Articles | Volume 14, issue 5
Geosci. Model Dev., 14, 3067–3077, 2021
https://doi.org/10.5194/gmd-14-3067-2021
Geosci. Model Dev., 14, 3067–3077, 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 et al.

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Interactive discussion

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
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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.