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

Rader, Jamin K.; Barnes, Elizabeth A.; Ebert‐Uphoff, Imme; Anderson, Chuck

doi:10.1029/2021ms002941

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

2022
By Rader, Jamin K. ; Barnes, Elizabeth A. ; Ebert‐Uphoff, Imme ; ...
Source: Journal of Advances in Modeling Earth Systems, 14(7)

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Journal Title:

Journal of Advances in Modeling Earth Systems
Personal Author:

Rader, Jamin K. ; Barnes, Elizabeth A. ; Ebert‐Uphoff, Imme ; Anderson, Chuck

Rader, Jamin K. ; Barnes, Elizabeth A. ; Ebert‐Uphoff, Imme ; Anderson, Chuck Less -
NOAA Program & Office:

CIRA (Cooperative Institute for Research in the Atmosphere)
Description:

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 allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single- and multi-variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate
Keywords:

[+]

Environmental Chemistry General Earth And Planetary Sciences Global And Planetary Change
Source:

Journal of Advances in Modeling Earth Systems, 14(7)
DOI:

https://doi.org/10.1029/2021ms002941
ISSN:

1942-2466;1942-2466;
Format:

PDF
Publisher:

American Geophysical Union (AGU)
Document Type:

Journal Article
Funding:

Grant no. NA19OAR4310289
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
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Library
Main Document Checksum:

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urn:sha256:408d6c1963c87a88fb99f845235199237c71192a9700d63f839d037460661644
Download URL:

https://repository.library.noaa.gov/view/noaa/54432/noaa_54432_DS1.pdf
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