Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-5613-2025
© 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-19-5613-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Four-dimensional variational data assimilation with a sea-ice thickness emulator
Charlotte Durand
CORRESPONDING AUTHOR
CEREA, École des Ponts and EDF R&D, Institut Polytechnique de Paris, Île-de-France, France
Tobias Sebastian Finn
CEREA, École des Ponts and EDF R&D, Institut Polytechnique de Paris, Île-de-France, France
Alban Farchi
CEREA, École des Ponts and EDF R&D, Institut Polytechnique de Paris, Île-de-France, France
now at: European Center for Medium-Range Weather Forecasts, Bonn, Germany
Marc Bocquet
CEREA, École des Ponts and EDF R&D, Institut Polytechnique de Paris, Île-de-France, France
Julien Brajard
Nansen Environmental and Remote Sensing Center, 5007 Bergen, Norway
Laurent Bertino
Nansen Environmental and Remote Sensing Center, 5007 Bergen, Norway
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Tobias Sebastian Finn, Lucas Disson, Alban Farchi, Marc Bocquet, and Charlotte Durand
Nonlin. Processes Geophys., 31, 409–431, https://doi.org/10.5194/npg-31-409-2024, https://doi.org/10.5194/npg-31-409-2024, 2024
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We train neural networks as denoising diffusion models for state generation in the Lorenz 1963 system and demonstrate that they learn an internal representation of the system. We make use of this learned representation and the pre-trained model in two downstream tasks: surrogate modelling and ensemble generation. For both tasks, the diffusion model can outperform other more common approaches. Thus, we see a potential of representation learning with diffusion models for dynamical systems.
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This paper focuses on predicting Arctic-wide sea-ice thickness using surrogate modeling with deep learning. The model has a predictive power of 12 h up to 6 months. For this forecast horizon, persistence and daily climatology are systematically outperformed, a result of learned thermodynamics and advection. Consequently, surrogate modeling with deep learning proves to be effective at capturing the complex behavior of sea ice.
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We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
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State Planet Discuss., https://doi.org/10.5194/sp-2025-18, https://doi.org/10.5194/sp-2025-18, 2025
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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.
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We introduce the first denoising diffusion model for wildfire spread prediction, a new kind of generative AI model that learns to simulate fires not just as one fixed outcome, but as a range of possible scenarios. This allows us to capture the inherent uncertainty of wildfire dynamics. Our model produces ensembles of forecasts that reflect physically meaningful distributions of where fire might go next.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
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We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
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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
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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.
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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
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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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2001, https://doi.org/10.5194/egusphere-2025-2001, 2025
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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 a physically-based model (Barents-2.5km). We show that MET-AICE is skillful and provides more accurate forecasts than Barents-2.5km.
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
The Cryosphere, 19, 731–752, https://doi.org/10.5194/tc-19-731-2025, https://doi.org/10.5194/tc-19-731-2025, 2025
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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.
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Ocean Sci., 20, 1707–1720, https://doi.org/10.5194/os-20-1707-2024, https://doi.org/10.5194/os-20-1707-2024, 2024
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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.
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Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
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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.
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Nonlin. Processes Geophys., 31, 409–431, https://doi.org/10.5194/npg-31-409-2024, https://doi.org/10.5194/npg-31-409-2024, 2024
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We train neural networks as denoising diffusion models for state generation in the Lorenz 1963 system and demonstrate that they learn an internal representation of the system. We make use of this learned representation and the pre-trained model in two downstream tasks: surrogate modelling and ensemble generation. For both tasks, the diffusion model can outperform other more common approaches. Thus, we see a potential of representation learning with diffusion models for dynamical systems.
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A novel approach, optimal transport data assimilation (OTDA), is introduced to merge DA and OT concepts. By leveraging OT's displacement interpolation in space, it minimises mislocation errors within DA applied to physical fields, such as water vapour, hydrometeors, and chemical species. Its richness and flexibility are showcased through one- and two-dimensional illustrations.
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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.
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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.
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The Cryosphere, 18, 1791–1815, https://doi.org/10.5194/tc-18-1791-2024, https://doi.org/10.5194/tc-18-1791-2024, 2024
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This paper focuses on predicting Arctic-wide sea-ice thickness using surrogate modeling with deep learning. The model has a predictive power of 12 h up to 6 months. For this forecast horizon, persistence and daily climatology are systematically outperformed, a result of learned thermodynamics and advection. Consequently, surrogate modeling with deep learning proves to be effective at capturing the complex behavior of sea ice.
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
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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.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
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Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
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
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau
The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023, https://doi.org/10.5194/tc-17-2965-2023, 2023
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We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023, https://doi.org/10.5194/gmd-16-3997-2023, 2023
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Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
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
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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.
Pierre J. Vanderbecken, Joffrey Dumont Le Brazidec, Alban Farchi, Marc Bocquet, Yelva Roustan, Élise Potier, and Grégoire Broquet
Atmos. Meas. Tech., 16, 1745–1766, https://doi.org/10.5194/amt-16-1745-2023, https://doi.org/10.5194/amt-16-1745-2023, 2023
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Instruments dedicated to monitoring atmospheric gaseous compounds from space will provide images of urban-scale plumes. We discuss here the use of new metrics to compare observed plumes with model predictions that will be less sensitive to meteorology uncertainties. We have evaluated our metrics on diverse plumes and shown that by eliminating some aspects of the discrepancies, they are indeed less sensitive to meteorological variations.
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
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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.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Geosci. Model Dev., 16, 1039–1052, https://doi.org/10.5194/gmd-16-1039-2023, https://doi.org/10.5194/gmd-16-1039-2023, 2023
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When radionuclides are released into the atmosphere, the assessment of the consequences depends on the evaluation of the magnitude and temporal evolution of the release, which can be highly variable as in the case of Fukushima Daiichi.
Here, we propose Bayesian inverse modelling methods and the reversible-jump Markov chain Monte Carlo technique, which allows one to evaluate the temporal variability of the release and to integrate different types of information in the source reconstruction.
Colin Grudzien and Marc Bocquet
Geosci. Model Dev., 15, 7641–7681, https://doi.org/10.5194/gmd-15-7641-2022, https://doi.org/10.5194/gmd-15-7641-2022, 2022
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Iterative optimization techniques, the state of the art in data assimilation, have largely focused on extending forecast accuracy to moderate- to long-range forecast systems. However, current methodology may not be cost-effective in reducing forecast errors in online, short-range forecast systems. We propose a novel optimization of these techniques for online, short-range forecast cycles, simultaneously providing an improvement in forecast accuracy and a reduction in the computational cost.
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
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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
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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
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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.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Atmos. Chem. Phys., 21, 13247–13267, https://doi.org/10.5194/acp-21-13247-2021, https://doi.org/10.5194/acp-21-13247-2021, 2021
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The assessment of the environmental consequences of a radionuclide release depends on the estimation of its source. This paper aims to develop inverse Bayesian methods which combine transport models with measurements, in order to reconstruct the ensemble of possible sources.
Three methods to quantify uncertainties based on the definition of probability distributions and the physical models are proposed and evaluated for the case of 106Ru releases over Europe in 2017.
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
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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.
Sourav Chatterjee, Roshin P. Raj, Laurent Bertino, Sebastian H. Mernild, Meethale Puthukkottu Subeesh, Nuncio Murukesh, and Muthalagu Ravichandran
The Cryosphere, 15, 1307–1319, https://doi.org/10.5194/tc-15-1307-2021, https://doi.org/10.5194/tc-15-1307-2021, 2021
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Sea ice in the Greenland Sea (GS) is important for its climatic (fresh water), economical (shipping), and ecological contribution (light availability). The study proposes a mechanism through which sea ice concentration in GS is partly governed by the atmospheric and ocean circulation in the region. The mechanism proposed in this study can be useful for assessing the sea ice variability and its future projection in the GS.
Tobias Sebastian Finn, Gernot Geppert, and Felix Ament
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-672, https://doi.org/10.5194/hess-2020-672, 2021
Revised manuscript not accepted
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Through the lens of recent developments in hydrological modelling and data assimilation, we hourly update the soil moisture with ensemble data assimilation and sparse 2-metre-temperature observations in a coupled limited area model system. In idealized experiments, we improve the soil moisture analysis by coupled data assimilation across the atmosphere-land interface. We conclude that we can merge the separated assimilation cycles for the atmosphere and land surface into one single cycle.
Cited articles
Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A., Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J., Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y., Downie, R., and Shuckburgh, E.: Seasonal Arctic sea ice forecasting with probabilistic deep learning, Nature Communications, 12, https://doi.org/10.1038/s41467-021-25257-4, 2021. a
Barthélémy, S., Brajard, J., Bertino, L., and Counillon, F.: Super-resolution data assimilation, Ocean Dynamics, 72, 661–678, https://doi.org/10.1007/s10236-022-01523-x, 2022. a
Bernard, B., Madec, G., Penduff, T., Molines, J.-M., Treguier, A.-M., Sommer, J. L., Beckmann, A., Biastoch, A., Böning, C., Dengg, J., Derval, C., Durand, E., Gulev, S., Remy, E., Talandier, C., Theetten, S., Maltrud, M., McClean, J., and Cuevas, B. D.: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution, Ocean Dynamics, 56, 543–567, https://doi.org/10.1007/s10236-006-0082-1, 2006. a
Bouchat, A., Hutter, N., Chanut, J., Dupont, F., Dukhovskoy, D., Garric, G., Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers, P. G., Ólason, E., Rampal, P., Rasmussen, T., Talandier, C., Tremblay, B., and Wang, Q.: Sea Ice Rheology Experiment (SIREx): 1. Scaling and Statistical Properties of Sea-Ice Deformation Fields, Journal of Geophysical Research: Oceans, 127, e2021JC017667, https://doi.org/10.1029/2021JC017667, 2022. a
Boutin, G., Regan, H., Ólason, E., Brodeau, L., Talandier, C., Lique, C., and Rampal, P.: Data accompanying the article “Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework” (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7277523, 2022. a
Boutin, G., Ólason, E., Rampal, P., Regan, H., Lique, C., Talandier, C., Brodeau, L., and Ricker, R.: Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework, The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, 2023. a, b
Broyden, C. G.: Quasi-Newton methods and their application to function minimisation, Mathematics of Computation, 21, 368–381, https://doi.org/10.1090/s0025-5718-1967-0224273-2, 1967. a
Chen, W., Mahmood, A., Tsamados, M., and Takao, S.: Deep Random Features for Scalable Interpolation of Spatiotemporal Data, ARXIV [preprint], https://doi.org/10.48550/ARXIV.2412.11350, 2024. a
Cheng, S., Chen, Y., Aydoğdu, A., Bertino, L., Carrassi, A., Rampal, P., and Jones, C. K. R. T.: Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020, The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, 2023a. a
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, https://doi.org/10.1109/jas.2023.123537, 2023b. a
Chennault, A., Popov, A. A., Subrahmanya, A. N., Cooper, R., Rafid, A. H. M., Karpatne, A., and Sandu, A.: Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation, ARXIV [preprint], https://doi.org/10.48550/ARXIV.2111.08626, 2021. a
Copernicus Climate Change Service, Climate Data Store: 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
Dansereau, V., Weiss, J., Saramito, P., and Lattes, P.: A Maxwell elasto-brittle rheology for sea ice modelling, The Cryosphere, 10, 1339–1359, https://doi.org/10.5194/tc-10-1339-2016, 2016. a, b
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis‐error statistics in observation space, Quarterly Journal of the Royal Meteorological Society, 131, 3385–3396, https://doi.org/10.1256/qj.05.108, 2005. a
Driscoll, S., Carrassi, A., Brajard, J., Bertino, L., Bocquet, M., and Ólason, E. O.: Parameter sensitivity analysis of a sea ice melt pond parametrisation and its emulation using neural networks, Journal of Computational Science, 79, 102231, https://doi.org/10.1016/j.jocs.2024.102231, 2024. a
Durand, C.: Code and data for `Four-dimensional variational data assimilation with a sea-ice thickness emulator', Zenodo [code and data set], https://doi.org/10.5281/zenodo.14418068, 2024. a
European Space Agency: SMOS-CryoSat L4 Sea Ice Thickness, European Space Agency [data set], https://doi.org/10.57780/SM1-4F787C3, 2023. a
European Union-Copernicus Marine Service: Arctic Ocean Sea Ice Analysis and Forecast, European Union-Copernicus Marine Service [data set], https://doi.org/10.48670/MOI-00004, 2020. a
Fenty, I. and Heimbach, P.: Coupled Sea Ice–Ocean-State Estimation in the Labrador Sea and Baffin Bay, Journal of Physical Oceanography, 43, 884–904, https://doi.org/10.1175/jpo-d-12-065.1, 2013. 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
Finn, T. S., Durand, C., Farchi, A., Bocquet, M., and Brajard, J.: Towards diffusion models for large-scale sea-ice modelling, ARXIV [preprint], https://doi.org/10.48550/ARXIV.2406.18417, 2024a. a
Finn, T. S., Durand, C., Farchi, A., Bocquet, M., Rampal, P., and Carrassi, A.: Generative diffusion for regional surrogate models from sea-ice simulations, Authorea [preprint], https://doi.org/10.22541/au.171386536.64344222/v1, 2024b. a
Girard, L., Bouillon, S., Weiss, J., Amitrano, D., Fichefet, T., and Legat, V.: A new modeling framework for sea-ice mechanics based on elasto-brittle rheology, Annals of Glaciology, 52, 123–132, https://doi.org/10.3189/172756411795931499, 2011. a, b
Goessling, H. F., Tietsche, S., Day, J. J., Hawkins, E., and Jung, T.: Predictability of the Arctic sea ice edge, Geophysical Research Letters, 43, 1642–1650, https://doi.org/10.1002/2015gl067232, 2016. a, b
Gregory, W., Bushuk, M., Adcroft, A., Zhang, Y., and Zanna, L.: Deep Learning of Systematic Sea Ice Model Errors From Data Assimilation Increments, Journal of Advances in Modeling Earth Systems, 15, https://doi.org/10.1029/2023ms003757, 2023. a
Gregory, W., Bushuk, M., Zhang, Y., Adcroft, A., and Zanna, L.: Machine Learning for Online Sea Ice Bias Correction Within Global Ice‐Ocean Simulations, Geophysical Research Letters, 51, https://doi.org/10.1029/2023gl106776, 2024a. a
Gregory, W., MacEachern, R., Takao, S., Lawrence, I. R., Nab, C., Deisenroth, M. P., and Tsamados, M.: Scalable interpolation of satellite altimetry data with probabilistic machine learning, Nature Communications, 15, https://doi.org/10.1038/s41467-024-51900-x, 2024b. a
Grigoryev, T., Verezemskaya, P., Krinitskiy, M., Anikin, N., Gavrikov, A., Trofimov, I., Balabin, N., Shpilman, A., Eremchenko, A., Gulev, S., Burnaev, E., and Vanovskiy, V.: Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting, Remote Sensing, 14, 5837, https://doi.org/10.3390/rs14225837, 2022. a
Guemas, V., Blanchard-Wrigglesworth, E., Chevallier, M., Day, J. J., Déqué, M., Doblas-Reyes, F. J., Fučkar, N. S., Germe, A., Hawkins, E., Keeley, S., Koenigk, T., y Mélia, D. S., and Tietsche, S.: A review on Arctic sea-ice predictability and prediction on seasonal to decadal time-scales, Quarterly Journal of the Royal Meteorological Society, 142, 546–561, https://doi.org/10.1002/qj.2401, 2014. a
Hatfield, S., Chantry, M., Dueben, P., Lopez, P., Geer, A., and Palmer, T.: Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks, Journal of Advances in Modeling Earth Systems, 13, https://doi.org/10.1029/2021ms002521, 2021. a
Hebert, D. A., Allard, R. A., Metzger, E. J., Posey, P. G., Preller, R. H., Wallcraft, A. J., Phelps, M. W., and Smedstad, O. M.: Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System, Journal of Geophysical Research: Oceans, 120, 8327–8345, https://doi.org/10.1002/2015jc011283, 2015. a
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., 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., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Ioffe, S. and Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, https://doi.org/10.48550/ARXIV.1502.03167, 2015. a
Isaksen, L., Bonavita, M., Buizza, R., Fisher, M., Haseler, J., Leutbecher, M., and Raynaud, L.: Ensemble of data assimilations at ECMWF, ECMWF [data set], https://doi.org/10.21957/OBKE4K60, 2010. a
Ji, Q., Zhu, X., Wang, H., Liu, G., Gao, S., Ji, X., and Xu, Q.: Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China, Acta Oceanologica Sinica, 34, 54–64, https://doi.org/10.1007/s13131-015-0691-y, 2015. a
Kauker, F., Kaminski, T., Karcher, M., Giering, R., Gerdes, R., and Voßbeck, M.: Adjoint analysis of the 2007 all time Arctic sea‐ice minimum, Geophysical Research Letters, 36, https://doi.org/10.1029/2008gl036323, 2009. a
Kimmritz, M., Counillon, F., Bitz, C., Massonnet, F., Bethke, I., and Gao, Y.: Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model, Tellus A: Dynamic Meteorology and Oceanography, 70, 1435945, https://doi.org/10.1080/16000870.2018.1435945, 2018. a
Koldunov, N. V., Köhl, A., Serra, N., and Stammer, D.: Sea ice assimilation into a coupled ocean–sea ice model using its adjoint, The Cryosphere, 11, 2265–2281, https://doi.org/10.5194/tc-11-2265-2017, 2017. a
Kurtz, N. and Harbeck, J.: CryoSat-2 Level-4 Sea Ice Elevation, Freeboard, and Thickness. (RDEFT4, Version 1), Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/96JO0KIFDAS8, 2017. 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, Journal of Geophysical Research: Oceans, 125, https://doi.org/10.1029/2019jc015820, 2020. a
Lemieux, J., Beaudoin, C., Dupont, F., Roy, F., Smith, G. C., Shlyaeva, A., Buehner, M., Caya, A., Chen, J., Carrieres, T., Pogson, L., DeRepentigny, P., Plante, A., Pestieau, P., Pellerin, P., Ritchie, H., Garric, G., and Ferry, N.: The Regional Ice Prediction System (RIPS): verification of forecast sea ice concentration, Quarterly Journal of the Royal Meteorological Society, 142, 632–643, https://doi.org/10.1002/qj.2526, 2015. a
Lindsay, R. W. and Zhang, J.: Assimilation of Ice Concentration in an Ice–Ocean Model, Journal of Atmospheric and Oceanic Technology, 23, 742–749, https://doi.org/10.1175/jtech1871.1, 2006. a
Liu, D. C. and Nocedal, J.: On the limited memory BFGS method for large scale optimization, Mathematical Programming, 45, 503–528, https://doi.org/10.1007/bf01589116, 1989. a
Liu, Q., Zhang, R., Wang, Y., Yan, H., and Hong, M.: Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network, Journal of Marine Science and Engineering, 9, 330, https://doi.org/10.3390/jmse9030330, 2021. a
Madec, G., Delecluse, P., Imbard, M., and Levy, C.: OPA 8 Ocean General Circulation Model - Reference Manual, Tech. rep., LODYC/IPSL Note 11, 1998. a
Michel, Y.: Diagnostics on the cost-function in variational assimilations for meteorological models, Nonlin. Processes Geophys., 21, 187–199, https://doi.org/10.5194/npg-21-187-2014, 2014. a, b
Nab, C., Mignac, D., Landy, J., Martin, M., Stroeve, J., and Tsamados, M.: Sensitivity to Sea Ice Thickness Parameters in a Coupled Ice‐Ocean Data Assimilation System, Journal of Advances in Modeling Earth Systems, 17, https://doi.org/10.1029/2024ms004276, 2025. a
Ólason, E., Boutin, G., Korosov, A., Rampal, P., Williams, T., Kimmritz, M., Dansereau, V., and Samaké, A.: A New Brittle Rheology and Numerical Framework for Large‐Scale Sea‐Ice Models, Journal of Advances in Modeling Earth Systems, 14, https://doi.org/10.1029/2021ms002685, 2022. a, b
Olonscheck, D., Mauritsen, T., and Notz, D.: Arctic sea-ice variability is primarily driven by atmospheric temperature fluctuations, Nature Geoscience, 12, 430–434, https://doi.org/10.1038/s41561-019-0363-1, 2019. a
Owens, R. and Hewson, T.: ECMWF Forecast User Guide, ECMWF [data set], https://doi.org/10.21957/M1CS7H, 2018. a
Palerme, C., Lavergne, T., Rusin, J., Melsom, A., Brajard, J., Kvanum, A. F., Macdonald Sørensen, A., Bertino, L., and Müller, M.: Improving short-term sea ice concentration forecasts using deep learning, The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024, 2024. a
Rampal, P., Bouillon, S., Ólason, E., and Morlighem, M.: neXtSIM: a new Lagrangian sea ice model, The Cryosphere, 10, 1055–1073, https://doi.org/10.5194/tc-10-1055-2016, 2016. a, b
Rampal, P., Dansereau, V., Olason, E., Bouillon, S., Williams, T., Korosov, A., and Samaké, A.: On the multi-fractal scaling properties of sea ice deformation, The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, 2019. a
Raynaud, L., Berre, L., and Desroziers, G.: Spatial averaging of ensemble‐based background‐error variances, Quarterly Journal of the Royal Meteorological Society, 134, 1003–1014, https://doi.org/10.1002/qj.245, 2008. 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. a, b, c
Robert, C., Durbiano, S., Blayo, E., Verron, J., Blum, J., and Le Dimet, F.-X.: A reduced-order strategy for 4D-Var data assimilation, Journal of Marine Systems, 57, 70–82, https://doi.org/10.1016/j.jmarsys.2005.04.003, 2005. a
Rousset, C., Vancoppenolle, M., Madec, G., Fichefet, T., Flavoni, S., Barthélemy, A., Benshila, R., Chanut, J., Levy, C., Masson, S., and Vivier, F.: The Louvain-La-Neuve sea ice model LIM3.6: global and regional capabilities, Geosci. Model Dev., 8, 2991–3005, https://doi.org/10.5194/gmd-8-2991-2015, 2015. 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, b
Sasaki, Y.: SOME BASIC FORMALISMS IN NUMERICAL VARIATIONAL ANALYSIS, Monthly Weather Review, 98, 875–883, https://doi.org/10.1175/1520-0493(1970)098<0875:sbfinv>2.3.co;2, 1970. a
Serreze, M. C., Maslanik, J. A., Barry, R. G., and Demaria, T. L.: Winter atmospheric circulation in the Arctic Basin and possible relationships to the great salinity anomaly in the northern North Atlantic, Geophysical Research Letters, 19, 293–296, https://doi.org/10.1029/91gl02946, 1992. a
Sitzmann, V., Martel, J., Bergman, A., Lindell, D., and Wetzstein, G.: Implicit neural representations with periodic activation functions, Advances in neural information processing systems, 33, 7462–7473, 2020. a
Talagrand, O. and Courtier, P.: Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory, Quarterly Journal of the Royal Meteorological Society, 113, 1311–1328, https://doi.org/10.1002/qj.49711347812, 1987. a
Talandier, C. and Lique, C.: CREG025.L75-NEMO_r3.6.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.5802028, 2021. a
Tian-Kunze, X., Kaleschke, L., Maaß, N., Mäkynen, M., Serra, N., Drusch, M., and Krumpen, T.: SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification, The Cryosphere, 8, 997–1018, https://doi.org/10.5194/tc-8-997-2014, 2014. a
Tietsche, S., Notz, D., Jungclaus, J. H., and Marotzke, J.: Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects, Ocean Sci., 9, 19–36, https://doi.org/10.5194/os-9-19-2013, 2013. a
Toyoda, T., Fujii, Y., Yasuda, T., Usui, N., Ogawa, K., Kuragano, T., Tsujino, H., and Kamachi, M.: Data assimilation of sea ice concentration into a global ocean–sea ice model with corrections for atmospheric forcing and ocean temperature fields, Journal of Oceanography, 72, 235–262, https://doi.org/10.1007/s10872-015-0326-0, 2015. a, b
Toyoda, T., Hirose, N., Urakawa, L. S., Tsujino, H., Nakano, H., Usui, N., Fujii, Y., Sakamoto, K., and Yamanaka, G.: Effects of Inclusion of Adjoint Sea Ice Rheology on Backward Sensitivity Evolution Examined Using an Adjoint Ocean–Sea Ice Model, Monthly Weather Review, 147, 2145–2162, https://doi.org/10.1175/mwr-d-18-0198.1, 2019. a
Usui, N., Wakamatsu, T., Tanaka, Y., Hirose, N., Toyoda, T., Nishikawa, S., Fujii, Y., Takatsuki, Y., Igarashi, H., Nishikawa, H., Ishikawa, Y., Kuragano, T., and Kamachi, M.: Four-dimensional variational ocean reanalysis: a 30-year high-resolution dataset in the western North Pacific (FORA-WNP30), Journal of Oceanography, 73, 205–233, https://doi.org/10.1007/s10872-016-0398-5, 2016. a
Wang, K., Debernard, J., Sperrevik, A. K., Isachsen, P. E., and Lavergne, T.: A combined optimal interpolation and nudging scheme to assimilate OSISAF sea-ice concentration into ROMS, Annals of Glaciology, 54, 8–12, https://doi.org/10.3189/2013aog62a138, 2013. a
Williams, T., Korosov, A., Rampal, P., and Ólason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207–3227, https://doi.org/10.5194/tc-15-3207-2021, 2021. a, b, c
Xiao, Y., Bai, L., Xue, W., Chen, K., Han, T., and Ouyang, W.: FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation, ARXIV [preprint], https://doi.org/10.48550/ARXIV.2312.12455, 2023. a
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
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.
This paper presents a four-dimensional variational data assimilation system based on a neural...