How to use generative diffusion for multivariate sea-ice modeling

In their newest pre-print submitted to the Journal of Advances in Modeling the Earth System, Finn et al. present their exciting and novel approach to learn sea-ice models from data based on generative deep learning. They show that it can outperform more classical approaches, while generating physical consistent forecasts. The completely data-driven model seems to generalize to benchmark-like cases (shown above), where the predicted fields resemble those predicted by geophysical models. With these results, Finn et al. show a large potential of generative deep learning to achieve similar results as classical geophysical models while being order of magnitude faster and solely learned from data.