气候变化领域集成服务门户(CCPortal): Network-based forecasting of climate phenomena

CCPortal

DOI	10.1073/pnas.1922872118
	Network-based forecasting of climate phenomena
	Ludescher J.; Martin M.; Boers N.; Bunde A.; Ciemer C.; Fan J.; Havlin S.; Kretschmer M.; Kurths J.; Runge J.; Stolbova V.; Surovyatkina E.; Schellnhuber H.J.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:47
英文摘要	Network theory, as emerging from complex systems science, can provide critical predictive power for mitigating the global warming crisis and other societal challenges. Here we discuss the main differences of this approach to classical numerical modeling and highlight several cases where the network approach substantially improved the prediction of high-impact phenomena: 1) El Niño events, 2) droughts in the central Amazon, 3) extreme rainfall in the eastern Central Andes, 4) the Indian summer monsoon, and 5) extreme stratospheric polar vortex states that influence the occurrence of wintertime cold spells in northern Eurasia. In this perspective, we argue that network-based approaches can gainfully complement numerical modeling. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Climate networks; Climate phenomena; Forecasting; Network theory
语种	英语
scopus关键词	rain; Article; artificial neural network; climate; drought; El Nino; extreme weather; forecasting; mathematical model; monsoon climate; network analysis; prediction; stratosphere; summer; winter
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/250983
作者单位	Potsdam Institute for Climate Impact Research, Potsdam, 14473, Germany; Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, 80333, Germany; Department of Mathematics and Global Systems Institute, University of Exeter, Exeter, EX4 4QE, United Kingdom; Institute for Theoretical Physics, Justus-Liebig-Universitat Giessen, Giessen, 35392, Germany; School of Systems Science, Beijing Normal University, Beijing, 100875, China; Department of Physics, Bar-Ilan University, Ramat-Gan52900, Israel; Department of Meteorology, University of Reading, Reading, RG6 6BB, United Kingdom; Department of Control Theory, Nizhny Novgorod State University, Nizhny Novgorod, 603950, Russian Federation; German Aerospace Center, Institute of Data Science, Jena, 07745, Germany; Swiss Federal Institute of Technology in Zurich, Zurich, 8092, Switzerland; Space Dynamics and Data Analysis Department, Space Research Institute, Russian Academy of Sciences, Moscow, 117997, Russian Federation
推荐引用方式 GB/T 7714	Ludescher J.,Martin M.,Boers N.,et al. Network-based forecasting of climate phenomena[J],2021,118(47).
APA	Ludescher J..,Martin M..,Boers N..,Bunde A..,Ciemer C..,...&Schellnhuber H.J..(2021).Network-based forecasting of climate phenomena.Proceedings of the National Academy of Sciences of the United States of America,118(47).
MLA	Ludescher J.,et al."Network-based forecasting of climate phenomena".Proceedings of the National Academy of Sciences of the United States of America 118.47(2021).