Accurate understanding and modeling of soil hydrothermal dynamics in permafrost regions is essential for reliably assessing future permafrost changes and their impacts. However, the inadequate representation of soil water-heat transport processes in current land surface models (LSMs) introduces large uncertainty in simulating permafrost dynamics, particularly on the Qinghai-Tibet Plateau (QTP). In this study, we modified the parameterizations of soil thermal conductivity, unfrozen water and soil evaporation resistance in version 5.0 of the Community Land Model (CLM5.0) and assessed their effects on soil hydrothermal dynamics in permafrost regions on the QTP using in-situ measurements the depths of 10-40 cm. The results showed that soil temperature was more sensitive to the modified soil thermal conductivity and unfrozen water schemes, with average RMSE reduced by approximately 0.60 degrees C compared to the default CLM5.0. Soil moisture was mainly affected the unfrozen water scheme during freezing and by the optimized soil evaporation resistance scheme during thawing, with maximum accuracy improvements of 8% and 25%, respectively. All three schemes significantly improved soil thermal conductivity simulations, reducing RMSE over 80%. Overall, our modifications remarkably reduced simulation errors compared to the default schemes, improving the average accuracy soil temperature, soil moisture and soil thermal conductivity by approximately 16%, 21% and 81% respectively. Additionally, this study emphasized the importance of accurately representing permafrost-related processes in LSMs, as they significantly affected simulation results. Specifically, soil thermodynamics is strongly sensitive to subtle changes in soil moisture transport processes, such as the hysteresis effect unfrozen water content, and parameterizations of snowpack and vegetation. Therefore, future work should focus on enhancing the accurate representations of these processes and optimized parameters in LSMs to improve the simulation accuracy in permafrost regions on the QTP. This study enhanced the understanding of soil hydrothermal processes in LSMs and provided valuable insights for the future model development for permafrost regions under the context of climate change.
气候变暖对北极多年冻土和植被产生了重要的影响。CLM(Community Land Model)是应用最广泛的陆面过程模式之一，但其中复杂的边界条件和参数化过程导致模式模拟结果存在一定的不确定性。本研究评估了CLM5.0对阿拉斯加多年冻土区表层土壤温度和碳循环的模拟能力，结果表明，CLM5.0可以捕捉到表层土壤温度的季节变化。在苔原和针叶林站点，CLM5.0在日尺度和月尺度都可以很好地模拟出总初级生产力（GPP）随时间的变化，但对净生态系统碳交换（NEE）的模拟结果存在一定的不确定性。CLM5.0可以较为合理地模拟高纬度多年冻土区的土壤温度季节变化，在未来的研究中可能还需要从结构、参数化方案等过程进行改进，从而进一步提升高纬度多年冻土区碳循环的模拟精度。
气候变暖对北极多年冻土和植被产生了重要的影响。CLM(Community Land Model)是应用最广泛的陆面过程模式之一，但其中复杂的边界条件和参数化过程导致模式模拟结果存在一定的不确定性。本研究评估了CLM5.0对阿拉斯加多年冻土区表层土壤温度和碳循环的模拟能力，结果表明，CLM5.0可以捕捉到表层土壤温度的季节变化。在苔原和针叶林站点，CLM5.0在日尺度和月尺度都可以很好地模拟出总初级生产力（GPP）随时间的变化，但对净生态系统碳交换（NEE）的模拟结果存在一定的不确定性。CLM5.0可以较为合理地模拟高纬度多年冻土区的土壤温度季节变化，在未来的研究中可能还需要从结构、参数化方案等过程进行改进，从而进一步提升高纬度多年冻土区碳循环的模拟精度。
气候变暖对北极多年冻土和植被产生了重要的影响。CLM(Community Land Model)是应用最广泛的陆面过程模式之一，但其中复杂的边界条件和参数化过程导致模式模拟结果存在一定的不确定性。本研究评估了CLM5.0对阿拉斯加多年冻土区表层土壤温度和碳循环的模拟能力，结果表明，CLM5.0可以捕捉到表层土壤温度的季节变化。在苔原和针叶林站点，CLM5.0在日尺度和月尺度都可以很好地模拟出总初级生产力（GPP）随时间的变化，但对净生态系统碳交换（NEE）的模拟结果存在一定的不确定性。CLM5.0可以较为合理地模拟高纬度多年冻土区的土壤温度季节变化，在未来的研究中可能还需要从结构、参数化方案等过程进行改进，从而进一步提升高纬度多年冻土区碳循环的模拟精度。
气候变暖对北极多年冻土和植被产生了重要的影响。CLM(Community Land Model)是应用最广泛的陆面过程模式之一，但其中复杂的边界条件和参数化过程导致模式模拟结果存在一定的不确定性。本研究评估了CLM5.0对阿拉斯加多年冻土区表层土壤温度和碳循环的模拟能力，结果表明，CLM5.0可以捕捉到表层土壤温度的季节变化。在苔原和针叶林站点，CLM5.0在日尺度和月尺度都可以很好地模拟出总初级生产力（GPP）随时间的变化，但对净生态系统碳交换（NEE）的模拟结果存在一定的不确定性。CLM5.0可以较为合理地模拟高纬度多年冻土区的土壤温度季节变化，在未来的研究中可能还需要从结构、参数化方案等过程进行改进，从而进一步提升高纬度多年冻土区碳循环的模拟精度。
气候变暖对北极多年冻土和植被产生了重要的影响。CLM(Community Land Model)是应用最广泛的陆面过程模式之一，但其中复杂的边界条件和参数化过程导致模式模拟结果存在一定的不确定性。本研究评估了CLM5.0对阿拉斯加多年冻土区表层土壤温度和碳循环的模拟能力，结果表明，CLM5.0可以捕捉到表层土壤温度的季节变化。在苔原和针叶林站点，CLM5.0在日尺度和月尺度都可以很好地模拟出总初级生产力（GPP）随时间的变化，但对净生态系统碳交换（NEE）的模拟结果存在一定的不确定性。CLM5.0可以较为合理地模拟高纬度多年冻土区的土壤温度季节变化，在未来的研究中可能还需要从结构、参数化方案等过程进行改进，从而进一步提升高纬度多年冻土区碳循环的模拟精度。
气候变暖对北极多年冻土和植被产生了重要的影响。CLM(Community Land Model)是应用最广泛的陆面过程模式之一，但其中复杂的边界条件和参数化过程导致模式模拟结果存在一定的不确定性。本研究评估了CLM5.0对阿拉斯加多年冻土区表层土壤温度和碳循环的模拟能力，结果表明，CLM5.0可以捕捉到表层土壤温度的季节变化。在苔原和针叶林站点，CLM5.0在日尺度和月尺度都可以很好地模拟出总初级生产力（GPP）随时间的变化，但对净生态系统碳交换（NEE）的模拟结果存在一定的不确定性。CLM5.0可以较为合理地模拟高纬度多年冻土区的土壤温度季节变化，在未来的研究中可能还需要从结构、参数化方案等过程进行改进，从而进一步提升高纬度多年冻土区碳循环的模拟精度。
气候变暖对北极多年冻土和植被产生了重要的影响。CLM(Community Land Model)是应用最广泛的陆面过程模式之一，但其中复杂的边界条件和参数化过程导致模式模拟结果存在一定的不确定性。本研究评估了CLM5.0对阿拉斯加多年冻土区表层土壤温度和碳循环的模拟能力，结果表明，CLM5.0可以捕捉到表层土壤温度的季节变化。在苔原和针叶林站点，CLM5.0在日尺度和月尺度都可以很好地模拟出总初级生产力（GPP）随时间的变化，但对净生态系统碳交换（NEE）的模拟结果存在一定的不确定性。CLM5.0可以较为合理地模拟高纬度多年冻土区的土壤温度季节变化，在未来的研究中可能还需要从结构、参数化方案等过程进行改进，从而进一步提升高纬度多年冻土区碳循环的模拟精度。
气候变暖对北极多年冻土和植被产生了重要的影响。CLM(Community Land Model)是应用最广泛的陆面过程模式之一，但其中复杂的边界条件和参数化过程导致模式模拟结果存在一定的不确定性。本研究评估了CLM5.0对阿拉斯加多年冻土区表层土壤温度和碳循环的模拟能力，结果表明，CLM5.0可以捕捉到表层土壤温度的季节变化。在苔原和针叶林站点，CLM5.0在日尺度和月尺度都可以很好地模拟出总初级生产力（GPP）随时间的变化，但对净生态系统碳交换（NEE）的模拟结果存在一定的不确定性。CLM5.0可以较为合理地模拟高纬度多年冻土区的土壤温度季节变化，在未来的研究中可能还需要从结构、参数化方案等过程进行改进，从而进一步提升高纬度多年冻土区碳循环的模拟精度。
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.