Articles | Volume 19, issue 12
https://doi.org/10.5194/tc-19-6639-2025
https://doi.org/10.5194/tc-19-6639-2025
Research article
 | 
08 Dec 2025
Research article |  | 08 Dec 2025

Arctic regional changes revealed by clustering of sea-ice observations

Amélie Simon, Pierre Tandeo, Florian Sévellec, and Camille Lique

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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 egusphere-2025-704', Francois Massonnet, 04 Apr 2025
  • RC2: 'Comment on egusphere-2025-704', Anonymous Referee #2, 10 Apr 2025
  • RC3: 'Comment on egusphere-2025-704', Marion Lebrun, 29 Apr 2025

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (25 Aug 2025) by Michel Tsamados
AR by Amelie Simon on behalf of the Authors (26 Aug 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (08 Nov 2025) by Michel Tsamados
AR by Amelie Simon on behalf of the Authors (20 Nov 2025)  Author's response 
EF by Polina Shvedko (21 Nov 2025)  Manuscript   Author's tracked changes 
ED: Publish subject to technical corrections (25 Nov 2025) by Michel Tsamados
AR by Amelie Simon on behalf of the Authors (26 Nov 2025)  Manuscript 
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Short summary
Through a machine learning technique based on seasonal cycles of sea-ice concentration from satellite data over the last 4 decades, our research shows that four regions are sufficient to best regionalize the Arctic. These regions are mainly organized into latitudinal bands and evolve in time and space. The descriptor proposed to monitor Arctic sea-ice changes is the probability to belong to each region. The probability to belong to the permanent sea-ice regions has decreased by 3.1 % per decade.
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