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

Deep learning for surrogate modelling of neXtSIM 

Charlotte Durand1, Tobias Finn1, Alban Farchi1, Marc Bocquet1, and Einar Olason2
Charlotte Durand et al.
  • 1CEREA, École des Ponts and EDF R&D, Île de France, France (
  • 2Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway

A novel generation of sea-ice models with Elasto-Brittle rheologies can represent the drift and deformation of sea-ice with an unprecedented resolution and accuracy. To speed-up these computationally heavy simulations and to facilitate subgrid-scale parameterizations, we investigate supervised deep learning techniques for surrogate modelling of large-scale, Arctic-wide, neXtSIM Lagrangian simulations. We tailor convolutional neural networks to emulate the sea-ice thickness for 12 hours in advance. In our most successful approach, the U-Net learns to make beneficially use of information from multiple temporal and spatial scales, an important feature of the neural network for sea-ice prediction. Consequently, cycling the neural network performs in average 36% better than persistence on a daily timescale and up to 43 % on a monthly timescale. These promising results therefore demonstrate a way towards surrogate modelling of Arctic-wide simulations. 

How to cite: Durand, C., Finn, T., Farchi, A., Bocquet, M., and Olason, E.: Deep learning for surrogate modelling of neXtSIM , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12810,, 2023.