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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, United Kingdom
* Author to whom correspondence should be addressed.
Received: 30 January 2013; in revised form: 11 May 2013 / Accepted: 14 May 2013 / Published: 24 May 2013
Abstract: 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.
Keywords: 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.
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.
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.