On the Effects of Lag-Times in Networks Constructed from Similarities of Monthly Fluctuations of Climate Fields

TL;DR

This study investigates how shifting time series data affects climate network structures based on temperature anomalies, revealing that such shifts do not significantly alter overall network connectivity despite modifying link strengths.

Contribution

It introduces a method of time-shifting temperature anomaly data to analyze seasonal similarity effects on climate networks, showing robustness of global connectivity measures.

Findings

Time-shifting data does not significantly change network area weighted connectivity.

Seasonal similarity analysis reveals consistent global connectivity despite hemispheric shifts.

Link strength modifications are averaged out in the overall network connectivity measure.

Abstract

The complex network framework has been successfully applied to the analysis of climatological data, providing, for example, a better understanding of the mechanisms underlying reduced predictability during El Ni\~no or La Ni\~na years. Despite the large interest that climate networks have attracted, several issues remain to be investigated. Here we focus in the influence of the periodic solar forcing in climate networks constructed via similarities of monthly averaged surface air temperature (SAT) anomalies. We shift the time series in each pair of nodes such as to superpose their seasonal cycles. In this way, when two nodes are located in different hemispheres we are able to quantify the similarity of SAT anomalies during the winters and during the summers. We find that data time-shifting does not significantly modify the network area weighted connectivity (AWC), which is the fraction of the total area of the Earth to which each node is con- nected. This unexpected network property can be understood in terms of how data time-shifting modifies the strength of the links connecting geographical regions in different hemispheres, and how these modifications are washed out by averaging the AWC.