Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning
Jordi Bolibar (Universiteit Utrecht, Université Grenoble Alpes, Institut National de Recherche Pour L’Agriculture, L’Alimentation et L’Environnement (INRAE))
Antoine Rabatel (Université Grenoble Alpes)
Isabelle Gouttevin (Université de Toulouse, Université Grenoble Alpes)
Harry Zekollari (Vrije Universiteit Brussel, TU Delft - Mathematical Geodesy and Positioning)
Clovis Galiez (Université Grenoble Alpes)
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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.
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