Articles | Volume 17, issue 21
https://doi.org/10.5194/bg-17-5335-2020
https://doi.org/10.5194/bg-17-5335-2020
Research article
 | 
06 Nov 2020
Research article |  | 06 Nov 2020

Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

Wei-Lei Wang, Guisheng Song, François Primeau, Eric S. Saltzman, Thomas G. Bell, and J. Keith Moore

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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
ED: Reconsider after major revisions (23 Jun 2020) by Koji Suzuki
AR by Weilei Wang on behalf of the Authors (23 Jun 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (23 Jun 2020) by Koji Suzuki
RR by Martí Galí (28 Jul 2020)
RR by Anonymous Referee #3 (04 Aug 2020)
ED: Publish subject to minor revisions (review by editor) (04 Aug 2020) by Koji Suzuki
AR by Weilei Wang on behalf of the Authors (13 Aug 2020)  Author's response   Manuscript 
ED: Publish subject to technical corrections (28 Aug 2020) by Koji Suzuki
AR by Weilei Wang on behalf of the Authors (29 Aug 2020)  Author's response   Manuscript 
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Short summary
Dimethyl sulfide, a volatile compound produced as a byproduct of marine phytoplankton activity, can be emitted to the atmosphere via gas exchange. In the atmosphere, DMS is oxidized to cloud condensation nuclei, thus contributing to cloud formation. Therefore, oceanic DMS plays an important role in regulating the planet's climate by influencing the radiation budget. In this study, we use an artificial neural network model to update the global DMS climatology and estimate the sea-to-air flux.
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