SamudrACE: Fast and Accurate Coupled Climate Modeling With 3D Ocean and Atmosphere Emulators

Abstract 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.

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Publication Details

Journal
Geophysical Research Letters
Published
2026-05-30
DOI
https://doi.org/10.1029/2025gl119340
Primary Topic
Methane Hydrates and Related Phenomena
Type
article
Field-Weighted Citation Impact
0.00

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article

SamudrACE: Fast and Accurate Coupled Climate Modeling With 3D Ocean and Atmosphere Emulators

Alistair Adcroft, Oliver Watt‐Meyer, James Duncan, Adam Subel et al.
Geophysical Research Letters
Methane Hydrates and Related Phenomena
article

SamudrACE: Fast and Accurate Coupled Climate Modeling With 3D Ocean and Atmosphere Emulators

Alistair Adcroft, Oliver Watt‐Meyer, James Duncan, Adam Subel, Laure Zanna, Anna Kwa, Julius Busecke, W. A. Perkins, Christopher S. Bretherton, Brian Henn, Spencer K. Clark, William J. Hurlin, Jeremy McGibbon, William King Gregory, Troy Arcomano, Surya Dheeshjith, Elynn Wu, Carlos Fernandez-Granda
article en

Abstract

Abstract 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.

Geophysical Research LettersVol. 53(11)
Lamont-Doherty Earth Observatory (US), NOAA Geophysical Fluid Dynamics Laboratory (US), Princeton University (US), Courant Institute of Mathematical Sciences (US), Allen Institute for Artificial Intelligence (US), New York University (US), Columbia University (US)
Princeton University, National Oceanic and Atmospheric Administration
Openalex Percentile: Top 100%
Methane Hydrates and Related Phenomena
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