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.

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).

The soil moisture active passive (SMAP) satellite mission distributes a product of CO2 flux estimates (SPL4CMDL) derived from a terrestrial carbon flux model, in which SMAP brightness temperatures are assimilated to update soil moisture (SM) and constrain the carbon cyclemodeling. While the SPL4CMDL product has demonstrated promising performance across the continental USA and Australia, a detailed assessment over the arctic and subarctic zones (ASZ) is still missing. In this study, SPL4CMDL net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (R-E) are evaluated against measurements from 37 eddy covariance towers deployed over the ASZ, spanning from 2015 to 2022. The assessment indicates that the NEE unbiased root-mean-square error falls within the targeted accuracy of 1.6 gC.m(-2).d(-1), as defined for the SPL4CMDL product. However, modeled GPP and R-E are overestimated at the beginning of the growing season over evergreen needleleaf forests and shrublands, while being underestimated over grasslands. Discrepancies are also found in the annual net CO2 budgets. SM appears to have a minimal influence on the GPP and R-E modeling, suggesting that ASZ vegetation is rarely subjected to hydric stress, which contradicts some recent studies. These results highlight the need for further carbon cycle process understanding and model refinements to improve the SPL4CMDL CO2 flux estimatesover the ASZ.

Hydrologic-land surface models (H-LSMs) offer a physically-based framework for representing and predicting the present and future states of the extensive high-latitude permafrost areas worldwide. Their primary challenge, however, is that soil temperature data are severely limited, and traditional model validation, based only on streamflow, can show the right fit to these data for the wrong reasons. Here, we address this challenge by (1) collecting existing data in various forms including in-situ borehole data and different large-scale permafrost maps in addition to streamflow data, (2) comprehensively evaluating the performance of an H-LSM with a wide range of possible process parametrizations and initializations, and (3) assessing possible trade-offs in model performance in concurrently representing hydrologic and permafrost dynamics, thereby pointing to the possible model deficiencies that require improvement. As a case study, we focus on the sub-arctic Liard River Basin in Canada, which typifies vast northern sporadic and discontinuous permafrost regions. Our findings reveal that different process parameterizations tend to align with different data sources or variables, which largely exhibit inconsistencies among themselves. We further observe that a model may fail to represent permafrost occurrence yet seemingly fit streamflows adequately. Nonetheless, we demonstrate that accurately representing essential permafrost dynamics, including the active soil layer and insulation effects from snow cover and soil organic matter, is crucial for developing high-fidelity models in these regions. Given the complexity of processes and the incompatibility among different data sources/variables, we conclude that employing an ensemble of carefully designed model parameterizations is essential to provide a reliable picture of the current conditions and future spatio-temporal co-evolution of hydrology and permafrost.

The climate in Northwest China (NWC) has undergone a warming and wetting trend (WWT) since the 1980s, which has attracted considerable attention from the scientific and policy communities. However, the majority of previous studies have focused on overall effects of WWT, and very few have examined how land surface system responds to climate warming or wetting trend, respectively. For this purpose, this study uses the Community Land Model (CLM5) driven by the Chinese Meteorological Forcing Dataset (CMFD) to conduct four modeling experiments: a control experiment (CTRL) and three sensitivity experiments, in which the annual trend of air temperature (NonWarm), precipitation (NonWet), and both (NonWWT) are removed from the CMFD from 1979 to 2018. Compared to CTRL, the land hydrological variables (i.e. soil moisture, runoff and evapotranspiration) show a visible reduction in magnitude, interannual variability, as well as annual trend in NonWet, while they are enhanced in NonWarm. In both NonWarm and NonWet, the magnitude and trend of both net radiation and sensible heat fluxes increase, with a more pronounced change in NonWWT. Further analysis indicates that the land surface processes are more sensitive to wetting trend than to warming trend. Among all land surface hydrological variables and energy variables, runoff and snow cover fraction are the most susceptible to climate change. Overall, the effects of climate change in Ta and Pr on surface hydrological variables are non-linearly offsetting, while the effects on surface energy budgets are non-linearly superimposed. Compared to warming trend, wetting trend plays a larger impact on the variability of land surface processes in NWC.

Tibetan Plateau (TP) is known as the Third Pole of the Earth. Any changes in land surface processes on the TP can have an unneglectable impact on regional and global climate. With the warming and wetting climate, the land surface of the TP saw a darkening trend featured by decreasing surface albedo over the past decades, primarily due to the melting of glaciers, snow, and greening vegetation. Recent studies have investigated the effects of the TP land surface darkening on the field of climate, but these assessments only address one aspect of the feedback loop. How do these darkening-induced climate changes affect the frozen ground and ecosystems on the TP? In this study, we investigated the impact of TP land surface darkening on regional frozen ground and ecosystems using the state-of-the-art land surface model ORCHIDEE-MICT. Our model results show that darkening-induced climate changes on the TP will lead to a reduction in the area of regional frozen ground by 1.1x104 +/- 0.019x104 km2, a deepening of the regional permafrost active layer by 0.06 +/- 0.0004 m, and a decrease in the maximum freezing depth of regional seasonal frozen ground by 0.06 +/- 0.0016 m compared to the scenario without TP land surface darkening. Furthermore, the darkening-induced climate change on the TP will result in an increase in the regional leaf area index and an enhancement in the regional gross primary productivity, ultimately leading to an increase in regional terrestrial carbon stock by 0.81 +/- 0.001 PgC. This study addresses the remaining piece of the puzzle in the feedback loop of TP land surface darkening, and improves our understanding of interactions across multiple spheres on the TP. The exacerbated regional permafrost degradation and increasing regional terrestrial carbon stock induced by TP land surface darkening should be considered in the development of national ecological security barrier.

Arctic land is characterized by a high surface and subsurface heterogeneity on different scales. However, the effects of land surface model resolution on fluxes and soil state variables in the Arctic have never been systematically studied, even though smaller scale heterogeneities are resolved in high-resolution land boundary condition datasets. Here, we compare 210 km and 5 km setups of the land surface model JSBACH3 for an idealized case study in eastern Siberia to investigate the effects of high versus low-resolution land boundary conditions on simulating the interactions of soil physics, hydrology and vegetation. We show for the first time that there are differences in the spatial averages of the simulated fluxes and soil state variables between resolution setups. Most differences are small in the summer mean, but larger within individual months. Heterogeneous soil properties induce large parts of the differences while vegetation characteristics play a minor role. Active layer depth shows a statistically significant increase of +20% in the 5 km setup relative to the 210 km setup for the summer mean and +43% for August. The differences are due to the nonlinear vertical discretization of the soil column amplifying the impact of the heterogeneous distributions of soil organic matter content and supercooled water. Resolution-induced differences in evaporation fluxes amount to +43% in July and are statistically significant. Our results show that spatial resolution significantly affects model outcomes due to nonlinear processes in heterogenous land surfaces. This suggests that resolution needs to be accounted in simulations of land surface models in the Arctic.

Permafrost in High Mountain Asia (HMA) is becoming increasingly vulnerable to thaw due to climate change. However, the lack of either in situ ground surface or borehole temperature data beyond the Tibetan Plateau prevents comprehensive assessments of its impact on the regional hydrologic cycle and local cascading hazards. Although past studies have generated estimates of permafrost extent in Central Asia, many are limited to the Tibetan Plateau, excluding the more remote reaches of the Tien Shan, Pamirs, and Himalayas. By leveraging surface temperatures from both the Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infra-Red Sounder (AIRS), this study advances further understanding of remotely sensed permafrost occurrence at high altitudes, which are prone to error due to frequent cloud cover. We demonstrate that the fusion of MODIS and AIRS products can accurately estimate long-term thermal regimes of the subsurface, with reported correlation coefficients of 0.773 and 0.560, RMSEs of 0.890 degree celsius and 0.680 degree celsius, and biases of 0.003 degree celsius and 0.462 degree celsius, respectively, for the ground surface and the depth of zero annual amplitude, during a reference period of 2003-2016. Furthermore, we provide a range of possible permafrost extents based on established equations for calculating the temperature at the top of the permafrost to demonstrate temperature sensitivity to soil moisture and snow cover. The MODIS-AIRS product is recommended to be a robust source of ground temperature estimates, which may be sufficient for inferring mountain permafrost presence in HMA. Incorporating the influence of soil moisture and snow depth, although limited by biased estimates, also produces estimates of permafrost regional areas comparable to previously reported permafrost indices. A total permafrost area of 1.69 (+/- 0.32) million km(2) is estimated for the entire HMA, across 15 mountain subregions.

The Tibetan Plateau (TP) is distributed with large areas of permafrost, which have received increasing attention as the climate warms. Accurately modeling the extent of permafrost and permafrost changes is now an important challenge for climate change research and climate modeling in this region. Uncertainty in land use and land cover (LULC), which is important information characterizing surface conditions, directly affects the accuracy of the simulation of permafrost changes in land surface models. In order to investigate the effect of LULC uncertainty on permafrost simulation, we conducted simulation experiments on the TP using the Community Land Model, version 5 (CLM5) with five high-resolution LULC products in this study. Firstly, we evaluated the simulation results using shallow soil temperature data and deep borehole data at several sites. The results show that the model performs well in simulating shallow soil temperatures and deep soil temperature profiles. The effect of different land use products on the shallow soil temperature and deep soil temperature contours is not obvious due to the small differences in land use products at these sites. Although there is little difference in the simulating results of different land use products when compared to the permafrost distribution map, the differences are noticeable for the simulation of the active layer. Land cover had a greater impact on soil temperature simulations in regions with greater land use inconsistency, such as at the junction of bare soil and grassland in the northwestern part of the TP, as well as in the southeast region with complex topography. The main way in which this effect occurs is that land cover affects the net surface radiation, which in turn causes differences in soil temperature simulations. In addition, we discuss other factors affecting permafrost simulation results and point out that increasing the model plant function types as well as carefully selecting LULC products is one of the most important ways to improve the simulation performance of land-surface models in permafrost regions.

Polar amplification appears in response to greenhouse gas forcing, which has become a focus of climate change research. However, polar amplification has not been systematically investigated over the Earth's three poles (the Arctic, Antarctica, and the Third Pole). An index of polar amplification is employed, and the annual and seasonal variations of land surface temperature over the Earth's three poles are examined using MODIS (Moderate Resolution Imaging Spectroradiometer) observations for the period 2001-2018. As expected, the warming of the Arctic is most conspicuous, followed by the Third Pole, and is weakest in Antarctica. Compared to the temperature changes for the global land region, positive polar amplification appears in the Arctic and the Third Pole on an annual scale, whereas Antarctic amplification disappears, with a negative amplification index of -0.72. The polar amplification for the Earth's three poles shows seasonal differences. Strong Arctic amplification appears in boreal spring and winter, with a surface warming rate of more than 3.40 times the global mean for land regions. In contrast, the amplification of the Third Pole is most conspicuous in boreal summer. The two poles located in the Northern Hemisphere have the weakest amplification in boreal autumn. Differently from the positive amplification for the Arctic and the Third Pole in all seasons, the faster variations in Antarctic temperature compared to the globe only appear in austral autumn and winter, and the amplification signal is negative in these seasons, with an amplification index of -1.68 and -2.73, respectively. In the austral winter, the strong negative amplification concentrates on West Antarctica and the coast of East Antarctica, with an absolute value of amplification index higher than 5 in general. Generally, the polar amplification is strongest in the Arctic except from June to August, and Antarctic amplification is the weakest among the Earth's three poles. The Earth's three poles are experiencing drastic changes, and the potential influence of climate change should receive attention.