Comparing Predictive Power in Climate Data: Clustering Matters

  • Karsten Steinhaeuser
  • Nitesh V. Chawla
  • Auroop R. Ganguly
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6849)

Abstract

Various clustering methods have been applied to climate, ecological, and other environmental datasets, for example to define climate zones, automate land-use classification, and similar tasks. Measuring the “goodness” of such clusters is generally application-dependent and highly subjective, often requiring domain expertise and/or validation with field data (which can be costly or even impossible to acquire). Here we focus on one particular task: the extraction of ocean climate indices from observed climatological data. In this case, it is possible to quantify the relative performance of different methods. Specifically, we propose to extract indices with complex networks constructed from climate data, which have been shown to effectively capture the dynamical behavior of the global climate system, and compare their predictive power to candidate indices obtained using other popular clustering methods. Our results demonstrate that network-based clusters are statistically significantly better predictors of land climate than any other clustering method, which could lead to a deeper understanding of climate processes and complement physics-based climate models.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Karsten Steinhaeuser
    • 1
    • 2
  • Nitesh V. Chawla
    • 1
  • Auroop R. Ganguly
    • 2
  1. 1.Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and ApplicationsUniversity of Notre DameNotre DameUSA
  2. 2.Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak RidgeUSA

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