SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Duncan, James P. C. Wu, Elynn Dheeshjith, Surya Subel, Adam Arcomano, Troy Clark, Spencer K. Henn, Brian Kwa, Anna McGibbon, Jeremy Perkins, W. Andre Gregory, William Fernandez-Granda, Carlos Busecke, Julius Watt-Meyer, Oliver Hurlin, William J. Adcroft, Alistair Zanna, Laure Bretherton, Christopher |
| author_facet | Duncan, James P. C. Wu, Elynn Dheeshjith, Surya Subel, Adam Arcomano, Troy Clark, Spencer K. Henn, Brian Kwa, Anna McGibbon, Jeremy Perkins, W. Andre Gregory, William Fernandez-Granda, Carlos Busecke, Julius Watt-Meyer, Oliver Hurlin, William J. Adcroft, Alistair Zanna, Laure Bretherton, Christopher |
| contents | Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other geophysical processes. This paradigm allows for distributed development of individual components within a common framework, unified by a coupler that handles translation between realms via spatial or temporal alignment and flux exchange. Following a similar approach adapted for machine learning-based emulators, we present SamudrACE: a coupled global climate model emulator which produces centuries-long simulations at 1-degree horizontal, 6-hourly atmospheric, and 5-daily oceanic resolution, with 145 2D fields spanning 8 atmospheric and 19 oceanic vertical levels, plus sea ice, surface, and top-of-atmosphere variables. SamudrACE is highly stable and has low climate biases comparable to those of its components with prescribed boundary forcing, with realistic variability in coupled climate phenomena such as ENSO that is not possible to simulate in uncoupled mode. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12490 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators Duncan, James P. C. Wu, Elynn Dheeshjith, Surya Subel, Adam Arcomano, Troy Clark, Spencer K. Henn, Brian Kwa, Anna McGibbon, Jeremy Perkins, W. Andre Gregory, William Fernandez-Granda, Carlos Busecke, Julius Watt-Meyer, Oliver Hurlin, William J. Adcroft, Alistair Zanna, Laure Bretherton, Christopher Atmospheric and Oceanic Physics Machine Learning Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other geophysical processes. This paradigm allows for distributed development of individual components within a common framework, unified by a coupler that handles translation between realms via spatial or temporal alignment and flux exchange. Following a similar approach adapted for machine learning-based emulators, we present SamudrACE: a coupled global climate model emulator which produces centuries-long simulations at 1-degree horizontal, 6-hourly atmospheric, and 5-daily oceanic resolution, with 145 2D fields spanning 8 atmospheric and 19 oceanic vertical levels, plus sea ice, surface, and top-of-atmosphere variables. SamudrACE is highly stable and has low climate biases comparable to those of its components with prescribed boundary forcing, with realistic variability in coupled climate phenomena such as ENSO that is not possible to simulate in uncoupled mode. |
| title | SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2509.12490 |