Weather and climate extremes are part of the natural variability. However, the frequency and intensity of precipitation extremes have increased in the globe following the global warming. Extreme precipitation impacts such as landslides and flooding with implications to vulnerability and adaptation are discussed for two regions of the state of São Paulo: the Metropolitan Region of Campinas and the Metropolitan Region of the Baixada Santista, located in southeastern South America. Simulations and projections obtained from four integrations of the Regional Eta model are analyzed to investigate the model behavior during the period of 1961-1990 and the projections within the period of 2011-2100. Uncertainties are discussed based on the standard deviation among the model spread. The projections show precipitation increase in the Metropolitan Region of Campinas during DJF for the near and distant future, while there are more uncertainties in the other seasons. In the Metropolitan Region of Baixada Santista, the precipitation increase is projected to all seasons, except JJA, when there is higher uncertainty. Daily rainfall indices suggest an increase of precipitation during the rainy days, but a reduction in the number of rainy days in both locations. The projections show a reduction of light rains and an increase of heavy rains at both regions. The model identifies the South Atlantic Convergence Zone and frontal systems as precipitation patterns associated with extremes in the two locations. The results can be useful for adaptation actions, since the regions are highly populated and have high vulnerabilities.

Brazil and consequently causes the temperature decrease. The energetics shows that the cold events kinetic energy maxima are more intense than those of cool events. For the cold events three maxima are observed, the first (K1) and the third (K3) maxima are developed by baroclinic conversion and ageostrophic flux convergence and the second one (K2) by ageostrophic flux convergence. For the cool events two maxima are found, the first maximum (K4) developed by baroclinic conversion and the second one by ageostrophic flux convergence.

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 Niño 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.

No abstract