Vast deserts and sandy lands in the mid-latitudes cover an area of 17.64 x 106 km2, with 6.98 x 106 km2 experiencing seasonal frozen soil (SFG). Freeze-thaw cycles of SFG significantly influence local surface processes in deserts, impacting meteorological disasters such as infrastructure failures and sandstorms. This study investigates the freeze-thaw dynamics of SFG in crescent dunes from three deserts in northern China: the Tengger Desert, Mu Us Sandy Land, and Ulan Buh Desert, over the period from 2019 to 2024.Freezing occurs from November to January, followed by thawing from January to March. The thawing rate (2.72 cm/day) was 1.8 times higher than the freezing rate (1.48 cm/day). The maximum seasonal freezing depth (MSFD) exceeded 0.80 mat all dune slopes, with depths surpassing 1.10 mat the leeward slope and lower slope positions. Soil moisture content, ranging from 1 % to 1.6 %, is critical for freezing, and this threshold varies depending on the dune's mechanical composition. The hardness of frozen desert soil is primarily controlled by moisture, along with temperature and particle size.Temperature initiates freezing, while moisture and particle size control the resulting hardness.These findings shed light on the seasonal freeze-thaw processes in desert soils and have practical implications for agricultural management, engineering design, and environmental hazard mitigation in arid regions.

Glaciers playa vital role in providing water resources for drinking, agriculture, and hydro-electricity in many mountainous regions. As global warming progresses, accurately reconstructing long-term glacier mass changes and comprehending their intricate dynamic relationships with environmental variables are imperative for sustaining livelihoods in these regions. This paper presents the use of eXplainable Machine Learning (XML) models with GRACE and GRACE-FO data to reconstruct long-term monthly glacier mass changes in the Upper Yukon Watershed (UYW), Canada. We utilized the H2O-AutoML regression tools to identify the best performing Machine Learning (ML) model for filling missing data and predicting glacier mass changes from hydroclimatic data. The most accurate predictive model in this study, the Gradient Boosting Machine, coupled with explanatory methods based on SHapley Additive eXplanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) analyses, led to automated XML models. The XML unveiled and ranked key predictors of glacier mass changes in the UYW, indicating a decrease since 2014. Analysis showed decreases in snow water equivalent, soil moisture storage, and albedo, along with increases in rainfall flux and air temperature were the main drivers of glacier mass loss. A probabilistic analysis hinging on these drivers suggested that the influence of the key hydrological features is more critical than the key meteorological features. Examination of climatic oscillations showed that high positive anomalies in sea surface temperature are correlated with rapid depletion in glacier mass and soil moisture, as identified by XML. Integrating H2OAutoML with SHAP and LIME not only achieved high prediction accuracy but also enhanced the explainability of the underlying hydroclimatic processes of glacier mass change reconstruction from GRACE and GRACE-FO data in the UYW. This automated XML framework is applicable globally, contingent upon sufficient high-quality data for model training and validation.

Future anthropogenic land use change (LUC) may alter atmospheric carbonaceous aerosol (black carbon and organic aerosol) burden by perturbing biogenic and fire emissions. However, there has been little investigation of this effect. We examine the global evolution of future carbonaceous aerosol under the Shared Socioeconomic Pathways projected reforestation and deforestation scenarios using the CESM2 model from present-day to 2100. Compared to present-day, the change in future biogenic volatile organic compounds emission follows changes in forest coverage, while fire emissions decrease in both projections, driven by trends in deforestation fires. The associated carbonaceous aerosol burden change produces moderate aerosol direct radiative forcing (-0.021 to +0.034 W/m2) and modest mean reduction in PM2.5 exposure (-0.11 mu g/m3 to -0.23 mu g/m3) in both scenarios. We find that future anthropogenic LUC may be more important in determining atmospheric carbonaceous aerosol burden than direct anthropogenic emissions, highlighting the importance of further constraining the impact of LUC.

Permafrost thaw has the potential to release ancient particulate and dissolved organic matter that had been stored for thousands of years. Previous studies have shown that dissolved organic matter from permafrost is very labile and can be used by heterotrophic microbes close to the thaw area. However, it is unknown if ancient particulate organic matter can also be utilized. This study aims to investigate whether arctic microbial communities (bacteria and Archaea) incorporate ancient organic matter potentially released from thawing permafrost into their biomass. We compare and contrast the radiocarbon signatures of microbial lipids and higher plant biomarkers (representing terrestrial organic matter) from five soil profiles and seven deltaic lake sediment cores from the Mackenzie River drainage basin, Arctic Canada. In the surface soils, modern to post-modern short-chain fatty acids (SCFA) ages indicate in situ microbial production, with differential rates of organic carbon (OC) cycling depending on soil moisture. In contrast, SCFA in deeper soils display millennial ages, which likely represent the microbial necromass preserved through mineral association. In deltaic lakes that are disconnected from the river, generally old SCFA suggests the uptake of pre-aged OC by bacteria. In perennially connected lakes, pre-aged SCFA could originate from in situ microbial uptake of old OC or from the Mackenzie River. Higher plant-derived long-chain fatty acids (LCFA) present older radiocarbon ages, reflecting mineral stabilization during either pre-aging in soils (for high closure lakes) or riverine transport (for no and low closure lakes). Archaeal lipids are younger than SCFA and LCFA in high closure lakes, and older in low and no closure lakes, mirroring bulk radiocarbon signatures due to their heterotrophic production. These radiocarbon signatures of bacterial biomarker lipids may therefore reflect microbial incorporation of ancient OC (e.g., derived from permafrost thaw) or exceptional preservation (e.g., through mineral stabilization). Hence, even in relatively high OC environments such as arctic aquatic ecosystems, microbes can rely on ancient OC for their growth.

Numerical modeling of permafrost dynamics requires adequate representation of atmospheric and surface processes, a reasonable parameter estimation strategy, and site-specific model development. The three main research objectives of the study are: (i) to propose a novel methodology that determines the required level of surface process complexity of permafrost models by conducting parameter sensitivity and calibration, (ii) to design and compare three numerical models of increasing surface process complexity, and (iii) to calibrate and validate the numerical models at the Yakou catchment on the Qinghai-Tibet Plateau as an exemplary study site. The calibration was carried out by coupling the Advanced Terrestrial Simulator (numerical model) and PEST (calibration tool). Simulation results showed that (i) A simple numerical model that considers only subsurface processes can simulate active layer development with the same accuracy as other more complex models that include surface processes. (ii) Peat and mineral soil layer permeability, Van Genuchten alpha, and porosity are highly sensitive. (iii) Liquid precipitation aids in increasing the rate of permafrost degradation. (iv) Deposition of snow insulated the subsurface during the thaw initiation period. We have developed and released an integrated code that couples the numerical software ATS to the calibration software PEST. The numerical model can be further used to determine the impacts of climate change on permafrost degradation.

Soil microorganisms play a pivotal role in the biogeochemical cycles of alpine meadow ecosystems, especially in the context of permafrost thaw. However, the mechanisms driving microbial community responses to environmental changes, such as variations in active layer thickness (ALT) of permafrost, remain poorly understood. This study utilized next-generation sequencing to explore the composition and co-occur rence patterns of soil microbial communities, focusing on bacteria and micro-eukaryotes along a permafrost thaw gradient. The results showed a decline in bacterial alpha diversity with increasing permafrost thaw, whereas micro-eukaryotic diversity exhibi ted an opposite trend. Although changes in microbial community composition were observed in permafrost and seasonally frozen soils, these shifts were not statistically significant. Bacterial communities exhibited a greater differentiation between frozen and seasonally frozen soils, a pattern not mirrored in eukaryotic communities. Linear discriminant analysis effect size analysis revealed a higher number of potential biomark ers in bacterial communities compared with micro-eukaryotes. Bacterial co-occurrence networks were more complex, with more nodes, edges, and positive linkages than those of micro-eukaryotes. Key factors such as soil texture, ALT, and bulk density significantly influenced bacterial community structures, particularly affecting the relative abundan ces of the Acidobacteria, Proteobacteria, and Actinobacteria phyla. In contrast, fungal communities (e.g., Nucletmycea, Rhizaria, Chloroplastida, and Discosea groups) were more affected by electrical conductivity, vegetation coverage, and ALT. This study highlights the distinct responses of soil bacteria and micro-eukaryotes to permafrost thaw, offering insights into microbial community stability under global climate change.

Study area: Urumqi Glacier No.1 Catchment in central Asia. Study focus: Chemical weathering at the basin scale is important process for understanding the feedback mechanism of the carbon cycle and climate change. This study mainly used the actual sampling data in 2013, 2014, and 2016, and the first collection from the literature in same catchment to analyze the seasonal and interannual characteristics of meltwater runoff, as well as cation denudation rate (CDR). New hydrological insights for the study region: The dominant ions of meltwater runoff are Ca2 +, HCO3- , and SO42-, which are mainly derived from calcite dissolution, feldspar weathering and sulfide oxidation. Meltwater runoff at Urumqi Glacier No.1 has higher concentrations of Ca2+ and lower concentrations of HCO3- than that from glaciers in Asia. Compared to 2006 and 2007, cation concentrations increased in 2013 and 2014, while SO42- concentration decreased. The daily ion concentration has seasonality and exhibits a negative relationship with discharge. Daily CDR is positively related to discharge and temperature. Annual CDR values range from 12.34 to 19.04 t/ km2/yr in 2013, 2014, and 2016, which are 1-1.7 times higher than those in 2006 and 2007 and higher than some glaciers in Asia. These results indicate that chemical weathering rate in the Urumqi Glacier No.1 catchment has increased with climate warming, and it is stronger than that of some glaciers in the Tibetan Plateau and surroundings.

Precipitation comes in various phases, including rainfall, snowfall, sleet, and hail. Shifts of precipitation phases, as well as changes in precipitation amount, intensity, and frequency, have significant impacts on regional climate, hydrology, ecology, and the energy balance of the land-atmosphere system. Over the past century, certain progress has been achieved in aspects such as the observation, discrimination, transformation, and impact of precipitation phases. Mainly including: since the 1980s, studies on the observation, formation mechanism, and prediction of precipitation phases have gradually received greater attention and reached a certain scale. The estimation of different precipitation phases using new detection theories and methods has become a research focus. A variety of discrimination methods or schemes, such as the potential thickness threshold method of the air layer, the temperature threshold method of the characteristic layer, and the near-surface air temperature threshold method, have emerged one after another. Meanwhile, comparative studies on the discrimination accuracy and applicability assessment of multiple methods or schemes have also been carried out simultaneously. In recent years, the shift of precipitation from solid to liquid (SPSL) in the mid-to-high latitudes of the Northern Hemisphere has become more pronounced due to global warming and human activities. It leads to an increase in rain-on-snow (ROS) events and avalanche disasters, affecting the speed, intensity, and duration of spring snow-melting, accelerating sea ice and glacier melting, releasing carbon from permafrost, altering soil moisture, productivity, and phenological characteristics of ecosystems, and thereby affecting their structures, processes, qualities, and service functions. Although some progress has been made in the study of precipitation phases, there remains considerable research potential in terms of completeness of basic data, reliability of discrimination schemes, and the mechanistic understanding of the interaction between SPSL and other elements or systems. The study on shifts of precipitation phases and their impacts will play an increasingly important role in assessing the impacts of global climate change, water cycle processes, water resources management, snow and ice processes, snow and ice-related disasters, carbon emissions from permafrost, and ecosystem safety.

We present an innovative approach to understanding permafrost degradation processes through the application of new environment-based particle image velocimetry (E-PIV) to time-lapse imagery and correlation with synchronous temperature and rainfall measurements. Our new approach to extracting quantitative vector movement from dynamic environmental conditions that can change both the position and the color balance of each image has optimized the trade-off between noise reduction and preserving the authenticity of movement data. Despite the dynamic polar environments and continuous landscape movements, the E-PIV provides the first quantitative real-time associations between environmental drivers and the responses of permafrost degradation mechanism. We analyze four event-based datasets from an island southwest of Tuktoyaktuk, named locally as Imnaqpaaluk or Peninsula Point near Tuktoyaktuk, NWT, Canada, spanning a 5-year period from 2017 to 2022. The 2017 dataset focuses on the interaction during a hot dry summer between slope movement and temperature changes, laying the foundation for subsequent analyses. In 2018, two datasets significantly expand our understanding of typical failure mechanisms in permafrost slopes: one investigates the relationship between slope movement and rainfall, while the other captures an overhang collapse, providing a rare quantitative observation of an acute landscape change event. The 2022 dataset revisits the combination of potential rain and air temperature-related forcing to explore the environment-slope response relationship around an ice wedge, a common feature of ice-rich permafrost coasts. These analyses reveal both a direct but muted association with air temperatures and a detectable delayed slope response to the occurrence of rainfall, potentially reflective of the time taken for the warm rainwater to infiltrate through the active layer and affect the frozen ground. Whilst these findings also indicate that other factors are likely to influence permafrost degradation processes, the associations have significant implications given the projections for a warmer, wetter Arctic. The ability to directly measure permafrost slope responses offers exciting new potential to quantitatively assess the sensitivity of different processes of degradation for the first time, improving the vulnerability components of hazard risk assessments, guiding mitigation efforts, and better constraining future projections of erosion rates and the mobilization of carbon-rich material.

As a key component of the cryosphere, permafrost is sensitive to climate change, but mapping permafrost, especially in the Tibetan Plateau, has been challenging due to the heterogeneous mountainous landscape and limited representativeness of ground observations. Using 155 compiled ground observations and more than 20,000 rock glacier records, we developed a machine learning model to map the distribution of permafrost and produce an improved permafrost zonation index (PZI) map. The model was applied by incorporating several control variables, including terrain (elevation and relief), soil (bulk density, clay, coarse fragments, sand, and silt), and temperature (MAAT, FDD, and TDDT) to estimate the PZI at a 1-km resolution in the southern Tibetan Plateau. Excluding glaciers and lakes, the area of permafrost estimated by the new map is approximately 103.5 x 103 km2, accounting for 47.8% of the total area of the region. The result was assessed with various datasets and compared with existing permafrost maps and achieved higher accuracy compared with previous studies. The overall classification accuracy was 96.1% in high plain areas and 84.4% in mountain areas. The results demonstrated the substantial potential for improving mapping permafrost and understanding the periglacial environment with rock glacier inventories and machine learning, especially in complex terrain and climate.