Predicted changes in future precipitation and air temperature across Bangladesh using CMIP6 GCMs

Mohammad Kamruzzaman; Shahriar Wahid; Shamsuddin Shahid; Edris Alam; Mohammed Mainuddin; H M Touhidul Islam; Jeapil Cho; Md Mizanur Rahman; Jatish Chandra Biswas; Kelly R Thorp

doi:10.1016/j.heliyon.2023.e16274

Predicted changes in future precipitation and air temperature across Bangladesh using CMIP6 GCMs

Heliyon. 2023 May 13;9(5):e16274. doi: 10.1016/j.heliyon.2023.e16274. eCollection 2023 May.

Authors

Affiliations

¹ Farm Machinery and Postharvest Technology Division, Bangladesh Rice Research Institute, Gazipur, 1701, Bangladesh.
² CSIRO Environment, Black Mountain Laboratories, Canberra, ACT, Australia.
³ Universiti Teknologi Malaysia (UTM), 81310, Johor, Malaysia.
⁴ Rabdan Academy, Abu Dhabi, United Arab Emirates.
⁵ Department of Geography and Environmental Studies, University of Chittagong, Chittagong, Bangladesh.
⁶ CSIRO Environment, Canberra, ACT, Australia.
⁷ Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh.
⁸ Convergence Center for Watershed Management, Integrated Watershed Management Institute (IWMI), Republic of Korea.
⁹ Krishi Gobeshona Foundation (KGF), BARC, Dhaka, Bangladesh.
¹⁰ USDA-ARS, Arid Land Agricultural Research Center, Maricopa, AZ, 85138, United States.

Abstract

Understanding spatiotemporal variability in precipitation and temperature and their future projections is critical for assessing environmental hazards and planning long-term mitigation and adaptation. In this study, 18 Global Climate Models (GCMs) from the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) were employed to project the mean annual, seasonal, and monthly precipitation, maximum air temperature (Tmax), and minimum air temperature (Tmin) in Bangladesh. The GCM projections were bias-corrected using the Simple Quantile Mapping (SQM) technique. Using the Multi-Model Ensemble (MME) mean of the bias-corrected dataset, the expected changes for the four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were evaluated for the near (2015-2044), mid (2045-2074), and far (2075-2100) futures in comparison to the historical period (1985-2014). In the far future, the anticipated average annual precipitation increased by 9.48%, 13.63%, 21.07%, and 30.90%, while the average Tmax (Tmin) rose by 1.09 (1.17), 1.60 (1.91), 2.12 (2.80), and 2.99 (3.69) °C for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. According to predictions for the SSP5-8.5 scenario in the distant future, there is expected to be a substantial rise in precipitation (41.98%) during the post-monsoon season. In contrast, winter precipitation was predicted to decrease most (11.12%) in the mid-future for SSP3-7.0, while to increase most (15.62%) in the far-future for SSP1-2.6. Tmax (Tmin) was predicted to rise most in the winter and least in the monsoon for all periods and scenarios. Tmin increased more rapidly than Tmax in all seasons for all SSPs. The projected changes could lead to more frequent and severe flooding, landslides, and negative impacts on human health, agriculture, and ecosystems. The study highlights the need for localized and context-specific adaptation strategies as different regions of Bangladesh will be affected differently by these changes.

Keywords: Downscaling; Global climate models; Multi-model ensemble; Projections; Shared socioeconomic pathways.