The South Atlantic convergence zone (SACZ) is the main summer-typical atmospheric phenomenon occurring in South America, and it is of great interest because it regulates the rainy season in the most populated regions of Brazil. Frequency variability, persistence, and geographical position of the SACZ and its relationship with intraseasonal variability is well described in the literature. However, the influence of extratropical forcing on the SACZ is not well understood. Consequently, the aim of this study is to evaluate the role of the Antarctic Oscillation (AAO) in SACZ events. The persistence and frequencies of SACZ events, mean, standard deviation of total precipitation per event, lag composite of daily precipitation and geopotential height anomalies were obtained for each phase of the AAO. Therefore, frequency, persistence and total precipitation of SACZ events were higher in positive AAO (AAO+) than negative AAO (AAO−). A teleconnection mechanism between the extratropics and the SACZ region is evident in AAO+, through intensification of the polar and subtropical jets, in the days preceding SACZ. The same was not observed in the AAO−, where the anomalies were confined in the subtropical region and displaced to the South Atlantic Ocean.

The strategy for assessing simulations produced by climate models established as part of the Atmospheric Model Intercomparison Project (AMIP) delivers an outline for model analysis, verification/validation, and intercomparison. Numerical models are continuously being developed to find the best representation for the amount and distribution of precipitation in Brazil to improve the country’s precipitation forecast. This article describes the key features of the Brazilian Global Atmospheric Model (BAM) (developed by the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE)) and analyses of its performance for annual rainfall climate simulations. This study considered the representation of the annual precipitation in Brazil mainly during the rainy season in the central part of Brazil by the BAM. The model was run over the 1990 to 2015 period using spectral Eulerian model dynamics with a 70-horizontal resolution of approximately 1.0${}^{\circ }$× 1.0${}^{\circ }$ and 42 vertical sigma levels. The analysis was divided into two stages: the annual precipitation and the rainy season precipitation. Model precipitation analyses were performed using statistical methods, such as the mean and standard deviation, comparing modeled data with observed data from two datasets, data from the XAV (observed data from INMET, ANA, and DAEE), and the Climate Prediction Center (CPC). In general, the BAM model simulations reasonably replicated the configuration of the spatial distribution of precipitation in the Brazilian territory almost entirely, especially compared with the XAV. The accumulated precipitation in the southern region presented great variation, accumulating from 750 mm year${}^{-1}$ in the extreme south to 1750 mm year${}^{-1}$ in the north of this region. Average values of the BAM accumulated precipitation ranged from 1000 to 2000 mm year${}^{-1}$, within the expected average, compared to observed values of 750–1500 mm year${}^{-1}$ (CPC and XAV, correspondingly). Although there was an underestimation of the accumulated precipitation by the model, the model reasonably reproduced the precipitation during the rainy season. The performed assessment identified model aspects that need to be improved.

Temperature is one of the most important variables used in climate studies. Changes in its time series can cause various social and political impacts, affecting diverse sectors of society, such as agriculture, for example. Southern Brazil is a region that relies heavily on agriculture, contributing to national production. Therefore, it is important to check the air temperature characteristic, i.e., its spatial pattern in this region. For this, daily data of minimum and maximum temperature of the air of the National Institute of Meteorology were used in the period from 1961 to 2011. The statistical technique of cluster analysis was applied to identify the homogeneous regions of temperature and the boxplot for the characterization of each an. In general, the results showed maximum values of temperatures in the west of Rio Grande do Sul. The regions of minimum temperatures presented a more homogenous pattern, with averages around 15 ºC. The regions of the maximum temperatures showed average values oscillating between 22 ºC and 28 ºC.