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Assessing the direction of climate interactions by means of complex networks and information theoretic tools

Deza, J. I.; Barreiro, M.; Masoller, C.

Authors

Ignacio Deza Ignacio.Deza@uwe.ac.uk
Research Fellow in Machine Learning for Building Energy Analysis

M. Barreiro

C. Masoller

Abstract

An estimate of the net direction of climate interactions in different geographical regions is made by constructing a directed climate network from a regular latitude-longitude grid of nodes, using a directionality index (DI) based on conditional mutual information (CMI). Two datasets of surface air temperature anomalies—one monthly averaged and another daily averaged—are analyzed and compared. The network links are interpreted in terms of known atmospheric tropical and extra- tropical variability patterns. Specific and relevant geographical regions are selected, the net direction of propagation of the atmospheric patterns is analyzed, and the direction of the inferred links is validated by recovering some well-known climate variability structures. These patterns are found to be acting at various time-scales, such as atmospheric waves in the extratropics or longer range events in the tropics. This analysis demonstrates the capability of the DI measure to infer the net direction of climate interactions and may contribute to improve the present understanding of cli- mate phenomena and climate predictability. The work presented here also stands out as an application of advanced tools to the analysis of empirical, real-world data.

Journal Article Type Article
Journal Chaos: An Interdisciplinary Journal of Nonlinear Science
Print ISSN 1054-1500
Publisher AIP Publishing
Peer Reviewed Peer Reviewed
Volume 25
Issue 3
Pages 033105
Institution Citation Deza, J. I., Barreiro, M., & Masoller, C. (in press). Assessing the direction of climate interactions by means of complex networks and information theoretic tools. Chaos, 25(3), 033105. https://doi.org/10.1063/1.4914101
DOI https://doi.org/10.1063/1.4914101
Keywords climate networks, nonlinear time series analysis, climate communities, information transfer
Publisher URL http://doi.org/10.1063/1.4914101