Articles | Volume 20, issue 3
https://doi.org/10.5194/tc-20-1841-2026
https://doi.org/10.5194/tc-20-1841-2026
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: 2,294 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,290 0 4 2,294 0 0
  • HTML: 2,290
  • PDF: 0
  • XML: 4
  • Total: 2,294
  • BibTeX: 0
  • EndNote: 0
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: 2,294 (including HTML, PDF, and XML) Thereof 2,288 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 31 Mar 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.
Share