Search Results (491)

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

Southeast Asia relies heavily on hydropower from dams and reservoir projects, but this dependence comes at the cost of ecological damage and increased vulnerability to extreme events. This dilemma necessitates a choice between continued dam development and adopting alternative renewable options. Concerns over these environmental impacts have already led to halts in dam construction across the region. This study assesses the potential of run-of-river hydropower plants (RHPs) across 199 hydrometric stations in Mainland Southeast Asia (MSEA). The assessment utilizes power duration curves for the historical period and projections from the HBV hydrological model, which is driven by an ensemble of 31 climate models for future scenarios. Energy production was analyzed at four levels (minimum, maximum, balanced, and optimal) for both historical and future periods under varying Shared Socioeconomic Pathways (SSPs). To promote sustainable development, environmental flow constraints and carbon dioxide (CO₂) emissions were evaluated for both historical and projected periods. The results indicate that the aggregate energy production potential during the historical period ranges from 111.15 to 229.62 MW (Malaysia), 582.78 to 3615.36 MW (Myanmar), 555.47 to 3142.46 MW (Thailand), 1067.05 to 6401.25 MW (Laos), 28.07 to 189.77 MW (Vietnam), and 566.13 to 2803.75 MW (Cambodia). The impact of climate change on power production varies significantly across countries, depending on the level and scenarios. At the optimal level, an average production change of −9.2–5.9% is projected for the near future, increasing to 15.3–19% in the far future. Additionally, RHP development in MSEA is estimated to avoid 32.5 Mt of CO₂ emissions at the optimal level. The analysis further shows avoidance change of 8.3–25.3% and −8.6–25.3% under SSP245 and SSP585, respectively. Full article

Dutch elm disease is one of the most devastating plant diseases, primarily spread through bark beetles. Scolytus scolytus is a key vector of this disease. In this study, distribution data of S. scolytus were collected and filtered. Combined with environmental and climatic variables, an ensemble model was developed using the Biomod2 platform to predict its potential geographical distribution in China. The selection of climate variables was critical for accurate prediction. Eight bioclimatic factors with high importance were selected from 19 candidate variables. Among these, the three most important factors are the minimum temperature of the coldest month (bio6), precipitation seasonality (bio15), and precipitation in the driest quarter (bio17). Under current climate conditions, suitable habitats for S. scolytus are mainly located in the temperate regions between 30° and 60° N latitude. These include parts of Europe, East Asia, eastern and northwestern North America, and southern and northeastern South America. In China, the low-suitability area was estimated at 37,883.39 km², and the medium-suitability area at 251.14 km². No high-suitability regions were identified. However, low-suitability zones were widespread across multiple provinces. Under future climate scenarios, low-suitability areas are still projected across China. Medium-suitability areas are expected to increase under SSP370 and SSP585, particularly along the eastern coastal regions, peaking between 2041 and 2060. High-suitability zones may also emerge under these two scenarios, again concentrated in coastal areas. These findings provide a theoretical basis for entry quarantine measures and early warning systems aimed at controlling the spread of S. scolytus in China. Full article

Abies nephrolepis and Picea jezoensis are native Pinaceae trees distributed in high mountainous regions of Northeast Asia (typically above ~1000 m a.s.l. on the Korean peninsula, northeastern China, Sakhalin, and the Russian Far East) and southern boreal forests, vulnerable to climate change and human disturbances, necessitating accurate habitat identification for effective conservation. While protected areas (PAs) are essential, merely expanding existing ones often fail to protect populations under human pressure and climate change. Using species distribution models with current and projected climate data, we mapped potential habitats across Northeast Asia. Spatial clustering analyses integrated with PA and land cover data helped identify optimal sites and priorities for new conservation areas. Ensemble species distribution models indicated extensive suitable habitats, especially in southern Sikhote-Alin, influenced by maritime-continental climates. Specific climate variables strongly affected habitat suitability for both species. The Kamchatka peninsula consistently emerged as an optimal habitat under future climate scenarios. Our study highlights essential environmental characteristics shaping the habitats of these species, reinforcing the importance of strategically enhancing existing PAs, and establishing new ones. These insights inform proactive conservation strategies for current and future challenges, by focusing on climate refugia and future habitat stability. 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

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

Projected changes in outdoor environmental conditions are expected to significantly alter building energy demand across the United States. Yet, policymakers and designers lack typology and climate-zone-specific guidance to support long-term planning. We simulated 10 U.S. Department of Energy (DOE) prototype buildings across all 16 ASHRAE climate zones with EnergyPlus. Future weather files generated in Meteonorm from a CMIP6 ensemble reflected two emissions pathways (RCP 4.5 and RCP 8.5) and two planning horizons (2050 and 2080), producing 800 simulations. Envelope parameters and schedules were held at DOE reference values to isolate the pure climate signal. Results show that cooling energy use intensity (EUI) in very hot-humid Zones 1A–2A climbs by 12% for full-service restaurants and 21% for medium offices by 2080 under RCP 8.5, while heating EUI in sub-arctic Zone 8 falls by 14–20%. Hospitals and large hotels change by < 6%, showing resilience linked to high internal gains. A simple linear-regression meta-model (R² > 0.90) links baseline EUI to future percentage change, enabling rapid screening of vulnerable stock without further simulation. These high-resolution maps supply actionable targets for state code updates, retrofit prioritization, and long-term decarbonization planning to support climate adaptation and sustainable development. Full article

Soil organic carbon (SOC) is a critical indicator of soil health and carbon sequestration potential. Accurate, efficient, and scalable SOC estimation is essential for sustainable orchard management and climate-resilient agriculture. However, traditional visible–near-infrared (Vis–NIR) spectroscopy often suffers from limited chemical specificity and weak adaptability in heterogeneous soil environments. To overcome these limitations, this study develops a five-stage modeling framework that systematically integrates Fourier Transform Infrared (FTIR) spectroscopy with hybrid machine learning techniques for non-destructive SOC prediction in citrus orchard soils. The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. The results showed that second-derivative (SD) preprocessing significantly enhanced the spectral signal-to-noise ratio. Among feature selection methods, the SPA reduced over 300 spectral bands to 10 informative wavelengths, enabling efficient modeling with minimal information loss. The SD + SPA + RF pipeline achieved the highest prediction performance (R² = 0.84, RMSE = 4.67 g/kg, and RPD = 2.51), outperforming the PLSR and BPNN models. This study presents a reproducible and scalable FTIR-based modeling strategy for SOC estimation in orchard soils. Its adaptive preprocessing, effective variable selection, and ensemble learning integration offer a robust solution for real-time, cost-effective, and transferable carbon monitoring, advancing precision soil sensing in orchard ecosystems. Full article

The evaluation of cloud distribution, properties, and their interaction with the radiation (longwave and shortwave) is of utmost importance for the proper assessment of future climate. Therefore, this study focuses on the Coupled Model Inter-Comparison Project Phase-6 (CMIP6) historical and future projections using the Shared Socio-Economic Pathways (SSPs) low- (ssp1–2.6), moderate- (ssp2–4.5), and high-emission (ssp5–8.5) scenarios along the South Asian region. For this purpose, a multi-model ensemble mean approach is employed to analyze the future projections in the low-, mid-, and high-emission scenarios. The cloud water content and cloud ice content in the CMIP6 models show an increase in upper and lower troposphere simultaneously in future projections as compared to ERA5 and historical projections. The longwave and shortwave cloud radiative effects at the top of the atmosphere are examined, as they offer a global perspective on radiation changes that influence atmospheric circulation and climate variability. The longwave cloud radiative effect (44.14 W/m²) and the shortwave cloud radiative effect (−73.43 W/m²) likely indicate an increase in cloud albedo. Similarly, there is an expansion of Hadley circulation (intensified subsidence) towards poleward, indicating the shifting of subtropical high-pressure zones, which can influence regional monsoon dynamics and cloud distributions. The impact of future projections on the tropospheric temperature (200–600 hPa) is studied, which seems to become more concentrated along the Tibetan Plateau in the moderate- and high-emission scenarios. This increase in the tropospheric temperature at 200–600 hPa reduces atmospheric stability, allowing stronger convection. Hence, the strengthening of convective activities may be favorable in future climate conditions. Thus, the correct representation of the model physics, cloud-radiative feedback, and the large-scale circulation that drives the Indian Summer Monsoon (ISM) is of critical importance in Coupled General Circulation Models (GCMs). Full article

Amid accelerating global climate change, research quantifying the uncertainty of mountain ecosystems in relation to CMIP6 multi-model ensemble (MME) simulations remains limited. This study addresses this gap by evaluating future temperature and precipitation trends in the Hengduan Mountains and quantifying the uncertainty associated with CMIP6 MME outputs. Utilizing data from 11 CMIP6 climate models, bilinear interpolation was employed to standardize model resolution, while inverse distance weighting (IDW) interpolation was applied to assess spatial distribution patterns. To mitigate systematic biases, the multi-model ensemble mean approach was adopted. Through an equal-weight model selection strategy, EC-Earth3-Veg and MPI-ESM1-2-HR were identified as the optimal model combination for the region. Key findings include the following: (1) During the reference period (1985–2014), model simulations exhibited systematic biases, with temperatures underestimated by 0.46 ± 0.08 °C/month and precipitation overestimated by 2.07 ± 0.32 mm/month relative to observations. (2) In the future period (2031–2070), projected regional warming rates in typical years under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios are −0.294 ± 0.021 °C/decade, 0.081 ± 0.009 °C/decade, and 0.171 ± 0.012 °C/decade, respectively. (3) Precipitation is projected to decline overall, with the most pronounced decrease under the SSP5-8.5 scenario (−0.68 ± 0.07%). This study is the first to systematically quantify CMIP6 model uncertainty in the Hengduan Mountains, revealing regional climate change trajectories, providing a scientific basis for formulating adaptive strategies, and identifying critical pathways for enhancing regional climate modeling efforts. Full article

Efficient utilization of wind energy resources, including advances in weather and seasonal forecasting and climate projections, is imperative for the sustainable progress of wind power generation. Although temperature and precipitation data receive considerable attention in interannual variability and seasonal forecasting studies, there is a notable gap in exploring correlations between climate indices and wind speeds. This paper proposes the use of an ensemble of artificial neural networks to forecast wind speeds based on climate oscillation indices and assesses its performance. An initial examination indicates a correlation signal between the climate indices and wind speeds of ERA5 for the selected case study in eastern Canada. Forecasts are made for the season April–May–June (AMJ) and are based on most correlated climate indices of preceding seasons. A pointwise forecast is conducted with a 20-member ensemble, which is verified by leave-on-out cross-validation. The results obtained are analyzed in terms of root mean squared error, bias, and skill score, and they show competitive performance with state-of-the-art numerical wind predictions from SEAS5, outperforming them in several regions. A relatively simple model with a single unit in the hidden layer and a regularization rate of ${10}^{-2}$ provides promising results, especially in areas with a higher number of indices considered. This study adds to global efforts to enable more accurate forecasting by introducing a novel approach. Full article

This research assesses four Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) concerning precipitation quantiles across Korea, utilizing the CMIP6 multi-model ensemble comprising 23 General Circulation Models alongside observational data to project future changes. Precipitation quantiles, derived from regional frequency analysis conducted at 615 sites, are calculated as annual averages for the period from 2015 to 2024. Each SSP scenario is evaluated for its spatial distribution through the application of observational data and chi-square tests, with the results indicating that the SSP3-7.0 ensemble most accurately reflects the current quantile estimates derived from observational data. Furthermore, interannual precipitation quantiles are projected to extend to the year 2100 to discern long-term trends within each reproducible period. It is anticipated that precipitation associated with the 100-year reproducible period will increase by over 20% in most regions across the nation by the century’s end, with this increase becoming more pronounced in accordance with the severity of the pathway. These findings underscore significant increases in extreme rainfall events under high-emission scenarios and highlight the critical need for the integration of enhanced flood mitigation, water resource management, and climate adaptation strategies within Korea’s planning framework. Full article

Climate and land use changes significantly affect runoff and hydrological drought, presenting challenges for water resource management. This study focuses on the Naoli River Basin, utilizing the SWAT model integrated with PLUS land use projections under the CMIP6 SSP245 and SSP585 scenarios to assess trends in runoff and drought characteristics from 2025 to 2100. The Standardized Runoff Index (SRI) and run theory are applied to analyze drought frequency and duration. Key findings include the following: (1) Under the SSP585 scenario (2061–2100), land use changes—specifically, a reduction in cropland and an increase in forest cover—resulted in a 12.59% decrease in runoff compared to the baseline period (1970–2014), with notable differences when considering climate-only scenarios. (2) The SSP585 scenario exhibits a significant rise in drought frequency and duration, particularly during summer, whereas SSP245 shows milder trends. (3) Based on the Taylor plot evaluation, the ensemble average MMM-Best (r = 0.80, RMSE = 26.15) has been identified as the optimal prediction model for the 2025–2100 period. Deviation analysis revealed that NorESM2-MM and IPSL-CM6A-LR demonstrated the greatest stability, while EC-Earth3 exhibited the largest deviation and highest uncertainty. (4) Land use changes under the SSP245 scenario help mitigate drought by enhancing water retention, although their effectiveness diminishes under SSP585 due to the dominant influence of climate factors, including increased temperature and precipitation variability. And (5) SRI-3 mutation analysis indicated that the mutation point occurred in July 2074 under the SSP245 scenario and in April 2060 under the SSP585 scenario (p < 0.05). The trend for SSP245 revealed significant fluctuations, with the number of crossover points rising to 40 following land use changes; conversely, the SSP585 trend remained stable with only seven crossover points, as high-emission scenarios predominantly influenced early mutations. These findings illuminate the interactive effects of land use and climate change, providing a scientific foundation for optimizing water resource management and developing effective drought mitigation strategies. Full article

Climate change poses a significant threat to the persistence of rear-edge populations, which are located at the margins of a species’ distribution range and are particularly vulnerable to environmental shifts. This study focuses on Yew (Taxus baccata L.) in the Iberian Peninsula, representing the southernmost extent of its range, where warming temperatures and decreasing moisture may compromise its survival. Our research aims to assess the climate sensitivity and habitat variability of Yew, addressing the hypothesis that future climate scenarios will significantly reduce the species’ climatic suitability, particularly in southern and low-altitude regions, and that this reduction will negatively impact individual growth performance. We used species distribution models (SDMs) based on ecological niche modeling (ENM) to project the current and future distribution of suitable habitats for Yew under two climate scenarios (SSP126 and SSP585). The models were calibrated using bioclimatic variables, and the resulting suitability maps were integrated with field data on individual growth performance, measured as basal area increment over the last five years (BAI5). The ensemble model showed high predictive performance, highlighting precipitation seasonality and annual mean temperature as the most influential variables explaining the climatic suitability distribution in the Iberian Peninsula. Our results indicate a substantial reduction in suitable habitats for Yew, especially under the high-emission scenario (SSP585), with southern populations experiencing the greatest losses. Furthermore, individual growth was positively correlated with climatic suitability, confirming that populations in favorable habitats exhibit better performance. These findings highlight the vulnerability of rear-edge populations of Yew to climate change and underscore the need for targeted conservation strategies, including the identification of climatic refugia and the potential use of assisted migration. Full article