Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell  elasto-brittle rheology

Finn, Tobias Sebastian; Durand, Charlotte; Farchi, Alban; Bocquet, Marc; Chen, Yumeng; Carrassi, Alberto; Dansereau, Véronique

doi:https://doi.org/10.5194/tc-17-2965-2023

Articles | Volume 17, issue 7

https://doi.org/10.5194/tc-17-2965-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-17-2965-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 7

Research article

|

21 Jul 2023

Research article |

| 21 Jul 2023

Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology

Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau

Model code and software

cerea-daml/hybrid_nn_meb_model: Preprint submission (Preprint) Tobias Finn https://doi.org/10.5281/zenodo.7997435

Short summary

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.