Articles | Volume 17, issue 7
Research article
21 Jul 2023
Research article |  | 21 Jul 2023

Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology

Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1342', Anonymous Referee #1, 14 Mar 2023
    • AC1: 'Reply on RC1', Tobias Finn, 19 Apr 2023
  • RC2: 'Comment on egusphere-2022-1342', Nils Hutter, 23 Mar 2023
    • AC2: 'Reply on RC2', Tobias Finn, 19 Apr 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (20 Apr 2023) by Yevgeny Aksenov
AR by Tobias Finn on behalf of the Authors (05 May 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 May 2023) by Yevgeny Aksenov
RR by Anonymous Referee #1 (23 May 2023)
ED: Publish as is (26 May 2023) by Yevgeny Aksenov
AR by Tobias Finn on behalf of the Authors (02 Jun 2023)  Manuscript 
Short summary
We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.