Articles | Volume 16, issue 16
Model description paper
18 Aug 2023
Model description paper |  | 18 Aug 2023

IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning

Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-293', Anonymous Referee #1, 28 Jan 2023
    • AC1: 'Reply on RC1', xiaodan luo, 04 Jun 2023
  • RC2: 'Comment on gmd-2022-293', Anonymous Referee #2, 10 Apr 2023
    • AC2: 'Reply on RC2', xiaodan luo, 04 Jun 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by xiaodan luo on behalf of the Authors (05 Jun 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (27 Jun 2023) by Christopher Horvat
RR by Anonymous Referee #1 (08 Jul 2023)
ED: Publish subject to technical corrections (09 Jul 2023) by Christopher Horvat
AR by xiaodan luo on behalf of the Authors (11 Jul 2023)  Author's response   Manuscript 
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.