Colombian climatology in CMIP5/CMIP6 models: Persistent biases and improvements

Abstract

Northern South America is among the regions with the highest vulnerability to climate change. General Circulation Models (GCMs) are among the different tools considered to analyze the impacts of climate change. In particular, GCMs have been proved to provide useful information, although they exhibit systematic biases and fail in reproducing regional climate, particularly in terrains with complex topography. This work evaluates the performance of GCMs included in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP), representing the annual cycle of precipitation and air surface temperature in Colombia. To evaluate this, we consider different observational and reanalysis datasets, including in situ gauges from the Colombian Meteorological Institute. Our results indicate that although the most recent generation of GCMs (CMIP6) show improvements with respect to the previous generation (CMIP5), they still have systematic biases in representing the Intertropical Convergence Zone and elevation-dependent processes, which highly determine intra-annual precipitation and air surface temperature in Colombia. In addition, CMIP6 models have larger biases in temperature over the Andes than CMIP5. We also analyze climate projections by the end of the 21st century according to the CMIP5/CMIP6 simulations under the highest greenhouse gases emission scenarios. Models show projections toward warmer air surface temperatures and mixed changes of precipitation, with decreases of precipitation over the Orinoco and Colombian Amazon in September-November and increases over the eastern equatorial Pacific during the entire year.

Assessment of CMIP6 Performance and Projected Temperature and Precipitation Changes Over South America

“…However, they have a strong bias along the Andes (Fig. 3c), which is partly due to the poor representation of highlands in coarse resolution GCMs (Bozkurt et al 2019;Pabón-Caicedo et al 2020;Arias et al 2021b;Ortega et al 2021).…”

“…Realistic representation of the complex climate characteristics of South America in numerical models remains a challenge. For instance, most studies using Global Climate Models (GCMs) show that while models are able to simulate the main precipitation, temperature and circulation features over the continent, they exhibit systematic errors in precipitation magnitudes, such as underestimation in tropical South America and overestimation in the Andes and La Plata basin (Yin et al 2013;Gulizia and Camilloni 2015;Sierra et al 2015;Zazulie et al 2017;Rivera and Arnould 2020;Arias et al 2021b;Dias and Reboita 2021;Ortega et al 2021). Several studies also document the limitations of GCMs in simulating mesoscale circulation features associated with the Andes (Pabón-Caicedo et al 2020;Arias et al 2021a), and the genesis of mesoscale convective systems over southeastern South America (Muñoz et al 2015(Muñoz et al , 2016Doss-Gollin 2018).…”

“…Therefore, a comprehensive understanding of climate change uncertainty over South America can only emerge if all factors affecting variations in time and space are fully explored. Recent studies highlight that CMIP6 models improve the representation of seasonal precipitation in South America with respect to CMIP3 and CMIP5 (Arias et al 2021b;Dias and Reboita 2021;Ortega et al 2021) although systematic large-scale biases such as the double Intertropical Convergence Zone still persist (Tian and Dong 2020;Ortega et al 2021).…”

Circulation Patterns and Associated Rainfall Over South Tropical South America: GCMs Evaluation During the Dry‐To‐Wet Transition Season

“…The evaluation of the historical simulations allows us to assess model performance during the present climate, which is necessary to properly interpret the GCMs future projections. In particular, GCMs from CMIP6 and previous Coupled Model Intercomparison Project Phase (CMIP) experiments exhibited systematic errors in PP magnitudes over STSA, such as an underestimation over tropical South America and overestimation in the Andes and La Plata basin (Almazroui et al., 2021; Arias, Ortega, et al., 2021; Díaz et al., 2021; Gulizia & Camilloni, 2015; Ortega et al., 2021; Pabón‐Caicedo et al., 2020; Sierra et al., 2015; Yin et al., 2013). In terms of long‐term variability, GCMs depicted general drying trends during the historical period, especially over most of Amazonia, which are expected to intensify in the future (Almazroui et al., 2021; Boisier et al., 2015; Fu et al., 2013; Reboita et al., 2021; Sena & Magnusdottir, 2020; Teodoro et al., 2021; Thaler et al., 2021; Wainwright et al., 2021).…”

A New Look into the South America Precipitation Regimes: Observation and Forecast

“…The realistic representation of South American climate features remains a challenge for numerical models [15,[68][69][70][71][72][73][74][75]. Global climate models (GCMs) reproduce and predict the main characteristics of precipitation, temperature, and circulation over the continent.…”