The Cryosphere
The Cryosphere
TC
Articles | Volume 18, issue 4
Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-1791-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-1791-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 4
Research article
 | 
18 Apr 2024
Research article |  | 18 Apr 2024

Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic

Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Guillaume Boutin, and Einar Ólason

Data sets

Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" (1.0) Guillaume Boutin et al. https://doi.org/10.5281/zenodo.7277523

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

Model code and software

Code for "Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic" published in The Cryosphere (Version v1) Charlotte Durand https://doi.org/10.5281/zenodo.10784995

Video supplement

Seasonal forecast of surrogate modeling of neXtSIM Charlotte Durand https://doi.org/10.5446/62131

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Short summary
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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