39 citations · 60 across the 7 of their papers we have counts for
9 papers
Extreme events and statistics in an AI emulator of a global storm-resolving model
Anna Kwa, W. Andre Perkins, Troy Arcomano +7
We present HiRO-ACEv2 (High Resolution Output Ai2 Climate Emulator), a two-stage framework for AI emulation of the 3~km resolution X-SHiELD global storm-resolving model (GSRM), and…
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…
HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model
W. Andre Perkins, Anna Kwa, Jeremy McGibbon +5
Kilometer-scale simulations of the atmosphere are an important tool for assessing local weather extremes and climate impacts, but computational expense limits their use to small re…
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…
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…
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…
Machine-learned climate model corrections from a global storm-resolving model
Anna Kwa, Spencer K. Clark, Brian Henn +6
Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution ( km) than is optimal for accurately res…
Emulating Fast Processes in Climate Models
Noah D. Brenowitz, W. Andre Perkins, Jacqueline M. Nugent +6
Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associate…
Machine Learning Climate Model Dynamics: Offline versus Online Performance
Noah D. Brenowitz, Brian Henn, Jeremy McGibbon +5
Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly r…