气候变化领域集成服务门户(CCPortal): Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies

CCPortal

DOI	10.1038/s41558-021-01168-6
	Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies
	Callaghan M.; Schleussner C.-F.; Nath S.; Lejeune Q.; Knutson T.R.; Reichstein M.; Hansen G.; Theokritoff E.; Andrijevic M.; Brecha R.J.; Hegarty M.; Jones C.; Lee K.; Lucas A.; van Maanen N.; Menke I.; Pfleiderer P.; Yesil B.; Minx J.C.
发表日期	2021
ISSN	1758-678X
起始页码	966
结束页码	972
卷号	11 期号:11
英文摘要	Increasing evidence suggests that climate change impacts are already observed around the world. Global environmental assessments face challenges to appraise the growing literature. Here we use the language model BERT to identify and classify studies on observed climate impacts, producing a comprehensive machine-learning-assisted evidence map. We estimate that 102,160 (64,958–164,274) publications document a broad range of observed impacts. By combining our spatially resolved database with grid-cell-level human-attributable changes in temperature and precipitation, we infer that attributable anthropogenic impacts may be occurring across 80% of the world’s land area, where 85% of the population reside. Our results reveal a substantial ‘attribution gap’ as robust levels of evidence for potentially attributable impacts are twice as prevalent in high-income than in low-income countries. While gaps remain on confidently attributabing climate impacts at the regional and sectoral level, this database illustrates the potential current impact of anthropogenic climate change across the globe. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
来源期刊	Nature Climate Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/237196
作者单位	Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany; Priestley International Centre for Climate, University of Leeds, Leeds, United Kingdom; Climate Analytics, Berlin, Germany; Integrative Research Institute on Transformations of Human-Environment Systems, Humboldt University, Berlin, Germany; IRI THESys and Geography Faculty, Humboldt University, Berlin, Germany; Institute of Atmospheric and Climate Sciences, ETH Zürich, Zürich, Switzerland; NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States; Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena, Germany; Robert Bosch Stiftung GmbH, Berlin, Germany; Hanley Sustainability Institute, Renewable and Clean Energy Program and Physics Department, University of Dayton, Dayton, OH, United States
推荐引用方式 GB/T 7714	Callaghan M.,Schleussner C.-F.,Nath S.,et al. Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies[J],2021,11(11).
APA	Callaghan M..,Schleussner C.-F..,Nath S..,Lejeune Q..,Knutson T.R..,...&Minx J.C..(2021).Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies.Nature Climate Change,11(11).
MLA	Callaghan M.,et al."Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies".Nature Climate Change 11.11(2021).