Multiple GCM-Based Climate Change Projections Across Northwest Region of Bangladesh Using Statistical Downscaling Model
Abstract
:1. Introduction
2. Study Area and Data Description
2.1. Climatic Features of the Study Area
2.2. General Circulation Models
3. Methodology
3.1. Description of Statistical Downscaling Model (SDSM)
3.2. Screening of NCEP Predictor List
3.3. Analysis of Model Performance
3.4. Assessment of Future Scenarios
4. Results
4.1. Selecting Key Predictors
4.2. Model Calibration
4.3. Model Validation Before and After Bias Correction
4.4. Mean Annual Change and Magnitude of Time-Series Trend of Tmean and Precipitation for Future Climates
5. Discussion
6. Conclusions
- SDSM has proven to be a highly suitable tool for establishing strong correlations between predictors and local predictands over the northwest region of Bangladesh.
- Based on the calibration results, it can be concluded that CanESM5 gives the best performance among the three GCMs for temperature predictions, with R2 = 0.961, KGE = 0.978, and NSE = 0.921. This outperformed HadCM3 (R2 = 0.923; KGE = 0.931; NSE = 0.901) and CanESM2 (R2 = 0.939; KGE = 0.945; NSE = 0.919), all of which showed good agreement with observed data.
- For rainfall projections, CanESM5 again performed well, with R2 = 0.967, KGE = 0.977, and NSE = 0.976. However, CanESM2 (R2 = 0.958, KGE = 0.966, NSE = 0.936) showed slightly lower performance.
- After applying bias correction, the errors of the models can be substantially minimized, leading to more robust projection results. The bias correction significantly enhanced the reliability of the future predictions in the current study, ensuring more accurate and dependable projections for both temperature and rainfall.
- Temperature showed an upward trend, while rainfall showed a downward trend, indicating potential warming and decreasing rainfall in the future for this study.
- The climate change trends under different scenarios obtained in the current study revealed that the SSP scenarios offer more stable projections than the RCP scenarios, which show slightly lower but consistent trends. In contrast, the SRES scenarios exhibit unstable in both temperature and rainfall, indicating larger uncertainty in future projections.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Station | Location | Elev (m) | Tmean (°C) | Precipitation (mm/year) | |
---|---|---|---|---|---|
Lat (deg.) | Lon (deg.) | ||||
Bogura | 24.85 | 89.37 | 23 | 25.67 | 1820 |
Dinajpur | 25.65 | 88.68 | 34 | 25.10 | 2125 |
Ishwardi | 24.15 | 89.33 | 16 | 25.74 | 1627 |
Rajshahi | 24.37 | 88.7 | 19 | 25.83 | 1526 |
Rangpur | 25.73 | 89.27 | 29 | 24.87 | 2386 |
Saidpur | 25.75 | 88.92 | 25 | 24.98 | 2267 |
GCMs | Scenarios | Resolution | References | CMIP Phase | Observed Data | |
---|---|---|---|---|---|---|
Calibration | Validation | |||||
HadCM3 | A2, B2 | 2.5 × 3.75 | Hadley Centre for climate predcition and research, UK | CMIP3 | 1975–1995 | 1996–2001 |
CanESM2 | RCP2.6 RCP4.5 RCP8.5 | 2.8° × 2.8° | Canadian Centre for Climate Modelling and Analysis (CCCma) | CMIP5 | 1975–1995 | 1996–2005 |
CanESM5 | SSP1-2.6 SSP2-4.5 SSP5-8.5 | 2.8° × 2.8° | Canadian Centre for Climate Modelling and Analysis (CCCma) | CMIP6 | 1985–2005 | 2006–2014 |
Predictor | Code | R1 | p.r | p-Values | Pd% |
---|---|---|---|---|---|
Temperature at 2 m * | temp | 0.808 | |||
1000 hpa specific humidity | shum | 0.793 | 0.102 | 0.000 | 87.137 |
Mean sea level Pressure | mslp | 0.772 | 0.195 | 0.000 | 74.741 |
850 hPa Specific humidity | s850 | 0.769 | 0.023 | 0.000 | 97.009 |
500 hPa Zonal wind component | p5_u | 0.733 | 0.060 | 0.000 | 91.814 |
500 hPa Wind speed | p5_f | 0.717 | 0.103 | 0.000 | 85.635 |
850 hPa Meridional wind component | p8_v | 0.62 | 0.110 | 0.000 | 82.258 |
850 hPa Geopotential | p850 | 0.564 | 0.173 | 0.000 | 69.326 |
1000 hPa Meridional wind component | p1_v | 0.55 | 0.096 | 0.000 | 82.545 |
500 hPa Specific humidity | s500 | 0.548 | 0.034 | 0.000 | 93.796 |
Total precipitation | prcp | 0.442 | 0.013 | 0.218 | 97.059 |
Weather Stations | Tmean | Precipitation | ||||
---|---|---|---|---|---|---|
HadCM3 | CanESM2 | CanESM5 | HadCM3 | CanESM2 | CanESM5 | |
Bogura | 26, 25 | 26 | 26 | 26, 25, 22 | 26, 25 | 26 |
Dinajpur | 26, 25 | 26 | 26 | 26, 25, 22 | 26, 25 | 26 |
Ishwardi | 26, 25 | 26 | 26 | 26, 25, 22 | 26, 25 | 26 |
Rajshahi | 26, 25 | 26 | 26 | 26, 25, 22 | 26, 25 | 26 |
Rangpur | 26, 25 | 26 | 26 | 26, 25, 22 | 26, 25 | 26 |
Saidpur | 26, 25 | 26 | 26 | 26, 25, 22 | 26, 25 | 26 |
GCMs | Climatic Variables | R2 | KGE | NSE |
---|---|---|---|---|
HadCM3 | Tmean | 0.923 | 0.931 | 0.901 |
Precipitation | 0.917 | 0.935 | 0.893 | |
CanESM2 | Tmean | 0.939 | 0.945 | 0.919 |
Precipitation | 0.958 | 0.966 | 0.936 | |
CanESM5 | Tmean | 0.961 | 0.978 | 0.921 |
Precipitation | 0.967 | 0.977 | 0.976 |
GCMs | Before Bias Correction | After Bias Correction | ||||
---|---|---|---|---|---|---|
RMSE | MAE | MBE | RMSE | MAE | MBE | |
HadCM3 | 0.986 | 0.783 | 0.130 | 0.758 | 0.608 | −0.068 |
CanESM2 | 0.946 | 0.759 | 0.060 | 0.942 | 0.753 | −0.002 |
CanESM5 | 0.927 | 0.745 | 0.037 | 0.922 | 0.743 | 0.001 |
GCMs | Before Bias Correction | After Bias Correction | ||||
---|---|---|---|---|---|---|
RMSE | MAE | MBE | RMSE | MAE | MBE | |
HadCM3 | 87.804 | 58.319 | 30.653 | 87.735 | 58.153 | 0.014 |
CanESM2 | 87.274 | 56.056 | 23.735 | 80.812 | 50.852 | −0.099 |
CanESM5 | 86.708 | 54.382 | 51.305 | 85.976 | 52.870 | 0.003 |
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Rana, M.M.; Adhikary, S.K.; Suzuki, T.; Mäll, M. Multiple GCM-Based Climate Change Projections Across Northwest Region of Bangladesh Using Statistical Downscaling Model. Climate 2025, 13, 62. https://doi.org/10.3390/cli13030062
Rana MM, Adhikary SK, Suzuki T, Mäll M. Multiple GCM-Based Climate Change Projections Across Northwest Region of Bangladesh Using Statistical Downscaling Model. Climate. 2025; 13(3):62. https://doi.org/10.3390/cli13030062
Chicago/Turabian StyleRana, Md Masud, Sajal Kumar Adhikary, Takayuki Suzuki, and Martin Mäll. 2025. "Multiple GCM-Based Climate Change Projections Across Northwest Region of Bangladesh Using Statistical Downscaling Model" Climate 13, no. 3: 62. https://doi.org/10.3390/cli13030062
APA StyleRana, M. M., Adhikary, S. K., Suzuki, T., & Mäll, M. (2025). Multiple GCM-Based Climate Change Projections Across Northwest Region of Bangladesh Using Statistical Downscaling Model. Climate, 13(3), 62. https://doi.org/10.3390/cli13030062