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.

Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of a rain gauge network. Given this, we aimed to evaluate the performance of nine databases that estimate rainfall in the BLA, four from gridded analyses based on pluviometry (Xavier, CPC, GPCC and CRU), four based on remote sensing (CHIRPS, IMERG, CMORPH and PERSIANN-CDR), and one from reanalysis (ERA5Land). We found that all the bases are efficient in characterizing the average annual cycle of accumulated precipitation in the BLA, but with a predominantly negative bias. Parameters such as Pearson’s correlation (r), root-mean-square error (RMSE) and Taylor diagrams (SDE), applied in a spatial analysis for the entire BLA as well as for six pluviometrically homogeneous regions, showed that, based on a skill ranking, the data from Xavier’s grid analysis, CHIRPS, GPCC and ERA5Land best represent precipitation in the BLA at monthly, seasonal and annual levels. The PERSIANN-CDR data showed intermediate performance, while the IMERG, CMORPH, CRU and CPC data showed the lowest correlations and highest errors, characteristics also captured in the Taylor diagrams. It is hoped that this demonstration of hierarchy based on skill will subsidize climate studies in this region of great relevance in terms of biodiversity, water resources and as an important climate regulator.

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.

Dynamic numerical models of the atmosphere are the main tools used for weather and climate forecasting as well as climate projections. Thus, this work evaluated the systematic errors and areas with large uncertainties in precipitation over the South American continent (SAC) based on regional climate simulations with the weather research and forecasting (WRF) model. Ten simulations using different convective, radiation, and microphysical schemes, and an ensemble mean among them, were performed with a resolution of 50 km, covering the CORDEX-South America domain. First, the seasonal precipitation variability and its differences were discussed. Then, its annual cycle was investigated through nine sub-domains on the SAC (AMZN, AMZS, NEBN, NEBS, SE, SURU, CHAC, PEQU, and TOTL). The Taylor Diagrams were used to assess the sensitivity of the model to different parameterizations and its ability to reproduce the simulated precipitation patterns. The results showed that the WRF simulations were better than the ERA-interim (ERAI) reanalysis when compared to the TRMM, showing the added value of dynamic downscaling. For all sub-domains the best result was obtained with the ensemble compared to the satellite TRMM. The largest errors were observed in the SURU and CHAC regions, and with the greatest dispersion of members during the rainy season. On the other hand, the best results were found in the AMZS, NEBS, and TOTL regions.

In this study, we evaluated the performance of the Brazilian Global Atmospheric Model (BAM), in its version 2.2.1, in the representation of the surface variables solar radiation, temperature (maximum, minimum, and average), and wind speed. Three experiments were carried out for the period from 2016 to 2022 under three different aerosol conditions (constant (CTE), climatological (CLIM), and equal to zero (ZERO)), discarding the first year as a spin-up period. The observations came from a high-resolution gridded analysis that provides Brazil with robust data based on observations from surface stations on a daily scale from 1961 to 2020; therefore, combining the BAM outputs with the observations, our intercomparison period took place from 2017 to 2020, for three timescales: daily, 10-day average, and monthly, targeting different applications. In its different simulations, BAM overestimated solar radiation throughout Brazil, especially in the Amazon; underestimated temperature in most of the northeast, southeast, and south regions; and overestimated in parts of the north and mid-west; while wind speed was only not overestimated in the Amazon region. In relative terms, the simulations with constant aerosol showed better performance than the others, followed by climatological conditions and zero aerosol. The dexterity indices applied in the intercomparison between BAM and observations indicate that BAM needs adjustments and calibration to better represent these surface variables. Where model deficiencies have been identified, these can be used to drive model development and further improve the predictive capabilities.