Evaluation of the CMIP3 and CMIP5 climate models' simulated precipitation over South America

INÉS CAMILLONI, CARLA GULIZIA

Exeter

Workshop; 4th WGNE workshop on systematic errors in weather and climate models; 2013

JSC/CAS Working Group on Numerical Experimentation (WGNE)

The main objective of this study is to compare the ability of 19 Global Climate Models (GCMs) from the CMIP3 and CMIP5 projects to represent the seasonal and annual precipitation mean fields in South America for the period 1960-99. Three sub-regions are particularly evaluated: the continental core of the monsoon region, the continental area of the South Atlantic Convergence Zone and the southern sector of Southeastern South America. Various statistical analysis are presented to detect if there have been any improvements in the new generation of GCMs relative to the previous one. The comparison between the biases of the CMIP3 and CMIP5 ensembles at the continental scale shows as the most distinctive feature that summer precipitation exhibits a better performance for the CMIP5 ensemble mean. However, this new generation of GCMs still has difficulties to simulate precipitation in NE Brazil, the regions over and near the Andes Mountains and the south end of the continent. Moreover, the median of the normalized RMSE of CMIP5 models (typical error) is higher for summer, winter and the annual mean than for the CMIP3 models not showing a necessarily improvement in time of rainfall representation for the analyzed region. However, the error of the ensemble mean is lower for the new generation of models for the winter and annual cases. Furthermore, for both CMIP3 and CMIP5, the models? ensembles agree with observations better than the typical error. The MRI-CGCM2.3.2 model in the CMIP3 analysis is superior to the multimodel ensemble while there is no individual model in the set of CMIP5 GCMs analyzed exhibiting the same superiority. Furthermore, results from the regional analysis show that the distance between the median of CMIP3 GCMs and the mean observed precipitation differs in the three sub-regions and among the different seasons although in all cases GCMs underestimate precipitation. Results from the same analysis but for CMIP5 models show considerable improvements