Generative diffusion surrogates for sea- ice modelling
Tobias S. Finn, Charlo2e Durand, Alban Farchi, Marc Bocquet, Pierre Rampal, Julien Brajard, Alberto Carrassi
We present the first generative diffusion surrogate model tailored to sea-ice processes. Trained on over 20 years of coupled neXtSIM-NEMO mesoscale simulations (~12 km resolution) in the region north of Svalbard, the model predicts 12-hour sea-ice evolution. Its inherent stochastic nature enables robust ensemble forecasting, outperforming all baselines in forecast error while resolving the smoothing limitations of deterministic surrogates. Crucially, we demonstrate for the first time that a fully data-driven model can generate physically consistent forecasts akin to those from neXtSIM. While this marks a significant advancement in realistic surrogate modelling, it incurs substantial computational costs compared to traditional approaches. Hence, we will showcase efforts to scale the model to Arctic-wide applications, including leveraging generative diffusion in a learned reduced space, and training at different resolutions for an enhanced performance.