Articles | Volume 29, issue 3
Nonlin. Processes Geophys., 29, 255–264, 2022
https://doi.org/10.5194/npg-29-255-2022
Nonlin. Processes Geophys., 29, 255–264, 2022
https://doi.org/10.5194/npg-29-255-2022
Research article
05 Jul 2022
Research article | 05 Jul 2022

Predicting sea surface temperatures with coupled reservoir computers

Benjamin Walleshauser and Erik Bollt

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2022-4', Anonymous Referee #1, 02 Mar 2022
    • AC1: 'Reply on RC1', Ben Walleshauser, 08 Apr 2022
  • RC2: 'Comment on npg-2022-4', Anonymous Referee #2, 10 Mar 2022
    • AC2: 'Reply on RC2', Ben Walleshauser, 08 Apr 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ben Walleshauser on behalf of the Authors (08 Apr 2022)  Author's response    Author's tracked changes    Manuscript
ED: Reconsider after major revisions (further review by editor and referees) (26 Apr 2022) by Reik Donner
AR by Ben Walleshauser on behalf of the Authors (02 May 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (20 May 2022) by Reik Donner
RR by Anonymous Referee #1 (30 May 2022)
ED: Publish as is (15 Jun 2022) by Reik Donner
AR by Ben Walleshauser on behalf of the Authors (18 Jun 2022)  Author's response    Manuscript
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Short summary
As sea surface temperature (SST) is vital for understanding the greater climate of the Earth and is also an important variable in weather prediction, we propose a model that effectively capitalizes on the reduced complexity of machine learning models while still being able to efficiently predict over a large spatial domain. We find that it is proficient at predicting the SST at specific locations as well as over the greater domain of the Earth’s oceans.