Learning-Based Methods and the Future of Numerical Ocean and Sea Ice Modeling

Charlotte Durand, Daria Botvynko, Hugo Frezat, Davide Grande, Andrea Storto, David Greenberg, Ronan Fablet, Said Ouala, Julien Le Sommer

The field of operational oceanography is undergoing a significant evolution with the increasing integration of artificial intelligence (AI) methods, which are complementing and, in some cases, redefining traditional numerical modeling approaches. This review explores how AI methods—particularly model-based autoregressive emulators, hybrid modeling, and end-to-end model-free approaches—are reshaping the representation of ocean and sea-ice dynamics in operational systems. We focus on three key objects: sea-ice parameters, near-surface ocean properties, and the 3D ocean state, each characterized by distinct observational and dynamical challenges. While AI-driven innovations offer new opportunities for improved monitoring, forecasting, and uncer- tainty quantification, their long-term impact on operational systems remains uncertain, especially given the sparsity of subsurface observations and the complexity of ocean dynamics. By synthesizing recent advances and identifying open questions, this paper aims to guide the ocean modeling community toward a future where AI and physics-based approaches coexist synergistically.