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

TL;DR

This paper uses complex network analysis and information theory to determine the net direction of climate interactions across regions, validating known atmospheric patterns and demonstrating the method's effectiveness on real-world temperature data.

Contribution

It introduces a novel application of the directionality index based on conditional mutual information to infer climate interaction directions from empirical temperature data.

Findings

Successfully identified known climate variability structures.

Demonstrated the method's ability to analyze different time scales.

Validated the directionality measure with real-world data.

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. 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 extratropical 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 extra-tropics 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 climate 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.