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

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Cited articles

Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A next-generation hyperparameter optimization framework, in: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2623–2631, https://doi.org/10.1145/3292500.3330701, 2019. a
Anilkumar, R., Bharti, R., Chutia, D., and Aggarwal, S. P.: Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques, The Cryosphere, 17, 2811–2828, https://doi.org/10.5194/tc-17-2811-2023, 2023. a, b
Auffarth, B.: Machine learning for time-series with Python, Packt Publishing United Kingdom, ISBN: 9781801819626, 2021. a
Bolibar, J., Rabatel, A., Gouttevin, I., Galiez, C., Condom, T., and Sauquet, E.: Deep learning applied to glacier evolution modelling, The Cryosphere, 14, 565–584, https://doi.org/10.5194/tc-14-565-2020, 2020. a
Bolibar, J., Rabatel, A., Gouttevin, I., Zekollari, H., and Galiez, C.: Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning, Nat. Commun., 13, 409, https://doi.org/10.1038/s41467-022-28033-0, 2022. a
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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.
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