S., see Hobbie, J.E. 326 Jayaweera, K., see Tsay, S. 148 Jennings Jr., J.C., see Gordon, L.I. 172, 173, 205, 242 Ji, P., see Li, C. 301 Jiang, L. 72 Jin, X. 149 Johannessen, O.M. 262, 323, 412 Johannessen, S.C., see Belzile, C. 170, 174, 230, 231 Johannessen, T., see Anderson, L.G. 167 John, J.G., see Fung, I.Y. 252, 259 Johnson, G.C., see Orsi, A.H. 6, 201, 206, 207 Johnson, J.E., see Huebert, B.J. 166, 178 Johnson, K.M., see Yager, P.L. 164, 169, 172, 176, 211, 325 Johnson, K.S., see Coale, K.H. 240, 251, 252 Johnson, K.S., see Fitzwater, S.E. 252 Johnson, K.S., see Fiztwater, S.E. 229 Johnson, M.A. 63, 75, 338 Johnson, M.A., see Minnett, P.J. 11, 338 Johnson, M.A., see Topp, R. 63 Johnson, S.R., see Renaud, W.E. 399 Johnston, G.C., see Orsi, A.H. 71 Johnston, I.A., see Peck, L.S. 331 Joiris, C.R. 403 Jones, D.R., see DiTullio, G.R. 164, 179 Jones, E.P. 209, 211, 240 Jones, E.P., see Anderson, L.G. 167, 170, 178, 209, 210 Jones, E.P., see Rudels, B. 68, 209 Jones, E.P., see Tait, V.K. 167 Jones, G.B., see Curran, M.A.J. 179 Jones, G.B., see Trevena, A.J. 180 Jones, R.A., see Key, E.L. 127, 136, 137, 139, 141, 143, 145, 148 Ju, S.-J. 298, 299 Jumars, P.A. 325, 339 Jumars, P.A., see Vetter, Y.-A. 328 Junge, K., see Krembs, C. 181 Jutterström, S., see Anderson, L.G. 167, 210 Juul-Pedersen, T., see Renaud, P.E. 368
Author Index Kadko, D., see Hansell, D.A. 329 Kadota, H., see Ishida, Y. 331 Kahl, J.D., see Serreze, M.C. 135 Kalff, J., see Bird, D.F. 336 Kalinowski, J., see Azzali, M. 294, 295, 397 Kaltin, S. 194 Kämpf, J. 33 Kampf, J. 73 Kampp, K. 399 Kampp, K., see Falk, K. 392, 400, 403 Kampp, K., see Joiris, C.R. 403 Kandler, O., see Woese, C.R. 326 Kantakov, G., see Gladyshev, S. 71 Kareiva, P.M., see Nevitt, G.A. 396 Karl, D.M. 325, 328–330, 336 Karl, D.M., see Bird, D.F. 329, 337, 340 Karl, D.M., see Carrillo, C.J. 326 Karl, D.M., see Christian, J.R. 328 Karl, D.M., see Church, M.J. 326 Karl, D.M., see Huntley, M.E. 332 Karl, D.M., see Karner, M.B. 328 Karl, D.M., see Martin, J.H. 375–377 Karl, D.M., see Murray, A.E. 326 Karl, D.M., see Smith Jr., K.L. 365 Karl, D.M., see Tilbrook, B.D. 170 Karner, M.B. 328 Karner, M.B., see Church, M.J. 326 Karnovsky, N.J. 240, 257, 399, 400, 403 Karnovsky, N.J., see Hobson, K.A. 257 Kasamatsu, N. 179 Kashino, Y., see Sampei, M. 283 Kassens, H., see Dmitrenko, I. 17 Kassens, H., see Dmitrenko, I.A. 414 Kato, A., see Ainley, D.G. 393, 412 Kattner, G. 211, 228, 298, 308, 338 Kattner, G., see Budéus, G. 175 Kattner, G., see Giannelli, V. 181 Kattner, G., see Hagen, W. 299, 308 Kattner, G., see Hirche, H.-J. 274, 275, 279, 282, 284 Kattner, G., see Lara, R.J. 401 Kattner, G., see Pesant, S. 229, 242, 338, 345, 401, 403 Kattner, G., see Robineau, B. 326 Kattner, G., see Rudels, B. 209 Kattner, G., see Skoog, A. 176, 211, 339 Kattner, G., see Smith Jr., W.O. 242, 247, 248, 338, 342, 366, 368, 401, 403 Kauffman, P., see Martin, S. 92, 93 Kaufmann, R.S. 296 Kawaguchi, K. 299, 304 Kawaguchi, S. 303, 304 Kawaguchi, S., see Kasamatsu, N. 179 Kawamura, T. 61 Kawamura, T., see Takizawa, T. 33 Kawamura, T., see Ushio, S. 5, 199
433 Keeling, R.F., see Stephens, B.B. 194 Keller, M.D. 178 Kelley, J., see Wendler, G. 232 Kelley, J.J., see Gosink, T.A. 181 Kelly, H.P., see Cooper, L.W. 349 Kelly, V., see Codispoti, L.A. 381 Kemp, W.M. 258 Kennedy, H., see Giannelli, V. 181 Ketchum, B.H., see Redfield, A.C. 210 Kettle, A.J. 179 Key, E.L. 127, 135–137, 139, 141, 143, 145, 148, 149 Key, E.L., see Marsden, R. 34 Key, E.L., see Marsden, R.F. 134 Key, E.L., see Minnett, P.J. 4, 70, 163 Key, J.R. 136, 139, 143 Key, J.R., see Liu, Y. 135 Key, J.R., see Maslanik, J.A. 225, 232 Key, J.R., see Schweiger, A.J. 143, 148 Key, R., see Broecker, W.S. 6, 26 Keys, H.J.R. 37 Khalil, M.A.K., see Shaw, G.E. 143 Khatiwala, S., see Weppernig, R. 203 Khen, G., see Gladyshev, S. 71 Kidson, J.W. 3 Kieber, D.J. 170 Kieber, D.J., see Sunda, W. 178 Kiene, R.P. 178, 179 Kiene, R.P., see Harada, H. 178 Kiene, R.P., see Sunda, W. 178 Killworth, P.D. 64, 71, 73 Killworth, P.D., see Carmack, E.C. 71 Killworth, P.D., see Martinson, D.G. 33, 64, 130 Kimura, N. 17 King, A.L., see Kaufmann, R.S. 296 King, B., see Rintoul, S.R. 6 King, D., see Wilson, K. 61 King, D.B., see Yvon-Lewis, S.A. 164, 180 King, D.J., see Wilson, K.J. 7, 34, 36 King, J.C. 3, 135, 136 King, J.C., see Ackley, S.F. 28 King, J.C., see König-Langlo, G. 130 King, J.C., see Marshall, G.J. 416 King, J.C., see Renfrew, I.A. 29, 107, 130 King, J.C., see Simmonds, I. 3 King, J.C., see Vaughan, D.G. 3 King, J.C., see Yu, Y. 5 King, R., see Nicol, S. 294, 298, 304 Kirchman, D.L. 326, 341–343 Kirchman, D.L., see Ducklow, H.W. 332, 334 Kirchman, D.L., see Rich, J. 324, 329 Kirillov, S.A., see Dmitrenko, I.A. 414 Kirilov, S., see Dmitrenko, I. 17 Kirkwood, J.M. 308, 309 Kirkwood, R. 392 Kirkwood, R., see Ikeda, T. 298, 299
434 Kirst, G.O. 179, 180 Kistler, R. 136 Kitchener, J.A., see Hosie, G.W. 292, 294 Klages, M., see Auel, H. 278 Klatt, O. 194 Klein, B. 242, 244–246, 248, 284, 346–348, 364, 368, 398–401, 403 Klein, B., see Acuna, J.L. 347 Klein, B., see Booth, B.C. 243, 282, 346, 347, 399 Klein, B., see Mei, Z.-P. 415 Klein, B., see Miller, L.A. 325, 347, 348 Klein, B., see Odate, T. 228 Klein, B., see Saunders, P. 257 Klein, B., see Saunders, P.A. 347, 399, 400 Kleine, E., see Dethleff, D. 5 Klinck, J.M., see Dinniman, M.S. 225, 372, 416 Klinck, J.M., see Prézelin, B.B. 252 Klinck, J.M., see Smith Jr., W.O. 240, 250, 252, 255 Knauer, G.A., see Karl, D.M. 328 Knauer, G.A., see Martin, J.H. 375–377 Knickmeier, K., see Kosobokova, K.N. 275, 279 Knowles, K., see Serreze, M.C. 2 Knowles, K., see Stroeve, J.C. 2 Knox, F. 324 Knox, G.A. 374, 412 Kobinata, K., see Kawamura, T. 61 Koch, A.L. 330 Kofeler, J., see Pfirman, S.L. 59 Kolosova, E.G., see Voronina, N.M. 288 König-Langlo, G. 130 Kooyman, G.L. 392 Korjeff-Bellows, W., see Keller, M.D. 178 Korsnes, R., see Zyranov, D. 117 Koshlyakov, M.N. 27 Kosobokova, K.N. 275, 279, 285 Kosobokova, K.N., see Hirche, H.-J. 272, 273, 275, 278, 279, 284 Kottmeier, C. 4, 5, 29, 127, 135, 145 Kottmeier, C., see Markus, T. 6, 29, 61 Kottmeier, C., see Timmerman, R. 108 Kowalik, Z. 66, 130 Kozo, T.L. 7, 68 Krembs, C. 181 Krieger-Brockett, B.B., see Vetter, Y.-A. 328 Krishfield, R.A., see Comiso, J.C. 167 Krishfield, R.A., see Yang, J. 245 Kriss, A.E. 330 Kristensen, R.M., see Falk, K. 392, 403 Kristensen, R.M., see Joiris, C.R. 403 Krueger, S.K., see Zulauf, M.A. 137, 140, 151 Krumholz, M., see Bauerfeind, E. 248, 283, 284, 325, 339 Kudoh, S., see Odate, T. 228 Kudoh, S., see Sampei, M. 283 Kujise, Y., see Ichii, T. 395
Author Index Kunze, H.B., see Garrison, D.L. 395 Kunze, H.B., see Gowing, M.M. 306 Kurbjeweit, F. 307 Kurbjeweit, F., see González, H.E. 305 Kurtz, D.D. 28, 61, 130, 204, 363, 372 Kurtz, D.D., see Bromwich, D. 61 Kurtz, D.D., see Bromwich, D.H. 21, 28, 130, 170, 171, 233 Kurz, K., see Quadfasel, D. 210 Kvenvolden, K.A. 170 Kwasniewski, S., see Hirche, H.J. 339 Kwasniewski, S., see Ringuette, M. 347 Kwok, R. 3, 4, 28, 262, 412 Kwok, R., see Liu, A.K. 90 Kwok, R., see Martin, S. 5, 60, 66 Labat, J.P., see Cuzin-Roudy, J. 308 Lachlan-Cope, T.A., see King, J.C. 3 Lachlan-Cope, T.A., see Marshall, G.J. 416 Lacis, A., see Hansen, J.E. 323 Laidre, K.L., see Heide-Jorgensen, M.P. 400 Lajzerowicz, C., see Rivkin, R.B. 324 Lake, I., see Rudels, B. 210 Lammers, R.B., see Peterson, B.J. 350 Lancelot, C. 349 Lancraft, T.M. 292, 296 Lancraft, T.M., see Hopkins, T.L. 307 Landry, M.R., see Brown, S.L. 326 Lane, P.V.Z., see Ashjian, C.J. 274, 275, 277–280, 282, 284, 339, 403 Langdon, C., see Sambrotto, R.N. 230 Langlois, A., see Papakyriakou, T.N. 181 Langone, L. 254, 255 Laprise, R., see Saucier, F.J. 117 Lara, R.J. 401 Lara, R.J., see Daly, K.L. 325, 339 Lara, R.J., see Skoog, A. 176, 211, 339 Larned, W.W., see Petersen, M.R. 392 Larouche, P., see Booth, B.C. 243, 282, 346, 347, 399 Larouche, P., see Klein, B. 346–348 Larouche, P., see Mei, Z.-P. 415 Larouche, P., see Ringuette, M. 347 Larsen, I.L., see Cooper, L.W. 349 Lascara, C.M., see Ross, R.M. 337 Laubacher, M., see Kaufmann, R.S. 296 Laws, E.A. 324 Laws, R.M. 395 Laws, R.M., see May, R.M. 395 Lazzara, L. 242 Lazzara, L., see Innamorati, M. 242, 256 Lazzara, L., see Nuccio, C. 256 Leakey, R.J.G. 349 Lebedev, V.L. 58, 89 LeBlanc, B., see Klein, B. 346–348 LeBlanc, B., see Lewis, E.L. 63, 398
Author Index LeBlanc, B., see Miller, L.A. 325, 347, 348 LeBlanc, B., see Odate, T. 228 LeBlanc, E., see Mei, Z.-P. 415 Leck, C. 149, 213 Leck, C., see Bigg, E.K. 143 Lee, P., see Karnovsky, N.J. 240 Lee, P.A. 179 Lee, P.A., see Bouillon, R.-C. 164, 179, 213 Lee, R.F. 278, 279, 284 Lee, S.-H., see Saunders, P.A. 257, 279–283, 347, 399, 400 LeFèvre, J., see Legendre, L. 325, 345 Legendre, L. 325, 345 Legendre, L., see Fortier, M. 244 Legendre, L., see Klein, B. 346–348 Legendre, L., see Lewis, E.L. 63, 398 Legendre, L., see Lovejoy, C. 229, 243, 245, 346, 399 Legendre, L., see Mei, Z.-P. 415 Legendre, L., see Pesant, S. 229, 242, 248, 338, 345, 401, 403 Legendre, L., see Robineau, B. 326 Legendre, L., see Smith Jr., W.O. 242, 247, 248, 338, 342, 366, 368, 401, 403 Legendre, L., see Touratier, F. 229, 247, 339 Legezynska, J., see Weslawski, J.M. 400 Lemke, P. 4, 31, 107 Lemke, P., see Timmerman, R. 108 Lennon, G.W., see Nunes Vaz, R.A. 130, 200, 207 Lennon, G.W., see Vaz, R.A.N. 28 Leonard, C.L., see Comiso, J.C. 372 Lerner, J., see Hansen, J.E. 323 Levasseur, M. 179, 180 Levasseur, M., see Bouillon, R.-C. 164, 179, 213 Levasseur, M., see Lee, P.A. 179 Levasseur, M., see Merzouk, A. 179 Leventer, A.R., see Arrigo, K.R. 31 Leventer, A.R., see DiTullio, G.R. 397 Leventer, A.R., see Dunbar, R.B. 254, 259, 305, 367, 373, 374, 397 Leventer, A.R., see Smith Jr., W.O. 294 Levin, 330 Levin, L.A. 365 Levinson, H. 282 Lewis, E. 165 Lewis, E.L. 63, 398 Lewis, E.L., see Melling, H. 64, 70, 71, 73 Li, C. 301 Li, S., see Xie, Y. 144 Li, W.K.W. 227 Licandro, P., see Sertorio, T.Z. 301 Ligett, K.A., see Perovich, D.K. 149 Light, B., see Perovich, D.K. 149 Lilley, M.D., see Kvenvolden, K.A. 170 Lindegren, R., see Anderson, L.G. 196, 203, 210 Lindsay, R.W. 121, 412
435 Lindsay, R.W., see Andreas, E.L. 151 Lindsey, R.W., see Schweiger, A.J. 143 Lingle, C.S., see Wendler, G. 41 Linley, E.A.S. 344 Linn, L.J., see Kiene, R.P. 178, 179 Liss, P.S. 166 Liss, P.S., see Malin, G. 178 Liu, A.K. 90 Liu, J. 3, 4 Liu, Y. 135 Liu, Z., see Bromwich, D.H. 6, 21, 26, 171, 372, 393 Livesey, N.J., see Manney, G.L. 230 Lizotte, M.P. 43, 194 Lizotte, M.P., see Arrigo, K.R. 31, 242, 332, 373, 378, 381, 394, 395, 416 Lizotte, M.P., see DiTullio, G.R. 397 Lobanov, V., see Talley, L.D. 71 Loeb, V. 304 Loeb, V., see Siegel, V. 303 Loewe, P., see Dethleff, D. 5 Loewen, M.D., see Fisk, A.T. 278 Long, R.A., see Azam, F. 328 Lønne, O.J., see Falk-Petersen, S. 297, 298, 308 Lonsdale, D.J., see Caron, D.A. 394, 397 Lorenson, T.D., see Kvenvolden, K.A. 170 Lorenti, M., see Gambi, M.C. 374 Louchouarn, P., see Benner, R. 325 Lovejoy, C. 229, 243, 245, 346, 399 Lovejoy, C., see Bouillon, R.-C. 164, 179, 213 Lovejoy, C., see Mei, Z.-P. 415 Lovejoy, C., see Tremblay, J.E. 246, 257 Lovejoy, C., see Vidussi, F. 399, 400 Lovelock, J.E., see Charlson, R.J. 178 Lovvorn, J.R. 367, 372, 379, 380 Lovvorn, J.R., see Cooper, L.W. 349 Lowings, M.G., see Anderson, L.G. 170 Lubin, D. 43 Lubin, D., see Arrigo, K.R. 231 Lukovich, J. 414, 415 Lynch, A.H. 117, 131 Lynch, A.H., see Arbetter, T.E. 33, 34, 127, 139, 147, 151 Lynch, A.H., see Bailey, D.A. 33, 34, 121 Lynch, A.H., see Pinto, J.O. 136 Lynn, R.J., see Reid, J.L. 64 Lyons, W.B., see Doran, P.T. 350 Lytle, V.I. 37 Lytle, V.I., see Massom, R.A. 5, 6, 21, 37, 39, 40, 42, 61, 226 Lytle, V.I., see Roberts, A. 6, 37, 129, 151 Macaulay, M.C., see Daly, K.L. 287, 303 Macdonald, R.W. 59, 170, 325 Macdonald, R.W., see Carmack, E.C. 74 Macdonald, R.W., see McLaughlin, F. 164, 178
436 Macdonald, R.W., see Omstedt, A. 64 Mace, G.G., see Dong, X. 143 Mackey, D.J., see Sedwick, P.N. 249, 394, 395 Macko, S.A., see Pomeroy, L.R. 325, 331 MacPherson, P., see Grant, J. 368 Maidment, D.R., see Dunton, K.H. 376 Mailhot, J. 137, 151 Makarov, R.R., see Bathmann, U.V. 288 Makinson, K. 29 Makinson, K., see Nicholls, K.W. 31 Makshatas, A., see Semiletov, I. 181 Malin, G. 178 Manabe, S., see Sarmiento, J.L. 262, 412 Manganini, S., see Collier, R. 254, 255, 373, 374, 378 Mangoni, O., see Saggiomo, V. 205 Manney, G.L. 230 Manning, K.W., see Monaghan, A.J. 147 Manno, C., see Accornero, A. 306 Manzella, G., see Picco, P. 242 Manzella, G.M.R. 28 Maqueda Morales, M.A., see Biggs, N.R.T. 59 Marchessault, R.H., see Ramsay, B.A. 344 Marin, V.H. 307 Marin, V.H., see Huntley, M.E. 304 Markowicz, K., see Vogelmann, A.M. 150 Markus, T. 6, 8, 9, 29–31, 59, 61, 70, 130, 136 Markus, T., see Arrigo, K.R. 30, 232 Markus, T., see Dokken, S.T. 209 Markus, T., see Martin, S. 60, 66, 113, 119, 130 Markus, T., see Renfrew, I.A. 29, 130 Marles, E.M., see Carmack, E.C. 67 Marra, J., see Ducklow, H.W. 334 Marra, J., see Smith Jr., W.O. 249, 262, 332–334, 394 Marschall, H.P. 287, 303 Marsden, R. 34 Marsden, R., see Barber, D. 173 Marsden, R.F. 134 Marsden, R.F., see Vincent, R.F. 129 Marshall, G.J. 416 Marshall, G.J., see King, J.C. 3 Marshall, G.J., see Vaughan, D.G. 3 Marsland, S.J. 6, 37, 64, 119, 177 Martin, J.H. 240, 375–377 Martin, J.H., see Karl, D.M. 328 Martin, K.A., see Welch, H.E. 399 Martin, S. 2, 5, 60, 61, 66, 71, 92, 93, 113, 119, 130, 147 Martin, S., see Alfultis, M.A. 61, 66 Martin, S., see Bauer, J. 92 Martin, S., see Cavalieri, D.J. 5, 6, 17, 37, 167 Martin, S., see Drucker, R. 10 Martin, S., see Gladyshev, S. 5 Martin, S., see Liu, A.K. 90 Martin, S., see Polyakov, I. 66, 75
Author Index Martineau, M.-J., see Lovejoy, C. 399 Martineau, M.J., see Lovejoy, C. 243, 245, 346 Martinson, D.G. 3, 33, 64, 130 Martinson, D.G., see Liu, J. 3, 4 Martinson, D.G., see Muench, R.D. 33 Martinson, D.G., see Yuan, X. 3 Maslanik, J.A. 225, 232 Maslanik, J.A., see Drobot, S.D. 2 Maslanik, J.A., see Pinto, J.O. 151 Maslanik, J.A., see Serreze, M.C. 2 Maslanik, J.A., see Stroeve, J.C. 2 Massi, L., see Innamorati, M. 242 Massi, L., see Nuccio, C. 256 Massom, R.A. 5, 6, 11, 19, 21, 23, 24, 28, 29, 37–42, 61, 130, 151, 206, 226, 255, 391, 392 Massom, R.A., see Barber, D.G. 55, 61, 68, 70, 129, 163, 201, 206, 209, 243, 247, 248, 272, 393, 398, 401, 413, 415 Massom, R.A., see Bindoff, N.L. 6, 21, 37 Massom, R.A., see Lubin, D. 43 Massom, R.A., see Lytle, V.I. 37 Matear, R.J., see McNeil, B.I. 207 Mathot, S., see DiTullio, G.R. 179, 180 Mathot, S., see Lancelot, C. 349 Mathot, S., see Smith Jr., W.O. 332, 337 Mathotm, S., see Garrison, D.L. 395 Matin, A. 331 Matrai, P.A. 213 Matrai, P.A., see Leck, C. 149 Matrai, P.A., see Sæmundsdóttir, S. 180 Matsuda, A., see Sambrotto, R.N. 230 Matsuda, A., see Vaillancourt, R.D. 207, 230, 242, 255 Matsuda, O., see Kawaguchi, K. 299, 304 Matsueda, H., see Ishii, M. 206 Matsuoka, K., see Ichii, T. 395 Matthews, J.B.L. 286 Mauchline, J. 305 May, D.A. 136 May, R.M. 395 Maykut, G.A. 57, 151, 225 Maykut, G.A., see Rothrock, D.A. 2, 412 Maykut, G.A., see Yu, Y. 2, 323, 412 McCarthy, J.J., see Laws, E.A. 324 McCarthy, M.S., see Talley, L.D. 64 McClain, C.R., see Arrigo, K.R. 225, 249, 256, 372 McClain, C.R., see Comiso, J.C. 372 McClain, C.R., see Sullivan, C.W. 324 McClelland, J.W., see Peterson, B.J. 350 McElroy, M.B., see Knox, F. 324 McGaffin, A.F., see Nicol, S. 294, 298, 304 McGillis, W.R. 166 McGillis, W.R., see Wanninkhof, R. 166 Mcinnes, K.L., see Curry, J.A. 137 McKay, C.P., see Doran, P.T. 350 Mckenzie, B.D., see May, D.A. 136
Author Index McKnight, D.M., see Doran, P.T. 350 McLaren, P.L. 399 McLaren, P.L., see Renaud, W.E. 399 McLaughlin, E., see Kvenvolden, K.A. 170 McLaughlin, F. 164, 178 McLaughlin, F.A. 66 McLaughlin, F.A., see Grebmeier, J.M. 369 McNamara, T.M., see Griffiths, R.P. 328, 331 McNeil, B.I. 207 McNutt, S.L., see Grebmeier, J.M. 369 McPhee, M.G. 64, 91, 111 McPhee, M.G., see Maykut, G.A. 57 McQuaid, C.D., see Froneman, P.W. 282 McRoy, C.P., see Grebmeier, J.M. 365, 374, 375, 379, 382 McRoy, C.P., see Springer, A.M. 275, 276, 364, 368, 379, 381, 384 McTaggart, A.R., see Gibson, J.A.E. 179, 180 Measures, C., see Sambrotto, R.N. 230 Meehl, G.A., see Cubasch, U. 323 Meehl, G.A., see Washington, W.M. 412 Mei, Z.-P. 211, 398–401, 403, 415 Mei, Z.-P., see Booth, B.C. 282, 399 Mei, Z.-P., see Tremblay, J.-E. 246, 257 Mei, Z.P. 74, 228, 246 Mei, Z.P., see Acuna, J.L. 347 Mei, Z.P., see Booth, B.C. 243, 346, 347 Mei, Z.P., see Klein, B. 346–348 Meier, W.N., see Stroeve, J.C. 2 Meier, W.N., see van Woert, M. 28 Mele, P.A., see Jacobs, S.S. 27, 393, 416 Melling, H., see Ingram, R.G. 34, 61, 63, 130, 173, 211, 225, 243, 272, 346 Melling, H., see Williams, W.J. 73 Melling, H.M. 8, 10, 34, 61, 63, 64, 70, 71, 73, 74, 173, 225, 243, 346, 398 Melnikov, I.A., see Voronina, N.M. 288 Meloni, R., see Langone, L. 254, 255 Meloni, R., see Manzella, G.M.R. 28 Meloni, R., see Picco, P. 242 Mengelt, C., see Prézelin, B.B. 252 Mensch, M. 178 Meon, B., see Amon, R.M.W. 339 Merlivat, L., see Bates, N.R. 166, 181 Merlivat, L., see Liss, P.S. 166 Mernicky, A., see Gloerson, P. 163 Merzouk, A. 179 Methe, B.A., see Peck, L.S. 331 Metz, C. 397 Metzl, N. 177 Meunch, R.D. 70 Meyer, B. 297, 299, 303, 309 Meyer, B., see Atkinson, A. 299 Meyer, M., see Hubold, G. 293, 309 Meyer-Reil, L.A., see Azam, F. 328 Meyn, S.K., see Fung, I.Y. 252, 259
437 Miceli, G., see Putt, M. 325, 328 Michael, K.J., see Massom, R.A. 5, 19, 23, 24, 28, 42, 61, 130, 151, 206, 392 Michael, W.C., see Stretch, J.J. 287 Michaud, J. 403 Michaud, J., see Fortier, L. 403 Michaud, J., see Klein, B. 346–348 Michaud, S., see Levasseur, M. 179, 180 Michaud, S., see Merzouk, A. 179 Michel, C. 174, 213, 229 Michel, C., see Caron, G. 245, 246 Michel, C., see Fortier, M. 244 Michel, C., see McLaughlin, F.A. 66 Michel, C., see Renaud, P.E. 368 Michel, C., see Robineau, B. 326 Michel, C., see Tremblay, J.E. 243, 245, 246, 257 Middelboe, M. 347, 348 Midttun, L. 71 Mikolajewicz, U., see Broecker, W.S. 6, 26 Miles, M.W., see Johannessen, O.M. 262, 323 Miller, C.B. 328 Miller, L.A. 164, 172–175, 205, 209, 212, 213, 325, 347, 348 Miller, L.A., see Papakyriakou, T.N. 181 Miller, R.L., see Shindell, D.T. 323 Miller, W.L. 174 Miller, W.L., see Belzile, C. 170, 174, 230, 231 Miller, W.L., see Miller, L.A. 325, 347, 348 Miller, W.L., see Scully, N.M. 174 Millero, F.J., see Gordon, L.I. 172, 173, 205, 242 Mills, A.L., see Tremaine, S.C. 345 Miloshevich, L.M. 136, 137 Minnett, P., see Barber, D. 173 Minnett, P.J. 4, 11, 63, 70, 129, 131, 145, 146, 148, 149, 163, 232, 247, 338 Minnett, P.J., see Bohm, B. 338 Minnett, P.J., see Böhm, E. 210 Minnett, P.J., see Bohm, E. 224 Minnett, P.J., see Gudmandsen, P. 338 Minnett, P.J., see Hanafin, J.A. 146, 148, 150, 232 Minnett, P.J., see Key, E.L. 127, 136, 137, 139, 141, 143, 145, 148 Minnett, P.J., see Marsden, R. 34 Minnett, P.J., see Marsden, R.F. 134 Minnett, P.J., see Vogelmann, A.M. 150 Minnett, P.J., see Wallace, D.W.R. 164, 176, 210, 211, 338, 339 Minnett, P.J., see Yager, P.L. 164, 169, 172, 176, 211, 325 Minnis, P., see Spangenberg, D.A. 143 Mishustina, I.E., see Kriss, A.E. 330 Misic, C., see Povero, P. 366 Mitchell, A.W., see Ikeda, T. 298 Mitchell, B.G., see Cota, G.F. 262 Mitchell, B.G., see Moisan, T.A. 249, 262 Mitchell, J.F.B., see Ingram, W.J. 326
438 Mitchell, J.F.B., see Vinnikov, K.Y. 2 Mitskevich, N., see Kriss, A.E. 330 Miyagaki, T., see Ishida, Y. 331 Mizdalski, E., see Schnack-Schiel, S.B. 287, 297 Mizuta, G., see Fukamachi, Y. 71 Møbjerg, N., see Falk, K. 392, 403 Mohn, C., see Beckmann, A. 121, 195 Moisan, T.A. 249, 262 Møller, S., see Falk, K. 392, 403 Monaghan, A.J. 147 Monaghan, A.J., see Bromwich, D.H. 139, 146, 147 Monod, J. 344 Montevecchi, W.A., see Stenhouse, I.J. 257, 400 Moore, B., see Wendler, G. 232 Moore, D.W., see Stommel, H.M. 111 Moore, G.W.K. 147 Moore, J.K., see Buesseler, K.O. 373, 378 Moore, J.K., see Smith Jr., W.O. 249, 250, 332 Moore, K. 68 Moore, R.M., see Scarratt, M.G. 180 Moore, S.E., see Grebmeier, J.M. 369 Moorhead, D.L., see Doran, P.T. 350 Mopper, K., see Kieber, D.J. 170 Morales Maqueda, M.A. 4, 7, 8, 27, 31–33, 43, 57, 59, 61, 64, 70, 74, 87, 88, 90, 97, 98, 101, 102, 195, 199, 369, 372 Morales Maqueda, M.A., see Biggs, N.R.T. 92, 93, 95–97, 107 Morales Maqueda, M.A., see Tear, S. 102–106 Morales Maqueda, M.A., see Willmott, A.J. 5, 43, 59, 91, 98–100, 129 Moran, D.M., see Caron, D.A. 394, 397 Moran, M.A., see Kiene, R.P. 178 Moran, S.B. 375–377 Moran, X.A.G. 329 Morata, N., see Renaud, P.E. 368 Morel, F.M.M., see Tortell, P.D. 252, 394 Morfley, R., see Steele, M. 88 Morgan, P. 330 Morgan, V.I., see Curran, M.A.J. 3, 167 Mori, G., see Innamorati, M. 242, 256 Mori, G., see Nuccio, C. 256 Morison, J.H., see Muench, R.D. 33 Morison, J.H., see Steele, M. 68 Morita, R.Y. 330 Morita, R.Y., see Amy, P.S. 330 Morita, R.Y., see Baross, J.A. 326, 330 Morita, R.Y., see Geesey, G.G. 331 Morita, R.Y., see Griffiths, R.P. 328, 331 Morita, R.Y., see Novitsky, J. 330 Moritz, R., see Drucker, R. 10, 60, 61, 70 Morrison, J.M., see Gordon, L.I. 172, 173, 205, 242 Morrison, J.M., see Smith Jr., W.O. 249, 250, 332 Morrow, E., see Arrigo, K.R. 231
Author Index Morrow, R., see Rintoul, S.R. 6 Mortazavi, B., see Yager, P.L. 324, 325 Mosbech, A., see Boertman, D. 257, 399 Mosbech, A., see Egevang, C. 399 Mostajir, B. 226 Mostajir, B., see Vidussi, F. 399, 400 Moyer, C.L., see Murray, A.E. 326 Mucciarone, D.A., see Dunbar, R.B. 254, 259, 305, 367, 373, 374, 397 Mucciarone, D.A., see Langone, L. 254, 255 Mucciarone, D.A., see Villinski, J.C. 374 Muench, R.D. 33, 64, 199, 239, 259 Muench, R.D., see Schauer, U. 209 Muench, R.D., see Smith, S.D. 2, 4, 31, 57, 129 Müller, P.J., see Bathmann, U. 305 Mulvaney, R., see Vaughan, D.G. 3 Mumm, N. 273, 279 Mundy, C.J. 34, 225, 398 Mundy, C.J., see Klein, B. 346–348 Mundy, C.J., see Mei, Z.-P. 415 Mundy, C.J., see Papakyriakou, T.N. 181 Munin, M., see Hirche, H.J. 403 Murata, A., see Guo, L.D. 325 Murphy, D.C., see Springer, A.M. 379 Murray, A.E. 326 Murray, A.E., see Peck, L.S. 331 Murray, B.W., see Hargrave, B. 245–247 Murray, D.W., see Hargrave, B.T. 347, 368 Mysak, L.A., see Armstrong, A.E. 136, 147 Mysak, L.A., see Darby, M. 74 Mysak, L.A., see Darby, M.S. 92, 96 Mysak, L.A., see Holland, D.M. 63, 117, 118 Mysak, L.A., see Tremblay, L.B. 112 Naganobu, M., see Kawaguchi, S. 304 Naganobu, M., see Wakatsuchi, M. 199, 201 Nagata, T. 328, 333 Napp, J.M., see Hunt, G.L. 233 Naqvi, S.W.A. 203 Nehring, S. 227 Nelson, D.M. 205, 252–254 Nelson, D.M., see Jones, E.P. 240 Nelson, D.M., see Smith Jr., W.O. 251, 294, 332 Nemoto, T., see Suh, H.-L. 304 Nettleship, D.N. 399 Nettleship, D.N., see Brown, R.G.B. 257, 391, 392 Nevitt, G.A. 396 NEWater Investigators, 325, 338 Newell, R.C., see Linley, E.A.S. 344 Nguyen, T., see Maslanik, J.A. 225, 232 Nicholls, K.W. 31 Nicholls, K.W., see Makinson, K. 29 Nichols, P., see Nicol, S. 294, 298, 304 Nicol, S. 294, 298, 304, 308 Niebauer, H.J. 392 Niebauer, H.J., see Johnson, M. 63, 75, 338
Author Index Niebauer, H.J., see Minnett, P.J. 11, 338 Niehoff, B. 307 Nielsen, T.-G., see Levinson, H. 282 Nielsen, T.-G., see Michel, C. 174 Nielsen, T.-G., see Middelboe, M. 347, 348 Nielsen, T.-G., see Rysgaard, S. 260, 280 Nightingale, P.D., see Baker, J.M. 180 Nihashi, S. 42 Nihoul, J.C.J. 369 Niiler, P.P., see Huntley, M.E. 332 Nilsson, J., see Rudels, B. 210 Nishiuchi, K., see Shiga, N. 379 Nishiwaki, S., see Ichii, T. 395 Nittrouer, C.A., see Langone, L. 254, 255 Nixon, S.W. 256 Noda, A., see Cubasch, U. 323 Nohr, C., see Rudels, B. 210 Nordhausen, W., see Huntley, M.E. 299, 303 Norstrom, R.J., see Fisk, A.T. 278 Nøst, O.A. 31 Nöthig, E.-M. 305 Nöthig, E.-M., see Bodungen, B.v. 304 Nöthig, E.-M., see Smetacek, V. 202 Nöthig, E.-M., see Spiridonov, V.A. 287, 288 Nothnagel, J., see Kirst, G.O. 179, 180 Novitsky, J. 330 Nowlin Jr., W.D., see Orsi, A.H. 203 Nozais, C., see Michel, C. 174, 213, 229 Nuccio, C. 256 Nuccio, C., see Innamorati, M. 242, 256 Nunes Vaz, R.A. 130, 200, 207 Nur, N., see Wilson, P.R. 412 Nurnberg, D., see Pfirman, S.L. 74 Nurnberg, D., see Reimnitz, E. 59, 60 Nygaard, E., see Foldvik, A. 29 Oberhuber, J.M., see Holland, D.M. 63, 117, 118 O’Brien, D.P. 303, 304 O’Brien, S.O., see Timco, G.W. 59 Obst, B.S., see Hamner, W.M. 303 O’Connor, E.F., see Ainley, D.G. 394–396 Odate, T. 228 Odate, T., see Kasamatsu, N. 179 O’Dwyer, J., see Haarpaintner, J. 167, 209 Oettl, B., see Meyer, B. 297, 299, 303, 309 Ohlson, M., see Anderson, L.G. 196, 203 Ohmura, A., see Enomoto, H. 201 Ohshima, K.I. 74 Ohshima, K.I., see Fukamachi, Y. 71 Ohshima, K.I., see Ishikawa, T. 5, 34 Ohshima, K.I., see Nihashi, S. 42 Ohshima, K.I., see Takizawa, T. 33 Ohshima, K.I., see Ushio, S. 5, 199 Ohshima, K.I., see Wakatsuchi, M. 199, 201 Ohtani, K., see Stabeno, P.J. 369
439 Ohtsuka, H., see Fukuchi, M. 293, 301 Olbers, D., see Rintoul, S.R. 6 Oliver, J.S., see Dayton, P.K. 374 Olson, R.J. 240, 249, 251, 259 Olszewski, D.S., see May, D.A. 136 Oltmans, S.J., see Miloshevich, L.M. 136, 137 Omstedt, A. 64 Ono, N., see Ishikawa, T. 5, 34 Opsahl, S. 325 Opsahl, S., see Yager, P.L. 324, 325 Orchardo, J., see Ducklow, H.W. 334 Orr, J., see Heide-Jorgensen, M.P. 400 Orr, J.R., see Richard, P.R. 257, 392 Orsi, A.H. 6, 71, 196, 201, 203, 206–208 Orsi, A.H., see Rintoul, S.R. 6 Ossola, C., see Sertorio, T.Z. 301 Østerhus, S., see Foldvik, A. 29 Østerhus, S., see Haarpaintner, J. 167, 209 Østerhus, S., see Nøst, O.A. 31 Ostermann, D., see Fischer, G. 306 Östlund, H.G., see Weiss, R.F. 194 Ostrom, P.H., see Pomeroy, L.R. 325, 331 Otero, M.P., see Carlson, C.A. 331 Ou, H.W. 6, 59, 64, 89, 92 Overland, J.E. 136, 137 Overland, J.E., see Grebmeier, J.M. 369 Overland, J.E., see Stabeno, P.J. 369 Overpeck, J.T. 412 Owens, O., see Papakyriakou, T.N. 181 Owens, W.B., see Lemke, P. 107 Paatero, P., see Xie, Y. 144 Pace, M.L., see Cole, J.J. 329, 336, 340–342, 347 Padman, L., see Muench, R.D. 33 Paget, M., see Massom, R.A. 5, 6, 21, 37, 39, 40, 42, 226 Paget, M.J., see Lytle, V.I. 37 Paget, M.J., see Massom, R.A. 61 Pagni, M. 344 Pakhomov, E.A. 288, 293, 294, 298, 301, 304, 308 Pakhomov, E.A., see Atkinson, A. 333, 337, 395 Pakhomov, E.A., see Froneman, P.W. 282 Pakhomov, E.A., see Perissinotto, R. 304 Pakhomov, E.A., see Scolardi, K.M. 296 Pakulski, J.D., see Klein, B. 346–348 Palmer, A.S., see Curran, M.A.J. 3, 167 Palmisano, A.C. 227 Palmisano, A.C., see Priscu, J.C. 227 Pandolfo, L., see Shindell, D.T. 323 Pane, L., see Carli, A. 295 Pang, S.S., see Kwok, R. 3 Papakyriakou, T.N. 181 Papakyriakou, T.N., see Mei, Z.-P. 415 Papakyriakou, T.N., see Yackel, J.J. 36 Paquette, R.G., see Bourke, R.H. 175 Parish, T.R. 5, 20, 24, 129
440 Parish, T.R., see Bromwich, D.H. 393 Parkinson, C., see Vaughan, D.G. 3 Parkinson, C.L. 2, 3, 33, 63, 64, 323, 393, 413 Parkinson, C.L., see Cavalieri, D.J. 43 Parkinson, C.L., see Gloersen, P. 2, 3, 57, 61, 225, 332 Parkinson, C.L., see Vinnikov, K.Y. 2 Parkinson, C.L., see Zwally, H.J. 3 Parmeter, M.M., see May, D.A. 136 Parrish, C.C., see Stevens, C.J. 277–279, 282, 284 Parsons, A.N., see Doran, P.T. 350 Parsons, R., see Carlson, C.A. 331 Paschini, E., see Picco, P. 242 Pasternak, A., see Arashkevich, E. 275, 279 Pasternak, A.F. 303 Paukkunen, A., see Miloshevich, L.M. 136, 137 Pauling, C., see Amy, P.S. 330 Paulson, C.A., see Andreas, E.L. 151 Paulson, C.A., see Meunch, R.D. 70 Pauly, D. 256 Payne, C.D., see Tremblay, J.-É. 211 Payne, C.D., see Tremblay, J.-E. 229 Payne, C.D., see Tremblay, J.-É. 399 Payne, C.D., see Tremblay, J.E. 243, 245 Payne, R. 147 Payne, T., see Shepherd, A. 350 Peacock, S.L., see Broecker, W.S. 6, 26 Pearson, J.G., see Gosink, T.A. 181 Pease, C.H. 4, 6, 7, 10, 58, 59, 66, 89, 92, 94, 96, 107, 129, 137 Pease, C.H., see Schumacher, J.D. 59, 369 Pease, C.H., see Smith, S.D. 2, 4, 31, 57, 129 Peck, L.S. 331 Pedersen, C.E., see Kampp, K. 399 Pedersen, L.T., see Comiso, J. 68 Pedersen, L.T., see Gudmandsen, P. 338 Pedros-Alio, C., see Moran, X.A.G. 329 Pellegrini, A., see van Woert, M. 28 Peloquin, J.A., see Smith Jr., W.O. 250, 251, 254 Peltzer, E.T., see Carlson, C.A. 325 Peng, T.-H., see Broecker, W.S. 6, 26 Penkett, S.A., see Baker, J.M. 180 Penry, D.L., see Jumars, P.A. 325, 339 Peperzak, L. 227 Pereira, A.F., see Beckmann, A. 121, 195 Perissinotto, R. 304 Perissinotto, R., see Froneman, P.W. 282 Perissinotto, R., see Pakhomov, E.A. 288, 293, 294, 298, 301, 304, 308 Perkin, R.G., see Smith, S.D. 66 Perkin, R.G., see Topham, D.R. 66 Perovich, D.K. 149, 181 Perovich, D.K., see Eicken, H. 149 Perovich, D.K., see Maykut, G.A. 57 Perry, M.J., see Jumars, P.A. 325, 339 Persson, C., see Leck, C. 213
Author Index Pesant, S. 229, 242, 247, 248, 280, 338, 345, 401, 403 Pesant, S., see Robineau, B. 326 Peters, F., see Choi, J.W. 331, 346 Petersen, G.H. 325 Petersen, M.R. 392 Peterson, B.J. 350 Peterson, R.G., see Fahrbach, E. 6 Peterson, R.G., see White, W.B. 373 Pettre, P., see König-Langlo, G. 130 Pfirman, S.L. 59, 74 Phillips, K.L., see Curran, M.A.J. 3, 167 Piatkowski, U. 288 Piatkowski, U., see Boysen-Ennen, E. 290, 293, 299, 301 Piatt, J.F. 379 Picco, P. 242 Picco, P., see Buffoni, G. 43 Picco, P., see Manzella, G.M.R. 28 Pike, D.J., see Streten, N.A. 28 Pillsbury, R.D. 26, 67, 372 Pinchuk, A.I., see Coyle, K.O. 365, 367 Pinette-Matthews, A.L. 347 Pinto, J.O. 136, 143, 151, 152 Pinto, J.O., see Curry, J.A. 137 Pivovarov, S., see Yamamoto-Kawai, M. 64 Piwowar, J., see Barber, D.G. 7, 8, 10, 34, 35, 61, 63, 129, 136, 147, 225, 398, 415 Plotnikov, V.V., see Niebauer, H.J. 392 Plummer, A.J., see Beaumont, K.L. 305 Poisson, A. 196, 207 Poisson, A., see Chen, C.T.A. 164, 178 Poisson, A., see Metzl, N. 177 Pollock, W.H., see Sturges, W.T.C. 180 Polyakov, I. 66, 75 Polyakov, I., see Kowalik, Z. 66 Polyakov, I., see Martin, S. 60, 66, 113, 119, 130 Polyakov, I.V. 167 Pomeroy, L.R. 324, 325, 328, 331, 344 Pomeroy, L.R., see Wiebe, W.J. 324 Ponomarev, V., see Talley, L.D. 71 Ponton, D., see Lewis, E.L. 63, 398 Pook, M.J., see Massom, R.A. 39, 40, 42 Popov, A.V. 136 Porter-Smith, R. 242 Potter, M.J., see Massom, R.A. 5, 19, 23, 24, 28, 42, 61, 130, 151, 206, 392 Povero, P. 366 Powers, J.G., see Monaghan, A.J. 147 Preston, C.M., see Church, M.J. 326 Prézelin, B.B. 252 Price, N.M., see Lovejoy, C. 229, 399 Price, N.M., see Tremblay, J.-E. 74 Price, N.M., see Tremblay, J.-É. 211–213 Price, N.M., see Tremblay, J.-E. 224, 229, 274 Price, N.M., see Tremblay, J.-É. 399, 415
Author Index Price, N.M., see Tremblay, J.E. 242–246, 346 Priestly, R.E. 170 Prinsenberg, S.J., see Conover, R.J. 273 Prinsenberg, S.J., see Saucier, F.J. 117 Priscu, J.C. 227 Priscu, J.C., see Doran, P.T. 350 Priscu, L.R., see Priscu, J.C. 227 Proshutinsky, A.Y., see Kowalik, Z. 130 Pucci, O.H., see Alvarez, H.M. 344 Pudsey, C.J., see Vaughan, D.G. 3 Putt, M. 325, 328 Quadfasel, D. 210 Quetin, L.B. 397 Quetin, L.B., see Haberman, K.L. 397 Quetin, L.B., see Ross, R.M. 303, 304, 336, 337 Racine, C., see Sturm, M. 412 Radke, L.F., see Garrett, T.J. 143 Rahmstorf, S., see Peterson, B.J. 350 Ramanathan, V. 127, 148 Ramsay, B.A. 344 Ramsay, J.A., see Ramsay, B.A. 344 Ramsay, M., see Heide-Joergensen, M.P. 257 Ramseier, R.O., see Bauerfeind, E. 248, 283, 284, 325, 339 Ramseier, R.O., see Michaud, J. 403 Ramseier, R.O., see Minnett, P.J. 11, 338 Ramseier, R.O., see Pesant, S. 229, 242, 338, 345, 401, 403 Rangno, A.L., see Hobbs, P.V. 145 Ransom, B., see Bano, N. 326 Raper, S., see Cubasch, U. 323 Rassmussen, R.A., see Barrie, L.A. 180 Ravaioli, M., see Langone, L. 254, 255 Redfield, A.C. 210 Reeder, R., see Curry, J.A. 146 Reeves, C.E., see Baker, J.M. 180 Reid, J.L. 64 Reimnitz, E. 59, 60 Reisenbichler, K.R., see Lancraft, T.M. 292, 296 Renaud, P.E. 368, 399, 400 Renaud, P.E., see Ambrose Jr., W.G. 368 Renfrew, I.A. 29, 107, 130 Renfrew, I.A., see Moore, G.W.K. 147 Renfrew, I.A., see Moore, K. 68 Renfrew, I.A., see Yu, Y. 5 Ribera d’Alcalà, M., see Saggiomo, V. 205 Ribic, C.A., see Chapman, E.W. 392 Rich, J. 324, 329 Richard, P., see Heide-Joergensen, M.P. 257 Richard, P.R. 257, 392 Richard, P.R., see Heide-Jorgensen, M.P. 400 Richards, F.A., see Redfield, A.C. 210 Richards, K., see Hosie, G.W. 292, 294
441 Richman, S.E., see Lovvorn, J.R. 372 Richter, C. 272, 273, 279 Richter, C., see Hirche, H.J. 403 Richter, C., see Mumm, N. 273, 279 Richter-Menge, J.A., see Eicken, H. 149 Riebesell, U., see Gervais, F. 229 Riedel, A., see Renaud, P.E. 368 Rigby, B., see Smith, M. 17, 130, 332 Riget, F.F., see Born, E.W. 400 Rignot, E. 350 Rigor, I.G. 2 Rigor, I.G., see Pfirman, S.L. 74 Rind, D., see Hansen, J.E. 323 Rind, D., see Liu, J. 3 Ringuette, M. 257, 272, 273, 277, 285, 286, 347, 400 Ringuette, M., see Tremblay, J.E. 246, 257 Rintoul, S.R. 6, 27, 28, 37, 71, 207 Rintoul, S.R., see Bindoff, N.L. 6, 21, 37 Riser, S.C., see Fukamachi, Y. 71 Riser, S.C., see Gladyshev, S. 5 Riser, S.C., see Talley, L.D. 71 Ritz, D.A., see Beaumont, K.L. 305 Ritzrau, W. 325, 343 Rivkin, R.B. 324 Rivkin, R.B., see Acuna, J.L. 347 Rivkin, R.B., see Deibel, D. 280, 283 Rivkin, R.B., see Klein, B. 346–348 Rivkin, R.B., see Saunders, P.A. 257, 279–283, 347, 399, 400 Roberts, A. 6, 37, 129, 151 Roberts, A., see Lytle, V.I. 37 Robertson, B., see Button, D.K. 327 Robertson, G.J., see Gilchrist, H.G. 392 Robertson, G.J., see Kirkwood, R. 392 Robineau, B. 326 Robinson, D.H., see Arrigo, K.R. 31, 229, 249, 250, 256, 258, 261, 262, 332, 373, 378, 394, 395, 416 Robinson, D.H., see DiTullio, G.R. 397 Robison, B.H., see Lancraft, T.M. 292, 296 Robock, A., see Vinnikov, K.Y. 2 Roether, W., see Hoppema, M. 207 Rogachev, K.A. 65, 66, 70, 75 Rogers, A., see Bromwich, D.H. 6, 21, 171 Rogers, A.D., see Peck, L.S. 331 Rogers, A.N. 136, 145 Rogers, A.N., see Bromwich, D.H. 393 Rohardt, G., see Bathmann, U.V. 288 Rohardt, G., see Fahrbach, E. 6, 196, 203 Römisch, K., see Peck, L.S. 331 Rosenberg, M. 181 Rosenberg, M.A., see Bindoff, N.L. 40, 207 Roseneau, D.G., see Springer, A.M. 379 Ross, J.C. 393 Ross, R.M. 303, 304, 336, 337
442 Ross, R.M., see Haberman, K.L. 397 Ross, R.M., see Quetin, L.B. 397 Rossow, W.B., see Schiffer, R.A. 143 Rothery, P., see Atkinson, A. 333, 337, 395 Rothrock, D.A. 2, 412 Rothrock, D.A., see Yu, Y. 2, 323, 412 Rouse, M.A., see Harada, H. 178 Rouse, W. 149 Rowe, P., see Fortier, L. 403 Rowe, P., see Michaud, J. 403 Roy, F., see Saucier, F.J. 117 Roy, S., see Klein, B. 346–348 Roy, S., see Vidussi, F. 399, 400 Rubin, S., see Broecker, W.S. 6, 26 Rudels, B. 68, 71, 209, 210 Rudels, B., see Anderson, L.G. 209, 210 Rudels, B., see Jones, E.P. 209 Rudels, B., see Quadfasel, D. 210 Rudels, B., see Schauer, U. 209 Rudnick, D.L., see Shcherbina, A.Y. 61, 71 Ruedy, R., see Hansen, J.E. 323 Ruffin, S., see Bano, N. 326 Runge, J.A., see Ringuette, M. 347 Russell, G., see Hansen, J.E. 323 Russell, N.J. 330 Russo, A., see Saggiomo, V. 205 Russo, A., see Sala, A. 294, 295, 301 Russo, G.F., see Gambi, M.C. 374 Rutgers van der Loeff, M.M. 201 Rutgers van der Loeff, M.M., see Usbeck, R. 197 Ryan, J.P., see Comiso, J.C. 372 Rysgaard, S. 260, 280 Ryther, J.H. 256 Sabine, C.L. 205, 207, 385 Saborowski, R., see Meyer, B. 299 Sabutis, J.L., see Manney, G.L. 230 Sæmundsdóttir, S. 180 Saggiomo, V. 205 Saggiomo, V., see Innamorati, M. 256 Saito, H., see Hattori, H. 273 Sakshaug, E. 364, 379 Sakshaug, E., see Hewes, C. 328 Sakshaug, E., see Smith Jr., W.O. 239 Sala, A. 294, 295, 301 Salmonsen, F. 399 Salomatin, A.S., see Rogachev, K.A. 65, 66, 70, 75 Saltzman, E.S., see Yvon-Lewis, S.A. 164, 180 Salyuk, A., see Talley, L.D. 71 Sambrotto, R.N. 206, 230, 242, 255, 294 Sambrotto, R.N., see Buesseler, K.O. 373, 378 Sambrotto, R.N., see Vaillancourt, R.D. 207, 230, 242, 255 Sampei, M. 283, 284 Santee, M.L., see Manney, G.L. 230
Author Index Saracovan, I., see Ramsay, B.A. 344 Sargent, J.R., see Falk-Petersen, S. 279, 297, 298, 308 Sarmiento, J.L. 262, 324, 412 Sasaki, H., see Fukuchi, M. 305 Sasaki, H., see Sampei, M. 283, 284 Sasaki, H., see Sato, M. 279 Sasaki, Y., see Weingartner, T.J. 5 Sass, B.H. 233 Satake, M., see Kawaguchi, S. 303 Sato, M. 279 Saucier, F.J. 117 Saunders, P.A. 257, 279–283, 347, 399, 400 Saunders, P.A., see Acuna, J.L. 347 Saunders, P.A., see Deibel, D. 280, 283 Saunders, P.A., see Lee, P.A. 179 Sautour, B., see Breton, E. 397 Sautour, B., see Cotonnec, G. 397 Savage, M.L. 163 Scambos, T.A., see Serreze, M.C. 2 Scarratt, M.G. 180 Scarratt, M.G., see Merzouk, A. 179 Scharek, R., see Smetacek, V. 202 Schauer, U. 71, 209, 210 Schauer, U., see Haarpaintner, J. 167, 209 Scheele, N., see Fahrbach, E. 203 Schiffer, R.A. 143 Schledermann, P. 392, 398 Schlesinger, W.H. 178 Schlitzer, R., see Usbeck, R. 197 Schlosser, P., see Fahrbach, E. 6 Schlosser, P., see Mensch, M. 178 Schlosser, P., see Muench, R.D. 33 Schlosser, P., see Weppernig, R. 203 Schmidt, G.A., see Shindell, D.T. 323 Schmidt, H.C., see Heide-Jorgensen, M.P. 400 Schmidt, K., see Atkinson, A. 299 Schmidt, K., see Stübing, D. 299 Schnack, K. 368 Schnack, S.B. 297, 298, 301 Schnack-Schiel, S.B. 287, 288, 297, 299, 307, 309 Schnack-Schiel, S.B., see Marin, V.H. 307 Schnack-Schiel, S.B., see Niehoff, B. 307 Schnack-Schiel, S.B., see Pasternak, A.F. 303 Schnack-Schiel, S.B., see Smith, S.L. 278 Schneider, W. 63, 75, 129, 175, 225, 338, 401 Schneider, W., see Budéus, G. 63, 175 Schneider, W., see Minnett, P.J. 11, 338 Schneider, W., see Robineau, B. 326 Schnell, A., see Arrigo, K.R. 242, 381 Schnell, R.C., see Barrie, L.A. 180 Schnell, R.C., see Serreze, M.C. 135 Schnell, R.C., see Sturges, W.T.C. 180 Schonberg, S.V., see Dunton, K.H. 376 Schramm, J.L., see Curry, J.A. 2, 143, 146 Schröder, M., see Fahrbach, E. 196, 203 Schröder, M., see Spiridonov, V.A. 287, 288
Author Index Schroeder, M., see Broecker, W.S. 6, 26 Schultz, M.B., see Hosie, G.W. 292, 294 Schumacher, J.D. 59, 369 Schwartz, S.E., see Stokes, G.M. 128 Schweiger, A.J. 143, 148 Schweiger, A.J., see Francis, J.A. 136 Schweiger, A.J., see Key, J.R. 139 Scipione, M.B., see Gambi, M.C. 374 Scolardi, K.M. 296 Scully, N.M. 174 Sedwick, P.N. 173, 229, 230, 240, 249, 259, 394, 395 Seelye, M., see Drucker, R. 60, 61, 70 Sehlstedt, P.-I., see Anderson, L.G. 210 Semiletov, I. 181 Senior, C.A., see Cubasch, U. 323 Senneville, S., see Saucier, F.J. 117 Serdula, J., see Marsden, R. 34 Serdula, J., see Marsden, R.F. 134 Serreze, M.C. 2, 135 Serreze, M.C., see Stroeve, J.C. 2 Sertorio, T.Z. 301 Shalapyonok, L., see Caron, D.A. 394, 397 Shalapyonok, L., see Olson, R.J. 240, 249, 251, 259 Shalina, E.V., see Johannessen, O.M. 262, 323 Shaw, G.E. 143, 144 Shcherbina, A.Y. 61, 71 Sheffield, G., see Simpkins, M.A. 371, 372, 376 Shepherd, A. 350 Sherr, B., see Rich, J. 324, 329 Sherr, E., see Rich, J. 324, 329 Shields, A.R., see Smith Jr., W.O. 250, 251, 254 Shiga, N. 379 Shiga, N., see Deibel, D. 280, 283 Shiklomanov, A.I., see Peterson, B.J. 350 Shiklomanov, I.A., see Peterson, B.J. 350 Shimoda, H., see Ohshima, K.I. 74 Shindell, D.T. 323 Shine, K.P. 143 Shinohara, N., see Ichii, T. 395 Shirasawa, K., see Kawamura, T. 61 Shupe, M.D. 145 Shyh-Chin, C., see White, W.B. 3 Sieburth, J.M. 330 Siegel, V. 303, 309 Siegel, V., see Atkinson, A. 333, 337, 395 Siegel, V., see Gutt, J. 293 Siegenthaler, U. 324 Siferd, T.D., see Welch, H.E. 399 Sigman, D.D., see Tortell, P.D. 252, 394 Signorini, S.R. 5 Simmonds, I. 3 Simon, M. 341, 349 Simon, M., see Smith, D.C. 328, 344 Simpkins, M.A. 371, 372, 376
443 Sinclair, E.N., see Hunt, G.L. 233 Sinclair, E.N., see Rogers, A.N. 136, 145 Sirenko, B.I., see Grebmeier, J.M. 376, 377 Skogseth, R. 71 Skogseth, R., see Fer, I. 71 Skoog, A. 176, 211, 339 Skoog, A., see Daly, K.L. 325, 339 Skriver, H., see Gudmandsen, P. 338 Skvarca, P., see Shepherd, A. 350 Smetacek, V. 202, 287, 288, 293 Smetacek, V., see Schnack, S.B. 301 Smethie Jr., W.M., see Orsi, A.H. 71, 196, 203, 207, 208 Smethie Jr., W.M., see Trumbore, S.E. 164, 173 Smith, C.R. 365 Smith, D.C. 328, 344 Smith, D.C., see Azam, F. 326, 328 Smith, D.C., see Ducklow, H.W. 332, 334 Smith, G., see Saucier, F.J. 117 Smith, J., see Barry, J.P. 365–368, 374, 376, 395 Smith, J., see McLaughlin, F. 164, 178 Smith, M. 17, 130, 332 Smith, R.C. 3, 323, 334, 336 Smith, R.C., see Carrillo, C.J. 326 Smith, R.C., see Ross, R.M. 304 Smith, R.E.H., see Pesant, S. 229, 242, 338, 345, 401, 403 Smith, S., see Grebmeier, J.M. 152 Smith, S.D. 2, 4, 31, 57, 66, 129 Smith, S.D., see den Hartog, G. 66 Smith, S.D., see Topham, D.R. 66 Smith, S.L. 278, 279, 285, 286 Smith, S.L., see Ashjian, C.J. 274, 275, 277–280, 282, 284, 339, 403 Smith, T.G., see Richard, P.R. 257 Smith, T.G., see Stirling, I. 257, 392 Smith, W.O., see Asper, V.L. 378 Smith, W.O., see DiTullio, G.R. 164, 179 Smith, W.O., see Tremblay, J.-E. 163, 167 Smith IV, D.C., see Meunch, R.D. 70 Smith Jr., K.L. 365 Smith Jr., W.O. 180, 202, 205, 208, 224, 229, 239, 240, 242, 247–252, 254, 255, 262, 294, 324, 332–335, 337–342, 366–368, 394, 401, 403 Smith Jr., W.O., see Arrigo, K.R. 225, 226, 233 Smith Jr., W.O., see Asper, V.L. 173, 249, 259 Smith Jr., W.O., see Booth, B.C. 226, 247 Smith Jr., W.O., see Carlson, C.A. 324, 325, 331 Smith Jr., W.O., see Cota, G.F. 262 Smith Jr., W.O., see Daly, K.L. 325, 339 Smith Jr., W.O., see Dinniman, M.S. 225, 372, 416 Smith Jr., W.O., see DiTullio, G.R. 396 Smith Jr., W.O., see Ducklow, H.W. 334, 335 Smith Jr., W.O., see Fitzwater, S.E. 252 Smith Jr., W.O., see Fiztwater, S.E. 229 Smith Jr., W.O., see Hiscock, M.R. 250, 252
444 Smith Jr., W.O., see Laws, E.A. 324 Smith Jr., W.O., see Nelson, D.M. 205, 252–254 Smith Jr., W.O., see Sullivan, C.W. 324 Smith Jr., W.O., see Tremblay, J.-É. 31, 74, 198, 364, 373 Smith Jr., W.O., see van Hilst, C.M. 251 Smith Jr., W.O., see Yager, P.L. 164, 169, 172, 176, 211, 325 Smolarkiewicz, P.K. 115 Snell, H.E., see Frederick, J.E. 230 Solomon, S., see Thompson, D.W.J. 3, 416 Solomon, S.M., see Macdonald, R.W. 325 Somerville, T., see Darby, M.S. 92, 96–98 Somerville, T.A., see Darby, M.S. 6 SooHoo, J.B., see Palmisano, A.C. 227 Sosik, H.M., see Olson, R.J. 240, 249, 251, 259 Sou, T., see Holloway, G. 2 Spangenberg, D.A. 143 Speer, K., see Rintoul, S.R. 6 Spezie, G., see Budillon, G. 6, 26 Spiridonov, V.A. 287, 288, 308, 309 Spiridonov, V.A., see Bathmann, U.V. 288 Springer, A.M. 275, 276, 364, 368, 379, 381, 384 Springer, A.M., see Piatt, J.F. 379 Stabeno, P.J. 369 Stabeno, P.J., see Hunt, G.L. 233 Stammerjohn, S.E, see Smith, R.C. 3 Stammerjohn, S.E., see Smith, R.C. 323, 334, 336 Stamnes, K.R., see Tsay, S. 148 Stamnes, K.R.G.E. 128 Starmans, A. 374 Starmans, A., see Gutt, J. 374 Stearns, C.R., see Bromwich, D.H. 21, 233, 393 Stearns, C.R., see Savage, M.L. 163 Steed, R.H.N., see Drewry, D.J. 372 Steele, M. 68, 88 Stefels, J. 178 Steffen, K. 34, 63, 130, 173, 398 Steffen, K., see Martin, S. 5 Stegmann, P., see Schnack, S.B. 301 Steinberg, D.K., see Dilling, L. 282 Steinberg, D.K., see Ducklow, H.W. 263 Steinbüchel, A., see Alvarez, H.M. 344 Stenhouse, I.J. 257, 400 Stepanova, A. 214 Stephens, B.B. 194 Stern, G., see Fisk, A.T. 278 Stevens, C.J. 277–279, 282, 284 Stevens, C.J., see Saunders, P.A. 257, 279–283, 347, 399, 400 Steward, G., see Ducklow, H.W. 332, 334 Stigebrandt, A. 67 Stirling, I. 60, 66, 215, 257, 391, 392, 398, 399, 403 Stirling, I., see Ainley, D.G. 391, 392 Stirling, I., see Harwood, L.A. 392
Author Index Stirling, I., see Holst, M. 399, 400 Stocchino, C., see Carli, A. 295 Stoecker, D.K., see Putt, M. 325, 328 Stokes, G.M. 128 Stoll, M.H.C., see Hoppema, M. 178, 197 Stolp, M., see Hosie, G.W. 290, 301 Stolp, M., see Nicol, S. 308 Stommel, H.M. 111 Stone, P., see Hansen, J.E. 323 Stone, R.C., see White, W.B. 3 Stone, R.S. 143 Stone, R.S., see Pinto, J.O. 151 Stott, P.A., see Marshall, G.J. 416 Stouffer, R.J., see Cubasch, U. 323 Stouffer, R.J., see Sarmiento, J.L. 262, 412 Stouffer, R.J., see Vinnikov, K.Y. 2 Strachan, W.J., see Fisk, A.T. 278 Strass, V., see Fahrbach, E. 203 Stretch, J.J. 287 Streten, N.A. 28 Stringer, W.J. 60, 248 Stroeve, J.C. 2 Stroeve, J.C., see Serreze, M.C. 2 Stübing, D. 299 Stübing, D., see Atkinson, A. 299 Stübing, D., see Meyer, B. 297, 299, 303, 309 Sturges, W.T.C. 180 Sturm, M. 412 Suh, H.-L. 304 Sui, Q., see Bodungen, B.v. 304 Sukhanova, I.N. 170 Sullivan, B., see Kaufmann, R.S. 296 Sullivan, C.W. 324 Sullivan, C.W., see Arrigo, K.R. 227 Sullivan, C.W., see Comiso, J.C. 372 Sullivan, C.W., see Palmisano, A.C. 227 Sullivan, C.W., see Priscu, J.C. 227 Sullivan, C.W., see Stretch, J.J. 287 Sullivan, W., see Sturges, W.T.C. 180 Sun, C.-H., see Buch, K.A.J. 131 Sun, S., see Li, C. 301 Sun-Mack, S., see Spangenberg, D.A. 143 Sunda, W. 178 Sunda, W., see Harada, H. 178 Sutherland, S.C. 164, 177 Sweeney, C. 164, 171–173, 194, 205 Sweeney, C., see Gordon, L.I. 172, 173, 205, 242 Sweeney, C., see Sutherland, S.C. 164, 177 Swietlicki, E., see Leck, C. 149 Swift, J.H., see Aagaard, K. 73 Swift, J.H., see Anderson, L.G. 167, 210 Swift, J.H., see Codispoti, L.A. 381 Swift, J.H., see Jones, E.P. 211 Sykes, P.F., see Huntley, M.E. 304 Szczodrak, M., see Vogelmann, A.M. 150
Author Index Tagliabue, A. 258, 261, 367, 378, 394, 397 Tahon, J., see Joiris, C.R. 403 Tait, V.K. 167 Takagi, S., see Shiga, N. 379 Takahashi, T. 164, 171–173 Takahashi, T., see Sutherland, S.C. 164, 177 Takizawa, T. 33 Takizawa, T., see Ushio, S. 5, 199 Taldenkova, E., see Stepanova, A. 214 Talley, L.D. 64, 71 Talley, L.D., see Fukamachi, Y. 71 Talley, L.D., see Gladyshev, S. 71 Talley, L.D., see Shcherbina, A.Y. 61, 71 Tamstorf, M.P., see Egevang, C. 399 Tanaka, N., see Guo, L.D. 325 Tanaka, N., see Yamamoto-Kawai, M. 64 Tanaka, T., see Guo, L.D. 325 Tande, K.S. 278, 281 Tande, K.S., see Falk-Petersen, S. 279 Tang, C.L., see Yao, T. 117 Tanimura, A. 293 Tanimura, A., see Fukuchi, M. 293, 301 Tanner, S.J., see Coale, K.H. 240, 251, 252 Tanoue, E., see Ishii, M. 206 Tape, K., see Sturm, M. 412 Tarakanov, R.Y., see Koshlyakov, M.N. 27 Tauber, G.M. 5 Taylor, G.T., see Karl, D.M. 329, 336 Tchernia, P., see Gordon, A.L. 207 Tear, S. 102–106 Tegen, I., see Fung, I.Y. 252, 259 Teilmann, J. 400 Teilmann, J., see Born, E.W. 400 Terbrüggen, A., see Hagen, W. 299 Theodorakis, C., see Cooper, L.W. 349 Thiel, C., see Kirst, G.O. 179, 180 Thiele, D., see Gill, P.C. 287 Thingstad, F., see Azam, F. 328 Thomas, D., see Schnack-Schiel, S.B. 287 Thomas, D.N., see Giannelli, V. 181 Thomas, P.G., see Harrington, S.A. 308, 309 Thompson, D.W.J. 3, 416 Thompson, D.W.J., see Gillett, N.P. 3 Thompson, W.T., see Burk, S.D. 136 Thomsen, B.B., see Gudmandsen, P. 338 Thomsen, L., see Ritzrau, W. 343 Thorne, L.R., see Buch, K.A.J. 131 Thoumelin, G., see Cotonnec, G. 397 Tien, G., see Karl, D.M. 329, 336 Tilbrook, B., see Metzl, N. 177 Tilbrook, B.D. 170 Tilbrook, B.D., see McNeil, B.I. 207 Tillmann, U., see Lara, R.J. 401 Timco, G.W. 59 Timlin, M.S., see Wolter, K. 233 Timmerman, R. 108
445 Timmermann, R., see Beckmann, A. 121, 195 Timofeev, S., see Falk-Petersen, S. 297, 298, 308 Timokhov, L.A. 59 Timokhov, L.A., see Schauer, U. 209 Tindale, N.W., see Duce, R.A. 259 Tishchenko, P., see Talley, L.D. 71 Tjernström, M., see Leck, C. 149 Tobin, D.C. 137 Toggweiler, J.R., see Sarmiento, J.L. 324 Toimil, L.J., see Reimnitz, E. 59 Tokarczyk, R., see Yvon-Lewis, S.A. 164, 180 Top, Z. 164, 176 Topham, D.R. 66 Topham, D.R., see den Hartog, G. 66 Topham, D.R., see Smith, S.D. 66 Topp, R. 63 Torres, J.J., see Hofmann, E.E 413 Torres, J.J., see Hopkins, T.L. 299, 307 Torres, J.J., see Lancraft, T.M. 292, 296 Torres, J.J., see Scolardi, K.M. 296 Tørresen, T., see Foldvik, A. 195 Tortell, P.D. 252, 394 Touratier, F. 229, 247, 339 Tozzi, S., see Smith Jr., W.O. 250, 251, 254 Treguer, P. 324 Tréguer, P., see Jones, E.P. 240 Tremaine, S.C. 345 Tremblay, A., see Mailhot, J. 137, 151 Tremblay, J.-É. 31, 74, 163, 167, 198, 211–213, 224, 229, 242–246, 257, 274, 346, 364, 373, 399, 415 Tremblay, J.-É., see Mei, Z.-P. 415 Tremblay, J.-É., see Vidussi, F. 399, 400 Tremblay, L.-B. 112 Tremblay, L.-B., see Armstrong, A.E. 136, 147 Trepte, Q., see Spangenberg, D.A. 143 Trevena, A.J. 180 Tripp, R.B., see Coachman, L.K. 369 Tripp, R.B., see Schumacher, J.D. 59, 369 Trivelpiece, W.Z., see Fraser, W.R. 392 Trull, T.W., see Gibson, J.A.E. 181, 194, 206, 207 Trumbore, S.E. 164, 173 Tsay, S. 148 Tucker, W.B., see Perovich, D.K. 149 Turco, K.R., see Springer, A.M. 275, 276 Turner, J. 373 Turner, J., see King, J.C. 3, 136 Turner, J., see Marshall, G.J. 416 Turner, J., see Vaughan, D.G. 3 Turner, J.T., see Levinson, H. 282 Turner, S.M., see Malin, G. 178 Tyler, P.A., see Gage, J.D. 365 Tynan, C.T., see Ainley, D.G. 391, 392 Tyshko, K., see Dmitrenko, I. 17 Tysko, K.N., see Dmitrenko, I.A. 414 Tzeng, R.-Y., see Bromwich, D.H. 26, 372
446 Ukita, J., see Ishikawa, T. 5, 34 Ulmke, R., see Kirst, G.O. 179, 180 Untersteiner, N. 57, 68 Usbeck, R. 197 Ushio, S. 5, 199 Ushio, S., see Takizawa, T. 33 Uttal, T., see Spangenberg, D.A. 143 Vaillancourt, R.D. 207, 230, 242, 255 Vaillancourt, R.D., see Sambrotto, R.N. 230 Valeur, H. 131 van den Enden, R.L., see Trevena, A.J. 180 van Dijken, G.L., see Arrigo, K.R. 17, 22, 25–28, 30, 31, 37, 163, 173, 178, 179, 201, 202, 206–208, 213, 224, 225, 231–233, 240, 248, 250, 252, 254, 256, 272, 288, 293–296, 332, 349, 364, 366–368, 372, 373, 375–377, 380, 392–394, 397, 416 van Hilst, C.M. 251 van Loon, H., see Hurrell, J.W. 3 van Ommen, T.D., see Curran, M.A.J. 3, 167 van Vleet, E.S., see Hagen, W. 299, 308 van Woert, M.L. 21, 28, 61, 171, 256, 372 van Woert, M.L., see Arrigo, K.R. 332, 373, 378, 394, 395, 416 van Woert, M.L., see Bromwich, D.H. 6, 21, 171, 393 van Woert, M.L., see Cullather, R.I. 136, 147 van Woert, M.L., see DiTullio, G.R. 397 Vasseur, C. 230 Vaughan, D.G. 3 Vaz, R.A.N. 28 Veit, R.R., see Nevitt, G.A. 396 Veldkamp, H., see Matin, A. 331 Vergin, K., see Carlson, C.A. 331 Vernet, M., see Matrai, P.A. 213 Vernet, M., see Ross, R.M. 304 Vernet, M., see Smith, R.C. 336 Veth, C., see Lancelot, C. 349 Vetter, Y.-A. 325, 328, 344, 348 Vezina, A., see Touratier, F. 229, 247, 339 Vickers, C.L., see Garrison, D.L. 395 Vidussi, F. 399, 400 Villinski, J.C. 374 Vincent, R.F. 129 Vinnikov, K.Y. 2 Vinnikov, K.Y., see Cavalieri, D.J. 43 Virginia, R.A., see Doran, P.T. 350 Virtue, P., see Nicol, S. 294, 298, 304 Visbeck, M., see Hall, A. 3, 416 Vogelmann, A.M. 150 Volk, T. 167 Vömel, H., see Miloshevich, L.M. 136, 137 von Quillfeldt, C.H., see Klein, B. 346–348 von Quillfeldt, C.H., see Lovejoy, C. 243, 245, 346, 399
Author Index von Quillfeldt, C.H., see Mei, Z.-P. 415 Voronina, N.M. 287, 288 Vorosmarty, C.J., see Peterson, B.J. 350 Voss, M., see Bauerfeind, E. 248, 283, 284, 325, 339 Wadhams, P. 57, 59 Wadhams, P., see Ackley, S.F. 3 Wadhams, P., see Comiso, J. 68 Wagenbach, D. 145 Wakatsuchi, M. 199, 201 Wakatsuchi, M., see Fukamachi, Y. 71 Wakatsuchi, M., see Ishikawa, T. 5, 34 Wakatsuchi, M., see Kimura, N. 17 Wakatsuchi, M., see Ohshima, K.I. 74 Wakeham, S.G., see Dacey, J.W.H. 165 Walker, S., see Broecker, W.S. 6, 26 Walkington, I.A. 101, 102, 106, 107 Wall, D.H., see Doran, P.T. 350 Wallace, D.W.R. 164, 176, 210, 211, 338, 339 Wallace, D.W.R., see Daly, K.L. 325, 339 Wallace, D.W.R., see Lewis, E. 165 Wallace, D.W.R., see Smith Jr., W.O. 242, 247, 248, 338, 342, 366, 368, 401, 403 Wallace, D.W.R., see Yager, P.L. 164, 169, 172, 176, 211, 325 Wallace, J.M., see Rigor, I.G. 2 Walsh, I.D., see Hargrave, B. 245–247 Walsh, I.D., see Hargrave, B.T. 347, 368 Walsh, I.D., see Smith Jr., W.O. 339–341 Walsh, I.D., see Yang, J. 245 Walsh, J.E. 148, 149 Walsh, J.E., see Doran, P.T. 350 Walsh, J.E., see Lynch, A.H. 117, 131 Walsh, J.E., see Stamnes, K.R.G.E. 128 Walsh, J.E., see Vinnikov, K.Y. 2 Walter, B.A. 151 Walters, G., see Hunt, G.L. 233 Wang, D., see Guo, L.D. 325 Wang, M., see Overland, J.E. 136 Wang, Q., see Wang, S. 232 Wang, S. 232 Wang, S.-H., see Bromwich, D.H. 139, 146, 147 Wang, S.W., see Gong, D.Y. 3 Wang, X., see Coale, K.H. 240, 251, 252 Wang, X., see Maslanik, J.A. 225, 232 Wanninkhof, R. 166 Wanzek, M., see Kirst, G.O. 179, 180 Warner, M.J. 165 Warner, M.J., see Bindoff, N.L. 40, 207 Warren, S.G., see Charlson, R.J. 178 Washington, W.M. 412 Wassman, P. 365, 367, 378 Wassman, P., see Arashkevich, E. 275, 279 Watanabe, S., see Kasamatsu, N. 179
Author Index Watatsuchi, M., see Gladyshev, S. 71 Waters, J.W., see Manney, G.L. 230 Watkins, J.M., see Wheeler, P.A. 325 Watkins, W.E., see Barnard, W.R. 165 Weaver, A., see McLaughlin, F. 164, 178 Weber, L.H. 328 Wedborg, M., see Skoog, A. 176 Wefer, G. 306 Wefer, G., see Fischer, G. 306 Weiber, P.H., see Hofmann, E.E 413 Weidner, G.A., see Savage, M.L. 163 Weingartner, T.J. 5 Weiss, A.M., see Arrigo, K.R. 225, 226, 233 Weiss, R.F. 165, 194 Weiss, R.F., see Broecker, W.S. 6, 26 Weiss, R.F., see Mensch, M. 178 Weiss, R.F., see Warner, M.J. 165 Welch, H.E. 399 Welch, H.E., see Macdonald, R.W. 325 Wendler, G. 21, 37, 41, 232 Wendler, G., see Adolphs, U. 21, 61, 134, 139 Wenk, T., see Siegenthaler, U. 324 Weppernig, R. 203 Werner, I., see Auel, H. 278 Weslawski, J.M. 400 Weslawski, J.M., see Falk, K. 392, 403 Weslawski, J.M., see Ringuette, M. 347 Wexels-Riser, C., see Arashkevich, E. 275, 279 Wheeler, B.R., see Carlson, C.A. 331 Wheeler, P.A. 325 Wheelis, M.L., see Woese, C.R. 326 White, W.B. 3, 373 Whitworth III, T., see Orsi, A.H. 203 Wickett, M.E., see Caldeira, K. 385 Wiebe, W.J. 324 Wiebe, W.J., see Pomeroy, L.R. 324, 325, 331, 344 Wiegand, R.C., see Carmack, E.C. 67 Wijffels, S., see Rintoul, S.R. 6 Williams, G.D. 37, 41, 61, 164, 177, 206, 207, 242 Williams, G.D., see Bindoff, N.L. 6, 21, 37 Williams, G.D., see Marsland, S.J. 6, 37, 119, 177 Williams, P.J.L. 344 Williams, R.M., see Andreas, E.L. 151 Williams, W.J. 73, 163, 164, 173 Williams, W.J., see McLaughlin, F.A. 66 Willmott, A.J. 5, 43, 91, 98–100, 129 Willmott, A.J., see Biggs, N.R.T. 59, 74, 92, 93, 95–97, 101, 102, 104, 105, 107 Willmott, A.J., see Darby, M.S. 6, 74, 92, 96–98 Willmott, A.J., see Morales Maqueda, M.A. 4, 7, 8, 27, 31–33, 43, 57, 59, 61, 64, 70, 74, 87, 88, 90, 97, 98, 101, 102, 195, 199, 369, 372 Willmott, A.J., see Tear, S. 102–106 Willmott, A.J., see Walkington, I.A. 101, 102, 106, 107 Wilson, C.A., see Ingram, W.J. 326
447 Wilson, J., see Dilling, L. 282 Wilson, K.J. 7, 34, 36, 61 Wilson, P.R. 412 Wilson, P.R., see Ainley, D.G. 393, 412 Winchell, C.J., see Gowing, M.M. 306 Wingham, D., see Shepherd, A. 350 Winsor, P. 5, 59–61, 71, 74, 92, 179, 209 Winsor, P., see Dokken, S.T. 209 Winsor, P., see Rudels, B. 210 Wisotzki, A., see Fahrbach, E. 203 Wisotzki, A., see Spiridonov, V.A. 287, 288 Witholt, B., see Huijberts, G.N. 344 Witte, H.J. 145 Woese, C.R. 326 Wolf-Gladrow, D., see Zeebe, R.E. 166 Wolff, E.W. 145 Wolff, H., see Kirst, G.O. 179, 180 Wolff, J.O., see Marsland, S.J. 64 Wolter, K. 233 Wommack, K.E., see Yager, P.L. 324, 325 Wong, A.P.S. 28, 207 Woodgate, R.A., see Fahrbach, E. 196 Woodgate, R.A., see Lindsay, R.W. 121 Worby, A.P. 151 Worby, A.P., see Ackley, S.F. 3 Worby, A.P., see Lytle, V.I. 37 Worby, A.P., see Massom, R.A. 5, 6, 21, 37, 39, 40, 42, 61, 226 Worthen, D.L. 261 Worthen, D.L., see Arrigo, K.R. 229, 242, 249, 250, 256, 258, 261, 262, 332, 373, 378, 381, 394, 395, 416 Worthington, L.V. 6 Wright, S.W., see Trevena, A.J. 180 Wu, K.Y., see Murray, A.E. 326 Wu, X. 3, 6 Wu, X., see Lytle, V.I. 37 Xiaogu, W., see Zilin, L. 206 Xie, Y. 144 Xiuren, N., see Zilin, L. 206 Yackel, J.J. 36 Yackel, J.J., see Hanesiak, J.M. 147, 149 Yager, P.L. 164, 169, 172, 176, 211, 227, 324, 325, 327, 331, 341–345 Yager, P.L., see Daly, K.L. 325, 339 Yager, P.L., see Deming, J.W. 331 Yager, P.L., see Ducklow, H.W. 167 Yager, P.L., see Hodges, L.R. 325 Yager, P.L., see Miller, L.A. 325, 347, 348 Yakunin, L.P., see Niebauer, H.J. 392 Yamamoto-Kawai, M. 64 Yamanouchi, T., see Ishikawa, T. 5, 34 Yamashita, K., see Martin, S. 5, 61, 66, 130, 147
448 Yang, J. 245 Yang, J., see Comiso, J.C. 167 Yao, T. 117 Yap, K.S., see Cubasch, U. 323 Yoshida, K., see Ohshima, K.I. 74 Yu, Y. 2, 5, 323, 412 Yu, Y., see Rothrock, D.A. 2, 412 Yuan, X. 3 Yuan, X., see Liu, J. 3 Yuming, C., see Zilin, L. 206 Yunker, M.B., see Macdonald, R.W. 325 Yvon-Lewis, S.A. 164, 180
Zak, B.D., see Stamnes, K.R.G.E. 128 Zakharov, V.F. 214
Author Index Zakharov, V.F., see Vinnikov, K.Y. 2 Zeebe, R.E. 166 Zemtsova, E.V., see Kriss, A.E. 330 Zhabin, I., see Talley, L.D. 71 Zhang, G., see Li, C. 301 Zhang, J., see Lindsay, R.W. 412 Zhao, X.M., see Button, D.K. 327 Zhou, C.Z., see van Woert, M. 28 Zilin, L. 206 Zimmerman, J.Z., see Daly, K.L. 309 Zubov, N.N. 59 Zulauf, M.A. 137, 140, 151 Zwally, H.J. 3, 5, 29, 32, 61, 89, 363, 372 Zwally, H.J., see Gloersen, P. 2, 3, 57, 61, 225, 332 Zwally, H.J., see Parkinson, C.L. 2, 63 Zyranov, D. 117
Subject Index Antarctic Silverfish, Pleuragramma antarcticum, 395 Antarctic species abundance, 287–296 Antarctic species composition, 287–296 Antarctic Toothfish, Dissostichus mawsoni, 395 Antarctic vertical flux, 304–306 Antarctic zooplankton diet, 301, 303, 304 dry weight, 296–301 feeding rates, 301, 303, 304 life history, 307–309 lipids, 296–299, 308 Antarctomysis spp., 294, 301 anthropogenic aerosols, 143 anthropogenic CO2 , 196, 198, 203, 207, 208 AO (Arctic Oscillation), 3 Apherusa glacialis, 403 appendicularian house, 283 appendicularians, 287, 293, 294, 301, 302, 305, 310, 399 Aptenodytes forsteri, Emperor Penguin, 392, 394, 395 Archaea, 326, 328 Arctic, 239, 240, 243, 245, 247, 248, 257, 259–262 Arctic Climate Impact Assessment, 74, 75 Arctic Cod, Boreogadus saida, 399, 400 Arctic macronutrients, 240 Arctic Minke Whale, Balaenoptera bonaerensis, 395, 396 Arctic Ocean, see also Canadian Arctic Archipelago dense water descent and transport, 71 flaw lead polynyas, 59–61 prevalence of leads, 56, 57 Arctic Oscillation (AO), 3 Arctic polynyas, 364, 366, 367 argon, 165 atmospheric temperature profile, 137 atmospheric transmittance, 148
AABW (Antarctic Bottom Water), 6, 71, 177, 178 ACC (Antarctic Circumpolar Current), 34, 67 Adelie Depression, 71 Adélie Penguin, Pygoscelis adeliae, 392, 394, 395, 397 Adolphus Washington Greely, 400 air–sea exchange, 163–190 factors influencing, 163, 164 future research directions, 180–182 gas flux defined, 166 gas partitioning, 164, 166 air–sea temperature difference, 134, 136, 148, 151 albedo, 127, 131, 139, 147, 149 alkalinity, seawater, 166, 170 Alle alle, Dovekie, 399, 400, 402, 403 Amery Ice Shelf, 21 polynya, 21, 23 amphipod, 399 Amundsen Sea polynya, 27, 394 annual production, 394 Antarctic, 239, 240, 243, 254–256, 259, 261, 262 biomass, 296–301 Antarctic Bottom Water (AABW), 6, 71, 177, 178 Antarctic Circumpolar Current (ACC), 34, 67 Antarctic Circumpolar Trough, 21 Antarctic climate changes, 262 Antarctic Coastal Current, 34, 39 Antarctic egg production, 307–309 Antarctic faecal pellet production, 304–306 Antarctic Krill, Euphausia superba, 287, 288, 290, 293–295, 298–305, 307–310, 395 Antarctic macronutrients, 240 Antarctic Ocean and climate change, 75, 76 convectively forced polynyas, 66 dense water descent and transport, 70, 71 flaw lead polynyas, 58, 61 prevalence of flaw leads, 57 wind-driven polynyas, 61, 62 Antarctic Oscillation, 3 Antarctic Peninsula, 289, 296, 298–301, 303, 304, 306–309, 397 Antarctic Petrel, Thalassoica antarctica, 394 Antarctic polynyas, 364, 375–377, 381–384 Antarctic secondary production, 309
bacteria, 326, 328 bacterial abundance, 335, 339–341, 347 bacterial biovolumes, 341 bacterial carbon demand (BCD), 327 bacterial cell abundance, 334, 340 bacterial growth, 323, 335, 347 efficiency (BGE), 327, 348 449
450 bacterial production (BP), 327, 341, 347 rates, 334 bacterial utilization, 343 bacteriovores, 346 bacterivory, 345 Baffin Bay, 346, 392, see also North Water polynya, 61, 63 Baffin Bay Gyre, 36 Baffin Current, 346 Balaena mysticetus, Bowhead Whale, 392 Balaenoptera bonaerensis, Antarctic Minke Whale, 395, 396 Barents Sea, 71 Barrier Bay polynya, 23 BCD (bacterial carbon demand), 327, 335 Bearded Seal, Erignathus barbatus, 392 Beaufort Sea, 58, see also Cape Bathurst polynya flaw lead polynyas, 59, 60 Marginal Ice Zone ‘polynyas’, 69 Belgica Bank, 338, 342 Bellingshausen Sea, 397 Bellot Strait polynya, 13, 14 Beluga Whale, Delphinapterus leucas, 392, 400 benthic biomass, 365, 369, 374, 376, 380, 382, 384 benthic carbon cycling, 365, 375, 380 benthic oxygen demand, 365, 375–377, 380–383 benthos, 364–367, 369, 374–376, 380–384 Bering Sea, 364, 367, 369, 370, 379, 380, 384, 392, see also St. Lawrence Island polynya, 61 BGE (bacterial growth efficiency), 327, 348 biogenic CO2 flux, 167, 169, 170 biogeochemical–physical coupling, 197 biological carbon pump, 324 biological processes impact of physical forcing on, 73, 74 biological pump, 324, 325, 346, see also primary production, 167, 169, 170 biomass, 279, 280 birds, 240, 257, 258, 260 Adelie Penguin, Pygoscelis adeliae, 392 Antarctic Petrel, Thalassoica antarctica, 394 Black Guillemot, Cepphus grylle, 400 Black-legged Kitiwake, Rissa tridactyla, 403 Dovekie, Alle alle, 399, 400, 402, 403 Emperor Penguin, Aptenodytes forsteri, 392, 394, 395 Northern Fulmar, Fulmarus glacialis, 403 Snow Petrel, Pagodroma nivea, 394 Thick-billed Murre, Uria lomvia, 403 Black-legged Kittiwake, Rissa tridactyla, 403 Blodgett Iceberg Tongue, 21 polynya, 21, 23 Boreogadus saida, Arctic Cod, 399, 400 bottom water (formation), 202, 205, 207, 208 Bowhead Whale, Balaena mysticetus, 392 Bowman Island polynya, 21, 23
Subject Index BP (bacterial production), 327, 341, 347 BP (bacterial production) : PP (primary production) ratio, 329, 335, 342, 348 Bransfield Strait, 329 Breid Bay polynya, 27 brine enriched waters descent and transport, 70–73 feedback processes, 67, 68 impact on biological processes, 73, 74 shelf waters, 164, 167, 168, 173, 175, 177 Bylot Island polynya, 11, 12 Calanoides acutus, 287–289, 291, 294, 296, 297, 299–303, 307, 309, 310 Calanus glacialis, 399 Calanus hyperboreus, 399 Calanus propinquus, 287–289, 291, 294, 296–303, 307, 309, 310 Calanus simillimus, 307 calcium carbonate, 170 Callianira antarctica, 301 Canadian Arctic Archipelago climate change, 75, 76 flaw leads, 60 ice bridges, 61–63 tidally-forced polynyas, 66 water mass transformation, 70 Cape Bathurst (CAB) polynya, 8, 17, 18, 164, 168, 178, 213, 248, 349, 392 Cape Borle polynya, 27 Cape Darnley polynya, 21, 23, 27 Cape Hudson polynya, 21, 23 Cape Poinsett polynya, 23, 27 carbon cycling, 164 air–sea exchange, 166–178 biogenic CO2 flux, 169, 170 carbon monoxide, 164, 178 carbonate system, 166, 170 CO2 equilibrium concentrations, 165–167 CO2 partial pressure (pCO2 ), 166, 167, 169–176 dissolved inorganic carbon (DIC), 166, 169, 173, 178 dissolved organic carbon (DOC), 170, 172–174, 176, 177 future research directions, 181, 182 methane, 164, 170, 178 methylhalides, 164, 180 particulate organic carbon (POC), 173 polynyas as CO2 sources/sinks, see also specific polynyas, 163, 164, 167–170, 181, 182 sea ice, 181 carbon export, 323, 378–380 carbon flux, 325, 403 carbonate system, 166, 170 Cardigan Strait polynya, 66
Subject Index Casey Bay polynya, 23 CDW (Circumpolar Deep Water), 28, 34, 61, 67, 74, 393, 396 Cepphus grylle, Black Guillemot, 400 CFCs (chlorofluorocarbons), 164, 194, 205, 207, 208 Chaetoceros, 399 Chaetoceros socialis, 346, 347 change, climate, 411 chlorofluorocarbons (CFCs), 164, 194, 205, 207, 208 Cape Bathurst polynya, 178 CFC-11, equilibrium concentration, 165 polynyas as sinks for, 164 Ross Sea, 173 Weddell Sea, 178 chlorophyll, 37, 242–251, 255–257 Chukchi Peninsula, 392 Chukchi Sea dense water descent and transport, 71 flaw lead polynyas, 59, 60 Circumpolar Deep Water (CDW), 28, 34, 61, 67, 74, 393, 396 circumpolar flaw lead, 17 Circumpolar Flaw lead (CFL) polynya, 5 climate change, 75, 76, 166, 167, 261, 262, 383–385, 411 impact on gas exchange in polynyas, 167, 181, 182 climate change/variability, 4–6, 42 climate warming, 215 climatological forcing carbon cycling in polynyas, 163, 164 cloud radiative forcing, 127, 139, 148 CO2 , 350 sink, 205, 206, 208, 213, 215 Coburg Island, 400 polynya, 11, 12 colored dissolved organic matter (CDOM), 174 Committee Bay polynya, 14, 15 Commonwealth Bay, 21 convective plumes, 136, 137, 151 convectively forced polynyas, 56, 57, 63–67 feedback processes, 67, 68 forced convection, 63–67 free convection, 63–65, 69 impact on biological processes, 74 modelling of, 64, 66 water mass transformation, 70 copepod, 248, 257, 261, 397, 399, 400 calanoid, 400 Calanoides acutus, 287–289, 291, 294, 296, 297, 299–303, 307, 309, 310 Calanus glacialis, 399 Calanus hyperboreus, 399
451 Calanus propinquus, 287–289, 291, 294, 296–303, 307, 309, 310 Calanus simillimus, 307 Ctenocalanus citer, 287, 289, 291, 307 Metridia gerlachei, 287–289, 291, 294, 296–303, 307, 309, 310 Metridia longa, 339 Microcalanus pygmaeus, 287, 289, 291, 296, 307, 310 Oithona spp., 288, 289, 291, 293, 294, 296, 307, 310 Oncaea curvata, 397 Oncaea spp., 288, 289, 291, 293, 296, 305, 310 Paraeuchaeta spp., 288, 289, 291, 294, 300–303 Paralabidocera antarctica, 293 Rhincalanus gigas, 287–289, 291, 294, 300–303, 307, 309 Cosmonaut Sea, 121, 272, 293, 301 polynya, 8, 33, 34, 65–68, 130, 199, 201 coupling, 329, 336, 337 Crabeater Seal, Lobodon carcinophaga, 395 Crystal Krill, Euphausia crystallorophias, 287, 288, 290, 291, 293–296, 298–302, 304, 305, 308–310, 395 Ctenocalanus citer, 287, 289, 291, 307 current driven polynyas, 62, 63 cycles, biogeochemical, 411 daily ration, 280, 281 Dalton Iceberg Tongue polynya, 21, 23 Davis Sea polynya, 27 deep and bottom water formation, 167 air–sea exchange, 164 Antarctic Bottom Water, 177, 178 Mertz Glacier polynya, 177, 178 North Water polynya, 175 Weddell Sea polynya, 178 deep water formation dense water descent and transport from polynyas, 70–73 flaw lead polynyas, 70 Greenland Sea, 64 deep ocean polynya, 33 deep-water polynya, 4, 7, 31, 32, 42 Delphinapterus leucas, Beluga Whale, 392, 400 Denman Glacier polynya, 23 Denmark Strait overflow, 72 dense water descent and transport, 70–73 fronts, 72 diatom, 225, 230, 243–245, 248–257, 259, 260–262, 338, 346, 394, 395, 397, 399, 401 Chaetoceros socialis, 346, 347 Thalassiosira antarctica, 397 Dibble Iceberg Tongue polynya, 21, 23
452 DIC (dissolved inorganic carbon), 166, 169, 173, 178, 196, 197, 204, 210, 212 production, 335 dimethyl sulfide (DMS), 144, 164, 178–180, 213, 396 benthic cycling, 179 equilibrium concentrations, 165 polynyas and climate change, 181, 182 sea ice, 180 dinoflagellates, 346 dissolved inorganic carbon (DIC), 166, 169, 173, 178, 196, 197, 204, 210, 212 North Water polynya, 173 Ross Sea, 173 Weddell Sea polynya, 178 dissolved organic carbon (DOC), 170, 172–174, 176, 177, 211, 331, 335, 339, 347, 348 consumption, 335 North Water polynya, 174 Northeast Water polynya, 176, 177 Ross Sea, 172, 173 dissolved organic matter (DOM), 325, 328, 339 dissolved organic nitrogen (DON), 339 Dissostichus mawsoni, Antarctic Toothfish, 395 DMSP (Defense Meteorological Satellite Program), 8, 9, 24, 31 SSM/I (Special Sensor Microwave/Imager), 33 DOC (dissolved organic carbon), 170, 172–174, 176, 177, 211, 331, 335, 339, 347, 348 DOM (dissolved organic matter), 325, 328, 339 DON (dissolved organic nitrogen), 339 Dovekie, Alle alle, 399, 400, 402, 403 Drake Passage, 297, 309 Drygalski Ice Tongue polynya, 28 Drygalski Island polynya, 21, 23 Dumont d’Urville polynya, 27 Dundas Island polynya, 16, 66 East Greenland Current, 62, 175, 177, 274, 338, 339 East Greenland Shelf Water, 338 East Lazarev Sea polynya, 27 East Siberian Sea, 59 flaw polynyas, 17 ECMWF (European Centre for Medium-range Weather Forecasts), 146 egg production, 284, 285 Ekman layers, 72, 73 El Niño, 28, 31, 393 El Niño-Southern Oscillation (ENSO), 28 Ellis Fjord, 305, 306 Eltanin Bay polynya, 27 Emperor penguin, Aptenodytes forsteri, 392, 394, 395 Enderbv Land polynya, 23 ENSO (El Nino-Southern Oscillation), 28
Subject Index Envisat, 43 EOS Aqua Advanced Scanning Microwave Radiometer-E (AMSR-E), 43 EOS Terra Moderate-resolution Imaging Spectro-radiometer (MODIS), 30 equilibrium gas concentrations, 164–166 Erignathus barbatus, Bearded Seal, 392 ESMR (Electrically Scanning Microwave Radiometer), see Nimbus-5 Electrically Scanning Microwave Radiometer, 32 Euphausia crystallorophias, Crystal Krill, 287, 288, 290, 291, 293–296, 298–302, 304, 305, 308–310, 395 Euphausia superba, Antarctic Krill, 287, 288, 290, 293–296, 298–305, 307–310, 395 European Centre for Medium-range Weather Forecasts (ECMWF), 146 export, 239, 246–248, 254, 260, 263, 339 fraction, 373, 375, 377–379, 381 faecal pellet, 280, 283 fatty acid, 281 fecal pellets, 397 feed-backs, 412 feedback processes air–sea exchange, 163, 164, 166, 170, 181, 182 polynya formation, 56, 57, 67, 68 polynyas and climate change, 75, 76, 167, 173, 181, 182 Filchner-Rønne Ice Shelf, 288 fish, 399 Antarctic Toothfish, Dissostichus mawsoni, 395 Arctic Cod, Boreogadus saida, 399, 400 fjord, 209, 210 flagellates, 397 flaw lead polynyas, 58–61 dense water descent and transport, 70, 71 flaw leads vs. polynyas, 55, 56 Marginal Ice Zone ‘polynyas’, 68, 69 flaw leads, 2, 5, 11, 181 fluxes of particulate matter, 347 food web, 239, 240, 256–261, 411 forced convection, 63–67 formation processes, 55–69 convective forcing, 57, 63–67 forced convection, 63–67 free convection, 63–65, 67 feedback processes, 56, 57, 67, 68 impact on biological processes, 73, 74 Marginal Ice Zone ‘polynyas’, 68, 69 mechanical forcing, 55, 56 current-driven, 62, 63 impact on biological processes, 73, 74 wind-driven, 57–63, 67, 68 Foxe Basin polynya, 17, 18
Subject Index Fragilariopsis spp., 399 Franklin Strait polynya, 17, 19 frazil ice, 38, 55–58, 67, 87 collection thickness, 92 concentration, 106 free convection, 63–65, 69 Fritillaria borealis, 290, 292, 293, 301, 302, 310 fronts, 72 Fulmarus glacialis, Northern Fulmar, 403 Fury and Hecla Strait polynya, 7 gas cycling, see air-sea exchange; specific gases GCM (general circulation model), 87, 108 general circulation (GCM) polynya models, 87, 108 generation time, 285 geostrophy, 133, 134 Gerlache Strait, 309, 329, 337, 340 global climate changes, 323 global warming, 323, 350, see also climate change grazing, 248, 254, 261, 262, 399 by, 348 impact, 282, 283 Phaeocystis sp., 397 Great Siberian Polynya, 130 greenhouse gas, 412 Greenland, 399–401 Greenland Sea, 167, 393, see also Northeast Water (NEW) polynya, 64 Nordbutka polynya, 68, 69 Northeast Water (NEW) polynya, 164, 175–177 grounded icebergs, 23, 28, 29, 31, 37, 39, 41, 42 growth efficiency, 327, 335, 344 growth rates, 340 gut chlorophyll, 281 Haklyut Island, 400 Halley Bay, 288–290, 293, 299–301, 305 polynya, 27 halocline, 5, 17 Hells Gate, 130 polynya, 66, 130 Hells Gate and Cardigan Strait polynyas, 14 Hells Gate—Cardigan Strait, 15 Henry Bay polynya, 27 Henry’s Law, 164, 165 heterotrophic nanoplankton (HNAN), 329, 337 heterotrophic potential, 343 high vertical ocean-heat fluxes, 3 high-latitude environmental change, 4 High-Salinity Shelf Water (HSSW), 27, 204, 205, 207, 209 HNAN (heterotrophic nanoplankton), 329, 337 HSSW (High-Salinity Shelf Water), 27, 204, 205, 207, 209
453 Hudson Bay, 117 Hull Bay polynya, 27 humidity profile, 136, 137 hybrid polynyas, 68 hydrolysis, 344, 348 Hydrurga leptonyx, Leopard Seal, 395 hyperproductivity, 205 ice algae, 169, 180 ice bridge, 7, 11, 34, 36, 42, 414 polynyas, 7 ice extent, reduction, 412 ice factories, 5, 38, 40 ice tongues, 28 ice-albedo feedback mechanism, 2 iceberg, 28–31, 34, 39, 42 B-15, 30, 31 B-15A, 28–30 B-15B, 30 B-9B, 37–39 C-08, 41 C-19, 31 icescape, 2, 7, 8, 22, 31, 37, 39, 40, 42 incorporation efficiency (IE), 328, 344 ingestion rate, 280, 281, 283 inter-annual variability, 2, 6, 17, 22, 28, 29, 34, 42 iron, 173, 240, 249–252, 254, 255, 259–262, 395 irradiance, 239, 243–245, 249–251, 262, 414 James Clarke Ross, 393 Jan Mayen Current, 68 Japanese Advanced Land Observing System (ALOS), 43 Jelbart Ice Shelf polynya, 27 Kane Basin, 398 Kapp Norvegia, 288, 289, 299, 305 Kara Sea, 17, 59 Kashevarov Bank polynya, 65, 66, 70, 75, 130 katabatic, 21, 37 wind, 5, 21, 23, 26, 28, 29, 34, 37 Killer Whale, Orcinus orca, 395, 396 kinetic, 327, 343 krill, 337, 397 Euphausia crystallorophias, Crystal Krill, 287, 288, 290, 291, 293–296, 298–302, 304, 305, 308–310, 395 Euphausia superba, Antarctic Krill, 287, 288, 290, 293–296, 298–305, 307–310, 395 La Niña, 393 Labrador Sea, 64, 167 Lady Anne Strait, 130, 398 polynya, 130 Lake Laberge polynyas, 65, 67
454 Lambert Channel polynya, 17, 19 Lancaster Sound, 398 polynya, 12, 13 land bridge, 42 polynyas, 7 Laptev Sea, 59 flaw polynya, 17 polynya, 21, 214, 275, 280, 285 Leptonychotes weddellii, Weddell Seal, 392, 395 Larsen Ice Shelf polynya, 27, 202, 203 larval fish, 399 Latady Island polynya, 27 latent heat, 223, 224, 231, 391, 393, 398 mechanisms, 17, 34 polynya, 4, 5, 8, 17, 23, 36, 129, 130, 131 polynyas, see mechanically forced polynyas Lazarev Sea, 293, 297, 301, 303, 304, 308 polynya, 349 leads, 2, 11 Leopard Seal, Hydrurga leptonyx, 395 light, 256, 260, 261 Limacina helicina, 397 Limacina spp., 287, 290, 292, 294, 300–302 Lincoln Sea, 346 lipid, 278, 284 Lobodon carcinophaga, Crabeater Seal, 395 Lützow-Holm Bay, 293, 301, 303, 305 polynya, 27 Lyddan Island polynya, 27 Mackenzie Bay polynya, 23, 27 Mackenzie River, 59 macronutrients, 255, 260 mammals Antarctic Minke Whale, Balaenoptera bonaerensis, 395, 396 Beluga Whale, Delphinapterus leucas, 392, 400 Bowhead Whale, Balaena mysticetus, 392 Crabeater Seal, Lobodon carcinophaga, 395 Killer Whale, Orcinus orca, 395, 396 Leopard Seal, Hydrurga leptonyx, 395 Polar Bear, Ursus maritimus, 392 Ringed Seal, Pusa hispida (formerly Phoca hispida), 400 Walrus, Odobenus rosmarus, 392 Weddell Seal, Leptonychotes weddellii, 392, 395 marginal ice zone (MIZ), 395–398, 403 ‘polynyas’, 68, 69 Marguerite Bay, 289, 296, 297, 300, 303, 307, 309, 415 polynya, 27 marine mammals, 240, 257 Maud Rise, 287, 288 polynya, 8, 64, 65, see also Weddell polynya Seamount, 130 Maudheim polynya, 27
Subject Index MCDW (Modified Circumpolar Deep Water), 26, 37, 416 McMurdo Sound, 291, 292, 294, 300, 301 mechanically forced polynyas, 55–63 current-driven, 62, 63 impact on biological processes, 73, 74 wind-driven, 57–63 feedback processes, 67, 68 flaw lead polynyas, 59–61, 70, 71 ice bridges, 61–63 in the Antarctic, 61, 62 modelling of, 58, 59 water mass transformation, 70 Mertz Glacier, 21, 119 carbon cycling, 177, 178 polynya (MGP), 21, 23, 27, 34, 37–41, 61, 62, 164, 177, 206, 207, 255 methylhalides, 180 shelf waters, 177, 178 Mertz Glacier Tongue (MGT), 23, 38 meteorological measurements, 128, 129, 131 methane, 164, 170, 178 methanesulfonic acid (MSA), 3 methylhalides, 164, 180 Metridia gerlachei, 287–289, 291, 294, 296–303, 307, 309, 310 Metridia longa, 339 MGP (Mertz Glacier Polynya), 21, 23, 27, 34, 37–41, 61, 62, 164, 177, 206, 207, 255 MGT (Mertz Glacier Tongue), 23, 38 microbial loop, 325, 328, 345 Microcalanus pygmaeus, 287, 289, 291, 296, 307, 310 microzooplankton grazers, 345 Mill Island polynya, 21, 23 Minke Whale, see Antarctic Minke Whale MIZ (Marginal Ice Zone), 395–398, 403 modelling convectively forced polynyas, 64, 66 dense water descent and transport, 71–73 future directions, 74, 75 mechanically forced polynyas, 58, 59 polynya response to climate change, 75, 76 sulfur cycling, 180 Modified Circumpolar Deep Water (MCDW), 26, 37, 416 MSA (methanesulfonic acid), 3 NAO (North Atlantic Oscillation), 3 Nares Strait, 68 Narwhal whale, 400 National Centers for Environmental Predictions (NCEP), 146 nepheloid, 343 NEW (Northeast Water) polynya, 9, 11, 29, 62, 63, 75, 98, 131, 164, 176, 210, 211, 240, 247,
Subject Index 272, 274, 277–279, 283, 338, 392, 393, 401, 403 new production, 245–248 NGT (Ninnis Glacier Tongue), 39, 41 Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR), 32 Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR), 31 Ninnis Glacier, 21 polynya, 21, 23, 27, 37 tongue (NGT), 39, 41 nitrate, 196, 210 nitrogen, 165, 176, 325, 339, 348 limitation, 344 Nordbutka polynya, 68, 69 Norske Ør, 338, 340 Norske Trough, 340 North Atlantic Oscillation (NAO), 3 North Water (NOW), 35, 117, 129, 130, 145, 147, 149, 323, 346, 392, 393, 401 polynya, 7, 8, 10–12, 34–36, 61–63, 68, 132, 164, 174, 211–213, 239, 240, 243, 257, 259, 398, 399, 401–403 carbon cycling, 173–175 deep water formation, 175 dimethylsulfide (DMS), 179 pCO2 , 172, 174, 175 Northeast Greenland Coastal Current, 63 Northeast Greenland Counter Current, 62 Northeast Water (NEW), 10, 117, 129, 145, 323 polynya, 11, 62, 63, 75, 98, 131, 164, 176, 210, 211, 240, 247, 272, 274, 277–279, 283, 338, 392, 393, 401, 403 carbon cycling, 175–177 dissolved organic carbon (DOC), 176 pCO2 , 172, 176 Northern Bering Sea polynya, 17 Northern Fulmar, Fulmarus glacialis, 403 Norwegian-Atlantic Current, 68 NOW (North Water), 35, 117, 129, 130, 145, 147, 149, 323, 346, 392, 393, 401 polynya, 7, 8, 10–12, 34–36, 61–63, 68, 132, 164, 174, 211–213, 239, 240, 243, 257, 259, 398, 399, 401–403 nutrient fluxes polynya formation processes, 74 nutrients, 196, 198, 202, 208, 213, 243, 248, 255, 260, 261, 415 Ob Bank, 338 ocean-strait, 42 polynya, 7, 13 Odobenus rosmarus, Walrus, 392 Oithona spp., 288, 289, 291, 293, 294, 296, 307, 310 Okhotsk Sea, 65, 119
455 dense water descent and transport, 71 flaw lead polynyas, 61 Kashevarov Bank polynya, 66 polynya, 17, 20 Oncaea curvata, 397 Oncaea spp., 288, 289, 291, 293, 296, 305, 310 Orcinus orca, Killer Whale, 395, 396 oxygen, 164, 194, 196, 200 equilibrium concentrations, 165 Mertz Glacier polynya (MGP), 177, 178 Pagodroma nivea, Snow Petrel, 394 Palmer Antarctica Long-term Ecological Research Program (LTER), 334, 336 Paraeuchaeta spp., 288, 289, 291, 294, 300–303 Paralabidocera antarctica, 293 partial pressure of CO2 (pCO2 ), 196, 204, 210, 211 particle export, 324 particulate organic carbon (POC), 173, 340 Ross Sea, 173, 340 particulate organic material (POM), 325, 342, 344 passive microwave, 2, 33 Paulding Bay, 21 polynya, 21, 23, 27 penetrative convection, 64 penguin, 256, 258, 260 Adelie Penguin, Pygocellis adeliae, 392 Emperor Penguin, Aptenodytes forsteri, 392, 394, 395 Pennell Bank, 393 polynya, 28 perennial ice, 414 petrels, 396, 397 Antarctic Petrel, Thalassoica antarctica, 394 Snow Petrel, Pagodroma nivea, 394 Phaeocystis, 226, 230, 243, 247, 249–252, 254, 256, 258, 261, 262, 294, 304–306, 310, 333, 335–337, 349, 395–397 Phaeocystis antarctica, 332, 394, 397 Phoca hispida, now Pusa hispida, Ringed Seal, 400 phosphate, 196, 210 physical forcing, see formation processes polynyas characteristics of, 55–57 vs. leads, 56, 57 phytoplankton, 143 assemblage, 239, 240, 252 biomass, 243, 249, 250, 398 growth, 239, 240, 243, 244, 252, 259 production, 245–248, 257, 262 Pim Island, 400 Pine Island Bay polynya, 27 Pleuragramma antarcticum, Antarctic Silverfish, 295
456 plume, 152 POC (particulate organic carbon), 173, 343 POC : PON ratio, 344 Polar Bear, Ursus maritimus, 392 polar-orbiting satellites, 143 polynya, 4–8, 11, 12, 14–17, 20–23, 25, 26, 28, 29, 31–34, 36–38, 40–43 closing, 102 time, 102 dynamics, 88 flux models, 87, 89 opening, 94 time, 94 règime, 28, 29, 37, 41 Polynya Signature Simulation Method (PSSM), 8, 9, 22, 30, 31 polynyas, 1, 2, 4–8, 11, 15–17, 21, 22, 25, 27–29, 42, 43 recurring, 392 POM (particulate organic matter), 325, 342, 344 Pomeroy, 331 hypothesis, 331 Porpoise Bay, 21 polynya, 21, 23, 27 post-polynya, 392, 397 primary production (PP), 194, 202, 211, 240, 243, 245, 247, 252, 253, 257, 261, 262, 327, 333, 398–400 biogenic CO2 flux, 167–170 DMS (dimethyl sulfide) cycling, 178, 179, 182 polynyas and climate change, 182 Ross Sea, 172, 173 primary productivity, 401 Prince Regent Inlet polynya, 12, 13 productivity, 239, 240, 242, 243, 245, 247, 252–254, 256–260, 262 protozoan, 328 Prydz Bay, 289, 293, 294, 301, 305, 349 polynya, 23, 27, 28, 177, 179, 200, 206, 207, 394 PSSM (Polynya Signature Simulation Method), 8, 9, 22, 30, 31 psychrophilic (cold-loving), 330 pteropod, 254, 397 Limacina helicina, 397 Limacina spp., 287, 290, 292, 294, 300–302 Pusa hispida (formerly Phoca hispida), Ringed Seal, 400 pycnocline, 7 Pygoscelis adeliae, Adelie Penguin, 392 PP (primary production), 194, 202, 211, 240, 243, 245, 247, 252, 253, 257, 261, 262, 327, 333, 398–400 BP : PP ratio, 329, 335, 342, 348
Subject Index Q10, 227, 228 Queens Channel and Penny Strait polynya, 14, 16 RACER (Research on Coastal Antarctic Ecosystem Rates), 329, 332, 336 Radarsat ScanSAR Wide image, 38, 40 Radarsat-2, 43 radiative fluxes, 131, 147, 148, 150 radiosonde, 137, 152 recruitment, 286 rectification, 326, 350 hypothesis, 211 respiration, 327, 335, 346 retentive system, 365, 367, 369, 378, 380, 382 Rhincalanus gigas, 287–289, 291, 294, 300–303, 307, 309 Ringed Seal, Pusa hispida, 400 Rissa tridactyla, Black-legged Kitiwake, 403 Ronne Ice Shelf, 133 polynya, 27, 130, 394 Ronne polynya, 28, 29, 31, 202 Ross Bay polynya, 130 Ross Ice Shelf polynya, 31 Ross Passage, 393 polynya, 28 Ross Sea, 119, 171, 287, 291, 294, 295, 299–301, 304–306, 308, 310, 395 carbon cycling, 170–173 continental slope, 395 dense water descent and transport, 70, 71 dimethyl sulfide (DMS), 179 dissolved organic carbon (DOC), 172, 173 flaw lead polynyas, 61 gas studies, 164 particulate organic carbon (POC), 173 pCO2 , 171, 172 polynya (RSP), 27–29, 61, 67, 68, 133, 170, 171, 173, 204–206, 239, 240, 249–255, 258, 259, 323, 331, 366, 371–374, 376, 378, 393, 394, 396–398, 401, 403 Polynya Project, 332 shelf, 395 shelf waters, 173 Terra Nova Bay polynya, 61, 62, 170, 171, 173 RSP (Ross Sea polynya), 27–29, 61, 67, 68, 133, 170, 171, 173, 204–206, 239, 240, 249–255, 258, 259, 323, 331, 366, 371–374, 376, 378, 393, 394, 396–398, 401, 403 SAM (Southern Annular Mode), 3, 416 salinity, 416 Salpa spp., 290, 292, 294, 301, 302, 304 SAR (synthetic aperture radar), 43 satellite, 43 sea ice
Subject Index anomalies, 36 CDOM, 174 concentration (SIC), 35 dimethyl sulfide (DMS), 180 gas fluxes, 181 ice algae, 169, 180 ice bridges, 61–63, 68 ice divergence, see also mechanically forced polynyas, 55, 56 ice flow motion, 63 ice tongues, 61, 62 sea ice–cloud–albedo feedback, 4 Sea of Okhotsk polynya, 129 sea salt aerosol, 145 sea-level pressure (SLP), 35 seals Bearded Seal, Erignathus barbatus, 392 Crabeater Seal, Lobodon carcinophaga, 395 Leopard Seal, Hydrurga leptonyx, 395 Ringed Seal, Pusa hispida (formerly Phoca hispida), 400 Weddell Seal, Leptonychotes weddellii, 392, 395 seasonal rectification hypothesis, 169, 170, 176 sediment community oxygen consumption, 367, 374, 375 sedimentation, 245, 248 sensible heat, 223–225, 228, 392, 393, 398 mechanisms, 17 polynya, 4, 129, 130 polynyas, see convectively forced polynyas sequestration, 197, 198 Shackleton Ice Shelf polynya, 21, 23, 27 shelf waters, 167 air–sea exchange, 164 Mertz Glacier polynya (MGP), 177, 178 North Water (NOW) polynya, 175 Ross Sea polynya (RSP), 173 shelf-water polynyas, 4, 5, 19, 21, 24, 29, 34 Siberian flaw polynyas, 74 sinking, 245–249, 254, 259, 260, 262, 263 export, 247 size and distribution, 55 Slava Bay, 21 polynya, 23 Smith Sound, 61, 398 SMMR (Scanning Multichannel Microwave Radiometer), see Nimbus-7 Multichannel Microwave Radiometer, 31, 413 Snow Petrel, Pagodroma nivea, 394 solar radiation Marginal Ice Zone (MIZ) ‘polynyas’, 68 solubility pump, 167 Southern Annular Mode (SAM), 3, 416 Southern Ocean, see also specific polynyas, 177 Special Sensor Microwave/Imager (SSM/I), 8, 9, 19, 24, 25, 28, 30, 31, 413 St. Lawrence Island, 117, 392
457 polynya (SLIP), 7, 8, 10, 58–61, 70, 73, 74, 101, 129, 133, 135, 168, 214, 240, 257, 272, 275, 276, 279, 349, 364, 365, 369 dimethylsulfide (DMS), 179 gas studies, 164 St. Matthew islands, 392 stamukhi, 59 standing stocks of “bacteria”, 326 Storfjorden, 71, 117 polynya, 17, 167, 168, 209, 210 stratification, 227–229, 234, 415 sulfur cycling, see also dimethyl sulfide, 178–180 Sulzberger Bay polynya, 27 superbloom, 202 surface film, 210 synoptic weather systems, 31 synoptic winds, 26, 29, 31, 37 synoptic-scale winds, 29 synthetic aperture radar (SAR), 43 Syowa, 21 polynya, 21, 23 Taylor Glacier, 21 polynya, 21, 23 temperature inversion, 135, 148 Terra Nova Bay, 119, 295, 306, 393, 397 polynya, 61, 62, 133, 170, 171, 173, 204, 205, 256 Terra Nova Island polynya, 21, 23 terrestrial, 325 Temora longicornis, 397 Thalassiosira spp., 399 Thalassoica antarctica, Antarctic Petrel, 394 Themisto libellula, 399, 400, 403 thermobaricity, 73 thermohaline, 6 Thick-billed Murre, Uria lomvia, 403 thickness, ice, 412 throughflow polynyas, 65, 67 Thysanoessa macrura, 288, 290, 291, 294, 295, 300, 302, 303, 308 tidally-forced polynyas, 64–66 timing, 239, 240, 243, 260, 261 of polynyas biogenic CO2 flux, 169, 170 topography, 64, 133, 134, 145 Trans-Polar Drift, 74 transport of dense water, 70–73 turbulent flux, 127, 136, 148, 150 UCDW (Upper Circumpolar Deep Water), 225, 230, 233 Upper Circumpolar Deep Water (UCDW), 225, 230, 233 Upper Halocline Arctic Water, 338
458 uptake, 327 upwelling, 61, 63, 65–67, 195, 197, 208, 415 Uria lomvia, Thick-billed Murre, 403 Ursus maritimus, Polar Bear, 392 Utstikkar Bay polynya, 27 ventilation, 195, 196, 208 vertical flux, 283 Vincennes Bay polynya, 23, 27 virus, 348 Voyeykov Ice Shelf polynya, 21, 23 Walrus, Odobenus rosmarus, 392 warming, 326, 342, 344, 348 water mass transformation, 68, 70 wax esters, 284 Weddell Gyre, 29 Weddell polynya, 164, 177, 178, 195–199, 208 Weddell Sea, 119, 167, 177, 272, 287–289, 299, 300, 303, 304, 306, 308, 309 carbon cycling, 178 dense water descent and transport, 70–72 flaw lead polynyas, 61 Maud Rise polynya, 64, 65 polynya, 7, 32, 133, 272, 287 Weddell Gyre, 64 Weddell polynya, 164, 177, 178 Weddell Sea Bottom Water, 178 Weddell Seal, Leptonychotes weddellii, 392, 395
Subject Index West Antarctic Peninsula, 329, 334, 336 West Greenland Current, 61, 63 West Ice Shelf polynya, 21, 23, 27 West Lazarev Sea polynya, 27 West Spitzbergen Current, 68 Westwind Trough, 338, 340 Whalers Bay polynya, 17, 20, 68, 69 whales, 412 Antarctic Minke Whale, Balaenoptera bonaerensis, 395, 396 Beluga Whale, Delphinapterus leucas, 392, 400 Bowhead Whale, Balaena mysticetus, 392 Killer Whale, Orcinus orca, 395, 396 wind-driven polynyas, 57–63 feedback processes, 67, 68 flaw lead polynyas, 59–61, 70, 71 ice bridges, 61–63 in the Antarctic, 61, 62 modelling of, 58, 59 water mass transformation, 70 Wrigley Gulf polynya, 27 zooplankton, 256–260 abundance, 273–276 diet, 281 dry weight, 277, 278 grazing, 246 species composition, 272 Themisto libellula, 400
