An important goal of the Climate Variability and Predictability (CLIVAR) research on the American monsoon systems is to determine the sources and limits of predictability of warm season precipitation, with emphasis on weekly to interannual time scales. This paper reviews recent progress in the understanding of the American monsoon systems and identifies some of the future challenges that remain to improve warm season climate prediction. Much of the recent progress is derived from complementary international programs in North and South America, namely, the North American Monsoon Experiment (NAME) and the Monsoon Experiment South America (MESA), with the following common objectives: 1) to understand the key components of the American monsoon systems and their variability, 2) to determine the role of these systems in the global water cycle, 3) to improve observational datasets, and 4) to improve simulation and monthly-to-seasonal prediction of the monsoons and regional water resources. Among the recent observational advances highlighted in this paper are new insights into moisture transport processes, description of the structure and variability of the South American low-level jet, and resolution of the diurnal cycle of precipitation in the core monsoon regions. NAME and MESA are also driving major efforts in model development and hydrologic applications. Incorporated into the postfield phases of these projects are assessments of atmosphere-land surface interactions and model-based climate predictability experiments. As CLIVAR research on American monsoon systems evolves, a unified view of the climatic processes modulating continental warm season precipitation is beginning to emerge.

This paper reviews recent progress made in our understanding of the functioning and variability of the South American Monsoon System (SAMS) on time scales varying from synoptic to long-term variability and climate change. The SAMS contains one of the most prominent summertime climate patterns in South America, featuring a strong seasonal variability in a region lying between the Amazon and the La Plata Basin. Much of the recent progress is derived from complementary international programs, such as the Monsoon Experiment South America (MESA), as well as from ongoing international programs such as the Large Scale Biosphere Atmosphere Experiment in the Amazon Basin (LBA) and the La Plata Basin (LPB) Regional Hydroclimate Project, which includes the CLARIS LPB Europe-South America Network for Climate Change Assessment and Impact Studies in La Plata Basin Project. The latter assesses atmosphere-land surface interactions, the role of land use changes and aerosols from biomass burning considered as sources of variability and change in the SAMS functioning, characteristics and behaviour.The SAMS region is particularly susceptible to variations of climate due to the importance of hydroelectricity generation and the agricultural base of local economies. Also addressed in this report are projections of climate change and extremes, which are important for impact and vulnerability assessments. This discussion includes the need to identify and understand important processes that control the monsoonal climate, how these processes may vary and change, and how they may interact with key societal sectors, including water resource management, hydroelectric generation, agriculture, and agribusiness. This paper reports on the major contributions of MESA to the knowledge of characteristics, functioning and variability of the SAMS, and is based on recent studies and publications, and can be considered as an update of a previous review by C. S. Vera et al. (2006a).

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A recent field campaign aimed at obtaining an improved temporal and spatial description of the tropospheric flow over central South America was essential for the validation, and improvement of, short-and long-term predictions in the region.

The Weather and Research Forecast model is tested over South America in different configurations to identify the one that gives the best estimates of observed surface variables. Systematic, nonsystematic, and total errors are computed for 48-h forecasts initialized with the NCEP Global Data Assimilation System (GDAS). There is no unique model design that best fits all variables over the whole domain, and nonsystematic errors for all configurations differ little from one another; such differences are in most cases smaller than the observed day-to-day variability. An ensemble mean consisting of runs with different parameterizations gives the best skill for the whole domain. Surface variables are highly sensitive to the choice of land surface models. Surface temperature is well represented by the Noah land model, but dewpoint temperature is best estimated by the simplest land surface model considered here, which specifies soil moisture based on climatology. This underlines the need for better understanding of humid processes at the subgrid scale. Surface wind errors decrease the intensity of the low-level jet, reducing expected heat and moisture advection over southeast South America (SESA), with negative precipitation errors over SESA and positive biases over the South Atlantic convergence zone (SACZ). This pattern of errors suggests feedbacks between wind errors, precipitation, and surface processes as follows: an increase of precipitation over the SACZ produces compensating descent in SESA, with more stable stratification, less rain, less soil moisture, and decreased rain. This is a clear example of how local errors are related to regional circulation, and suggests that improvement of model performance requires not only better parameterizations at the subgrid scales, but also improved regional models.