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Open AccessArticle

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information

Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod vodarenskou vezi 2, 182 07, Prague 8, Czech Republic
Potsdam Institute for Climate Impact Research (PIK), 14473 Potsdam, Germany
Department of Physics, Humboldt University, 12489 Berlin, Germany
Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, UK
Author to whom correspondence should be addressed.
Entropy 2013, 15(6), 2023-2045;
Received: 30 January 2013 / Revised: 11 May 2013 / Accepted: 14 May 2013 / Published: 24 May 2013
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences)
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. View Full-Text
Keywords: causality; climate; nonlinearity; transfer entropy; network; stability causality; climate; nonlinearity; transfer entropy; network; stability
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MDPI and ACS Style

Hlinka, J.; Hartman, D.; Vejmelka, M.; Runge, J.; Marwan, N.; Kurths, J.; Paluš, M. Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information. Entropy 2013, 15, 2023-2045.

AMA Style

Hlinka J, Hartman D, Vejmelka M, Runge J, Marwan N, Kurths J, Paluš M. Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information. Entropy. 2013; 15(6):2023-2045.

Chicago/Turabian Style

Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Runge, Jakob; Marwan, Norbert; Kurths, Jürgen; Paluš, Milan. 2013. "Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information" Entropy 15, no. 6: 2023-2045.

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