气候变化领域集成服务门户(CCPortal): Deep learning for early warning signals of tipping points

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DOI	10.1073/pnas.2106140118
	Deep learning for early warning signals of tipping points
	Bury T.M.; Sujith R.I.; Pavithran I.; Scheffer M.; Lenton T.M.; Anand M.; Bauch C.T.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:39
英文摘要	Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems simplify down to a limited number of possible "normal forms" that determine qualitative aspects of the new state that lies beyond the tipping point, such as whether it will oscillate or be stable. In several of those forms, indicators like increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) of the tipping point by detecting how dynamics slow down near the transition. But they do not predict the nature of the new state. Here we develop a deep learning algorithm that provides EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behavior of dynamics near tipping points that are common to many dynamical systems. The algorithm provides EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that characterizes the oncoming tipping point, thus providing qualitative information on certain aspects of the new state. Such approaches can help humans better prepare for, or avoid, undesirable state transitions. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally occurring tipping points. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Bifurcation theory; Dynamical systems; Early warning signals; Machine learning; Theoretical ecology
语种	英语
scopus关键词	algorithm; article; climate; conformational transition; cosmos; deep learning; ecology; human; sensitivity and specificity; thermoacoustics; time series analysis
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238370
作者单位	Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada; School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada; Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai, 600036, India; Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India; Department of Environmental Sciences, Wageningen University, Wageningen, 6708 PB, Netherlands; Global Systems Institute, University of Exeter, Exeter, EX4 4PY, United Kingdom
推荐引用方式 GB/T 7714	Bury T.M.,Sujith R.I.,Pavithran I.,et al. Deep learning for early warning signals of tipping points[J],2021,118(39).
APA	Bury T.M..,Sujith R.I..,Pavithran I..,Scheffer M..,Lenton T.M..,...&Bauch C.T..(2021).Deep learning for early warning signals of tipping points.Proceedings of the National Academy of Sciences of the United States of America,118(39).
MLA	Bury T.M.,et al."Deep learning for early warning signals of tipping points".Proceedings of the National Academy of Sciences of the United States of America 118.39(2021).