气候变化领域集成服务门户(CCPortal): Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning

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DOI	10.1038/s41467-022-28033-0
	Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning
	Bolibar J.; Rabatel A.; Gouttevin I.; Zekollari H.; Galiez C.
发表日期	2022
ISSN	2041-1723
卷号	13 期号:1
英文摘要	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. © 2022, The Author(s).
语种	英语
scopus关键词	air temperature; climate change; future prospect; glacier mass balance; ice cap; machine learning; nonlinearity; sensitivity analysis; air temperature; article; climate change; deep learning; glacier; ice cap; precipitation; sea level rise; temperature sensitivity; warming; water availability; Alps; mountain; Alps; France
来源期刊	Nature Communications
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/250527
作者单位	Univ. Grenoble Alpes, CNRS, IRD, G-INP, Institut des Géosciences de l’Environnement, Grenoble, France; INRAE, UR RiverLy, Lyon-Villeurbanne, France; Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, Netherlands; Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France; Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, Netherlands; Laboratoire de Glaciologie, Université Libre de Bruxelles, Brussels, Belgium; Univ. Grenoble Alpes, CNRS, G-INP, Laboratoire Jean Kuntzmann, Grenoble, France
推荐引用方式 GB/T 7714	Bolibar J.,Rabatel A.,Gouttevin I.,et al. Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning[J],2022,13(1).
APA	Bolibar J.,Rabatel A.,Gouttevin I.,Zekollari H.,&Galiez C..(2022).Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning.Nature Communications,13(1).
MLA	Bolibar J.,et al."Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning".Nature Communications 13.1(2022).