Search Results (799)

This study presents a scenario-based assessment of the future sensitivity of minimal low-water runoff to climate change in Western Kazakhstan. An ensemble of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), combined with dynamically downscaled projections for Central Asia, was applied to estimate minimal monthly runoff during the summer–autumn and winter low-water periods for the rivers of the Zhaiyk–Caspian water management basin. The analysis covers three future time horizons: 2040 (2031–2050), 2060 (2051–2070), and 2080 (2071–2090), under two greenhouse gas concentration scenarios: SSP3-7.0 (moderately high emissions) and SSP5-8.5 (high emissions). The results reveal a pronounced seasonal contrast in the projected hydrological response. During the winter low-water period, a steady increase in minimal runoff is projected for all rivers, with the most significant changes observed for the Or, Zhem, Temir, and Shagan rivers. This increase is primarily driven by higher winter precipitation, increased thaw frequency, and enhanced infiltration recharge. Conversely, despite modest increases in summer–autumn precipitation, minimal runoff during the summer–autumn low-water period is projected to decline significantly, particularly in the southern basins, due to elevated evapotranspiration rates and soil moisture deficits associated with rising air temperatures. These findings emphasize the importance of developing seasonally differentiated, climate-resilient water management strategies to mitigate low-flow risks and ensure water security under future climate conditions in arid and semi-arid regions. Full article

The Indian heat low, a strong atmospheric circulation pattern marked by low pressure and high temperatures, significantly influences weather patterns over Pakistan and northern India. Understanding how terrestrial heating patterns changes over time is crucial for understanding climate change. Such changes can profoundly affect large-scale systems such as the heat low and monsoon. The evolving characteristics of the heat low area over the Indian subcontinent from 1940 to 2020 are investigated using a decadal analysis of reanalysis data (ERA5). The results indicate an eastward shift in the heat low over the historical period. Furthermore, ensembles of CMIP5 Global Climate Models (GCMs) under RCP6.0 and RCP8.5 scenarios project a continued eastward shift in this heat low pattern between 2050 and 2100. The eastward shift in the heat low pattern significantly influences the monsoon circulation system, potentially leading to substantial changes in rainfall distribution across the Indian subcontinent, as projected under RCP6.0 and RCP8.5 scenarios—with some differences between them. The results from the GCMs support the idea of an eastward shift in rainfall, indicating the potential for altered precipitation distribution in the future. Furthermore, the study emphasizes the possible drying of the western Indian subcontinent, including Pakistan and western India, because of the altering heat low trend. These findings highlight the necessity of considering changing atmospheric circulation patterns in climate change assessments and the need for a better understanding of the associated implications for regional climate dynamics. Full article

This study assesses future agricultural drought risk in the Ganjiang River Basin under climate change and land use change. A coupled analysis framework was established using the SWAT hydrological model, the CMIP6 climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5), and the PLUS land use simulation model. Key methods included the Standardized Soil Moisture Index (SSMI), travel time theory for drought event identification and duration analysis, Mann–Kendall trend test, and the Pettitt change-point test to examine soil moisture dynamics from 2027 to 2100. The results indicate that the CMIP6 ensemble performs excellently in temperature simulations, with a correlation coefficient of R² = 0.89 and a root mean square error of RMSE = 1.2 °C, compared to the observational data. The MMM-Best model also performs well in precipitation simulations, with R² = 0.82 and RMSE = 15.3 mm, compared to observational data. Land use changes between 2000 and 2020 showed a decrease in forestland (−3.2%), grassland (−2.8%), and construction land (−1.5%), with an increase in water (4.8%) and unused land (2.7%). Under all emission scenarios, the SSMI values fluctuate with standard deviations of 0.85 (SSP1-2.6), 1.12 (SSP2-4.5), and 1.34 (SSP5-8.5), with the strongest drought intensity observed under SSP5-8.5 (minimum SSMI = −2.8). Drought events exhibited spatial and temporal heterogeneity across scenarios, with drought-affected areas ranging from 25% (SSP1-2.6) to 45% (SSP5-8.5) of the basin. Notably, abrupt changes in soil moisture under SSP5-8.5 occurred earlier (2045–2050) due to intensified land use change, indicating strong human influence on hydrological cycles. This study integrated the CMIP6 climate projections with high-resolution human activity data to advance drought risk assessment methods. It established a framework for assessing agricultural drought risk at the regional scale that comprehensively considers climate and human influences, providing targeted guidance for the formulation of adaptive water resource and land management strategies. Full article

In the past three decades, localized research has highlighted shifts in rainfall patterns and temperature trends in central Ethiopia, a region vital for agriculture and economic activities and heavily dependent on climate conditions to sustain livelihoods and ensure food security. However, comprehensive analyses of long-term climate data remain limited for this area. Understanding local climate trends is essential for enhancing agricultural resilience in the study area, a region heavily dependent on rainfall for crop production. This study analyzes historical rainfall and temperature patterns over the past 30 years and projects future climate conditions using downscaled CMIP6 models under SSP4.5 and SSP8.5 scenarios. Results indicate spatial variability in rainfall trends, with certain areas showing increasing rainfall while others experience declines. Temperature has shown a consistent upward trend across all seasons, with more pronounced warming during the short rainy season (Belg). Climate projections suggest continued warming and moderate increases in annual rainfall, particularly under SSP8.5 by the end of the 21st century. It is concluded that both temperature and rainfall are projected to increase in magnitude by 2080, with higher Sen’s slope values compared to earlier periods, indicating a continued upward trend. These findings highlight potential breaks in agricultural calendars, such as shifts in rainfall onset and cessation, shortened or extended growing seasons, and increased risk of temperature-induced stress. This study highlights the need for localized adaptation strategies to safeguard agriculture production and enhance resilience in the face of future climate variability. Full article

As global climate change accelerates, the urban heat island (UHI) phenomenon has become increasingly pronounced, posing significant challenges to urban energy balance, atmospheric processes, and public health. This study used the Weather Research and Forecasting (WRF) model to dynamically downscale two CMIP6 scenarios—moderate forcing (SSP245) and high forcing (SSP585)—focusing on Zhengzhou, a rapidly urbanizing city in central China. High-resolution simulations captured fine-scale intra-urban temperature patterns and analyze the spatial and seasonal variations in UHI intensity in 2030 and 2060. The results demonstrated significant seasonal variations in UHI effects in Zhengzhou for both 2030 and 2060 under SSP245 and SSP585 scenarios, with the most pronounced warming in summer. Notably, under the SSP245 scenario, elevated autumn temperatures in suburban areas reduced the urban–rural temperature gradient, while intensified rural cooling during winter enhanced the UHI effect. These findings underscore the importance of integrating high-resolution climate modeling into urban planning and developing targeted adaptation strategies based on future UHI patterns to address climate challenges. Full article

This study investigates changes in Near-Surface Air Temperature (NSAT) over the South African region using weather station data, reanalysis products, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. It is shown that, based on ERA5 reanalysis, the average NSAT increase in the region (45–10° S, 0–50° E) for the period 1940–2023 was 0.11 ± 0.04 °C. Weak multi-decadal changes in NSAT were observed from 1940 to the mid-1970s, followed by a rapid warming trend starting in the mid-1970s. Weather station data generally confirm these results, although they exhibit considerable inter-station variability. An ensemble of 33 CMIP6 models also reproduces these multi-decadal NSAT change characteristics. Specifically, the average model-simulated NSAT values for the region increased by 0.63 ± 0.12 °C between the periods 1940–1969 and 1994–2023. Based on the results of the comparison between weather station observations, reanalysis, and models, we utilize projections of NSAT changes from the analyzed ensemble of 33 CMIP6 models until the end of the 21st century under various Shared Socioeconomic Pathway (SSP) scenarios. These projections indicate that the average NSAT of the South African region will increase between 1994–2023 and 2070–2099 by 0.92 ± 0.36 °C under the SSP1-2.6 scenario, by 1.73 ± 0.44 °C under SSP2-4.5, by 2.52 ± 0.50 °C under SSP3-7.0, and by 3.17 ± 0.68 °C under SSP5-8.5. Between 1994–2023 and 2025–2054, the increase in average NSAT for the studied region, considering inter-model spread, will be 0.49–1.15 °C, depending on the SSP scenario. Furthermore, climate warming in South Africa, both in the next 30 years and by the end of the 21st century, is projected to occur according to all 33 CMIP6 models under all considered SSP scenarios. The main spatial feature of this warming is a more significant increase in NSAT over the landmass of the studied region compared to its surrounding waters, due to the stabilizing role of the ocean. Full article

Previous research has primarily focused on assessing seasonal mean or annual extreme climate events, whereas intraseasonal variability in extreme climate has received comparatively little attention, despite its importance for understanding short-term climate dynamics and associated risks. This study evaluates the performance of nine climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing summer maximum temperature (Tmax) variability across China during 1979–2014, with the variability defined as the standard deviation of daily Tmax anomalies for each summer. Results show that most CMIP6 models fail to reproduce the observed north–south gradient of Tmax variability with significant regional biases and limited agreement on temporal trends. The multi-model ensemble (MME) outperforms most individual models in terms of root-mean-square error and spatial correlation, but it still under-represents the observed temporal trends, especially over southeastern and central China. Taylor diagram analysis reveals that EC-Earth3, GISS-E2-1-G, IPSL-CM6A-LR, and the MME perform relatively well in capturing the spatial characteristics of Tmax variability, whereas MIROC6 shows the poorest performance. These findings highlight the persistent limitations in simulating intraseasonal Tmax variability and underscore the need for improved model representations of regional climate dynamics over China. Full article

Understanding future changes in crop phenology and climate suitability is essential for sustaining winter wheat production in the Huang-Huai-Hai (3H) Plain under climate change. This study integrates bias-corrected CMIP6 climate projections, the DSSAT CERES-Wheat crop model, and Random Forest analysis to assess spatiotemporal shifts in winter wheat phenology and climate suitability. The assessment focuses on the mid- (2041–2060) and late 21st century (2081–2100) under the SSP2-4.5 and SSP5-8.5 scenarios. The results indicate that the vegetative and whole growing periods (VGP and WGP) will be extended in the mid-century but shorten by the late century. In contrast, the reproductive growing period (RGP) will be slightly reduced in the mid-century and extended under high emissions in the late century. Temperature suitability is projected to increase during the VGP and WGP but decline during the RGP. Precipitation suitability generally improves, except for a decrease during the reproductive period south of 32° N. Solar radiation suitability is expected to decline across all stages. Temperature is identified as the primary driver of phenological changes, with solar radiation and precipitation playing increasingly important roles in the mid- and late 21st century, respectively. Adaptive strategies, including the adoption of heat-tolerant varieties, longer reproductive periods, and earlier sowing, are recommended to enhance yield stability under future climate conditions. Full article

Watershed ecosystem resilience (RES) plays a vital role in supporting ecosystem sustainability. However, comprehensive assessments and investigations into the complex mechanisms driving RES remain limited, particularly in ecologically sensitive basins. To address this gap, this study proposes a multidimensional RES evaluation framework tailored to watershed-specific natural characteristics. The framework integrates five core dimensions: ecosystem resistance, ecosystem recovery capacity, ecosystem adaptability, ecosystem services, and ecosystem vitality. RES patterns under 2030 different future scenarios were simulated using the PLUS model combined with CMIP6 climate projections. Spatial and temporal dynamics of RES from 2010 to 2020 were quantified using Geodetector and Partial Least Squares Path Modeling, offering insights into the interactions among natural and anthropogenic drivers. The results reveal that RES in the Upper Yangtze River Basin exhibits a spatial gradient of “high in the east and west, low in the middle” with an overall 2.80% decline during the study period. Vegetation coverage and temperature emerged as dominant natural drivers, while land use change exerted significant indirect effects by altering ecological processes. This study emphasizes the importance of integrated land-climate strategies and offers valuable guidance for enhancing RES and supporting sustainable watershed management in the context of global environmental change. Full article

Rostov Oblast is one of the key grain-producing regions in Russia, accounting for 6% of the total grain production. However, it faces an increasing risk of climate aridization, which requires an accurate scientific assessment to ensure the food security of the country. The present study analyzes the spatial and temporal dynamics of climate aridification in the Rostov region for the period 1951–2054. This analysis is based on ERA5 reanalysis data and CMIP6 forecast models (MPI-ESM1-2-HR, CanESM5, BCC-CSM2-MR). The analysis indicates that the annual mean temperature in the region has increased by 2–3 °C since the 1950s, reaching 12 °C in 2023. At the same time, precipitation shows significant interannual variability with no detectable long-term trend. Spatial analysis reveals a stable meridional temperature gradient and zonality of precipitation distribution. The southeastern parts of the region are characterized by the highest degree of aridification. Projection models indicate further warming (+1.5–3 °C by 2054) and increasing contrasts between western (wetter) and eastern (drier) areas. Projections derived from the CMIP6 models indicate an intensification of aridification, accompanied by a decrease in the De Martonne index of 15–25% by the year 2054. The area of territories with arid climates is expected to increase from 30% to 40%. The most vulnerable regions will be in the southeast part of Rostov Oblast, where the De Martonne index values are predicted to decrease to less than 10. The potential increase in temperature and evapotranspiration, coupled with spatial differentiation, could pose significant risks to the sustainability of the agro-industrial complex, particularly in the southeastern part of the region. Full article

Aerosols are critical to the Earth’s atmosphere, influencing climate through interactions with solar radiation and clouds. However, accurately replicating the interactions between aerosols and clouds remains challenging due to the complexity of the physical processes involved. This study evaluated the performance of Coupled Model Intercomparison Project phase 6 (CMIP6) models in simulating aerosol optical depth (AOD), cloud fraction (CF), and liquid water path (LWP) by comparing them with satellite observations from MODIS and AMSR-E. Using 30 years of CMIP6 model simulations and available satellite observations during the satellite era, the results show that most CMIP6 models underestimate CF and LWP by 24.3% for LWP in the Northern Hemisphere. An assessment of spatial patterns indicates that models generally align more closely with observations in the Northern Hemisphere than in the Southern Hemisphere. Latitudinal profiles reveal that while most models capture the overall distribution patterns, they struggle to accurately reproduce observed magnitudes. A quantitative scoring system is applied to evaluate each model’s ability to replicate the spatial characteristics of multi-year mean aerosol and cloud properties. Overall, the findings suggest that CMIP6 models perform better in simulating AOD and CF than LWP, particularly in the Southern Hemisphere. Full article

Over the past few decades, occurrences of extreme climatic events have escalated significantly, with severe repercussions for global ecosystems and socio-economics. northern China (NC), characterized by its complex topography and diverse climatic conditions, represents a typical ecologically vulnerable region where vegetation is highly sensitive to climate change. Therefore, monitoring vegetation dynamics and analyzing the influence of extreme climatic events on vegetation are crucial for ecological conservation efforts in NC. Based on extreme climate indicators and the Normalized Difference Vegetation Index (NDVI), this study employed trend analysis, Ensemble Empirical Mode Decomposition, all subsets regression analysis, and random forest to quantitatively investigate the spatiotemporal variations in historical and projected future NDVI trends in NC, as well as their responses to extreme climatic conditions. The results indicate that from 1982 to 2018, the NDVI in NC generally exhibited a recovery trend, with an average growth rate of 0.003/a and a short-term variation cycle of approximately 3 years. Vegetation restoration across most areas was primarily driven by short-term high temperatures and long-term precipitation patterns. Future projections under different emission scenarios (SSP245 and SSP585) suggest that extreme climate change will continue to follow historical trends. However, increased radiative forcing is expected to constrain both the rate of vegetation growth and its spatial expansion. These findings provide a scientific basis for mitigating the impacts of climate anomalies and improving ecological quality in NC. Full article

Catalpa bungei C. A. Mey, an economically and ecologically important tree species endemic to China, exhibits notable drought resistance; however, the spatial dynamics of its habitat under future climate change have not been thoroughly investigated. We employed a parameter-optimized MaxEnt modeling framework to project current and future suitable habitats for C. bungei under two Shared Socioeconomic Pathway scenarios, SSP126 (low-emission) and SSP585 (high-emission), based on CMIP6 climate data. We incorporated 126 spatially rarefied occurrence records and 22 environmental variables into a rigorous modeling workflow that included multicollinearity assessment and systematic variable screening. Parameter optimization was performed using the kuenm package in R version 4.2.3, and the best-performing model configuration was selected (Regularization Multiplier = 2.5; Feature Combination = LQT) based on the AICc, omission rate, and evaluation metrics (AUC, TSS, and Kappa). Model validation demonstrated robust predictive accuracy. Four primary environmental predictors obtained from WorldClim version 2.1—the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), maximum temperature of the warmest month (Bio5), and elevation—collectively explained over 90% of habitat suitability. Currently, the optimal habitats are concentrated in central and eastern China. By the 2090s, the total suitable habitats are projected to increase by approximately 4.25% under SSP126 and 18.92% under SSP585, coupled with a significant northwestward shift in the habitat centroid. Conversely, extremely suitable habitats are expected to markedly decline, particularly in southern China, due to escalating climatic stress. These findings highlight the need for adaptive afforestation planning and targeted conservation strategies to enhance the climate resilience of C. bungei under future climate change. Full article

Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO₂, NO₂) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO₂ and 45–55% for NO₂ concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m⁻³ for SO₂ and exceed 100 μg m⁻³ for NO₂ in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO₂ under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article

Vapour pressure deficit (VPD) is frequently used to assess the impact of climate change on wildfires, vegetation, and other phenomena dependent on atmospheric moisture. A common aim of projection studies is to sample the full range of changes projected by climate models. Although characterization of model spread in projected temperature and rainfall is common, similar analyses are lacking for VPD. Here, we analyze the range of change in projected VPD from a 15-member CMIP6 model ensemble using the SSP-370 scenario. Projected changes are calculated for 2015–2100 relative to the historical period 1850–2014, and the resulting changes are analyzed on a seasonal and regional basis, the latter using continents based on IPCC reference regions. We find substantial regional differences including higher increases in VPD in areas towards the equatorial regions, indicating increased vulnerability to climate change in these areas. Seasonal assessments reveal that regions in the Northern Hemisphere experience peak VPD changes in summer (JJA), correlating with higher temperatures and lower relative humidity, while Southern Hemisphere areas like South America see notable increases in all seasons. We find that the mean projected change in seasonal VPD ranges from 0.02–0.23 kPa in Europe, 0.04–0.19 kPa in Asia, 0.02–0.16 kPa in North America, 0.15–0.33 kPa in South America, 0.10–0.18 kPa in Oceania, and 0.21–0.31 kPa in Africa. Our analysis suggests that for most regions, no two models span the range of projected change in VPD for all seasons. The overall projected change space for VPD identified here can be used to interpret existing studies and support model selection for future climate change impact assessments that seek to span this range. Full article