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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
(1.南京信息工程大学地理与遥感学院,国家气象信息中心;2.南京信息工程大学地理与遥感学院;3.国家气象信息中心;4.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;5.中国气象局国家气候中心气候研究开放实验室)
The effects of improved precipitation CLDAS on the snow simulation in China
shichunxiang1, zhangshuai2, sunshuai3, jianglipeng4, liangxiao4, jiabinghao5, wujie6
(1.School of Geography and Remote Sensing, Nanjing Unversity of Information Science &Technology,National meteorological information center;2.School of Geography and Remote Sensing, Nanjing Unversity of Information Science DdDdTechnology;3.School of Geography and Remote Sensing, Nanjing Unversity of Information Science &Technology;4.National meteorological information center;5.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences;6.Laboratory for Climate Studies, National Climate Center, China Meteorological Administration)
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投稿时间:2017-10-19    修订日期:2018-06-15
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,在大气和陆地表面起到了调节能量和水交换的显著作用。驱动数据的质量对模式模拟的结果有重大的影响。鉴于CLDAS在冬季降水存在低估的问题,导致有雪季节模拟积雪低估,本文采用CLDAS和降水改进后的CLDAS(CLDAS-Prcp)驱动NOAH3.6陆面模式分别对积雪变量进行模拟,并在中国主要的积雪区东北区域、新疆区域、青藏高原区域对积雪覆盖率、雪深、雪水当量进行了评估。评估结果说明,改进降水后的驱动改善了CLDAS冬季降雪量偏少的情况,在三个积雪区域中在青藏高原积雪模拟能力最强,东北区域模拟与观测最为一致,而积雪变量中雪水当量的改进效果最明显。同时,CLDAS-Prcp不仅对长序列具有良好的模拟能力,而且能够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, which regulating energy and water exchange for the atmosphere and land surface. The quality of the forcing data has a great influence on the result of model simulation. The underestimates of precipitation of CLDAS leads to fewer snow simulation in snow season. This paper adopts CLDAS and CLDAS-Prcp data driving on NOAH3.6 land surface model to simulate snow variables, and assessment snow cover fraction (SCF), snow depth (SD), the snow water equivalent (SWE) in major snow area , such as Northeast China, Xinjiang region and Tibetan Plateau region. The result shows that CLDAS-Prcp can improve snow simulation in the winter. The simulation capacity of Tibetan Plateau region is strongest in the three snow areas, model result of Northeast China is the most consistent with observations. The improvement of snow water equivalent is most obvious. CLDAS-Prcp not only has better simulation capability but also reflects the extreme snow enevnts.
文章编号:201710190462     中图分类号:    文献标志码:
基金项目:国家自然科学基金重点项目(91437220);国家公益性行业专项(GYHY201506002,GYHY201206008);中国气象局“气象资料质量控制及多源数据融合与再分析”项目
引用文本:
师春香,张帅,孙帅,姜立鹏,梁晓,贾炳浩,吴捷,0.[en_title][J].Meteor Mon,():-.
shichunxiang,zhangshuai,sunshuai,jianglipeng,liangxiao,jiabinghao,wujie,0.The effects of improved precipitation CLDAS on the snow simulation in China[J].Meteor Mon,():-.