The Cryosphere
The Cryosphere
TC
Articles | Volume 19, issue 7
Articles | Volume 19, issue 7
https://doi.org/10.5194/tc-19-2583-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-2583-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 7
Research article
 | 
18 Jul 2025
Research article |  | 18 Jul 2025

Calibrating calving parameterizations using graph neural network emulators: application to Helheim Glacier, East Greenland

Younghyun Koo, Gong Cheng, Mathieu Morlighem, and Maryam Rahnemoonfar

Viewed

Total article views: 5,318 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,927 1,166 225 5,318 70 239 343
  • HTML: 3,927
  • PDF: 1,166
  • XML: 225
  • Total: 5,318
  • Supplement: 70
  • BibTeX: 239
  • EndNote: 343
Views and downloads (calculated since 12 Jul 2024)
Cumulative views and downloads (calculated since 12 Jul 2024)

Viewed (geographical distribution)

Total article views: 5,318 (including HTML, PDF, and XML) Thereof 5,237 with geography defined and 81 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 20 May 2026
Download
Short summary
Calving, the breaking of ice bodies from the terminus of a glacier, plays an important role in the mass losses of Greenland ice sheets. However, calving parameters have been poorly understood because of the intensive computational demands of traditional numerical models. To address this issue and find the optimal calving parameter that best represents real observations, we develop deep-learning emulators based on graph neural network architectures.
Read more
Share