Downscaling and Projection of Spatiotemporal Changes in Temperature of Bangladesh

Alamgir, Mahiuddin; Ahmed, Kamal; Homsi, Rajab; Dewan, Ashraf; Wang, Jiao-Jun; Shahid, Shamsuddin

doi:10.1007/s41748-019-00121-0

Cited by 35 publications

(40 citation statements)

References 40 publications

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“…4). This rise of temperature is consistent with CMIP5 data analysis over South Asia under RCP8.5, which is in the range 3.2-5.1 °C for the period 2070-2099 (Alamgir et al 2019). Overall, the change in the future temperature over the northern Peninsula is bigger (exceeding 6.0 °C) than over the southern Peninsula, which is in line with the CMIP3 and CMIP5 projections, as shown in Almazroui et al (2016Almazroui et al ( , 2017b.…”

Section: Changes In Temperature For the Near And Far Futuressupporting

confidence: 87%

Future Changes in Climate over the Arabian Peninsula based on CMIP6 Multimodel Simulations

et al. 2020

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This paper presents the changes in projected temperature and precipitation over the Arabian Peninsula for the twenty-first century using the Coupled Model Intercomparison Project phase 6 (CMIP6) dataset. The changes are obtained by analyzing the multimodel ensemble from 31 CMIP6 models for the near (2030–2059) and far (2070–2099) future periods, with reference to the base period 1981–2010, under three future Shared Socioeconomic Pathways (SSPs). Observations show that the annual temperature is rising at the rate of 0.63 ˚C decade–1 (significant at the 99% confidence level), while annual precipitation is decreasing at the rate of 6.3 mm decade–1 (significant at the 90% confidence level), averaged over Saudi Arabia. For the near (far) future period, the 66% likely ranges of annual-averaged temperature is projected to increase by 1.2–1.9 (1.2–2.1) ˚C, 1.4–2.1 (2.3–3.4) ˚C, and 1.8–2.7 (4.1–5.8) ˚C under SSP1–2.6, SSP2–4.5, and SSP5–8.5, respectively. Higher warming is projected in the summer than in the winter, while the Northern Arabian Peninsula (NAP) is projected to warm more than Southern Arabian Peninsula (SAP), by the end of the twenty-first century. For precipitation, a dipole-like pattern is found, with a robust increase in annual mean precipitation over the SAP, and a decrease over the NAP. The 66% likely ranges of annual-averaged precipitation over the whole Arabian Peninsula is projected to change by 5 to 28 (–3 to 29) %, 5 to 31 (4 to 49) %, and 1 to 38 (12 to 107) % under SSP1–2.6, SSP2–4.5, and SSP5–8.5, respectively, in the near (far) future. Overall, the full ranges in CMIP6 remain higher than the CMIP5 models, which points towards a higher climate sensitivity of some of the CMIP6 climate models to greenhouse gas (GHG) emissions as compared to the CMIP5. The CMIP6 dataset confirmed previous findings of changes in future climate over the Arabian Peninsula based on CMIP3 and CMIP5 datasets. The results presented in this study will be useful for impact studies, and ultimately in devising future policies for adaptation in the region.

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Section: Changes In Temperature For the Near And Far Futuressupporting

confidence: 87%

Future Changes in Climate over the Arabian Peninsula based on CMIP6 Multimodel Simulations

et al. 2020

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“…This study found an increase in annual mean temperature from 2.6 to 4.8 °C by 2100, relative to the reference period. Alamgir et al (2019) used eight CMIP5 GCMs to statistically downscale over Bangladesh and reported an increase in temperature by 2.7 to 4.7 °C under RCP 8.5 at the end of this century.…”

Section: Discussionmentioning

confidence: 99%

Climate Change Analysis for Bangladesh Using CMIP5 Models

Bosu¹,

Rashid²,

Mannan³

et al. 2021

Dhaka Univ J Earth Env Sci

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Using the Coupled Model Inter-comparison Project phase 5 (CMIP5) global climate modeling predictions, the study analyzes the distribution of rainfall and temperature in Bangladesh in recent years (1981–2005) and in three future periods (2025–2049, 2050–2074 and 2075–2099) considering RCP 4.5 and RCP 8.5 scenarios. In the historical period, all three CMIP5 models (MPI-ESM-LR, MPI-ESM-MR and NorESM1-M) mostly underestimated the observed mean rainfall data collected from Bangladesh Meteorological Department (BMD), but for temperature the result is found almost similar between models and observation. The CMIP5 models simulation reveal biases in monthly mean rainfall and temperature over Bangladesh in the past, so bias-correction is performed for future data. Quantile mapping bias-correction reduces significant amount of biases from the projection data of rainfall and temperature. By the end of the twenty-first century, the multi-model ensemble annual mean rainfall averaged over Bangladesh is projected to change between -2% to 9% and -3% to 15% under RCP 4.5 and RCP 8.5, respectively. The changes in spatial patterns of annual rainfall indicate a decrease in rainfall over a major portion in the west and the northwestern and an increase in the southeast, east, and the northeastern part of the country. The multi-model ensemble projection reveals a continuous increase in the annual mean temperature and shows a larger increase over the northwestern part and west-central part of Bangladesh. By the end of the twenty-first century, the annual mean temperature over Bangladesh is projected to increase by 1.9 (0.9-2.8) °C and 4 (2.8-4.6) °C under the RCP 4.5 and RCP 8.5 scenarios respectively, relative to the reference climate (1981-2005). The Dhaka University Journal of Earth and Environmental Sciences, Vol. 9(1), 2020, P 1-12

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“…Large-scale features can be represented in different ways in ESD, either based on the nearest grid-box values from reanalyses and GCMs (e.g., Liu et al, 2016), or by an index that represents some spatially aggregated quantity or a statistic describing the spatio-temporal patterns in a larger region (e.g., Benestad, 2011). Common Empirical Orthogonal Function (common EOF) analysis applied to GCM and reanalysis data can be used to represent the large-scale predictors (Flury, 1988;Barnett, 1999;Benestad, 2001Benestad, , 2011. This means that EOF analysis is applied to an object combining the GCM and reanalysis data, which decomposes the combined data into a common set of spatial patterns, eigenvalues representing the relative importance of each pattern, and principal components (PCs) describing the temporal variations associated with the spatial patterns for the GCM and reanalysis data separately (see schematic in Fig.…”

Section: Introductionmentioning

confidence: 99%

Climate change projections of maximum temperature in the pre-monsoon season in Bangladesh using statistical downscaling of global climate models

Rashid¹,

Hossain

Mannan³

et al. 2021

Adv. Sci. Res.

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Abstract. The climate of Bangladesh is very likely to be influenced by global climate change. To quantify the influence on the climate of Bangladesh, Global Climate Models were downscaled statistically to produce future climate projections of maximum temperature during the pre-monsoon season (March–May) for the 21st century for Bangladesh. The future climate projections are generated based on three emission scenarios (RCP2.6, RCP4.5 and RCP8.5) provided by the fifth Coupled Model Intercomparison Project. The downscaling process is undertaken by relating the large-scale seasonal mean temperature, taken from the ERA5 reanalysis data set, to the leading principal components of the observed maximum temperature at stations under Bangladesh Meteorological Department in Bangladesh, and applying the relationship to the GCM ensemble. The in-situ temperature data has only recently been digitised, and this is the first time they have been used in statistical downscaling of local climate projections for Bangladesh. This analysis also provides an evaluation of the local data, and the local temperatures in Bangladesh show a close match with the ERA5 reanalysis. Compared to the reference period of 1981–2010, the projected maximum pre-monsoon temperature in Bangladesh indicate an increase by 0.7/0.7/0.7 ∘C in the near future (2021–2050) and 2.2/1.2/0.8 ∘C in the far future (2071–2100) assuming the RCP8.5/RCP4.5/RCP2.6 scenario, respectively.

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Downscaling and Projection of Spatiotemporal Changes in Temperature of Bangladesh

Cited by 35 publications

References 40 publications

Future Changes in Climate over the Arabian Peninsula based on CMIP6 Multimodel Simulations

Future Changes in Climate over the Arabian Peninsula based on CMIP6 Multimodel Simulations

Climate Change Analysis for Bangladesh Using CMIP5 Models

Climate change projections of maximum temperature in the pre-monsoon season in Bangladesh using statistical downscaling of global climate models

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