Assessment of CMIP6 models' performance in simulating present-day climate in Brazil

Abstract

Brazil is one of the most vulnerable regions to extreme climate events, especially in recent decades, where these events posed a substantial threat to the socio-ecological system. This work underpins the provision of actionable information for society's response to climate variability and change. It provides a comprehensive assessment of the skill of the state-of-art Coupled Model Intercomparison Project, Phase 6 (CMIP6) models in simulating regional climate variability over Brazil during the present-day period. Different statistical analyses were employed to identify systematic biases and to choose the best subset of models to reduce uncertainties. The results show that models perform better for winter than summer precipitation, consistent with previous results in the literature. In both seasons, the worst performances were found for Northeast Brazil. Results also show that the models present deficiencies in simulating temperature over Amazonian regions. A good overall performance for precipitation and temperature in the La Plata Basin was found, in agreement with previous studies. Finally, the models with the highest ability in simulating monthly rainfall, aggregating all five Brazilian regions, were HadGEM3-GC31-MM, ACCESS-ESM1-5, IPSL-CM6A-LR, IPSL-CM6A-LR-INCA, and INM-CM4-8, while for monthly temperatures, they were CMCC-ESM2, CMCC-CM2-SR5, MRI-ESM2-0, BCC-ESM1, and HadGEM3-GC31-MM. The application of these results spans both past and possible future climates, supporting climate impact studies and providing information to climate policy and adaptation activities.

Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America

“…However, even with a wetter simulation pattern, these models underestimate rainfall The ensemble mean maintains the warmer behavior of the models in sectors such as the Andean region and north-central Argentina, as well as the colder pattern in extreme southern Argentina and northern SA. Other studies have also found a colder bias of climate models in SA [27,[102][103][104][105][106] due to the limitation of the models in simulating the complex topography of the continent, especially over regions such as the Andes and the La Plata Basin [107], as well as the initial soil moisture conditions since this variable modifies the surface temperature amplitude [108].…”

“…. Other studies also reported the drier bias of CMIP5 [100] and CMIP6 [4,24,27,28] models in sectors such as northern SA and northern Brazil. These systematic model errors are generally associated with a less satisfactory representation of the ITCZ, which is attributed to the models' oversensitivity to sea surface temperature (SST) and failure to simulate surface wind convergence in the equatorial zone [100].…”

“…Monthly data of precipitation and 2 m air temperature from 50 GCMs (table 1) were obtained from the Earth System Grid Federation platform (available at (https://esgf-data.dkrz.de/projects/cmip6-dkrz/) for the historical period from January 1995 to December 2014. Although the historical CMIP6 simulations cover the period 1850-2014, in this study, we analyzed climate simulations for 1995-2014, as also established by the AR6 Working Group [40] and used in several studies [1,3,4,7,13,27].…”

“…They concluded that in the Amazon region, the models performed best during the months of MAM and JJA, while in the Southeast, the best performances were for the months of DJF. Likewise, Firpo et al [27] investigated the performance of 35 CMIP6-GCMs in simulating the temperature and precipitation of the historical period in Brazil. They observed that the models perform better for winter rainfall than summer, and the La Plata Basin (Brazilian Northeast) showed the best (worst) results.…”

“…Given the framework, this study aims to evaluate CMIP6 models in terms of their ability to recreate historical climate statistics (1995-2014) for five South American subdomains. In this context, we point out that previous studies have also evaluated the performance of CMIP6 models in representing the statistical characteristics of the present climate in SA [3,4,26,27,35], but with different purposes and subdomains analyzed. Here, we evaluate critical regions for energy studies in SA, as we are interested in the renewable resources present in these sectors.…”