Climatology and significant trends in air temperature in Alagoas, Northeast Brazil

Abstract

The increase in air temperature can generate several environmental and socioeconomic impacts, so the main objective of this study was to characterize the monthly and annual pro les of maximum, minimum and average air temperatures and to identify whether there are signi cant trends in the time series of each, for the state of Alagoas, Brazil. For this, monthly data on maximum and minimum air temperatures (1980 to 2013) were used for 98 of the 102 municipalities in Alagoas. With the data in hand, the average air temperature was calculated and cluster analysis was applied to determine homogeneous sub-regions of the average air temperature. Trend analyzes were veri ed using nonparametric Mann-Kendall (sign of trend), Sen (magnitude of trend) and Pettitt (beginning of trend) tests. Analysis of variance (ANOVA) was used to verify differences between the decennial averages air temperatures. The statistical signi cance adopted was 5%. The results showed that the state of Alagoas has four sub-regions with homogeneous characteristics according to average air temperature. Sub-region 4 (S4), located in the Sertão (scrubland region) of Alagoas, had the highest average, maximum and minimum air temperatures, monthly and annually. Annually, signi cantly increasing trends were observed at 5% in the four sub-regions, with emphasis on S4, with increases above 1.0°C/décade in average, maximum and minimum temperatures. It is noteworthy that throughout the state of Alagoas, a signi cant trend of increase in the average air temperature was identi ed during the 34 years analyzed, highlighting the possible impacts of climate change.3. S1 had the lowest thermal amplitudes, associated with the joint in uence of geographic location, relief con guration, sea breeze and also greatest rainfall, although S3 had a similar amplitude, but with higher and greater differences internally (between municipalities).4. Signi cant trends of increase in average, maximum and minimum temperatures were found for the entire state of Alagoas, with emphasis on the sub-regions located in the west of the state, especially S4, with increases of 1.3, 1.4 and 1 °C.decade -1 , respectively.5. The increase in air temperature in Alagoas may be aggravating vulnerability in this area, especially in the semiarid region (west of the state), which in turn is already characterized by the predominance of water de cit.

Effects of extreme phases of El Niño–Southern Oscillation on rainfall extremes in Alagoas, Brazil

“…The IDW (Inverse Distance Weighting) method was consistent and suitable for spatially representing rainfall data in the state of Alagoas, according to the adopted statistical indicators. It is worth noting that previous studies had already pointed out the use of this method for the spatialization of rainfall data in Alagoas (Amorim et al, 2008;Wanderley et al, 2012Wanderley et al, , 2013aWanderley et al, , 2013b) and more recently for identifying homogeneous regions of air temperature in Alagoas (Silva et al, 2023).…”

The Alagoas Antwren Myrmotherula snowi is in imminent danger of extinction

“…Since 2000, Alagoas has experienced drier conditions in many more years than occurred in the 1970s, 1980s or 1990s (Oliveira-Júnior et al 2021). The climate has warmed significantly since the 1980s (Silva et al 2023). Deforestation and removal of large trees leaves the remaining fragments of forest less able to buffer adverse climatic conditions (Betts et al 2018).…”

Intercomparison of Different Sources of Precipitation Data in the Brazilian Legal Amazon

“…XAV provides daily surface data for all of Brazil from 1961 to 2020 with a spatial resolution of 0.1 • × 0.1 • , with precipitation being the variable that had the largest number of surface observations for the construction of the grid, interpolated by the inverse distance weighting method (IDW). This grid is updated from its previous version [19] and has been used in several studies as a reliable basis for surface observation [31,[34][35][36][37][38].…”

“…With the distribution of stations shown in Figure 2, we used the multivariate statistical technique known as cluster analysis to identify homogeneous areas based on the monthly distribution of rainfall in the BLA. The use of this technique is common for this purpose in climate sciences, defining groups that involve seasons with similar characteristics [34,[74][75][76]. The similarity structure of the elements of each group was obtained using the Euclidean distance method [77], expressed in Equation (1) as follows:…”

“…The sum of squares within each group was verified from the square of the Euclidean distance of each element to each group, according to [34] Equation ( 2):…”