Comparative analysis of the ability of a set of CMIP3 and CMIP5 global climate models to represent precipitation in South America

Abstract

The purpose of this study is to evaluate the ability of two sets of global climate models (GCMs) derived from the Coupled Model Intercomparison Projects Phase 3 (CMIP3) and Phase 5 (CMIP5) to represent the summer, winter, and annual precipitation mean patterns in South America south of the equator and in three particular sub‐regions, between years 1960 and 1999. Different metrics (relative bias, spatial correlation, RMSE, and relative errors) were calculated and compared between both projects to determine if there has been improvement from CMIP3 to CMIP5 models in the representation of regional rainfall. Results from this analysis indicate that for the analysed seasons, precipitation simulated by both CMIP3 and CMIP5 models' ensembles exhibited some differences. In DJF, the relative bias over Amazonia, central South America, eastern Argentina, and Uruguay is reduced in CMIP5 compared with CMIP3. In JJA, the same occurs in some areas of Amazonia. Annual precipitation is also better represented by the CMIP5 than CMIP3 GCMs as they underestimate precipitation to a lesser extent, although in NE Brazil the overestimation values are much larger in CMIP5 than in CMIP3 analysis. In line with previous studies, the multi‐model ensembles show the best representation of the observed patterns in most seasons and regions. Only in some cases, single GCMs [MIROC3.2(hires) – CMIP3– and MIROC4h – CMIP5] presented better results than the ensemble. The high horizontal resolution of these models suggests that this could be a relevant issue for a more adequate estimation of rainfall at least in the analysed regions.

Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China

“…Relative to earlier phases, CMIP5 focuses on a set of experiments that include higher spatial resolution models, improved model physics, and a richer set of output fields (Gulizia and Camilloni, 2015;Taylor et al, 2012). Additionally, the CMIP5 climate change projections are driven by new climate scenarios that use a time series of emissions and concentrations from the representative concentration pathways (RCPs) described in Moss et al (2010).…”

“…Additionally, the CMIP5 climate change projections are driven by new climate scenarios that use a time series of emissions and concentrations from the representative concentration pathways (RCPs) described in Moss et al (2010). Accordingly, GCMs provided by the CMIP5 have been widely used in the assessment of climate change (Gulizia and Camilloni, 2015;Pierce et al, 2013;Smith et al, 2013).…”

Future Changes in Drought Characteristics over Southern South America Projected by a CMIP5 Multi-Model Ensemble

“…The discrepancies between the multi-model ensemble and the GPCC database could respond to biases in the simulation of mean precipitation patterns in SSA, which was identified in [28,29]; and also to biases unrelated to errors in the mean patterns, like the representation of seasonal and inter-annual precipitation variabilities [30] that are important for drought development.…”

Impact of moisture divergence on systematic errors in precipitation around the Tibetan Plateau in a general circulation model

“…The ensemble of Coupled Model Intercomparison Project Phase 3 (CMIP3) models overestimates the amount of precipitation over the Tibetan Plateau by up to 100 % (Xu et al 2010). This is also true for other regions with high mountains, e.g., the Andes mountains, where both regional and global models tend to produce excessive precipitation (Alves and Marengo 2010;Gulizia and Camilloni 2015).…”