Complex networks for climate model evaluation with application to statistical versus dynamical modeling of South American climate

Jan H. Feldhoff, Stefan Lange*, Jan Volkholz, Jonathan F. Donges, Juergen Kurths, Friedrich-Wilhelm Gerstengarbe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
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In this study we introduce two new node-weighted difference measures on complex networks as a tool for climate model evaluation. The approach facilitates the quantification of a model's ability to reproduce the spatial covariability structure of climatological time series. We apply our methodology to compare the performance of a statistical and a dynamical regional climate model simulating the South American climate, as represented by the variables 2 m temperature, precipitation, sea level pressure, and geopotential height field at 500 hPa. For each variable, networks are constructed from the model outputs and evaluated against a reference network, derived from the ERA-Interim reanalysis, which also drives the models. We compare two network characteristics, the (linear) adjacency structure and the (nonlinear) clustering structure, and relate our findings to conventional methods of model evaluation. To set a benchmark, we construct different types of random networks and compare them alongside the climate model networks. Our main findings are: (1) The linear network structure is better reproduced by the statistical model statistical analogue resampling scheme (STARS) in summer and winter for all variables except the geopotential height field, where the dynamical model CCLM prevails. (2) For the nonlinear comparison, the seasonal differences are more pronounced and CCLM performs almost as well as STARS in summer (except for sea level pressure), while STARS performs better in winter for all variables.

Original languageEnglish
Pages (from-to)1567-1581
Number of pages15
JournalClimate dynamics
Issue number5-6
Early online date31 May 2014
Publication statusPublished - Mar 2015

Bibliographical note

Acknowledgments: This paper was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP. Furthermore, this work has been financially supported by the Leibniz Society (project ECONS), and the Stordalen Foundation (JFD). For certain calculations, the software packages pyunicorn (Donges et al. 2013a) and igraph (Csa´rdi and Nepusz 2006) were used. The authors would like to thank Manoel F. Cardoso, Niklas Boers, and the
reviewers for helpful comments on the manuscript.

Open Access: This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.


  • climate model evaluation
  • complex networks
  • South American climate
  • network comparison
  • Amazon rain-forest
  • El-Nino
  • simulations
  • precipitation
  • performance
  • variability
  • schemes
  • systems
  • CLM


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