Articles | Volume 15, issue 9
https://doi.org/10.5194/tc-15-4281-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-4281-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Sep 2021
Research article |
| 07 Sep 2021
| 07 Sep 2021
Southern Ocean polynyas in CMIP6 models
Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden
Céline Heuzé
Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
Sebastiaan Swart
Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden
Department of Oceanography, University of Cape Town, Rondebosch, South Africa
Related authors
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
Short summary
Short summary
For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Baylor Fox-Kemper, Patricia DeRepentigny, Anne Marie Treguier, Christian Stepanek, Eleanor O’Rourke, Chloe Mackallah, Alberto Meucci, Yevgeny Aksenov, Paul J. Durack, Nicole Feldl, Vanessa Hernaman, Céline Heuzé, Doroteaciro Iovino, Gaurav Madan, André L. Marquez, François Massonnet, Jenny Mecking, Dhrubajyoti Samanta, Patrick C. Taylor, Wan-Ling Tseng, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2025-3083, https://doi.org/10.5194/egusphere-2025-3083, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The earth system model variables needed for studies of the ocean and sea ice are prioritized and requested.
Céline Heuzé and Carmen Hau Man Wong
EGUsphere, https://doi.org/10.5194/egusphere-2025-2747, https://doi.org/10.5194/egusphere-2025-2747, 2025
Short summary
Short summary
Polynyas are areas with no- or thin-ice within the ice pack. They play a crucial role for the Earth system, yet their monitoring in the Arctic is challenging because polynya detection is non-trivial. We here demonstrate that polynyas can successfully be detected with a novel, machine-learning based method. In fact, we argue that they are better detected than with traditional methods, which seem to fail as sea ice decreases because of climate change.
Flor Vermassen, Clare Bird, Tirza M. Weitkamp, Kate F. Darling, Hanna Farnelid, Céline Heuzé, Allison Y. Hsiang, Salar Karam, Christian Stranne, Marcus Sundbom, and Helen K. Coxall
Biogeosciences, 22, 2261–2286, https://doi.org/10.5194/bg-22-2261-2025, https://doi.org/10.5194/bg-22-2261-2025, 2025
Short summary
Short summary
We provide the first systematic survey of planktonic foraminifera in the high Arctic Ocean. Our results describe the abundance and species composition under summer sea ice. They indicate that the polar specialist N. pachyderma is the only species present, with subpolar species absent. The data set will be a valuable reference for continued monitoring of the state of planktonic foraminifera communities as they respond to the ongoing sea-ice decline and the “Atlantification” of the Arctic Ocean.
Céline Heuzé, Linn Carlstedt, Lea Poropat, and Heather Reese
EGUsphere, https://doi.org/10.5194/egusphere-2025-700, https://doi.org/10.5194/egusphere-2025-700, 2025
Short summary
Short summary
Extreme sea level kills and will worsen under climate change. In Northern Europe what drives these extreme events will not change so determining these drivers is of use for planning coastal defenses. Here, using two machine learning methods on hourly tide gauge and weather data at nine locations around the North Sea – Baltic, we determine that the driver of prolonged periods of high sea level is the westerly winds, whereas the drivers of the most extreme peaks depend on the coastline geometry.
Salar Karam, Céline Heuzé, Mario Hoppmann, and Laura de Steur
Ocean Sci., 20, 917–930, https://doi.org/10.5194/os-20-917-2024, https://doi.org/10.5194/os-20-917-2024, 2024
Short summary
Short summary
A long-term mooring array in the Fram Strait allows for an evaluation of decadal trends in temperature in this major oceanic gateway into the Arctic. Since the 1980s, the deep waters of the Greenland Sea and the Eurasian Basin of the Arctic have warmed rapidly at a rate of 0.11°C and 0.05°C per decade, respectively, at a depth of 2500 m. We show that the temperatures of the two basins converged around 2017 and that the deep waters of the Greenland Sea are now a heat source for the Arctic Ocean.
Lea Poropat, Dani Jones, Simon D. A. Thomas, and Céline Heuzé
Ocean Sci., 20, 201–215, https://doi.org/10.5194/os-20-201-2024, https://doi.org/10.5194/os-20-201-2024, 2024
Short summary
Short summary
In this study we use a machine learning method called a Gaussian mixture model to divide part of the ocean (northwestern European seas and part of the Atlantic Ocean) into regions based on satellite observations of sea level. This helps us study each of these regions separately and learn more about what causes sea level changes there. We find that the ocean is first divided based on bathymetry and then based on other features such as water masses and typical atmospheric conditions.
Céline Heuzé, Oliver Huhn, Maren Walter, Natalia Sukhikh, Salar Karam, Wiebke Körtke, Myriel Vredenborg, Klaus Bulsiewicz, Jürgen Sültenfuß, Ying-Chih Fang, Christian Mertens, Benjamin Rabe, Sandra Tippenhauer, Jacob Allerholt, Hailun He, David Kuhlmey, Ivan Kuznetsov, and Maria Mallet
Earth Syst. Sci. Data, 15, 5517–5534, https://doi.org/10.5194/essd-15-5517-2023, https://doi.org/10.5194/essd-15-5517-2023, 2023
Short summary
Short summary
Gases dissolved in the ocean water not used by the ecosystem (or "passive tracers") are invaluable to track water over long distances and investigate the processes that modify its properties. Unfortunately, especially so in the ice-covered Arctic Ocean, such gas measurements are sparse. We here present a data set of several passive tracers (anthropogenic gases, noble gases and their isotopes) collected over the full ocean depth, weekly, during the 1-year drift in the Arctic during MOSAiC.
Amy Solomon, Céline Heuzé, Benjamin Rabe, Sheldon Bacon, Laurent Bertino, Patrick Heimbach, Jun Inoue, Doroteaciro Iovino, Ruth Mottram, Xiangdong Zhang, Yevgeny Aksenov, Ronan McAdam, An Nguyen, Roshin P. Raj, and Han Tang
Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, https://doi.org/10.5194/os-17-1081-2021, 2021
Short summary
Short summary
Freshwater in the Arctic Ocean plays a critical role in the global climate system by impacting ocean circulations, stratification, mixing, and emergent regimes. In this review paper we assess how Arctic Ocean freshwater changed in the 2010s relative to the 2000s. Estimates from observations and reanalyses show a qualitative stabilization in the 2010s due to a compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the Amerasian and Eurasian basins.
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
Short summary
Short summary
For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Céline Heuzé
Ocean Sci., 17, 59–90, https://doi.org/10.5194/os-17-59-2021, https://doi.org/10.5194/os-17-59-2021, 2021
Short summary
Short summary
Dense waters sinking by Antarctica and in the North Atlantic control global ocean currents and carbon storage. We need to know how these change with climate change, and thus we need accurate climate models. Here we show that dense water sinking in the latest models is better than in the previous ones, but there is still too much water sinking. This impacts how well models represent the deep ocean density and the deep currents globally. We also suggest ways to improve the models.
Lovisa Waldrop Bergman and Céline Heuzé
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-122, https://doi.org/10.5194/os-2018-122, 2018
Preprint withdrawn
Short summary
Short summary
How to force a model where no suitable observation exists? We here determine using MITgcm the relative influence of the choice of wind, initial hydrography, and sea ice cover on the resulting ocean circulation in Nares Strait, northwest Greenland. The input with the largest effect is the density gradient in the upper layer. We argue that it should be prioritised over high resolution wind for cost-effective simulations of the Arctic straits, crucial for modelling the Arctic freshwater export.
Céline Heuzé
Ocean Sci., 13, 609–622, https://doi.org/10.5194/os-13-609-2017, https://doi.org/10.5194/os-13-609-2017, 2017
Short summary
Short summary
Climate models are the best tool available to estimate the ocean’s response to climate change, notably sea level rise. To trust the models, we need to compare them to the real ocean in key areas. Here we do so in the North Atlantic, where deep waters form, and show that inaccurate location, extent and frequency of the formation impact the representation of the global ocean circulation and how much heat enters the Arctic. We also study the causes of the errors in order to improve future models.
Mathew A. Stiller-Reeve, Céline Heuzé, William T. Ball, Rachel H. White, Gabriele Messori, Karin van der Wiel, Iselin Medhaug, Annemarie H. Eckes, Amee O'Callaghan, Mike J. Newland, Sian R. Williams, Matthew Kasoar, Hella Elisa Wittmeier, and Valerie Kumer
Hydrol. Earth Syst. Sci., 20, 2965–2973, https://doi.org/10.5194/hess-20-2965-2016, https://doi.org/10.5194/hess-20-2965-2016, 2016
Short summary
Short summary
Scientific writing must improve and the key to long-term improvement of scientific writing lies with the early-career scientist (ECS). We introduce the ClimateSnack project, which aims to motivate ECSs to start writing groups around the world to improve their skills together. Writing groups offer many benefits but can be a challenge to keep going. Several ClimateSnack writing groups formed, and this paper examines why some of the groups flourished and others dissolved.
C. Heuzé, J. K. Ridley, D. Calvert, D. P. Stevens, and K. J. Heywood
Geosci. Model Dev., 8, 3119–3130, https://doi.org/10.5194/gmd-8-3119-2015, https://doi.org/10.5194/gmd-8-3119-2015, 2015
Short summary
Short summary
Most ocean models, including NEMO, have unrealistic Southern Ocean deep convection. That is, through extensive areas of the Southern Ocean, they exhibit convection from the surface of the ocean to the sea floor. We find this convection to be an issue as it impacts the whole ocean circulation, notably strengthening the Antarctic Circumpolar Current. Using sensitivity experiments, we show that counter-intuitively the vertical mixing needs to be enhanced to reduce this spurious convection.
Related subject area
Discipline: Sea ice | Subject: Antarctic
A new dataset of Southern Ocean sea-ice leads: First insights into regional lead patterns, seasonality and trends, 2003–2023
Inferring the seasonality of sea ice floes in the Weddell Sea using ICESat-2
Impacts of Atmospheric Dynamics on Sea-Ice and Snow Thickness at a Coastal Site in East Antarctica
Brief communication: New perspectives on the skill of modelled sea ice trends in light of recent Antarctic sea ice loss
Quantifying the influence of snow over sea ice morphology on L-band passive microwave satellite observations in the Southern Ocean
The role of atmospheric conditions in the Antarctic sea ice extent summer minima
Sources of low-frequency variability in observed Antarctic sea ice
A contrast in sea ice drift and deformation between winter and spring of 2019 in the Antarctic marginal ice zone
Multidecadal variability and predictability of Antarctic sea ice in the GFDL SPEAR_LO model
Signature of the stratosphere–troposphere coupling on recent record-breaking Antarctic sea-ice anomalies
Southern Ocean polynyas and dense water formation in a high-resolution, coupled Earth system model
A decade-plus of Antarctic sea ice thickness and volume estimates from CryoSat-2 using a physical model and waveform fitting
Annual evolution of the ice–ocean interaction beneath landfast ice in Prydz Bay, East Antarctica
The response of sea ice and high-salinity shelf water in the Ross Ice Shelf Polynya to cyclonic atmosphere circulations
Antarctic sea ice regime shift associated with decreasing zonal symmetry in the Southern Annular Mode
Evolution of the dynamics, area, and ice production of the Amundsen Sea Polynya, Antarctica, 2016–2021
Modulation of the seasonal cycle of the Antarctic sea ice extent by sea ice processes and feedbacks with the ocean and the atmosphere
Ice Sheet and Sea Ice Ultrawideband Microwave radiometric Airborne eXperiment (ISSIUMAX) in Antarctica: first results from Terra Nova Bay
Influence of fast ice on future ice shelf melting in the Totten Glacier area, East Antarctica
A comparison between Envisat and ICESat sea ice thickness in the Southern Ocean
An indicator of sea ice variability for the Antarctic marginal ice zone
Physical and mechanical properties of winter first-year ice in the Antarctic marginal ice zone along the Good Hope Line
Altimetric observation of wave attenuation through the Antarctic marginal ice zone using ICESat-2
Flexural and compressive strength of the landfast sea ice in the Prydz Bay, East Antarctic
The sensitivity of landfast sea ice to atmospheric forcing in single-column model simulations: a case study at Zhongshan Station, Antarctica
An evaluation of Antarctic sea-ice thickness from the Global Ice-Ocean Modeling and Assimilation System based on in situ and satellite observations
Rectification and validation of a daily satellite-derived Antarctic sea ice velocity product
Weddell Sea polynya analysis using SMOS–SMAP apparent sea ice thickness retrieval
Eighteen-year record of circum-Antarctic landfast-sea-ice distribution allows detailed baseline characterisation and reveals trends and variability
Brief communication: The anomalous winter 2019 sea-ice conditions in McMurdo Sound, Antarctica
Airborne mapping of the sub-ice platelet layer under fast ice in McMurdo Sound, Antarctica
Evaluation of sea-ice thickness from four reanalyses in the Antarctic Weddell Sea
The Antarctic sea ice cover from ICESat-2 and CryoSat-2: freeboard, snow depth, and ice thickness
Seasonal and interannual variability of landfast sea ice in Atka Bay, Weddell Sea, Antarctica
Influence of sea-ice anomalies on Antarctic precipitation using source attribution in the Community Earth System Model
Retrieval of snow freeboard of Antarctic sea ice using waveform fitting of CryoSat-2 returns
Three years of sea ice freeboard, snow depth, and ice thickness of the Weddell Sea from Operation IceBridge and CryoSat-2
Umesh Dubey, Sascha Willmes, and Günther Heinemann
EGUsphere, https://doi.org/10.5194/egusphere-2025-736, https://doi.org/10.5194/egusphere-2025-736, 2025
Short summary
Short summary
Sea-ice leads facilitate the exchange of heat and moisture between the ocean and atmosphere during wintertime. We present a new dataset on monthly wintertime sea-ice leads in the Southern Ocean from 2003 to 2023. Our study reveals distinct regional patterns, seasonal variability, and small but significant trends. Here, we present initial findings on Southern Ocean lead dynamics to support future research into the complex pan-Antarctic interactions among sea ice, ocean and atmosphere.
Mukund Gupta, Heather Regan, Younghyun Koo, Sean Minhui Tashi Chua, Xueke Li, and Petra Heil
The Cryosphere, 19, 1241–1257, https://doi.org/10.5194/tc-19-1241-2025, https://doi.org/10.5194/tc-19-1241-2025, 2025
Short summary
Short summary
The sea ice cover is composed of floes, whose shapes set the material properties of the pack. Here, we use a satellite product (ICESat-2) to investigate these floe shapes within the Weddell Sea in Antarctica. We find that floes tend to become smaller during the melt season, while their thickness distribution exhibits different behavior between the western and southern regions of the pack. These metrics will help calibrate models and improve our understanding of sea ice physics across scales.
Diana Francis, Ricardo Fonseca, Narendra Nelli, Petra Heil, Jonathan Wille, Irina Gorodetskaya, and Robert Massom
EGUsphere, https://doi.org/10.5194/egusphere-2024-3535, https://doi.org/10.5194/egusphere-2024-3535, 2025
Short summary
Short summary
This study investigates the impact of atmospheric rivers and associated atmospheric dynamics on sea-ice thickness and snow depth at a coastal site in East Antarctica during July–November 2022 using in-situ measurements and numerical modelling. The passage of an atmospheric river induced a reduction of up to 0.06 m in both fields. Precipitation occurred from the convergence of katabatic winds with advected low-latitude moist air.
Caroline R. Holmes, Thomas J. Bracegirdle, Paul R. Holland, Julienne Stroeve, and Jeremy Wilkinson
The Cryosphere, 18, 5641–5652, https://doi.org/10.5194/tc-18-5641-2024, https://doi.org/10.5194/tc-18-5641-2024, 2024
Short summary
Short summary
Until recently, satellite data showed an increase in Antarctic sea ice area since 1979, but climate models simulated a decrease over this period. This mismatch was one reason for low confidence in model projections of 21st-century sea ice loss. We show that following low Antarctic sea ice in 2022 and 2023, we can no longer conclude that modelled and observed trends differ. However, differences in the manner of the decline mean that model sea ice projections should still be viewed with caution.
Lu Zhou, Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Shiming Xu, Weixin Zhu, Sahra Kacimi, Stefanie Arndt, and Zifan Yang
The Cryosphere, 18, 4399–4434, https://doi.org/10.5194/tc-18-4399-2024, https://doi.org/10.5194/tc-18-4399-2024, 2024
Short summary
Short summary
Snow over Antarctic sea ice, influenced by highly variable meteorological conditions and heavy snowfall, has a complex stratigraphy and profound impact on the microwave signature. We employ advanced radiation transfer models to analyse the effects of complex snow properties on brightness temperatures over the sea ice in the Southern Ocean. Great potential lies in the understanding of snow processes and the application to satellite retrievals.
Bianca Mezzina, Hugues Goosse, François Klein, Antoine Barthélemy, and François Massonnet
The Cryosphere, 18, 3825–3839, https://doi.org/10.5194/tc-18-3825-2024, https://doi.org/10.5194/tc-18-3825-2024, 2024
Short summary
Short summary
We analyze years with extraordinarily low sea ice extent in Antarctica during summer, until the striking record in 2022. We highlight common aspects among these events, such as the fact that the exceptional melting usually occurs in two key regions and that it is related to winds with a similar direction. We also investigate whether the summer conditions are preceded by an unusual state of the sea ice during the previous winter, as well as the physical processes involved.
David B. Bonan, Jakob Dörr, Robert C. J. Wills, Andrew F. Thompson, and Marius Årthun
The Cryosphere, 18, 2141–2159, https://doi.org/10.5194/tc-18-2141-2024, https://doi.org/10.5194/tc-18-2141-2024, 2024
Short summary
Short summary
Antarctic sea ice has exhibited variability over satellite records, including a period of gradual expansion and a period of sudden decline. We use a novel statistical method to identify sources of variability in observed Antarctic sea ice changes. We find that the gradual increase in sea ice is likely related to large-scale temperature trends, and periods of abrupt sea ice decline are related to specific flavors of equatorial tropical variability known as the El Niño–Southern Oscillation.
Ashleigh Womack, Alberto Alberello, Marc de Vos, Alessandro Toffoli, Robyn Verrinder, and Marcello Vichi
The Cryosphere, 18, 205–229, https://doi.org/10.5194/tc-18-205-2024, https://doi.org/10.5194/tc-18-205-2024, 2024
Short summary
Short summary
Synoptic events have a significant influence on the evolution of Antarctic sea ice. Our current understanding of the interactions between cyclones and sea ice remains limited. Using two ensembles of buoys, deployed in the north-eastern Weddell Sea region during winter and spring of 2019, we show how the evolution and spatial pattern of sea ice drift and deformation in the Antarctic marginal ice zone were affected by the balance between atmospheric and oceanic forcing and the local ice.
Yushi Morioka, Liping Zhang, Thomas L. Delworth, Xiaosong Yang, Fanrong Zeng, Masami Nonaka, and Swadhin K. Behera
The Cryosphere, 17, 5219–5240, https://doi.org/10.5194/tc-17-5219-2023, https://doi.org/10.5194/tc-17-5219-2023, 2023
Short summary
Short summary
Antarctic sea ice extent shows multidecadal variations with its decrease in the 1980s and increase after the 2000s until 2015. Here we show that our climate model can predict the sea ice decrease by deep convection in the Southern Ocean and the sea ice increase by the surface wind variability. These results suggest that accurate simulation and prediction of subsurface ocean and atmosphere conditions are important for those of Antarctic sea ice variability on a multidecadal timescale.
Raúl R. Cordero, Sarah Feron, Alessandro Damiani, Pedro J. Llanillo, Jorge Carrasco, Alia L. Khan, Richard Bintanja, Zutao Ouyang, and Gino Casassa
The Cryosphere, 17, 4995–5006, https://doi.org/10.5194/tc-17-4995-2023, https://doi.org/10.5194/tc-17-4995-2023, 2023
Short summary
Short summary
We investigate the response of Antarctic sea ice to year-to-year changes in the tropospheric–stratospheric dynamics. Our findings suggest that, by affecting the tropospheric westerlies, the strength of the stratospheric polar vortex has played a major role in recent record-breaking anomalies in Antarctic sea ice.
Hyein Jeong, Adrian K. Turner, Andrew F. Roberts, Milena Veneziani, Stephen F. Price, Xylar S. Asay-Davis, Luke P. Van Roekel, Wuyin Lin, Peter M. Caldwell, Hyo-Seok Park, Jonathan D. Wolfe, and Azamat Mametjanov
The Cryosphere, 17, 2681–2700, https://doi.org/10.5194/tc-17-2681-2023, https://doi.org/10.5194/tc-17-2681-2023, 2023
Short summary
Short summary
We find that E3SM-HR reproduces the main features of the Antarctic coastal polynyas. Despite the high amount of coastal sea ice production, the densest water masses are formed in the open ocean. Biases related to the lack of dense water formation are associated with overly strong atmospheric polar easterlies. Our results indicate that the large-scale polar atmospheric circulation must be accurately simulated in models to properly reproduce Antarctic dense water formation.
Steven Fons, Nathan Kurtz, and Marco Bagnardi
The Cryosphere, 17, 2487–2508, https://doi.org/10.5194/tc-17-2487-2023, https://doi.org/10.5194/tc-17-2487-2023, 2023
Short summary
Short summary
Antarctic sea ice thickness is an important quantity in the Earth system. Due to the thick and complex snow cover on Antarctic sea ice, estimating the thickness of the ice pack is difficult using traditional methods in radar altimetry. In this work, we use a waveform model to estimate the freeboard and snow depth of Antarctic sea ice from CryoSat-2 and use these values to calculate sea ice thickness and volume between 2010 and 2021 and showcase how the sea ice pack has changed over this time.
Haihan Hu, Jiechen Zhao, Petra Heil, Zhiliang Qin, Jingkai Ma, Fengming Hui, and Xiao Cheng
The Cryosphere, 17, 2231–2244, https://doi.org/10.5194/tc-17-2231-2023, https://doi.org/10.5194/tc-17-2231-2023, 2023
Short summary
Short summary
The oceanic characteristics beneath sea ice significantly affect ice growth and melting. The high-frequency and long-term observations of oceanic variables allow us to deeply investigate their diurnal and seasonal variation and evaluate their influences on sea ice evolution. The large-scale sea ice distribution and ocean circulation contributed to the seasonal variation of ocean variables, revealing the important relationship between large-scale and local phenomena.
Xiaoqiao Wang, Zhaoru Zhang, Michael S. Dinniman, Petteri Uotila, Xichen Li, and Meng Zhou
The Cryosphere, 17, 1107–1126, https://doi.org/10.5194/tc-17-1107-2023, https://doi.org/10.5194/tc-17-1107-2023, 2023
Short summary
Short summary
The bottom water of the global ocean originates from high-salinity water formed in polynyas in the Southern Ocean where sea ice coverage is low. This study reveals the impacts of cyclones on sea ice and water mass formation in the Ross Ice Shelf Polynya using numerical simulations. Sea ice production is rapidly increased caused by enhancement in offshore wind, promoting high-salinity water formation in the polynya. Cyclones also modulate the transport of this water mass by wind-driven currents.
Serena Schroeter, Terence J. O'Kane, and Paul A. Sandery
The Cryosphere, 17, 701–717, https://doi.org/10.5194/tc-17-701-2023, https://doi.org/10.5194/tc-17-701-2023, 2023
Short summary
Short summary
Antarctic sea ice has increased over much of the satellite record, but we show that the early, strongly opposing regional trends diminish and reverse over time, leading to overall negative trends in recent decades. The dominant pattern of atmospheric flow has changed from strongly east–west to more wave-like with enhanced north–south winds. Sea surface temperatures have also changed from circumpolar cooling to regional warming, suggesting recent record low sea ice will not rapidly recover.
Grant J. Macdonald, Stephen F. Ackley, Alberto M. Mestas-Nuñez, and Adrià Blanco-Cabanillas
The Cryosphere, 17, 457–476, https://doi.org/10.5194/tc-17-457-2023, https://doi.org/10.5194/tc-17-457-2023, 2023
Short summary
Short summary
Polynyas are key sites of sea ice production, biological activity, and carbon sequestration. The Amundsen Sea Polynya is of particular interest due to its size and location. By analyzing radar imagery and climate and sea ice data products, we evaluate variations in the dynamics, area, and ice production of the Amundsen Sea Polynya. In particular, we find the local seafloor topography and associated grounded icebergs play an important role in the polynya dynamics, influencing ice production.
Hugues Goosse, Sofia Allende Contador, Cecilia M. Bitz, Edward Blanchard-Wrigglesworth, Clare Eayrs, Thierry Fichefet, Kenza Himmich, Pierre-Vincent Huot, François Klein, Sylvain Marchi, François Massonnet, Bianca Mezzina, Charles Pelletier, Lettie Roach, Martin Vancoppenolle, and Nicole P. M. van Lipzig
The Cryosphere, 17, 407–425, https://doi.org/10.5194/tc-17-407-2023, https://doi.org/10.5194/tc-17-407-2023, 2023
Short summary
Short summary
Using idealized sensitivity experiments with a regional atmosphere–ocean–sea ice model, we show that sea ice advance is constrained by initial conditions in March and the retreat season is influenced by the magnitude of several physical processes, in particular by the ice–albedo feedback and ice transport. Atmospheric feedbacks amplify the response of the winter ice extent to perturbations, while some negative feedbacks related to heat conduction fluxes act on the ice volume.
Marco Brogioni, Mark J. Andrews, Stefano Urbini, Kenneth C. Jezek, Joel T. Johnson, Marion Leduc-Leballeur, Giovanni Macelloni, Stephen F. Ackley, Alexandra Bringer, Ludovic Brucker, Oguz Demir, Giacomo Fontanelli, Caglar Yardim, Lars Kaleschke, Francesco Montomoli, Leung Tsang, Silvia Becagli, and Massimo Frezzotti
The Cryosphere, 17, 255–278, https://doi.org/10.5194/tc-17-255-2023, https://doi.org/10.5194/tc-17-255-2023, 2023
Short summary
Short summary
In 2018 the first Antarctic campaign of UWBRAD was carried out. UWBRAD is a new radiometer able to collect microwave spectral signatures over 0.5–2 GHz, thus outperforming existing similar sensors. It allows us to probe thicker sea ice and ice sheet down to the bedrock. In this work we tried to assess the UWBRAD potentials for sea ice, glaciers, ice shelves and buried lakes. We also highlighted the wider range of information the spectral signature can provide to glaciological studies.
Guillian Van Achter, Thierry Fichefet, Hugues Goosse, and Eduardo Moreno-Chamarro
The Cryosphere, 16, 4745–4761, https://doi.org/10.5194/tc-16-4745-2022, https://doi.org/10.5194/tc-16-4745-2022, 2022
Short summary
Short summary
We investigate the changes in ocean–ice interactions in the Totten Glacier area between the last decades (1995–2014) and the end of the 21st century (2081–2100) under warmer climate conditions. By the end of the 21st century, the sea ice is strongly reduced, and the ocean circulation close to the coast is accelerated. Our research highlights the importance of including representations of fast ice to simulate realistic ice shelf melt rate increase in East Antarctica under warming conditions.
Jinfei Wang, Chao Min, Robert Ricker, Qian Shi, Bo Han, Stefan Hendricks, Renhao Wu, and Qinghua Yang
The Cryosphere, 16, 4473–4490, https://doi.org/10.5194/tc-16-4473-2022, https://doi.org/10.5194/tc-16-4473-2022, 2022
Short summary
Short summary
The differences between Envisat and ICESat sea ice thickness (SIT) reveal significant temporal and spatial variations. Our findings suggest that both overestimation of Envisat sea ice freeboard, potentially caused by radar backscatter originating from inside the snow layer, and the AMSR-E snow depth biases and sea ice density uncertainties can possibly account for the differences between Envisat and ICESat SIT.
Marcello Vichi
The Cryosphere, 16, 4087–4106, https://doi.org/10.5194/tc-16-4087-2022, https://doi.org/10.5194/tc-16-4087-2022, 2022
Short summary
Short summary
The marginal ice zone (MIZ) in the Antarctic is the largest in the world ocean. Antarctic sea ice has large year-to-year changes, and the MIZ represents its most variable component. Processes typical of the MIZ have also been observed in fully ice-covered ocean and are not captured by existing diagnostics. A new statistical method has been shown to address previous limitations in assessing the seasonal cycle of MIZ extent and to provide a probability map of sea ice state in the Southern Ocean.
Sebastian Skatulla, Riesna R. Audh, Andrea Cook, Ehlke Hepworth, Siobhan Johnson, Doru C. Lupascu, Keith MacHutchon, Rutger Marquart, Tommy Mielke, Emmanuel Omatuku, Felix Paul, Tokoloho Rampai, Jörg Schröder, Carina Schwarz, and Marcello Vichi
The Cryosphere, 16, 2899–2925, https://doi.org/10.5194/tc-16-2899-2022, https://doi.org/10.5194/tc-16-2899-2022, 2022
Short summary
Short summary
First-year sea ice has been sampled at the advancing outer edge of the Antarctic marginal ice zone (MIZ) along the Good Hope Line. Ice cores were extracted from five pancake ice floes and subsequently analysed for their physical and mechanical properties. Of particular interest was elucidating the transition of ice composition within the MIZ in terms of differences in mechanical stiffness and strength properties as linked to physical and textural characteristics at early-stage ice formation.
Jill Brouwer, Alexander D. Fraser, Damian J. Murphy, Pat Wongpan, Alberto Alberello, Alison Kohout, Christopher Horvat, Simon Wotherspoon, Robert A. Massom, Jessica Cartwright, and Guy D. Williams
The Cryosphere, 16, 2325–2353, https://doi.org/10.5194/tc-16-2325-2022, https://doi.org/10.5194/tc-16-2325-2022, 2022
Short summary
Short summary
The marginal ice zone is the region where ocean waves interact with sea ice. Although this important region influences many sea ice, ocean and biological processes, it has been difficult to accurately measure on a large scale from satellite instruments. We present new techniques for measuring wave attenuation using the NASA ICESat-2 laser altimeter. By measuring how waves attenuate within the sea ice, we show that the marginal ice zone may be far wider than previously realised.
Qingkai Wang, Zhaoquan Li, Peng Lu, Yigang Xu, and Zhijun Li
The Cryosphere, 16, 1941–1961, https://doi.org/10.5194/tc-16-1941-2022, https://doi.org/10.5194/tc-16-1941-2022, 2022
Short summary
Short summary
A large area of landfast sea ice exists in the Prydz Bay, and it is always a safety concern to transport cargos on ice to the research stations. Knowing the mechanical properties of sea ice is helpful to solve the issue; however, these data are rarely reported in this region. We explore the effects of sea ice physical properties on the flexural strength, effective elastic modulus, and uniaxial compressive strength, which gives new insights into assessing the bearing capacity of landfast sea ice.
Fengguan Gu, Qinghua Yang, Frank Kauker, Changwei Liu, Guanghua Hao, Chao-Yuan Yang, Jiping Liu, Petra Heil, Xuewei Li, and Bo Han
The Cryosphere, 16, 1873–1887, https://doi.org/10.5194/tc-16-1873-2022, https://doi.org/10.5194/tc-16-1873-2022, 2022
Short summary
Short summary
The sea ice thickness was simulated by a single-column model and compared with in situ observations obtained off Zhongshan Station in the Antarctic. It is shown that the unrealistic precipitation in the atmospheric forcing data leads to the largest bias in sea ice thickness and snow depth modeling. In addition, the increasing snow depth gradually inhibits the growth of sea ice associated with thermal blanketing by the snow.
Sutao Liao, Hao Luo, Jinfei Wang, Qian Shi, Jinlun Zhang, and Qinghua Yang
The Cryosphere, 16, 1807–1819, https://doi.org/10.5194/tc-16-1807-2022, https://doi.org/10.5194/tc-16-1807-2022, 2022
Short summary
Short summary
The Global Ice-Ocean Modeling and Assimilation System (GIOMAS) can basically reproduce the observed variability in Antarctic sea-ice volume and its changes in the trend before and after 2013, and it underestimates Antarctic sea-ice thickness (SIT) especially in deformed ice zones. Assimilating additional sea-ice observations with advanced assimilation methods may result in a more accurate estimation of Antarctic SIT.
Tian R. Tian, Alexander D. Fraser, Noriaki Kimura, Chen Zhao, and Petra Heil
The Cryosphere, 16, 1299–1314, https://doi.org/10.5194/tc-16-1299-2022, https://doi.org/10.5194/tc-16-1299-2022, 2022
Short summary
Short summary
This study presents a comprehensive validation of a satellite observational sea ice motion product in Antarctica by using drifting buoys. Two problems existing in this sea ice motion product have been noticed. After rectifying problems, we use it to investigate the impacts of satellite observational configuration and timescale on Antarctic sea ice kinematics and suggest the future improvement of satellite missions specifically designed for retrieval of sea ice motion.
Alexander Mchedlishvili, Gunnar Spreen, Christian Melsheimer, and Marcus Huntemann
The Cryosphere, 16, 471–487, https://doi.org/10.5194/tc-16-471-2022, https://doi.org/10.5194/tc-16-471-2022, 2022
Short summary
Short summary
In this paper we show that the activity leading to the open-ocean polynyas near the Maud Rise seamount that have occurred repeatedly from 1974–1976 as well as 2016–2017 does not simply stop for polynya-free years. Using apparent sea ice thickness retrieval, we have identified anomalies where there is thinning of sea ice on a scale that is comparable to that of the polynya events of 2016–2017. These anomalies took place in 2010, 2013, 2014 and 2018.
Alexander D. Fraser, Robert A. Massom, Mark S. Handcock, Phillip Reid, Kay I. Ohshima, Marilyn N. Raphael, Jessica Cartwright, Andrew R. Klekociuk, Zhaohui Wang, and Richard Porter-Smith
The Cryosphere, 15, 5061–5077, https://doi.org/10.5194/tc-15-5061-2021, https://doi.org/10.5194/tc-15-5061-2021, 2021
Short summary
Short summary
Landfast ice is sea ice that remains stationary by attaching to Antarctica's coastline and grounded icebergs. Although a variable feature, landfast ice exerts influence on key coastal processes involving pack ice, the ice sheet, ocean, and atmosphere and is of ecological importance. We present a first analysis of change in landfast ice over an 18-year period and quantify trends (−0.19 ± 0.18 % yr−1). This analysis forms a reference of landfast-ice extent and variability for use in other studies.
Greg H. Leonard, Kate E. Turner, Maren E. Richter, Maddy S. Whittaker, and Inga J. Smith
The Cryosphere, 15, 4999–5006, https://doi.org/10.5194/tc-15-4999-2021, https://doi.org/10.5194/tc-15-4999-2021, 2021
Short summary
Short summary
McMurdo Sound sea ice can generally be partitioned into two regimes: a stable fast-ice cover forming south of approximately 77.6° S and a more dynamic region north of 77.6° S that is regularly impacted by polynyas. In 2019, a stable fast-ice cover formed unusually late due to repeated break-out events. This subsequently affected sea-ice operations in the 2019/20 field season. We analysed the 2019 sea-ice conditions and found a strong correlation with unusually large southerly wind events.
Christian Haas, Patricia J. Langhorne, Wolfgang Rack, Greg H. Leonard, Gemma M. Brett, Daniel Price, Justin F. Beckers, and Alex J. Gough
The Cryosphere, 15, 247–264, https://doi.org/10.5194/tc-15-247-2021, https://doi.org/10.5194/tc-15-247-2021, 2021
Short summary
Short summary
We developed a method to remotely detect proxy signals of Antarctic ice shelf melt under adjacent sea ice. It is based on aircraft surveys with electromagnetic induction sounding. We found year-to-year variability of the ice shelf melt proxy in McMurdo Sound and spatial fine structure that support assumptions about the melt of the McMurdo Ice Shelf. With this method it will be possible to map and detect locations of intense ice shelf melt along the coast of Antarctica.
Qian Shi, Qinghua Yang, Longjiang Mu, Jinfei Wang, François Massonnet, and Matthew R. Mazloff
The Cryosphere, 15, 31–47, https://doi.org/10.5194/tc-15-31-2021, https://doi.org/10.5194/tc-15-31-2021, 2021
Short summary
Short summary
The ice thickness from four state-of-the-art reanalyses (GECCO2, SOSE, NEMO-EnKF and GIOMAS) are evaluated against that from remote sensing and in situ observations in the Weddell Sea, Antarctica. Most of the reanalyses can reproduce ice thickness in the central and eastern Weddell Sea but failed to capture the thick and deformed ice in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis in Antarctic climate research.
Sahra Kacimi and Ron Kwok
The Cryosphere, 14, 4453–4474, https://doi.org/10.5194/tc-14-4453-2020, https://doi.org/10.5194/tc-14-4453-2020, 2020
Short summary
Short summary
Our current understanding of Antarctic ice cover is largely informed by ice extent measurements from passive microwave sensors. These records, while useful, provide a limited picture of how the ice is responding to climate change. In this paper, we combine measurements from ICESat-2 and CryoSat-2 missions to assess snow depth and ice thickness of the Antarctic ice cover over an 8-month period (April through November 2019). The potential impact of salinity in the snow layer is discussed.
Stefanie Arndt, Mario Hoppmann, Holger Schmithüsen, Alexander D. Fraser, and Marcel Nicolaus
The Cryosphere, 14, 2775–2793, https://doi.org/10.5194/tc-14-2775-2020, https://doi.org/10.5194/tc-14-2775-2020, 2020
Hailong Wang, Jeremy G. Fyke, Jan T. M. Lenaerts, Jesse M. Nusbaumer, Hansi Singh, David Noone, Philip J. Rasch, and Rudong Zhang
The Cryosphere, 14, 429–444, https://doi.org/10.5194/tc-14-429-2020, https://doi.org/10.5194/tc-14-429-2020, 2020
Short summary
Short summary
Using a climate model with unique water source tagging, we found that sea-ice anomalies in the Southern Ocean and accompanying SST changes have a significant influence on Antarctic precipitation and its source attribution through their direct impact on moisture sources and indirect impact on moisture transport. This study also highlights the importance of atmospheric dynamics in affecting the thermodynamic impact of sea-ice anomalies on regional Antarctic precipitation.
Steven W. Fons and Nathan T. Kurtz
The Cryosphere, 13, 861–878, https://doi.org/10.5194/tc-13-861-2019, https://doi.org/10.5194/tc-13-861-2019, 2019
Short summary
Short summary
A method to measure the snow freeboard of Antarctic sea ice from CryoSat-2 data is developed. Through comparisons with data from airborne campaigns and another satellite mission, we find that this method can reasonably retrieve snow freeboard across the Antarctic and shows promise in retrieving snow depth in certain locations. Snow freeboard data from CryoSat-2 are important because they enable the calculation of sea ice thickness and help to better understand snow depth on Antarctic sea ice.
Ron Kwok and Sahra Kacimi
The Cryosphere, 12, 2789–2801, https://doi.org/10.5194/tc-12-2789-2018, https://doi.org/10.5194/tc-12-2789-2018, 2018
Short summary
Short summary
The variability of snow depth and ice thickness in three years of repeat surveys of an IceBridge (OIB) transect across the Weddell Sea is examined. Retrieved thicknesses suggest a highly variable but broadly thicker ice cover compared to that inferred from drilling and ship-based measurements. The use of lidar and radar altimeters to estimate snow depth for thickness calculations is analyzed, and the need for better characterization of biases due to radar penetration effects is highlighted.
Cited articles
Adcroft, A., Anderson, W., Balaji, V., Blanton, C., Bushuk, M., Dufour, C. O.,
Dunne, J. P., Griffies, S. M., Hallberg, R., Harrison, M. J., Held, I. M., Jansen, M. F., John, J. G., Krasting, J. P., Langenhorst, A. R., Legg, S., Liang, Z., McHugh, C., Radhakrishnan, A., Reichl, B. G., Rosati, T., Samuels, B. L., Shao, A., Stouffer, R., Winton, M., Wittenberg, A. T., Xiang, B., Zadeh, N., and Zhang, R.: The
GFDL global ocean and sea ice model OM4.0: Model description and simulation
features, J. Adv. Model. Earth Sy., 11, 3167–3211,
2019. a, b
Aguiar, W., Mata, M. M., and Kerr, R.: On deep convection events and Antarctic Bottom Water formation in ocean reanalysis products, Ocean Sci., 13, 851–872, https://doi.org/10.5194/os-13-851-2017, 2017. a
Andreas, E. L. and Ackley, S. F.: On the differences in ablation seasons of
Arctic and Antarctic sea ice, J. Atmos. Sci., 39,
440–447, 1982. a
Arbetter, T. E., Lynch, A. H., and Bailey, D. A.: Relationship between synoptic
forcing and polynya formation in the Cosmonaut Sea: 1. Polynya climatology,
J. Geophys. Res.-Oceans, 109, C04022, https://doi.org/10.1029/2003JC001837, 2004. a
Arrigo, K. R. and Van Dijken, G. L.: Phytoplankton dynamics within 37 Antarctic
coastal polynya systems, J. Geophys. Res.-Oceans, 108, 3271, https://doi.org/10.1029/2002JC001739, 2003. a
Barber, D. G. and Massom, R. A.: The role of sea ice in Arctic and Antarctic
polynyas, Elsev. Oceanogr. Series, 74, 1–54, 2007. a
Beadling, R., Russell, J., Stouffer, R., Mazloff, M., Talley, L., Goodman, P.,
Sallée, J., Hewitt, H., Hyder, P., and Pandde, A.: Representation of
Southern Ocean Properties across Coupled Model Intercomparison Project
Generations: CMIP3 to CMIP6, J. Climate, 33, 6555–6581, 2020. a, b, c, d, e, f, g, h, i, j, k, l
Bi, D., Dix, M., Marsland, S. J., O’Farrell, S., Rashid, H., Uotila, P.,
Hirst, A. C., Kowalczyk, E., Golebiewski, M., Sullivan, A., Yan, Y., Hannah, N., Franklin, C., Sun, Z., Vohralik, P., Watterson, I., Fiedler, R., Collier, M., and Puri, K.: The
ACCESS coupled model: description, control climate and evaluation, Aust.
Meteorol. Oceanogr. J., 63, 41–64, 2013. a, b, c
oucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y.,
Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P.,
Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A.,
Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes,
J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C.,
Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S.,
Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, Lionel, E.,
Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A.,
Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur,
G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G.,
Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L.,
Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y.,
Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A.,
Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J.,
Vialard, J., Viovy, N., and Vuichard, N.: Presentation and evaluation of the
IPSL-CM6A-LR climate model, J. Adv. Model. Earth Sy.,
12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020. a
CMIP: Coupled Model Intercomparison Project Phase 6 (CMIP6) data, Working Group on Coupled Modeling of the World Climate Research Programme, Earth System Grid Federation [data set],
available at: https://esgf-data.dkrz.de/projects/cmip6-dkrz/, last access: December 2020. a
Counillon, F., Keenlyside, N., Bethke, I., Wang, Y., Billeau, S., Shen, M. L.,
and Bentsen, M.: Flow-dependent assimilation of sea surface temperature in
isopycnal coordinates with the Norwegian Climate Prediction Model, Tellus A, 68, 32437, https://doi.org/10.3402/tellusa.v68.32437 2016. a, b
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van
Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer,
C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E.,
Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch,
P. J., and Strand, W. G.: The
Community Earth System Model version 2 (CESM2), J. Adv.
Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a, b, c, d
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart,
F.: The
ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, 2011. a
Dong, Y., Armour, K. C., Zelinka, M. D., Proistosescu, C., Battisti, D. S.,
Zhou, C., and Andrews, T.: Intermodel Spread in the Pattern Effect and Its
Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models, J.
Climate, 33, 7755–7775, https://doi.org/10.1175/JCLI-D-19-1011.1, 2020. a, b
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John,
J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E.,
Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K. A., Dupuis, C.,
Durachta, J., Dussin, R., Gauthier, P. P. G., Griffies, S. M., Guo, H.,
Hallberg, R. W., Harrison, M., He, J., Hurlin, W., McHugh, C., Menzel, R.,
Milly, P. C. D., Nikonov, S., Paynter, D. J., Ploshay, J., Radhakrishnan, A.,
Rand, K., Reichl, B. G., Robinson, T., Schwarzkopf, D. M., Sentman, L. T.,
Underwood, S., Vahlenkamp, H., Winton, M., Wittenberg, A. T., Wyman, B.,
Zeng, Y., and Zhao, M.: The GFDL Earth
System Model version 4.1 (GFDL-ESM4.1): Model description and simulation
characteristics (under review), J. Adv. Model. Earth
Sy., 11, 3167–3211, 2019. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a, b
Francis, D., Eayrs, C., Cuesta, J., and Holland, D.: Polar cyclones at the
origin of the reoccurrence of the Maud Rise Polynya in austral winter 2017,
J. Geophys. Res.-Atmos., 124, 5251–5267, 2019. a
Good, S., Martin, M., and Rayner, N.: EN4: quality controlled ocean temperature
and salinity profiles and monthly objective analyses with uncertainty
estimates, J. Geophys. Res.-Oceans, 118, 6704–6716, 2013. a
Gutjahr, O., Putrasahan, D., Lohmann, K., Jungclaus, J. H., von Storch, J.-S., Brüggemann, N., Haak, H., and Stössel, A.: Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 12, 3241–3281, https://doi.org/10.5194/gmd-12-3241-2019, 2019. a, b, c
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K., Watanabe, S., and Kawamiya, M.: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks, Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, 2020. a
Hasumi, H.: CCSR ocean component model (COCO), Atmosphere and Ocean Research
Institute,The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba
277-8568, Japan, 2015. a
Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J.,
Shevliakova, E., Winton, M., Zhao, M., Bushuk, M., Wittenberg, A. T., Wyman,
B., Xiang, B., Zhang, R., Anderson, W., Balaji, V., Donner, L., Dunne, K.,
Durachta, J., Gauthier, P. P. G., Ginoux, P., Golaz, J.-C., Griffies, S. M.,
Hallberg, R., Harris, L., Harrison, M., Hurlin, W., John, J., Lin, P., Lin,
S.-J., Malyshev, S., Menzel, R., Milly, P. C. D., Ming, Y., Naik, V.,
Paynter, D., Paulot, F., Rammaswamy, V., Reichl, B., Robinson, T., Rosati,
A., Seman, C., Silvers, L. G., Underwood, S., and Zadeh, N.: Structure and
performance of GFDL's CM4.0 climate model, J. Adv. Model.
Earth Sy., 11, 3691–3727, 2019. a, b, c, d, e
Heuzé, C., Ridley, J. K., Calvert, D., Stevens, D. P., and Heywood, K. J.: Increasing vertical mixing to reduce Southern Ocean deep convection in NEMO3.4, Geosci. Model Dev., 8, 3119–3130, https://doi.org/10.5194/gmd-8-3119-2015, 2015. a
Heuzé, C., Garric, G., and Lavergne, T.: The Weddell Sea
Polynya, J. Oper. Oceanogr., 12, S91–S123, 2019. a
Huntemann, M. and Heygster, G.: SMOS, thickness of thin sea ice (SIT), available at: https://seaice.uni-bremen.de/data/smos/ncs/,
last access: December 2020. a
Johnson, K., Riser, S., Boss, E., Talley, L., Sarmiento, J., Swift, D., Plant,
J., Maurer, T., Key, R., Williams, N., Wanninkhof, R. H., Dickson, A. G.,
Feely, R. A., and Russell, J. L.: SOCCOM float data—Snapshot
2018-12-31, Southern Ocean Carbon and Climate Observations and Modeling
(SOCCOM) Float Data Archive [data set], https://doi.org/10.6075/J02J6968, 2018. a, b, c
Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell,
G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V.,
Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio,
A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A.,
Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E.,
McDermid, S., Mezuman, K., Miller, R. L., Murray, L. T., Oinas, V., Orbe, C.,
García-Pando, C. P., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A.,
Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng,
E., Wu, J., and Yao, M.-S.: GISS-E2.
1: Configurations and Climatology, J. Adv. Model. Earth
Sy., e2019MS002025,
https://doi.org/10.1029/2019MS002025, 2019. a
Kern, S., Spreen, G., Kaleschke, L., De La Rosa, S., and Heygster, G.: Polynya
Signature Simulation Method polynya area in comparison to AMSR-E 89GHz
sea-ice concentrations in the Ross Sea and off the Adélie Coast,
Antarctica, for 2002–05: first results, Ann. Glaciol., 46, 409–418,
2007. a, b, c, d, e
Kuhlbrodt, T., Jones, C. G., Sellar, A., Storkey, D., Blockley, E., Stringer,
M., Hill, R., Graham, T., Ridley, J., Blaker, A., Calvert, D., Copsey, D.,
Ellis, R., Hewitt, H., Hyder, P., Ineson, S., Mulcahy, J., Siahaan, A., and
Walton, J.: The low-resolution
version of HadGEM3 GC3. 1: Development and evaluation for global climate,
J. Adv. Model. Earth Sy., 10, 2865–2888, 2018. a
Kurtakoti, P., Veneziani, M., Stössel, A., and Weijer, W.: Preconditioning
and formation of Maud Rise polynyas in a high-resolution Earth system model,
J. Climate, 31, 9659–9678, 2018. a
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019. a, b, c, d
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C., Bruciaferri, D.,
Calvert, D., Chanut, J., Clementi, E., Coward, A., Delrosso, D., Ethé, C.,
Flavoni, S., Graham, T., Harle, J., Iovino, D., Lea, D., Lévy, C., Lovato,
T., Martin, N., Masson, S., Mocavero, S., Paul, J., Rousset, C., Storkey, D.,
Storto, A., and Vancoppenolle, M.: NEMO ocean engine, Zenodo,
https://doi.org/10.5281/zenodo.3248739, 2017. a
Markus, T. and Burns, B. A.: A method to estimate subpixel-scale coastal
polynyas with satellite passive microwave data, J. Geophys.
Res.-Oceans, 100, 4473–4487, 1995. a
Martinson, D. G. and Iannuzzi, R. A.: Antarctic ocean-ice interaction:
Implications from ocean bulk property distributions in the Weddell Gyre,
in: Antarctic sea ice: physical processes, interactions and variability, 74,
243–271, 1998. a
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann,
S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la
Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S.,
Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner,
K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E.
M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K.,
Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S.,
Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H.,
Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens,
B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H.,
Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the
MPI-M Earth System Model version 1.2 (MPI-ESM1. 2) and its response to
increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038,
2019. a, b, c
Mchedlishvili, A., Spreen, G., Melsheimer, C., and Huntemann, M.: Weddell Sea Polynya analysis using SMOS-SMAP Sea Ice Thickness Retrieval, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-138, in review, 2021. a
Mohrmann, M., Heuzé, C., and Swart, S.: Southern Ocean polynyas in CMIP6 models – Codes 1.0, Zenodo [code], https://doi.org/10.5281/zenodo.5187947, 2021. a
Morales Maqueda, M., Willmott, A., and Biggs, N.: Polynya dynamics: A review of
observations and modeling, Rev. Geophys., 42, RG1004, https://doi.org/10.1029/2002RG000116, 2004. a
Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M.,
Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine,
T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E.,
Stemmler, I., Tian, F., and Marotzke, J.: A
Higher-resolution Version of the Max Planck Institute Earth System Model
(MPI-ESM1. 2-HR), J. Adv. Model. Earth Sy., 10,
1383–1413, 2018. a
Notz, D., Jahn, A., Holland, M., Hunke, E., Massonnet, F., Stroeve, J., Tremblay, B., and Vancoppenolle, M.: The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): understanding sea ice through climate-model simulations, Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, 2016. a
Ohshima, K. I., Nihashi, S., and Iwamoto, K.: Global view of sea-ice production
in polynyas and its linkage to dense/bottom water formation, Geosci.
Lett., 3, 13, https://doi.org/10.1186/s40562-016-0045-4, 2016. a, b, c
Orsi, A. H., Johnson, G. C., and Bullister, J. L.: Circulation, mixing, and
production of Antarctic Bottom Water, Prog. Oceanogr., 43, 55–109,
1999. a
OSI SAF: Global Sea Ice Concentration Climate Data Record v2.0 – Multimission, EUMETSAT SAF on Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_0008, 2017. a
Park, S., Shin, J., Kim, S., Oh, E., and Kim, Y.: Global climate simulated by
the Seoul National University atmosphere model version 0 with a unified
convection scheme (SAM0-UNICON), J. Climate, 32, 2917–2949, 2019. a
Roach, L. A., Dörr, J., Holmes, C. R., Massonnet, F., Blockley, E. W., Notz,
D., Rackow, T., Raphael, M. N., O'Farrell, S. P., Bailey, D. A., and Bitz,
C. M.: Antarctic sea ice area in CMIP6, Geophys. Res. Lett., 47,
e2019GL086729, https://doi.org/10.1029/2019GL086729, 2020. a, b
Rong, X., Li, J., Chen, H., Xin, Y., jing Zhi, S., Hua, L., and Zhang, Z.: Introduction of CAMS-CSM model and its
participation in CMIP6, Climate Change Res., 15, 540–544, 2019. a
Sallée, J.-B., Shuckburgh, E., Bruneau, N., Meijers, A. J., Bracegirdle,
T. J., Wang, Z., and Roy, T.: Assessment of Southern Ocean water mass
circulation and characteristics in CMIP5 models: Historical bias and forcing
response, J. Geophys. Res.-Oceans, 118, 1830–1844, 2013. a
Santoso, A., England, M. H., and Hirst, A. C.: Circumpolar deep water
circulation and variability in a coupled climate model, J. Phys.
Oceanogr., 36, 1523–1552, 2006. a
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A.,
Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., et al.:
Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System
Processes in Present-Day and Future Climate, J. Adv. Model.
Earth Sy., 11, 4182–4227, 2019. a
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A.,
O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora,
L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E.,
Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T.,
Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J.,
Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A.,
Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat,
S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A.,
Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1:
Description and evaluation of the UK Earth System Model, J. Adv.
Model. Earth Sy., 11, 4513–4558, 2019. a, b, c, d
Shu, Q., Song, Z., and Qiao, F.: Assessment of sea ice simulations in the CMIP5 models, The Cryosphere, 9, 399–409, https://doi.org/10.5194/tc-9-399-2015, 2015. a
Smedsrud, L. H.: Warming of the deep water in the Weddell Sea along the
Greenwich meridian: 1977–2001, Deep-Sea Res. Pt. I, 52, 241–258, 2005. a
Stoessel, A., Notz, D., Haumann, F. A., Haak, H., Jungclaus, J., and
Mikolajewicz, U.: Controlling high-latitude Southern Ocean convection in
climate models, Ocean Model., 86, 58–75, 2015. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019. a
Tamura, T., Ohshima, K. I., and Nihashi, S.: Mapping of sea ice production for
Antarctic coastal polynyas, Geophys. Res. Lett., 35, L07606, https://doi.org/10.1029/2007GL032903, 2008. a
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019. a, b
Tietsche, S., Alonso-Balmaseda, M., Rosnay, P., Zuo, H., Tian-Kunze, X., and Kaleschke, L.: Thin Arctic sea ice in L-band observations and an ocean reanalysis, The Cryosphere, 12, 2051–2072, https://doi.org/10.5194/tc-12-2051-2018, 2018. a
Turner, J., Bracegirdle, T. J., Phillips, T., Marshall, G. J., and Hosking,
J. S.: An initial assessment of Antarctic sea ice extent in the CMIP5 models,
J. Climate, 26, 1473–1484, 2013. a
Van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F.,
Warner, J. D., Yager, N., Gouillart, E., and Yu, T.: scikit-image: image
processing in Python, PeerJ, 2, e453,
https://doi.org/h10.7717/peerj.453, 2014. a
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A.,
Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat,
P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke, S., Beau, I.,
Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville,
H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G.,
Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and
Waldman, R.: Evaluation of CMIP6 deck experiments with CNRM-CM6-1, J.
Adv. Model. Earth Sy., 11, 2177–2213, 2019. a
Williams, W., Carmack, E., and Ingram, R.: Physical oceanography of polynyas,
Elsev. Oceanogr. Ser., 74, 55–85, 2007. a
Willmott, A., Holland, D., and Maqueda, M. M.: Polynya modelling, Elsev.
Oceanogr. Ser., 74, 87–125, 2007. a
Wilson, E. A., Riser, S. C., Campbell, E. C., and Wong, A. P.: Winter
upper-ocean stability and ice–ocean feedbacks in the sea ice–covered
Southern Ocean, J. Phys. Oceanogr., 49, 1099–1117, 2019. a
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L., Zhang, F., Zhang, Y., Wu, F., Li, J., Chu, M., Wang, Z., Shi, X., Liu, X., Wei, M., Huang, A., Zhang, Y., and Liu, X.: The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6, Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019. a, b
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S.,
Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Shokichi, Y., Hiromasa, Y.,
Eiki, S., Ryo, M., Atsushi, O., Yukimasa, A., and Masayoshi, I.:The Meteorological
Research Institute Earth System Model version 2.0, MRI-ESM2. 0: Description
and basic evaluation of the physical component, J. Meteorol.
Soc. Jpn. Ser. II, 97, 931–965,
https://doi.org/10.2151/jmsj.2019-051, 2019. a
Zanowski, H., Hallberg, R., and Sarmiento, J. L.: Abyssal ocean warming and
salinification after Weddell polynyas in the GFDL CM2G coupled climate model,
J. Phys. Oceanogr., 45, 2755–2772, 2015. a
Ziehn, T., Lenton, A., Law, R. M., Matear, R. J., and Chamberlain, M. A.: The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations, Geosci. Model Dev., 10, 2591–2614, https://doi.org/10.5194/gmd-10-2591-2017, 2017. a
Short summary
Polynyas are large open-water areas within the sea ice. We developed a method to estimate their area, distribution and frequency for the Southern Ocean in climate models and observations. All models have polynyas along the coast but few do so in the open ocean, in contrast to observations. We examine potential atmospheric and oceanic drivers of open-water polynyas and discuss recently implemented schemes that may have improved some models' polynya representation.
Polynyas are large open-water areas within the sea ice. We developed a method to estimate their...
