Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient

RESEARCH ARTICLE Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient Florêncio Mendes Oliveira FilhoID1,2☯*, Everaldo Freitas Guedes3☯, Paulo Canas Rodrigues4☯ 1 Senai Cimatec University Center, Computer Engineering, Salvador, Brazil, 2 Earth Sciences and Environment Modeling Program, State University of Feira de Santana, Feira de Santana, BA, Brazil, 3 Clime´ rio de Oliveira Maternity School, Federal University of Bahia, Salvador, Brazil, 4 Department of Statistics, Federal University of Bahia, Salvador, Brazil ☯ These authors contributed equally to this work. * florenciofh@yahoo.com.br Abstract Climate change is one of the most relevant challenges that the world has to deal with. Stud- ies that aim to understand the behavior of environmental and atmospheric variables and the way they relate to each other can provide helpful insights into how the climate is changing. However, such studies are complex and rarely found in the literature, especially in dealing with data from the Brazilian territory. In this paper, we analyze four environmental and atmo- spheric variables, namely, wind speed, radiation, temperature, and humidity, measured in 27 Weather 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 time series, as well as the cross-correlation coefficient ρDCCA to quantify the long-range cross- correlation between stations, and a network analysis that considers the top 10% ρDCCA val-

ues to represent the cross-correlations between stations better. The methodology used in this paper represents a step forward in the field of hybrid methodologies, combining time series and network analysis that can be applied to other regions, other environmental vari- ables, and also to other fields of research. The application results are of great importance to better understand the behavior of environmental and atmospheric variables in the Brazilian territory and to provide helpful insights about climate change and renewable energy production. Introduction Due to its territorial immensity (8, 511, 965Km2), with 93% of the territory located in the Southern Hemisphere and 7% in the Northern Hemisphere, with a location in the inter-tropi- cal zone of the planet, Brazil has different types of climates, such as equatorial, tropical, high- land tropical, humid tropical, semi-arid and subtropical. This distribution of climates, depending on the region, can have well-distributed rainfall (e.g., the South region of Brazil), high temperatures throughout the year, insufficient and poorly distributed rainfall (e.g., the PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0290838 September 15, 2023 1 / 18 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Oliveira Filho FM, Guedes EF, Rodrigues PC (2023) Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient. PLoS ONE 18(9): e0290838. https://doi. org/10.1371/journal.pone.0290838 Editor: Pak Wai Chan, Hong Kong Observatory, HONG KONG Received: April 21, 2023

Accepted: August 15, 2023 Published: September 15, 2023 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0290838 Copyright: © 2023 Oliveira Filho et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are available at: https://portal.inmet.gov.br/ dadoshistoricos. Others would be able to access these data in the same manner as the authors. The