Articles | Volume 14, issue 11
Geosci. Model Dev., 14, 6977–6999, 2021
https://doi.org/10.5194/gmd-14-6977-2021
Geosci. Model Dev., 14, 6977–6999, 2021
https://doi.org/10.5194/gmd-14-6977-2021

Model description paper 17 Nov 2021

Model description paper | 17 Nov 2021

ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler

Bin Mu et al.

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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 gmd-2021-213', Anonymous Referee #1, 10 Aug 2021
    • AC1: 'Reply on RC1', Bo Qin, 14 Aug 2021
  • CEC1: 'Comment on gmd-2021-213', Juan Antonio Añel, 10 Aug 2021
    • AC2: 'Reply on CEC1', Bo Qin, 14 Aug 2021
  • RC2: 'Comment on gmd-2021-213', Anonymous Referee #2, 26 Aug 2021
    • AC3: 'Reply on RC2', Bo Qin, 31 Aug 2021
      • RC3: 'Reply on AC3', Anonymous Referee #2, 31 Aug 2021
        • AC4: 'Reply on RC3', Bo Qin, 02 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Bo Qin on behalf of the Authors (15 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (23 Sep 2021) by Xiaomeng Huang
AR by Bo Qin on behalf of the Authors (03 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (10 Oct 2021) by Xiaomeng Huang
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Short summary
Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we subjectively incorporate the prior physical knowledge into the modeling process and build up an ENSO deep learning forecast model with a multivariate air–sea coupler, named ENSO-ASC, the performance of which outperforms the other state-of-the-art models. The extensive experiments indicate that ENSO-ASC is a powerful tool for both the ENSO prediction and for the analysis of the underlying complex mechanisms.