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气象:2018,44(8):985-997
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改进的CLDAS降水驱动对中国区域积雪模拟的影响评估
师春香,张帅,孙帅,姜立鹏,梁晓,贾炳浩,吴捷
(南京信息工程大学地理科学学院,南京 210044; 国家气象信息中心,北京 100081; 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京 100029; 中国气象局气候研究开放实验室,北京 100081)
Effect of Improved Precipitation CLDAS on Snow Simulation in China
SHI Chunxiang,ZHANG Shuai,SUN Shuai,JIANG Lipeng,LIANG Xiao,JIA Binghao,WU Jie
(School of Geographic Sciences, Nanjing Unversity of Information Science and Technology, Nanjing 210044; National Meteorological Information Centre, Beijing 100081; State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029; Laboratory for Climate Studies, CMA, Beijing 100081)
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投稿时间:2017-10-19    修订日期:2018-03-19
中文摘要: 积雪因为其特定的属性在气候变化和水文循环中扮演着重要角色,在大气和陆面之间起到了调节能量和水交换的显著作用,而陆面驱动数据的质量直接决定着模式对积雪的模拟效果。本文采用CLDAS(CMA Land Data Assimilation System)和改进后的降水驱动(CLDAS Prcp)分别驱动Noah3.6陆面模式对积雪变量进行模拟,并对中国主要的积雪区东北区域、新疆区域、青藏高原区域的积雪覆盖率、雪深、雪水当量的模拟效果进行了评估。结果表明,CLDAS Prcp改善了原有驱动在冬季由于低估降水所造成的模拟积雪量偏少的情况;东北区域模拟结果与观测的时间变率最为一致,积雪覆盖率、雪深、雪水当量的相关系数分别为0.42,0.78,0.93;而雪水当量的改进效果最明显,均方根误差和偏差分别减小了54.8%和83.1%,相关系数提高了0.47;同时,CLDAS Prcp不仅能反映积雪变量的年际变率,而且能够较准确地反映出强度较大的突发降雪事件。
Abstract:Snow cover plays a significant role in climate change and hydrological cycle due to its specific properties, regulating energy and water exchange for atmosphere and land surface. The quality of the forcing data has a great influence on the result of model simulation. This paper adopts CLDAS and CLDAS Prcp data driving on Noah 3.6 land surface model to simulate snow variables, and assesses snow cover fraction (SCF), snow depth (SD), the snow water equivalent (SWE) in major snow areas, such as Northeast China, Xinjiang and Tibetan Plateau Region. The result shows that CLDAS Prcp can improve snow simulation in the winter, removes poor snow simulation due to underestimating precipitation of CLDAS. Model result of Northeast China is the most consistent with observations, CORR of SCF, SD and SWE are 0.42, 0.78 and 0.93 respectively. The improvement of snow water equivalent is most obvious, RMSE and BIAS are reduced by 54.8% and 83.1% respectively, while CORR is increased by 0.47. Thus, CLDAS Prcp not only has better simulation capability but also reflects the extreme snow enevnts.
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基金项目:国家自然科学基金重点项目(91437220)、公益性行业(气象)科研专项(GYHY201506002和GYHY201206008)及国家气象科技创新工程攻关任务(气象资料质量控制及多源数据融合与再分析)共同资助
引用文本:
师春香,张帅,孙帅,姜立鹏,梁晓,贾炳浩,吴捷,2018.改进的CLDAS降水驱动对中国区域积雪模拟的影响评估[J].气象,44(8):985-997.
SHI Chunxiang,ZHANG Shuai,SUN Shuai,JIANG Lipeng,LIANG Xiao,JIA Binghao,WU Jie,2018.Effect of Improved Precipitation CLDAS on Snow Simulation in China[J].Meteor Mon,44(8):985-997.