Future precipitation projection in Bangladesh using <scp>SimCLIM</scp> climate model: A multi‐model ensemble approach

Islam, H. M. Touhidul; Islam, Abu Reza Md. Towfiqul; Shahid, Shamsuddin; Alam, G. M. Monirul; Biswas, JC; Rahman, Md. Mizanur; Roy, Dilip Kumar; Kamruzzaman, Mohammad

doi:10.1002/joc.7605

Cited by 19 publications

(15 citation statements)

References 93 publications

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“…SimCLIM 4.0 was employed to anticipate future Tmax and Tmin using the MME of 40 GCMs, presented in Table 1. The earth System Grid (ESG) was used to collect CMIP5 GCMs temperature projection data (Taylor et al, 2012;Islam et al, 2022). CMIP5 models included multiple emission scenarios, namely RCP2.6, 4.5, 6.0, and 8.5 (IPCC, 2014).…”

Section: Data Sourcesmentioning

confidence: 99%

“…SimCLIM 4.0 for Desktop can handle both spatial and site data. It includes several tools, such as spatial scenario generation and impact models, to provide meaningful and (Warrick et al, 2012;Islam et al, 2022). SimCLIM, first utilized in New Zealand, was primarily inspired by CLIMPACTS (Warrick, 2009).…”

Section: Simclim 40mentioning

confidence: 99%

“…SimCLIM, first utilized in New Zealand, was primarily inspired by CLIMPACTS (Warrick, 2009). It now covers many other countries and regions worldwide, including Bangladesh, to create climate datasets (Amin et al, 2018a;Rahman and Rob, 2019;Zheng et al, 2020;Islam et al, 2022). This study used SimCLIM 4.0 to generate climate projections for RCP4.5 and RCP8.5.…”

Section: Simclim 40mentioning

confidence: 99%

“…where m is the future sample periods number in any 5-year average between 2021 and 2100. Pattern-scaling downscaling enables SimCLIM 4.0 to anticipate multiple climatic parameters at fine accuracy and resolution for diverse spatiotemporal scales (Amin et al, 2018a;Amin et al, 2018b;Islam et al, 2022). It has easily configurable downscaling capabilities for downscaling GCMs to the geographic resolution necessary for the CC impact evaluation (Yin et al, 2013).…”

Section: Simclim 40mentioning

confidence: 99%

“…The RCPs have lately been developed in CMIP5 to provide projections for different radiative forcings (Rahman and Rob, 2019). CMIP5 models have thoroughly outlined the earth system with high geospatial resolution and integrated physics (Almazroui et al, 2020a) and, therefore, shown better skill in climate projections than CMIP3 models (Song et al, 2021;Islam et al, 2022). GCMs have been extensively used for climate modeling (IPCC, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data

Islam

Kamruzzaman

Shahid

et al. 2023

Front. Environ. Sci.

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Temperature rise is a concern for future agriculture in different regions of the globe. This study aimed to reveal the future changes and variabilities in minimum temperature (Tmin) and maximum temperature (Tmax) in the monthly, seasonal, and annual scale over Bangladesh using 40 General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 5 (CMIP5) for two radiative concentration pathways (RCPs, RCP4.5 and RCP8.5). The statistical downscaling climate model (SimCLIM) was used for downscaling and to ensemble temperature projections (Tmax and Tmin) for the near (2021–2060) and far (2071–2100) periods compared to the base period (1986–2005). Multi-model ensemble (MME) exhibited increasing Tmax and Tmin for all the timescales for all future periods and RCPs. Sen’s slope (SS) analysis showed the highest increase in Tmax and Tmin in February and relatively less increase in July and August. The mean annual Tmax over Bangladesh would increase by 0.61°C and 1.75°C in the near future and 0.91°C and 3.85°C in the far future, while the mean annual Tmin would rise by 0.65°C and 1.85°C in the near future and 0.96°C and 4.07°C in the far future, for RCP4.5 and RCP8.5, respectively. The northern and northwestern parts of the country would experience the highest rise in Tmax and Tmin, which have traditionally been exposed to temperature extremes. In contrast, the southeastern coastal region would experience the least rise in temperature. A higher increase in Tmin than Tmax was detected for all timescales, signifying a future decrease in the diurnal temperature range (DTR). The highest increase in Tmax and Tmin will be in winter compared to other seasons for both the periods and RCPs. The spatial variability of Tmax and Tmin changes can be useful for the long-term planning of the country.

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Section: Data Sourcesmentioning

confidence: 99%

Section: Simclim 40mentioning

confidence: 99%

Section: Simclim 40mentioning

confidence: 99%

Section: Simclim 40mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data

Islam

Kamruzzaman

Shahid

et al. 2023

Front. Environ. Sci.

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Performance evaluation of CMIP6 HighResMIP models in simulating precipitation over Madagascar

Randriatsara

et al. 2023

Intl Journal of Climatology

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The present study evaluates the performance of high‐resolution global climate models derived from Coupled Model Intercomparison Project Phase 6 (CMIP6 HighResMIP), in simulating rainfall characteristics over Madagascar on an annual and seasonal scales for the period 1981–2014. The models and their ensemble mean are assessed based on two observational datasets sourced from Climate Hazards Group Infrared Precipitation with Station data version 2 (CHIRPS v2.0) data and the European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis fifth generation‐Land dataset (ERA5) as the references throughout the diverse analyses. A Taylor diagram, accompanied by the Taylor skill score (TSS), is used for the annual and seasonal model‐rankings and the overall performance of the models. The best‐performing models are EC‐Earth3P‐HR, ECMWF‐IFS‐HR, ECMWF‐IFS‐LR and HadGEM3‐GC31‐MM. The least‐recommended models with remarkable biases are BCC‐CSM2‐HR, CAMS‐CSM1‐0, FGOALS‐f3‐H, MPI‐ESM1‐2‐HR and MPI‐ESM1‐2‐XR. It is worth mentioning that FGOALS‐f3‐H tends to overestimate rainfall in most analyses, while MPI‐ESM1‐2‐HR and MPI‐ESM1‐2‐XR underestimate it. The findings of this study are of great importance to climatologists and present an opportunity for further investigation of underlying processes responsible for the observed wet/dry biases in order to improve the forecast skills in the models over the study area.

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Interannual Climate Variability and Its Impacts on Major Crop Productivity

Kamruzzaman,

Biswas,

Islam

et al. 2024

Climate Change and Soil-Water-Plant Nexus

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Future precipitation projection in Bangladesh using SimCLIM climate model: A multi‐model ensemble approach

Cited by 19 publications

References 93 publications

Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data

Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data

Performance evaluation of CMIP6 HighResMIP models in simulating precipitation over Madagascar

Interannual Climate Variability and Its Impacts on Major Crop Productivity

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