Search Results (756)

The assessment of wind energy resources is critical for the transition from fossil fuel to renewable energy sources. Using the outputs from high-resolution global climate models (GCMs), such as the High Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6), has become one of the most important tools in wind energy research. This study evaluated the reliability of the 22 GCMs available in the HighResMIP-PRIMAVERA project by simulating the wind energy climatology and variability over the Tibetan Plateau (TP) with reference to observations and investigated the differences in performance of the GCMs between high-resolution (HR) and low-resolution (LR) simulations. The results show that most models performed relatively well in simulating the probability distribution of the observed wind speed over the TP, but nearly half of the models generally underestimated the wind speed, whereas the others tended to overestimated the wind speed. Compared with the wind speed, the GCMs showed larger biases in reproducing the wind power density (WPD) and other wind energy resources, whereas the biases in multi-model ensembles were relatively smaller than those in most individual models. With respect to interannual variability, both the HR and LR models failed to capture interannual variations in WPD over the TP. Furthermore, more than half of the HR GCMs had a reduced bias relative to the corresponding LR GCMs, indicating the good performance of most HR models in simulating wind energy resources over the TP in terms of spatial pattern and temporal variability. However, the overall performance of HR GCMs varied among models, which suggests that solely improving the horizontal resolution is not sufficient to completely solve the uncertainties and deficiencies in the simulation of wind energy over complex terrain. Full article

Northeast China plays a crucial role as a major grain-producing region, and attention to its land use and land cover changes (LUCC), especially farmland changes, are crucial to ensure food security and promote sustainable development. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) data and a decision tree model, land types, especially those of paddy fields in Northeast China from 2000 to 2020, were extracted, and the spatiotemporal changes in paddy fields and their drivers were analyzed. The development trends of paddy fields under different future scenarios were explored alongside the Coupled Model Intercomparison Project Phase 6 (CMIP6) data. The findings revealed that the kappa coefficients of land use classification from 2000 to 2020 reached 0.761–0.825, with an overall accuracy of 80.5–87.3%. The proposed land classification method can be used for long-term paddy field monitoring in Northeast China. The LUCC in Northeast China is dominated by the expansion of paddy fields. The centroids of paddy fields gradually shifted toward the northeast by a distance of 292 km, with climate warming being the main reason for the shift. Under various climate scenarios, the temperature in Northeast China and its surrounding regions is projected to rise. Each scenario is anticipated to meet the temperature conditions necessary for the northeastward expansion of paddy fields. This study provides support for ensuring sustainable agricultural development in Northeast China. Full article

In this study, a CMIP6 ensemble of 26 GCMs and SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios from CanDCS-U6 is used to project drought conditions in the South Saskatchewan River Watershed. The near-current period (2015–2030) and two future periods (2041–2060 and 2071–2100) are analyzed based on the Standardized Precipitation Evapotranspiration Index (SPEI) at 1-, 3-, 6-, 12-, and 24-month timescales. Projections indicate a shift in average SPEI values from above zero (no drought) in the base period (1951–1990) to more negative values in the future. Results show an increase in drought severity and frequency under climate change conditions. The percentage of time with no drought conditions is projected to decline from 55–70% in the base period to 25–45% by 2071–2100. Severe and extreme droughts, rare in the base period (below 4%), are projected to increase to up to 19% by 2071–2100. The area experiencing drought is expected to expand from 36–49% (for different SPEI timescales) in the base period to up to 76% by 2071–2100. Drought frequency is projected to be higher under SSP1-2.6 and less frequent under SSP2-4.5. Results showed that longer SPEI timescales are associated with higher drought occurrence rates and severity. The spatial pattern of drought is also projected to significantly change, with higher frequencies expected in the eastern parts of the watershed under climate change. Full article

The hydrological cycle is altered by climate change and human activities, amplifying extreme precipitation and heightening the flood risk regionally and globally. It is imperative to explore the future possible alterations in flood risk at the regional scale. Focusing on the Hanjiang river basin (HRB), this study develops a framework for establishing a scientific assessment of spatio-temporal dynamics of future flood risks under multiple future scenarios. In this framework, a GCMs statistical downscaling method based on machine learning is used to project future precipitation, the PLUS model is used to project future land use, the digitwining watershed model (DWM) is used to project future runoff, and the entropy weight method is used to calculate risk. Six extreme precipitation indices are calculated to project the spatio-temporal patterns of future precipitation extremes in the HRB. The results of this study show that the intensity (Rx1day, Rx5day, PRCPTOT, SDII), frequency (R20m), and duration (CWD) of future precipitation extremes will be consistently increasing over the HRB during the 21st century. The high values of extreme precipitation indices in the HRB are primarily located in the southeast and southwest. The future annual average runoff in the upper HRB during the near-term (2023–2042) and mid-term (2043–2062) is projected to decrease in comparison to the baseline period (1995–2014), with the exception of that during the mid-term under the SSP5-8.5 scenario. The high flood risk center in the future will be distributed in the southwestern region of the upper HRB. The proportions of areas with high and medium–high flood risk in the upper HRB will increase significantly. Under the SSP5-8.5 scenario, the area percentage with high flood risk during the future mid-term will reach 24.02%. The findings of this study will facilitate local governments in formulating effective strategic plans for future flood control management. Full article

The skill, assumptions, and uncertainty of machine learning techniques (MLTs) for downscaling global climate model’s precipitation to the local level in Bolivia were assessed. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machine (SVM) techniques, was used on four zones (highlands, Andean slopes, Amazon lowlands, and Chaco lowlands). The downscaled series’ skill was evaluated in terms of relative errors. The uncertainty was analyzed through variance decomposition. In most cases, MLTs’ skill was adequate, with relative errors less than 50%. Moreover, RF tended to outperform SVM. Robust (weak) stationary (perfect prognosis) assumptions were found in the highlands and Andean slopes. The weakness was attributed to topographical complexity. The downscaling methods were shown to be the dominant source of uncertainties. This analysis allowed the derivation of robust future projections, showing higher annual rainfall, shorter dry spell duration, and more frequent but less intense high rainfall events in the highlands. Apart from the dry spell’s duration, a similar pattern was found for the Andean slopes. A decrease in annual rainfall was projected in the Amazon lowlands and an increase in the Chaco lowlands. Full article

The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias in the CMIP6 model data was adjusted using data from meteorological stations. Additionally, this study uses daily CMIP6 precipitation and temperature data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the near (2015–2044), mid (2045–2074), and far (2075–2100) periods. Power transformation and distribution mapping bias correction techniques were used to adjust biases in precipitation and temperature data from seven CMIP6 models. To validate the model data against observed data, statistical evaluation techniques were employed. Mann–Kendall (MK) and Sen’s slope estimator were also performed to identify trends and magnitudes of variations in rainfall and temperature, respectively. The performance evaluation revealed that the INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. The precipitation projections in all agro-climatic zones under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show a significant (p < 0.01) positive trend. The mean annual maximum temperature over UBNB is estimated to increase by 1.8 °C, 2.1 °C, and 2.8 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5 between 2015 and 2100, respectively. Similarly, the mean annually minimum temperature is estimated to increase by 1.5 °C, 2.1 °C, and 3.1 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. These significant changes in climate variables are anticipated to alter the incidence and severity of extremes. Hence, communities should adopt various adaptation practices to mitigate the effects of rising temperatures. Full article

The accelerating impact of climate change on golf course conditions has led to a significant increase in pesticide dependency, underscoring the importance of innovative management strategies. The shift from Coupled Model Intercomparison Project Phase 5 (CMIP5) to the latest CMIP6 phase has drawn the attention of professionals, including engineers, decision makers, and golf course managers. This study evaluates how climate projections from CMIP6, using Canadian Earth System Models (CanESM2 and CanESM5), impact pesticide application trends on Quebec’s golf courses. Through the comparison of temperature and precipitation projections, it was found that a more substantial decline in precipitation is exhibited by CanESM2 compared to CanESM5, while the latter projects higher temperature increases. A comparison between historical and projected pesticide use revealed that, in most scenarios and projected periods, the projected pesticide use was substantially higher, surpassing past usage levels. Additionally, in comparing the two climate change models, CanESM2 consistently projected higher pesticide use across various scenarios and projected periods, except for RCP2.6, which was 27% lower than SSP1-2.6 in the second projected period (PP2). For all commonly used pesticides, the projected usage levels in every projected period, according to climate change models, surpass historical levels. When comparing the two climate models, CanESM5 consistently forecasted greater pesticide use for fungicides, with a difference ranging from 65% to 222%, and for herbicides, with a difference ranging from 114% to 247%, across all projected periods. In contrast, insecticides, growth regulators, and rodenticides displayed higher AAIR values in CanESM2 during PP1 and PP3, showing a difference of 28% to 35.6%. However, CanESM5 again projected higher values in PP2, with a difference of 1.5% to 14%. Full article

Investigating the spatial-temporal evolutionary trends of future temperature and precipitation considering various emission scenarios is crucial for developing effective responses to climate change. However, researchers in Iraq have not treated this issue under CMIP6 in much detail. This research aims to examine the spatiotemporal characteristics of temperature and rainfall in northern Iraq by applying LARS-WG (8) under CMIP6 general circulation models (GCMs). Five GCMs (ACCESS-ESM1-5, CNRM-CM6-1, MPI-ESM1-2-LR, HadGEM3-GC31-LL, and MRI-ESM2-0) and two emissions scenarios (SSP245 and SSP585) were applied to project the upcoming climate variables for the period from 2021 to 2040. The research relied on satellite data from fifteen weather sites spread over northern Iraq from 1985 to 2015 to calibrate and validate the LARS-WG model. Analysis of spatial-temporal evolutionary trends of future temperature and precipitation compared with the baseline period revealed that seasonal mean temperatures will increase throughout the year for both scenarios. However, the SSP585 scenario reveals the highest increase during autumn when the spatial coverage of class (15–20) °C increased from 27.7 to 96.29%. At the same time, the average seasonal rainfall will rise in all seasons for both scenarios except autumn for the SSP585 scenario. The highest rainfall increment percentage is obtained using the SSP585 for class (120–140) mm during winter. The spatial extent of the class increased from 25.49 to 50.19%. Full article

A major challenge for climate system models in simulating the impacts of El Niño–Southern Oscillation (ENSO) on the interannual variations of East Asian rainfall anomalies is the wide inter-model spread of outputs, which causes considerable uncertainty in physical mechanism understanding and short-term climate prediction. This study investigates the fidelity of 40 models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in representing the impacts of ENSO on South China Spring Rainfall (SCSR) during the ENSO decaying spring. The response of SCSR to ENSO, as well as the sea surface temperature anomalies (SSTAs) over the tropical Indian Ocean (TIO), is quite different among the models; some models even simulate opposite SCSR anomalies compared to the observations. However, the models capturing the ENSO-related warm SSTAs over TIO tend to simulate a better SCSR-ENSO relationship, which is much closer to observation. Therefore, models are grouped based on the simulated TIO SSTAs to explore the modulating processes of the TIO SSTAs in ENSO affecting SCSR anomalies. Comparing analysis suggests that the warm TIO SSTA can force the equatorial north–south antisymmetric circulation in the lower troposphere, which is conducive to the westward extension and maintenance of the western North Pacific anticyclone (WNPAC). In addition, the TIO SSTA enhances the upper tropospheric East Asian subtropical westerly jet, leading to anomalous divergence over South China. Thus, the westward extension and strengthening of WNPAC can transport sufficient water vapor for South China, which is associated with the ascending motion caused by the upper tropospheric divergence, leading to the abnormal SCSR. Full article

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems’ responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive region, remains underexplored. This study aimed to develop a data-driven approach to predict the seasonal and annual variations in GPP in the Tibetan Plateau up to the year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed to investigate the relationships between GPP and various environmental factors, including climate variables, CO₂ concentrations, and terrain attributes. This study analyzed the projected seasonal and annual GPP from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four future scenarios: SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The results suggest that the annual GPP is expected to significantly increase throughout the 21st century under all future climate scenarios. By 2100, the annual GPP is projected to reach 1011.98 Tg C, 1032.67 Tg C, 1044.35 Tg C, and 1055.50 Tg C under the four scenarios, representing changes of 0.36%, 4.02%, 5.55%, and 5.67% relative to 2021. A seasonal analysis indicates that the GPP in spring and autumn shows more pronounced growth under the SSP3–7.0 and SSP5–8.5 scenarios due to the extended growing season. Furthermore, the study identified an elevation band between 3000 and 4500 m that is particularly sensitive to climate change in terms of the GPP response. Significant GPP increases would occur in the east of the Tibetan Plateau, including the Qilian Mountains and the upper reaches of the Yellow and Yangtze Rivers. These findings highlight the pivotal role of climate change in driving future GPP dynamics in this region. These insights not only bridge existing knowledge gaps regarding the impact of future climate change on the GPP of the Tibetan Plateau over the coming decades but also provide valuable guidance for the formulation of climate adaptation strategies aimed at ecological conservation and carbon management. Full article

Alterations in the hydrological cycle due to climate change are one of the key threats to the future accessibility of natural resources. This study used 12 GCM climate models from CMIP6 to evaluate future climate change scenarios by applying model performance measures and trend analysis in Kobo Valley, Ethiopia. The models were ranked based on their ability to analyze the historical datasets. The result of this study showed that the outputs of the FIO-ESM-2-0 CIMP6 model had a good overall ranking for both precipitation and temperature. After bias correction of the model-based projections with the observed data, the average annual precipitation in the average scenario (SSP2-4.5) decreased by 4.4% and 13% in 2054 and 2084, respectively. Similarly, in the worst-case scenario (SSP5-8.5), by the end of 2054 and 2084, decreases of 4% and 12.8%, respectively, were predicted. The average annual maximum temperature under the SSP2-4.5 scenario increased by 1.5 °C in 2054 and by 2.1 °C in 2084. The average annual maximum temperature under the worst-case (SSP5-8.5) scenario increased by 1.7 °C in 2054 and by 3.2 °C in 2084. In the middle scenario (SSP4.5), the average annual minimum temperature increased by 2.2 °C in 2054 and by 3 °C in 2084. The average annual minimum temperature under the worst-case (SSP5-8.5) scenario increased by 2.6 °C in 2054 and by 4.3 °C in 2084. The seasonal variability in precipitation in the studied valley will decrease in the winter and increase in the summer. A decrease in precipitation combined with an increase in temperature will strengthen the risk of drought events in the future. Full article

Vegetation temperature sensitivity is a key indicator to understand the response of vegetation to temperature changes and predict potential shifts in ecosystem functions. However, under the context of global warming, the impact of future extreme heat events on vegetation temperature sensitivity remains poorly understood. Such research is crucial for predicting the dynamic changes in terrestrial ecosystem structure and function. To address this issue, we utilized historical (1850–2014) and future (2015–2100) simulation data derived from CMIP6 models to explore the spatiotemporal dynamics of vegetation temperature sensitivity under different carbon emission scenarios. Moreover, we employed correlation analysis to assess the impact of extreme heat events on vegetation temperature sensitivity. The results indicate that vegetation temperature sensitivity exhibited a declining trend in the historical period but yielded an increasing trend under the SSP245 and SSP585 scenarios. The increasing trend under the SSP245 scenario was less pronounced than that under the SSP585 scenario. By contrast, vegetation temperature sensitivity exhibited an upward trend until 2080 and it began to decline after 2080 under the SSP126 scenario. For all the three future scenarios, the regions with high vegetation temperature sensitivity were predominantly located in high latitudes of the Northern Hemisphere, the Tibetan Plateau, and tropical forests. In addition, the impact of extreme heat events on vegetation temperature sensitivity was intensified with increasing carbon emission intensity, particularly in the boreal forests and Siberian permafrost. These findings provide important insights and offer a theoretical basis and guidance to identify climatically sensitive areas under global climate change. Full article

This study assesses the impact of climate change on streamflow characteristics in the Lualaba River Basin (LRB), an important yet ungauged watershed in the Congo River Basin. Two conceptual hydrological models, HBV-MTL and GR4J, were calibrated using the reanalysis datasets and outputs of Generalized Circulation Models (GCMs) under CMIP6 during the historical period. The hydrological models were fed with outputs of GCMs under shared socioeconomic pathways (SSPs) 2-45 and 5-85, moderate- and high-radiative future scenarios. The results demonstrate that hydrological models successfully simulate observed streamflow, but their performance varies significantly with the choice of climate data and model structure. Interannual streamflow (Q) percentiles (10, 50, 90) were used to describe flow conditions under future climate. Q10 is projected to increase by 33% under SSP2-45 and 44% under SSP5-85, suggesting higher flow conditions that are exceeded 90% of the time. Q50 is also expected to rise by almost the same rate. However, a considerably higher Q90 is projected to increase by 56% under the moderate- and 80% under the high-radiative scenario. These indicate the overall higher water availability in this watershed to be used for energy and food production and the need for flood risk management. Full article

Latoia consocia Walker is an important phytophagous pest that has rapidly spread across North China in recent years, posing a severe threat to related plants. To study the impact of climatic conditions on its distribution and to predict its distribution under current and future climate conditions, the MaxEnt niche model and ArcGIS 10.8 software were used. The results showed that the MaxEnt model performs well in predicting the distribution of L. consocia, with an AUC value of 0.913. The annual precipitation (Bio12), the precipitation of the driest month (Bio14), the temperature annual range (Bio7), and the minimum temperature of the coldest month (Bio6) are key environmental factors affecting the potential distribution of L. consocia. Under current climate conditions, L. consocia has a highly suitable growth area of 2243 km² in China, among which Taiwan has the largest high-suitable area with a total area of 1450 km². With climate warming, the potential habitat area for L. consocia shows an overall decreasing trend in future. This work provides a scientific basis for research on pest control and ecological protection. A “graded response” detection and early warning system, as well as prevention and control strategies, can be developed for potentially suitable areas to effectively address this pest challenge. Full article

This study simulated the spatiotemporal changes in coastal ecosystem services (ESs) in the Jiaodong Peninsula from 2000 to 2050 and analyzed the driving mechanisms of climate change and human activities with respect to ESs, aiming to provide policy recommendations that promote regional sustainable development. Future climate change and land use were forecast based on scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to assess ESs such as water yield (WY), carbon storage (CS), soil retention (SR), and habitat quality (HQ). Key drivers of ESs were identified using Structural Equation Modeling (SEM). Results demonstrate the following: (1) High WY services are concentrated in coastal built-up areas, while high CS, HQ, and SR services are mainly found in the mountainous and hilly regions with extensive forests and grasslands. (2) By 2050, CS and HQ will show a gradual degradation trend, while the annual variations in WY and SR are closely related to precipitation. Among the different scenarios, the most severe ES degradation occurs under the SSP5-8.5 scenario, while the SSP1-2.6 scenario shows relatively less degradation. (3) SEM analysis indicates that urbanization leads to continuous declines in CS and HQ, with human activities and topographic factors controlling the spatial distribution of the four ESs. Climate factors can directly influence WY and SR, and their impact on ESs is stronger in scenarios with higher human activity intensity than in those with lower human activity intensity. (4) Considering the combined effects of human activities and climate change on ESs, we recommend that future development decisions be made to rationally control the intensity of human activities and give greater consideration to the impact of climate factors on ESs in the context of climate change. Full article