No abstract

The Northeast region of Brazil (NRB) is the most populous semiarid area in the world and is extremely susceptible to droughts. The severity and duration of these droughts depend on several factors, and they do not necessarily follow the same behavior. The aim of this work is to evaluate the frequency of droughts in the NRB and calculate the return period of each drought event using the copula technique, which integrates the duration and severity of the drought in the NRB in a joint bivariate distribution. Monthly precipitation data from 96 meteorological stations spatially distributed in the NRB, ranging from 1961 to 2017, are used. The copula technique is applied to the Standardized Precipitation Index (SPI) on the three-month time scale, testing three families of Archimedean copula functions (Gumbel–Hougaard, Clayton and Frank) to reveal which model is best suited for the data. Averagely, the most frequent droughts observed in the NRB are concentrated in the northern sector of the region, with an observed duration varying from three and a half to five and a half months. However, the eastern NRB experiences the most severe droughts, lasting for 14 to 24 months. The probability distributions that perform better in modeling the series of severity and duration of droughts are exponential, normal and lognormal. The observed severity and duration values show that, for average values, the return period across the region is approximately 24 months. Still in this regard, the southernmost tip of the NRB stands out for having a return period of over 35 months. Regarding maximum observed values of severity and duration, the NRB eastern strip has the longest return period (>60 months), mainly in the southeastern portion where a return period above 90 months was observed. The northern NRB shows the shortest return period (~45 months), indicating that it is the NRB sector with the highest frequency of intense droughts. These results provide useful information for drought risk management in the NRB.

Resumo A Evapotranspiração (ET) é a segunda variável mais importante do ciclo hidrológico e influencia inúmeros processos da atmosfera. Desta forma, é interessante estudar as mudanças desta variável sob o contexto das mudanças climáticas antropogênicas e da constante ocorrência de secas no Nordeste Brasileiro (NEB). Foram analisadas as tendências da ET no NEB entre 1980 e 2016 a partir de dados em grade com alta resolução de 0.25° x 0.25º, constituindo 2071 pontos no NEB. Uma análise de agrupamentos mostrou que é plausível dividir a região em quatro grupos homogêneos. O grupo 1 é referente a área do semiárido na porção central do NEB. O grupo 2 é a faixa que circunda o semiárido. O grupo 3 localiza-se ao norte do Maranhão e o 4 na costa e noroeste do NEB. Analisando-se a tendência média da ET, observa-se que os grupos 2, 3 e 4 apresentaram tendências significativas de aumento da ET de 2,7 mm/ano, 3,51 mm/ano e 2,57 mm/ano, respectivamente. Em análise ponto por ponto, a porção central do NEB e semiárido apresentaram tendências positivas de ET anual (~0.8 mm/ano), enquanto o litoral noroeste e uma parte da região central da Bahia apresentaram tendências negativas (~1 mm/ano). Tais resultados devem estar associados as recentes tendências de temperatura e chuvas observadas no NEB, com impactos importantes nos núcleos de desertificação observados em muitas áreas da região, servindo de alerta a gestores ambientais e de recursos hídricos.

In this work, we used the MICE (Multivariate Imputation by Chained Equations) technique to impute missing daily data from six meteorological variables (precipitation, temperature, relative humidity, atmospheric pressure, wind speed and insolation) from 96 stations located in the northeast region of Brazil (NEB) for the period from 1961 to 2014. We then applied tests with a quality control system (QCS) developed for the detection, correction and possible replacement of suspicious data. Both the applied gap filling technique and the QCS showed that it was possible to solve two of the biggest problems found in time series of daily data measured in meteorological stations: the generation of plausible values for each variable of interest, in order to remedy the absence of observations, and how to detect and allow proper correction of suspicious values arising from observations.

This study quantifies the impacts of the Atlantic Meridional Overturning Circulation (AMOC) on the El Niño–Southern Oscillation (ENSO) under anthropogenic warming by comparing climate change model simulations with declining and fixed strengths of the overturning. After the 1980s, a weakened AMOC is shown to reduce the strength of the annual cycle of sea surface temperature (SST) in the eastern equatorial Pacific and induce anomalous cross‐equatorial northerly winds there, which strengthens ENSO variability by about 11%. An analysis of the Bjerknes stability index reveals that this intensification of ENSO results mainly from enhanced Ekman upwelling feedback due to amplified atmospheric wind response to SST anomalies and oceanic upwelling response to equatorial wind stress anomalies. The weakened AMOC also promotes the occurrence of Central Pacific El Niño events and reduces ENSO skewness. These AMOC impacts on ENSO magnitude and complexity throughout the twenty‐first century are however smaller than ENSO variations expected from internal climate variability.

Resumo O cultivo de milho em regime de sequeiro no estado de Alagoas é determinado pela variabilidade climática, especialmente sob as recorrentes secas que influenciam a recarga dos recursos hídricos em toda a região Nordeste do Brasil. Uma das formas de minimizar o risco de perdas é estabelecer uma janela climática ótima para o plantio. Nessa pesquisa utilizou-se um modelo agrometeorológico de penalização por déficit hídrico para simular a produtividade em todos os municípios de Alagoas no período de 1980 a 2015. A alta correlação entre simulações e observações, e o erro médio absoluto baixo para estações de referência validaram o modelo. Há diferentes janelas favoráveis ao plantio, mais curta no sertão durante o mês de abril, entre o terceiro decêndio de março e o terceiro decêndio de maio no agreste, e entre o primeiro decêndio de março e o segundo decêndio de junho no leste alagoano. Em média, as perdas relativas de produtividade no sertão são de 45%, no agreste de 40% a 45%, e em torno de 20% no leste. Estes resultados podem auxiliar o Zoneamento Agrícola de Risco Climático de Culturas do Ministério da Agricultura, Pecuária e Abastecimento, a estabelecer um calendário mais criterioso para a semeadura do milho no estado de Alagoas.

Since the early 2000s, Brazil has been one of the world’s leading grain producers, with agribusiness accounting for around 28% of the Brazilian GDP in 2021. Substantial investments in research, coupled with the expansion of arable areas, owed to the advent of new agriculture frontiers, led the country to become the world’s greatest producer of soybean. One of the newest agricultural frontiers to be emerging in Brazil is the one known as SEALBA, an acronym that refers to the three Brazilian states whose areas it is comprised of—Sergipe, Alagoas, and Bahia—all located in the Northeast region of the country. It is an extensive area with a favorable climate for the production of grains, including soybeans, with a rainy season that takes place in autumn/winter, unlike the Brazilian regions that are currently the main producers of these kinds of crops, in which the rainfall regime has the wet period concentrated in spring/summer. Considering that precipitation is the main determinant climatic factor for crops, the scarcity of weather stations in the SEALBA region poses an obstacle to an accurate evaluation of the actual feasibility of the region to a given crop. Therefore, the aim of this work was to carry out an assessment of the performance of four different precipitation databases of alternative sources to observations: two from gridded analyses, MERGE and CHIRPS, and the other two from ECMWF reanalyses, ERA5, and ERA5Land, and by comparing them to observational records from stations along the region. The analysis was based on a comparison with data from seven weather stations located in SEALBA, in the period 2001–2020, through three dexterity indices: the mean absolute error (MAE), the root mean squared errors (RMSE), and the coefficient of Pearson’s correlation (r), showing that the gridded analyzes performed better than the reanalyses, with MERGE showing the highest correlations and the lowest errors (global average r between stations of 0.96, followed by CHIRPS with 0.85, ERA5Land with 0.83, and ERA5 with 0.70; average MAE 14.3 mm, followed by CHIRPS with 21.3 mm, ERA5Land with 42.1 mm and ERA5 with 50.1 mm; average RMSE between stations of 24.6 mm, followed by CHIRPS with 50.8 mm, ERA5Land with 62.3 mm and ERA5 with 71.4 mm). Since all databases provide up-to-date data, our findings indicate that, for any research that needs a complete daily precipitation dataset for the SEALBA region, preference should be given to use the data in the following order of priority: MERGE, CHIRPS, ERA5Land, and ERA5.

The Northeast region of Brazil (NEB) is characterized by large climate variability that causes extreme and long unseasonal wet and dry periods. Despite significant model developments to improve seasonal forecasting for the NEB, the achievement of a satisfactory accuracy often remains a challenge, and forecasting methods aimed at reducing uncertainties regarding future climate are needed. In this work, we implement and assess the performance of an empirical model (EmpM) based on a decomposition of historical data into dominant modes of precipitation and seasonal forecast applied to the NEB domain. We analyzed the model’s performance for the February-March-April quarter and compared its results with forecasts based on data from the North American Multi-model Ensemble (NMME) project for the same period. We found that the first three leading precipitation modes obtained by empirical orthogonal functions (EOF) explained most of the rainfall variability for the season of interest. Thereby, this study focuses on them for the forecast evaluations. A teleconnection analysis shows that most of the variability in precipitation comes from sea surface temperature (SST) anomalies in various areas of the Pacific and the tropical Atlantic. The modes exhibit different spatial patterns across the NEB, with the first being concentrated in the northern half of the region and presenting remarkable associations with the El Niño-Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), both linked to the latitudinal migration of the intertropical convergence zone (ITCZ). As for the second mode, the correlations with oceanic regions and its loading pattern point to the influence of the incursion of frontal systems in the southern NEB. The time series of the third mode implies the influence of a lower frequency mode of variability, probably related to the Interdecadal Pacific Oscillation (IPO). The teleconnection patterns found in the analysis allowed for a reliable forecast of the time series of each mode, which, combined, result in the final rainfall prediction outputted by the model. Overall, the EmpM outperformed the post-processed NMME for most of the NEB, except for some areas along the northern region, where the NMME showed superiority.