Systematic temperature and precipitation biases in the CLARIS-LPB ensemble simulations over South America and possible implications for climate projections

• Autores: Solman, Silvina Alicia
• Año de publicación: 2016
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: REGIONAL CLIMTE MODELS; REGIONAL CLIMATE CHANGE; SOUTH AMERICA; SYSTEMATIC BIAS
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Within the framework of the CLARIS-LPB EU Project, a suite of 7 coordinated Regional Climate Model (RCM) simulations over South America driven by both the ERA-Interim reanalysis and a set of Global Climate Models (GCMs) were evaluated. The systematic biases in simulating monthly mean temperature and precipitation from the 2 sets of RCM simulations were identified. The Climate Research Unit dataset was used as a reference. The systematic model errors were more dependent on the RCMs than on the driving GCMs. Most RCMs showed a systematic temperature overestimation and precipitation underestimation over the La Plata Basin region. Model biases were not invariant, but a temperature-dependent temperature bias and a precipitation-dependent precipitation bias were apparent for the region, with the warm bias amplified for warm months and the dry bias amplified for wet months. In a climate change scenario, the relationship between model bias behaviour and the projected climate change for each individual model revealed that the models with the largest temperature bias amplification projected the largest warming and the models with the largest dry bias amplification projected the smallest precipitation increase, suggesting that models’ bias behaviour may affect the future climate projections. After correcting model biases by means of a quantile-based mapping bias correction method, projected temperature changes were systematically reduced, and projected precipitation changes were systematically increased. Though applying bias correction methodologies to projected climate conditions is controversial, this study demonstrates that bias correction methodologies should be considered in order to better interpret climate change signals.
Fil: Solman, Silvina Alicia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina