Downscaling extreme month-long anomalies in southern South America

We investigate the performance of one stretched-grid atmospheric global model, five different regional climate models and a statistical downscaling technique in simulating 3 months (January 1971, November 1986, July 1996) characterized by anomalous climate conditions in the southern La Plata Basin. Models were driven by reanalysis (ERA-40). The analysis has emphasized on the simulation of the precipitation over land and has provided a quantification of the biases of and C. G. Menéndez (B) · A. A. Sörensson · J. Blazquez · M. N. Núñez · N. Pessacg · A. Rolla · S. A. Solman Centro de Investigaciones del Mar y la Atmósfera, CONICET-UBA, Pabellón 2, Piso 2, Ciudad Universitaria, 1428 Buenos Aires, Argentina e-mail: [email protected] M. de Castro · E. Sanchez · A. Elizalde Facultad de Ciencias del Medio Ambiente, Universidad de Castilla-La Mancha, Toledo, Spain J.-P. Boulanger Laboratoire d’Océanographie et du Climat, UMR CNRS/IRD/UPMC, Paris, France A. D’Onofrio DC, FCEN, Universidad de Buenos Aires, Buenos Aires, Argentina C. G. Menéndez · S. A. Solman DCAO, FCEN, Universidad de Buenos Aires, Buenos Aires, Argentina A. Elizalde · D. Jacob · S. Pfeiffer · C. Teichmann Max Planck Institute for Meteorology, Hamburg, Germany H. Le Treut · Z. X. Li Laboratoire de Météorologie Dynamique, CNRS, Paris, France M. Rojas Departamento de Geofísica, Universidad de Chile, Santiago, Chile P. Samuelsson Rossby Centre, SMHI, Norrköping, Sweden 380 Climatic Change (2010) 98:379–403 scatter between the different regional simulations. Most but not all dynamical models underpredict precipitation amounts in south eastern South America during the three periods. Results suggest that models have regime dependence, performing better for some conditions than others. The models’ ensemble and the statistical technique succeed in reproducing the overall observed frequency of daily precipitation for all periods. But most models tend to underestimate the frequency of dry days and overestimate the amount of light rainfall days. The number of events with strong or heavy precipitation tends to be under simulated by the models.