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20232026
most citedACE: A fast, skillful learned global atmospheric model for climate prediction

29 citations · 40 across the 6 of their papers we have counts for

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physics.ao-ph2026

Disentangling the effects of sea surface temperature and CO in global machine learned weather-climate emulators

Spencer K. Clark, Troy Arcomano, James P. C. Duncan +8

While previous versions of the Ai2 Climate Emulator (ACE) have been trained with CO as a forcing, they are only accurate within a narrow range of scenarios, for example climate…

physics.ao-ph20261 cited

AIMIP Phase 1: systematic evaluations of AI weather and climate models

Brian Henn, Christopher S. Bretherton, Nikolay Koldunov +18

We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a…

physics.ao-ph2025

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

James P. C. Duncan, Elynn Wu, Surya Dheeshjith +15

Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land sur…

physics.ao-ph20244 cited

ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the sensitivity of climate to changed CO

Spencer K. Clark, Oliver Watt-Meyer, Anna Kwa +6

While autoregressive machine-learning-based emulators have been trained to produce stable and accurate rollouts in the climate of the present-day and recent past, none so far have…

physics.ao-ph20243 cited

ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses

Oliver Watt-Meyer, Brian Henn, Jeremy McGibbon +6

Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temper…

physics.ao-ph202329 cited

ACE: A fast, skillful learned global atmospheric model for climate prediction

Oliver Watt-Meyer, Gideon Dresdner, Jeremy McGibbon +9

Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a…