Os eventos climáticos extremos demonstram um papel significativo das sociedades, seja por sua intensidade, pela frequência de ocorrência ou pela vulnerabilidade socioambiental. Objetiva-se classificar e quantificar as precipitações na porção leste da região Nordeste (NE) do Brasil através do índice SPI, como também detectar maiores déficits e/ou excesso de precipitação. O Standardized Precipitation Index (SPI) foi utilizado para quantificar déficits de precipitação e identificar eventos secos e chuvosos em diferentes escalas temporais, auxiliando no monitoramento da sua dinâmica temporal. No cálculo do SPI foi utilizado a distribuição gama, e estimados os limites de precipitação que representam a cada categoria do índice. Foram utilizados dados pluviométricos das capitais dos estados que compõem no leste do Nordeste do Brasil, no período de 1961 a 2014 provenientes da Agência Nacional das Águas (ANA). A análise de Ondeletas foi utilizada com objetivo de identificar ciclos de extremos pluviométricos e de suas causas através das escalas temporais detectadas em séries de precipitação para as capitais do leste do Nordeste do Brasil. Os resultados mostraram que as ocorrências de secas foram as maiores em todas as cidades, todavia na categoria extrema os eventos chuvosos revelaram-se mais frequentes. Os anos normais foram os mais persistentes em todas cidades analisadas. Recife apresentou máximas ocorrências de eventos chuvosos. Os eventos com intensidade extrema, seja chuvoso ou seco, ocorreram em boa parte da série em anos de ENOS. O SPI revelou-se uma excelente ferramenta na detecção e no monitoramento de seca/chuvas na região analisada. A presença de escalas temporais relacionadas com eventos ENOS, Dipolo do Atlântico, ciclo de manchas solares e Oscilação Decadal do Pacífico foram identificadas em todas as capitais do leste do NEB. Characterization of Drought Events Based on the Standardized of Precipitation Index for the East NortheastA B S T R A C TExtreme weather events demonstrate a significant role for societies, whether by their intensity, frequency of occurrence or socio-environmental vulnerability. Objective-classify and quantify as precipitation in the eastern portion of the Northeast (NE) of Brazil through the SPI index, as well as detect larger deficits and / or excess occurrence. The Standardized Precipitation Index (SPI) was used to quantify use deficits and to identify dry and rainy events in different temporal variations, helping to monitor their temporal utilization. No SPI calculations were used for gamma distribution, and estimated capture limits representing each category of the index. Rainfall data were used from the capitals of the states that make up the eastern Northeast of Brazil, with no period from 1961 to 2014, Registration of the National Water Agency (ANA). A wave analysis was used to identify extreme rainfall cycles and their causes caused by temporary variations detected in monitoring series for the eastern capitals of Brazil. The results shown as drought occurrences were the highest in all cities, however in the extreme category of rain events most frequently revealed. The normal years were the most persistent in all cities analyzed. Recife presents maximum occurrences of rain events. Extreme intensity events, whether rainy or dry, occur in much of the series in ENSO years. The SPI revealed an excellent tool for detection and monitoring of drought / gloves in the analyzed region. The presence of temporary variations related to ENOS, Atlantic Dipole, sunspot cycle and Pacific Oscillation events are identified in all eastern NEB capitals.Keywords: SPI; extreme rainfall; drought; Wavelet analysis.

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.

Previous work suggests that changes in seasonality could lead to a 70% reduction in the extent of the Amazon rainforest. The primary cause of the dieback of the rainforest is a lengthening of the dry season due to a weakening of the large-scale tropical circulation. Here we examine these changes in the seasonal cycle. Under present day conditions the Amazon climate is characterized by a zonal separation of the dominance of the annual and semi-annual seasonal cycles. This behavior is strongly modified under greenhouse warming conditions, with the annual cycle becoming dominant throughout the Amazon basin, increasing differences between the dry and wet seasons. In particular, there are substantial changes in the annual cycle of temperature due to the increase in the temperature of the warmest month, but the lengthening of the dry season is believed to be particularly important for vegetation-climate feedbacks. Harmonic analysis performed to regional climate model simulations yields results that differ from the global climate model that it is forced from, with the regional model being more sensitive to changes in the seasonal cycle.

Resumo Neste estudo, um modelo de downscaling estatístico (MDE) baseado na metodologia de análogos foi usado para projetar padrões futuros de precipitação e temperatura na cidade de Maceió, litoral leste do Nordeste (9,7° S; 35,7° W; 64,5m), e avaliar seus impactos na produtividade da cana de açúcar. Dados observados históricos foram usados para o downscaling da precipitação e temperatura usando diferentes modelos climáticos globais (CanESM2, CNRM-CM5, IPSL-CM5A-MR, GFDL-ESM2M, MIROC-ESM, MPI-ESM-MR, e NorESM1-M) em relação ao período de referencia (1961-1990), e, em seguida, para cenários futuros de 2021 a 2080. Os dados foram analisados em decêndios, mesmo passo de tempo do modelo agrometeorológico usado para a cultura. Ao contrário do que se esperava, a combinação de redução das chuvas e aumento da evapotranspiração nos cenários futuros não diminuiu a estimativa da produtividade, com previsão de aumento. A este fato pode-se atribuir que a redução das chuvas previstas, mais acentuadas no período chuvoso, não serão decisivas para a diminuição da produtividade, pois na maioria dos modelos climáticos há previsão de aumento das chuvas no período seco de setembro a dezembro, crítico para o crescimento inicial da cultura, que tem calendário de plantio a partir do primeiro decêndio de setembro.