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

Forest microclimates regulate biodiversity, ecosystem functioning, and species' thermal tolerance, yet tropical forest disturbances (logging, fire and fragmentation) increasingly disrupt these conditions. Southeast Asia, home to 15% of the world's tropical forests, faces rapid deforestation and degradation, but large‐scale changes in maximum microclimate temperature (Tmicromax) remain poorly quantified. Here, we estimated baseline average Tmicromax (1984–2014), measured 15 cm above the surface and projected changes by 2050 under different scenarios combining climate forcing and forest cover change. Baseline Tmicromax ranged from 11.6°C to 25.1°C, driven mainly by topography, macroclimate, and vegetation structure. Projections indicate regional mean increases of 1.4°C (SSP2‐4.5) to 2.1°C (SSP5‐8.5). By 2050%, 52%–66% of forests may experience Tmicromax values warmer than any in the baseline period on average with Brunei, Indonesia, and Malaysia showing the strongest changes. These results reveal substantial sub‐canopy warming, posing potential risks for tropical biodiversity and highlighting the need for spatially informed conservation planning.

Solar photovoltaic (PV) energy is one of the fastest growing renewable energy sources globally. However, the dependency of PV generation on climatological factors such as the intensity of radiation, temperature, wind speed, cloud cover, etc can impact future power generation capacity. Considering the future large-scale deployment of PV systems, accurate climate information is essential for PV site selection, stable grid regulation, planning and energy output projections. In this study, the long-term changes in the future PV potential are estimated over Australia using regional climate projections for the near-future (2020–2039) and far-future (2060–2079) periods under a high emission scenario that projects 3.4 °C warming by 2100. The effects of projected changes in shortwave downwelling radiation, temperature and wind speed on the future performance of PV systems over Australia is also examined. Results indicate decline in the future PV potential over most of the continent due to reduced insolation and increased temperature. Northern coastal Australia experiences negligible increase in PV potential during the far future period due to increase in radiation and wind speed in that region. On further investigation, we find that the cell temperatures are projected to increase in the future under a high emission scenario (2.5 °C by 2079), resulting in increased degradation and risks of failure. The elevated cell temperatures significantly contribute to cell efficiency losses, that are expected to increase in the future (6–13 d yr −1 for multi-crystalline silicon cells) mostly around Western and central Australia indicating further reductions in PV power generation. Therefore, long-term PV power projections can help understand the variations in future power generation and identify regions where PV systems will be highly susceptible to losses in Australia.

Facing the western Pacific Ocean and backed by the Eurasian continent, the coastal area of China (hereafter as CAC) is sensitive and vulnerable to climate change due to the compound effects of land-ocean-atmosphere, and thus is prone to suffer huge climate-related disaster losses because of its large population density and fast developed economy in the context of global warming. Here in this study the near- (2040), mid- (2070), and long-future (2100) mean, minimum, and maximum temperature (Tmean, Tmin, and Tmax) projections based on the statistic downscaling climate prediction model (SimCLIM) integrated with 44 General Circulation Models (GCMs) of CMIP5 under three representative concentration pathway (RCP4.5, RCP6.0, and RCP8.5) scenarios are evaluated over CAC and its sub-regions. Multi-model ensemble of the selected GCMs demonstrated that there was a dominating and consistent warming trend of Tmean, Tmin, and Tmax in the Chinese coastal area in the future. Under RCP4.5, RCP6.0, and RCP8.5 scenarios, the annual temperature increase was respectively projected to be in the range of 0.8–1.2°C for 2040, 1.5–2.7°C for 2070, and 1.6–4.4°C for 2100 over the entire CAC. Moreover, a spatial differentiation of temperature changes both on the sub-regional and meteorological station scales was also revealed, generally showing an increment with “high south and low north” for annual average Tmean but “high north and low south” for Tmin and Tmax. An obvious lower increase of Tmean in the hotter months like July and August in the south and a significant sharper increment of Tmin and Tmax in the colder months such as January, February, and December in the north were expected in the future. Results derived from this study are anticipated to provide insights into future temperature changes and also assist in the development of target climate change mitigation and adaptation measures in the coastal area of China.

Equilibrium climate sensitivity (ECS) and transient climate response (TCR) are both measures of the sensitivity of the climate system to external forcing, in terms of temperature response to CO 2 doubling. Here it is shown that, of the two, TCR in current-generation coupled climate models is better correlated with the model projected temperature change from the pre-industrial state, not only on decadal time scales but throughout much of the 21st century. For strong mitigation scenarios the difference persists until the end of the century. Historical forcing on the other hand has a significant degree of predictive power of past temperature evolution in the models, but is not relevant to the magnitude of temperature change in their future projections. Regional analysis shows a superior predictive power of ECS over TCR during the latter half of the 21st century in areas with slow warming, illustrating that although TCR is a better predictor of warming on a global scale, it does not capture delayed regional feedbacks, or pattern effects. The transient warming at CO 2 quadrupling (T140) is found to be correlated with global mean temperature anomaly for a longer time than TCR, and it also better describes the pattern of regional temperature anomaly at the end of the century. Over the 20th century, there is a weak correlation between total forcing and ECS, contributing to, but not determining, the model agreement with observed warming. ECS and aerosol forcing in the models are not correlated.

The Australian Alps are the highest mountain range in Australia, which are important for biodiversity, energy generation and winter tourism. Significant increases in temperature in the past decades has had a huge impact on biodiversity and ecosystem in this region. In this study, observed temperature is used to assess how temperature changed over the Australian Alps and surrounding areas. We also use outputs from two generations of NARCliM (NSW and Australian Regional Climate Modelling) to investigate spatial and temporal variation of future changes in temperature and its extremes. The results show temperature increases faster for the Australian Alps than the surrounding areas, with clear spatial and temporal variation. The changes in temperature and its extremes are found to be strongly correlated with changes in albedo, which suggests faster warming in cool season might be dominated by decrease in albedo resulting from future changes in natural snowfall and snowpack. The warming induced reduction in future snow cover in the Australian Alps will have a significant impact on this region.

In optical sparse projection reconstruction, the reconstruction of the tested field often requires the utilization of a priori knowledge to compensate for the lack of information due to the sparse projection angle. For situations where the radiation field of unknown materials is reconstructed or prior knowledge cannot be obtained, this paper proposes a multi-spectral temperature field reconstruction technology under a sparse projection. This technology utilizes the principles of multi-spectral temperature measurement technology, takes the correlation of radiation information between sub-regions of the temperature field as the optimization objective, and establishes statistical rules between the missing information by combining the equation constraint optimization algorithm and multi-spectral temperature measurement technology. Finally, the temperature field to be measured is reconstructed. The simulation and experimental tests show that, without any prior knowledge, the proposed method can reconstruct the temperature field under two projection angles, with an accuracy of 1.64~12.25%. Moreover, the projection angle is lower, and the robustness is stronger than that of the other methods.

Changes in near surface air temperature (Δ T ) in response to anthropogenic greenhouse gas forcing are expected to show spatial heterogeneity because energy and moisture fluxes are modulated by features of the landscape that are also heterogeneous at these spatial scales. Detecting statistically meaningful heterogeneity requires a combination of high spatial resolution and a large number of simulations. To investigate spatial variability of projected Δ T , we generated regional, high-resolution (25-km horizontal), large ensemble (100 members per year), climate simulations of western United States (US) for the periods 1985–2014 and 2030–2059, the latter with atmospheric constituent concentrations from the Representative Concentration Pathway 4.5. Using the large ensemble, 95 % confidence interval sizes for grid-cell-scale temperature responses were on the order of 0.1 °C, compared to 1 °C from a single ensemble member only. In both winter and spring, the snow-albedo feedback statistically explains roughly half of the spatial variability in Δ T . Simulated decreases in albedo exceed 0.1 in places, with rates of change in T per 0.1 decrease in albedo ranging from 0.3 to 1.4 °C. In summer, Δ T pattern in the northwest US is correlated with the pattern of decreasing precipitation. In all seasons, changing lapse rates in the low-to-middle troposphere may account for up to 0.2 °C differences in warming across the western US. Near the coast, a major control of spatial variation is the differential warming between sea and land.

This study examined the temperature variations in West Africa's Volta River Basin (VRB) from 2021 to 2050 in comparison to the historical period (1985–2014) under two Shared Socioeconomic Pathway Scenarios (SSP2-4.5 and SSP5-8.5). Datasets from three Global Climate Models (GCMs) of the sixth Coupled Model Intercomparison Project (CMIP6) were used. The GCMs and their ensemble were evaluated on a monthly scale. The study used the ensemble mean to analyse the changes in annual and monthly temperature over the Sahel, Savannah, Guinea Coast, and the entire Volta basin. The results demonstrate the individual GCMs reproduced the observed temperature pattern at the VRB, though with some overestimations, but the ensemble mean indicated a better representation of the observed temperature. A warming trend in the basin is projected under both climate scenarios, with higher temperatures projected under SSP5-8.5 compared to SSP2-4.5 in all three zones. The mean annual temperature is projected to increase by 0.8 and 1.0 °C, with a statistically increasing trend under SSP2-4.5 and SSP5-8.5, respectively. Without a doubt, high temperatures, if unchecked, can erupt into resource conflict among the competing interest groups, thereby affecting the achievement of economic development at the VRB.

This study used an ERA5 reanalysis SST dataset re-gridded to a common grid with a 0.25° × 0.25° spatial resolution (latitude × longitude) for the historical (1940–2014) and projected (2015–2100) periods. The SST simulation under the SSP5-8.5 scenario was carried out with outputs from eight General Circulation Models (GCMs). The bias-corrected dataset was developed using Empirical Quantile Mapping (EQM) for the historical (1940–2015) and future (2030–2100) periods while the CMIP6 model simulation was evaluated against the ERA5 monthly observed reanalysis data for temperatures over the Gulf of Guinea. Overall, the CMIP6 models’ future simulations in 2030–20100 based on the SSP5-8.5 scenario indicate that SSTs are projected, for the Gulf of Guinea, to increase by 4.61 °C, from 31 °C in the coast in 2030 to 35 °C in 2100, and 2.6 °C in the Western GOG (Sahel). The Linux-based Ncview, Ferret, and the CDO (Climate Data Operator) software packages were used to perform further data re-gridding and assess statistical functions concerning the data. In addition, ArcGIS was used to develop output maps for visualizing the spatial trends of the historical and future outputs of the GCM. The correlation coefficient (r) was used to evaluate the performance of the CMIP6 models, and the analysis showed ACCESS 0.1, CAMS CSM 0.2, CAN ESM 0.3, CMCC 0.3, and MCM 0.4, indicating that all models performed well in capturing the climatological patterns of the SSTs. The CMIP6 bias-corrected model simulations showed that increased SST warming over the GOG will be higher in the far period than the near-term climate scenario. This study affirms that the CMIP6 projections can be used for multiple assessments related to climate and hydrological impact studies and for the development of mitigation measures under a warming climate.