23 Aug 2023
 | 23 Aug 2023
Status: this preprint is open for discussion.

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

Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, and Einar Òlason

Abstract. A novel generation of sea-ice models with Elasto-Brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale, for resolutions of around 10 km. As these models are computationally expensive, we introduce supervised deep learning techniques for surrogate modeling of the sea-ice thickness from neXtSIM simulations. We adapt a convolutional UNet architecture to an Arctic-wide setup by taking the land-sea mask with partial convolutions into account. Trained to emulate the sea-ice thickness on a lead time of 12 hours, the neural network can be iteratively applied to predictions up to a year. The improvements of the surrogate model over a persistence forecast prevail from 12 hours to roughly a year, with improvements of up to 50 % in the forecast error. The predictability of the sea-ice thickness measured against a daily climatology additionally lays by around 8 months. By using atmospheric forcings as additional input, the surrogate model can represent advective and thermodynamical processes, which influence the sea-ice thickness and the growth and melting therein. While iterating, the surrogate model experiences diffusive processes, which result into a loss of fine-scale structures. However, this smoothing increases the coherence of large-scale features and hereby the stability of the model. Therefore, based on these results, we see a huge potential for surrogate modelling of state-of-art sea-ice models with neural networks.

Charlotte Durand et al.

Status: open (until 04 Nov 2023)

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Charlotte Durand et al.

Charlotte Durand et al.


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Short summary
This paper focuses on predicting the Arctic-wide sea-ice thickness using surrogate modeling with deep learning. The model has a predictive power from 12 hours up to eight months. For this forecast horizon, persistence and daily climatology are systematically outperformed, a result of learned thermodynamics and advection. Consequently, surrogate modelling with deep learning proves to be effective in capturing the complex behavior of sea-ice.