A Framework for Hybrid Physics-AI Coupled Ocean Models
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Zanna, Laure Gregory, William Perezhogin, Pavel Sane, Aakash Zhang, Cheng Adcroft, Alistair Bushuk, Mitch Fernandez-Granda, Carlos Reichl, Brandon Balwada, Dhruv Busecke, Julius Chapman, William Connolly, Alex Du, Danni Everard, Kelsey Falasca, Fabrizio Falga, Renaud Kamm, David Meunier, Etienne Liu, Qi Nasser, Antoine Pudig, Matthew Shao, Andrew Simpson, Julia L. Vogt, Linus Wu, Jiarong |
| author_facet | Zanna, Laure Gregory, William Perezhogin, Pavel Sane, Aakash Zhang, Cheng Adcroft, Alistair Bushuk, Mitch Fernandez-Granda, Carlos Reichl, Brandon Balwada, Dhruv Busecke, Julius Chapman, William Connolly, Alex Du, Danni Everard, Kelsey Falasca, Fabrizio Falga, Renaud Kamm, David Meunier, Etienne Liu, Qi Nasser, Antoine Pudig, Matthew Shao, Andrew Simpson, Julia L. Vogt, Linus Wu, Jiarong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22676 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Framework for Hybrid Physics-AI Coupled Ocean Models Zanna, Laure Gregory, William Perezhogin, Pavel Sane, Aakash Zhang, Cheng Adcroft, Alistair Bushuk, Mitch Fernandez-Granda, Carlos Reichl, Brandon Balwada, Dhruv Busecke, Julius Chapman, William Connolly, Alex Du, Danni Everard, Kelsey Falasca, Fabrizio Falga, Renaud Kamm, David Meunier, Etienne Liu, Qi Nasser, Antoine Pudig, Matthew Shao, Andrew Simpson, Julia L. Vogt, Linus Wu, Jiarong Atmospheric and Oceanic Physics 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. |
| title | A Framework for Hybrid Physics-AI Coupled Ocean Models |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2510.22676 |