Snow cover variation significantly impacts alpine vegetation dynamics on the Tibetan Plateau (TP), yet this effect under climate change remains underexplored. This study uses a survival analysis model to assess the influence of snow on vegetation green-up dynamics, while controlling for key temperature and water availability factors. This analysis integrates multi-source data, including satellite-derived vegetation green-up dates (GUDs), snow depth, accumulated growing degree days (AGDD), downward shortwave radiation (SRAD), precipitation, and soil moisture. Our survival analysis model effectively simulated GUD on the TP, achieving an R of 0.62 (p < 0.01), a root mean square error (RMSE) of 11.20 days, and a bias of -1.41 days for 2020 GUD predictions. It outperformed both the model excluding snow depth and a linear regression model. By isolating snow's impact, we found it exerts a stronger influence on vegetation GUD than precipitation in snow-covered areas of the TP. Furthermore, snow depth effects varied seasonally: a 1-cm increase in preseason snow depth reduced green-up rates by 8.48% before 156(th) day but increased them by 4.74% afterward. This indicates that deeper preseason snow cover delays GUD before June, but advances it from June onward, rather than having a uniform effect. These findings highlight the critical role of snow and underscore the need to incorporate its distinct effects into vegetation phenology models in alpine regions.

Northeastern China (NEC) is the largest grain base in China. Improving understanding of the effect of climate change on grain production over NEC is conducive to providing immediate response strategies for grain production. In this study, the relationships of the maize production with the dry state during the different maize growth stage have been investigated using the year-to-year increment method. Results showed that the severe drought that occurred from the jointing to maturity period have exerted severe effects on the maize growth. Further analysis indicated that the sea surface temperature (SST) anomalies over North Atlantic and Maritime Continent in later spring are the important factors affecting the summer droughts over NEC. The late spring SST anomaly over North Atlantic can excite the Rossby waves from the western North Atlantic and propagate eastward to NEC. The snow anomaly over western Siberia in late spring and the soil moisture anomaly over NEC in summer are key factors linking the SST anomaly to drought over the NEC. On the other hand, the Maritime Continent SST anomaly in late spring can modulate the activity of the East Asian jet stream via the East AsiaPacific (EAP) teleconnection, which can provide the favorable conditions for the soil moisture reduction over NEC. Eventually, a predictive model for maize yield over NEC is successfully developed by using the predictive indices of the North Atlantic and the Maritime Continental SST during late spring. Both the cross-validation and independent sample tests show that the calibrated prediction model is robust and exhibits high skill in predicting maize yield over NEC.

Empirical orthogonal function (EOF) and correlation analyses were employed to investigate the winter and spring snow depth in Eurasia and its relationship with Eastern China precipitation based on the observed and reanalyzed data from 1980 to 2016. The results show that the winter and spring snow cover in Eurasia not only highlights a decreasing trend due to global warming (the first EOF mode, its variance accounted for 24.4% and 22.6% of the total variance) but also exhibits notable interdecadal variation (the second EOF mode, its variance accounted for 10.2% and 11.5% of the total variance). The second EOF mode of winter snow depth in Eurasia is characterized by a west-east dipole pattern. It was observed that the spatial correlation pattern between the EOF2 of Eurasian snow depth and summer precipitation in China closely resembles the meridional quadrupole structure of the third EOF mode of summer precipitation in China. This pattern is characterized by excessive rainfall in Northeast China and the lower-middle reaches of the Yangtze River, and less rainfall over the Yellow River basin and southern China. The EOF mode of spring snow depth not only reflects the declining trend but also regulates precipitation in Eastern China. The possible mechanisms by which snow depth causes changes in soil moisture and subsequently affects atmospheric circulation are then explored from the perspective of the hydrological effects of snow cover. Decreased (Increased) snow depth in Eurasia during the winter and spring directly leads to diminished (increased) soil moisture while increasing (decreasing) net radiation and sensible heat flux at the surface. The meridional distribution of surface temperature also exhibits a dipole pattern, leading to enhanced subtropical westerly jet in the upper troposphere. The Eurasian snow cover anomalies pattern triggered an anomalous mid-latitude Eurasian wave train, which strengthened significantly in the Western Siberian Plain. It then splits into two branches, one continuing to propagate eastward at high latitudes and the other shifting towards East Asia, thereby impacting precipitation in Eastern China. This work indicates that the second EOF mode of Eurasian snow cover can impact the precipitation variability in Eastern China during the same period and in summer on an interdecadal scale.

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock-ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate-cryosphere-hydrology-hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze-thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

Projected future changes in snow cover patterns associated with global warming in cold zone ecosystems could affect soil biochemical cycling. However, the effects of snow cover changes on soil available carbon, nitrogen and enzyme activities and their potential response mechanisms have not been clarified. Therefore, from November 2021 to April 2022, this study conducted a snow depth manipulation test of four treatments in the northeast black soil region, and divided the test period into five stages to measure soil temperature and humidity, microbial biomass, enzyme activity, and available carbon and nitrogen. The results showed that the decrease of snow cover increased the freeze-thaw cycle frequency and freezing temperature of soil, but decreased the soil water content. Soil total organic carbon and inorganic nitrogen contents were increased in early and deep snow periods, while snow treatment was on the contrary. Due to the release of soluble nutrients caused by frequent freeze-thaw processes, Soil soluble organic carbon and Soil soluble organic nitrogen contents increased with the decrease of snow depth in deep snow period, snowmelt period and subsequent early crop growth period. Snow treatment increased soil microbial carbon and nitrogen content in early winter and early spring because snow provided heat insulation. Soil enzyme activities increased with the increase of snow cover. Compared with the control, soil urease activities and sucrase activities increased by 18.5 % and 11.5 % under snow treatment, and decreased by 23.2 % and 10.8 % under snow reduction treatment. In addition, soil soluble organic matter was a controlling factor for soil microbial biomass and enzyme activity throughout winter. The direct effect of soil soluble organic carbon and nitrogen on soil enzymes will make soil enzymes participate in the cyclic transformation process of available carbon, thus forming a closed loop of mutual feedback between soil available carbon and nitrogen and enzymes. These results demonstrated that the changes of snow cover in the future will have certain effects on soil carbon and nitrogen cycles and enzyme activities and hence biogeochemical cycling in terrestrial system of earth.

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are -0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

As a vital source of the climate change predictability, the snow depth predictability originates from its own persistence and the external forcing factors. In order to investigate the root of snow depth predictability at the North Hemisphere, this study conducted an ensemble of 20 simulations spanning 50 years with the Community Earth System Model (CESM). With a regression model constructed via the canonical correlation analysis method, we analyzed the temporal and spatial distribution characteristics of snow depth predictability on the global scale, as well as the effects of snow depth persistence and sea surface temperature (SST) on snow depth predictability. The results show that the predictability due to snow depth persistence depends on both season and location. The persistence of snow depth can reach more than 3 months in high latitude region. After considering the SST forcing, the predictability is increased in many parts of the Northern Hemisphere, such as northern North America, Europe, and central Siberia. The areas where SST significantly influences snow depth predictability mainly overlap the snow cover transition zones. We further investigated the possible pathways of the impact of SST on snow depth predictability, and found that in North America and Europe, SST improves the predictability mainly through affecting the surface temperature, while in central Siberia and eastern Europe, the pathway also includes snowfall and shortwave radiation, respectively. Additionally, we conducted a similar analysis with three other climate models from the Atmospheric Model Intercomparison Project phase 6 (AMIP6), and the results can also verify the conclusions of CESM ensemble simulations.

Climate change is destabilizing permafrost landscapes, affecting infrastructure, ecosystems, and human livelihoods. The rate of permafrost thaw is controlled by surface and subsurface properties and processes, all of which are potentially linked with each other. However, no standardized protocol exists for measuring permafrost thaw and related processes and properties in a linked manner. The permafrost thaw action group of the Terrestrial Multidisciplinary distributed Observatories for the Study of the Arctic Connections (T-MOSAiC) project has developed a protocol, for use by non-specialist scientists and technicians, citizen scientists, and indigenous groups, to collect standardized metadata and data on permafrost thaw. The protocol introduced here addresses the need to jointly measure permafrost thaw and the associated surface and subsurface environmental conditions. The parameters measured along transects include: snow depth, thaw depth, vegetation height, soil texture, and water level. The metadata collection includes data on timing of data collection, geographical coordinates, land surface characteristics (vegetation, ground surface, water conditions), as well as photographs. Our hope is that this openly available dataset will also be highly valuable for validation and parameterization of numerical and conceptual models, and thus to the broad community represented by the T-MOSAiC project.

As the buffer layer between the atmosphere and permafrost, the active layer is vulnerable to climate change. The variation in the active layer thickness (ALT) has important effects on surface energy balance, ecosystem, hydrological cycle, vegetation cover, and engineering construction in permafrost regions. The goal of this study is to discuss the active layer variation under different shared socioeconomic pathways (SSPs) for specific warming levels and to reveal the potential interactions between the ALT and the associated driving factors in typical hydrological basins. We revised the Stefan solution using the edaphic factor and the thawing index calculated by multimodel data from the Coupled Model Intercomparison Project Phase 6 to estimate the variation in the ALT. During 2015 to 2100, the ALT will increase by 14 cm (SSP1-2.6), 43 cm (SSP2-4.5), and 1.44 m (SSP5-8.5), with average increase rates of 2.5 cm/decade, 5.8 cm/decade, and 17.5 cm/decade, respectively. The rates of increase of the ALT in the Hexi basin, Inner basin, Mekong basin, Yangtze basin, and Yellow basin are 12.6 cm/decade, 6.7 cm/decade, 5.2 cm/decade, 8.0 cm/decade, and 5 cm/decade, respectively. These results illustrate that air temperature is the primary determinant of ALT variation and normalized difference vegetation index (NDVI) and snow depth may influence the ALT change. The most significant correlations are between the ALT and NDVI in the Yangtze basin. In different seasons, the spring snow depth has the greatest impact on the ALT in the Hexi basin.

In this study, a backpropagation artificial neural network snow simulation model (BPANNSIM) is built using data collected from the National Climate Reference Station to obtain simulation data of China's future daily snow depth in terms of representative concentration pathways (RCP4.5 and RCP8.5). The input layer of the BPANNSIM comprises the current day's maximum temperature, minimum temperature, snow depth, and precipitation data, and the target layer comprises snow depth data of the following day. The model is trained and validated based on data from the National Climate Reference Station over a baseline period of 1986-2005. Validation results show that the temporal correlations of the observed and the model iterative simulated values are 0.94 for monthly cumulative snow cover duration and 0.88 for monthly cumulative snow depth. Subsequently, future daily snow depth data (2016-2065) are retrieved from the NEX-GDPP dataset (Washington, DC/USA: the National Aeronautics and Space Administration(NASA)Earth Exchange/Global Daily Downscaled Projections data), revealing that the simulation data error is highly correlated with that of the input data; thus, a validation method for gridded meteorological data is proposed to verify the accuracy of gridded meteorological data within snowfall periods and the reasonability of hydrothermal coupling for gridded meteorological data.