Articles | Volume 19, issue 12
https://doi.org/10.5194/tc-19-6639-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-6639-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Dec 2025
Research article |
| 08 Dec 2025
| 08 Dec 2025
Arctic regional changes revealed by clustering of sea-ice observations
IMT Atlantique, Lab-STICC, UMR CNRS 6285, 29238, Brest, France
Univ Brest CNRS Ifremer IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), Brest, France
Pierre Tandeo
IMT Atlantique, Lab-STICC, UMR CNRS 6285, 29238, Brest, France
ODYSSEY Team-Project, INRIA CNRS, Brest, France
Florian Sévellec
Univ Brest CNRS Ifremer IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), Brest, France
ODYSSEY Team-Project, INRIA CNRS, Brest, France
Camille Lique
Univ Brest CNRS Ifremer IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), Brest, France
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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.
Through a machine learning technique based on seasonal cycles of sea-ice concentration from...
