Disentangling different types of El Nino episodes by evolving climate network analysis

Alexander Radebach, Reik V. Donner, Jakob Runge, Jonathan F. Donges, Juergen Kurths

Research output: Contribution to journalArticlepeer-review

73 Citations (Scopus)
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Complex network theory provides a powerful toolbox for studying the structure of statistical interrelationships between multiple time series in various scientific disciplines. In this work, we apply the recently proposed climate network approach for characterizing the evolving correlation structure of the Earth's climate system based on reanalysis data for surface air temperatures. We provide a detailed study of the temporal variability of several global climate network characteristics. Based on a simple conceptual view of red climate networks (i.e., networks with a comparably low number of edges), we give a thorough interpretation of our evolving climate network characteristics, which allows a functional discrimination between recently recognized different types of El Nino episodes. Our analysis provides deep insights into the Earth's climate system, particularly its global response to strong volcanic eruptions and large-scale impacts of different phases of the El Nino Southern Oscillation.

Original languageEnglish
Article number052807
Number of pages19
JournalPhysical Review. E, Statistical, Nonlinear and Soft Matter Physics
Issue number5
Publication statusPublished - 12 Nov 2013


  • North-Atlantic oscillation
  • visibility graph analysis
  • complex brain networks
  • time-series
  • hierarchical organization
  • surface-temperature
  • dynamics
  • Pacific
  • fluctuations
  • evolution


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