Optimizing Multi-model Ensemble of CMIP6 GCMs for Climate Simulation: A Case Study over Bangladesh

Abstract

Abstract This study aims to enhance the precision of climate simulations by optimizing a multi-model ensemble of General Circulation Models (GCMs) for simulating rainfall, maximum temperature (Tmax), and minimum temperature (Tmin). Bangladesh, with its susceptibility to rapid seasonal shifts and various forms of flooding, is the focal point of this research. Historical simulations of 19 CMIP6 GCMs are meticulously compared with ERA5 data for 1986–2014. The bilinear interpolation technique is used to harmonize the resolution of GCM data with the observed grid points. Seven distinct error metrics quantify the grid-to-grid agreement between GCMs and ERA5 data. The metrics are integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for seasonal and annual rankings of GCMs. Finally, the ensemble means of top-performing models are estimated using Bayesian Model Averaging (BMA) and Arithmetic Mean (AM) for relative comparison. The outcomes of this study underscore the variability in GCM performance across different seasons, necessitating the development of an overarching ranking system. Results reveal ACCESS.CM2 is the preeminent GCM for rainfall, while INM.CM4.8 excels in replicating Tmax and UKESM1.0.LL in replicating Tmin. In contrast, FGOALS.g3, KACE.1.0.G and CanESM5 are the most underperformed models in estimating rainfall, Tmx and Tmn, respectively. Overall, there are five models, ACCESS.ESM1.5, ACCESS.CM2, UKESM1.0.LL, MRI.ESM2.0, EC.Earth3 performed best in simulating both rainfall and temperature. The relative comparison of the ensemble means of the top five models revealed that the accuracy of BMA surpasses AM in capturing rainfall and temperature spatial patterns. This study offers invaluable insights into the selection of GCMs and ensemble methodologies for climate simulations in Bangladesh. Improving the accuracy of climate projections in this region can contribute significantly to climate science.