Comparative analysis of soil temperature simulated by two land surface models in China
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摘要: 为研究不同陆面模式对中国区域土壤温度的模拟效果，基于中国气象局陆面数据同化系统（CMA Land Data Assimilation System，CLDAS）大气驱动数据分别驱动Noah和Noah-MP陆面模式进行中国区域土壤温度的模拟（简称：CLDAS_Noah和CLDAS_Noah-MP试验），使用2010—2018年中国气象局2380个土壤温度观测站点10和40 cm观测数据以及美国全球陆面数据同化系统（The Global Land Data Assimilation System，GLDAS）驱动的Noah模式（GLDAS_Noah试验）模拟的土壤温度结果，从空间分布、季节、分区等角度进行了评估，实现了不同驱动数据相同陆面模式和相同驱动数据不同陆面模式的对比分析。结果表明： GLDAS_Noah、CLDAS_Noah和CLDAS_Noah-MP试验均能合理模拟出中国区域土壤温度空间分布，但在量级上有一定差异，主要表现在中国东北、新疆、青藏高原等积雪区。对于相同陆面模式不同驱动数据，均方根误差显示CLDAS_Noah试验在季节与分区上均优于GLDAS_Noah试验，间接表明CLDAS大气驱动数据优于GLDAS大气驱动数据，且大气驱动数据是提高土壤温度模拟精度的重要因素之一；对于相同驱动数据不同陆面模式，总体上CLDAS_Noah-MP试验棋拟效果优于CLDAS_Noah试验，其中CLDAS_Noah试验模拟的10和40 cm深度土壤温度在冬季积雪区误差明显大于CLDAS_Noah-MP试验，可能与Noah-MP模式改进了积雪方案有关，但10和40 cm深度下CLDAS_Noah-MP试验在东北、华北、青藏高原地区对春季土壤温度模拟误差明显大于CLDAS_Noah试验，可能与Noah-MP模式融雪方案有关。总之，本研究对于后续开展土壤温度多模式集成、土壤温度站点资料同化，最终研制中国区域高质量土壤温度数据集具有一定的参考意义。
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关键词:
- 土壤温度 /
- 中国气象局陆面数据同化系统 /
- 全球陆面数据同化系统 /
- Noah /
- Noah-MP /
- 分析
Abstract: In order to study the simulation effects of different land surface models on soil temperature in China, the Noah and Noah-MP land surface models are driven by the atmospheric driving data of the China Meteorological Administration (CMA) land surface data assimilation system (CLDAS) to simulate soil temperature in China. Soil temperature simulations of GLDAS_Noah, CLDAS_Noah and CLDAS_Noah-MP are evaluated from the perspectives of spatial distribution, different seasons, time series in different regions, etc. based on soil temperature observations collected at 2380 sites during 2010—2018 in China and the Noah soil temperature from the United States Global Land Data Assimilation System (GLDAS_Noah). This study realizes the comparative analysis of soil temperature simulated with different driving data, with same land surface models and same driving data, and with different land surface models. The results show that the GLDAS_Noah, CLDAS_Noah and CLDAS_Noah-MP can reasonably simulate spatial distributions of soil temperature at 10 and 40 cm-depth in China from a qualitative point of view, but there are certain differences in magnitude, which mainly occur in snow-covered areas of Northeast China, Xinjiang and Qinghai-Tibet Plateau. From a quantitative perspective, with the same land surface model and different driving data, CLDAS_Noah is better than GLDAS_Noah in different seasonal assessments based on bias spatial distributions and RMSE (Root Mean Square Error) time series in different regions. This result can indirectly show that CLDAS atmospheric driving data is better than GLDAS Atmospheric driving data and the atmospheric driving data is one of the important factors to improve the accuracy of soil temperature simulation. With the same driving data and different land surface models, the overall effect of CLDAS_Noah-MP is better than that of CLDAS_Noah. Among them, the errors of CLDAS_Noah winter soil temperature at 10 and 40 cm depths in snow-covered areas are significantly greater than that of CLDAS_Noah-MP, which may be related to the improvement of Noah-MP parameterization scheme in snow-covered areas. However, the spring soil temperature simulation errors of CLDAS_Noah-MP at 10 and 40 cm depths in Northeast, North China, and Qinghai-Tibet Plateau are significantly larger than that of CLDAS_Noah, which may be related to the snow melting parameterization scheme in the model. In short, this research provides certain references for subsequent development of soil temperature multi-model integration research and in-situ soil temperature data assimilation research and the final development of high-quality soil temperature dataset in China. -
图 1 用于评估的中国气象局2380个土壤温度观测站点分布 （a. 东北，b. 华北，c. 江淮，d. 华南，e. 西北东部，f. 西南，g. 西北西部，h. 青藏高原）
Figure 1. Distribution map of 2380 soil temperature observation stations of China Meteorological Administration used for evaluation （a. Northeast，b. North China，c. Jianghuai，d. South China，e. Northwest east，f. Southwest，g. Northwest west，h. Tibet）
图 2 2010—2018年GLDAS_Noah （a—d）、CLDAS_Noah （e—h）、CLDAS_Noah-MP （i—l）模式模拟的10 cm土壤温度 （色阶，单位：℃） 不同季节 （a、e、i. 春季，b、f、j. 夏季，c、g、k. 秋季，d、h、l. 冬季） 平均空间分布
Figure 2. Season average （a，e，i. Spring；b，f，j. Summer；c，g，k. Autumn；d，h，l. Winter） spatial distributions of 10 cm soil temperature （shading，unit：℃） of GLDAS_Noah （a—d），CLDAS_Noah （e—h），CLDAS_Noah-MP （i—l） from 2010 to 2018
图 3 GLDAS_Noah、CLDAS_Noah、CLDAS_Noah-MP模拟的不同季节 （a—c. 春季，d—f. 夏季，g—i. 秋季，j—l. 冬季） 10 cm土壤温度与观测比较
Figure 3. Overall evaluation results of 10 cm soil temperature of GLDAS_Noah，CLDAS_Noah and CLDAS_Noah-MP in different seasons （a—c. Spring，d—f. Summer，g—i. Autumn，j—l. Winter）
图 4 GLDAS_Noah、CLDAS_Noah、CLDAS_Noah-MP模拟的不同季节 （a—c. 春季，d—f. 夏季，g—i. 秋季，j—l. 冬季） 40 cm土壤温度与观测比较
Figure 4. Overall evaluation results of 40 cm soil temperature of GLDAS_Noah，CLDAS_Noah，CLDAS_Noah-MP in different seasons （a—c. Spring，d—f. Summer，g—i. Autumn，j—l. Winter）
图 5 2010—2018年CLDAS_Noah、CLDAS_Noah-MP模式模拟的春 （a）、冬 （b） 季雪深差异 （CLDAS_Noah-MP−CLDAS_Noah，单位：cm）
Figure 5. Snow depth comparison of CLDAS_Noah，CLDAS_Noah-MP in spring （a） and winter （b） from 2010 to 2018 （CLDAS_Noah-MP−CLDAS_Noah，unit：cm）
图 6 GLDAS_Noah、CLDAS_Noah、CLDAS_Noah-MP模拟的不同分区10 cm土壤温度均方根误差时间序列 （a. 东北，b. 华北，c. 江淮，d. 华南，e. 西北东部，f. 西南，g. 西北西部，h. 青藏高原）
Figure 6. Time series of 10 cm soil temperature RMSE for GLDAS_Noah and CLDAS_Noah，CLDAS_Noah-MP in different regions （a. Northeast，b. North China，c. Jianghuai，d. South China，e. Northwest east，f. Southwest，g. Northwest west，h. Tibet）
图 7 GLDAS_Noah、CLDAS_Noah、CLDAS_Noah-MP模拟的不同分区40 cm土壤温度均方根误差时间序列 （a. 东北，b. 华北，c. 江淮，d. 华南，e. 西北东部，f. 西南，g. 西北西部，h. 青藏高原）
Figure 7. Time series of 40 cm soil temperature RMSE for GLDAS_Noah，CLDAS_Noah and CLDAS_Noah-MP in different regions （a. Northeast，b. North China，c. Jianghuai，d. South China，e. Northwest East，f. Southwest，g. Northwest West，h. Tibet）
表 1 试验方案设计
Table 1. Design of experiments
大气驱动数据 模式 简称 试验1 GLDAS Noah GLDAS_Noah 试验2 CLDAS Noah CLDAS_Noah 试验3 CLDAS Noah-MP CLDAS_Noah-MP 
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