Articles | Volume 20, issue 4
https://doi.org/10.5194/tc-20-2557-2026
© Author(s) 2026. 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-20-2557-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Apr 2026
Research article |
| 29 Apr 2026
| 29 Apr 2026
The importance of initial conditions in seasonal predictions of Antarctic sea ice
Elio Campitelli
CORRESPONDING AUTHOR
School of Earth, Atmosphere and Environment, Monash University, Kulin Nations, Clayton, Victoria, Australia
ARC Special Research Initiative for Securing Antarctica's Environmental Future, Clayton, Kulin Nations, Victoria, Australia
Ariaan Purich
School of Earth, Atmosphere and Environment, Monash University, Kulin Nations, Clayton, Victoria, Australia
ARC Special Research Initiative for Securing Antarctica's Environmental Future, Clayton, Kulin Nations, Victoria, Australia
Julie Arblaster
School of Earth, Atmosphere and Environment, Monash University, Kulin Nations, Clayton, Victoria, Australia
ARC Special Research Initiative for Securing Antarctica's Environmental Future, Clayton, Kulin Nations, Victoria, Australia
Eun-Pa Lim
Research, Bureau of Meteorology, Melbourne, Australia
Matthew C. Wheeler
Research, Bureau of Meteorology, Melbourne, Australia
Phillip Reid
Research, Bureau of Meteorology, Melbourne, Australia
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Andrew G. Pauling, Inga J. Smith, Torge Martin, Jeff K. Ridley, David P. Stevens, Max Thomas, Rebecca L. Beadling, Christopher Danek, Tore Hattermann, Qian Li, John Marshall, Morven Muilwijk, Ariaan Purich, and Neil C. Swart
EGUsphere, https://doi.org/10.5194/egusphere-2026-658, https://doi.org/10.5194/egusphere-2026-658, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Climate models typically do not include meltwater entering the Southern Ocean due to Antarctic ice sheet mass loss. Previous work shows this meltwater drives sea ice growth, but the varying responses have been difficult to compare across models. We ran 11 climate models using the same meltwater input and found a wide range of sea ice responses depending on the background state in each model. Understanding this uncertainty in response is important for future projections of Antarctic sea ice.
Morven Muilwijk, Tore Hattermann, Rebecca L. Beadling, Neil C. Swart, Aleksi Nummelin, Chuncheng Guo, David M. Chandler, Petra M. Langebroek, Shenjie Zhou, Pierre Dutrieux, Jia-Jia Chen, Christopher Danek, Matthew H. England, Stephen M. Griffies, F. Alexander Haumann, André Jüling, Ombeline Jouet, Qian Li, Torge Martin, John Marshall, Andrew G. Pauling, Ariaan Purich, Zihan Song, Inga J. Smith, Max Thomas, Irene Trombini, Eveline C. van der Linden, and Xiaoqi Xu
The Cryosphere, 20, 1087–1117, https://doi.org/10.5194/tc-20-1087-2026, https://doi.org/10.5194/tc-20-1087-2026, 2026
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Antarctic meltwater affects ocean stratification and temperature, which in turn influences the rate of ice shelf melting – a coupling missing in most climate models. We analyze a suite of climate models with added meltwater to explore this feedback in different regions. While meltwater generally enhances ocean warming and melt, in West Antarctica most models simulate coastal cooling, suggesting a negative feedback that could slow future ice loss there.
William J. M. Seviour, Justin Finkel, Philip Rupp, Regan Mudhar, Amy H. Butler, Chaim I. Garfinkel, Peter Hitchcock, Blanca Ayarzagüena, Dong-Chan Hong, Yu-Kyung Hyun, Hera Kim, Eun-Pa Lim, Daniel De Maeseneire, Gabriele Messori, Gerbrand Koren, Michael Sigmond, Isla R. Simpson, and Seok-Woo Son
EGUsphere, https://doi.org/10.5194/egusphere-2026-230, https://doi.org/10.5194/egusphere-2026-230, 2026
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Variability of the stratospheric polar vortex is thought to play a role in driving weather extremes, but quantifying this role for a given event has proved challenging. Using a new set of perturbed subseasonal forecast experiments from 7 modelling centres we determine the stratospheric contribution to the risk and severity of three recent extreme weather events. The forecast-based methodology that we develop is applicable to understanding a range of other drivers of weather extremes.
Zhaoyang Kong, Andrew T. Prata, Peter T. May, Ariaan Purich, Yi Huang, and Steven T. Siems
Weather Clim. Dynam., 6, 1643–1660, https://doi.org/10.5194/wcd-6-1643-2025, https://doi.org/10.5194/wcd-6-1643-2025, 2025
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To investigate why ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) does not accurately capture the observed increase in annual precipitation at Macquarie Island during 1979 to 2023, we classify daily synoptic systems using k-means clustering. Find that the increase in mean intensity across all systems is the main contributor to the observed annual precipitation trend and the resulting discrepancy, rather than changes in the frequency. And this increase may also have a substantial impact on the freshwater fluxes over the Southern Ocean.
Helen J. Shea, Ailie Gallant, Ariaan Purich, and Tessa R. Vance
Clim. Past, 21, 2009–2030, https://doi.org/10.5194/cp-21-2009-2025, https://doi.org/10.5194/cp-21-2009-2025, 2025
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Ice core data from Mount Brown South (MBS), East Antarctica links high sea salt years to stronger westerly winds and increased sea ice near MBS's northeast coast. Low pressure storms off the coast might transport sea salts from sea ice regions to MBS. The tropical Pacific influences sea salt levels with El Niño events affecting wind patterns around MBS, impacting sea salt sources. Identifying these mechanisms aids in the understanding of climate variability before instrumental records.
Raina Roy, Julie M. Arblaster, Matthew C. Wheeler, Eun-Pa Lim, and Jadwiga H. Richter
EGUsphere, https://doi.org/10.5194/egusphere-2025-4453, https://doi.org/10.5194/egusphere-2025-4453, 2025
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A key pattern of tropical weather, the Madden-Julian Oscillation, has become significantly harder to predict since the late 1990s. We discovered this by comparing forecasts from major models across two time periods. The decrease in forecast skill is linked to changes in large-scale climate patterns, not just model errors. This means to improve long-range weather forecasts, models must better simulate how these large-scale patterns interact with tropical weather.
John P. Dunne, Helene T. Hewitt, Julie M. Arblaster, Frédéric Bonou, Olivier Boucher, Tereza Cavazos, Beth Dingley, Paul J. Durack, Birgit Hassler, Martin Juckes, Tomoki Miyakawa, Matt Mizielinski, Vaishali Naik, Zebedee Nicholls, Eleanor O'Rourke, Robert Pincus, Benjamin M. Sanderson, Isla R. Simpson, and Karl E. Taylor
Geosci. Model Dev., 18, 6671–6700, https://doi.org/10.5194/gmd-18-6671-2025, https://doi.org/10.5194/gmd-18-6671-2025, 2025
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The seventh phase of the Coupled Model Intercomparison Project (CMIP7) coordinates efforts to answer key and timely climate science questions and facilitate delivery of relevant multi-model simulations for prediction and projection; characterization, attribution, and process understanding; and vulnerability, impact, and adaptation analysis. Key to the CMIP7 design are the mandatory Diagnostic, Evaluation and Characterization of Klima and optional Assessment Fast Track experiments.
Robert Massom, Phillip Reid, Stephen Warren, Bonnie Light, Donald Perovich, Luke Bennetts, Petteri Uotila, Siobhan O'Farrell, Michael Meylan, Klaus Meiners, Pat Wongpan, Alexander Fraser, Alessandro Toffoli, Giulio Passerotti, Peter Strutton, Sean Chua, and Melissa Fedrigo
EGUsphere, https://doi.org/10.5194/egusphere-2025-3166, https://doi.org/10.5194/egusphere-2025-3166, 2025
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Ocean waves play a previously-neglected role in the rapid annual melting of Antarctic sea ice by flooding and pulverising floes, removing the snow cover and reducing the albedo by an estimated 0.38–0.54 – to increase solar absorption and enhance the vertical melt rate by up to 5.2 cm/day. Ice algae further decrease the albedo, to increase the melt-rate enhancement to up to 6.1 cm/day. Melting is accelerated by four previously-unconsidered wave-driven positive feedbacks.
Jessica M. A. Macha, Andrew N. Mackintosh, Felicity S. McCormack, Benjamin J. Henley, Helen V. McGregor, Christiaan T. van Dalum, and Ariaan Purich
The Cryosphere, 19, 1915–1935, https://doi.org/10.5194/tc-19-1915-2025, https://doi.org/10.5194/tc-19-1915-2025, 2025
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Extreme El Niño–Southern Oscillation (ENSO) events have global impacts, but their Antarctic impacts are poorly understood. Examining Antarctic snow accumulation anomalies of past observed extreme ENSO events, we show that accumulation changes differ between events and are insignificant during most events. Significant changes occur during 2015/16 and in Enderby Land during all extreme El Niños. Historical data limit conclusions, but future greater extremes could cause Antarctic accumulation changes.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
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This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Roseanna C. McKay, Julie M. Arblaster, and Pandora Hope
Weather Clim. Dynam., 3, 413–428, https://doi.org/10.5194/wcd-3-413-2022, https://doi.org/10.5194/wcd-3-413-2022, 2022
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Understanding what makes it hot in Australia in spring helps us better prepare for harmful impacts. We look at how the higher latitudes and tropics change the atmospheric circulation from early to late spring and how that changes maximum temperatures in Australia. We find that the relationship between maximum temperatures and the tropics is stronger in late spring than early spring. These findings could help improve forecasts of hot months in Australia in spring.
Adam A. Scaife, Mark P. Baldwin, Amy H. Butler, Andrew J. Charlton-Perez, Daniela I. V. Domeisen, Chaim I. Garfinkel, Steven C. Hardiman, Peter Haynes, Alexey Yu Karpechko, Eun-Pa Lim, Shunsuke Noguchi, Judith Perlwitz, Lorenzo Polvani, Jadwiga H. Richter, John Scinocca, Michael Sigmond, Theodore G. Shepherd, Seok-Woo Son, and David W. J. Thompson
Atmos. Chem. Phys., 22, 2601–2623, https://doi.org/10.5194/acp-22-2601-2022, https://doi.org/10.5194/acp-22-2601-2022, 2022
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Great progress has been made in computer modelling and simulation of the whole climate system, including the stratosphere. Since the late 20th century we also gained a much clearer understanding of how the stratosphere interacts with the lower atmosphere. The latest generation of numerical prediction systems now explicitly represents the stratosphere and its interaction with surface climate, and here we review its role in long-range predictions and projections from weeks to decades ahead.
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
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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.
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Short summary
By comparing sea-ice forecasts made by the same model but using different initial conditions, we show that better initial sea-ice initial conditions improve sea-ice forecasts a lot in Summer and Autumn, but not as much in winter. This means that forecasting groups should focus efforts in assimilating observations in Summer and that research on how easy it is to predict sea ice need to analyse all seasons.
By comparing sea-ice forecasts made by the same model but using different initial conditions,...
