Quantification of information exchange in idealized and climate system applications

  • 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.

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Author:Praveen Kumar PothapakulaORCiDGND, Cristina PrimoORCiD, Bodo AhrensORCiDGND
Parent Title (English):Entropy
Place of publication:Basel
Document Type:Article
Year of Completion:2019
Date of first Publication:2019/11/08
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2020/02/10
Tag:El Niño; Indian Ocean dipole; climate informatics; information flow; transfer entropy
Issue:11, Art. 1094
Page Number:28
First Page:1
Last Page:28
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Institutes:Geowissenschaften / Geographie / Geowissenschaften
Fachübergreifende Einrichtungen / Biodiversität und Klima Forschungszentrum (BiK-F)
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Open-Access-Publikationsfonds:Geowissenschaften / Geographie
Licence (German):License LogoCreative Commons - Namensnennung 4.0