Optimizing the multi-model ensemble of CMIP6 GCMs for climate simulation over Bangladesh
Afifa Talukder1, Shamsuddin Shaid2, Syewoon Hwang3
1Department of Environmental Sciences, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh.
Scientific Reports
|April 2, 2025
Summary
This study optimizes climate models for Bangladesh, finding ACCESS.CM2 best for precipitation and INM.CM4.8/UKESM1.0.LL for temperature. Bayesian Model Averaging (BMA) improved climate projection accuracy over Arithmetic Mean (AM).
Area of Science:
- Climate Science
- Atmospheric Science
- Environmental Science
Background:
- Bangladesh faces significant climate change impacts, including extreme weather events and flooding.
- Accurate climate simulations are crucial for regional adaptation and mitigation strategies.
- General Circulation Models (GCMs) are vital tools for climate projection but require careful evaluation and optimization.
Purpose of the Study:
- To enhance the precision of climate simulations in Bangladesh by optimizing a multi-model ensemble of GCMs.
- To evaluate and rank 19 CMIP6 GCMs for their ability to simulate precipitation, maximum temperature (Tmax), and minimum temperature (Tmin).
- To compare ensemble techniques, Bayesian Model Averaging (BMA) and Arithmetic Mean (AM), for improved climate projection accuracy.
Main Methods:
- Historical GCM simulations (1986-2014) were compared against ERA5 reanalysis data.
- Bilinear interpolation harmonized GCM and ERA5 data resolutions.
- Seven error metrics and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were used for GCM ranking.
- Bayesian Model Averaging (BMA) and Arithmetic Mean (AM) were applied to ensemble means of top-performing models.
Main Results:
- ACCESS.CM2 demonstrated superior performance for precipitation simulation (rating 0.99).
- INM.CM4.8 and UKESM1.0.LL excelled in simulating Tmax (1.0) and Tmin (0.88), respectively.
- Five models (ACCESS.ESM1.5, ACCESS.CM2, UKESM1.0.LL, MRI.ESM2.0, EC.Earth3) showed strong overall performance for both precipitation and temperature.
- Bayesian Model Averaging (BMA) significantly outperformed Arithmetic Mean (AM) in capturing spatial patterns, achieving higher Kling-Gupta Efficiency (KGE) values.
Conclusions:
- GCM performance varies significantly across seasons, highlighting the need for robust ranking systems.
- The study provides a framework for selecting optimal GCMs and ensemble methods for climate studies in Bangladesh.
- Enhanced climate projections through optimized GCM ensembles can significantly aid regional climate adaptation planning and contribute to climate science advancements.
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