Soil freeze-thaw state influences multiple terrestrial ecosystem processes, such as soil hydrology and carbon cycling. However, knowledge of historical long-term changes in the timing, duration, and temperature of freeze-thaw processes remains insufficient, and studies exploring the combined or individual contributions of climatic factors-such as air temperature, precipitation, snow depth, and wind speed-are rare, particularly in current thermokarst landscapes induced by abrupt permafrost thawing. Based on ERA5-Land reanalysis, MODIS observations, and integrated thermokarst landform maps, we found that: 1) Hourly soil temperature from the reanalysis effectively captured the temporal variations of in-situ observations, with Pearson' r of 0.66-0.91. 2) Despite an insignificant decrease in daily freeze-thaw cycles in 1981-2022, other indicators in the Qinghai-Tibet Plateau (QTP) changed significantly, including delayed freezing onset (0.113 d yr- 1), advanced thawing onset (-0.22 d yr- 1), reduced frozen days (-0.365 d yr- 1), increased frozen temperature (0.014 degrees C yr- 1), and decreased daily freeze-thaw temperature range (-0.015 degrees C yr- 1). 3) Total contributions indicated air temperature was the dominant climatic driver of these changes, while indicators characterizing daily freeze-thaw cycles were influenced mainly by the combined effects of increased precipitation and air temperature, with remarkable spatial heterogeneity. 4) When regionally averaged, completely thawed days increased faster in the thermokarstaffected areas than in their primarily distributed grasslands-alpine steppe (47.69%) and alpine meadow (22.64%)-likely because of their stronger warming effect of precipitation. Locally, paired comparison within 3 x 3 pixel windows from MODIS data revealed consistent results, which were pronounced when the thermokarst-affected area exceeded about 38% per 1 km2. Conclusively, the warming and wetting climate has significantly altered soil freeze-thaw processes on the QTP, with the frozen soil environment in thermokarstaffected areas, dominated by thermokarst lakes, undergoing more rapid degradation. These insights are crucial for predicting freeze-thaw dynamics and assessing their ecological impacts on alpine grasslands.

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere (NH) is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network is presented. This framework defines the landscape FT-cycle retrieval as a time-series anomaly detection problem, considering the frozen states as normal and the thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is assessed over Alaska using in situ observations, demonstrating an 11% improvement in accuracy and reduced uncertainties compared to traditional methods that rely on thresholding the normalized polarization ratio (NPR).

Observations from 1,047 meteorological stations from September 1, 2006 to August 31, 2015 revealed regional differences in the freezing and thawing processes of seasonally frozen ground (SFG) across China. SFG generally undergoes a one-way freezing process (i.e., top-down), and the stations with a large freeze depth generally experienced long freeze durations. During the thawing process, soil is generally characterized by two-way thawing (i.e., top-down and bottom-up) in the region north of 35 ' N, ' N, especially north of 30 ' N ' N (except in northeastern China). The onset of thawing from the bottom occurs earlier than that from the top at most stations in the two-way thawing region. The stations exhibiting one-way thawing (i.e., bottom-up) were mainly located on the southern edge of eastern China (east of 110 degrees E) degrees E) and in southern part of Xinjiang and southeast part of the Qinghai-Tibet Plateau. The freezing process lasts several days to more than four months longer than the soil thawing process, and this difference tends to be larger in high-latitude and high-altitude regions. All of the sites experienced a discontinuous freeze-thaw process, the station-average duration of which was less than a quarter of that of the continuous freeze-thaw process. Strong associations of soil freeze depth with air temperature (as characterized by the air freezing index and air thawing index) implied a dominant influence of air temperature on the soil freeze-thaw process. During the freezing process, this relationship was partially modulated by snow cover in snowy regions, such as northeast China, northwest China, and the eastern Tibetan Plateau. This paper provides the first overview of regional differences in the freezing and thawing processes of SFG over China, and the findings improve our understanding of the soil freeze-thaw process and provide important information to support research into regional landscapes, ecosystems, and hydrological processes.

Introduction: Permafrost and seasonally frozen soil are widely distributed on the Qinghai-Tibetan Plateau, and the freezing-thawing cycle can lead to frequent phase changes in soil water, which can have important impacts on ecosystems.Methods: To understand the process of soil freezing-thawing and to lay the foundation for grassland ecosystems to cope with complex climate change, this study analyzed and investigated the hydrothermal data of Xainza Station on the Northern Tibet from November 2019 to October 2021.Results and Discussion: The results showed that the fluctuation of soil temperature showed a cyclical variation similar to a sine (cosine) curve; the deep soil temperature change was not as drastic as that of the shallow soil, and the shallow soil had the largest monthly mean temperature in September and the smallest monthly mean temperature in January. The soil water content curve was U-shaped; with increased soil depth, the maximum and minimum values of soil water content had a certain lag compared to that of the shallow soil. The daily freezing-thawing of the soil lasted 179 and 198 days and the freezing-thawing process can be roughly divided into the initial freezing period (November), the stable freezing period (December-early February), the early ablation period (mid-February to March), and the later ablation period (March-end of April), except for the latter period when the average temperature of the soil increased with the increase in depth. The trend of water content change with depth at all stages of freezing-thawing was consistent, and negative soil temperature was one of the key factors affecting soil moisture. This study is important for further understanding of hydrothermal coupling and the mechanism of the soil freezing-thawing process.

Revegetation is an effective approach for restoring extremely degraded grassland (DG) in the Qinghai-Tibetan Plateau (QTP). However, little is known about its effects on permafrost stability. Our study investigated changes in the characteristics of DG and revegetated grassland (RG) in alpine permafrost regions of the QTP by means of in situ monitoring and sampling. Compared with DG, soil temperature was lower in warm months and slightly higher in cool months both at 2 and 10 cm depths after revegetation, while soil moisture generally decreased. Revegetation advanced the onset and increased the duration of completely frozen stage. The number of freeze-thaw days decreased at 2 cm but increased at 10 cm depth. The freeze-thaw strength weakened at 2 cm depth in spring and autumn, and at 10 cm depth in autumn, but increased at 10 cm depth in spring. The thawing index at the two depths and active layer thickness in RG were also significantly lower than those in DG. Revegetation significantly affected the particle size distribution and stability of soil aggregates by increasing the proportion of large macroaggregates. Thus, revegetation can effectively improve the permafrost stability of degraded grassland in the QTP and enhance the service functions of alpine grassland ecosystems.

The soil freeze/thaw (FT) state has emerged as a critical role in the ecosystem, hydrological, and biogeochemical processes, but obtaining representative soil FT state datasets with a long time sequence, fine spatial resolution, and high accuracy remains challenging. Therefore, we propose a decision-level spatiotemporal data fusion algorithm based on Convolutional Long Short-Term Memory networks (ConvLSTM) to expand the SMAP-enhanced L3 landscape freeze/thaw product (SMAP_E_FT) temporally. In the algorithm, the Freeze/Thaw Earth System Data Record product (ESDR_FT) is sucked in the ConvLSTM and fused with SMAP_E_FT at the decision level. Eight predictor datasets, i.e., soil temperature, snow depth, soil moisture, precipitation, terrain complexity index, area of open water data, latitude and longitude, are used to train the ConvLSTM. Direct validation using six dense observation networks located in the Genhe, Maqu, Naqu, Pali, Saihanba, and Shandian river shows that the fusion product (ConvLSTM_FT) effectively absorbs the high accuracy characteristics of ESDR_FT and expands SMAP_E_FT with an overall average improvement of 2.44% relative to SMAP_E_FT, especially in frozen seasons (averagely improved by 7.03%). The result from indirect validation based on categorical triple collocation also shows that ConvLSTM_FT performs stable regardless of land cover types, climate types, and terrain complexity. The findings, drawn from preliminary analyses on ConvLSTM_FT from 1980 to 2020 over China, suggest that with global warming, most parts of China suffer from different degrees of shortening of the frozen period. Moreover, in the Qinghai-Tibet region, the higher the permafrost thermal stability, the faster the degradation rate.

Permafrost soils in the northern hemisphere are known to harbor large amounts of soil organic matter (SOM). Global climate warming endangers this stable soil organic carbon (SOC) pool by triggering permafrost thaw and deepening the active layer, while at the same time progressing soil formation. But depending, e.g., on ice content or drainage, conditions in the degraded permafrost can range from water-saturated/anoxic to dry/oxic, with concomitant shifts in SOM stabilizing mechanisms. In this field study in Interior Alaska, we investigated two sites featuring degraded permafrost, one water-saturated and the other well-drained, alongside a third site with intact permafrost. Soil aggregate- and density fractions highlighted that permafrost thaw promoted macroaggregate formation, amplified by the incorporation of particulate organic matter, in topsoils of both degradation sites, thus potentially counteracting a decrease in topsoil SOC induced by the permafrost thawing. However, the subsoils were found to store notably less SOC than the intact permafrost in all fractions of both degradation sites. Our investigations revealed up to net 75% smaller SOC storage in the upper 100 cm of degraded permafrost soils as compared to the intact one, predominantly related to the subsoils, while differences between soils of wet and dry degraded landscapes were minor. This study provides evidence that the consideration of different permafrost degradation landscapes and the employment of soil fractionation techniques is a useful combination to investigate soil development and SOM stabilization processes in this sensitive ecosystem.

Simple Summary Microorganisms and their enzymes are crucial to ensuring soil quality, health, and carbon sequestration. Their numerous reactions are essential for biogeochemical cycles. However, a comprehensive review is lacking to summarize the latest findings in agricultural and enzymatic research. Although the relationships between soil enzyme activities and different soil ecosystems, such as arctic and permafrost regions, tropics and subtropics, tundra, steppes, etc., have been intensively investigated, particularly in the context of climate changes, only a few reviews summarize the impact of climate change on soil enzyme activity. This review aims to highlight the main groups of microbial enzymes found in soil (such as alpha-glucosidases and beta-glucosidases, phosphatases, ureases, N-acetyl-glucosaminidases, peptidases, etc.), their role in the global nutrient cycles of carbon, nitrogen, phosphorus, sulfur, carbon sequestration, and the influence of intensive agriculture on microbial enzymatic activity, and the variations in enzyme activity across different climate zones and soil ecosystems. Furthermore, the review will emphasize the importance of microbial enzymes for soil fertility and present both current challenges and future perspectives.Abstract The extracellular enzymes secreted by soil microorganisms play a pivotal role in the decomposition of organic matter and the global cycles of carbon (C), phosphorus (P), and nitrogen (N), also serving as indicators of soil health and fertility. Current research is extensively analyzing these microbial populations and enzyme activities in diverse soil ecosystems and climatic regions, such as forests, grasslands, tropics, arctic regions and deserts. Climate change, global warming, and intensive agriculture are altering soil enzyme activities. Yet, few reviews have thoroughly explored the key enzymes required for soil fertility and the effects of abiotic factors on their functionality. A comprehensive review is thus essential to better understand the role of soil microbial enzymes in C, P, and N cycles, and their response to climate changes, soil ecosystems, organic farming, and fertilization. Studies indicate that the soil temperature, moisture, water content, pH, substrate availability, and average annual temperature and precipitation significantly impact enzyme activities. Additionally, climate change has shown ambiguous effects on these activities, causing both reductions and enhancements in enzyme catalytic functions.

This paper presents a convolutional autoencoder deep learning framework for probabilistic characterization of the ground freeze-thaw (FT) dynamics in the Northern Hemisphere to enhance our understanding of permafrost response to global warming and shifts in the high-latitude carbon cycle, using Soil Moisture Active Passive (SMAP) satellite brightness temperatures (TB) observations. The autoencoder recasts the FT-cycle retrieval as an anomaly detection problem in which the peak winter (summer) represents the normal (anomaly) segments of the TB time series. The results demonstrate that the new framework outperforms the widely used fixed-thresholding of the Normalized Polarization Ratio (NPR) by learning the land surface structural and radiometric complexities that might arise in TB times series due to snow cover and vegetation. Validation against ground-based measurements over Alaska shows that the accuracy of the FT-cycle retrievals can be improved by 12%, primarily due to a marked reduction in false detection of short snowmelt episodes as ground thawing by the NPR thresholding approach.

In the context of global warming, the soil freeze depth (SFD) over the Tibetan Plateau (TP) has undergone significant changes, with a series of profound impacts on the hydrological cycle and ecosystem. The complex terrains and high elevations of the TP pose great challenges in data acquisition, presenting difficulties for studying SFD in this region. This study employs Stefan's solution and downscaled datasets from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate the future SFDs over the TP. The changing trends of the projected SFDs under different Shared Socio-economic Pathways (SSP) scenarios are investigated, and; the responses of SFDs to potential climatic factors, such as temperature and precipitation, are analyzed. The potential impacts of SFD changes on eco-hydrological processes are analyzed based on the relationships between SFDs, the distribution of frozen ground, soil moisture, and the Normalized Difference Vegetation Index (NDVI). Results show that the projected SFDs of the TP are estimated to decrease at rates of 0.100 cm/yr under the SSP126, 0.330 cm/yr under the SSP245, 0.565 cm/yr under the SSP370, and 0.750 cm/yr under the SSP585. Additionally, the SFD decreased at a rate of 0.160 cm/yr during the historical period from 1950 to 2014, which was between the decreasing rates of the SSP126 and SSP245 scenarios. The projected SFDs are negatively correlated with air temperature and precipitation, more significant under the higher emissions scenario. The projected decrease in SFDs will significantly impact eco-hydrological processes. A rapid decrease in SFD may lead to a decline in soil moisture content and have adverse impacts on vegetation growth. This research provides valuable insights into the future changes in SFD on the TP and their impacts on eco-hydrological processes.