Articles | Volume 16, issue 9
https://doi.org/10.5194/tc-16-3753-2022
https://doi.org/10.5194/tc-16-3753-2022
Research article
 | 
22 Sep 2022
Research article |  | 22 Sep 2022

Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region

Nazanin Asadi, Philippe Lamontagne, Matthew King, Martin Richard, and K. Andrea Scott

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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 tc-2021-282', Anonymous Referee #1, 27 Oct 2021
  • RC2: 'Comment on tc-2021-282', Anonymous Referee #2, 27 Oct 2021
  • RC3: 'Comment on tc-2021-282', Anonymous Referee #3, 23 Nov 2021

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) (24 Jan 2022) by Lars Kaleschke
AR by Nazanin Asadi on behalf of the Authors (07 Mar 2022)  Author's response 
EF by Polina Shvedko (10 Mar 2022)  Author's tracked changes 
EF by Polina Shvedko (10 Mar 2022)  Manuscript 
ED: Reconsider after major revisions (further review by editor and referees) (10 Mar 2022) by Lars Kaleschke
AR by Nazanin Asadi on behalf of the Authors (14 Apr 2022)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (24 Apr 2022) by Lars Kaleschke
RR by Anonymous Referee #1 (07 May 2022)
RR by Anonymous Referee #3 (02 Jun 2022)
ED: Publish subject to revisions (further review by editor and referees) (02 Jun 2022) by Lars Kaleschke
AR by Nazanin Asadi on behalf of the Authors (22 Jun 2022)  Author's response 
EF by Polina Shvedko (23 Jun 2022)  Manuscript   Author's tracked changes 
ED: Referee Nomination & Report Request started (23 Jun 2022) by Lars Kaleschke
RR by Anonymous Referee #3 (08 Jul 2022)
ED: Publish subject to revisions (further review by editor and referees) (11 Jul 2022) by Lars Kaleschke
AR by Nazanin Asadi on behalf of the Authors (29 Jul 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (02 Aug 2022) by Lars Kaleschke
AR by Nazanin Asadi on behalf of the Authors (12 Aug 2022)  Manuscript 
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Short summary
Machine learning approaches are deployed to provide accurate daily spatial maps of sea ice presence probability based on ERA5 data as input. Predictions are capable of predicting freeze-up/breakup dates within a 7 d period at specific locations of interest to shipping operators and communities. Forecasts of the proposed method during the breakup season have skills comparing to Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system.