Advances in Climate Change Research最新文献

Freeze‒thaw induced landslides (FTILs) in grasslands on the Tibetan Plateau are a geological disaster leading to soil erosion. These landslides reduce biodiversity and intensify landscape fragmentation, which in turn are strengthen by the persistent climate change and increased anthropogenic activities. However, conventional techniques for mapping FTILs on a regional scale are impractical due to their labor-intensive, costly, and time-consuming nature. This study focuses on improving FTILs detection by implementing image fusion-based Google Earth Engine (GEE) and a random forest algorithm. Integration of multiple data sources, including texture features, index features, spectral features, slope, and vertical‒vertical polarization data, allow automatic detection of the spatial distribution characteristics of FTILs in Zhidoi county, which is located within the Qinghai‒Tibet Engineering Corridor (QTEC). We employed statistical techniques to elucidate the mechanisms influencing FTILs occurrence. The enhanced method identifies two schemes that achieve high accuracy using a smaller training sample (scheme A: 94.1%; scheme D: 94.5%) compared to other methods (scheme B: 50.0%; scheme C: 95.8%). This methodology is effective in generating accurate results using only ∼10% of the training sample size necessitated by other methods. The spatial distribution patterns of FTILs generated for 2021 are similar to those obtained using various other training sample sources, with a primary concentration observed along the central region traversed by the QTEC. The results highlight the slope as the most crucial feature in the fusion images, accounting for 93% of FTILs occurring on gentle slopes ranging from 0° to 14°. This study provides a theoretical framework and technological reference for the identification, monitoring, prevention and control of FTILs in grasslands. Such developments hold the potential to benefit the management of grassland ecosystem, reduce economic losses, and promote grassland sustainability.

Glaciers have retreated and shrunk in High Mountain Asia since the mid-20th century because of global warming, leading to glacier instability and hazardous ice–snow avalanches. However, the complex relationship between ice–snow avalanches and factors such as climate and potential triggers are difficult to understand because of the lack of observational data. Here, we addressed ice–snow avalanches on the Annapurna II glacier in Nepal, Central Himalaya. We constructed an ice–snow avalanche history using long-term multi-source remote sensing images (1988–2021) and mapped the velocity fields of glaciers using cross-correlation analysis on SAR and optical images. Then, we investigated the impact of climate change and earthquakes on the frequency and size of ice–snow avalanches. The results demonstrate that the frequency of ice–snow avalanches has increased from 10 in 1988 to 27 in 2020, but the average area of ice–snow avalanche deposits has decreased by approximately 70%, from 3.4 $\times$ 10⁵ m² in 1988 to 1.2 $\times$ 10⁵ m² in 2020. The evolutionary characteristic of ice avalanches is linked to the impact of glacier retreat (reduction in ice material supply) and increased activity under climate change. The glacier movement velocity controls the size of ice–snow avalanches and can be set as an indicator for ice–snow avalanche warnings. On the Annapurna II glacier, an ice–snow avalanche occurred when the glacier velocities were greater than 1.5 m d⁻¹. These results offer insights into ice–snow avalanche risk assessment and prediction in high-mountain areas, particularly in regions characterised by dense glacier distribution.

The increasingly frequent and severe regional-scale compound heatwave‒drought extreme events (CHDEs), driven by global warming, present formidable challenges to ecosystems, residential livelihoods, and economic conditions. However, uncertainty persists regarding the future trend of CHDEs and their insights into regional spatiotemporal heterogeneity. By integrating daily meteorological data from observations in 1961–2022 and global climate models (GCMs) based on the Shared Socioeconomic Pathways, the evolution patterns of CHDEs were compared and examined among three sub-catchments of the Yangtze River Basin, and the return periods of CHDE in 2050s and 2100s were projected. The findings indicate that the climate during the 2022 CHDE period was the warmest and driest recorded in 1961–2022, with precipitation less than 154.5 mm and a mean daily maximum temperature 3.4 °C higher than the average of 1981–2010, whereas the characteristics in the sub-catchments exhibited temporal and spatial variation. In July–August 2022, the most notable feature of CHDE was its extremeness since 1961, with return periods of ∼200-year in upstream, 80-year in midstream, and 40-year in downstream, respectively. By 2050, the return periods witnessed 2022 CHDE would likely be reduced by one-third. Looking towards 2100, under the highest emission scenario of SSP585, it was projected to substantially increase the frequency of CHDEs, with return periods reduced to one-third in the upstream and downstream, as well as halved in the midstream. These findings provide valuable insights into the changing risks associated with forthcoming climate extremes, emphasizing the urgency of addressing these challenges in regional management and sustainable development.

Understanding how hydrological factors interrelate is crucial when examining the impact of climate warming on snowmelt. However, these connections are often overlooked, leading to an unclear relationship between temperature and snowmelt. This study investigates the complex interplay between temperature and snowmelt in the Tibetan Plateau from 1961 to 2020, focusing on how extreme high-temperature events affect the frequency of extreme snowmelt. Using a structural equation model, we detected three temperature-related factors that predominantly influenced snowmelt and extreme snowmelt. The annual average temperature was found to have a significant indirect impact on snowmelt, mediated by changes in snowfall, snow depth and snow cover. By contrast, high-temperature days (daily maximum temperatures exceeding the 90th percentile) and heat waves (at least three consecutive high-temperature days) negatively affected extreme snowmelt directly or indirectly. The direct effect of increasing extreme temperature events was associated with an earlier onset of high-temperature periods, which accelerated snowmelt and shortened the duration of extreme snowmelt periods. Additionally, the reduction in snow cover owing to warming emerged as a main factor suppressing snowmelt and extreme snowmelt frequencies. We also revealed spatiotemporal variations in the temperature‒snowmelt relationship that highly depended on changes in snowmelt patterns. The study elucidated why warming suppresses snowmelt and extreme snowmelt events in the Tibetan Plateau, highlighting the mediating roles of snow-related and phenological factors. The findings will provide scientific support for climate simulation and water management policymaking in alpine regions worldwide.

Climate change alters snowpack evolution, which in turn influences the likelihood of snow avalanches and flood risks. The lack of systemic observational data on key snow characteristics in high mountains remains a scientific challenge in terms of systematically elucidating the dynamic chain of variations in climate–snowpack–snow disasters. This restricts our understanding and poses challenges in the prediction of snow-related disaster risks. As such, this study analysed the variations of temperature and snowfall and the physical characteristics of snowpacks based on ground-based observations from the Kunse River Valley situated in the Tianshan Mountains from 1967 to 2021. The results reveal that the temperature increased significantly by 0.32 °C per decade (p < 0.01) during the snow season, along with more extreme snowfall events. The snow-cover duration was observed to have been shortened by 4.77 d per decade (p < 0.01) from 1967 to 2021, which is characterised by later snow-cover onset and earlier snowmelt. Concurrently, average and maximum snow depths increased along with an increase in peak snow water equivalent, thus indicating a higher frequency of extremely scarce or abundant snow years. The low snowpack temperature gradient and earlier snowmelt dates in spring lead to earlier occurrences of snowmelt floods and wet avalanches. As the risks of these events increase, they pose greater threats to farmlands, road transportation, water–electricity infrastructure and several other human activities. Therefore, these insights are critical for providing vital information that can deepen our understanding of the impact of climate change on snowpack characteristics and improve management strategies for snow-related disaster prevention and mitigation.

In 2022, the Pakistan witnessed the hottest spring and wettest summer in history. And devastating floods inundated a large portion of Pakistan and caused enormous damages. However, the primary water source and its contributions to these unprecedented floods remain unclear. Based on the reservoir inflow measurements, Multi-Source Weighted-Ensemble Precipitation (MSWEP), the fifth generation ECMWF atmospheric reanalysis (ERA5) products, this study quantified the contributions of monsoon precipitation, antecedent snowmelts, and orographic precipitation enhancement to floods in Pakistan. We found that the Indus experienced at least four inflow uprushes, which was mainly supplied by precipitation and snowmelt; In upper Indus, abnormally high temperature continued to influence the whole summer and lead to large amounts of snowmelts which not only was a key water supply to the flood but also provided favorable soil moisture conditions for the latter precipitation. Before July, the snowmelt has higher contributions than the precipitation to the streamflow of Indus River, with contribution value of more than 60%. Moreover, the snowmelt could still supply 20%–40% water to the lower Indus in July and August; The leading driver of 2022 mega-floods over the southern Pakistan in July and August was dominated by the precipitation, where terrain disturbance induced precipitation account to approximately 33% over the southern Pakistan. The results help to understand the mechanisms of flood formation, and to better predict future flood risks over complex terrain regions.

Quantification of the impact of winter wheat irrigation on the climate and the occurrence of extreme climatic events over North China is crucial for regional adaptation planning. Previous related studies mainly focused on the impact on surface processes; however, few focused on the effects of extreme events using high-resolution nonhydrostatic regional climate models. Here, the 9-km-resolution nonhydrostatic RegCM4.7 was coupled with a crop irrigation scheme and an updated winter wheat irrigation dataset to better simulate irrigation effects. Two experiments were conducted with and without winter wheat irrigation to isolate the effects of irrigation. Results showed that irrigation simulation reduces the model biases in temperature, precipitation, latent heat flux, soil moisture, sensitive heat flux, and top-layer soil moisture. Moreover, it also reduces the bias and increases the correlation with observations obtained in irrigated areas, especially in summer, indicating better representation of irrigation schemes. Winter wheat irrigation tends to cause substantial cooling of the local surface maximum, minimum, and mean air temperatures (by −1.68, −0.34, and −0.79 °C, respectively) over irrigated areas of North China, with the largest changes observed in relation to maximum temperature. Additionally, precipitation is found to increase during spring and summer, which is strongly related to water vapor transport in the lower levels of the atmosphere. Further analyses indicated that the number of annual mean hot days decrease (−13.9 d), whereas the number of both comfort days (+10.2 d) and rainy days (days with total precipitation greater than 1 mm: +6.6 d) increase over irrigated areas, demonstrating beneficial feedback to human perception and agriculture. Fortunately, although the heat wave risk increases (number of annual mean heat wave days: +5.8 d), the impact is limited to small areas within irrigated region. Additionally, no notable change was found in terms of heavy rainfall events and precipitation intensity, which might be an undereastimation caused by the less water use in model simulation. Although winter wheat irrigation does not have notable impact on the climate of the surrounding region, it is an important factor for the local-scale climate.

Permafrost is degrading globally, particularly those with low thermal stability on the Qinghai‒Tibet Plateau, owing to climate change. However, the inadequacy of direct research on permafrost degradation based on in-situ monitoring limits the prediction of permafrost degradation and engineering practices. This study explored the processes and modes of permafrost degradation into talik by analyzing ground temperature data from five points in the hinterland of the Qinghai‒Tibet Plateau from 2006 to 2021. The results showed that the degradation of the warm permafrost layer with a geothermal gradient of zero occurred simultaneously in the top and bottom directions. The rate of permafrost degradation from the top down and bottom up increase during the degradation process, but the increase of the former is more drastic after the formation of thawed interlayer. Additionally, the construction of the Qinghai‒Tibet Railway changed the degradation modes of the permafrost in adjacent natural sites through horizontal heat transfer, particularly after through talik formation under the embankment. The findings suggest that taking countermeasures before or immediately after forming thawed interlayer is more effective. When evaluating the thermal impact of projects in warm permafrost regions, special attention should be given to the horizontal heat transfer process that may result from the formation of a through talik.

This study provides a comprehensive overview of the challenges to achieving carbon neutrality at the county level in China and offers targeted recommendations, laying the groundwork for future specialized research in this area. A total of 283 relevant studies (2004–2023) were analyzed to assess county-level carbon emissions through three phases: bibliometric analysis, frontier analysis, and future prospects. Bibliometric findings reveal that publication trends were largely influenced by domestic and foreign policies. Keyword cluster discerns ten primary themes, ranging from conceptual frameworks to research methodologies. The frontier analysis of the literature highlights the leading research areas, which include carbon neutrality pathway, driving factors, spatiotemporal variation of carbon emissions, the co-effects of pollutants and carbon reduction, and carbon emissions in China's rural areas. Drawing from the results of bibliometric and frontier analyses, this study elucidates the recommendations for achieving carbon neutrality at the county level from three perspectives: effective regional policy guidance, emphasis on ecological conservation, and the deployment of advanced carbon reduction and sequestration technologies. This study enriches the body of knowledge on carbon emissions at the county level and holds significant implications for China's comprehensive push towards achieving its carbon neutrality objectives.

Frozen ground (FG) plays an important role in global and regional climates and environments through changes in land freeze‒thaw processes, which have been conducted mainly in different regions. However, the changes in land surface freeze‒thaw processes under climate change on a global scale are still unclear. Based on ERA5-Land hourly land skin temperature data, this study evaluated changes in the global FG area, global land surface first freeze date (FFD), last freeze date (LFD) and frost-free period (FFP) from 1950 to 2020. The results show that the current FG areas (1991–2020 mean) in the Northern Hemisphere (NH), Southern Hemisphere (SH), and globe are 68.50 × 10⁶, 9.03 × 10⁶, and 77.53 × 10⁶ km², which account for 72.4%, 26.8%, and 60.4% of the exposed land (excluding glaciers, ice sheets, and water bodies) in the NH, SH and the globe, respectively; further, relative to 1951–1980, the FG area decreased by 1.9%, 8.8%, and 2.8%, respectively. Seasonally FG at lower latitudes degrades to intermittently FG, and intermittently FG degrades to non-frozen ground, which caused the global FG boundary to retreat to higher latitudes from 1950 to 2020. The annual FG areas in the NH, SH, and globe all show significant decreasing trends (p < 0.05) from 1950 to 2020 at −0.32 × 10⁶, −0.22 × 10⁶, and −0.54 × 10⁶ km² per decade, respectively. The FFP prolongation in the NH is mainly influenced by LFD advance, while in the SH it is mainly controlled by FFD delay. The prolongation trend of FFP in the NH (1.34 d per decade) is larger than that in the SH (1.15 d per decade).