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

The scaling exponent αDFA quantifies the empirical strength of the long-range power-law correlations of the signal. If the signal is not random, it is characterized by long-range correla- tion features. The αDFA can be interpreted as shown in Table 2 [30, 31]. The DFA method permits the detection of long-range correlations embedded in seemingly non-stationary time series, and also avoids the spurious detection of apparent long-range cor- relations, which are an artifact of non-stationarity. Table 2. Interpretation of the αDFA, a self-affinity parameter representing the long-range power-law correlation of the signal of a time series. Exponent Type of signal αDFA < 0.5 Antipersistent αDFA ⋍ 0.5 Uncorrelated, white noise αDFA > 0.5 Long-range correlated persistent αDFA ⋍ 1.0 1/f noise αDFA > 1.0 Non-stationary aDFA ⋍ 3 2 Brownian noise https://doi.org/10.1371/journal.pone.0290838.t002 Table 1. Region, state, the state capital, latitude, longitude, altitude, and percentage of missing values for each atmospheric variable for all 27 stations. More details about the geographical location of each station can be found on the map in S1 Fig in S1 File of the supplementary material. Region State City (state capital) Latitude Longitude Altitude Percentage of missing values Radiation Temperature Humidity Wind speed North PA Bele´m -1.41 -48.44 24.00 8.65 1.18 1.20 1.21 North RR Boa Vista 2.82 -60.69 94.00 23.44 12.13 12.13 12.21 North AP Macapa´ 3.81 -51.86 21.00 36.95 32.19 32.20 32.23 North AM Manaus -3.10 -60.02 61.25 11.98 1.77 1.90 8.58

North TO Palmas -10.18 -48.30 280.00 11.12 5.20 5.20 5.20 North RO Porto Velho -8.76 -63.47 98.00 32.22 24.76 24.77 47.77 North AC Rio Branco -9.96 -68.17 220.00 44.81 37.00 42.19 37.03 Northeast SE Aracaju -10.95 37.05 4.72 1.90 1.33 12.02 12.68 Northeast CE Fortaleza -3.83 -38.54 26.45 24.01 17.83 17.83 18.05 Northeast PB João Pessoa -7.17 -34.82 47.00 11.15 11.04 11.89 10.59 Northeast AL Maceio´ -9.55 -35.77 80.00 8.88 6.04 6.08 6.05 Northeast RN Natal -5.90 -35.20 48.60 5.38 1.46 1.46 7.59 Northeast PE Recife -8.06 -34.96 10.00 2.57 1.56 4.14 13.58 Northeast BA Salvador -13.02 -38.52 51.41 3.35 1.95 1.95 1.98 Northeast MA São Luı´s -2.53 -44.21 56.00 11.79 4.76 6.96 4.81 Northeast PI Teresina -5.07 -42.81 74.36 9.52 7.90 7.90 7.91 Central-West DF Brası´lia -15.79 -47.93 1159.54 6.20 0.35 0.35 0.89 Central-West MS Campo Grande -20.45 -54.60 530.00 18.52 3.95 4.02 3.96 Central-West MT Cuiaba´ -15.56 -56.73 240.00 26.67 13.14 13.14 16.93 Central-West GO Goiania -16.64 -49.22 770.00 5.74 0.25 0.29 0.25 Southeast MG Belo Horizonte -19.88 -43.97 869.00 4.73 0.21 0.21 0.19 Southeast RJ Rio de Janeiro -22.99 -43.19 42.00 5.89 1.04 4.70 1.47 Southeast SP São Paulo -23.48 -46.62 792.06 6.28 0.65 0.48 0.48 Southeast ES Vito´ria -20.27 -40.30 90.00 6.86 4.01 5.22 4.02 South PR Curitiba -25.43 -49.27 923.50 8.98 4.59 11.83 4.59

South SC Forianopo´lis -27.60 -48.62 1.80 8.39 0.30 0.30 0.31 South RS Porto Alegre -30.05 -51.17 46.97 8.10 0.39 0.39 1.33 https://doi.org/10.1371/journal.pone.0290838.t001 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 4 / 18