Generative Diffusion for Regional Surrogate Models From Sea-Ice Simulations

Abstract

We introduce deep generative diffusion for multivariate and regional surrogate modeling learned from sea-ice simulations. Given initial conditions and atmospheric forcings, the model is trained to generate forecasts for a 12-hr lead time from simulations by the state-of-the-art sea-ice model neXtSIM. For our regional model setup, the diffusion model outperforms as ensemble forecast all other tested models, including a free-drift model and a stochastic extension of a deterministic data-driven surrogate model. The diffusion model additionally retains information at all scales, resolving smoothing issues of deterministic models. Furthermore, by generating physically consistent forecasts, previously unseen for such kind of completely data-driven surrogates, the model can almost match the scaling properties of neXtSIM, as similarly deduced from sea-ice observations. With these results, we provide a strong indication that diffusion models can achieve similar results as traditional geophysical models with the significant advantage of being orders of magnitude faster and solely learned from data.