Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on resolved variables, known as parameterizations. Parameterizations often lead to inaccuracies in climate models, with significant biases in the physics of key climate phenomena. Advances in artificial intelligence (AI) are now directly enabling the learning of unresolved processes from data to improve the physics of climate simulations. Here, we introduce a flexible framework for developing and implementing physics- and scale-aware machine learning parameterizations within climate models. We focus on the ocean and sea-ice components of a state-of-the-art climate model by implementing a spectrum of data-driven parameterizations, ranging from complex deep learning models to more interpretable equation-based models. Our results showcase the viability of AI-driven parameterizations in operational models, advancing the capabilities of a new generation of hybrid simulations, and include prototypes of fully coupled atmosphere-ocean-sea-ice hybrid simulations. The tools developed are open source, accessible, and available to all.
@article{arxiv.2510.22676,
title = {A Framework for Hybrid Physics-AI Coupled Ocean Models},
author = {Laure Zanna and William Gregory and Pavel Perezhogin and Aakash Sane and Cheng Zhang and Alistair Adcroft and Mitch Bushuk and Carlos Fernandez-Granda and Brandon Reichl and Dhruv Balwada and Julius Busecke and William Chapman and Alex Connolly and Danni Du and Kelsey Everard and Fabrizio Falasca and Renaud Falga and David Kamm and Etienne Meunier and Qi Liu and Antoine Nasser and Matthew Pudig and Andrew Shao and Julia L. Simpson and Linus Vogt and Jiarong Wu},
journal= {arXiv preprint arXiv:2510.22676},
year = {2025}
}