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+## Generative diffusion surrogates for sea- ice modelling
+### *Tobias S. Finn*, Charlo2e Durand, Alban Farchi, Marc Bocquet, Pierre Rampal, Julien Brajard, Alberto Carrassi
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+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.
+
+[back to the Workshop page](https://nansencenter.github.io/superice-nersc/workshop/)