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.

The hydrothermal dynamics of the active layer is a key issue in the study of surface processes in permafrost regions. Even though the soil energy budget is controlled by thermal conduction and latent heat transfer, few studies have focused on their effects upon the active layer thickness (ALT). In the present study, the community land model (CLM) version 5.0 is used to simulate the soil temperature and moisture of the active layers at the Tanggula (TGL) and Beiluhe (BLH) stations in permafrost regions of the Qinghai-Tibet Plateau based on the theory of soil enthalpy in order to estimate the soil energy state and analyze the energy changes in the active layer during freezing and thawing. The results indicate that the soil enthalpy has significant seasonal variation characteristics, which accurately reflected the freezing and thawing processes of the active layer. The change in soil enthalpy is significantly related to the thawing depth of the active layer in TGL and BLH, and its changing process can be expressed as an exponential relationship. Near the surface, the variation of the energy due to temperature gradient and actual evaporation can also be expressed as an exponential relationship. The promoting effect of heat conduction on the ALT is greater than the inhibiting effect of latent heat transfer, with the energy contribution from the phase change accounting for about 20-40% of the energy due to the temperature gradient. The thawing depth increases by 14.16-18.62 cm as the energy due to the temperature gradient increases by 1 MJ/m(2) and decreases by 2.75-7.16 cm as the energy due to the phase change increases by 1 MJ/m(2). Thus, the present study quantifies the effects of soil energy upon the ALT and facilitates an understanding of the hydrothermal processes in soils in permafrost regions.

Soil thermal conductivity (STC) is essential parameter for revealing thermodynamic changes and projecting changes in soil thermal regimes. However, the incorporation of different STC schemes into land surface process models (LSMs) can afford large errors. Thus, to accurately simulate soil thermal regimes in permafrost regions, a suitable STC scheme in LSMs is important. Herein, we selected nine normalized STC schemes and evaluated their performance in simulating STC and soil temperatures with in situ measurements in permafrost regions on the Tibetan Plateau (TP). These schemes were divided into three categories and incorporated into the latest version of the Community Land Model (CLM5.0). The results showed that the category comprising minerals, soil organic matter, and gravel soil afforded better performance at most sites than the other categories. The Balland and Arp (BA2005), Chadburn (C2015), and Bao (B2016) schemes had better performances in their affiliated categories, respectively. The BA2005 scheme ranked the best among the selected schemes with an average root-mean-square error decreased of 56.2% and 15.0% in simulating STC and soil temperatures compared to the default scheme, respectively. Additionally, the different schemes yielded a maximum difference of 2.69 W.m(-1) K-1 and 2.55 degrees C in simulating STC and soil temperature, respectively. Possible causes affecting the results were also investigated. The results indicated that soil moisture is a determinant: slight changes in soil moisture may cause large changes in thermal processes. However, the CLM5.0 yields large uncertainties of soil moisture. In addition, soil properties, atmospheric forcing data, and model structures also yielded errors in the simulated results. Note that no single STC scheme can be applied to all regions with satisfactory results. Therefore, multiple schemes need to be employed depending on their suitability in different regions. And more studies should focus on the accuracy of the hydraulic processes, especially soil hydraulic conductivity, unfrozen water, and snow processes.

Surface energy budget and soil hydrothermal regime are crucial for understanding the interactions between the atmosphere and land surface. However, large uncertainties in current land surface process models exist, espe-cially for the permafrost regions in the Qinghai-Tibet Plateau. In this study, observed soil temperature, moisture, and surface energy fluxes at four sites in permafrost regions are chosen to evaluate the performance of CLM5.0. Furthermore, the soil property data, different thermal roughness length schemes, and dry surface layer (DSL) scheme are investigated. The results show that the soil property data is important for CLM5.0. The default scheme in CLM5.0 yields large errors for surface energy fluxes. The combination of the thermal roughness length and DSL scheme significantly improved the simulation of surface energy fluxes, especially for latent heat flux. The optimization of DSL scheme significantly improved soil temperature simulation and decreased the RMSE from 1.95 degrees C, 2.07 degrees C, 2.02 degrees C, and 2.95 degrees C to 1.34 degrees C, 1.35 degrees C, 1.35 degrees C and 2.29 degrees C in TGL site, respectively. The combination of the thermal roughness length and DSL scheme performed the best in shallow soil moisture, decreasing the RMSE from 0.136 m3 m- 3 to 0.049 m3 m- 3 in the XDT site but slightly enhancing the errors in middle soil. The interactions between surface energy and soil hydrothermal regime also discussed. However, the thermal roughness length and the DSL schemes are highly dependent on the condition of the underlying surface. Different schemes should be selected for different regions.

Changes in soil temperature (ST) and soil moisture (SM) are essential for climate change and ecosystem assessments. Previous investigations on the ST and SM on the Tibetan Plateau (TP) are mainly based on the situ observation and the satellite products. In this study, the improved Community Land Model version 4.5 (CLM4.5), with proper parameter optimization and surface datasets update, is used to estimate the response of ST and SM in the TP to climate change in the long-term time series from 1961 to 2010. After validating the reasonability of the simulated results using the observations, the spatial distribution of changes in ST and SM in annual and seasonal time series since 1960s, 1980s, 1990s, are investigated respectively and the changes of precipitation (Pr) and surface evaporation (Ev) are also analysed to understand the cause of changes objectively. As a whole, the soil was warming and wetting at the maximum value of 0.31 degrees C/decade and 0.77%/decade since the 1960s. However, the warming process in soil mainly occurred in the 1980s while the wetting tendency is detected since the 1990s extensively. Except for the influence of air warming, the enhanced Pr and Ev might also be indispensable factors that caused the intensive wetting process but damped warming process in soil. Summer is the favourable season for the thermal and hydraulic variation since the 1980s. There exists the striking warmer and drier trend in the eastern TP since 1980s while the colder and wetter condition in the western TP since the 1990s. The magnitude of variation in soil is magnified from 1990s under the continuing impact of climate change.

Numerical simulation is of great importance to the investigation of changes in frozen ground on large spatial and long temporal scales. Previous studies have focused on the impacts of improvements in the model for the simulation of frozen ground. Here the sensitivities of permafrost simulation to different atmospheric forcing data sets are examined using the Community Land Model, version 4.5 (CLM4.5), in combination with three sets of newly developed and reanalysis-based atmospheric forcing data sets (NOAA Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-I), and NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA)). All three simulations were run from 1979 to 2009 at a resolution of 0.5 degrees x 0.5 degrees and validated with what is considered to be the best available permafrost observations (soil temperature, active layer thickness, and permafrost extent). Results show that the use of reanalysis-based atmospheric forcing data set reproduces the variations in soil temperature and active layer thickness but produces evident biases in their climatologies. Overall, the simulations based on the CFSR and ERA-I data sets give more reasonable results than the simulation based on the MERRA data set, particularly for the present-day permafrost extent and the change in active layer thickness. The three simulations produce ranges for the present-day climatology (permafrost area: 11.31-13.57 x 10(6) km(2); active layer thickness: 1.10-1.26 m) and for recent changes (permafrost area: -5.8% to -9.0%; active layer thickness: 9.9%-20.2%). The differences in air temperature increase, snow depth, and permafrost thermal conditions in these simulations contribute to the differences in simulated results.

A growing body of simulation research has considered the dynamics of permafrost, which has an important role in the climate system of a warming world. Previous studies have concentrated on the future degradation of permafrost based on global climate models (GCMs) or data from GCMs. An accurate estimation of historical changes in permafrost is required to understand the relations between changes in permafrost and the Earth's climate and to validate the results from GCMs. Using the Community Land Model 4.5 driven by the Climate Research Unit -National Centers for Environmental Prediction (CRUNCEP) atmospheric data set and observations of changes in soil temperature and active layer thickness and present-day areal extent of permafrost, this study investigated the changes in permafrost in the Northern Hemisphere from 1901 to 2010. The results showed that the model can reproduce the interannual variations in the observed soil temperature and active layer thickness. The simulated area of present-day permafrost fits well with observations, with a bias of 2.02x10(6)km(2). The area of permafrost decreased by 0.06 (0.62)x10(6)km(2)decade(-1) from 1901 to 2009 (1979 to 2009). A clear decrease in the area of permafrost was found in response to increases in air temperatures during the period from about the 1930s to the 1940s, indicating that permafrost is sensitive to even a temporary increase in temperature. From a regional perspective, high-elevation permafrost decreases at a faster rate than high-latitude permafrost; permafrost in China shows the fastest rate of decrease, followed by Alaska, Russia, and Canada. Discrepancies in the rate of decrease in the extent of permafrost among different regions were mostly linked to the sensitivity of permafrost in the regions to increases in air temperatures rather than to the amplitude of the increase in air temperatures. An increase in the active layer thickness of 0.009 (0.071)mdecade(-1) was shown during the period of 1901-2009 (1979-2009). These results are useful in understanding the response of permafrost to a historical warming climate and for validating the results from GCMs.