Articles | Volume 15, issue 10
https://doi.org/10.5194/gmd-15-4105-2022
https://doi.org/10.5194/gmd-15-4105-2022
Model description paper
 | 
25 May 2022
Model description paper |  | 25 May 2022

Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0

Bin Mu, Yuehan Cui, Shijin Yuan, and Bo Qin

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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-2021-396', Anonymous Referee #1, 24 Jan 2022
    • AC1: 'Reply on RC1', Yuehan Cui, 25 Feb 2022
    • AC3: 'Reply on RC1', Yuehan Cui, 04 Apr 2022
  • RC2: 'Comment on gmd-2021-396', Anonymous Referee #2, 10 Feb 2022
    • AC2: 'Reply on RC2', Yuehan Cui, 25 Feb 2022
    • AC4: 'Reply on RC2', Yuehan Cui, 04 Apr 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Yuehan Cui on behalf of the Authors (04 Apr 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (16 Apr 2022) by Xiaomeng Huang
RR by Anonymous Referee #2 (22 Apr 2022)
RR by Anonymous Referee #1 (23 Apr 2022)
ED: Publish as is (07 May 2022) by Xiaomeng Huang
AR by Yuehan Cui on behalf of the Authors (09 May 2022)
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Short summary
An ENSO deep learning forecast model (ENSO-MC) is built to simulate the spatial evolution of sea surface temperature, analyse the precursor and identify the sensitive area. The results reveal the pronounced subsurface features before different types of events and indicate that oceanic thermal anomaly in the central and western Pacific provides a key long-term memory for predictions, demonstrating the potential usage of the ENSO-MC model in simulation, understanding and observations of ENSO.