Reliability of inference of directed climate networks using conditional mutual information
type: Item
instance of: scholarly article
title: Reliability of inference of directed climate networks using conditional mutual information
zbMATH Open document ID: 1296.62218
author: J. Hlinka
author: David Hartman
author: Martin Vejmelka
author: Jakob Runge
author: N. Marwan
author: M. Paluš
author: J. Kurths
published in: Entropy
publication date: 2014-09-19T00:00:00Z
review text: Summary: Across geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical complex systems can be represented by a directed graph, where each link denotes an existence of a causal relation, or information exchange between the nodes. For geophysical systems such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods. These include bivariate nonlinear methods based on information theory and their linear counterpart. The trade-off between the valuable sensitivity of nonlinear methods to more general interactions and the potentially higher numerical reliability of linear methods may affect inference regarding structure and variability of climate networks. We investigate the reliability of directed climate networks detected by selected methods and parameter settings, using a stationarized model of dimensionality-reduced surface air temperature data from reanalysis of 60-year global climate records. Overall, all studied bivariate causality methods provided reproducible estimates of climate causality networks, with the linear approximation showing higher reliability than the investigated nonlinear methods. On the example dataset, optimizing the investigated nonlinear methods with respect to reliability increased the similarity of the detected networks to their linear counterparts, supporting the particular hypothesis of the near-linearity of the surface air temperature reanalysis data.
Mathematics Subject Classification ID: 62P12
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 6346151
zbMATH Keywords: causality
zbMATH Keywords: climate
zbMATH Keywords: nonlinearity
zbMATH Keywords: transfer entropy
zbMATH Keywords: network
zbMATH Keywords: stability
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e15062023
OpenAlex ID: W2115737548
Wikidata QID: Q124683774
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DOI: 10.3390/E15062023
description: scientific article; zbMATH DE number 6346151