Climate Dynamics

, Volume 42, Issue 5–6, pp 1665–1670 | Cite as

A climate model intercomparison at the dynamics level

  • Karsten Steinhaeuser
  • Anastasios A. Tsonis


Until now, climate model intercomparison has focused primarily on annual and global averages of various quantities or on specific components, not on how well the general dynamics in the models compare to each other. In order to address how well models agree when it comes to the dynamics they generate, we have adopted a new approach based on climate networks. We have considered 28 pre-industrial control runs as well as 70 20th-century forced runs from 23 climate models and have constructed networks for the 500 hPa, surface air temperature (SAT), sea level pressure (SLP), and precipitation fields for each run. We then employed a widely used algorithm to derive the community structure in these networks. Communities separate “nodes” in the network sharing similar dynamics. It has been shown that these communities, or sub-systems, in the climate system are associated with major climate modes and physics of the atmosphere (Tsonis AA, Swanson KL, Wang G, J Clim 21: 2990–3001 in 2008; Tsonis AA, Wang G, Swanson KL, Rodrigues F, da Fontura Costa L, Clim Dyn, 37: 933–940 in 2011; Steinhaeuser K, Ganguly AR, Chawla NV, Clim Dyn 39: 889–895 in 2012). Once the community structure for all runs is derived, we use a pattern matching statistic to obtain a measure of how well any two models agree with each other. We find that, with the possible exception of the 500 hPa field, consistency for the SAT, SLP, and precipitation fields is questionable. More importantly, none of the models comes close to the community structure of the actual observations (reality). This is a significant finding especially for the temperature and precipitation fields, as these are the fields widely used to produce future projections in time and in space.


Climate networks Large-scale dynamics Climate variability Model intercomparison Spatial pattern analysis 



AAT is supported by Department of Energy USA grant DE-0005305. KS is supported by the National Science Foundation grant IIS-1029711. Access to computing facilities was provided by the Minnesota Supercomputing Institute.

Supplementary material

382_2013_1761_MOESM1_ESM.doc (133 kb)
Supplementary material 1 (DOC 133 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Atmospheric Sciences Group, Department of Mathematical SciencesUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

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