Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: model performance and uncertainties

The capability of a set of 7 coordinated regional climate model simulations performed in the framework of the CLARIS-LPB Project in reproducing the mean climate conditions over the South American conti- nent has been evaluated. The model simulations were forced by the ERA-Interim reanalysis dataset for the period 1990?2008 on a grid resolution of 50 km, follow- ing the CORDEX protocol. The analysis was focused on evaluating the reliability of simulating mean precipitation and surface air temperature, which are the variables most commonly used for impact studies. Both the common features and the differences among individual models have been evaluated and compared against several observational datasets. In this study the ensemble bias and the degree of agreement among individual models have been quantified. The evaluation was focused on the sea- sonal means, the area-averaged annual cycles and the frequency distributions of monthly means over target sub- regions. Results show that the Regional Climate Model ensemble reproduces adequately well these features, with biases mostly within ±2 °C and ±20 % for temperature and precipitation, respectively. However, the multi-model ensemble depicts larger biases and larger uncertainty (as defined by the standard deviation of the models) over tropical regions compared with subtropical regions. Though some systematic biases were detected particularly over the La Plata Basin region, such as underestimation of rainfall during winter months and overestimation of tem- perature during summer months, every model shares a similar behavior and, consequently, the uncertainty in simulating current climate conditions is low. Every model is able to capture the variety in the shape of the frequency distribution for both temperature and precipitation along the South American continent. Differences among indi- vidual models and observations revealed the nature of individual model biases, showing either a shift in the distribution or an overestimation or underestimation of the range of variability.Fil: Solman, Silvina Alicia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Sanchez, E.. Universidad de Castilla-La Mancha. Facultad Ciencias Ambientales y Bioquímica; EspañaFil: Samuelsson, P.. Rossby Centre, Meteorological And Hydrological Institute Of Sweden; SueciaFil: da Rocha, R. P.. Universidade de São Paulo. Departamento de Ciências Atmosféricas. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Li, L.. Laboratoire de Météorologie Dynamique; FranciaFil: Marengo, J.. Instituto Nacional de Pesquisas Espaciais. Centro de Ciencia do Sistema Terrestre; BrasilFil: Pessacg, Natalia Liz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; ArgentinaFil: Remedio, A. R. C.. Max Planck Institute for Meteorology; AlemaniaFil: Chou, S. C.. Centro de Previsao de Tempo e Estudos Climaticos. Instituto Nacional de Pesquisas Espaciais; BrasilFil: Berbery, Ernesto Hugo. University of Maryland. Earth System Science Interdisciplinary Center; Estados UnidosFil: Le Treut, H.. Laboratoire de Météorologie Dynamique; FranciaFil: De Castro, M.. Universidad de Castilla-La Mancha. Facultad Ciencias Ambientales y Bioquímica; EspañaFil: Jacob, D.. Max Planck Institute for Meteorology; Alemania. Climate Service Center; Estados Unido