How complex climate networks complement eigen techniques for the statistical analysis of climatological data

Jonathan F. Donges*, Irina Petrova, Alexander Loew, Norbert Marwan, Juergen Kurths

*Corresponding author for this work

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2407-2424
Number of pages18
JournalClimate dynamics
Volume45
Issue number9
Early online date28 Jan 2015
DOIs
Publication statusPublished - Nov 2015

Keywords

  • Climate networks
  • Empirical orthogonal functions
  • Coupled patterns
  • Maximum covariance analysis
  • Climate data analysis
  • Nonlinear dimensionality reduction
  • El-Nino
  • Time-Series
  • Atmospheric teleconnections
  • Southern-oscillation
  • Surface-temperature
  • Visibility graph
  • Cautionary note
  • Prediction

Cite this

How complex climate networks complement eigen techniques for the statistical analysis of climatological data. / Donges, Jonathan F.; Petrova, Irina; Loew, Alexander; Marwan, Norbert; Kurths, Juergen.

In: Climate dynamics, Vol. 45, No. 9, 11.2015, p. 2407-2424.

Research output: Contribution to journalArticle

Donges, Jonathan F. ; Petrova, Irina ; Loew, Alexander ; Marwan, Norbert ; Kurths, Juergen. / How complex climate networks complement eigen techniques for the statistical analysis of climatological data. In: Climate dynamics. 2015 ; Vol. 45, No. 9. pp. 2407-2424.
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abstract = "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.",
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note = "Acknowledgments: This work has been financially supported by the Leibniz association (Project ECONS), the German National Academic Foundation, the Potsdam Institute for Climate Impact Research, the Stordalen Foundation, BMBF (Project GLUES), the Max Planck Society, and DFG Grants KU34-1 and MA 4759/4-1. For climate network analysis, the software package pyunicorn was used that is available at http://tocsy.pik-potsdam.de/pyunicorn.php (Donges et al. 2013). We thank Reik V. Donner and Doerthe Handorf for discussions and comments on an earlier version of the manuscript",
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N1 - Acknowledgments: This work has been financially supported by the Leibniz association (Project ECONS), the German National Academic Foundation, the Potsdam Institute for Climate Impact Research, the Stordalen Foundation, BMBF (Project GLUES), the Max Planck Society, and DFG Grants KU34-1 and MA 4759/4-1. For climate network analysis, the software package pyunicorn was used that is available at http://tocsy.pik-potsdam.de/pyunicorn.php (Donges et al. 2013). We thank Reik V. Donner and Doerthe Handorf for discussions and comments on an earlier version of the manuscript

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AB - 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.

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