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The reliability of climate prediction by a global model is directly related to the ability to simulate the observed climate variability and the main teleconnection patterns. Precipitation anomalies in certain regions are strongly affected by these features, and it is important to know if models are able to reproduce such patterns and influences. The main objective of this article is to analyse some global features of the Brazilian Atmospheric Model with simplified physics (BAM-v0), and to discuss several aspects of climate variability over South America. Especially, the ability of the model in simulating the main teleconnection patterns that affect South America and the precipitation variability in several regions of Brazil associated with the Pacific and Atlantic Sea Surface Temperature. The model is the atmospheric component of the Brazilian Earth System Model-Ocean-Atmosphere (BESM), which can be used to long integrations due to the simplified physics, considering computer limitations. Climate variability is investigated through analyses of variance and correlations, and teleconnections such as Southern Annular Mode (SAM) and Pacific South American (PSA) are obtained from EOF analyses. El Nino Southern Oscillation (ENSO) features are analysed through the Southern Oscillation Index and precipitation anomalies. BAM-v0, even at coarse resolution, represents many climate variability features. It captures the influences of tropical Pacific and Atlantic Oceans on Northeast Brazil precipitation and reproduces the influences of ENSO over South America. SAM and PSA teleconnections are well simulated. Observed features of the South America Monsoon System are captured by the model, although the intensities of precipitation variability need to be improved. There are some deficiencies related to global budget, precipitation variance in some regions of the globe and precipitation anomalies in certain regions of South America. Identification of model deficiencies and variability analyses are important to model development and contribute to climate prediction improvements.