An off-line simulation of land surface processes over the northern Tibetan Plateau

作     者：MinHong Song YaoMing Ma Yu Zhang WeiQiang Ma SiQiong Luo 

作者机构：Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences Key Laboratory of Tibetan Environment Changes and Land Surface Processes Institute of Tibetan Plateau ResearchChinese Academy of Sciences 

出 版 物：《Research in Cold and Arid Regions》 (寒旱区科学（英文版）)

年 卷 期：2014年第6卷第3期

页      面：236-246页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：the National Natural Science Foundation of China (Nos. 41075053 and 41275016) 

主　　题：Noah Land Surface Model off-line northern Tibetan Plateau radiation flux 

摘      要：In order to further understand the land surface processes over the northern Tibetan Plateau, this study produced an off-line simulated examination at the Bujiao site on the northern Tibetan Plateau from June 2002 to April 2004, using the Noah Land Surface Model （Noah LSM） and observed data from the CAMP/Tibet experiment. The observed data were neces- sarily corrected and the number of soil layers in the Noah LSM was changed from 4 to 10 to enable this off-line simulation and analysis. The main conclusions are as follows： the Noah LSM performed well on the northern Tibetan Plateau. The simulated net radiation, upward longwave radiation, and upward shortwave radiation demonstrated the same remarkable annual and seasonal variation as the observed data, especially the upward longwave radiation. The simulated soil temperatures were acceptably close to the observed temperatures, especially in the shallow soil layers. The simulated freezing and melting processes were shown to start from the surface soil layer and spread down to the deep soil layers, but they took longer than the observed processes. However, Noah LSM did not adequately simulate the soil moisture. Therefore, additional high-quality, long-term observations of land surface-atmosphere processes over the Tibetan Plateau will be a key factor in proper adiustments of the model parameters in the future.