Preprints
https://doi.org/10.5194/egusphere-2022-1342
https://doi.org/10.5194/egusphere-2022-1342
02 Jan 2023
 | 02 Jan 2023

Deep learning of 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

Abstract. We introduce a scalable approach to parametrise the unresolved subgrid-scale of sea-ice dynamics with deep learning techniques. We apply this data-driven approach to a regional sea-ice model that accounts exclusively for dynamical processes with a Maxwell-Elasto-Brittle rheology. Our channel-like model setup is driven by a wave-like wind forcing, which generates examples of sharp transitions between unfractured and fully-fractured sea ice. Using a convolutional U-Net architecture, the parametrising neural network extracts multiscale and anisotropic features and, thus, includes important inductive biases needed for sea-ice dynamics. The neural network is trained to correct all nine model variables at the same time. With the initial and forecast state as input into the neural network, we cast the subgrid-scale parametrisation as model error correction, needed to correct unresolved model dynamics. We test the here-proposed approach in twin experiments, where forecasts of a low-resolution forecast model are corrected towards high-resolution truth states for a forecast lead time of about 10 min. At this lead time, our approach reduces the forecast errors by more than 75 %, averaged over all model variables. The neural network learns hereby a physically-explainable input-to-output relation. Furthermore, cycling the subgrid-scale parametrisation together with the geophysical model improves the short-term forecast up to one hour. We consequently show that neural networks can parametrise the subgrid-scale for sea-ice dynamics. We therefore see this study as an important first step towards hybrid modelling to forecast sea-ice dynamics on an hourly to daily timescale.

Tobias Sebastian Finn et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1342', Anonymous Referee #1, 14 Mar 2023
    • AC1: 'Reply on RC1', Tobias Finn, 19 Apr 2023
  • RC2: 'Comment on egusphere-2022-1342', Nils Hutter, 23 Mar 2023
    • AC2: 'Reply on RC2', Tobias Finn, 19 Apr 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1342', Anonymous Referee #1, 14 Mar 2023
    • AC1: 'Reply on RC1', Tobias Finn, 19 Apr 2023
  • RC2: 'Comment on egusphere-2022-1342', Nils Hutter, 23 Mar 2023
    • AC2: 'Reply on RC2', Tobias Finn, 19 Apr 2023

Tobias Sebastian Finn et al.

Model code and software

Code for experiments and neural networks (Git repository that includes all code except the regional sea-ice model) Tobias Sebastian Finn https://github.com/cerea-daml/hybrid_nn_meb_model

Tobias Sebastian Finn et al.

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Short summary
We combine machine learning with a simplified regional sea-ice model to correct errors in sea-ice dynamics of low-resolution forecasts towards a high-resolution truth. The combined model improves the forecast by up to 75 % and surpasses thereby the performance of persistence. As the error correction can also represent physically-explainable relations, this study highlights the potential of combined modelling for short-term sea-ice forecasting.