Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-5801-2025
https://doi.org/10.5194/tc-19-5801-2025
Research article
 | 
17 Nov 2025
Research article |  | 17 Nov 2025

Machine learning improves seasonal mass balance prediction for unmonitored glaciers

Kamilla Hauknes Sjursen, Jordi Bolibar, Marijn van der Meer, Liss Marie Andreassen, Julian Peter Biesheuvel, Thorben Dunse, Matthias Huss, Fabien Maussion, David R. Rounce, and Brandon Tober

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Cited articles

Andreassen, L. M., Elvehøy, H., Kjøllmoen, B., Engeset, R., and Haakensen, N.: Glacier mass-balance and length variation in Norway, Annals of Glaciology, 42, 317–325, https://doi.org/10.3189/172756405781812826, 2005. a, b
Andreassen, L. M., Winsvold, S., Paul, F., and Hausberg, J.: Inventory of Norwegian Glaciers, Tech. rep., Norwegian Water Resources and Energy Directorate, ISBN 978-82-410-0826-9, https://doi.org/10.5167/uzh-73855, 2012. a
Andreassen, L. M., Elvehøy, H., Kjøllmoen, B., and Engeset, R. V.: Reanalysis of long-term series of glaciological and geodetic mass balance for 10 Norwegian glaciers, The Cryosphere, 10, 535–552, https://doi.org/10.5194/tc-10-535-2016, 2016. a, b, c, d, e, f, g, h, i
Andreassen, L. M., Elvehøy, H., Kjøllmoen, B., and Belart, J. M.: Glacier change in Norway since the 1960s – an overview of mass balance, area, length and surface elevation changes, Journal of Glaciology, 66, 313–328, https://doi.org/10.1017/jog.2020.10, 2020. a, b, c, d, e, f, g
Andreassen, L. M., Nagy, T., Kjøllmoen, B., and Leigh, J. R.: An inventory of Norway's glaciers and ice-marginal lakes from 2018-19 Sentinel-2 data, Journal of Glaciology, 68, 1085–1106, https://doi.org/10.1017/jog.2022.20, 2022. a, b
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Short summary
Understanding glacier mass changes is crucial for assessing freshwater availability in many regions of the world. We present the Mass Balance Machine, a machine learning model that learns from sparse measurements of glacier mass change to make predictions on unmonitored glaciers. Using data from Norway, we show that the model provides accurate estimates of mass changes at different spatiotemporal scales. Our findings show that machine learning can be a valuable tool to improve such predictions.
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