Articles | Volume 20, issue 6
https://doi.org/10.5194/tc-20-3313-2026
https://doi.org/10.5194/tc-20-3313-2026
Research article
 | 
03 Jun 2026
Research article |  | 03 Jun 2026

Learning to melt: Emulating Greenland surface melt from a polar RCM with machine learning

Elke Schlager, Sebastian Scher, Ruth H. Mottram, and Peter L. Langen

Viewed

Total article views: 2,910 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,947 816 147 2,910 127 214
  • HTML: 1,947
  • PDF: 816
  • XML: 147
  • Total: 2,910
  • BibTeX: 127
  • EndNote: 214
Views and downloads (calculated since 27 Jan 2026)
Cumulative views and downloads (calculated since 27 Jan 2026)

Viewed (geographical distribution)

Total article views: 2,910 (including HTML, PDF, and XML) Thereof 2,868 with geography defined and 42 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 03 Jun 2026
Download
Short summary
Predicting Greenland's surface melt is critical for understanding sea-level rise, but traditional firn models are too slow for exploring many climate scenarios. We developed a neural network optimized through systematic input selection and network tuning to identify the necessary information to accurately emulate surface melt. This approach cuts computation costs by orders of magnitude and can be retrained for different climate forcings or extended to other surface mass balance properties.
Share