Networks Analysis Of Brazilian Climate Data Based On The DCCA Cross-Correlation Coefficient

Abstract

Climate change is one of the most relevant challenges that the world has to deal with. A better understanding about the behaviour of environmental and atmospheric variables, their long range cross correlation and the way they relate to each other, can provide helpful insights about how the climate is changing. However, such studies are complex and rarely found in the literature, especially dealing with data from the Brazilian territory. In this paper we consider four environmental and atmospheric variables, wind speed, radiation, temperature and humidity, measured in 27 stations (the capital of each of the 26 Brazilian states plus the federal district). We use the detrended fluctuation analysis to evaluate the statistical self-affinity of the signal, the cross correlation coefficient $\rho_{DCCA}$ to quantify the long range cross correlation between stations, and a network analysis that considers the top $10\%$ $\rho_{DCCA}$ values for each atmospheric variable to better understand the cross correlation between stations. The methodology used and combined in this paper represents a step forward in the field of time series and network analysis, that can be applied to other regions, other environmental variables and also to other fields of research. The results of the application are of great importance to better understand the behaviour of environmental and atmospheric variables in the Brazilian territory and to provide helpful insights about climate change and renewable energy production.