Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning

Jordi Bolibar (), Antoine Rabatel, Isabelle Gouttevin, Harry Zekollari and Clovis Galiez
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Jordi Bolibar: Univ. Grenoble Alpes, CNRS, IRD, G-INP, Institut des Géosciences de l’Environnement
Antoine Rabatel: Univ. Grenoble Alpes, CNRS, IRD, G-INP, Institut des Géosciences de l’Environnement
Isabelle Gouttevin: Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige
Harry Zekollari: Delft University of Technology
Clovis Galiez: Univ. Grenoble Alpes, CNRS, G-INP, Laboratoire Jean Kuntzmann

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract Glaciers and ice caps are experiencing strong mass losses worldwide, challenging water availability, hydropower generation, and ecosystems. Here, we perform the first-ever glacier evolution projections based on deep learning by modelling the 21st century glacier evolution in the French Alps. By the end of the century, we predict a glacier volume loss between 75 and 88%. Deep learning captures a nonlinear response of glaciers to air temperature and precipitation, improving the representation of extreme mass balance rates compared to linear statistical and temperature-index models. Our results confirm an over-sensitivity of temperature-index models, often used by large-scale studies, to future warming. We argue that such models can be suitable for steep mountain glaciers. However, glacier projections under low-emission scenarios and the behaviour of flatter glaciers and ice caps are likely to be biased by mass balance models with linear sensitivities, introducing long-term biases in sea-level rise and water resources projections.

Date: 2022
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DOI: 10.1038/s41467-022-28033-0

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