COMPARISONS BETWEEN OBSERVED AND MODELED PRECIPITATION AND TEMPERATURE EXTREMES IN SOUTH AMERICA DURING THE XX CENTURY (IPCC 20C3M).

MATILDE RUSTICUCCI, JOSÉ MARENGO, OLGA PENALBA, AND MADELEINE RENOM

Foz de iguazu

Conferencia; 8th International Conference on Southern Hemisphere Meteorology and Oceanography Society; 2006

One of the key aspects of Climate Change is to understand the behavior of extremes. It is recognized that the changes in the frequency and intensity of extreme events are likely to have a larger impact than changes in mean climate. Because of differences among model formulation in the various IPCC AR4 global coupled models, some differences can be expected in the projection of mean climate and extremes in the present and also in the future. A trend analyses performed by Marengo et al (2006) using various indices of extremes used by Tebaldi et al (2005) have shown that even though all models simulate quite well the observed warming trends in mean and extremes temperatures for 1950-2002, the situation with rainfall indices is not as good, and basically all models show tendencies that are different that the observed trends in various regions of South America. Marengo et al (2006) analyzes the simulations of the IPCC 20C3M, where all models are run with the same forcing for present climates. And Tebaldi et al (2005) analyzes future climate changes in extremes for 2071-2100 for an ensemble of IPCC AR4 model projections, and while almost all models show a common signal of warming in many regions of the planet, the common signal for rainfall anomalies in the future climate is restricted to few regions around the globe. We propose to assess the expected changes in climate extremes over southern South America through the analysis of the indices of the IPCC 4th Assessment Model Output for the present climate (IPCC20C3M). These "extreme indices" are derived data, from simulated daily temperature and precipitation, in the form of annual indicator time series. In this paper, for the common period 1960- 2000, the mean, standard deviation and mean square error between the grid point from different models and the nearest station was calculated.