globalchange  > 过去全球变化的重建
DOI: 10.1007/s00382-015-2479-3
Scopus记录号: 2-s2.0-84945456842
论文题名:
How complex climate networks complement eigen techniques for the statistical analysis of climatological data
作者: Donges J.F.; Petrova I.; Loew A.; Marwan N.; Kurths J.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2015
卷: 45, 期:2017-09-10
起始页码: 2407
结束页码: 2424
语种: 英语
英文关键词: Climate data analysis ; Climate networks ; Coupled patterns ; Empirical orthogonal functions ; Maximum covariance analysis
英文摘要: Eigen techniques such as empirical orthogonal function (EOF) or coupled pattern (CP)/maximum covariance analysis have been frequently used for detecting patterns in multivariate climatological data sets. Recently, statistical methods originating from the theory of complex networks have been employed for the very same purpose of spatio-temporal analysis. This climate network (CN) analysis is usually based on the same set of similarity matrices as is used in classical EOF or CP analysis, e.g., the correlation matrix of a single climatological field or the cross-correlation matrix between two distinct climatological fields. In this study, formal relationships as well as conceptual differences between both eigen and network approaches are derived and illustrated using global precipitation, evaporation and surface air temperature data sets. These results allow us to pinpoint that CN analysis can complement classical eigen techniques and provides additional information on the higher-order structure of statistical interrelationships in climatological data. Hence, CNs are a valuable supplement to the statistical toolbox of the climatologist, particularly for making sense out of very large data sets such as those generated by satellite observations and climate model intercomparison exercises. © 2015, Springer-Verlag Berlin Heidelberg.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53960
Appears in Collections:过去全球变化的重建

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作者单位: Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, Potsdam, Germany; Stockholm Resilience Center, Stockholm University, Kräftriket 2B, Stockholm, Sweden; Max-Planck-Institute for Meteorology, KlimaCampus, Hamburg, Germany; Department of Geography, University of Munich (LMU), Luisenstr. 37, Munich, Germany; Department of Physics, Humboldt University, Newtonstr. 15, Berlin, Germany; Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, United Kingdom; Department of Control Theory, Nizhny Novgorod State University, Nizhny Novgorod, Russian Federation

Recommended Citation:
Donges J.F.,Petrova I.,Loew A.,et al. How complex climate networks complement eigen techniques for the statistical analysis of climatological data[J]. Climate Dynamics,2015-01-01,45(2017-09-10)
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