HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

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

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.
Document type :
Journal articles
Complete list of metadata

https://hal-insu.archives-ouvertes.fr/insu-03668394
Contributor : Nathalie Pothier Connect in order to contact the contributor
Submitted on : Saturday, May 14, 2022 - 5:37:58 PM
Last modification on : Tuesday, May 17, 2022 - 7:40:07 AM

File

s41467-022-28033-0.pdf
Publisher files allowed on an open archive

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Collections

INSU | LJK | OMP | IRD | INRIA | IGE | METEO | CNRS | UGA

Citation

Jordi Bolibar, Antoine Rabatel, Isabelle Gouttevin, Harry Zekollari, Clovis Galiez. Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning. Nature Communications, Nature Publishing Group, 2022, 13, ⟨10.1038/s41467-022-28033-0⟩. ⟨insu-03668394⟩

Share

Metrics

Record views

0

Files downloads

0