Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle

Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle

Abstract One approach to improving the accuracy of a coarse‐grid global climate model is to add machine‐learned (ML) state‐dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine‐grid global storm‐resolving model (GSRM). Our past work d...

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Bibliographic Details
Main Authors: Anna Kwa, Spencer K. Clark, Brian Henn, Noah D. Brenowitz, Jeremy McGibbon, Oliver Watt‐Meyer, W. Andre Perkins, Lucas Harris, Christopher S. Bretherton
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2023-05-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
climate modeling
machine learning
global storm‐resolving model
coarse‐graining
Online Access:https://doi.org/10.1029/2022MS003400

Internet

https://doi.org/10.1029/2022MS003400

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