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

Abstract 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 U-Net architecture to an Arctic-wide setup by taking the land–sea mask with partial convolutions into account. Trained to emulate the sea-ice thickness at a lead time of 12 h, the neural network can be iteratively applied to predictions for up to 1 year. The improvements of the surrogate model over a persistence forecast persist from 12 h to roughly 1 year, with improvements of up to 50 % in the forecast error. Moreover, the predictability gain for the sea-ice thickness measured against the daily climatology extends to over 6 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 in a loss of fine-scale structures. However, this smoothing increases the coherence of large-scale features and thereby the stability of the model. Therefore, based on these results, we see huge potential for surrogate modeling of state-of-the-art sea-ice models with neural networks.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic ; volume:18 ; number:4 ; year:2024 ; pages:1791-1815 ; extent:25 ; The Cryosphere ; 18, Heft 4 (2024), 1791-1815 (gesamt 25)

Contributors, Places and Time

• Creator: Durand, Charlotte ; Finn, Tobias Sebastian ; Farchi, Alban ; Bocquet, Marc ; Boutin, Guillaume ; Olason, Einar

Further information

• DOI: 10.5194/tc-18-1791-2024
• URN: urn:nbn:de:101:1-2404250457507.815590759288
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:55 AM CEST