孙帅,师春香,梁晓,姜立鹏,徐宾,韩帅,谷军霞,粟运. 2022. 两种陆面模式对中国区域土壤温度模拟的对比分析[J]. 气象学报, (0):-, doi:[doi]
两种陆面模式对中国区域土壤温度模拟的对比分析
Comparative analysis of soil temperature simulated by two land surface models in China
投稿时间:2021-10-17  修订日期:2022-04-13
DOI:
中文关键词:  土壤温度, CLDAS, GLDAS, Noah, Noah-MP,分析
英文关键词:Soil temperature  CLDAS  GLDAS  Noah  Noah-MP  analysis
基金项目:国家自然科学基金(42161054)、国家重点研发计划(2019YFA0606904,2018YFC1506600)、国家气象信息中心结余项目(NMICJY202104)
作者单位邮编
孙帅 国家气象信息中心 100081
师春香  100081
梁晓  100081
姜立鹏  100081
徐宾  100081
韩帅  100081
谷军霞  100081
粟运  100081
摘要点击次数: 31
全文下载次数: 18
中文摘要:
      为研讨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),使用2010-2018年中国气象局2380个10cm和40cm土壤温度站点观测数据,以及美国全球陆面数据同化系统GLDAS中Noah模式土壤温度(简称:GLDAS_Noah),从空间分布、不同季节、不同分区等角度对GLDAS_Noah、CLDAS_Noah和CLDAS_Noah-MP土壤温度进行了评估,实现不同驱动数据、相同陆面模式和相同驱动数据、不同陆面模式的土壤温度对比分析。结果表明:从定性角度分析,GLDAS_Noah、CLDAS_Noah和CLDAS_Noah-MP均能够合理地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,主要表现在我国东北、新疆、青藏高原等积雪区;从定量角度分析,对于相同陆面模式、不同驱动数据,CLDAS_Noah在不同季节与不同分区均方根误差时间序列上均优于GLDAS_Noah,可间接地表明CLDAS大气驱动数据优于GLDAS大气驱动数据,大气驱动数据是提高土壤温度模拟精度的重要因素之一;而对于相同驱动数据、不同陆面模式,总体上CLDAS_Noah-MP效果优于CLDAS_Noah, 其中10cm和40cm下CLDAS_Noah土壤温度在冬季积雪区误差明显大于CLDAS_Noah-MP,可能与Noah-MP改进了积雪方案有关,但10cm和40cm下CLDAS_Noah-MP在东北、华北、青藏高原地区春季土壤温度模拟误差明显大于CLDAS_Noah,可能与Noah-MP模式融雪方案有关。总之,该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研究,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。
英文摘要:
      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 were driven to simulate the soil temperature in China by the atmospheric driving data of the China Meteorological Administration land surface data assimilation system (CLDAS). The soil temperature of GLDAS_Noah, CLDAS_Noah and CLDAS_Noah-MP was evaluated by using the 2010-2018 China Meteorological Administration"s 2380 soil temperature observation data and the Noah soil temperature United States Global Land Surface Data Assimilation System (GLDAS_Noah) , from the perspectives of spatial distribution, different seasons, time series in different regions, etc. So as to realize the comparative analysis of soil temperature with different driving data, the same land surface models and the same driving data, different land surface models. The results show that the GLDAS_Noah, CLDAS_Noah and CLDAS_Noah-MP can reasonably simulate the spatial distribution of soil temperature of 10cm and 40cm in China from a qualitative point of view, but there are certain differences in magnitude, which mainly manifested in the snow-covered areas of Northeast China, Xinjiang and Qinghai-Tibet Plateau; From a quantitative perspective, under the same land surface models and different driving data, CLDAS_Noah is better than GLDAS_Noah in different seasonal assessments, bias spatial distributions, and RMSE time series in different regions, which 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; For the same driving data and different land surface modes, the overall effect of CLDAS_Noah-MP is better than CLDAS_Noah. Among them, the error of CLDAS_Noah soil temperature in the winter snow area at 10cm and 40cm is significantly greater than that of CLDAS_Noah-MP, which It may be related to the improvement of Noah-MP in snow cover parametric scheme. However, the spring soil temperature simulation error of CLDAS_Noah-MP at 10cm and 40cm in Northeast, North China, and Qinghai-Tibet Plateau is significantly larger than CLDAS_Noah, which may be related to the snowmelt parametric scheme in the model. In short, this research has certain reference significance for the subsequent development of soil temperature multi-model integration research and soil temperature sites data assimilation research, and the final development of a high-quality soil temperature data set in China.
查看全文   查看/发表评论  下载PDF阅读器
分享按钮