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.

Study region: The study focuses on the Indus River Basin and southern Pakistan, severely affected by flooding in 2022. Study focus: This study assessed how land surface temperature, snow cover, soil moisture, and precipitation contributed to the deluge of 2022. This study mainly investigated MODIS-AIRS land surface temperature, MODIS snow cover (NDSI), SMAP soil moisture, and GPM IMERG precipitation accumulation. Furthermore, different flood visualization and mapping techniques were applied to delineate the flood extent map using Landsat 8-9, Sentinel-2 MSI, and Sentinel-1 SAR data. New hydrological insights for the region: The region experienced some of the most anomalous climatic events in 2022, such as prolonged heatwaves as observed with higher-than-average land surface temperatures and subsequent rapid decline in snow cover extent during the spring, increased soil moisture followed by an abnormal amount of extreme monsoon precipitation in the summer. The upper subbasins experienced more than 8 degrees C in positive temperature anomaly, indicating a warmer climate in spring. Subsequently, the snow cover declined by more than 25 % in the upper subbasins. Further, higher surface soil moisture values (> 0.3 m3/m3) were observed in the basin during the spring due to the rapid snow and ice melt. Furthermore, the basin received more than 200 mm of rainfall compared to the long-term average rainfall of about 98 mm, translating to about 300 % more rainfall than usual in July and August. The analysis helps understand the spatial and temporal variability within the basin and facilitates the understanding of factors and their intricate connections contributing to flooding.

Ambient seismic noise and microseismicity analyses are increasingly applied for the monitoring of landslides and natural hazards. These methodologies can offer a valuable monitoring tool also for glacial and periglacial bodies, to understand the internal processes driven by external modifications in air temperature and rainfall/snowfall regimes and to forecast possible melting-related hazards in the light of climate change adaptation. We applied the methods to an almost continuous year of data recorded by a network of four passive seismic stations deployed in the frontal portion of the Gran Sometta rock glacier (Aosta Valley, NW Italian Alps). The spectral analysis of ambient seismic noise revealed frequency peaks related to stratigraphic resonances inside the rock glacier. Although the resonance frequency related to the bedrock interface was constant over time, a second higher resonance frequency was identified as the effect of variations in the active layer thickness driven by external air temperature modifications at the daily and seasonal scales. Ambient seismic noise cross-correlation highlighted coherent shear wave velocity modifications inside the periglacial body. The microseismicity dataset extracted from the continuous ambient noise recordings was analyzed and clustered to further investigate the ongoing internal processes and gain insight into their source mechanism and location. The first cluster of events was found to be likely related to the basal movements of the rock glacier and to falls and slides of the debris material. The second cluster was possibly related to shallow ice and rock fracturing processes. The validation of the seismic results through simple models of the rock glacier physical and mechanical layering, the internal thermal regime and the surface displacements allowed for a comprehensive understanding of the rock glacier's reaction to the external conditions.

Soil thermal conductivity (STC) plays a crucial role in regulating the energy distribution of both the surface and underground soil layers. It is widely applied in various fields, including engineering design, geothermal resource development and climate change research. A rapid and accurate estimation of STC remains a key focus in the study of soil thermodynamic parameters. However, the methods for estimating STC and their distinct characteristics have yet to be systematically reviewed. In this study, we used bibliometrics to comprehensively and systematically review the literature on STC, focusing on knowledge graph characteristics to analyze the development trend of calculation schemes. The main conclusions drawn from the study are as follows: (1) In recent years, most studies have been focused on soil thermal characteristics and their main contributing factors, the soil hydrothermal process in the Qinghai-Tibet Plateau, geothermal equipment and numerical simulations, and the exploration of geothermal resources. (2) A systematic review of various schemes indicates that no single scheme is universally applicable to all soil types. Moreover, a single parameterization scheme fails to meet the practical requirements of land surface process models. We evaluated the advantages and disadvantages of the traditional heat conduction schemes, parameterization schemes, and machine learning-based schemes and the findings suggest that a comprehensive scheme that integrates these three different schemes for STC simulations should be urgently developed.

Periglacial processes and permafrost-related landforms, such as rock glaciers, are particularly vulnerable to climate change because of their reliance on sustained low temperatures to maintain permafrost integrity. Rising temperatures lead to permafrost thawing, increased active layer thickness, and ground instability, which disrupt the structural and ecological stability of these environments. Rock glaciers, which are ubiquitous in high mountain systems, are especially sensitive to these changes and serve as key geo-indicators of current or past alpine permafrost conditions, reflecting the multifaceted impacts of warming on both ecological and abiotic components. In this review, we synthesize current scientific knowledge on the complex and divergent responses of alpine rock glaciers to climate change, highlighting a wide range of methodologies employed to study the complex interactions between climatic drivers and rock glacier dynamics. We first explore ecological impacts, focusing on how climatic changes influence vegetation patterns, species composition, and overall biodiversity associated with rock glaciers. Subsequently, we examine the dynamic behavior of rock glaciers, including their structural integrity, movement patterns, and hydrological roles within high mountain ecosystems. By integrating findings from various disciplines, this review underscores the importance of multidisciplinary approaches and long-term monitoring to advance our understanding of rock glacier ecosystem dynamics and their role in periglacial processes under climate change. Our synthesis identifies critical knowledge gaps, such as the uncertain drivers of divergent rock glacier responses and the limited integration of ecological and abiotic data in existing studies. We highlight research priorities, including the establishment of regional monitoring networks and the development of predictive models that incorporate vegetation and permafrost interactions. These insights provide actionable guidance for adaptive management strategies to mitigate the ecological and geological impacts of climate change on these unique and sensitive environments.

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.