Recent climate warming has accelerated permafrost thaw and dynamics of thermokarst lakes (TLs) on the Tibetan Plateau (TP). Yet, owing to the lack of long-term monitoring of TLs, our understanding of lake evolution processes and their driving factors remains uncertain. Here, using the global surface water product and timeseries Landsat imagery, we identified 58,538 TLs (0.01-3 km2) and determined the primary occurrence year of lake changes from 1990 to 2022. Our results indicated that TLs on the TP are primarily located in the central inland region, over 82 % of lakes experienced area expansion, and only 15 % in the northwest show decrease in area. Annual number of lake expansion peaked in 2016, whereas lake shrinkage was most common in 2019. The calculated lake area errors, field investigations, and validation of lake disturbance time demonstrated high accuracy and consistency. We applied the optimal machine learning regression model to distinguish the different drivers for lake expansion and shrinkage. The topographic and climatic factors are primary drivers for lake expansion, while differences in evaporation trend and soil temperature trend might contribute to lake shrinkage. This study highlights the vulnerability of permafrost on the TP to climate change, which can contribute to carbon sequestration estimation and infrastructure maintenance.

The Himalayan glacier valleys are encountering escalating environmental challenges. One of the contributing factors is thought to be the rising amounts of light-absorbing carbonaceous aerosols, particularly brown carbon (BrC) and black carbon (BC), that are reaching glacier valleys. The present study examines the optical and radiative characteristics of BC at Bhojbasa, near Gaumukh (similar to 3800amsl). Real-time in-situ BC data, optical characteristics, radiative forcing, heating rate, several meteorological parameters, and BC transport pathways to this high-altitude site are investigated. The daily mean concentration of equivalent black carbon (eBC) was 0.28 +/- 0.21 mu g/m(3) over the research period, and the eBC from fossil fuel (BCFF) is dominant with 78 % with a daily mean of 0.22 +/- 0.19 mu g/m(3)(,) and eBC from biomass burning (BCBB) is 22 % with a daily mean of 0.06 +/- 0.08 mu g/m(3). Meteorological data, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) imaging, and backward air-mass trajectory analysis demonstrate the presence of BC particles and their plausible transit pathways from multiple source locations to the pristine Gangotri Glacier Valley. The estimated daily mean BC radiative forcing values are +6.71 +/- 1.80 W/m(2) in the atmosphere, +1.87 +/- 1.16 W/m(2) at the top of the atmosphere, and -4.84 +/- 1.01 W/m(2) at the surface with a corresponding atmospheric heating rate of 0.19 +/- 0.05 K/day. These findings highlight the critical role of ground-based measurements in monitoring the fluctuations of BC over such varied Himalayan terrain, as they offer important information on the localized trends and effects. Long-term measurements of glacier valleys are essential for a comprehensive evaluation of the impact of BC particles on Himalayan ecology and climate.

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.

Massive stores of ancient soil organic carbon (SOC) in permafrost can decompose with Arctic warming and accelerate global climate change. Declining SOC stocks are central to the permafrost carbon feedback, but direct measures of SOC loss are extremely rare due to methodological challenges related to subsidence in the Arctic. To fully capture changing SOC dynamics during thaw, we directly measured SOC stock and bulk soil radiocarbon (C-14) changes, while accounting for subsidence, during 13 years of permafrost thaw in a warming experiment in Interior Alaska. We found significant declines in SOC stocks: 14% (+/- 6%) in ambient plots that experienced regional warming and 23% (+/- 5%) in snow fence warmed plots, entirely in deep, mineral soil layers. Losses were largely driven by winter soil warming but were mediated by changing soil moisture and vegetation conditions. Plots with low shrub biomass had greater SOC losses, suggesting that vegetation community composition may play an important role in SOC storage. Surface soil C-14 measurements suggest that carbon inputs were three times greater in warming plots compared to ambient plots, but that decomposition increased proportionally leading to no detectable change in surface organic layers. We observed significant SOC losses of 5.2-8.1 kg C m(-2) from deeper soil layers where carbon was sequestered similar to 2400 to similar to 4500 years ago. Our findings indicate that warmer soils in the winter will accelerate SOC losses, but that increasing density of shrub species through shrub expansion could help to mitigate SOC losses in deep soils. The significant loss of SOC from deep, mineral soils observed over just 13 years of ambient and experimental permafrost thaw highlights the vulnerability of this old C pool as it enters the active global carbon cycle.

Thaw hazards in high-latitude and glaciated regions are becoming increasingly frequent because of global climate warming and human activities, posing significant threats to infrastructure stability and environmental sustainability. However, despite these risks, comprehensive investigations of thaw-hazard susceptibility in permafrost regions remain limited. Here, this gap is addressed by a systematic and long-term investigation of thaw hazards in China's Qinghai Province as a representative permafrost area. A detailed inventory of 534 thawhazard sites was developed based on remote sensing, field verification, and surveys by a UAV, providing critical data for susceptibility analysis. Eleven environmental factors influencing thaw hazards were identified and analyzed using information gain and Shapley additive explanation. By using the random forest model, a susceptibility map was generated, categorizing the study area into five susceptibility classes: very low, low, moderate, high, and very high. The key influencing factors include precipitation, permafrost type, temperature change rate, and human activity. The results reveal that 17.5 % of the permafrost region within the study area is classified as high to very high susceptibility, concentrated primarily near critical infrastructure such as the Qinghai-Tibet Railway, potentially posing significant risks to its structural stability. The random forest model shows robust predictive capability, achieving an accuracy of 0.906 and an area under the receiver operating characteristic curve of 0.965. These findings underscore the critical role of advanced modeling in understanding the spatial distribution and drivers of thaw hazards, offering actionable insights for hazard mitigation and infrastructure protection in permafrost regions under a changing climate.

The retreat of glaciers due to climate change is reshaping mountain landscapes and biodiversity. While previous research has documented vegetation succession after glacier retreat, our understanding of functional diversity dynamics is still limited. In this case study, we address the effects of glacier retreat on plant functional diversity by integrating plant traits with ecological indicator values across a 140-year chronosequence in a subalpine glacier landscape. We reveal that functional richness and functional dispersion decrease with glacier retreat, while functional evenness and functional divergence increase, suggesting a shift toward more specialized and competitive communities. Our findings highlight the critical role of ecological factors related to soil moisture, soil nutrients and light availability in shaping plant community dynamics. As years since deglaciation was a key factor in regression and machine learning models, encapsulating time-lagged, spatial and historical processes, we highlight the need of including time into phenomenological or mechanistic models predicting biodiversity change following glacier retreat. The integrative approach of this case study provides novel insights into the potential response of alpine plant communities to climate change, offering a deeper understanding of how to predict and anticipate the effects of glacier extinction on biodiversity in rapidly changing environments. (sic)(sic): (sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)140(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).

Aeolian sand, as a primary medium of desertification, changes surface energy budgets and consequently affects both ecological systems and infrastructure stability on the Tibetan Plateau. Accurate interpretation of upper boundary conditions is critical for assessing aeolian sand's effects on subsurface hydrothermal processes. Nevertheless, current numerical simulations typically rely on empirical thermal boundaries and neglect surface radiation components and latent heat exchange, causing, significant deviations between simulations and field observations. This study establishes a thermal boundary model to calculate net surface energy (Q) based on energy balance theory and 13 sets of reflectance experiments. Using meteorological data from 2003 to 2019, soil temperature evolution was simulated under three aeolian sand coverage conditions: dry, 5 % moisture, and 10 % moisture. Results indicated that the simulated outputs exhibit strong correlations with observed data in terms of trend direction, phase timing of peaks and troughs, and temperature amplitude (R > 0.93, p < 0.0001). At the sand-atmosphere interface (-0.05 m), the annual mean temperature under dry aeolian sand cover reached 5.300 degrees C, which is 4.823 degrees C higher than that of the exposed surface (0.477 degrees C) during 2005-2006. When including moisture, the latent heat-driven cooling effect became evident, and the annual mean temperature at the sand surface dropped significantly to 0.930 degrees C (5 % moisture) and 1.461 degrees C (10 % moisture). More importantly, moisture cooling effects in shallow layers (-0.05 to-0.4 m) exhibit non-monotonic behavior: 10 % moisture yields higher annual mean temperatures than 5 % moisture (e.g., 1.370 degrees C vs. 0.858 degrees C at-0.2 m), suggesting aeolian sand's thermal impact on underlying permafrost involves critical moisture thresholds.

Studying permafrost changes under different (e.g., glacial/interglacial) and changing (e.g., current various scenarios) climates can potentially advance our understanding of permafrost's responses to climate change and further enable informed policy making for mitigating impacts from permafrost changes. Despite existing studies generally focusing on permafrost change during certain periods, here, we have synthetically examined the changes of the Northern Hemisphere near-surface permafrost during the six periods (Last Glacial Maximum (LGM, similar to 21 ka), mid-Holocene (MH, similar to 6 ka), preindustrial (PI, ca 1850), future 1.5 degrees C and 2.0 degrees C global warming periods, and end of the 21st century), using the surface frost index (SFI) model and outputs of six climate models. Simulated climate anomalies plus present-day observed climatology are used to drive the SFI model in this study. This helps correct systematic biases in permafrost change simulations.The results show that multi-model ensemble extent of present-day near-surface permafrost in the Northern Hemisphere agree well with the observations, with an area bias of 0.27x106 km2 in area (1.8% of the total observed area). Minor deviations (1.55x106 km2) in the simulated present-day permafrost extents across the climate models indicate a low inter-model diversity. In response to changes in annual mean surface air temperature of -10.3 +/- 2.3 degrees C (LGM), +0.1 +/- 0.5 degrees C (MH), +2.6 +/- 0.7 degrees C (1.5 degrees C global warming, RCP4.5), +3.6 +/- 1.0 degrees C (2.0 degrees C global warming, RCP4.5), and +5.0 +/- 1.3 degrees C (end of the 21st century, RCP4.5) in present-day permafrost regions relative to the PI, the changes in near-surface permafrost area are +33%+/- 30% (LGM), -13%+/- 6% (MH), -25%+/- 8% (1.5 degrees C warming, RCP4.5), -35% +/- 10% (2.0 degrees C warming, RCP4.5), and -55%+/- 12% (end of the 21st century, RCP4.5), respectively. From the LGM to the future, near-surface permafrost extent substantially decreases, underlining its high sensitivity to climate change and implying its potentially profound impacts in a warming future.

The Arctic has been warming much faster than the global average, known as Arctic amplification. The active layer is seasonally frozen in winter and thaws in summer. In the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign, airborne L- and P- band synthetic aperture radar (SAR) was used to acquire a dataset of active layer thickness (ALT) and vertical soil moisture profile, at 30 m resolution for 51 swaths across the ABoVE domain. Using a thawing degree day (TDD) model, ALT=K root TDD, we estimated ALT along the ABoVE swaths employing the 2-m air temperature from ERA5. The coefficient (K) calibrated has an R2=0.9783. We also obtained an excellent fit between ALT and K root(TDD/theta) where theta is the soil moisture from ERA5 (R2=0.9719). Output based on shared-social economic pathway (SSP) climate scenarios SSP 1-2.6, SSP 2-4.5, and SSP 5-8.5 from seven global climate models (GCMs), statistically downscaled to 25-km resolution, was used to project the impacts of climate warming on ALT. Assuming ALT=K root TDD, the projections of UKESM1-0-LL GCM resulted in the largest projected ALT, up to about 0.7 m in 2080s under SSP5-8.5. Given that the mean observed ALT of the study sites is about 0.482 m, this implies that ALT will increase by 0.074 to 0.217 m (15% and 45%) in 2080s. This will have substantial impacts on Arctic infrastructure. The projected settlement Iset (cm) of 1 to 7 cm will also impact the infrastructure, especially by differential settlement due to the high spatial variability of ALT and soil moisture, given at local scale the actual thawing will partly depend on thaw sensitivity of the material and potential thaw strain, which could vary widely from location to location.