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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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ACP | Articles | Volume 20, issue 4
Atmos. Chem. Phys., 20, 2303–2317, 2020
https://doi.org/10.5194/acp-20-2303-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 20, 2303–2317, 2020
https://doi.org/10.5194/acp-20-2303-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Technical note 26 Feb 2020

Technical note | 26 Feb 2020

Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

Theodore Weber et al.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ben Kravitz on behalf of the Authors (19 Nov 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (25 Nov 2019) by Martina Krämer
RR by Anonymous Referee #1 (08 Dec 2019)
ED: Publish subject to minor revisions (review by editor) (28 Dec 2019) by Martina Krämer
AR by Ben Kravitz on behalf of the Authors (02 Jan 2020)  Author's response    Manuscript
ED: Publish as is (27 Jan 2020) by Martina Krämer
Publications Copernicus
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Short summary
Climate model emulators can save computer time but are less accurate than full climate models. We use neural networks to build emulators of precipitation, trained on existing climate model runs. By doing so, we can capture nonlinearities and how the past state of a model (to some degree) shapes the future state. Our emulator outperforms a persistence forecast of precipitation.
Climate model emulators can save computer time but are less accurate than full climate models....
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