Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-3067-2021
https://doi.org/10.5194/gmd-14-3067-2021
Development and technical paper
 | 
28 May 2021
Development and technical paper |  | 28 May 2021

Physically regularized machine learning emulators of aerosol activation

Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg

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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 Sam Silva on behalf of the Authors (26 Mar 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (11 Apr 2021) by David Topping
AR by Sam Silva on behalf of the Authors (11 Apr 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (14 Apr 2021) by David Topping
AR by Sam Silva on behalf of the Authors (19 Apr 2021)  Manuscript 
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Short summary
The activation of aerosol into cloud droplets is an important but uncertain process in the Earth system. The physical and chemical interactions that govern this process are too computationally expensive to explicitly resolve in modern Earth system models. Here, we demonstrate how hybrid machine learning approaches can provide a potential path forward, enabling the representation of the more detailed physics and chemistry at a reduced computational cost while still retaining physical information.