Articles | Volume 19, issue 2
https://doi.org/10.5194/tc-19-731-2025
© Author(s) 2025. 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-19-731-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2025
Research article |
| 18 Feb 2025
| 18 Feb 2025
Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach
Nansen Environmental and Remote Sensing Center, Bergen 5007, Norway
Bjerknes Center, Bergen 5007, Norway
Jiping Xie
Nansen Environmental and Remote Sensing Center, Bergen 5007, Norway
Bjerknes Center, Bergen 5007, Norway
Anton Korosov
Nansen Environmental and Remote Sensing Center, Bergen 5007, Norway
Bjerknes Center, Bergen 5007, Norway
Julien Brajard
Nansen Environmental and Remote Sensing Center, Bergen 5007, Norway
Bjerknes Center, Bergen 5007, Norway
Laurent Bertino
Nansen Environmental and Remote Sensing Center, Bergen 5007, Norway
Bjerknes Center, Bergen 5007, Norway
Related authors
Anton Korosov, Léo Edel, Heather Regan, Thomas Lavergne, Signe Aaboe, and Emily Jane Down
Earth Syst. Sci. Data, 18, 721–740, https://doi.org/10.5194/essd-18-721-2026, https://doi.org/10.5194/essd-18-721-2026, 2026
Short summary
Short summary
We present a new long-term record of Arctic sea ice age spanning from 1991 to 2024. Using satellite data and a new tracking method, it maps fractions of sea ice from first- to sixth-year and includes uncertainty estimates. The dataset shows a decline in older ice and more first-year ice, it agrees well with buoy data, and supports Arctic monitoring, climate research, navigation, and model evaluation.
Valentin Ludwig, Caroline Ribere, Sara Fleury, Christian Haas, Michel Tsamados, Mahmoud El Hajj, Jerome Bouffard, Michele Scagliola, Marion Bocquet, Eric de Boisseson, Vincent Boulenger, Guillaume Boutin, Laurence Connor, Léo Edel, Stefan Hendricks, Ferran Hernández Macià, Marcus Huntemann, Lars Kaleschke, Frank Kauker, Jack Landy, Tom Megain, Alek Petty, Till Soya Rasmussen, Mads Hvid Ribergaard, Robert Ricker, Axel Schweiger, Hoyeon Shi, Xiangshan Tian-Kunze, Donghui Yi, and Alessandro Di Bella
EGUsphere, https://doi.org/10.5194/egusphere-2025-6201, https://doi.org/10.5194/egusphere-2025-6201, 2026
Short summary
Short summary
Our paper compares Arctic sea-ice thickness datasets from models, reanalyses, satellite-only, and multi-product sources. We validate them against Beaufort Sea reference data, compare large-scale products, and analyse time series. Cross-product biases range from 0.2–0.4 m, RMSDs from 0.4–0.9 m, and correlations from 0.5–0.8. We find no 2010–2023 trend, but 1995–2023 thinning of ~ 0.5 m in November and ~ 0.3 m in March.
Anton Korosov, Jonathan Rheinlænder, Richard Davy, François Massonnet, and David Babb
State Planet Discuss., https://doi.org/10.5194/sp-2026-5, https://doi.org/10.5194/sp-2026-5, 2026
Preprint under review for SP
Short summary
Short summary
The Arctic sea ice cover has changed dramatically over the last few decades. Thick, old ice that has survived several years is being replaced by thinner ice that forms each winter and melts easily. We examined Arctic sea ice age from 1991 to 2025 using satellite observations and found that during the rapid transition from 2003 to 2008, the area covered by multi-year ice decreased sharply, likely due to altered sea ice dynamics. Then, the Arctic has remained in a new state with much less old ice.
Einar Ólason, Guillaume Boutin, Timothy Williams, Anton Korosov, Heather Regan, Jonathan Rheinlænder, Pierre Rampal, Daniela Flocco, Abdoulaye Samaké, Richard Davy, Timothy Spain, and Sean Chua
Geosci. Model Dev., 19, 6467–6496, https://doi.org/10.5194/gmd-19-6467-2026, https://doi.org/10.5194/gmd-19-6467-2026, 2026
Short summary
Short summary
This paper introduces a new version of the neXtSIM sea-ice model. NeXtSIM is unique among sea-ice models in how it represents sea-ice dynamics, focusing on features such as cracks and ridges and how these impact interactions between the atmosphere and ocean where sea ice is present. The new version introduces some physical parameterisations and model options detailed and explained in the paper. Following the paper's publication, the neXtSIM code will be released publicly for the first time.
Achref Othmani, Annette Samuelsen, Jiping Xie, Laurent Bertino, Fabio Mangini, and Roshin Pappukutty Raj
EGUsphere, https://doi.org/10.5194/egusphere-2026-1520, https://doi.org/10.5194/egusphere-2026-1520, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We developed a high-resolution (6–10 km) HYCOM-CICE configuration to better understand ocean and sea ice conditions in the Arctic and North Atlantic from 2009 to 2019. By comparing the model with available datasets, we found it reliably captures major patterns and seasonal changes. This can support forecasting, helping improve environmental monitoring and decision-making in regions sensitive to climate change.
Julien Brajard, Anton Korosov, Fabio Mangini, Richard Davy, and Yiguo Wang
EGUsphere, https://doi.org/10.5194/egusphere-2026-2318, https://doi.org/10.5194/egusphere-2026-2318, 2026
Short summary
Short summary
Small details in Arctic sea ice thickness, such as ridges, cracks and leads, are difficult to observe with satellites and are rarely represented in climate models, even though they strongly influence sea ice motion and its interaction with the climate system. In this study, we introduce an artificial intelligence method that reconstructs realistic small‑scale ice thickness features from coarse observations. The results show more accurate estimates and physically realistic sea ice patterns.
Anton Korosov, Léo Edel, Heather Regan, Thomas Lavergne, Signe Aaboe, and Emily Jane Down
Earth Syst. Sci. Data, 18, 721–740, https://doi.org/10.5194/essd-18-721-2026, https://doi.org/10.5194/essd-18-721-2026, 2026
Short summary
Short summary
We present a new long-term record of Arctic sea ice age spanning from 1991 to 2024. Using satellite data and a new tracking method, it maps fractions of sea ice from first- to sixth-year and includes uncertainty estimates. The dataset shows a decline in older ice and more first-year ice, it agrees well with buoy data, and supports Arctic monitoring, climate research, navigation, and model evaluation.
Valentin Ludwig, Caroline Ribere, Sara Fleury, Christian Haas, Michel Tsamados, Mahmoud El Hajj, Jerome Bouffard, Michele Scagliola, Marion Bocquet, Eric de Boisseson, Vincent Boulenger, Guillaume Boutin, Laurence Connor, Léo Edel, Stefan Hendricks, Ferran Hernández Macià, Marcus Huntemann, Lars Kaleschke, Frank Kauker, Jack Landy, Tom Megain, Alek Petty, Till Soya Rasmussen, Mads Hvid Ribergaard, Robert Ricker, Axel Schweiger, Hoyeon Shi, Xiangshan Tian-Kunze, Donghui Yi, and Alessandro Di Bella
EGUsphere, https://doi.org/10.5194/egusphere-2025-6201, https://doi.org/10.5194/egusphere-2025-6201, 2026
Short summary
Short summary
Our paper compares Arctic sea-ice thickness datasets from models, reanalyses, satellite-only, and multi-product sources. We validate them against Beaufort Sea reference data, compare large-scale products, and analyse time series. Cross-product biases range from 0.2–0.4 m, RMSDs from 0.4–0.9 m, and correlations from 0.5–0.8. We find no 2010–2023 trend, but 1995–2023 thinning of ~ 0.5 m in November and ~ 0.3 m in March.
Cyril Palerme, Johannes Röhrs, Thomas Lavergne, Jozef Rusin, Are Frode Kvanum, Atle Macdonald Sørensen, Arne Melsom, Julien Brajard, Martina Idžanović, Marina Durán Moro, and Malte Müller
Geosci. Model Dev., 18, 9751–9766, https://doi.org/10.5194/gmd-18-9751-2025, https://doi.org/10.5194/gmd-18-9751-2025, 2025
Short summary
Short summary
We present MET-AICE, a sea ice prediction system based on artificial intelligence techniques that has been running operationally since March 2024. The forecasts are produced daily and provide sea ice concentration predictions for the next 10 days. We evaluate the MET-AICE forecasts from the first year of operation, and we compare them to forecasts produced by three physically-based models. We show that MET-AICE is skillful and provides more accurate forecasts than the physically-based models.
Jean Rabault, Trygve Halsne, Ana Carrasco, Anton Korosov, Joey Voermans, Patrik Bohlinger, Jens Boldingh Debernard, Malte Müller, Øyvind Breivik, Takehiko Nose, Gaute Hope, Fabrice Collard, Sylvain Herlédan, Tsubasa Kodaira, Nick Hughes, Qin Zhang, Kai Håkon Christensen, Alexander Babanin, Lars Willas Dreyer, Cyril Palerme, Lotfi Aouf, Konstantinos Christakos, Atle Jensen, Johannes Röhrs, Aleksey Marchenko, Graig Sutherland, Trygve Kvåle Løken, and Takuji Waseda
The Cryosphere, 19, 6229–6260, https://doi.org/10.5194/tc-19-6229-2025, https://doi.org/10.5194/tc-19-6229-2025, 2025
Short summary
Short summary
We observe strongly modulated waves-in-ice significant wave height using buoys deployed East of Svalbard. We show that these observations likely cannot be explained by wave-current interaction or tide-induced modulation alone. We also demonstrate a strong correlation between the waves height modulation, and the rate of sea ice convergence. Therefore, our data suggest that the rate of sea ice convergence and divergence may modulate wave in ice energy dissipation.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Julien Brajard, and Laurent Bertino
The Cryosphere, 19, 5613–5637, https://doi.org/10.5194/tc-19-5613-2025, https://doi.org/10.5194/tc-19-5613-2025, 2025
Short summary
Short summary
This paper presents a four-dimensional variational data assimilation system based on a neural network emulator for sea-ice thickness, learned from neXtSIM (neXt generation Sea Ice Model) simulation outputs. Testing with simulated and real observation retrievals, the system improves forecasts and bias error, performing comparably to operational methods, demonstrating the promise of sea-ice data-driven data assimilation systems.
Zikang He, Julien Brajard, Yiguo Wang, Xidong Wang, and Zheqi Shen
Nonlin. Processes Geophys., 32, 397–409, https://doi.org/10.5194/npg-32-397-2025, https://doi.org/10.5194/npg-32-397-2025, 2025
Short summary
Short summary
Climate prediction is challenging due to systematic errors in traditional climate models. We addressed this by training a machine learning model to correct these errors and then integrating it with the traditional climate model to form an AI-physics hybrid model. Our study demonstrates that the hybrid model outperforms the original climate model on both short-term and long-term predictions of the atmosphere and ocean.
Roshin P. Raj, Vidar S. Lien, Sourav Chatterjee, Saradhy Surendran, Antonio Bonaduce, and Laurent Bertino
State Planet Discuss., https://doi.org/10.5194/sp-2025-18, https://doi.org/10.5194/sp-2025-18, 2025
Revised manuscript accepted for SP
Short summary
Short summary
Dense water formed and exiting from the Barents Sea constitutes an important part of the global ocean circulation. Considering the significant impact of salinity changes in dense water formation, we investigate the salinity changes in the Barents Sea during the past 3 decades. Our results highlight the recent freshening and its drivers in the northern and southern Barents Sea and show its impact on the density of the waters exiting the Barents Sea.
Zikang He, Yiguo Wang, Julien Brajard, Xidong Wang, and Zheqi Shen
The Cryosphere, 19, 3279–3293, https://doi.org/10.5194/tc-19-3279-2025, https://doi.org/10.5194/tc-19-3279-2025, 2025
Short summary
Short summary
Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and economic activities. To address prediction errors in dynamical models, we apply machine learning for error correction during prediction (online) or post-processing (offline). Our results show that both methods enhance sea ice predictions, particularly from September to January, with offline corrections outperforming online corrections.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet, 5-opsr, 14, https://doi.org/10.5194/sp-5-opsr-14-2025, https://doi.org/10.5194/sp-5-opsr-14-2025, 2025
Short summary
Short summary
Forecasts of sea ice are in high demand in the polar regions, and they are also quickly improving and becoming more easily accessible to non-experts. We provide here a brief status of the short-term forecasting services – typically 10 d ahead – and an outlook of their upcoming developments.
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet, 5-opsr, 9, https://doi.org/10.5194/sp-5-opsr-9-2025, https://doi.org/10.5194/sp-5-opsr-9-2025, 2025
Short summary
Short summary
Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity, and currents will evolve over the coming days and weeks and to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025, https://doi.org/10.5194/gmd-18-885-2025, 2025
Short summary
Short summary
We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Georges Baaklini, Julien Brajard, Leila Issa, Gina Fifani, Laurent Mortier, and Roy El Hourany
Ocean Sci., 20, 1707–1720, https://doi.org/10.5194/os-20-1707-2024, https://doi.org/10.5194/os-20-1707-2024, 2024
Short summary
Short summary
Understanding the flow of the Levantine Sea surface current is not straightforward. We propose a study based on learning techniques to follow interactions between water near the shore and further out at sea. Our results show changes in the coastal currents past 33.8° E, with frequent instances of water breaking away along the Lebanese coast. These events happen quickly and sometimes lead to long-lasting eddies. This study underscores the need for direct observations to improve our knowledge.
Alexander Barth, Julien Brajard, Aida Alvera-Azcárate, Bayoumy Mohamed, Charles Troupin, and Jean-Marie Beckers
Ocean Sci., 20, 1567–1584, https://doi.org/10.5194/os-20-1567-2024, https://doi.org/10.5194/os-20-1567-2024, 2024
Short summary
Short summary
Most satellite observations have gaps, for example, due to clouds. This paper presents a method to reconstruct missing data in satellite observations of the chlorophyll a concentration in the Black Sea. Rather than giving a single possible reconstructed field, the discussed method provides an ensemble of possible reconstructions using a generative neural network. The resulting ensemble is validated using techniques from numerical weather prediction and ocean modelling.
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
Preprint archived
Short summary
Short summary
The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Andreas Stokholm, Jørgen Buus-Hinkler, Tore Wulf, Anton Korosov, Roberto Saldo, Leif Toudal Pedersen, David Arthurs, Ionut Dragan, Iacopo Modica, Juan Pedro, Annekatrien Debien, Xinwei Chen, Muhammed Patel, Fernando Jose Pena Cantu, Javier Noa Turnes, Jinman Park, Linlin Xu, Katharine Andrea Scott, David Anthony Clausi, Yuan Fang, Mingzhe Jiang, Saeid Taleghanidoozdoozan, Neil Curtis Brubacher, Armina Soleymani, Zacharie Gousseau, Michał Smaczny, Patryk Kowalski, Jacek Komorowski, David Rijlaarsdam, Jan Nicolaas van Rijn, Jens Jakobsen, Martin Samuel James Rogers, Nick Hughes, Tom Zagon, Rune Solberg, Nicolas Longépé, and Matilde Brandt Kreiner
The Cryosphere, 18, 3471–3494, https://doi.org/10.5194/tc-18-3471-2024, https://doi.org/10.5194/tc-18-3471-2024, 2024
Short summary
Short summary
The AutoICE challenge encouraged the development of deep learning models to map multiple aspects of sea ice – the amount of sea ice in an area and the age and ice floe size – using multiple sources of satellite and weather data across the Canadian and Greenlandic Arctic. Professionally drawn operational sea ice charts were used as a reference. A total of 179 students and sea ice and AI specialists participated and produced maps in broad agreement with the sea ice charts.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
Short summary
Short summary
We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024, https://doi.org/10.5194/tc-18-2161-2024, 2024
Short summary
Short summary
Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin
The Cryosphere, 18, 1597–1619, https://doi.org/10.5194/tc-18-1597-2024, https://doi.org/10.5194/tc-18-1597-2024, 2024
Short summary
Short summary
Individual satellite passes instead of daily means of sea ice concentration are used to correct the sea ice model forecast in the Barents Sea. The use of passes provides a significantly larger improvement of the forecasts even after a 7 d period due to the more precise information on temporal and spatial variability contained in the passes. One major advantage of the use of satellite passes is that there is no need to wait for the daily mean availability in order to update the forecast.
Marta Umbert, Eva De Andrés, Maria Sánchez, Carolina Gabarró, Nina Hoareau, Veronica González-Gambau, Aina García-Espriu, Estrella Olmedo, Roshin P. Raj, Jiping Xie, and Rafael Catany
Ocean Sci., 20, 279–291, https://doi.org/10.5194/os-20-279-2024, https://doi.org/10.5194/os-20-279-2024, 2024
Short summary
Short summary
Satellite retrievals of sea surface salinity (SSS) offer insights into freshwater changes in the Arctic Ocean. This study evaluates freshwater content in the Beaufort Gyre using SMOS and reanalysis data, revealing underestimation with reanalysis alone. Incorporating satellite SSS measurements improves freshwater content estimation, especially near ice-melting areas. Adding remotely sensed salinity aids in monitoring Arctic freshwater content and in understanding its impact on global climate.
Anton Korosov, Pierre Rampal, Yue Ying, Einar Ólason, and Timothy Williams
The Cryosphere, 17, 4223–4240, https://doi.org/10.5194/tc-17-4223-2023, https://doi.org/10.5194/tc-17-4223-2023, 2023
Short summary
Short summary
It is possible to compute sea ice motion from satellite observations and detect areas where ice converges (moves together), forms ice ridges or diverges (moves apart) and opens leads. However, it is difficult to predict the exact motion of sea ice and position of ice ridges or leads using numerical models. We propose a new method to initialise a numerical model from satellite observations to improve the accuracy of the forecasted position of leads and ridges for safer navigation.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Heather Regan, Pierre Rampal, Einar Ólason, Guillaume Boutin, and Anton Korosov
The Cryosphere, 17, 1873–1893, https://doi.org/10.5194/tc-17-1873-2023, https://doi.org/10.5194/tc-17-1873-2023, 2023
Short summary
Short summary
Multiyear ice (MYI), sea ice that survives the summer, is more resistant to changes than younger ice in the Arctic, so it is a good indicator of sea ice resilience. We use a model with a new way of tracking MYI to assess the contribution of different processes affecting MYI. We find two important years for MYI decline: 2007, when dynamics are important, and 2012, when melt is important. These affect MYI volume and area in different ways, which is important for the interpretation of observations.
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
Short summary
Short summary
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, https://doi.org/10.5194/essd-15-1675-2023, 2023
Short summary
Short summary
Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Jiping Xie, Roshin P. Raj, Laurent Bertino, Justino Martínez, Carolina Gabarró, and Rafael Catany
Ocean Sci., 19, 269–287, https://doi.org/10.5194/os-19-269-2023, https://doi.org/10.5194/os-19-269-2023, 2023
Short summary
Short summary
Sea ice melt, together with other freshwater sources, has effects on the Arctic environment. Sea surface salinity (SSS) plays a key role in representing water mixing. Recently the satellite SSS from SMOS was developed in the Arctic region. In this study, we first evaluate the impact of assimilating these satellite data in an Arctic reanalysis system. It shows that SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
Georges Baaklini, Roy El Hourany, Milad Fakhri, Julien Brajard, Leila Issa, Gina Fifani, and Laurent Mortier
Ocean Sci., 18, 1491–1505, https://doi.org/10.5194/os-18-1491-2022, https://doi.org/10.5194/os-18-1491-2022, 2022
Short summary
Short summary
We use machine learning to analyze the long-term variation of the surface currents in the Levantine Sea, located in the eastern Mediterranean Sea. We decompose the circulation into groups based on their physical characteristics and analyze their spatial and temporal variability. We show that most structures of the Levantine Sea are becoming more energetic over time, despite those of the western area remaining the most dominant due to their complex bathymetry and strong currents.
Fabio Mangini, Léon Chafik, Antonio Bonaduce, Laurent Bertino, and Jan Even Ø. Nilsen
Ocean Sci., 18, 331–359, https://doi.org/10.5194/os-18-331-2022, https://doi.org/10.5194/os-18-331-2022, 2022
Short summary
Short summary
We validate the recent ALES-reprocessed coastal satellite altimetry dataset along the Norwegian coast between 2003 and 2018. We find that coastal altimetry and conventional altimetry products perform similarly along the Norwegian coast. However, the agreement with tide gauges slightly increases in terms of trends when we use the ALES coastal altimetry data. We then use the ALES dataset and hydrographic stations to explore the steric contribution to the Norwegian sea-level anomaly.
Justino Martínez, Carolina Gabarró, Antonio Turiel, Verónica González-Gambau, Marta Umbert, Nina Hoareau, Cristina González-Haro, Estrella Olmedo, Manuel Arias, Rafael Catany, Laurent Bertino, Roshin P. Raj, Jiping Xie, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 307–323, https://doi.org/10.5194/essd-14-307-2022, https://doi.org/10.5194/essd-14-307-2022, 2022
Short summary
Short summary
Measuring salinity from space is challenging since the sensitivity of the brightness temperature to sea surface salinity is low, but the retrieval of SSS in cold waters is even more challenging. In 2019, the ESA launched a specific initiative called Arctic+Salinity to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product.
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.
Cited articles
Aksenov, Y., Popova, E. E., Yool, A., Nurser, A. G., Williams, T. D., Bertino, L., and Bergh, J.: On the future navigability of Arctic sea routes: High-resolution projections of the Arctic Ocean and sea ice, Mar. Policy, 75, 300–317, 2017. a
Arrigo, K. R.: Sea Ice Ecosystems, Annu. Rev. Mar. Sci., 6, 439–467, https://doi.org/10.1146/annurev-marine-010213-135103, 2014. a
Beaufort Gyre Exploration Program: Mooring data, Woods Hole Oceanographic Institution in collaboration with researchers from Fisheries and Oceans Canada at the Institute of Ocean Sciences [data set], https://www2.whoi.edu/site/beaufortgyre/data/mooring-data/ (last access: 5 February 2025), 2003. a
Bleck, R.: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates, Ocean Model., 4, 55–88, 2002. a
Bocquet, M. and Fleury, S.: Arctic and Antarctic sea ice thickness climate data record (ERS-1, ERS-2, Envisat, CryoSat-2), Odatis [data set], https://doi.org/10.6096/ctoh_sit_2023_01, 2023. a
Bocquet, M., Fleury, S., Piras, F., Rinne, E., Sallila, H., Garnier, F., and Rémy, F.: Arctic sea ice radar freeboard retrieval from the European Remote-Sensing Satellite (ERS-2) using altimetry: toward sea ice thickness observation from 1995 to 2021, The Cryosphere, 17, 3013–3039, https://doi.org/10.5194/tc-17-3013-2023, 2023. a, b, c
Bourke, R. H. and Paquette, R. G.: Estimating the thickness of sea ice, J. Geophys. Res.-Oceans, 94, 919–923, 1989. a
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model, Journal of Computational Science, 44, 101171, https://doi.org/10.1016/j.jocs.2020.101171, 2020. a, b, c
Cheng, S., Quilodrán-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., Xiao, D., Janjic, T.,Ding, W., Guo, Y., Carrassi, A., Bocquet, M., and Arcucci, R.: Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review, IEEE/CAA Journal of Automatica Sinica, 10, 1361–1387, 2023. a
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated decline in the Arctic sea ice cover, Geophys. Res. Lett., 35, https://doi.org/10.1029/2007GL031972, 2008. a
Drange, H. and Simonsen, K.: Formulation of air-sea fluxes in the ESOP2 version of MICOM, Nansen Environmental and Remote Sensing Center, report number 125, 1996. a
Driscoll, S., Carrassi, A., Brajard, J., Bertino, L., Bocquet, M., and Olason, E.: Parameter sensitivity analysis of a sea ice melt pond parametrisation and its emulation using neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.05407, 2023. a
Edel, L.: TOPAZ4-ML Sea Ice Thickness and Volume (1992-2022), TIB [video], https://doi.org/10.5446/68161, 2024. a, b
Edel, L., Xie, J., and Bertino, L.: TOPAZ4-ML Sea Ice Thickness (1992–2022), Zenodo [code and data set], https://doi.org/10.5281/zenodo.11191853, 2024. a, b
E.U. Copernicus Marine Service Information (CMEMS): Global Ocean Along Track L 3 Sea Surface Heights Reprocessed 1993 Ongoing Tailored For Data Assimilation, E.U. Copernicus Marine Service Information (CMEMS), Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00146, last access: 5 February 2025a. a
E.U. Copernicus Marine Service Information (CMEMS): ESA SST CCI and C3S reprocessed sea surface temperature analyses, E.U. Copernicus Marine Service Information (CMEMS), Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00169, last access: 5 February 2025b. a
E.U. Copernicus Marine Service Information (CMEMS): Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF), E.U. Copernicus Marine Service Information (CMEMS), Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00136, last access: 5 February 2025c. a
E.U. Copernicus Marine Service Information (CMEMS): Arctic Ocean Sea Ice Drift REPROCESSED, E.U. Copernicus Marine Service Information (CMEMS), Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00126, last access: 5 February 2025d. a
E.U. Copernicus Marine Service Information (CMEMS): Arctic Ocean Physics Reanalysis, E.U. Copernicus Marine Service Information (CMEMS), Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00007, last access: 5 February 2025e. a
Finn, T. S., Durand, C., Farchi, A., Bocquet, M., Chen, Y., Carrassi, A., and Dansereau, V.: Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology, The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023, 2023. a
Frainer, A., Primicerio, R., Kortsch, S., Aune, M., Dolgov, A. V., Fossheim, M., and Aschan, M. M.: Climate-driven changes in functional biogeography of Arctic marine fish communities, P. Natl. Acad. Sci. USA, 114, 12202–12207, 2017. a
Gregory, W., Bushuk, M., Adcroft, A., Zhang, Y., and Zanna, L.: Deep learning of systematic sea ice model errors from data assimilation increments, J. Adv. Model. Earth Sy., 15, e2023MS003757, https://doi.org/10.1029/2023MS003757, 2023. a, b
Hendricks, S., Paul, S., and Rinne, E.: ESA Sea Ice Climate Change Initiative: Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0, CEDA Archive [data set], https://doi.org/10.5285/f4c34f4f0f1d4d0da06d771f6972f180, 2018. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput., 9, 1735–1780, 1997. a
Johannessen, O. M., Bobylev, L. P., Shalina, E. V., and Sandven, S.: Sea Ice in the Arctic, Past, Present and Future, Springer Nature, series Springer Polar Sciences, ISBN 978-3-030-21300-8, https://doi.org/10.1007/978-3-030-21301-5, 2020. a
Johnson, M., Proshutinsky, A., Aksenov, Y., Nguyen, A. T., Lindsay, R., Haas, C., Zhang, J., Diansky, N., Kwok, R., Maslowski, W., Häkkinen, S., Ashik, I., and de Cuevas, B.: Evaluation of Arctic sea ice thickness simulated by Arctic Ocean Model Intercomparison Project models, J. Geophys. Res.-Oceans, 117, https://doi.org/10.1029/2011JC007257, 2012. a, b, c
Kahru, M., Brotas, V., Manzano-Sarabia, M., and Mitchell, B.: Are phytoplankton blooms occurring earlier in the Arctic?, Glob. Change Biol., 17, 1733–1739, 2011. a
Kaleschke, L., Tian-Kunze, X., Maaß, N., Mäkynen, M., and Drusch, M.: Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period, Geophys. Res. Lett., 39, https://doi.org/10.1029/2012GL050916, 2012. a
Kim, J.-M., Sohn, B.-J., Lee, S.-M., Tonboe, R. T., Kang, E.-J., and Kim, H.-C.: Differences between ICESat and CryoSat-2 sea ice thicknesses over the Arctic: Consequences for analyzing the ice volume trend, J. Geophys. Res.-Atmos., 125, e2020JD033103, https://doi.org/10.1029/2020JD033103, 2020. a
Korosov, A. A., Rampal, P., Pedersen, L. T., Saldo, R., Ye, Y., Heygster, G., Lavergne, T., Aaboe, S., and Girard-Ardhuin, F.: A new tracking algorithm for sea ice age distribution estimation, The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, 2018. a, b
Krishfield, R. and Proshutinsky, A.: BGOS ULS data processing procedure, Woods Hole Oceanographic Institution, Corpus ID: 130370100, https://api.semanticscholar.org/CorpusID:130370100 (last access: 10 February 2025), 2006. a
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage: losses and coupled variability (1958–2018), Environ. Res. Lett., 13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018. a
Kwok, R. and Cunningham, G.: ICESat over Arctic sea ice: Estimation of snow depth and ice thickness, J. Geophys. Res.-Oceans, 113, https://doi.org/10.1029/2008JC004753, 2008. a
Labe, Z., Magnusdottir, G., and Stern, H.: Variability of Arctic sea ice thickness using PIOMAS and the CESM large ensemble, J. Climate, 31, 3233–3247, 2018. a
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., and Battaglia, P.: Learning skillful medium-range global weather forecasting, Science, 382, 1416–1421, 2023. a
Landy, J. C., Petty, A. A., Tsamados, M., and Stroeve, J. C.: Sea ice roughness overlooked as a key source of uncertainty in CryoSat-2 ice freeboard retrievals, J. Geophys. Res.-Oceans, 125, e2019JC015820, https://doi.org/10.1029/2019JC015820, 2020. a
Liu, G., Bracco, A., and Brajard, J.: Systematic Bias Correction in Ocean Mesoscale Forecasting Using Machine Learning, J. Adv. Model. Earth Sy., 15, e2022MS003426, https://doi.org/10.1029/2022MS003426, 2023. a
Liu, Y., Key, J. R., Wang, X., and Tschudi, M.: Multidecadal Arctic sea ice thickness and volume derived from ice age, The Cryosphere, 14, 1325–1345, https://doi.org/10.5194/tc-14-1325-2020, 2020. a
Morison, J. H., Aagaard, K. D., Moritz, R., McPhee, M., Heiberg, A., Steele, M., and Andersen, R.: North Pole Environmental Observatory (NPEO) Oceanographic Mooring Data, Arctic Data Center [data set], https://doi.org/10.5065/D6P84921, 2016. a
Nab, C., Mallett, R., Nelson, C., Stroeve, J., and Tsamados, M.: Optimising interannual sea ice thickness variability retrieved from CryoSat-2, Geophys. Res. Lett., 51, e2024GL111071, https://doi.org/10.1029/2024GL111071, 2024. a
OSI SAF: Global Low Resolution Sea Ice Drift – Multimission, EUMETSAT SAF on Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_NRT_2007, 2010. a
Paul, S., Hendricks, S., Ricker, R., Kern, S., and Rinne, E.: Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative, The Cryosphere, 12, 2437–2460, https://doi.org/10.5194/tc-12-2437-2018, 2018. a, b
Regan, H., Rampal, P., Ólason, E., Boutin, G., and Korosov, A.: Modelling the evolution of Arctic multiyear sea ice over 2000–2018, The Cryosphere, 17, 1873–1893, https://doi.org/10.5194/tc-17-1873-2023, 2023. a
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017 (data available at: ftp://ftp.awi.de/sea_ice/product/cryosat2_smos/v204/, last access: 5 February 2025). a, b, c, d, e
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C., Ng, A. Y., Hassabis, D., Platt, J. C., Creutzig, F., Chayes, J., and Bengio, Y.: Tackling climate change with machine learning, ACM Computing Surveys (CSUR), 55, 1–96, https://doi.org/10.48550/arXiv.1906.05433, 2022. a
Sakov, P. and Oke, P. R.: A deterministic formualtion of the ensemble Kalman filter: an alternative to ensemble square root filters, Tellus, 60, 361–371, 2008. a
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012. a
Sallila, H., Farrell, S. L., McCurry, J., and Rinne, E.: Assessment of contemporary satellite sea ice thickness products for Arctic sea ice, The Cryosphere, 13, 1187–1213, https://doi.org/10.5194/tc-13-1187-2019, 2019. a
Schutz, B. E., Zwally, H. J., Shuman, C. A., Hancock, D., and DiMarzio, J. P.: Overview of the ICESat mission, Geophys. Res. Lett., 32, https://doi.org/10.1029/2005GL024009, 2005. a
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., and Stern, H.: Uncertainty in modeled arctic sea ice volume, J. Geophys. Res., 116, https://doi.org/10.1029/2011JC007084, 2011. a, b
SIN'XS: SIN'XS – Sea Ice-thickness product iNter-comparison eXerciSe, NOVELTIS [data set], https://sinxs.noveltis.fr (last access: 10 February 2025), 2025. a
Solomon, A., Heuzé, C., Rabe, B., Bacon, S., Bertino, L., Heimbach, P., Inoue, J., Iovino, D., Mottram, R., Zhang, X., Aksenov, Y., McAdam, R., Nguyen, A., Raj, R. P., and Tang, H.: Freshwater in the Arctic Ocean 2010–2019, Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, 2021. a
Soriot, C., Prigent, C., and Jimenez, C.: PMW Sea Ice Thickness – from SSM/I and SSMIS CDR (v1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.13880123, 2024a. a
Soriot, C., Vancoppenolle, M., Prigent, C., Jimenez, C., and Frappart, F.: Winter arctic sea ice volume decline: uncertainties reduced using passive microwave-based sea ice thickness, Scientific Reports, 14, 21000, https://doi.org/10.1038/s41598-024-70136-9, 2024b. a, b, c, d
Szekely, T., Gourrion, J., Pouliquen, S., and Reverdin, G.: CORA, Coriolis Ocean Dataset for Reanalysis, SEANOE [data set], https://doi.org/10.17882/46219, 2024. a
Tilling, R. L., Ridout, A., and Shepherd, A.: Estimating Arctic sea ice thickness and volume using CryoSat-2 radar altimeter data, Adv. Space Res., 62, 1203–1225, 2018. a
Trenberth, K. E., Zhang, Y., Fasullo, J. T., and Cheng, L.: Observation-Based Estimates of Global and Basin Ocean Meridional Heat Transport Time Series, J. Climate, 32, 4567–4583, https://doi.org/10.1175/JCLI-D-18-0872.1, 2019. a
Uotila, P., Goosse, H., Haines, K., Chevallier, M., Barthélemy, A., Bricaud, C., Carton, J., Fučkar, N., Garric, G., Iovino, D., Kauker, F., Korhonen, M., Lien, V. S., Marnela, M., Massonnet, F., Mignac, D., Peterson, K. A., Sadikni, R., Shi, L., Tietsche, S., Toyoda, T., Xie, J., and Zhang, Z.: An assessment of ten ocean reanalyses in the polar regions, Clim. Dynam., 52, 1613–1650, 2019. a, b, c
Wang, Z., Lu, Y., Wright, D. G., Dupont, F., and Division, O. S.: Sea ice sensitivity to the parameterisation of open water area, J. Oper. Oceanogr., 3, 3–9, https://doi.org/10.1080/1755876X.2010.11020113, 2010. a
Watts, M., Maslowski, W., Lee, Y. J., Kinney, J. C., and Osinski, R.: A spatial evaluation of Arctic sea ice and regional limitations in CMIP6 historical simulations, J. Climate, 34, 6399–6420, 2021. a
Wingham, D., Francis, C., Baker, S., Bouzinac, C., Brockley, D., Cullen, R., de Chateau-Thierry, P., Laxon, S., Mallow, U., Mavrocordatos, C., Phalippou, L., Ratier, G., Rey, L., Rostan, F., Viau, P., and Wallis, D. W.: CryoSat: A mission to determine the fluctuations in Earth’s land and marine ice fields, Adv. Space Res., 37, 841–871, 2006. a
Xie, J., Bertino, L., Counillon, F., Lisæter, K. A., and Sakov, P.: Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013, Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017, 2017. a, b
Xie, J., Counillon, F., and Bertino, L.: Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis, The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, 2018. a, b
Xie, J., Raj, R. P., Bertino, L., Martínez, J., Gabarró, C., and Catany, R.: Assimilation of sea surface salinities from SMOS in an Arctic coupled ocean and sea ice reanalysis, Ocean Sci., 19, 269–287, https://doi.org/10.5194/os-19-269-2023, 2023. a
Xiu, Y., Min, C., Xie, J., Mu, L., Han, B., and Yang, Q.: Evaluation of sea-ice thickness reanalysis data from the coupled ocean-sea-ice data assimilation system TOPAZ4, J. Glaciol., 67, 353–365, 2021. a
Yi, D. and Zwally, H. J.: Arctic Sea Ice Freeboard and Thickness, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/SXJVJ3A2XIZT, 2009. a, b
Zygmuntowska, M., Rampal, P., Ivanova, N., and Smedsrud, L. H.: Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends, The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, 2014. a, b
Short summary
This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011 to 2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes in sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a...
