Learning to Correct Climate Projection Biases

Learning to Correct Climate Projection Biases

Abstract The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post‐process model outputs to match observations, existing appro...

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Bibliographic Details
Main Authors: Baoxiang Pan, Gemma J. Anderson, André Goncalves, Donald D. Lucas, Céline J. W. Bonfils, Jiwoo Lee, Yang Tian, Hsi‐Yen Ma
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2021-10-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
deep learning
climate projection
bias correction
generative adversarial net
Online Access:https://doi.org/10.1029/2021MS002509

Internet

https://doi.org/10.1029/2021MS002509

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