Unravelling the backbone of climate networks from the analysis of collective dynamics and time reversal

Abstract

Many complex systems are characterized by interconnected units often difficult, or even impossible, to map through direct observations, while the dynamics of units is mostly easier to measure. The reconstruction of an unknown structure from the observed dynamics is a typical inverse problem in network-based approaches to overcome such obstacles. However, this procedure is affected by the interplay between the structure and dynamics, as well as by the choice of the statistical method. In this study, collective behaviour of the Earth's climate system was investigated by focusing on three distinct climatological variables in monthly time step using a causality measure to separately construct climate networks through a robust statistical analysis. Multiplex network (MUX) was used to integrate the information obtained from collective dynamics and time reversal for better filtering spurious connectivity. In fact, the proposed approach mitigates undesired effects of pairwise statistical inference by adjusting for unphysical relationships in two layers of the constructed networks. The results indicate that the mere existence of statistical errors allowed by the hypothesis testing lead, inevitably, to reconstruct spurious connections in the null models even when the collective dynamics consists of white-noise datasets from uncoupled units. The impact of spurious connections with the MUX dynamics is not remarkable to reconstruct the underlying plausible interactions; therefore, the concepts of MUX might be employed with the statistical analysis to unravel the backbone of climate networks. In this way that the inferred connections could distinguish highly connected regions for better understanding interconnected dynamical patterns, yielding deep insights into underlying physical interactions between the oceans and atmosphere. More specifically, edge directionality features provide a complementary tool for analysing topology of the networks with respect to the climatological variables, identifying relevant patterns in the climate system.