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Open AccessFeature PaperArticle

Quantification of Information Exchange in Idealized and Climate System Applications

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Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt am Main, Altenhöferallee 1, 60438 Frankfurt am Main, Germany
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Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage, 25, 60325 Frankfurt am Main, Germany
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Author to whom correspondence should be addressed.
Entropy 2019, 21(11), 1094; https://doi.org/10.3390/e21111094
Received: 30 September 2019 / Revised: 24 October 2019 / Accepted: 5 November 2019 / Published: 8 November 2019
Often in climate system studies, linear and symmetric statistical measures are applied to quantify interactions among subsystems or variables. However, they do not allow identification of the driving and responding subsystems. Therefore, in this study, we aimed to apply asymmetric measures from information theory: the axiomatically proposed transfer entropy and the first principle-based information flow to detect and quantify climate interactions. As their estimations are challenging, we initially tested nonparametric estimators like transfer entropy (TE)-binning, TE-kernel, and TE k-nearest neighbor and parametric estimators like TE-linear and information flow (IF)-linear with idealized two-dimensional test cases along with their sensitivity on sample size. Thereafter, we experimentally applied these methods to the Lorenz-96 model and to two real climate phenomena, i.e., (1) the Indo-Pacific Ocean coupling and (2) North Atlantic Oscillation (NAO)–European air temperature coupling. As expected, the linear estimators work for linear systems but fail for strongly nonlinear systems. The TE-kernel and TE k-nearest neighbor estimators are reliable for linear and nonlinear systems. Nevertheless, the nonparametric methods are sensitive to parameter selection and sample size. Thus, this work proposes a composite use of the TE-kernel and TE k-nearest neighbor estimators along with parameter testing for consistent results. The revealed information exchange in Lorenz-96 is dominated by the slow subsystem component. For real climate phenomena, expected bidirectional information exchange between the Indian and Pacific SSTs was detected. Furthermore, expected information exchange from NAO to European air temperature was detected, but also unexpected reversal information exchange. The latter might hint to a hidden process driving both the NAO and European temperatures. Hence, the limitations, availability of time series length and the system at hand must be taken into account before drawing any conclusions from TE and IF-linear estimations. View Full-Text
Keywords: transfer entropy; information flow; climate informatics; Indian Ocean dipole; El Niño transfer entropy; information flow; climate informatics; Indian Ocean dipole; El Niño
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Pothapakula, P.K.; Primo, C.; Ahrens, B. Quantification of Information Exchange in Idealized and Climate System Applications. Entropy 2019, 21, 1094.

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