Articles | Volume 20, issue 6
https://doi.org/10.5194/tc-20-3299-2026
© 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-20-3299-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jun 2026
Research article |
| 03 Jun 2026
| 03 Jun 2026
Sea ice data assimilation in ORAS6
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Eric de Boisseson
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Sarah Keeley
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Charles Pelletier
European Centre for Medium-Range Weather Forecasts, Bonn, Germany
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
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Charles Pelletier, Thierry Fichefet, Hugues Goosse, Konstanze Haubner, Samuel Helsen, Pierre-Vincent Huot, Christoph Kittel, François Klein, Sébastien Le clec'h, Nicole P. M. van Lipzig, Sylvain Marchi, François Massonnet, Pierre Mathiot, Ehsan Moravveji, Eduardo Moreno-Chamarro, Pablo Ortega, Frank Pattyn, Niels Souverijns, Guillian Van Achter, Sam Vanden Broucke, Alexander Vanhulle, Deborah Verfaillie, and Lars Zipf
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coupling interfacesrepresenting the feedbacks between the distinct models used for contribution. PARASO is stable and ready to use but is still characterized by significant biases.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
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We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.
Cited articles
Abraham, C., Steiner, N., Monahan, A., and Michel, C.: Effects of subgrid-scale snow thickness variability on radiative transfer in sea ice, J. Geophys. Res,-Oceans, 120, 5597–5614, https://doi.org/10.1002/2015JC010741, 2015. a
Balan-Sarojini, B., Tietsche, S., Mayer, M., Balmaseda, M., Zuo, H., de Rosnay, P., Stockdale, T., and Vitart, F.: Year-round impact of winter sea ice thickness observations on seasonal forecasts, The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, 2021. a, b
Bernard, B., Madec, G., Penduff, T., Molines, J.-M., Treguier, A.-M., Le Sommer, J., 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 De Cuevas, B.: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution, Ocean Dynam., 56, 543–567, 2006. a
Bitz, C. M., Holland, M. M., Weaver, A. J., and Eby, M.: Simulating the ice‐thickness distribution in a coupled climate model, J. Geophys. Res-Oceans, 106, 2441–2463, https://doi.org/10.1029/1999jc000113, 2001. a
Blockley, E. W. and Peterson, K. A.: Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness, The Cryosphere, 12, 3419–3438, https://doi.org/10.5194/tc-12-3419-2018, 2018. a
Bocquet, M., Fleury, S., Rémy, F., and Piras, F.: Arctic and Antarctic Sea Ice Thickness and Volume Changes From Observations Between 1994 and 2023, J. Geophys. Res.-Oceans, 129, e2023JC020848, https://doi.org/10.1029/2023JC020848, 2024. a
Bouillon, S., Morales Maqueda, M. Á., Legat, V., and Fichefet, T.: An elastic-viscous-plastic sea ice model formulated on Arakawa B and C grids, Ocean Model., 27, 174–184, https://doi.org/10.1016/j.ocemod.2009.01.004, 2009. a
Chrust, M., Weaver, A. T., Browne, P., Zuo, H., and Balmaseda, M. A.: Impact of an Ensemble of Ocean Data Assimilations in ECMWF's next generation ocean reanalysis system, arxiv [preprint], https://doi.org/10.48550/arXiv.2407.04488, 2024. a
Cipollone, A., Banerjee, D. S., Iovino, D., Aydogdu, A., and Masina, S.: Bivariate sea-ice assimilation for global-ocean analysis–reanalysis, Ocean Sci., 19, 1375–1392, https://doi.org/10.5194/os-19-1375-2023, 2023. a
de Rosnay, P., Browne, P., de Boisséson, E., Fairbairn, D., Hirahara, Y., Ochi, K., Schepers, D., Weston, P., Zuo, H., Alonso-Balmaseda, M., Balsamo, G., Bonavita, M., Borman, N., Brown, A., Chrust, M., Dahoui, M., Chiara, G., English, S., Geer, A., Healy, S., Hersbach, H., Laloyaux, P., Magnusson, L., Massart, S., McNally, A., Pappenberger, F., and Rabier, F.: Coupled data assimilation at ECMWF: current status, challenges and future developments, Q. J. Roy. Meteor. Soc., 148, 2672–2702, https://doi.org/10.1002/qj.4330, 2022. a
Fritzner, S., Graversen, R., Christensen, K. H., Rostosky, P., and Wang, K.: Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system, The Cryosphere, 13, 491–509, https://doi.org/10.5194/tc-13-491-2019, 2019. a
Geer, A. J.: Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting, Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.4797, 2024. a
Good, S., Fiedler, E., Mao, C., Martin, M. J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., and Worsfold, M.: The current configuration of the OSTIA system for operational production of foundation sea surface temperature and ice concentration analyses, Remote Sensing, 12, 720, https://doi.org/10.3390/rs12040720, 2020. a
Jean-Michel, L., Eric, G., Romain, B.-B., Gilles, G., Angélique, M., Marie, D., Clément, B., Mathieu, H., Olivier, L. G., Charly, R., Tony, C., Charles-Emmanuel, T., Florent, G., Giovanni, R., Mounir, B., Yann, D., and Pierre-Yves, L. T.: The Copernicus Global
Keeley, S. and Mogensen, K.: Dynamic sea ice in the IFS, ECMWF Newsletter, 23–29, https://doi.org/10.21957/4ska25furb, 2018. a
Landy, J. C., Dawson, G. J., Tsamados, M., Bushuk, M., Stroeve, J. C., Howell, S. E., Krumpen, T., Babb, D. G., Komarov, A. S., Heorton, H. D. B. S., Belter, H. J., and Aksenov, Y.: A year-round satellite sea-ice thickness record from CryoSat-2, Nature, 609, 517–522, 2022. a
Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., Good, S. A., Mittaz, J., Rayner, N. A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., and Donlon, C.: Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Sci. Data, 6, 223, https://doi.org/10.1038/s41597-019-0236-x, 2019. a
Mignac, D., Martin, M., Fiedler, E., Blockley, E., and Fournier, N.: Improving the Met Office's Forecast Ocean Assimilation Model (FOAM) with the assimilation of satellite-derived sea-ice thickness data from CryoSat-2 and SMOS in the Arctic, Q. J. Roy. Meteor. Soc., 148, 1144–1167, 2022. a
Mogensen, K., Balmaseda, M. A., and Weaver, A.: The NEMOVAR ocean data assimilation system as implemented in the ECMWF ocean analysis for System 4, ECMWF Reading, UK, https://doi.org/10.21957/x5y9yrtm, 2012. a
Mu, L., Nerger, L., Tang, Q., Loza, S. N., Sidorenko, D., Wang, Q., Semmler, T., Zampieri, L., Losch, M., and Goessling, H. F.: Toward a data assimilation system for seamless sea ice prediction based on the AWI climate model, J. Adv. Model. Earth Sy., 12, e2019MS001937, https://doi.org/10.1029/2019MS001937, 2020. a
OSI-450-a: OSI SAF Global sea ice concentration climate data record 1978–2020 (v3.0, 2022), EUMETSAT Ocean and Sea Ice Satellite Application Facility, [data set], https://doi.org/10.15770/EUM_SAF_OSI_0013, 2022. a
Prather, M. J.: Numerical advection by conservation of second-order moments, J. Geophys. Res.-Atmos., 91, 6671–6681, 1986. 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
Smith, G. C., Roy, F., Reszka, M., Surcel Colan, D., He, Z., Deacu, D., Belanger, J.-M., Skachko, S., Liu, Y., Dupont, F., Lemieux, J.-F., Beaudoin, C., Tranchant, B., Drévillon, M., Garric, G., Testut, C.-E., Lellouche, J.-M., Pellerin, P., Ritchie, H., Lu, Y., Davidson, F., Buehner, M., Caya, A., and Lajoie, M.: Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System, Q. J. Roy. Meteor. Soc., 142, 659–671, https://doi.org/10.1002/qj.2555, 2016. a, b
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness distribution of sea ice, J. Geophys. Res., 80, 4501–4513, https://doi.org/10.1029/JC080i033p04501, 1975. a
Vancoppenolle, M., Fichefet, T., and Bitz, C. M.: On the sensitivity of undeformed Arctic sea ice to its vertical salinity profile, Geophys. Res. Lett., 32, https://doi.org/10.1029/2005GL023427, 2005. a
Vancoppenolle, M., Rousset, C., Blockley, E., Aksenov, Y., Feltham, D., Fichefet, T., Garric, G., Guémas, V., Iovino, D., Keeley, S., Madec, G., Massonnet, F., Ridley, J., Schroeder, D., and Tietsche, S.: SI3, the NEMO Sea Ice Engine, Zenodo, https://doi.org/10.5281/zenodo.7534900, 2023. a, b
Wieringa, M. M., Riedel, C., Anderson, J. L., and Bitz, C. M.: Bounded and categorized: targeting data assimilation for sea ice fractional coverage and nonnegative quantities in a single-column multi-category sea ice model, The Cryosphere, 18, 5365–5382, https://doi.org/10.5194/tc-18-5365-2024, 2024. a
Williams, N., Byrne, N., Feltham, D., Van Leeuwen, P. J., Bannister, R., Schroeder, D., Ridout, A., and Nerger, L.: The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system, The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, 2023. a
Yang, Q., Losa, S. N., Losch, M., Tian-Kunze, X., Nerger, L., Liu, J., Kaleschke, L., and Zhang, Z.: Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter, J. Geophys. Res.-Oceans, 119, 6680–6692, https://doi.org/10.1002/2014JC009963, 2014. a
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019. a
Zuo, H., Alonso-Balmaseda, M., de Boisseson, E., Browne, P., Chrust, M., Keeley, S., Mogensen, K., Pelletier, C., de Rosnay, P., and Takakura, T.: ECMWF’s next ensemble reanalysis system for ocean and sea ice: ORAS6, ECMWF Newsletter, 30–36, https://doi.org/10.21957/hzd5y821lk, 2024. a
Zuo, H., Balmaseda, M. A., Boisseson, E. D., Browne, P., Chrust, M., Cobb, A., Day, J., Keeley, S., Mogensen, K., Johnson, S., Mayer, M., Pelletier, C., Roberts, C., Rosnay, P. D., Takakura, T., and Tietsche, S.: ORAS6: The new ECMWF ensemble ocean and sea-ice reanalysis-analysis system and impact on coupled forecasts, Q. J. Roy. Meteor. Soc., in preparation, 2026. a
Short summary
An initial estimate of sea ice conditions are required to start modern weather forecasting models. To get these we have to combine observations, typically from satellites, with previous estimates of the sea ice conditions. This paper talks about how we do this in the latest version of the model, where the sea ice state has to be specified for ice of different thicknesses. We describe the method that is used when we produce an estimate for every hour for the past 50 years.
An initial estimate of sea ice conditions are required to start modern weather forecasting...
