Regional and inter-regional effects in evolving climate networks

J Hlinka,D Hartman,R Donner,J Kurths,N Jajcay,M Paluš,M Vejmelka,N Marwan

doi:10.5194/npg-21-451-2014

Abstract

Abstract. Complicated systems composed of many interacting subsystems are frequently studied as complex networks. In the simplest approach, a given real-world system is represented by an undirected graph composed of nodes standing for the subsystems and non-oriented unweighted edges for interactions present among the nodes; the characteristic properties of the graph are subsequently studied and related to the system's behaviour. More detailed graph models may include edge weights, orientations or multiple types of links; potential time-dependency of edges is conveniently captured in so-called evolving networks. Recently, it has been shown that an evolving climate network can be used to disentangle different types of El Niño episodes described in the literature. The time evolution of several graph characteristics has been compared with the intervals of El Niño and La Niña episodes. In this study we identify the sources of the evolving network characteristics by considering a reduced-dimensionality description of the climate system using network nodes given by rotated principal component analysis. The time evolution of structures in local intra-component networks is studied and compared to evolving inter-component connectivity.

Highlights

Complex networks (Newman, 2003; Boccaletti et al, 2006) represent a relatively young scientific discipline that has already influenced many research fields ranging from technological areas such as the Internet (Faloutsos et al, 1999), the World Wide Web (Albert et al, 1999), power grids or transportation networks (Guimera et al, 2005; Rosvall et al, 2005), through socially oriented research topics such as social networks (Wasserman and Faust, 1994), scientific collaboration networks (Newman, 2001) or financial markets (Mantegna, 1999), to networks dealing with complex natural systems
To assess the extent to which the features of the evolution of the main graph-theoretical characteristics are already reproduced using the much smaller network of 68 components obtained from rotated principal component analysis (PCA), we plot these along in the same Fig. 1
The detailed analysis of the evolution of connectivity in the surface air temperature network has shown that the temporal changes of both localised and inter-regional connectivity are reflected in the global graph evolution

Summary

Introduction

Complex networks (Newman, 2003; Boccaletti et al, 2006) represent a relatively young scientific discipline that has already influenced many research fields ranging from technological areas such as the Internet (Faloutsos et al, 1999), the World Wide Web (Albert et al, 1999), power grids or transportation networks (Guimera et al, 2005; Rosvall et al, 2005), through socially oriented research topics such as social networks (Wasserman and Faust, 1994), scientific collaboration networks (Newman, 2001) or financial markets (Mantegna, 1999), to networks dealing with complex natural systems The latter comprise systems like protein–protein interaction networks (Jeong et al, 2001), brain networks (Bullmore and Sporns, 2009) or climate networks (Tsonis and Roebber, 2004). The analysis is usually based on computing-specific characteristics of the networks under study

Results

Discussion

Conclusion