The Cryosphere
The Cryosphere
TC
Articles | Volume 20, issue 3
Articles | Volume 20, issue 3
https://doi.org/10.5194/tc-20-1841-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-1841-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 3
Research article
 | 
30 Mar 2026
Research article |  | 30 Mar 2026

Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature

Nils Bochow, Philipp Hess, and Alexander Robinson

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 6,983 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
6,802 145 36 6,983 55 51
  • HTML: 6,802
  • PDF: 145
  • XML: 36
  • Total: 6,983
  • BibTeX: 55
  • EndNote: 51
Views and downloads (calculated since 28 Aug 2025)
Cumulative views and downloads (calculated since 28 Aug 2025)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 6,983 (including HTML, PDF, and XML) Thereof 6,972 with geography defined and 11 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Jun 2026
Download
Short summary
This study presents a fast, physics-guided machine-learning method that downscales coarse climate fields to fine resolution while enforcing conservation of large-scale totals. Trained on regional climate simulations and driven by Earth system model output, it handles extremes and outperforms linear interpolation, providing realistic, high-resolution forcing for ice-sheet models and improving projections of Greenland’s sea-level contribution.
Read more
Share