{"refrec":{"BRefID":352791,"RR":"<b>Bolibar, J.; Rabatel, A.; Gouttevin, I.; Zekollari, H.; Galiez, C.</b> (2022). Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning. <i>Nature Comm. 13(1)</i>: 409. <a href=\"https://dx.doi.org/10.1038/s41467-022-28033-0\" target=\"_blank\">https://dx.doi.org/10.1038/s41467-022-28033-0</a>","BEntID":350500,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Nature Comm. 13(1)</i>: 409. <a href=\"https://dx.doi.org/10.1038/s41467-022-28033-0\" target=\"_blank\">https://dx.doi.org/10.1038/s41467-022-28033-0</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Bolibar, J.; Rabatel, A.; Gouttevin, I.; Zekollari, H.; Galiez, C.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Bolibar, J. <i>et al.</i>","Englishabstract":"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.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-04-30 01:31:59.208110","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2022,"MonPub":null,"DateUpdate":"2022-06-22","DateCreate":"2022-06-21","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000745469500022","VABBcode":null,"OpenAcc":1,"DOI":"10.1038/s41467-022-28033-0"},"refs":null,"anarec":{"AnaID":352791,"PubliDate":2022,"Pagination":"409","XtraPublOfAnaID":null,"ISBN":null,"Volume":"13","Issue":"1","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":198717,"SerRR":"Nature Communications. 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