Thermal Bioclimatic Transformations in the Coastal Regions of Ganges Delta: Insights from CMIP6 Multi-Model Ensembles

Thermal Bioclimatic Transformations in the Coastal Regions of Ganges Delta: Insights from CMIP6 Multi-Model Ensembles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Thermal Bioclimatic Transformations in the Coastal Regions of Ganges Delta: Insights from CMIP6 Multi-Model Ensembles Mohammad Kamruzzaman, H. M. Touhidul Islam, Mohammad Mainuddin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4101730/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The effects of climatic alteration caused by global warming on people, the environment, and ecosystems can be better understood by examining thermal bioclimatic indicators (TBIs) changes. Evaluating such alterations is of utmost significance for the Ganges Delta (GD) coastal region, which offers the world's most extensive biological variety. This study utilizes a multi-model ensemble (MME) of 16 CMIP6 Global Climate Models (GCMs) to assess prospective alterations in thermal bioclimatic indicators (TBIs) across the coastal region of the Ganges Delta (GD) for two Shared Socioeconomic Pathways (SSPs): SSP245 (moderate) and SSP585 (severe). We employ ensemble median, 5th, and 95th percentiles to analyze temporal shifts and associated uncertainty in TBIs during the near (2020–2059) and far (2060–2100) futures. Our projections reveal a significant escalation in annual temperatures throughout the GD, with MME median average in-crease anticipated to range from 0.77–2.80°C (SSP2-4.5) to 1.03–4.65°C (SSP5-8.5) by 2059. Moreover, notable transformations in thermal patterns are expected, with a projected decrease in both diurnal temperature range (DTR) by 0.02–0.87°C and isothermality by 3.30-12.09%. Additionally, the average temperature during the driest months is anticipated to rise higher than in the wettest months. These findings underscore climate change's existential threat to the GD and its rich biodiversity. They provide vital information for formulating crucial mitigation strategies to curb greenhouse gas emissions and robust adaptation measures to bolster the resilience of communities and eco-systems. Urgent action is paramount to safeguard the future of this invaluable ecological treasure. Earth and environmental sciences/Climate sciences/Climate change Earth and environmental sciences/Climate sciences Bioclimatic indicators Climate projections temperature and precipitation Scenario-based analysis Climate change impacts Ganges Delta Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Climate change (CC) is a pressing global challenge that profoundly threatens the environment, ecosystem, and public health 1,2 . Thermal Bioclimatic indicators (TBIs) are commonly utilized to assess the effects of CC on biodiversity, pollution, agricultural production, and human thermal comfort 3–5 . The balance of energy and comfort levels of the human body are influenced by several climatic parameters, such as humidity, temperature, and wind, along with their fluctuations 6 . Thus, bioclimatic indicators are extensively employed for the areas where human comfort is optimal 7 . Additionally, it has been employed to determine the impact of CC on crop production and the suitability of a region for cultivating a specific type of crop like rice 8 , banana 9 , sweet potato 10 , coffee 11 and maize 12 . The majority of species are capable of surviving within a certain bioclimatic niche. Consequently, even a slight alteration in the bioclimate may profoundly impact the geographical distribution of species and the overall ecology of a particular area 13 . Thus, its broad application and high efficacy make it an appealing method for assessing the impacts of CC. Global climate models (GCMs) are frequently utilized to explore different scenarios of CC. Scholars have reported notable improvements in GCMs in the most recent edition of the Coupled Model Intercomparison Project (CMIP), known as CMIP6 14,15 . The improvements include refining the model's architecture, increasing the geographic resolution, reducing uncertainty, enhancing the simulation of clouds, and improving the ability to replicate synoptic progressions 16 . Hence, employing data obtained from CMIP6 GCMs to examine forthcoming alterations in TBIs is crucial. The inhabitants of the Ganges delta's coastal areas (Bangladesh and West Bengal) depend significantly on fishing and agriculture to nourish and sustain their livelihoods 17,18 . Nevertheless, this area is exceedingly susceptible to climate change and encounters a diverse array of severe climatic fluctuations, which can substantially influence agricultural output 19–21 . CC may worsen public health conditions in Bangladesh, particularly by increasing the prevalence of waterborne (diarrhoea and cholera) as well as vector-borne (dengue fever and malaria) diseases 22 . As climatic extremes in this region are now experiencing an increased frequency 2,21 this trend is projected to persist [ 23 ], increasing the region's vulnerability to CC risks and consequences. Evaluating TBIs can assist policymakers and stakeholders in Bangladesh in comprehending and proactively addressing these consequences. This can be achieved by pinpointing the most vulnerable regions and formulating suitable adaptation plans. Many studies have employed raw and downscaled GCMs to assess the effects of CC on bioclimatic indicators and biodiversity worldwide 23–29 . Zhang et al. 25 employed GCMs to examine the geographical distribution of a medicinal plant in China. Wang et al. 30 investigated the anticipated future range of six species of flowering plants, both presently and in the future, utilizing CMIP5. Zahoor et al. 26 performed a study to analyze alterations in the distribution of bears by utilizing GCMs for two Representative Concentration Pathways (RCPs). Therefore, it is essential to evaluate bioclimatic indicators in historical and future scenarios to promote sustainable development in any region. Despite this, there is a notable gap in academic research specifically addressing this topic in the context of the Ganges delta. A recent study by Kamruzzaman et al. 4 investigated the TBIs across Bangladesh using a multi-model ensemble (MME) projection of 18 CMIP6 GCMs. It is noteworthy that they relied on observed daily climate data from 28 meteorological stations for TBIs projections. Significantly, there is currently no research specifically focusing on the coastal region of the Ganges delta, encompassing Bangladesh and West Bengal (India). This research aims to fill the current gaps in previous studies. The coastal area of the Ganges delta holds unique ecological significance, and a comprehensive examination of bioclimatic indicators in this coastal region is essential for understanding and addressing the potential impacts of CC. Hence, this work aims to investigate the bioclimatic indicators in both historical and future scenarios, specifically within the coastal region of the Ganges delta, incorporating Bangladesh and India (West Bengal) for SSP245 and SSP585 CC scenarios. A bias correction technique based on quantile mapping was applied to enhance the precision of the GCM projections 31 . Furthermore, to address uncertainties in the projections, a model ensemble approach was utilized 32,33 . Such research is imperative for providing information to guide in decision-making and develop viable climate change adaptation approaches in this ecologically sensitive and vulnerable region. 2. Data and Methods 2.2 Study area This work encompasses the southwestern coastal area of the Ganges Delta (GD), which includes Bangladesh, as well as the southeastern coastal region of West Bengal in India. The region is situated within the latitudes of around 21°30'N to 23°30'N and longitudes of 88°00'E to 90°30'E (Fig. 1 ). This area included 70 Upazilas in nine coastal districts of Bangladesh and 20 blocks in the South 24 Parganas district of West Bengal. It is located in the GD and is known for being close to the Bay of Bengal and having a complex system of water bodies. The region experiences varying average annual rainfall levels, roughly 2000 mm in the eastern and 1800 mm in the western region 34 . The Sundarbans, the world's biggest mangrove forest, is located in this area, where the local population depends mostly on agriculture, fishing, and the exploitation of resources from the mangrove forest 35 . Moreover, regional problems like salinity tend to deteriorate in the absence of precipitation and the reduced influx of freshwater from rivers. The area is exceptionally vulnerable to natural calamities, specifically cyclones, flooding, and storm surges, all presenting substantial threats to habitation and facilities. 2.2 Data Sources As the distribution of meteorological stations in the study area is not uniform and limited, this study utilized ERA5 data ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ) as observed data with a spatial resolution of 0.25°. It spanned the period from 1995 to 2014. To validate the data, we examined the relationship between precipitation, Tmax, and Tmin in the In Situ and ERA5 observation datasets (Figure S1 ). The In Situ observation data consisted of the average measurements from nine stations, specifically Sathkhira, Khulna, Barishal, Patuakhali, Khepurpara, Hatia, Sandwip, and Canning (West Bengal). ERA5 precipitation, Tmax, and Tmin strongly correlate with In Situ observations, indicating they can be reliable substitutes for In Situ data. This study examined the spatial and temporal variations in thermal bioclimatic indicators (TBIs) throughout two future periods: the near (2021–2059) and the far (2060–2100) periods. The analysis utilized 16 CMIP6 Global Climate Models (GCMs) can be found in Table S1 and focused on two SSPs: SSP245 and SSP585. The CMIP6 GCMs downscaled data files have been downloaded from the Earth System Grid Federation (ESGF) portal ( https://esgf-node.llnl.gov/search/cmip6/ ). 2.4 Methodologies The GCM simulations display a notable disparity in geographical resolution and frequently have systematic biases that limit their direct usefulness in assessing the effects of CC. This work utilized a statistical technique to downscale GCMs at each grid point. The raw GCM downscaled data were initially re-gridded to match the observed points. Subsequently, a quantile mapping (QM) technique was utilized to rectify the discrepancy in the GCM projections. Consequently, it exhibits a stronger resemblance to the distribution that is seen, reduces biases, and enhances the dependability of climate forecasts 36,37 . Further details concerning the bias-correction methodology employed in the research are given in Kamruzzaman et al. 38 . In this study we used three types of bioclimatic indicators like annual, seasonal and limiting environment indicators. An elaborate explanation of the thermal bioclimatic indicators (TBIs) that were examined in this work are presented in Table 1 . The TBIs were calculated for each model's data during the historical timeframe and both projected scenarios. The months with the highest and lowest temperatures and rainfall were not predetermined for both the historical and future timeframes. They were selected again for future times 29,39 . For example, a dynamic approach was used to determine the month with the highest or lowest temperature for Bio-8 to Bio-11. By combining the results of 23 GCMs, we were able to construct a historical median multi-model ensemble (MME) that displayed the 5th and 95th percentiles, or confidence interval (CI), for the alteration of each index and reduced uncertainty. For both the SSP245 and SSP585, the MME median and percentiles were also calculated for the near (2021–2059) and far (2060–2100) periods. Table 1 Definition of the bioclimatic indicators where T avg is the mean temperature ((T max + T min )/2), and i is the month of the year Indicator Equation Unit Annual indicators Bio-1: Annual average temperature \(\text{Bio-1 }=\frac{\sum _{i=1}^{i=12} {\text{T}\text{a}\text{v}\text{g}}_{i}}{12}\) °C Bio-2: Diurnal temperature range \(\text{Bio-2 }=\frac{\sum _{i=1}^{i=12} \left({\text{T}\text{m}\text{a}\text{x}}_{i}-T\underset{i}{min} \right)}{12}\) °C Bio-3: Isothermality \(\text{Bio3 }=\frac{\text{ Bio }2}{\text{ Bio }7}\times 100\) % Bio-4: Temperature variation in a year \(\text{Bio-4 }=Standard deviation\left\{{\text{T}\text{a}\text{v}\text{g}}_{1},\dots ,{\text{ Tavg }}_{12}\right\}\times 100\) % Bio-7: Annual temperature range \(\text{Bio7 }=\text{ Bio5 }-\text{ Bio }6\) °C Seasonal temperature indicators Bio-5: Maximum monthly temperature \(\text{Bio-5 }=max\left(\left\{{\text{T}\text{m}\text{a}\text{x}}_{1},\dots ,{\text{T}\text{m}\text{a}\text{x}}_{12}\right\}\right)\) °C Bio-6: Minimum monthly temperature \(\text{Bio-6 }=min\left(\left\{{\text{T}\text{m}\text{i}\text{n}}_{1},\dots ,{\text{T}\text{m}\text{i}\text{n}}_{12}\right\}\right)\) °C Bio-10: Average temperature of the warmest quarter \({Q}_{max}=max\left(\begin{array}{c}\sum _{i=1}^{i=3} {\text{T}\text{a}\text{v}\text{g}}_{i}\\ \sum _{i=2}^{i=4} {\text{T}\text{a}\text{v}\text{g}}_{i}\\ \dots ,\\ \sum _{i=11}^{i=1} {\text{T}\text{a}\text{v}\text{g}}_{i}\\ \sum _{i=12}^{i=2} {\text{T}\text{a}\text{v}\text{g}}_{i}\end{array}\right)\) Bio- \(10=\frac{{\sum }_{i=1}^{i=3} {\text{T}\text{a}\text{v}\text{g}}_{i}}{3}\) , based on the three selected months of \({Q}_{\text{m}\text{a}\text{x}}\) °C Bio-11: Average temperature of the coldest quarter \({Q}_{\text{m}\text{i}\text{n}}=min\left(\begin{array}{c}\sum _{i=1}^{i=3} {\text{T}\text{a}\text{v}\text{g}}_{i}\\ \sum _{i=2}^{i=4} {\text{T}\text{a}\text{v}\text{g}}_{i}\\ \dots ,\\ \sum _{i=11}^{i=1} {\text{T}\text{a}\text{v}\text{g}}_{i}\\ \sum _{i=12}^{i=2} {\text{T}\text{a}\text{v}\text{g}}_{i}\end{array}\right)\) Bio-11 \(=\frac{{\sum }_{i=1}^{i=3} {\text{T}\text{a}\text{v}\text{g}}_{i}}{3}\) , based on the three selected months of \({Q}_{\text{m}\text{i}\text{n}}\) °C Limiting environment indicators Bio-8: Average temperature of the wettest quarter \({Q}_{\text{m}\text{a}\text{x}}=max\left(\begin{array}{c}\sum _{i=1}^{i=3} {\text{ Rainfall }}_{i}\\ \sum _{i=2}^{i=4} {\text{ Rainfall }}_{i}\\ \dots ,\\ \sum _{i=11}^{i=1} {\text{ Rainfall }}_{i}\\ \sum _{i=12}^{i=2} {\text{ Rainfall }}_{i}\end{array}\right)\) Bio-8 \(=\frac{{\sum }_{i=1}^{i=3} {\text{T}\text{a}\text{v}\text{g}}_{i}}{3}\) , based on the three selected months of \({Q}_{\text{m}\text{a}\text{x}}\) °C Bio-9: Average temperature of the driest quarter \({Q}_{\text{m}\text{i}\text{n}}=min\left(\begin{array}{c}\sum _{i=1}^{i=3} {\text{ Rainfall }}_{i}\\ \sum _{i=2}^{i=4} {\text{ Rainfall }}_{i}\\ \dots ,\\ \sum _{i=11}^{i=1} {\text{ Rainfall }}_{i}\\ \sum _{i=12}^{i=2} {\text{ Rainfall }}_{i}\end{array}\right)\) Bio-9 \(=\frac{{\sum }_{i=1}^{i=3} {\text{T}\text{a}\text{v}\text{g}}_{i}}{3}\) , based on the three selected months of \({Q}_{\text{m}\text{i}\text{n}}\) °C In Bio-8 and Bio-9 (Bio-10 and Bio-11) equations, the rainfall (temperature) is evaluated for consecutive 3 months, which may span over two consecutive years 3 Results 3.1 Validation of Climate Models Figure 2 illustrates the simulated precipitation, Tmax, and Tmin of the MME median, consisting of 16 CMIP6 GCMs. The comparison is made between the bias-corrected data before and after being adjusted to match the observed (ERA5) values. The findings indicated that the MME median of CMIP6 GCMs had a notable bias, as seen in Figs. 2 a, c, and e. Specifically, the raw mean annual precipitation in the southeastern region overestimated the ERA5 value. It exhibited a wet bias of up to 25%. In comparison, the rest of the study area noticed a dry bias of up to 30%, especially in the northwestern region (Fig. 2 a). The raw downscaled MME median Tmax overestimated the ERA5 Tmax up to 3°C in the northern region while underestimating it up to 1°C in the southcentral and southeastern regions. Nevertheless, the raw downscaled MME median Tmin overestimated the ERA5 Tmax by about 1.42–5°C. Therefore, the bias-correction approach is employed to reduce these errors. Because of the implementation of the quantile mapping (QM) technique, a significant bias reduction was seen for all three climatic variables (Fig. 2 b, d, and f). This work utilized a simple quantile mapping (SQM) technique for bias correction. The biases in precipitation in the bias-corrected MME data have been decreased to less than 3 mm. However, the biases in Tmax and Tmin are nearly identical to the ERA5 data. 3.2 Annual indicators 3.2.1 Annual average temperature and diurnal temperature range The spatial distribution of the annual average temperature (Bio-1) over the coastal region of the GD is depicted in Fig. 3 . In contrast, Fig. 4 illustrates the diurnal temperature range (DTR) or Bio-2. The average historical Bio-1 and Bio-2 is 25.41°C (Fig. 3 a) and 9.16°C (Fig. 4 a), respectively, from 1985 to 2014. The MME median average (including the 5th and 95th percentiles) projected a Bio-1 rise of 0.93°C (0.57–1.60°C) and 2.02°C (1.47–3.08°C) for the near and far periods, respectively, in the coastal region of the GD under the SSP245 scenario. The anticipated MME median average and confidence interval (CI) in Bio-1 are nearly the same for both SSP585 and SSP245 in the near period. Nevertheless, the projected temperature rise in the far period is estimated to be 3.13°C (with a range of 1.17–5.50°C) (Fig. 3 b). This suggests greater uncertainty in the far period and SSP585 compared to the near future and SSP245 scenario. A higher rise in Bio-1 is expected in the southeastern coastal area (Fig. 3 b). In contrast, the MME median predicted that the average temperature in Bio-2 would vary by -0.29°C (with a CI of -0.81 to 0.08°C) and − 0.39°C (with a CI of -0.94 to 0.23°C) for SSP245, in the near and far future, respectively. Similarly, the MME expected a Bio-2 alteration of -0.30°C (with a CI of -0.78 to 0.26°C) and − 0.64°C (with a CI of -1.24 to 0.30°C) for SSP585, in the near and far period, correspondingly (Fig. 4 b). 3.3.2 Isothermality, seasonality, and range Isothermality, also known as Bio-3, is the percentage ratio between the DTR (Bio-2) and the annual temperature range (Bio-7). The average historical Bio-3 was 69.08% (Fig. 5 a). Figure 5 shows how the isothermality (Bio-3) is expected to change in the coastal area of the GD for two future scenarios and periods, relative to the historical phase spanning from 1985–2014. The projected MME median mean (CI) changes for SSP245 are estimated to be -3.93% (-9.25 to -0.94%) and − 6.92% (-13.80 to -1.69%) for the near and far periods, correspondingly. For SSP585, the mean (CI) alternations are projected to be -4.88% (-9.53 to -0.93%) and − 9.38% (-17.50 to -0.28%) for the near and far periods, correspondingly (Fig. 5 b). Seasonality, often known as Bio-4, refers to the average temperature variation seen across the years. It is obtained by computing the standard deviation (SD) as a percentage. The mean historical Bio-4 was about 4.57% (Fig. 6 a). The MME projected very small alternations (between − 0.65–1.2%) in Bio-4 for all future scenarios, indicating that Bio-4 is expected to be minimally affected by CC. The comparatively greater increase in Bio-4 was anticipated in the southeastern region of the coastal area of the GD for SSP585 (Fig. 6 b). Figure 7 displays the annual temperature range (Bio-7) over the historical timeframe and potential future variations within the study area. Bio-7 refers to the fluctuation in temperature over a specific period or the deviation between Bio-5 and Bio-6. The average historical Bio-7 was about 13.61°C (Fig. 7 a). For SSP245, the expected change for the Bio-7 is 0.47°C (with a CI of − 0.19 to 1.12°C) and 1.11°C (with a CI of -0.18 to 2.42°C) in the near and far periods, respectively, while 0.55°C (with a CI of − 0.27 to 1.77°C) and 1.12°C (with a CI of -0.59 to 3.06°C) in the near and far periods, respectively for SSP585. Figure 7 b shows that the southern region of the coastal area of the GD is expected to see a substantially greater rise in Bio-7 under the SSP585 scenario. 3.4 Seasonal indicators 3.4.1 Average maximum and minimum monthly temperature The historical and predicted alterations in maximum monthly (Bio-5) and minimum monthly (Bio-6) temperature are shown in Figs. 7 and 8 . The average historical Bio-5 and Bio-6 is 30.32°C (Fig. 8 a) and 17.18°C (Fig. 9 a), respectively. The predicted alteration in Bio-5 by about 1.32°C (with a CI of 0.58–2.45°C) and 2.76°C (with a CI 1.79–4.88°C) for the near and far futures for SSP245, while 1.69°C (with a CI of 0.79–2.91°C) and 4.80°C (3.15–6.98°C) for the near and far futures for SSP585. Nevertheless, the expected changes in Bio-6 are estimated to be 0.80°C (0.51–1.57°C) and 2.04°C (1.24–2.95°C) for SSP245 and 1.09°C (with a CI of 0.40–2°C) and 3.92°C (with a CI of 2.40–5.27°C) for SSP585, for the near and far periods, respectively. For both indicators, the southernmost region of the study area might experience a more significant temperature rise (Figs. 8 b, 9 b). The data suggests a more incredible rise in Bio-6 compared to Bio-5 in the coastal region of the GD. The most significant future shift for both indicators is projected to happen in the far period for the SSP585 scenario. 3.4.2 Average temperature of the warmest quarters and coldest quarters The warmest (Bio-10) and coldest (Bio-11) quarters were calculated based on the mean temperature over three months at each grid. For this purpose, we utilized a dynamic methodology to identify the quarters with the highest and lowest temperatures. The mean historical Bio-10 and Bio-11 are 19.70°C (Fig. 10 a) and 18.85°C (Fig. 11 a), respectively. For SSP245, the CI for the expected shifts in Bio-10 was 0.57 to 2.04°C for the near period and 1.52 to 4.21°C for the far period. Moreover, the CI for the temperature changes in Bio-11 are estimated to be within 0.48 and 1.71°C for the near future and within 1.18 and 3.10°C for the far period. The confidence interval (CI) for the expected changes in Bio-10 under SSP585 was 0.68 to 2.37°C for the near period and 2.86 to 6.36°C for the far period. Furthermore, the CI for the temperature fluctuations in Bio-11 is calculated to range from 0.37 to 2.11°C for the near period and from 2.34 to 5.28°C for the far period. It is noted that the rise in average temperature during the warmest quarters surpasses that of the coldest quarters in all periods and scenarios (Figs. 10 b, 11 b). 3.5 Limiting environment indicators While Bangladesh experiences a monsoon climate, rainfall distribution differs across various regions. This study calculated the cumulative precipitation over three consecutive months to determine each point's wettest (Bio-8) and driest (Bio-9) quarters. The average historical values of Bio-8 and Bio-9 are 28.58°C (Fig. 12 a) and 19.93°C (Fig. 13 a), respectively. The anticipated alteration in the MME median average and CI of Bio-8 is estimated to vary by 0.66°C (with a CI of 0.33–1.46°C) for the near period and 1.44°C (with a CI of 0.86–2.68°C) for the far period under the SSP245 scenario. The projections for Bio-9 were 1.05°C (with a CI of 0.27–2.08°C) in the near future and 2.13°C (with a CI of 1.14–3.71°C) in the far period. For SSP585, the projected changes for Bio-8 are likely to be 0.78°C (with a CI of 0.40–1.69°C) in the near period and 2.63°C (with a CI of 1.59–4.39°C) in the far period. Similarly, for Bio-9, the anticipated shifts in temperature are expected to be 1.21°C (with a CI of 0.12–2.40°C) in the near period and 4.32°C (with a CI of 2.21–5.89°C) in the far period. Both indicators exhibited an identical spatial change in the future scenarios (Figs. 12 b, 13 b). 4. Discussion This work evaluated the alterations in 11 thermal bioclimatic indicators using the MME median obtained from the modelled precipitation, Tmax, and Tmin of 16 CMIP6 GCMs. These projections were made for two scenarios: medium (SSP245) and high (SSP585). The eleven indicators convey significant information about various temperature conditions closely associated with the biology and ecology of the coastal region of the GD. The study's findings that the average temperature (Bio-1) would rise by 0.93–2.02°C for SSP245, while 1.21–3.63 for SSP585, are consistent with the findings of earlier works 4,40,41 . DTR (Bio-2) and Isothermality (Bio-3) are expected to decrease in the study region, which also coincides with the studies conducted by the MME of CMIP5 40 and CMIP6 4 GCMs. The drop in DTR can be attributed to the fact that Tmin was increased more than Tmax. Several investigations have established a correlation between Bio-2 and the occurrence of illness and death 42 . The vast population of Bangladesh is particularly vulnerable to changes in DTR, which can have serious consequences for their health. Research has shown that changes in DTR can affect agricultural production. For example, Peng et al. 43 found that lower DTR negatively affected rice yield. People would feel more thermal discomfort during heatwaves with less DTR since a higher summer Tmin won't provide enough nighttime cooling to counteract the high Tmax. 29,44 . Hence, a significant decrease in DTR may be associated with the growing likelihood of heat waves 45 . However, reduced Isothermality (Bio-3) levels indicate less temperature variance, which reduces species' thermal tolerances 46 and might affect the coastal region of the Ganges Delta, which offers the world's most extensive biological variety. Moreover, a decline in isothermality (Bio-3) might substantially impact agricultural production. According to Salvacion 9 , isothermality (Bio-3) has a greater effect on Philippine banana yields than other climate factors. Therefore, a drop in isothermality (Bio-3) levels might profoundly affect agriculture, given that most individuals in the coastal part of the GD depend on agriculture for survival. In this study, all indicators are projected to increase except for DTR (Bio-2) and Isothermality (Bio-3). Consistent with earlier work utilizing MME from the CMIP5 40 and CMIP6 41 model, this analysis demonstrated that monthly Tmin (Bio-6) rose more than monthly Tmax (Bio-5). However, the study also demonstrated that the driest quarter's (Bio-8) temperature increases surpassed the wettest quarter’s (Bio-9). Hotter weather in the driest region might put more strain on the nation's water supply in the future. It might exacerbate the salinity problem in the coastal region of the GD. Additionally, the dual impact of heightened warmth and dryness might potentially induce detrimental stress on rice at many physiological levels 47 . The predicted rise in temperature, leading to a reduction in cold days and an upsurge in hot days, might have major consequences for rice cultivation 8 , especially for the existing varieties cultivated in this coastal region. Elevated temperatures can lead to a decrease in both the growth period of the rice crop and the amount of time of the grain filling stage. Consequently, this will result in a decline in the quality and quantity of the rice grains produced 48 . These findings also imply that the wettest quarter is expected to have a rise in both humidity and temperature, which might worsen human thermal discomfort 29 . Global warming has changed the natural ecology and environment; hence, it is critical to understand how climate impacts biota distribution patterns, particularly how their habitats change over time 39 . Using CMIP5 models, Tan et al. 49 revealed that CC significantly affects several species in Southeastern Asia, causing disturbance, habitat loss, migration, and loss. The information and geographical maps of TBIs from this study might be valuable for understanding the distribution of niches under CC circumstances. The results of this research may be applied in developing policies and strategies concerning water, agriculture, public health, and environmental management and advancement to alleviate the impacts of CC. Future research endeavours should prioritize boosting the precision of climate models by integrating more extensive physical mechanisms, broadening the observational network, and considering local feedback mechanisms. Furthermore, the research suggests choosing the most appropriate models from CMIP6 for regional climate forecast and utilizing MME with weights depending on GCM performance to improve the precision of climate projections. 5. Conclusions The present work examined the projected changes and associated uncertainties in 11 Thermal Bioclimatic Indicators (TBIs) in the coastal region of the Ganges Delta, using the MME median of CMIP6 GCMs. The study examined scenarios with medium and high levels of emissions. The study revealed a high probability of an increase in both mean and seasonal temperatures in Bangladesh, especially during the driest and warmest periods than the wettest or coldest periods. The temperature rise caused by human-induced CC will become increasingly noticeable in the coming years. TBIs are projected to keep increasing in all possible scenarios. A reduction in the difference between daytime and nighttime temperatures (Bio-2) and an increase in the annual temperature range (Bio-7) might result in a fall in the ratio between them and, consequently, a loss in isothermality. Ecological and conservation specialists can utilize the maps and information produced in this work to comprehend potential alterations or transitions in biodiversity in relation to CC. The governments and policymakers might utilize it to formulate sustainable development strategies. Further research might be undertaken to assess alterations in additional bioclimatic variables associated with precipitation and moisture levels. Declarations Conflicts of Interest The authors declare no conflict of interest. Funding statement This research received no specific grant from funding agencies in public, commercial, or not-for-profit sectors. Author Contributions H.M.T.I. and M.K. collaborated as the principal authors, contributing to the design of the research, data analysis, and manuscript writing. M.M. oversaw the study, offering essential insights and critically evaluating the manuscript. A.A. added valuable intellectual content. S.A. and A.R. assisted in manuscript preparation and subsequent revisions. Acknowledgments This research was conducted with the generous support of the Australian Centre for International Agricultural Research (ACIAR) and the Krishi Gobeshona Foundation (KGF) of Bangladesh, as part of the project titled "Cropping System Intensification in the Salt-Affected Coastal Zone of Bangladesh and West Bengal, India (CSI4CZ)" (Project ID: LWR/2014/073). We extend our appreciation for their financial backing, which made it possible to carry out this vital research. Data availability In response to a formal request, we will provide the requested data. References Zauli Sajani, S., Tibaldi, S., Scotto, F. & Lauriola, P. Bioclimatic characterisation of an urban area: a case study in Bologna (Italy). Int J Biometeorol 52, 779–785 (2008). Islam, H. M. T. et al. 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First Velarifictorus (Orthoptera: Gryllidae, Gryllinae) cricket described from Borneo (Southeast Asia) and notes on a co-occurring congener. Zootaxa 4282, (2017). Additional Declarations (Not answered) Supplementary Files Supply13.01.2024.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4101730","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":280229809,"identity":"0399bce5-7970-432f-ab6b-020b288c4241","order_by":0,"name":"Mohammad Kamruzzaman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDACZuYGBsYGBgY2diDBYGBBjBZGqBaeAyAtEsRYA9XCIJEA4hGhRbedsfFx4Y57+XySz69u+FEgwcDf3p2AV4vZYcZm45lnii3bpHPKbvYAHSZx5uwGQlrapHnbEgzYpHPSbvAAtRhI5BKrRfJM2s0/pGmRYD92m1hbmo15zwC18OSw3ZYxkOAh7Jfzhw8+5t2RYCDffvzZzTd/bOT423vxa0ECPAZgkljlIMD+gBTVo2AUjIJRMIIAANSQQa1L6jmfAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6640-8082","institution":"Bangladesh Rice Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Kamruzzaman","suffix":""},{"id":280229810,"identity":"61de32b2-7ed9-465c-a967-46925516cafe","order_by":1,"name":"H. 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Panels (a) and (b) show the median annual precipitation (%) before and after bias correction, respectively. Panels (c) and (d) present the maximum temperature (◦C) before and after bias correction, respectively. Similarly, panels (e) and (f) illustrate the minimum temperature before and after bias correction.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/37d015588dbc4799e1130343.png"},{"id":52961051,"identity":"e14a1a4a-cfce-4a32-84eb-8ab2e6d5c216","added_by":"auto","created_at":"2024-03-19 06:17:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":448844,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual mean surface temperature (Bio-1) changes for SSP245 and SSP585. Panel (a) depicts historical changes, while panel (b) presents projected changes for the near and far future.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/cc5aba052abdb628df09fc3a.png"},{"id":52960129,"identity":"95e70a16-3b58-49c4-9a55-e0e622286586","added_by":"auto","created_at":"2024-03-19 06:09:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":459820,"visible":true,"origin":"","legend":"\u003cp\u003eDiurnal temperature range (Bio-2) changes for SSP245 and SSP585. 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Panel (a) depicts historical changes, while panel (b) presents projected changes for the near and far future.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/0939ef6c10e9deff4235aa8f.png"},{"id":52961049,"identity":"a1201d72-dba9-49c4-9f1c-bbe2e676a967","added_by":"auto","created_at":"2024-03-19 06:17:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":459097,"visible":true,"origin":"","legend":"\u003cp\u003eTemperature seasonality (Bio-4) changes for SSP245 and SSP585. Panel (a) depicts historical changes, while panel (b) presents projected changes for the near and far future.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/d7db33c043ae9b8b0d5a1eec.png"},{"id":52961050,"identity":"ebeca007-2148-4b2b-a3a2-60a545fb29c6","added_by":"auto","created_at":"2024-03-19 06:17:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":450951,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual temperature range (Bio-7) changes for SSP245 and SSP585. Panel (a) depicts historical changes, while panel (b) presents projected changes for the near and far future.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/d09566c085de2fd9ad4e1aa9.png"},{"id":52960127,"identity":"c275752b-09c5-4350-9a6a-96dd078b5167","added_by":"auto","created_at":"2024-03-19 06:09:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":451393,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly maximum temperature (Bio-5) changes for SSP245 and SSP585. Panel (a) depicts historical changes, while panel (b) presents projected changes for the near and far future.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/e7b831bc9f957348c33b50c2.png"},{"id":52960126,"identity":"cc39bfa8-a777-46f5-b5f5-8d36d40b954d","added_by":"auto","created_at":"2024-03-19 06:09:32","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":442887,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly minimum temperature (Bio-6) changes for SSP245 and SSP585. 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Panel (a) depicts historical changes, while panel (b) presents projected changes for the near and far future.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/ab538837e6e6c15d3b276c32.png"},{"id":52960134,"identity":"8f8e565b-1b0d-4ae5-be75-990d8fe11f98","added_by":"auto","created_at":"2024-03-19 06:09:32","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":447877,"visible":true,"origin":"","legend":"\u003cp\u003eColdest quarters (Bio-11) changes for SSP245 and SSP585. Panel (a) depicts historical changes, while panel (b) presents projected changes for the near and far future.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/ef2e4a4c310524ea5fb61dc1.png"},{"id":52960131,"identity":"1f301317-5545-4922-a78b-a54917b1091a","added_by":"auto","created_at":"2024-03-19 06:09:32","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":415404,"visible":true,"origin":"","legend":"\u003cp\u003eWettest quarters (Bio-8) changes for SSP245 and SSP585. Panel (a) depicts historical changes, while panel (b) presents projected changes for the near and far future.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/7d594214086f023ddb3db317.png"},{"id":52960124,"identity":"740ecab9-6212-477e-a761-763fb980bdd9","added_by":"auto","created_at":"2024-03-19 06:09:32","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":418829,"visible":true,"origin":"","legend":"\u003cp\u003eDriest quarters (Bio-9) changes for SSP245 and SSP585. Panel (a) depicts historical changes, while panel (b) presents projected changes for the near and far future.\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/b57681399e778ead0c793a3a.png"},{"id":62761272,"identity":"4549bdce-9959-41eb-8710-b3af008b58bc","added_by":"auto","created_at":"2024-08-19 07:35:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5127868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/b87344a3-84be-4190-8430-eeefc73c917b.pdf"},{"id":52960122,"identity":"86bde9a6-e86e-4a1f-bba6-9acfb9769e4a","added_by":"auto","created_at":"2024-03-19 06:09:31","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":26689,"visible":true,"origin":"","legend":"","description":"","filename":"Supply13.01.2024.docx","url":"https://assets-eu.researchsquare.com/files/rs-4101730/v1/962fe49efc5d2145cb41141a.docx"}],"financialInterests":"(Not answered)","formattedTitle":"Thermal Bioclimatic Transformations in the Coastal Regions of Ganges Delta: Insights from CMIP6 Multi-Model Ensembles","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change (CC) is a pressing global challenge that profoundly threatens the environment, ecosystem, and public health \u003csup\u003e1,2\u003c/sup\u003e. Thermal Bioclimatic indicators (TBIs) are commonly utilized to assess the effects of CC on biodiversity, pollution, agricultural production, and human thermal comfort \u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. The balance of energy and comfort levels of the human body are influenced by several climatic parameters, such as humidity, temperature, and wind, along with their fluctuations \u003csup\u003e6\u003c/sup\u003e. Thus, bioclimatic indicators are extensively employed for the areas where human comfort is optimal \u003csup\u003e7\u003c/sup\u003e. Additionally, it has been employed to determine the impact of CC on crop production and the suitability of a region for cultivating a specific type of crop like rice \u003csup\u003e8\u003c/sup\u003e, banana \u003csup\u003e9\u003c/sup\u003e, sweet potato \u003csup\u003e10\u003c/sup\u003e, coffee \u003csup\u003e11\u003c/sup\u003e and maize\u003csup\u003e12\u003c/sup\u003e. The majority of species are capable of surviving within a certain bioclimatic niche. Consequently, even a slight alteration in the bioclimate may profoundly impact the geographical distribution of species and the overall ecology of a particular area \u003csup\u003e13\u003c/sup\u003e. Thus, its broad application and high efficacy make it an appealing method for assessing the impacts of CC.\u003c/p\u003e \u003cp\u003eGlobal climate models (GCMs) are frequently utilized to explore different scenarios of CC. Scholars have reported notable improvements in GCMs in the most recent edition of the Coupled Model Intercomparison Project (CMIP), known as CMIP6 \u003csup\u003e14,15\u003c/sup\u003e. The improvements include refining the model's architecture, increasing the geographic resolution, reducing uncertainty, enhancing the simulation of clouds, and improving the ability to replicate synoptic progressions \u003csup\u003e16\u003c/sup\u003e. Hence, employing data obtained from CMIP6 GCMs to examine forthcoming alterations in TBIs is crucial.\u003c/p\u003e \u003cp\u003eThe inhabitants of the Ganges delta's coastal areas (Bangladesh and West Bengal) depend significantly on fishing and agriculture to nourish and sustain their livelihoods \u003csup\u003e17,18\u003c/sup\u003e. Nevertheless, this area is exceedingly susceptible to climate change and encounters a diverse array of severe climatic fluctuations, which can substantially influence agricultural output \u003csup\u003e19\u0026ndash;21\u003c/sup\u003e. CC may worsen public health conditions in Bangladesh, particularly by increasing the prevalence of waterborne (diarrhoea and cholera) as well as vector-borne (dengue fever and malaria) diseases \u003csup\u003e22\u003c/sup\u003e. As climatic extremes in this region are now experiencing an increased frequency \u003csup\u003e2,21\u003c/sup\u003ethis trend is projected to persist [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], increasing the region's vulnerability to CC risks and consequences. Evaluating TBIs can assist policymakers and stakeholders in Bangladesh in comprehending and proactively addressing these consequences. This can be achieved by pinpointing the most vulnerable regions and formulating suitable adaptation plans.\u003c/p\u003e \u003cp\u003eMany studies have employed raw and downscaled GCMs to assess the effects of CC on bioclimatic indicators and biodiversity worldwide \u003csup\u003e23\u0026ndash;29\u003c/sup\u003e. Zhang et al. \u003csup\u003e25\u003c/sup\u003e employed GCMs to examine the geographical distribution of a medicinal plant in China. Wang et al. \u003csup\u003e30\u003c/sup\u003e investigated the anticipated future range of six species of flowering plants, both presently and in the future, utilizing CMIP5. Zahoor et al. \u003csup\u003e26\u003c/sup\u003e performed a study to analyze alterations in the distribution of bears by utilizing GCMs for two Representative Concentration Pathways (RCPs). Therefore, it is essential to evaluate bioclimatic indicators in historical and future scenarios to promote sustainable development in any region. Despite this, there is a notable gap in academic research specifically addressing this topic in the context of the Ganges delta. A recent study by Kamruzzaman et al. \u003csup\u003e4\u003c/sup\u003e investigated the TBIs across Bangladesh using a multi-model ensemble (MME) projection of 18 CMIP6 GCMs. It is noteworthy that they relied on observed daily climate data from 28 meteorological stations for TBIs projections. Significantly, there is currently no research specifically focusing on the coastal region of the Ganges delta, encompassing Bangladesh and West Bengal (India). This research aims to fill the current gaps in previous studies.\u003c/p\u003e \u003cp\u003eThe coastal area of the Ganges delta holds unique ecological significance, and a comprehensive examination of bioclimatic indicators in this coastal region is essential for understanding and addressing the potential impacts of CC. Hence, this work aims to investigate the bioclimatic indicators in both historical and future scenarios, specifically within the coastal region of the Ganges delta, incorporating Bangladesh and India (West Bengal) for SSP245 and SSP585 CC scenarios. A bias correction technique based on quantile mapping was applied to enhance the precision of the GCM projections\u003csup\u003e31\u003c/sup\u003e. Furthermore, to address uncertainties in the projections, a model ensemble approach was utilized \u003csup\u003e32,33\u003c/sup\u003e. Such research is imperative for providing information to guide in decision-making and develop viable climate change adaptation approaches in this ecologically sensitive and vulnerable region.\u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study area\u003c/h2\u003e \u003cp\u003eThis work encompasses the southwestern coastal area of the Ganges Delta (GD), which includes Bangladesh, as well as the southeastern coastal region of West Bengal in India. The region is situated within the latitudes of around 21\u0026deg;30'N to 23\u0026deg;30'N and longitudes of 88\u0026deg;00'E to 90\u0026deg;30'E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This area included 70 Upazilas in nine coastal districts of Bangladesh and 20 blocks in the South 24 Parganas district of West Bengal. It is located in the GD and is known for being close to the Bay of Bengal and having a complex system of water bodies. The region experiences varying average annual rainfall levels, roughly 2000 mm in the eastern and 1800 mm in the western region \u003csup\u003e34\u003c/sup\u003e. The Sundarbans, the world's biggest mangrove forest, is located in this area, where the local population depends mostly on agriculture, fishing, and the exploitation of resources from the mangrove forest \u003csup\u003e35\u003c/sup\u003e. Moreover, regional problems like salinity tend to deteriorate in the absence of precipitation and the reduced influx of freshwater from rivers. The area is exceptionally vulnerable to natural calamities, specifically cyclones, flooding, and storm surges, all presenting substantial threats to habitation and facilities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Sources\u003c/h2\u003e \u003cp\u003eAs the distribution of meteorological stations in the study area is not uniform and limited, this study utilized ERA5 data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5\u003c/span\u003e\u003cspan address=\"https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as observed data with a spatial resolution of 0.25\u0026deg;. It spanned the period from 1995 to 2014. To validate the data, we examined the relationship between precipitation, Tmax, and Tmin in the In Situ and ERA5 observation datasets (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The In Situ observation data consisted of the average measurements from nine stations, specifically Sathkhira, Khulna, Barishal, Patuakhali, Khepurpara, Hatia, Sandwip, and Canning (West Bengal). ERA5 precipitation, Tmax, and Tmin strongly correlate with In Situ observations, indicating they can be reliable substitutes for In Situ data.\u003c/p\u003e \u003cp\u003eThis study examined the spatial and temporal variations in thermal bioclimatic indicators (TBIs) throughout two future periods: the near (2021\u0026ndash;2059) and the far (2060\u0026ndash;2100) periods. The analysis utilized 16 CMIP6 Global Climate Models (GCMs) can be found in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and focused on two SSPs: SSP245 and SSP585. The CMIP6 GCMs downscaled data files have been downloaded from the Earth System Grid Federation (ESGF) portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://esgf-node.llnl.gov/search/cmip6/\u003c/span\u003e\u003cspan address=\"https://esgf-node.llnl.gov/search/cmip6/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Methodologies\u003c/h2\u003e \u003cp\u003eThe GCM simulations display a notable disparity in geographical resolution and frequently have systematic biases that limit their direct usefulness in assessing the effects of CC. This work utilized a statistical technique to downscale GCMs at each grid point. The raw GCM downscaled data were initially re-gridded to match the observed points. Subsequently, a quantile mapping (QM) technique was utilized to rectify the discrepancy in the GCM projections. Consequently, it exhibits a stronger resemblance to the distribution that is seen, reduces biases, and enhances the dependability of climate forecasts \u003csup\u003e36,37\u003c/sup\u003e. Further details concerning the bias-correction methodology employed in the research are given in Kamruzzaman et al. \u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study we used three types of bioclimatic indicators like annual, seasonal and limiting environment indicators. An elaborate explanation of the thermal bioclimatic indicators (TBIs) that were examined in this work are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The TBIs were calculated for each model's data during the historical timeframe and both projected scenarios. The months with the highest and lowest temperatures and rainfall were not predetermined for both the historical and future timeframes. They were selected again for future times\u003csup\u003e29,39\u003c/sup\u003e. For example, a dynamic approach was used to determine the month with the highest or lowest temperature for Bio-8 to Bio-11. By combining the results of 23 GCMs, we were able to construct a historical median multi-model ensemble (MME) that displayed the 5th and 95th percentiles, or confidence interval (CI), for the alteration of each index and reduced uncertainty. For both the SSP245 and SSP585, the MME median and percentiles were also calculated for the near (2021\u0026ndash;2059) and far (2060\u0026ndash;2100) periods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefinition of the bioclimatic indicators where T\u003csub\u003eavg\u003c/sub\u003e is the mean temperature ((T\u003csub\u003emax\u003c/sub\u003e + T\u003csub\u003emin\u003c/sub\u003e)/2), and i is the month of the year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-1: Annual average temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Bio-1 }=\\frac{\\sum _{i=1}^{i=12} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}}{12}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-2: Diurnal temperature range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Bio-2 }=\\frac{\\sum _{i=1}^{i=12} \\left({\\text{T}\\text{m}\\text{a}\\text{x}}_{i}-T\\underset{i}{min} \\right)}{12}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-3: Isothermality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Bio3 }=\\frac{\\text{ Bio }2}{\\text{ Bio }7}\\times 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-4: Temperature variation in a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Bio-4 }=Standard deviation\\left\\{{\\text{T}\\text{a}\\text{v}\\text{g}}_{1},\\dots ,{\\text{ Tavg }}_{12}\\right\\}\\times 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-7: Annual temperature range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Bio7 }=\\text{ Bio5 }-\\text{ Bio }6\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeasonal temperature indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-5: Maximum monthly temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Bio-5 }=max\\left(\\left\\{{\\text{T}\\text{m}\\text{a}\\text{x}}_{1},\\dots ,{\\text{T}\\text{m}\\text{a}\\text{x}}_{12}\\right\\}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-6: Minimum monthly temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Bio-6 }=min\\left(\\left\\{{\\text{T}\\text{m}\\text{i}\\text{n}}_{1},\\dots ,{\\text{T}\\text{m}\\text{i}\\text{n}}_{12}\\right\\}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-10: Average temperature of the warmest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{max}=max\\left(\\begin{array}{c}\\sum _{i=1}^{i=3} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}\\\\ \\sum _{i=2}^{i=4} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}\\\\ \\dots ,\\\\ \\sum _{i=11}^{i=1} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}\\\\ \\sum _{i=12}^{i=2} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}\\end{array}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eBio-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(10=\\frac{{\\sum }_{i=1}^{i=3} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}}{3}\\)\u003c/span\u003e\u003c/span\u003e, based on the three selected months of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{\\text{m}\\text{a}\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-11: Average temperature of the coldest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{\\text{m}\\text{i}\\text{n}}=min\\left(\\begin{array}{c}\\sum _{i=1}^{i=3} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}\\\\ \\sum _{i=2}^{i=4} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}\\\\ \\dots ,\\\\ \\sum _{i=11}^{i=1} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}\\\\ \\sum _{i=12}^{i=2} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}\\end{array}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eBio-11 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(=\\frac{{\\sum }_{i=1}^{i=3} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}}{3}\\)\u003c/span\u003e\u003c/span\u003e, based on the three selected months of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{\\text{m}\\text{i}\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLimiting environment indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-8: Average temperature of the wettest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{\\text{m}\\text{a}\\text{x}}=max\\left(\\begin{array}{c}\\sum _{i=1}^{i=3} {\\text{ Rainfall }}_{i}\\\\ \\sum _{i=2}^{i=4} {\\text{ Rainfall }}_{i}\\\\ \\dots ,\\\\ \\sum _{i=11}^{i=1} {\\text{ Rainfall }}_{i}\\\\ \\sum _{i=12}^{i=2} {\\text{ Rainfall }}_{i}\\end{array}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eBio-8 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(=\\frac{{\\sum }_{i=1}^{i=3} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}}{3}\\)\u003c/span\u003e\u003c/span\u003e, based on the three selected months of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{\\text{m}\\text{a}\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-9: Average temperature of the driest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{\\text{m}\\text{i}\\text{n}}=min\\left(\\begin{array}{c}\\sum _{i=1}^{i=3} {\\text{ Rainfall }}_{i}\\\\ \\sum _{i=2}^{i=4} {\\text{ Rainfall }}_{i}\\\\ \\dots ,\\\\ \\sum _{i=11}^{i=1} {\\text{ Rainfall }}_{i}\\\\ \\sum _{i=12}^{i=2} {\\text{ Rainfall }}_{i}\\end{array}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eBio-9 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(=\\frac{{\\sum }_{i=1}^{i=3} {\\text{T}\\text{a}\\text{v}\\text{g}}_{i}}{3}\\)\u003c/span\u003e\u003c/span\u003e, based on the three selected months of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{\\text{m}\\text{i}\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eIn Bio-8 and Bio-9 (Bio-10 and Bio-11) equations, the rainfall (temperature) is evaluated for consecutive 3 months, which may span over two consecutive years\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Validation of Climate Models\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the simulated precipitation, Tmax, and Tmin of the MME median, consisting of 16 CMIP6 GCMs. The comparison is made between the bias-corrected data before and after being adjusted to match the observed (ERA5) values. The findings indicated that the MME median of CMIP6 GCMs had a notable bias, as seen in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, c, and e. Specifically, the raw mean annual precipitation in the southeastern region overestimated the ERA5 value. It exhibited a wet bias of up to 25%. In comparison, the rest of the study area noticed a dry bias of up to 30%, especially in the northwestern region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The raw downscaled MME median Tmax overestimated the ERA5 Tmax up to 3\u0026deg;C in the northern region while underestimating it up to 1\u0026deg;C in the southcentral and southeastern regions. Nevertheless, the raw downscaled MME median Tmin overestimated the ERA5 Tmax by about 1.42\u0026ndash;5\u0026deg;C. Therefore, the bias-correction approach is employed to reduce these errors. Because of the implementation of the quantile mapping (QM) technique, a significant bias reduction was seen for all three climatic variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, d, and f).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis work utilized a simple quantile mapping (SQM) technique for bias correction. The biases in precipitation in the bias-corrected MME data have been decreased to less than 3 mm. However, the biases in Tmax and Tmin are nearly identical to the ERA5 data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Annual indicators\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Annual average temperature and diurnal temperature range\u003c/h2\u003e \u003cp\u003eThe spatial distribution of the annual average temperature (Bio-1) over the coastal region of the GD is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In contrast, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the diurnal temperature range (DTR) or Bio-2. The average historical Bio-1 and Bio-2 is 25.41\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and 9.16\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), respectively, from 1985 to 2014.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe MME median average (including the 5th and 95th percentiles) projected a Bio-1 rise of 0.93\u0026deg;C (0.57\u0026ndash;1.60\u0026deg;C) and 2.02\u0026deg;C (1.47\u0026ndash;3.08\u0026deg;C) for the near and far periods, respectively, in the coastal region of the GD under the SSP245 scenario. The anticipated MME median average and confidence interval (CI) in Bio-1 are nearly the same for both SSP585 and SSP245 in the near period. Nevertheless, the projected temperature rise in the far period is estimated to be 3.13\u0026deg;C (with a range of 1.17\u0026ndash;5.50\u0026deg;C) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). This suggests greater uncertainty in the far period and SSP585 compared to the near future and SSP245 scenario. A higher rise in Bio-1 is expected in the southeastern coastal area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eIn contrast, the MME median predicted that the average temperature in Bio-2 would vary by -0.29\u0026deg;C (with a CI of -0.81 to 0.08\u0026deg;C) and \u0026minus;\u0026thinsp;0.39\u0026deg;C (with a CI of -0.94 to 0.23\u0026deg;C) for SSP245, in the near and far future, respectively. Similarly, the MME expected a Bio-2 alteration of -0.30\u0026deg;C (with a CI of -0.78 to 0.26\u0026deg;C) and \u0026minus;\u0026thinsp;0.64\u0026deg;C (with a CI of -1.24 to 0.30\u0026deg;C) for SSP585, in the near and far period, correspondingly (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Isothermality, seasonality, and range\u003c/h2\u003e \u003cp\u003eIsothermality, also known as Bio-3, is the percentage ratio between the DTR (Bio-2) and the annual temperature range (Bio-7). The average historical Bio-3 was 69.08% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows how the isothermality (Bio-3) is expected to change in the coastal area of the GD for two future scenarios and periods, relative to the historical phase spanning from 1985\u0026ndash;2014. The projected MME median mean (CI) changes for SSP245 are estimated to be -3.93% (-9.25 to -0.94%) and \u0026minus;\u0026thinsp;6.92% (-13.80 to -1.69%) for the near and far periods, correspondingly. For SSP585, the mean (CI) alternations are projected to be -4.88% (-9.53 to -0.93%) and \u0026minus;\u0026thinsp;9.38% (-17.50 to -0.28%) for the near and far periods, correspondingly (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeasonality, often known as Bio-4, refers to the average temperature variation seen across the years. It is obtained by computing the standard deviation (SD) as a percentage. The mean historical Bio-4 was about 4.57% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The MME projected very small alternations (between \u0026minus;\u0026thinsp;0.65\u0026ndash;1.2%) in Bio-4 for all future scenarios, indicating that Bio-4 is expected to be minimally affected by CC. The comparatively greater increase in Bio-4 was anticipated in the southeastern region of the coastal area of the GD for SSP585 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e displays the annual temperature range (Bio-7) over the historical timeframe and potential future variations within the study area. Bio-7 refers to the fluctuation in temperature over a specific period or the deviation between Bio-5 and Bio-6. The average historical Bio-7 was about 13.61\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). For SSP245, the expected change for the Bio-7 is 0.47\u0026deg;C (with a CI of \u0026minus;\u0026thinsp;0.19 to 1.12\u0026deg;C) and 1.11\u0026deg;C (with a CI of -0.18 to 2.42\u0026deg;C) in the near and far periods, respectively, while 0.55\u0026deg;C (with a CI of \u0026minus;\u0026thinsp;0.27 to 1.77\u0026deg;C) and 1.12\u0026deg;C (with a CI of -0.59 to 3.06\u0026deg;C) in the near and far periods, respectively for SSP585. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb shows that the southern region of the coastal area of the GD is expected to see a substantially greater rise in Bio-7 under the SSP585 scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Seasonal indicators\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Average maximum and minimum monthly temperature\u003c/h2\u003e \u003cp\u003eThe historical and predicted alterations in maximum monthly (Bio-5) and minimum monthly (Bio-6) temperature are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The average historical Bio-5 and Bio-6 is 30.32\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea) and 17.18\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea), respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe predicted alteration in Bio-5 by about 1.32\u0026deg;C (with a CI of 0.58\u0026ndash;2.45\u0026deg;C) and 2.76\u0026deg;C (with a CI 1.79\u0026ndash;4.88\u0026deg;C) for the near and far futures for SSP245, while 1.69\u0026deg;C (with a CI of 0.79\u0026ndash;2.91\u0026deg;C) and 4.80\u0026deg;C (3.15\u0026ndash;6.98\u0026deg;C) for the near and far futures for SSP585. Nevertheless, the expected changes in Bio-6 are estimated to be 0.80\u0026deg;C (0.51\u0026ndash;1.57\u0026deg;C) and 2.04\u0026deg;C (1.24\u0026ndash;2.95\u0026deg;C) for SSP245 and 1.09\u0026deg;C (with a CI of 0.40\u0026ndash;2\u0026deg;C) and 3.92\u0026deg;C (with a CI of 2.40\u0026ndash;5.27\u0026deg;C) for SSP585, for the near and far periods, respectively. For both indicators, the southernmost region of the study area might experience a more significant temperature rise (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb,\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). The data suggests a more incredible rise in Bio-6 compared to Bio-5 in the coastal region of the GD. The most significant future shift for both indicators is projected to happen in the far period for the SSP585 scenario.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Average temperature of the warmest quarters and coldest quarters\u003c/h2\u003e \u003cp\u003eThe warmest (Bio-10) and coldest (Bio-11) quarters were calculated based on the mean temperature over three months at each grid. For this purpose, we utilized a dynamic methodology to identify the quarters with the highest and lowest temperatures. The mean historical Bio-10 and Bio-11 are 19.70\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea) and 18.85\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea), respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor SSP245, the CI for the expected shifts in Bio-10 was 0.57 to 2.04\u0026deg;C for the near period and 1.52 to 4.21\u0026deg;C for the far period. Moreover, the CI for the temperature changes in Bio-11 are estimated to be within 0.48 and 1.71\u0026deg;C for the near future and within 1.18 and 3.10\u0026deg;C for the far period. The confidence interval (CI) for the expected changes in Bio-10 under SSP585 was 0.68 to 2.37\u0026deg;C for the near period and 2.86 to 6.36\u0026deg;C for the far period. Furthermore, the CI for the temperature fluctuations in Bio-11 is calculated to range from 0.37 to 2.11\u0026deg;C for the near period and from 2.34 to 5.28\u0026deg;C for the far period. It is noted that the rise in average temperature during the warmest quarters surpasses that of the coldest quarters in all periods and scenarios (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb, \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Limiting environment indicators\u003c/h2\u003e \u003cp\u003eWhile Bangladesh experiences a monsoon climate, rainfall distribution differs across various regions. This study calculated the cumulative precipitation over three consecutive months to determine each point's wettest (Bio-8) and driest (Bio-9) quarters. The average historical values of Bio-8 and Bio-9 are 28.58\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea) and 19.93\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ea), respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe anticipated alteration in the MME median average and CI of Bio-8 is estimated to vary by 0.66\u0026deg;C (with a CI of 0.33\u0026ndash;1.46\u0026deg;C) for the near period and 1.44\u0026deg;C (with a CI of 0.86\u0026ndash;2.68\u0026deg;C) for the far period under the SSP245 scenario. The projections for Bio-9 were 1.05\u0026deg;C (with a CI of 0.27\u0026ndash;2.08\u0026deg;C) in the near future and 2.13\u0026deg;C (with a CI of 1.14\u0026ndash;3.71\u0026deg;C) in the far period. For SSP585, the projected changes for Bio-8 are likely to be 0.78\u0026deg;C (with a CI of 0.40\u0026ndash;1.69\u0026deg;C) in the near period and 2.63\u0026deg;C (with a CI of 1.59\u0026ndash;4.39\u0026deg;C) in the far period. Similarly, for Bio-9, the anticipated shifts in temperature are expected to be 1.21\u0026deg;C (with a CI of 0.12\u0026ndash;2.40\u0026deg;C) in the near period and 4.32\u0026deg;C (with a CI of 2.21\u0026ndash;5.89\u0026deg;C) in the far period. Both indicators exhibited an identical spatial change in the future scenarios (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eb, \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis work evaluated the alterations in 11 thermal bioclimatic indicators using the MME median obtained from the modelled precipitation, Tmax, and Tmin of 16 CMIP6 GCMs. These projections were made for two scenarios: medium (SSP245) and high (SSP585). The eleven indicators convey significant information about various temperature conditions closely associated with the biology and ecology of the coastal region of the GD. The study's findings that the average temperature (Bio-1) would rise by 0.93\u0026ndash;2.02\u0026deg;C for SSP245, while 1.21\u0026ndash;3.63 for SSP585, are consistent with the findings of earlier works \u003csup\u003e4,40,41\u003c/sup\u003e. DTR (Bio-2) and Isothermality (Bio-3) are expected to decrease in the study region, which also coincides with the studies conducted by the MME of CMIP5 \u003csup\u003e40\u003c/sup\u003e and CMIP6 \u003csup\u003e4\u003c/sup\u003e GCMs. The drop in DTR can be attributed to the fact that Tmin was increased more than Tmax. Several investigations have established a correlation between Bio-2 and the occurrence of illness and death \u003csup\u003e42\u003c/sup\u003e. The vast population of Bangladesh is particularly vulnerable to changes in DTR, which can have serious consequences for their health. Research has shown that changes in DTR can affect agricultural production. For example, Peng et al. \u003csup\u003e43\u003c/sup\u003e found that lower DTR negatively affected rice yield. People would feel more thermal discomfort during heatwaves with less DTR since a higher summer Tmin won't provide enough nighttime cooling to counteract the high Tmax. \u003csup\u003e29,44\u003c/sup\u003e. Hence, a significant decrease in DTR may be associated with the growing likelihood of heat waves \u003csup\u003e45\u003c/sup\u003e. However, reduced Isothermality (Bio-3) levels indicate less temperature variance, which reduces species' thermal tolerances\u003csup\u003e46\u003c/sup\u003e and might affect the coastal region of the Ganges Delta, which offers the world's most extensive biological variety.\u003c/p\u003e \u003cp\u003eMoreover, a decline in isothermality (Bio-3) might substantially impact agricultural production. According to Salvacion \u003csup\u003e9\u003c/sup\u003e, isothermality (Bio-3) has a greater effect on Philippine banana yields than other climate factors. Therefore, a drop in isothermality (Bio-3) levels might profoundly affect agriculture, given that most individuals in the coastal part of the GD depend on agriculture for survival.\u003c/p\u003e \u003cp\u003eIn this study, all indicators are projected to increase except for DTR (Bio-2) and Isothermality (Bio-3). Consistent with earlier work utilizing MME from the CMIP5 \u003csup\u003e40\u003c/sup\u003e and CMIP6 \u003csup\u003e41\u003c/sup\u003e model, this analysis demonstrated that monthly Tmin (Bio-6) rose more than monthly Tmax (Bio-5). However, the study also demonstrated that the driest quarter's (Bio-8) temperature increases surpassed the wettest quarter\u0026rsquo;s (Bio-9). Hotter weather in the driest region might put more strain on the nation's water supply in the future. It might exacerbate the salinity problem in the coastal region of the GD.\u003c/p\u003e \u003cp\u003eAdditionally, the dual impact of heightened warmth and dryness might potentially induce detrimental stress on rice at many physiological levels \u003csup\u003e47\u003c/sup\u003e. The predicted rise in temperature, leading to a reduction in cold days and an upsurge in hot days, might have major consequences for rice cultivation \u003csup\u003e8\u003c/sup\u003e, especially for the existing varieties cultivated in this coastal region. Elevated temperatures can lead to a decrease in both the growth period of the rice crop and the amount of time of the grain filling stage. Consequently, this will result in a decline in the quality and quantity of the rice grains produced \u003csup\u003e48\u003c/sup\u003e. These findings also imply that the wettest quarter is expected to have a rise in both humidity and temperature, which might worsen human thermal discomfort \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlobal warming has changed the natural ecology and environment; hence, it is critical to understand how climate impacts biota distribution patterns, particularly how their habitats change over time \u003csup\u003e39\u003c/sup\u003e. Using CMIP5 models, Tan et al. \u003csup\u003e49\u003c/sup\u003e revealed that CC significantly affects several species in Southeastern Asia, causing disturbance, habitat loss, migration, and loss. The information and geographical maps of TBIs from this study might be valuable for understanding the distribution of niches under CC circumstances. The results of this research may be applied in developing policies and strategies concerning water, agriculture, public health, and environmental management and advancement to alleviate the impacts of CC. Future research endeavours should prioritize boosting the precision of climate models by integrating more extensive physical mechanisms, broadening the observational network, and considering local feedback mechanisms. Furthermore, the research suggests choosing the most appropriate models from CMIP6 for regional climate forecast and utilizing MME with weights depending on GCM performance to improve the precision of climate projections.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe present work examined the projected changes and associated uncertainties in 11 Thermal Bioclimatic Indicators (TBIs) in the coastal region of the Ganges Delta, using the MME median of CMIP6 GCMs. The study examined scenarios with medium and high levels of emissions. The study revealed a high probability of an increase in both mean and seasonal temperatures in Bangladesh, especially during the driest and warmest periods than the wettest or coldest periods. The temperature rise caused by human-induced CC will become increasingly noticeable in the coming years. TBIs are projected to keep increasing in all possible scenarios. A reduction in the difference between daytime and nighttime temperatures (Bio-2) and an increase in the annual temperature range (Bio-7) might result in a fall in the ratio between them and, consequently, a loss in isothermality. Ecological and conservation specialists can utilize the maps and information produced in this work to comprehend potential alterations or transitions in biodiversity in relation to CC. The governments and policymakers might utilize it to formulate sustainable development strategies. Further research might be undertaken to assess alterations in additional bioclimatic variables associated with precipitation and moisture levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from funding agencies in public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eH.M.T.I. and M.K. collaborated as the principal authors, contributing to the design of the research, data analysis, and manuscript writing. M.M. oversaw the study, offering essential insights and critically evaluating the manuscript. A.A. added valuable intellectual content. S.A. and A.R. assisted in manuscript preparation and subsequent revisions.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis research was conducted with the generous support of the Australian Centre for International Agricultural Research (ACIAR) and the Krishi Gobeshona Foundation (KGF) of Bangladesh, as part of the project titled \"Cropping System Intensification in the Salt-Affected Coastal Zone of Bangladesh and West Bengal, India (CSI4CZ)\" (Project ID: LWR/2014/073). We extend our appreciation for their financial backing, which made it possible to carry out this vital research.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eIn response to a formal request, we will provide the requested data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZauli Sajani, S., Tibaldi, S., Scotto, F. \u0026amp; Lauriola, P. Bioclimatic characterisation of an urban area: a case study in Bologna (Italy). 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Zootaxa 4282, (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bioclimatic indicators, Climate projections, temperature and precipitation, Scenario-based analysis, Climate change impacts, Ganges Delta","lastPublishedDoi":"10.21203/rs.3.rs-4101730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4101730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe effects of climatic alteration caused by global warming on people, the environment, and ecosystems can be better understood by examining thermal bioclimatic indicators (TBIs) changes. Evaluating such alterations is of utmost significance for the Ganges Delta (GD) coastal region, which offers the world's most extensive biological variety. This study utilizes a multi-model ensemble (MME) of 16 CMIP6 Global Climate Models (GCMs) to assess prospective alterations in thermal bioclimatic indicators (TBIs) across the coastal region of the Ganges Delta (GD) for two Shared Socioeconomic Pathways (SSPs): SSP245 (moderate) and SSP585 (severe). We employ ensemble median, 5th, and 95th percentiles to analyze temporal shifts and associated uncertainty in TBIs during the near (2020\u0026ndash;2059) and far (2060\u0026ndash;2100) futures. Our projections reveal a significant escalation in annual temperatures throughout the GD, with MME median average in-crease anticipated to range from 0.77\u0026ndash;2.80\u0026deg;C (SSP2-4.5) to 1.03\u0026ndash;4.65\u0026deg;C (SSP5-8.5) by 2059. Moreover, notable transformations in thermal patterns are expected, with a projected decrease in both diurnal temperature range (DTR) by 0.02\u0026ndash;0.87\u0026deg;C and isothermality by 3.30-12.09%. Additionally, the average temperature during the driest months is anticipated to rise higher than in the wettest months. These findings underscore climate change's existential threat to the GD and its rich biodiversity. They provide vital information for formulating crucial mitigation strategies to curb greenhouse gas emissions and robust adaptation measures to bolster the resilience of communities and eco-systems. Urgent action is paramount to safeguard the future of this invaluable ecological treasure.\u003c/p\u003e","manuscriptTitle":"Thermal Bioclimatic Transformations in the Coastal Regions of Ganges Delta: Insights from CMIP6 Multi-Model Ensembles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 06:09:26","doi":"10.21203/rs.3.rs-4101730/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"750c8ffc-af28-4373-bf13-0a2db3690564","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29514839,"name":"Earth and environmental sciences/Climate sciences/Climate change"},{"id":29514840,"name":"Earth and environmental sciences/Climate sciences"}],"tags":[],"updatedAt":"2024-08-19T07:27:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 06:09:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4101730","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4101730","identity":"rs-4101730","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}