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

TL;DR

This paper presents a multi-step approach for selecting appropriate dependence measures in climate data analysis, emphasizing the importance of nonlinearity and nonstationarity, and demonstrates its application on temperature datasets.

Contribution

It introduces a comprehensive method to evaluate nonlinear contributions and nonstationarities in climate data, guiding better choice of connectivity measures.

Findings

Nonlinear coupling is quantitatively negligible at the analyzed scale.

Several sources of nonstationarity significantly affect dependence structures.

Linear methods are generally sufficient for climate temperature data analysis.

Abstract

Quantification of relations between measured variables of interest by statistical measures of dependence is a common step in analysis of climate data. The term "connectivity" is used in the network context including the study of complex coupled dynamical systems. 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 connectivity 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 connectivity method 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 nodes 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 connectivity measure, namely mutual information, focusing on its relevance and potential alterations in the discovered dependence structure. We document the method by applying it on 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 this scale has proven to be almost negligible, providing quantitative support for the use of linear methods for this type of data.