Slope failures resulting from thaw slumps in permafrost regions, have developed widely under the influence of climate change and engineering activities. The shear strength at the interface between the active layer and permafrost (IBALP) at maximum thawing depth is a critical factor to evaluate stability of permafrost slopes. Traditional direct shear, triaxial shear, and large-scale in-situ shear experiments are unsuitable for measuring the shear strength parameter of the IBALP. Based on the characteristics of thaw slumps in permafrost regions, this study proposes a novel test method of self-weight direct shear instrument (SWDSI), and its principle, structure, measurement system and test steps are described in detail. The shear strength of the IBALP under maximum thaw depth conditions is measured using this method. The results show that under the condition that the permafrost layer is thick underground ice and the active layer consists of silty clay with 20% water content, the test results are in good agreement with the results of field large-scale direct shear tests and are in accordance with previous understandings and natural laws. The above analysis indicates that the method of the SWDSI has a reliable theoretical basis and reasonable experimental procedures, and meets the needs of stability assessment of thaw slumps in permafrost regions. The experimental data obtained provide important parameter support for the evaluation of related geological hazards.

The thermal coupling between the atmosphere and the subsurface on the Qinghai-Tibetan Plateau (QTP) governs permafrost stability, surface energy balance, and ecosystem processes, yet its spatiotemporal dynamics under accelerated warming are poorly understood. This study quantifies soil-atmosphere thermal coupling ((3) at the critical 0.1 m root-zone depth using in-situ data from 99 sites (1980-2020) and a machine learning framework. Results show significantly weaker coupling in permafrost (PF) zones (mean (3 = 0.42) than in seasonal frost (SF) zones (mean (3 = 0.50), confirming the powerful thermal buffering of permafrost. Critically, we find a widespread trend of weakening coupling (decreasing (3) at 66.7 % of sites, a phenomenon most pronounced in SF zones. Our driver analysis reveals that the spatial patterns of (3 are primarily controlled by surface insulation from summer rainfall and soil moisture. The temporal trends, however, are driven by a complex and counter-intuitive interplay. Paradoxically, rapid atmospheric warming is the strongest driver of a strengthening of coupling, likely due to the loss of insulative snow cover, while trends toward wetter conditions drive a weakening of coupling by enhancing surface insulation. Spatially explicit maps derived from our models pinpoint hotspots of accelerated decoupling in the eastern and southern QTP, while also identifying high-elevation PF regions where coupling is strengthening, signaling a loss of protective insulation and increased vulnerability to degradation. These findings highlight a dynamic and non-uniform response of land-atmosphere interactions to climate change, with profound implications for the QTP's cryosphere, hydrology, and ecosystems.

Infrastructure in northern regions is increasingly threatened by climate change, mainly due to permafrost thaw. Prediction of permafrost stability is essential for assessing the long-term stability of such infrastructure. A key aspect of geotechnical problems subject to climate change is addressing the surface energy balance (SEB). In this study, we evaluated three methodologies for applying surface boundary conditions in longterm thermal geotechnical analyses, including SEB heat flux, n-factors, and machine learning (ML) models by using ERA5-Land climate reanalysis data until 2100. We aimed to determine the most effective approach for accurately predicting ground surface temperatures for climate-resilient design of northern infrastructure. The evaluation results indicated that the ML-based approach outperformed both the SEB heat flux and n-factors methods, demonstrating significantly lower prediction errors. The feasibility of long-term thermal analysis of geotechnical problems using ML-predicted ground surface temperatures was then demonstrated through a permafrost case study in the community of Salluit in northern Canada, for which the thickness of the active layer and talik were calculated under moderate and extreme climate scenarios by the end of the 21st century. Finally, we discussed the application and limitations of surface boundary condition methodologies, such as the limited applicability of the n-factors in long-term analysis and the sensitivity of the SEB heat flux to inputs and thermal imbalance. The findings highlight the importance of selecting suitable boundary condition methodologies in enhancing the reliability of thermal geotechnical analyses in cold regions.

Black carbon (BC) is a major short-lived climate pollutant (SLCP) with significant climate and environmentalhealth impacts. This review synthesizes critical advancements in the identification of emerging anthropogenic BC sources, updates to global warming potential (GWP) and global temperature potential (GTP) metrics, technical progress in characterization techniques, improvements in global-regional monitoring networks, emission inventory, and impact assessment methods. Notably, gas flaring, shipping, and urban waste burning have slowly emerged as dominant emission sources, especially in Asia, Eastern Europe, and Arctic regions. The updated GWP over 100 years for BC is estimated at 342 CO2-eq, compared to 658 CO2-eq in IPCC AR5. Recent CMIP6-based Earth System Models (ESMs) have improved attribution of BC's microphysics, identifying a 22 % increase in radiative forcing (RF) over hotspots like East Asia and Sub-Saharan Africa. Despite progress, challenges persist in monitoring network inter-comparability, emission inventory uncertainty, and underrepresentation of BC processes in ESMs. Future efforts could benefit from the integration of satellite data, artificial intelligence (AI)assisted methods, and harmonized protocols to improve BC assessment. Targeted mitigation strategies could avert up to four million premature deaths globally by 2030, albeit at a 17 % additional cost. These findings highlight BC's pivotal roles in near-term climate and sustainability policy.

Widespread dieback of natural Mongolian pine (Pinus sylvestris var. mongolica) forests in Hulunbuir sandy land since 2018 has raised concerns about their sustainability in afforestation programs. We hypothesized that this dieback is driven by two interrelated mechanisms: (1) anthropogenic fire suppression disrupting natural fire regime, and (2) climate change-induced winter warming reducing snow cover duration and depth. To test these, we quantified dieback using Green Normalized Difference Vegetation Index (GNDVI) across stands with varying fire histories via UAV-based multispectral imagery, alongside long-term climatic observations (1960-2024) of temperature, precipitation, and snow dynamics from meteorological stations combined with remote sensing datasets. Results showed that an abrupt change point in 2018 for both annual precipitation and mean temperature was identified, coinciding with dieback. Significant negative linear relationship between GNDVI (forest health) and last fire interval indicated prolonged fire exclusion exacerbating dieback, possibly via pathogen/pest accumulation. Winter temperature rose significantly during 1960-2023, with notable acceleration following abrupt change point in 1987. Concurrently, winters during 2018-2023 exhibited pronounced warming, with snow cover duration reduced by 23 days and snow depth diminished by 7.6 cm. These conditions reduced snowmelt -derived soil moisture (critical water source) recharge in early spring, exacerbating drought stress during critical growth periods and predisposing trees to pest and disease infestations. Our results support both hypotheses, demonstrating that dieback is synergistically driven by fire regime alteration and climate-mediated snowpack reductions. Converting pure pine forests into mixed pine-broadleaf forests via differentiated water sources was proposed to restore ecological resilience in sandy ecosystems.

Against the backdrop of global warming, the increasing spatiotemporal variability in precipitation patterns has intensified the frequency and risk of dry-wet abrupt alternation (DWAA) events in semi-arid regions. This study investigates the Hailar River Basin in northern China (1980-2019) and develops the Soil Moisture Concentration Index (SMCI) using daily soil moisture (SM) data simulated by the VIC hydrological model. A high-resolution temporal framework is introduced to detect DWAA events and evaluate the impact of precipitation pattern variations on dry-wet transitions in the basin. The results indicate: (1) Annual precipitation in the basin has significantly increased (0.47 mm y(-1) in the south, P < 0.05), while precipitation intensity follows a gradient pattern, increasing in the upstream (3.65 mm d1 y1) and decreasing in the downstream (-2.34 mm y(-1)). Additionally, the number of dry days and short-duration, high-intensity precipitation events has risen; (2) Soil moisture (SM) data simulated by the VIC model effectively capture DWAA events, showing significantly higher | SMCI| values downstream than upstream (P < 0.05) and indicating more intense dry-wet transitions in the downstream region. Furthermore, 78 % of the area exhibits an increasing trend in |SMCI|(1980-2019), with dry-to-wet transition events occurring more frequently than wet-to-dry events. For instance, in 2013, the maximum coverage area reached 48 % in a single day; (3) The random forest model highlights the spatial heterogeneity of DWAA driving factors: upstream water yield is the dominant factor, whereas downstream variations are closely associated with precipitation intensity (R-2 = 0.76) and the frequency of heavy rainfall days. Permafrost degradation and land use changes further heighten hydrological sensitivity in the downstream region. This study offers a transferable methodological framework for understanding extreme hydrological events and reveals that the driving mechanisms of DWAA are spatially heterogeneous, shifting from being dominated by terrestrial factors in the headwaters to meteorological factors downstream-a finding with significant implications for water resource management in other large, heterogeneous semi-arid basins.

Light-absorbing carbonaceous aerosols, comprising black carbon (BC) and brown carbon (BrC), significantly influence air quality and radiative forcing. Unlike traditional approaches that use a fixed value of absorption & Aring;ngstrom exponent (AAE), this study investigated the absorption and optical properties of carbonaceous aerosols in Beijing for both local emission and regional transport events during a wintertime pollution event by using improved AAE results that employs wavelength-dependent AAE (WDA). By calculating the difference of BC AAE at different wavelengths using Mie theory and comparing the calculated results to actual measurements from an Aethalometer (AE31), a more accurate absorption coefficient of BrC can be derived. Through the analysis of air mass sources, local emission was found dominated the pollution events during this study, accounting for 81 % of all cases, while regional transport played a minor role. Carbonaceous aerosols exhibited a continuous increasing trend during midday, which may be attributed to the re-entrainment of nighttime-accumulated carbonaceous aerosols to the surface during the early planetary boundary layer (PBL) development phase, as the mixed layer rises, combined with the variation of PBL and anthropogenic activity. At night, variations in the PBL height, in addition to anthropogenic activities, effectively contributed to surface aerosol concentrations, leading to peak surface aerosol values during local pollution episodes. The diurnal variation of AAE470/880 exhibited a decreasing trend, with a total decrease of approximately 12 %. Furthermore, the BrC fraction showed a constant diurnal variation, suggesting that the declining AAE470/880 was primarily influenced by BC, possibly due to enhanced traffic contributions.

The long-term trend for aerosol optical properties and climate impact sensitivity in terms of radiative forcing efficiency were analyzed at a suburban station in Athens, Southeast Mediterranean, using an extensive dataset from 2008 to 2022. The study examined scattering (nsc) and absorption (nap) coefficients, scattering & Aring;ngstrom exponent (SAE), absorption & Aring;ngstrom exponent (AAE), single scattering albedo (SSA), asymmetry parameter (g), and radiative forcing efficiency (RFE). Seasonal variability was linked to meteorological conditions and human activities. Single Scattering Albedo (SSA) was lowest (0.86), and Radiative Forcing Efficiency (RFE) was highest (-61 W/m2) in winter, confirming enhanced contributions from traffic and biomass burning. Lower SAE values (1.5) in spring indicate a greater presence of coarse particles due to frequent Saharan dust events (SDEs). Daily patterns of nap and SSA reflect local emissions, with pronounced traffic-related peaks. Aerosol classification revealed that Black Carbon (BC) dominates the suburban aerosol (51 %), with mixed BrC-BC (16 %) peaking in winter and dust-pollution mixtures (13 %) increasing in spring. The presence of large particles mixed with BC (11 %) was more frequent in spring, further highlighting seasonal variability. Trend analysis showed statistically significant (ss) decreases in nsc (-0.611) and SSA (-0.003), alongside increases in nap (+0.027) and RFE (+0.270) at a 95 % confidence level, suggesting a shift toward more absorbing aerosols. The findings provide new insights and reveal a new aerosol regime, where a reduction in anthropogenic emissions is affecting the scattering rather than the absorbing aerosol component, while the impact from forest fires as a climate feedback mechanism has a significant effect in the Eastern Mediterranean. It is important for future studies and climate modelling to account for the regionally observed changes of the state of mixing of ambient aerosol leading to a shift in radiative forcing efficiency through the reduction in SSA. This is evident in the long term for the east Mediterranean region and must be accounted for in radiative forcing estimates and future climate projections.

The Three-Rivers Headwater Region (TRHR) is located on the Tibetan Plateau, within a transitional zone between seasonally frozen ground and continuous permafrost. Over 70 % of the region is predominantly covered by alpine grasslands, a vulnerable ecosystem increasingly threatened by ongoing permafrost degradation. This study utilized satellite data to analyze permafrost degradation by measuring active layer thickness (ALT) and the soil non-frozen period (NFP), and to investigate their impacts on alpine grassland growth. Results showed significant permafrost degradation from 2000 to 2020, with ALT thickening at a rate of 7.79 cm/decade (p < 0.05) and NFP lengthening by 1.1 days/yr (p < 0.05). Simultaneously, grassland vegetation exhibited a significant greening trend (0.0014 yr(-1), p < 0.01). Using the partial least squares (PLS) regression method, the study evaluated the relationships between grassland dynamics and permafrost degradation, while jointly accounting for climate variables (temperature, precipitation, and sunshine duration). ALT thickening was the dominant explanatory variable for grassland growth in 11.09 % of the region, and it was positively correlated in relatively cold western and alpine areas, but negatively correlated in the relatively warm eastern and central regions. NFP extension was the dominant explanatory variable for grassland growth in 10.38 % of the region, although its positive correlation weakened as climate conditions transitioned from relatively cold-dry to relatively warm-wet. Although permafrost degradation was positively correlated with grassland greening in relatively cold regions, the diminishing benefit of NFP extension and the adverse effects of ALT thickening may increasingly undermine grassland stability in relatively warm regions under further climate warming.

An anomalous warm weather event in the Antarctic McMurdo Dry Valleys on 18 March 2022 created an opportunity to characterize soil biota communities most sensitive to freeze-thaw stress. This event caused unseasonal melt within Taylor Valley, activating stream water and microbial mats around Canada Stream. Liquid water availability in this polar desert is a driver of soil biota distribution and activity. Because climate change impacts hydrological regimes, we aimed to determine the effect on soil communities. We sampled soils identified from this event that experienced thaw, nearby hyper-arid areas, and wetted areas that did not experience thaw to compare soil bacterial and invertebrate communities. Areas that exhibited evidence of freeze-thaw supported the highest live and dead nematode counts and were composed of soil taxa from hyper-arid landscapes and wetted areas. They received water inputs from snowpacks, hyporheic water, or glacial melt, contributing to community differences associated with organic matter and salinity gradients. Inundated soils had higher organic matter and lower conductivity (p < .02) and hosted the most diverse microbial and invertebrate communities on average. Our findings suggest that as liquid water becomes more available under predicted climate change, soil communities adapted to the hyper-arid landscape will shift toward diverse, wetted soil communities.

The Tibetan Railway has introduced pressures on the fragile grassland ecosystems of the Tibetan Plateau. However, the impact of the railway on the carbon sequestration remains unclear, as existing studies primarily focus on in-situ vegetation observations. In this study, we extracted the start and end of the growing season (SOS, EOS) and maximum daily GPP (GPPmax) along the railway corridor from the satellite-derived Gross Primary Productivity (GPP) data, and quantified the extent and intensity of the railway's disturbance on these indicators. We further employed the Statistical Model of Integrated Phenology and Physiology (SMIPP) to translate these disturbances into annual cumulative GPP (GPPann). Results show that Tibetan Railway significantly influences grassland within 50-meters, causing earlier SOS (0.1086 d m-1), delayed EOS (0.0646 d m-1), and reduced GPPmax (0.0069 gC m-2 d-1 m-1) as the distance to the railway gets closer. The advanced SOS and delayed EOS contributed gains of 28.82 and 104.26 MgC y-1, but reduction in GPPmax accounted for a loss of 2952.79 MgC y-1. Railway-induced phenology-physiology trade-off causes GPPann loss of 2819.71 MgC y-1. This study reveals Tibetan Railway's impact on grassland carbon cycling, offering insights for grassland conservation and sustainable transportation infrastructure projects.

Understanding soil organic carbon (SOC) distribution and its environmental controls in permafrost regions is essential for achieving carbon neutrality and mitigating climate change. This study examines the spatial pattern of SOC and its drivers in the Headwater Area of the Yellow River (HAYR), northeastern Qinghai-Xizang Plateau (QXP), a region highly susceptible to permafrost degradation. Field investigations at topsoils of 86 sites over three summers (2021-2023) provided data on SOC, vegetation structure, and soil properties. Moreover, the spatial distribution of key permafrost parameters was simulated: temperature at the top of permafrost (TTOP), active layer thickness (ALT), and maximum seasonal freezing depth (MSFD) using the TTOP model and Stefan Equation. Results reveal a distinct latitudinal SOC gradient (high south, low north), primarily mediated by vegetation structure, soil properties, and permafrost parameters. Vegetation coverage and above-ground biomass showed positive correlation with SOC, while soil bulk density (SBD) exhibited a negative correlation. Climate warming trends resulted in increased ALT and TTOP. Random Forest analysis identified SBD as the most important predictor of SOC variability, which explains 38.20% of the variance, followed by ALT and vegetation coverage. These findings likely enhance the understanding of carbon storage controls in vulnerable alpine permafrost ecosystems and provide insights to mitigate carbon release under climate change.