Performance evaluation and ranking of CMIP6 global climate models over Vietnam

Abstract

This study comprehensively assesses the performance of 29 Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) and their ensemble mean (ENS_MEAN) over Vietnam. The spatiotemporal variability of near-surface temperature and precipitation is thoroughly evaluated for the 30-year historical period of 1985–2014. Results show that the models can reasonably reproduce the observational annual cycles and spatial distribution of temperature and precipitation, though their performances vary across the seven climatic sub-regions of Vietnam. Due to their coarse resolutions, the models produce warm biases over certain highland and mountainous areas, and many of them cannot reproduce the rainy season shift from summer in most sub-regions toward the end of the year in Central Vietnam. Finally, these results are summarized, and a performance score is assigned to each model, inferring a ranking. The top three models are EC-Earth3-Veg, EC-Earth3, and HadGEM3-GC31-MM. Although created by simply averaging all the models, ENS_MEAN can compete with the best GCMs and ranks 4th overall. In the same model family, the higher-resolution experiment exhibits a higher ranking than the lower-resolution one. The results of this study can provide a reference for the choice of appropriate CMIP6 GCMs in the upcoming downscaling activities in Vietnam.

Selecting CMIP6 GCMs for CORDEX Dynamical Downscaling over Southeast Asia Using a Standardised Benchmarking Framework

“…Note that over SEA, observations are sparse with large uncertainties, particularly for rainfall (Nguyen et al, 2020), making GCM evaluations more complicated (Nguyen et al, 2022;. To date, the performance of various CMIP6 GCMs has been evaluated and ranked over the whole region of SEA (Desmet and Ngo-Duc, 2022;Pimonsree et al, 2023) and its sub-regions [e.g., Philippines (Ignacio-Reardon and Luo, 2023); Thailand (Kamworapan et al, 2021); Vietnam (Nguyen-Duy et al, 2023)]. Although there are groups of GCMs that consistently perform well (e.g., EC-Earth3, EC-Earth3-Veg, GFDL-ESM4, MPI-ESM1-2-HR, E3SM1-0, CESM2) and poorly (e.g., FGOALS-g3, CanESM, NESM3, IPSL-CM6A-LR) across available literature, their ranking varies differently given inconsistencies in evaluation metrics and observational reference datasets.…”

Future climate projections for Vietnam: temperature and precipitation changes under SSP2-4.5 and SSP5-8.5 scenarios

“…While some researchers have started utilizing the IPCC CMIP6 dataset, their main emphasis has centered on validating General Circulation Model (GCM) models specific to the Vietnam domain or forecasting large-scale climate and ocean circulations (Desmet and Ngo-Duc, 2022;Nguyen-Duy et al, 2023;Tran-Anh et al, 2023). However, the applicability of their findings for policymaking is limited, and their resolution may not offer the regional specificity necessary for effective decision-making.…”

Unveiling Deviations from IPCC Temperature Projections through Bayesian Downscaling and Assessment of CMIP6 General Circulation Models in a Climate-Vulnerable Region

“…Thus, the evaluation of GCMs at a local scale is absolutely necessary before incorporating them into a Multi-Model Ensemble (MME), aiming to identify possible deviations or abnormal discrepancies in certain GCMs that may undermine the predictive capacity of studies. This locally based evaluation approach, combined with MME, is prevalent in numerous research endeavors [32,35,[52][53][54][55][56]. Nevertheless, a notable absence of such a localized assessment for the European Mediterranean region has been identified [57], with few studies conducting a comprehensive comparison of model-performance disparities [58][59][60][61].…”