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

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

doi:10.5194/tc-20-3313-2026

Articles | Volume 20, issue 6

https://doi.org/10.5194/tc-20-3313-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-20-3313-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 6

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

Data sets

Output of Learning to melt: Emulating Greenland surface melt from a polar RCM with machine learning Elke Schlager https://doi.org/10.5281/zenodo.19627367

Model code and software

Zenodo archive of code for MeltEmulation Elke Schlager https://doi.org/10.5281/zenodo.20271069

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.