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

Northeast region of Brazil), high temperature, high humidity, and low thermal amplitude, with high levels of rainfall (e.g., the North region of Brazil, where the Amazon forest is located), mountain and plateau regions (e.g., the Southeast region of Brazil, which is character- ized by low thermal amplitude and average temperature oscillating between 17˚C and 22˚C) [1–9]. In countries with a vast territory, studies with climatic variables such as radiation, tempera- ture, humidity and wind speed have been carried out to localize sandstorms in Asia [10]. This research explored the relationship between dust transported from various sources and ocean biological activities with different nutrient conditions and explores signs of global warming. Already in 2021, climate variables were investigated to explain hotter summers, frequent heavy rains, prolonged droughts, more severe forest fires, violent storms (blizzards), cyclones and the melting of the polar ice caps [11]. We also explore the relationship between climate variables (temperature, radiation, humidity and wind speed) and different aspects of the performance of buildings and energy systems [12–15]. In Brazil, the scalable behavior of wind speed along the coast was analyzed to determine long-range correlations and acquire more information about cross-sectional behavior at various scales. It was also investigated in Brazil with a focus on sus- tainable energy, variables such as radiation, temperature, and humidity, taking into account

cross-correlation and multiple cross-correlations, by considering three stations located in the Brazilian state of Bahia [16]. Research with climate variables has also investigated significant energy sources in terms of their capacity, reliability, cost, safety, and effects on the environ- ment [17]. For both studies, the concepts of auto-correlation and cross-correlation were applied to understand behavior, trends, and markings. Regarding the topic of networks associated with climate variables, it has gained prominence in recent years. A study combines species distribution models with ecological network analysis to test the potential impacts of climate change on more than 700 plant and animal species in Central Europe’s pollination and seed dispersal networks [18]. The networks have also been applied in studies to assess the impact of climate change on groundwater resources for regions without a pumping well. The network demonstrated the impact of climate change on average spring flow. Discharge increased by about +8% in spring and summer and decreased by about -7% in autumn and winter [19]. Network theory has also been used for the decision-making process in agriculture to effectively predict Fujian rice production under typical climatic con- ditions of the mountainous region and evaluate the performance of the network model against variations in development parameters by comparing the effectiveness of [20] multiple linear regression models to study soil water loss by evaporation and plant water loss by transpiration

[21]. Regardless of the type of network, application and/or improvement of models expanded worldwide and in scientific domains. Despite its vast territory and the importance of a better understanding of the behavior that climate variables such as wind speed, radiation, temperature, and humidity have in terms of cli- mate change and renewable energy production, to the best of our knowledge, no study provides a portrait of the Brazilian regions using long-range cross-correlation and network analysis. In this paper, we consider four important environmental 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 [22] to evaluate the statistical self-affinity of the signal and use the cross-correlation coefficient ρDCCA [23] to quan- tify the long-range cross correlation between Brazilian regions. Then, network analysis is made for each environmental variable, where the top 10% values of ρDCCA are highlighted and discussed. The rest of the paper is organized as follows. The next section includes the materials and methods, where the data collection and organization are described, and the methodological PLOS ONE Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient PLOS ONE | https://doi.org/10.1371/journal.pone.0290838 September 15, 2023 2 / 18 authors did not have any special access privileges that others would not have. Funding: Florêncio Mendes Oliveira Filho is grateful

to Centro Universita´rio SENAI CIMATEC and the National Council for Scientific and Technological Development (CNPq 25/2021) scholarship PDJ - 150655/2022-3. Paulo Canas Rodrigues would like to thank the National Council for Scientific and Technological Development of Brazil (CNPq) for the PQ - 2305852/2019 -1. Competing interests: As described in the manuscript itself, the authors declare that there are no conflicting interests.