The Cryosphere
The Cryosphere
TC
Articles | Volume 19, issue 12
Articles | Volume 19, issue 12
https://doi.org/10.5194/tc-19-6381-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-6381-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 12
Research article
 | 
01 Dec 2025
Research article |  | 01 Dec 2025

Extended seasonal prediction of Antarctic sea ice concentration using ANTSIC-UNet

Ziying Yang, Jiping Liu, Mirong Song, Yongyun Hu, Qinghua Yang, Ke Fan, Rune Grand Graversen, and Lu Zhou

Viewed

Total article views: 3,524 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,139 1,210 175 3,524 260 96 139
  • HTML: 2,139
  • PDF: 1,210
  • XML: 175
  • Total: 3,524
  • Supplement: 260
  • BibTeX: 96
  • EndNote: 139
Views and downloads (calculated since 03 Jun 2024)
Cumulative views and downloads (calculated since 03 Jun 2024)

Viewed (geographical distribution)

Total article views: 3,524 (including HTML, PDF, and XML) Thereof 3,430 with geography defined and 94 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 04 May 2026
Download
Short summary
Antarctic sea ice has changed rapidly in recent years. Here we developed a deep learning model trained by multiple climate variables for extended seasonal Antarctic sea ice prediction. Our model shows high predictive skills up to 6 months in advance, particularly in predicting extreme events. It also shows skillful predictions at the sea ice edge and year-to-year sea ice changes. Variable importance analyses suggest what variables are more important for prediction at different lead times.
Read more
Share