{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bolibar J"],"funding":["Région Auvergne-Rhône-Alpes","Dutch Research Council (NWO)","Fonds De La Recherche Scientifique - FNRS"],"pagination":["409"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8776880"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_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 pr"],"journal":["Nature communications"],"pubmed_title":["Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning."],"pmcid":["PMC8776880"],"funding_grant_id":["Postdoctoral grant- chargé de recherches","016.Vidi.171.063","BERGER project"],"pubmed_authors":["Bolibar J","Zekollari H","Rabatel A","Gouttevin I","Galiez C"],"additional_accession":[]},"is_claimable":false,"name":"Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning.","description":"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 pr","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2025-04-18T16:34:27.761Z","creation":"2025-04-07T03:50:40.054Z"},"accession":"S-EPMC8776880","cross_references":{"pubmed":["35058461"],"doi":["10.1038/s41467-022-28033-0"]}}