Dry and Wet Climate Periods over Eastern South America: Identification and Characterization through the SPEI Index

Abstract

A large part of the population and the economic activities of South America are located in eastern regions of the continent, where extreme climate events are a recurrent phenomenon. This study identifies and characterizes the dry and wet climate periods at domain-scale occurring over the eastern South America (ESA) during 1980–2018 through the multi-scalar Standardized Precipitation–Evapotranspiration Index (SPEI). For this study, the spatial extent of ESA was defined according to a Lagrangian approach for moisture analysis. It consists of the major continental sink of the moisture transported from the South Atlantic Ocean throughout the year, comprising the Amazonia, central Brazil, and the southeastern continental areas. The SPEI for 1, 3, 6, and 12 months of accumulation was calculated using monthly precipitation and potential evapotranspiration time series averaged on ESA. The analysis of the climate periods followed two different approaches: classification of the monthly SPEI values as mild, moderate, severe, and extreme; the computation of the events and their respective parameters (duration, severity, intensity, and peak). The results indicate that wet periods prevailed in the 1990s and 2000s, while dry conditions predominated in the 2010s, when the longest and more severe dry events have been identified at the four scales.

Drought Assessment in São Francisco River Basin, Brazil: Characterization through SPI and Associated Anomalous Climate Patterns

“…These anomalous precipitation and low-level circulation patterns were also similar to the ones registered in the energy crisis in 2001 [91]. In general, the anomalous atmospheric patterns shown in this study were similar to those that occurred in the water crisis in the SEB in 2014 [54], indicating that the dry conditions observed during the 2012-2020 episode over the Upper-SF investigated here have also affected other regions in the country [89,94]. The predominance of the wave pattern with positive pressure anomaly over the SEB may inhibit cloud formation, which can consequently warm the SST, as noted by the positive anomalies of the SST in the South Atlantic (Figure 5a).…”

“…Although not the scope of this study, further investigation of the potential forcing that triggers the wave propagated in the vicinity of Australia is necessary, as well as a more in-depth study on the IASAS and the Southern Atlantic variability. Previous studies showed the importance of the South Atlantic Ocean as a moisture source for Eastern South America [89,[94][95][96].…”

“…The onset of a drought episode can be identified when the SPI changes from a positive to a negative value, and it ends when the index returns to a positive value; however, to define the event as dry, the SPI must reach a value less than or equal to −1 [17]. Works such as Gozzo et al [52], Stojanovic et al [88] and Drumond et al [89] also applied a similar methodology to assess the onset of events in order to include the mild dry conditions that lead to the establishment of a drought in the analysis. In addition to the criteria to define a drought, McKee et al [17] created a system to classify the SPI values, as shown in Table 1.…”

Anthropogenic influence of temperature changes across East Asia using CMIP6 simulations

“…For the long-term variability analysis of SAT, we used the CRU time series version 4.03 (resolution = 0.5° × 0.5°) and HadCRUT4 (resolution = 5° × 5°) observational datasets for the period 1901–2018 and 1850–2019, respectively; the HadCRUT4 dataset (available at https://crudata.uea.ac.uk/cru/data/temperature/ ) was primarily used to cover the period not considered by the CRU dataset (1850–1900). The CRU data are derived from the spatial interpolation of monthly climate anomalies of worldwide weather station observations 26 – 28 and are available at https://crudata.uea.ac.uk/cru/data/hrg/ . These CRU data have been used by many researchers studying long-term temperature and precipitation changes over the EA region 26 , 27 , 29 , 30 and they correlate well with ground station datasets.…”

Anthropogenic Influence on Long-Term Surface Air Temperature Trends: Attribution of Temperature Changes Across East Asia

“…We used the CRU time series version 4.03 (resolution = 0.5° × 0.5°) and HadCRUT4 (resolution = 5° × 5°) observational datasets for the period 1901-2018 and 1850-2019, respectively; the HadCRUT4 dataset (available at https://crudata.uea.ac.uk/cru/data/temperature/) was primarily used to cover the period not considered by the CRU dataset . The CRU data are derived from the interpolation of monthly climate anomalies of worldwide weather station observations (Peng et al 2018;Harris et al 2020;Drumond et al 2021) and are available at https://crudata.uea.ac.uk/cru/data/hrg/. These CRU data have been used by many researchers studying long-term temperature and precipitation changes over the EA region (Li et al 2018b;Peng et al 2018;Chen et al 2019;Harris et al 2020) and they correlate well with ground station datasets.…”