Articles | Volume 11, issue 3
https://doi.org/10.5194/esd-11-835-2020
https://doi.org/10.5194/esd-11-835-2020
Research article
 | 
18 Sep 2020
Research article |  | 18 Sep 2020

Reconstructing coupled time series in climate systems using three kinds of machine-learning methods

Yu Huang, Lichao Yang, and Zuntao Fu

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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
ED: Reconsider after major revisions (28 Mar 2020) by C.T. Dhanya
AR by Yu Huang on behalf of the Authors (07 Apr 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (18 Apr 2020) by C.T. Dhanya
RR by Anonymous Referee #2 (05 May 2020)
RR by Anonymous Referee #1 (11 May 2020)
ED: Reconsider after major revisions (22 May 2020) by C.T. Dhanya
AR by Yu Huang on behalf of the Authors (28 May 2020)
ED: Referee Nomination & Report Request started (10 Jun 2020) by C.T. Dhanya
RR by Anonymous Referee #2 (30 Jun 2020)
ED: Publish subject to minor revisions (review by editor) (12 Jul 2020) by C.T. Dhanya
AR by Yu Huang on behalf of the Authors (15 Jul 2020)  Author's response   Manuscript 
ED: Publish as is (09 Aug 2020) by C.T. Dhanya
AR by Yu Huang on behalf of the Authors (10 Aug 2020)
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Short summary
We investigate the applicability of machine learning (ML) on time series reconstruction and find that the dynamical coupling relation and nonlinear causality are crucial for the application of ML. Our results could provide insights into causality and ML approaches for paleoclimate reconstruction, parameterization schemes, and prediction in climate studies.
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