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

Fig 6 shows the results for global radiation in the South region. For small scales (n � 13) an increasing trend is observed, followed by a decrease for 13 < n � 576, and by another increase for n > 576. Here the correlation varies between 0.18 < ρDCCA < 0.99. For temperature (S17 Fig in S1 File), after n > 38 (3 days), increasing the scale also increases the cross-correlation. For these three capitals, a variation between 0.38 < ρDCCA < 0.99 is observed. Humidity (S18 Fig in S1 File) and wind speed (S19 Fig in S1 File), some stability between 24 < n < 809 was observed, being more evident for humidity in Floriano´polis (0.13 < ρDCCA < 0.22) and for the wind speed in Curitiba, Floriano´polis and Porto Alegre with the cross-correlation coefficient varying between 0.17 < ρDCCA < 0.20. Differently from other regions, the cross-correlation coefficient tends to fall for wind speed when considering n > 5068, with a variation between 0.75 < ρDCCA < 0.39. Network analysis In this third phase of our analysis, we use the concept of network analysis to recognize hidden patterns in the data and to group and classify them. Our analysis takes into account the stations located in each Brazilian state capital and Federal District (Table 1). The nodes of the intercon- nected networks are represented by the capitals, and the edges are represented by the strength of the cross-correlation coefficient, which satisfies ρDCCA  90%. The choice of a threshold of 90% takes into account the representation of strong correlations and also avoids overcrowding

in the representation of networks. The networks shown in Figs 7–10, are associated to the vari- ables radiation, temperature, humidity and wind speed, respectively. The criteria adopted Fig 3. Cross-correlation, ρDCCA(n), for global radiation in the Northeast region of Brazil. The plots show the cross- correlations between the state capital written in the plot and all others in the region. https://doi.org/10.1371/journal.pone.0290838.g003 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 10 / 18