Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks

Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks

Abstract Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and all...

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Bibliographic Details
Main Authors: Jamin K. Rader, Elizabeth A. Barnes, Imme Ebert‐Uphoff, Chuck Anderson
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2022-07-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
climate signals
climate models
explainable AI
neural networks
indicator patterns
CMIP6
Online Access:https://doi.org/10.1029/2021MS002941

Internet

https://doi.org/10.1029/2021MS002941

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