Articles | Volume 19, issue 10
https://doi.org/10.5194/tc-19-4149-2025
https://doi.org/10.5194/tc-19-4149-2025
Research article
 | 
02 Oct 2025
Research article |  | 02 Oct 2025

Developing a deep learning forecasting system for short-term and high-resolution prediction of sea ice concentration

Are Frode Kvanum, Cyril Palerme, Malte Müller, Jean Rabault, and Nick Hughes

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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, Nat. Commun., 12, https://doi.org/10.1038/s41467-021-25257-4, 2021. a, b, c, d
Batrak, Y. and Müller, M.: On the warm bias in atmospheric reanalyses induced by the missing snow over Arctic sea-ice, Nat. Commun., 10, https://doi.org/10.1038/s41467-019-11975-3, 2019. a
Blair, B., Müller, M., Palerme, C., Blair, R., Crookall, D., Knol-Kauffman, M., and Lamers, M.: Coproducing sea ice predictions with stakeholders using simulation, Weather Clim. Soc., 14, 399–413, https://doi.org/10.1175/wcas-d-21-0048.1, 2022. a
Bommer, P., Kretschmer, M., Hedström, A., Bareeva, D., and Höhne, M. M. C.: Finding the right XAI method – A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science, arXiv [preprint], https://doi.org/10.48550/arXiv.2303.00652, 2023. a
Cavalieri, D., Parkinson, C., Gloersen, P., and Zwally, H. J.: Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data. (NSIDC-0051, Version 1), NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/8GQ8LZQVL0VL, 1996. a
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Short summary
Recent studies have shown that machine learning models are effective at predicting sea ice concentration, yet few have explored the development of such models in an operational context. In this study, we present the development of a machine learning forecasting system which can predict sea ice concentration at 1 km resolution up to 3 d ahead using real-time operational data. The developed forecasts predict the sea ice edge position with better accuracy than physical and baseline forecasts.
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