Climate Dynamics

, Volume 42, Issue 7–8, pp 1873–1886

Non-linear dependence and teleconnections in climate data: sources, relevance, nonstationarity

  • Jaroslav Hlinka
  • David Hartman
  • Martin Vejmelka
  • Dagmar Novotná
  • Milan Paluš
Article

DOI: 10.1007/s00382-013-1780-2

Cite this article as:
Hlinka, J., Hartman, D., Vejmelka, M. et al. Clim Dyn (2014) 42: 1873. doi:10.1007/s00382-013-1780-2

Abstract

Quantification of relations between measured variables of interest by statistical measures of dependence is a common step in analysis of climate data. The choice of dependence measure is key for the results of the subsequent analysis and interpretation. The use of linear Pearson’s correlation coefficient is widespread and convenient. On the other side, as the climate is widely acknowledged to be a nonlinear system, nonlinear dependence quantification methods, such as those based on information-theoretical concepts, are increasingly used for this purpose. In this paper we outline an approach that enables well informed choice of dependence measure for a given type of data, improving the subsequent interpretation of the results. The presented multi-step approach includes statistical testing, quantification of the specific non-linear contribution to the interaction information, localization of areas with strongest nonlinear contribution and assessment of the role of specific temporal patterns, including signal nonstationarities. In detail we study the consequences of the choice of a general nonlinear dependence measure, namely mutual information, focusing on its relevance and potential alterations in the discovered dependence structure. We document the method by applying it to monthly mean temperature data from the NCEP/NCAR reanalysis dataset as well as the ERA dataset. We have been able to identify main sources of observed non-linearity in inter-node couplings. Detailed analysis suggested an important role of several sources of nonstationarity within the climate data. The quantitative role of genuine nonlinear coupling at monthly scale has proven to be almost negligible, providing quantitative support for the use of linear methods for monthly temperature data.

Keywords

Climate networks Nonlinearity Mutual information Teleconnections Seasonality in variance Nonstationarity 

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jaroslav Hlinka
    • 1
  • David Hartman
    • 1
  • Martin Vejmelka
    • 1
  • Dagmar Novotná
    • 2
  • Milan Paluš
    • 1
  1. 1.Institute of Computer ScienceAcademy of Sciences of the Czech RepublicPrague 8Czech Republic
  2. 2.Institute of Atmospheric PhysicsAcademy of Sciences of the Czech RepublicPrague 8Czech Republic

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