Seasonal Mean Temperature Research Articles

Observed climate changes in the Toplica river valley : Trend analysis of temperature, precipitation and river discharge

Changes in air temperature, precipitation, and the Toplica river discharge were investigated. Annual and seasonal climatic data were collected at weather stations Kursumlija and Prokuplje, and discharge data on hydrological gauges Pepeljevac and Doljevac. The data covered a period of 62 years (1957–2018). Mann-Kendall and Pettitt’s tests have been applied for the periods 1957–2018, 1957–1987, 1988–2018, and 1975–1994, which we find as very important period when atmospheric circulation was altered. Mean annual temperature and precipitation were greater in the second half-period, while the discharge was smaller, even all the signals had growth trend. Mean seasonal temperature increased for all seasons, as well as precipitation, except for summer (JJA). The discharge is lower in the second half-period for almost all seasons, with signs of recovery for all seasons except summer.

Reliability of Antarctic air temperature changes from Polar WRF: A comparison with observations and MAR outputs

This study investigates the near-surface air temperature changes over the Antarctic Ice Sheet (AIS) since 1979 using the Polar Weather Research and Forecasting model (Polar WRF, version 3.9.1) at a horizontal resolution of 27 km. Its performance is estimated by quality-controlled monthly temperature measurements from 46 automatic and manual weather stations, and the outputs of the Regional Atmosphere Model (Modèle Atmosphérique Régional, MAR) forced by ERA-Interim (MAR-ERAI) and ERA-5 (MAR-ERA5). Although warm mean biases at interior stations and cold mean biases at coastal stations are observed for the annual and seasonal mean temperatures, all simulations exhibit robust consistency with the year-to-year variability in the observed temperatures. Compared to MAR, Polar WRF presents a warmer temperature, especially on the West AIS (WAIS). Moreover, while Polar WRF shows a poorer performance than MAR over the AP (Antarctic Peninsula) with respect to the observations, Polar WRF can generally capture the trends on the East AIS (EAIS) and WAIS and shows smaller mean biases in winter and autumn. For 1979–2018, Polar WRF shows a warming trend east of 60°E in the EAIS and parts of the AP and WAIS and an insignificant cooling trend in other regions for the annual temperature. The MAR simulations agree on the warming trend in most regions of the EAIS and WAIS but show different annual warming trends over the AP and East Antarctic Plateau.

Spatio-temporal hydro-climate variability in Omo-Gibe river Basin, Ethiopia

Hydroclimatic variables have changed globally mainly due to human intervention; those hydroclimatic changes alter the distribution behavior of rainfall and temperature at different scales. This study examined the magnitude of temporal and spatial variation of hydroclimatic variables in the Omo-Gibe River Basin (OGRB) using rainfall and temperature data from 1981 to 2016 and river flow data from 1981 to 2014. Mann-Kendall and Sen's slope estimator trend test were used to analyze the distribution and trends of rainfall, temperature, surface runoff, and river flow. The result shows rainfall variability has been higher in the northern and central parts and decreasing towards the southern part of the OGRB to its southern part. Results also show that seasonal and annual mean temperatures have increased significantly in the recent two decades. The northern and central OGRB summer and autumn seasons' rainfall shows a statically insignificant positive trend. However, the basin has been experiencing less to high spatio-temporal seasonal and monthly rainfall variability. Runoff and flow of the basin were constant in all rivers during the period 1981 to 1997. However, the basin experienced peaks and diminished runoff and flowed, particularly in the recent two decades. The mean seasonal trend of runoff increased insignificantly, whereas the river flow increased significantly during the summer, spring, and autumn seasons by 0.069, 0.014 and 0.197 m3s−1/year, respectively. Overall, the mean annual runoff and river flow observed an insignificant increasing trend in the entire basin. In general, the erraticity of rainfall, rising temperature, and the highest spatial and temporal variation of river flow and runoff witnessed in OGRB suggest that the basin water resource is at risk due to these reinforcing challenges, and much needs to be done by all the concerned actors to save water resources.

Simulation of future land surface temperature under the scenario of climate change using remote sensing & GIS techniques of northwestern Rajshahi district, Bangladesh

Identification of change dynamics of land surface temperature on regional scale is a very convenient way to describe the issues related global warming and climate change. This study aims to assess the spatio-temporal pattern of land surface temperature during 2014–2020 along with future simulation and to investigate the meteorological condition of northwestern Rajshahi district of Bangladesh during last few decades. Surface temperature and land cover indices were derived in this study from the multi-temporal Landsat imagery. Besides, unsupervised classification method was utilized to recognize the land cover classes. The trend in annual and seasonal mean temperature and total rainfall together with the frequency of hot and cold days were computed in this study from daily based meteorological data. The impact of land use and climate change was assessed through regression analysis. Finally, land surface temperature for the years 2022 and 2024 was predicted by utilizing a cellular automata model integrated with the artificial neural network. This study revealed that mean surface temperature of both post-monsoon and winter season has increased during 2014–2020 where its seasonal based variation has gradually declined. Annual mean temperature has increased by +0.0195 °C/year with the increase in seasonal mean temperature of pre-monsoon (0.0162 °C/year), monsoon (0.043 °C/year) and post-monsoon (0.0124 °C/year) during 1985–2018. The frequency of hot days is also increasing (0.4127 days/year) significantly. The maximum rate of rainfall during the monsoon has decreased (9.0098 mm/year) which leaves a chance of future drought conditions to occur at Rajshahi even in the rainy season. Land surface temperature is highly influenced by land cover pattern and it increases with expansion of impervious land cover like settlement. Simulation result considering the changing trend of meteorological elements and land cover reveals that surface temperature greater than 27.5 °C would slightly decrease in 2022 but overall lands with surface temperature 23.5–25.5 °C would increase during the post-monsoon period in 2022 and 2024.

Assessing the impact of climate change on crop yields in Gangetic Plains Region, India


 Against the increasing vulnerability of agriculture and farm livelihoods to climate change, the study attempted to analyse the trend in climate variables and their impact on major crop yields during the period from 1966-2011, across 4 agro-climatic zones forming Gangetic Plains Region. A rising trend was observed in annual and seasonal (kharif and rabi) mean maximum and minimum temperature across the zones. Rainfall on the other hand, showed a declining trend. Overall, climate change adversely impacted crop yield, but the magnitudes of such effects vary spatially. The results reveal that rice and wheat yield will decline in the entire Gangetic region. By 2050s, maize yield will be higher by 6 percent in Lower Gangetic Plains; pearl millet will increase by 15 percent and rapeseed & mustard by 3.8 percent in Trans-Gangetic Plains. Amongst the crops, sugarcane yield was the most impacted to climate change and is expected to reduce by 21 percent in Middle Gangetic Plains towards end of the century. Hence, there is a need to formulate sustainable adaptation measures and practices suitable to location-specific needs for enhancing climate resiliency and capacity of agricultural system to withstand climatic shocks.

Seasonal evolution and interseasonal changes in polar mesospheric clouds at high latitudes in the Southern Hemisphere

The total areas and other related characteristics of polar mesospheric clouds of the Southern Hemisphere are determined on the basis of the processing of images obtained from the AIM satellite mission. Daily images have been analyzed from 2007 to 2008 to 2019–2020 austral summer seasons. The set of the analyzed data allowed us to study the features of seasonal and inter-seasonal changes in the development of polar mesospheric clouds (PMCs). Long-term temporal changes in such parameters as the maximum and integral areas of the cloud field, the date of the first appearance and disappearance of PMCs, as well as their season durations have been considered in detail. The conclusion about the non-random nature of changes in these characteristics in some seasons is substantiated. It is possible that the PMC evolution is influenced by fluctuations in weather and climatic conditions in the Antarctic region. This assumption is supported by both the relationship between the intensity of mesospheric clouds development in the Southern Hemisphere and variations in seasonal mean (in the context of the clouds existence) temperatures in Antarctica as well as between the stable similarity in seasonal changes in temperature and PMC field areas.

Impacts of forestation on the annual and seasonal water balance of a tropical catchment under climate change

BackgroundThis study aims to assess the effects of a forestation program and climate change on the annual and seasonal water balance of the Bogowonto catchment (597 km2) in Java, Indonesia. The catchment study is rare example in Indonesia where forestation has been applied at the catchment level. However, since the forestation program has been initiated, evaluations of the program only focus on the planting area targets, while the environmental success e.g., impacts on the hydrological processes have never been assessed. This study used a calibrated Soil and Water Assessment Tool (SWAT) model to diagnose the isolated and combined effects of forestation and climate change on five water balance components, namely streamflow (Q), evapotranspiration (ET), surface runoff (Qs), lateral flow (Ql) and base flow (Qb).ResultsThe results show that from 2006 to 2019, forest cover has increased from 2.7% to 12.8% of the total area, while in the same period there was an increase in the mean annual and seasonal temperature, rainfall, and streamflow. Results of SWAT simulations show that changes in the mean annual and seasonal water balance under the forestation only scenario were relatively minor, while changes were more pronounced under the climate change only scenario. Based on the combined impacts scenario, it was observed that the effects of a larger forest area on the water balance were smaller than the effects of climate change.ConclusionsAlthough we found that forestation program has minor impacts compared to that of climate change on the hydrological processes in the Bogowonto catchment, seasonally, forestation activity has decreased the streamflow and surface runoff during the wet season which may reduce the risk of moderate floods. However, much attention should be paid to the way how forestation may result in severe drought events during the dry season. Finally, we urge the importance of accounting for the positive and negative effects in future forestation programs.

Fine-scale variation in microhabitat conditions influences physiology and metabolism in an Antarctic insect.

Microhabitats with distinct biotic and abiotic properties exist within landscapes, and this microhabitat variation can have dramatic impacts on the phenology and physiology of the organisms occupying them. The Antarctic midge Belgica antarctica inhabits diverse microhabitats along the Western Antarctic Peninsula that vary in macrophyte composition, hygric qualities, nutrient input, and thermal patterns. Here, we compare seasonal physiological changes in five populations of B. antarctica living in close proximity but in different microhabitats in the vicinity of Palmer Station, Antarctica. Thermal regimes among our sample locations differed in both mean temperature and thermal stability. Between the warmest and coldest sites, seasonal mean temperatures differed by 2.6˚C and degree day accumulations above freezing differed by a factor of 1.7. Larval metabolic and growth rates varied among the sites, and adult emergence occurred at different times. Distinct microhabitats also corresponded with differences in body composition, as lipid and carbohydrate content of larvae differed across sites. Further, seasonal changes in carbohydrate and protein content were dependent on site, indicating fine-scale variation in the biochemical composition of larvae as they prepare for winter. Together, these results demonstrate that variation in microhabitat properties influences the ontogeny, phenology, physiology, and biochemical makeup of midge populations living in close proximity. These results have implications for predicting responses of Antarctic ecosystems to environmental change.

A New Framework for Identifying and Investigating Seasonal Climate Extremes

AbstractPrevious studies have recognized the societal relevance of climatic extremes on the seasonal time scale and examined physical processes leading to individual high-impact extreme seasons (e.g., extremely wet or warm seasons). However, these findings have not yet been generalized beyond case studies since at any specific location only very few seasonal events of such rarity occurred in the observational record. In this concept paper, a pragmatic approach to pool seasonal extremes across space is developed and applied to investigate hot summers and cold winters in ERA-Interim and the Community Earth System Model Large Ensemble (CESM-LENS). We identify spatial extreme season objects as contiguous regions of extreme seasonal mean temperatures based on statistical modeling. Regional pooling of extreme season objects in CESM-LENS then yields considerable samples of analogs to even the most extreme ERA-Interim events. This approach offers numerous opportunities for systematically analyzing large samples of extreme seasons, and several such analyses are illustrated. We reveal a striking co-occurrence of El Niño to La Niña transitions and the largest ERA-Interim midlatitude extreme summer events. Moreover, we perform a climate model evaluation with regard to extreme season size and intensity measures and estimate how often an extreme winter like the cold North American 2013/14 winter is expected anywhere in midlatitude regions. Furthermore, we present a large set of simulated analogs to this event, which makes it possible to study commonalities and differences of their underlying physical processes. Finally, substantial but spatially varying climatological differences in the size of extreme summer and extreme winter objects are identified.

The potential benefit of the use of seasonal forecast during the agricultural economic year 2019-2020 in Bulgaria

Seasonal forecasting gained ground in the last decades by building up knowledge on the processes staying behind the climate variability at the seasonal time scale, constructing ever more sophisticated general circulation models and ensemble prediction systems and thus enhancing forecast skill. The seasonal forecast is a climate forecast and is therefore probabilistic in nature. The predictability of the atmospheric circulation at the seasonal scale is limited in the middle latitudes, where Europe and Bulgaria are situated, by its chaotic nature. The current standard is to give forecast of the potential anomalies of the mean seasonal temperature and the seasonal amount of precipitation. The National Institute of Meteorology and Hydrology of Bulgaria has been issuing operationally seasonal forecast for the country since 2005. The goal of this work is to discuss the seasonal forecast for the last agricultural year 2019-2020. The year was characterized by its drought conditions especially in Eastern Bulgaria. This work would show the extent to which it was successfully predicted and how the seasonal forecast could have been used for decision making. The use of agrometeorological indices for the analysis of the skill of the seasonal forecast has been shown.

Seasonal Profile and Five-Year Trend Analysis of Malaria Prevalence in Maygaba Health Center, Welkait District, Northwest Ethiopia.

Background Malaria is a serious public health problem of most developing countries, including Ethiopia. The burden of malaria is severely affecting the economy and lives of people, particularly among the productive ages of rural society. Thus, this study was targeted to analyze the past five-year retrospective malaria data among the rural setting of Maygaba town, Welkait district, northwest Ethiopia. Methods The study was done on 36,219 outpatients attending for malaria diagnosis during January 2015 to 2019. Data was extracted from the outpatient medical database. Chi-square (χ2) test and binary logistic regression model were used to analyze the retrospective data. Statistical significance was defined at p < 0.05. Results Of 36,219 outpatients examined, 7,309 (20.2%) malaria-positive cases were reported during 2015-2019. There was a fluctuating trend in the number of malaria-suspected and -confirmed cases in each year. Male slide-confirmed (61.4%, N = 4,485) were significantly higher than females (38.6%, N = 2,824) (p < 005). Plasmodium falciparum and Plasmodium vivax were the dominant parasites detected, which accounted for 66.1%; N = 4832, 33.9%; N = 2477, respectively. Despite the seasonal abundance of malaria cases, the highest prevalence was recorded in autumn (September to November) in the study area. Binary logistic regression analysis revealed that statistically significant associations were observed between sexes, interseasons, mean seasonal rainfall, and mean seasonal temperature with the prevalence of P. vivax. However, P. falciparum has shown a significant association with interseasons and mean seasonal temperature. Conclusions Although the overall prevalence of malaria was continually declined from 2015-2019, malaria remains the major public health problem in the study area. The severe species of P. falciparum was found to be the dominant parasite reported in the study area. A collaborative action between the national malaria control program and its partners towards the transmission, prevention, and control of the two deadly species is highly recommended.

How does the CMIP6 ensemble change the picture for European climate projections?

We compare Coupled Model Intercomparison Project (CMIP) ensemble (CMIP6) projections for seasonal mean temperature and precipitation to CMIP5 for northern Europe, central Europe and the Mediterranean. The CMIP6 ensemble shows increased projected summer warming compared to CMIP5, which was found to be statistically significant in central Europe and the Mediterranean. Precipitation projections for Central Europe in CMIP6 were found to have a stronger drying trend in the summer months, there was also a substantially narrower projection range. Spatial comparisons indicate that this stronger drying trend also extends into a large part northern Europe. We show that warmer projected summer temperatures in northern Europe and the Mediterranean are largely driven by the higher global climate sensitivities in CMIP6 models, while regional changes are broadly similar. In central Europe a significant difference in the regional responses was found and in these cases the picture can be said to have changed. We find the difference in regional sensitivity is important in central Europe where it accounts for roughly 40 of the differences between ensembles in projected regional temperature.

Calling phenology of anurans in a tropical rainforest in South Mexico: testing predictive models

ABSTRACT Anuran calling activity has been widely associated with abiotic factors, such as temperature, rain and photoperiod. In this sense, mathematical modelling could help us to decipher the mechanisms that underlie these phenomena in order to be able to predict possible scenarios. Here, we propose 15 models that incorporate some environmental variables with biologic relevance: photoperiod, accumulated rain, dry/wet season, and annual mean temperature. The calling activity patterns were obtained with an automated recording system (ARS) for 7 months in Nahá, Chiapas, Mexico, where nine species were recorded. Those models were compared through the Akaike information criterion corrected for small samples (AICC ) to find the one that best fits. Our results point to a sinusoidal model containing photoperiod, dry-wet season and accumulated rain as the best model. Photoperiod resulted as a critical variable driving phenological calling activity patterns since whenever it was incorporated in a model, its values were reduced. It is interesting because the photoperiod, through photoreception, allows anurans to synchronise their physiology and behaviour throughout the year, being able to control the growth, development and maintenance of organisms by starting the reproduction phases in optimal seasons for their development. However, it is also important to highlight that phenological structure of the anuran calls in Nahá, México is better explained by a combination of mechanisms, which includes photoperiod, rainfall and seasonality, but not temperature.

Spatio-temporal variations of land surface temperature and precipitation due to climate change in the Jhelum river basin, India

The present study investigated the spatio-temporal variations of precipitation and temperature for the projected period (2011-2100) in the Jhelum basin, India. The precipitation and temperature variables are projected under RCP 8.5 scenario using statistical down scaling techniques such as Artificial Neural Network (ANN) and Wavelet Artificial Neural Network (WANN) models. Firstly, the screened predictors were downscaled to predictand using ANN and WANN models for all the study stations. On the basis of the performance criteria, the WANN model is selected as an efficient model for downscaling of precipitation and temperature. The future screened predictor data pertaining to RCP 8.5 of CanESM2 model were downscaled to monthly temperature and precipitation for future periods (2011-2100) using WANN models. The investigation of the future projections revealed an average increase of 17-25% in the mean annual precipitation and 20-25% average increase in the monthly mean precipitation for all the selected stations towards the end of 21st century. The monthly mean temperature also showed an increase of 2-3 °C for all the study stations towards the end of 21st century. The mean seasonal temperature of the projected period is found to be increasing for all the four seasons in most parts of the basin.

Grain yield and greenhouse gas emissions from maize and wheat fields under plastic film and straw mulching: A meta-analysis

Understanding the effects of soil mulching on crop yields and greenhouse gas (N 2 O, CO 2 and CH 4 ) emissions is important for developing sustainable agriculture. However, no systematic conclusions have been drawn yet because the environmental factors vary among various studies. A meta-analysis was carried out to evaluate the impact of plastic film mulching (PM) and straw mulching (SM) on grain yields and greenhouse gas emissions from two major cereal crops (maize and wheat) based on data from 61 peer-reviewed publications. The results showed that PM and SM, when averaged across all studies, significantly increased maize yield (13.3 % and 7.3 %, respectively) and wheat yield (12.5 % and 3.5 %, respectively) compared with no-mulching (NM). Generally, PM and SM significantly increased N 2 O emission by 18.6 % and by 24.7 % compared with NM, respectively. CO 2 emission was significantly increased by PM and CH 4 uptake was significantly inhibited by PM, while the increase in CO 2 emission under SM was not significant but CH 4 uptake was significantly promoted. The impact of soil mulching on crop yields and GHG emissions was also affected by temperature, water input amount, cultivation practice and nitrogen (N) application rate. Specifically, ridge-furrow cultivation with plastic film on the ridge was highly recommended for maize production, especially when mean seasonal air temperature was <20℃ and water input was <350 mm. Straw mulching was more suitable for maize production under high temperature (>20℃) and water input (>350 mm). The effect of SM on the increase in wheat yield under low water input (<350 mm) and moderate temperature (>10℃) was not significantly worse than that of PM. N 2 O and CO 2 emissions under SM in the wheat field were not significantly increased compared to NM, while these emissions under PM were significantly increased when water input was <350 mm and temperatue >10℃. However, PM should be given more consideration in wheat production because its greater effect size on wheat yield and lower effect size on N 2 O emission relative to SM under most classifications, especially under partial mulching. These results have important implications for choosing proper mulching practices to increase crop yields while decreasing the adverse environmental effects in agricultural production.

Quantifying maize grain yield losses caused by climate change based on extensive field data across China

Climate change can have significant impacts on maize (Zea mays L.) grain yield. In this study, we used extensive multi-year field experimental data (n = 423) across different climatic regions of China to distinguish effects of different climate factors on maize grain yield and mimicking the effect of climate change, in particular, temperature and solar radiation. Across the years and climatic regions, the major driver for the yield difference was found to be the diurnal temperature range (DTR) and accumulated photosynthetically active radiation (PAR). 1 °C decrease in DTR and 100 MJ decrease in accumulated PAR resulted in 1.0 t ha−1 and 0.85 t ha−1 reduction in maize grain yield, respectively. 1 °C increase in growing season mean temperature (Tmean) and minimum temperature (Tmin) resulted in 0.83 t ha−1 and 0.67 t ha−1 maize grain yield reduction. By mimicking the effect of climate change on maize grain yield, it was found that 1 °C increase in Tmean resulted in 0.83 t ha−1 (5.8%) maize grain yield reduction. Decrease in PAR also decreased maize grain yield significantly and the 1% decrease in PAR decreased maize grain yield by 0.15 tons per ha. Among all the five cultivars in this study, yield reductions of ZD958 and NH101 induced by 1 °C increase in Tmean were the smallest compared to other three cultivars. The quantitative analysis can provide important guidance to predict possible quantitative outcomes of future changes in crop productivity due to climate change and search for effective mitigation strategies such as cultivar selection.

Effect of Temperature on Sowing Dates of Wheat under Arid and Semi-Arid Climatic Regions and Impact Quantification of Climate Change through Mechanistic Modeling with Evidence from Field

Rising temperature from climate change is the most threatening factor worldwide for crop production. Sustainable wheat production is a challenge due to climate change and variability, which is ultimately a serious threat to food security in Pakistan. A series of field experiments were conducted during seasons 2013–2014 and 2014–2015 in the semi-arid (Faisalabad) and arid (Layyah) regions of Punjab-Pakistan. Three spring wheat genotypes were evaluated under eleven sowing dates from 16 October to 16 March, with an interval of 14–16 days in the two regions. Data for the model calibration and evaluation were collected from field experiments following the standard procedures and protocols. The grain yield under future climate scenarios was simulated by using a well-calibrated CERES-wheat model included in DSSAT v4.7. Future (2051–2100) and baseline (1980–2015) climatic data were simulated using 29 global circulation models (GCMs) under representative concentration pathway (RCP) 8.5. These GCMs were distributed among five quadrants of climatic conditions (Hot/Wet, Hot/Dry, Cool/Dry, Cool/Wet, and Middle) by a stretched distribution approach based on temperature and rainfall change. A maximum of ten GCMs predicted the chances of Middle climatic conditions during the second half of the century (2051–2100). The average temperature during the wheat season in a semi-arid region and arid region would increase by 3.52 °C and 3.84 °C, respectively, under Middle climatic conditions using the RCP 8.5 scenario during the second half-century. The simulated grain yield was reduced by 23.5% in the semi-arid region and 35.45% in the arid region under Middle climatic conditions (scenario). Mean seasonal temperature (MST) of sowing dates ranged from 16 to 27.3 °C, while the mean temperature from the heading to maturity (MTHM) stage was varying between 12.9 to 30.4 °C. Coefficients of determination (R2) between wheat morphology parameters and temperature were highly significant, with a range of 0.84–0.96. Impacts of temperature on wheat sown on 15 March were found to be as severe as to exterminate the crop before heading. The spikes and spikelets were not formed under a mean seasonal temperature higher than 25.5 °C. In a nutshell, elevated temperature (3–4 °C) till the end-century can reduce grain yield by about 30% in semi-arid and arid regions of Pakistan. These findings are crucial for growers and especially for policymakers to decide on sustainable wheat production for food security in the region.

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

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

Response of Crop Water Requirement and Yield of Irrigated Rice to Elevated Temperature in Bangladesh

In the changing climatic condition, temperature is the most vulnerable parameter and is projecting a trend of increase in the future. Crop growth and development process depend largely on air temperature. This study aims to determine the role of increasing air temperature in yield, crop water requirement (CWR), and other agronomic parameters of irrigated rice. Ceres-rice model associated in the Decision Support System for Agrotechnology Transfer (DSSAT) was used in 15 different locations of Bangladesh. Grain yield, growth duration, and crop water requirement of widely cultivated irrigated rice (Boro rice) variety BRRI dhan28 were analysed in normal temperature and elevated air temperature by 1°C, 2°C, 3°C, and 4°C. The result revealed detrimental effect of elevated temperature on growth duration and grain yield. The estimated highest growth duration reduction of 30 days was found in Moulvibazar for 4°C temperature rise. The grain yield reduction was projected by 0–17%, 16–35%, 31–49%, and 39–61% from the normal condition if the seasonal mean temperature increased by 1°C, 2°C, 3°C, and 4°C, respectively. The country average crop water requirement was found to be 405 mm of which the highest 445 mm and the lowest 358 mm were recorded in Moulvibazar and Chandpur, respectively. The study revealed that the country average rice CWR reduced by 5%, 8%, 12%, and 17% over the normal condition for 1°C, 2°C, 3°C, and 4°C rising temperature, respectively. For 1°C temperature rise, BRRI dhan28 life span shortened by 6.4 days, grain yield reduced by 695 kg, and estimated CWR decreased by 14 mm. The projected declining CWR indicated that irrigated rice will require less irrigation water, but it will cause considerable yield loss under elevated temperature. Though elevated temperature will save huge irrigation water used in country-wide Boro rice cultivation, the crop developers need to introduce new heat-tolerant cultivar to minimize yield loss.

Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study

The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community.