| 中国区域夏季再分析资料高空变量可信度的检验 |
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| 引用本文： | 韦芬芬,汤剑平,王淑瑜.中国区域夏季再分析资料高空变量可信度的检验[J].地球物理学报,2015,58(2):383-397. |
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| 作者姓名： | 韦芬芬 汤剑平 王淑瑜 |
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| 作者单位： | 1. 南京大学 大气科学学院, 南京 210093;
2. 苏州市气象局, 苏州 215131;
3. 南京大学 气候与全球变化研究院, 南京 210093 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)(2011CB952004,2010CB428500)资助 |
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| 摘 要： | 利用全球探空资料(IGRA)对1989—2008年美国国家环境预报中心(NCEP)和大气研究中心(NCAR)再分析资料、NCEP和美国能源部(DOE)再分析资料、NCEP气候预测系统再分析资料(CFSR)、日本气象厅25年再分析资料(JRA-25)、欧洲数值预报中心再分析资料(ERA-Interim)和美国国家航空航天局(NASA)现代回顾性再分析资料(MERRA)的高空变量在中国地区对流层中高层的可信度进行了初步的检验.分析结果表明:再分析资料对中高层位势高度和温度的夏季平均气候态具有较好的再现能力,其EOF的时空变化特征与观测吻合也较好;再分析资料的绝对湿度值较观测结果要偏大,其中MERRA与观测最为接近.再分析资料不能很好地反映经向风的夏季平均气候态及年际变化特征,EOF的时空模态和观测偏离也较大.总体而言,NCEP/NCAR、NCEP/DOE及NCEP/CFSR对这些变量的再现能力较JRA-25、ERA-Interim和MERRA弱.
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| 关 键 词： | 再分析资料 探空资料 高空变量 可信度 |
| 收稿时间： | 2013-07-01 |
A reliability assessment of upper-level reanalysis datasets over China |
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| WEI Fen-Fen;TANG Jian-Ping;WANG Shu-Yu.A reliability assessment of upper-level reanalysis datasets over China[J].Chinese Journal of Geophysics,2015,58(2):383-397. |
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| Authors: | WEI Fen-Fen;TANG Jian-Ping;WANG Shu-Yu |
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| Institution: | 1. School of Atmospheric Sciences, Nanjing University, Nanjing 210093, China;
2. Suzhou Meteorological Bureau, Suzhou 215131, China;
3. Institute for Climate and Global Change Research, Nanjing University, Nanjing 210093, China |
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| Abstract: | East Asia is one of the most sensitive and vulnerable areas in the world to the global climate changes. The study of the climate changes in this region is much more difficult than other places. With high quality,long time series and high resolution,atmosphere reanalysis has become the most widely used datasets in atmospheric science. However,the systematic biases can influence the quantity of reanalysis datasets. Therefore, an evaluation of the reliability and accuracy of reanalysis datasets is significant for global and regional climate research. In recent years, lots of work have been made to investigate the reliability of reanalysis. However, most of them focused on surface variables, few reanalysis and the time period were limited. This work evaluated the upper-level variables extracted from following sources: the National Centers for Environment Prediction (NCEP)-National Centers for Atmospheric Research (NCAR) reanalysis-1, the NCEP-Department of Energy (DOE) renalysis-2, the NCEP-Climate Forecast System Reanalysis (CFSR), the 25-year Japanese Meteorological Agency (JRA-25) reanalysis, the European Centre for Medium-Range Weather Forecast (ECMWF) Interim reanalysis (ERA-Interim) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis products, and Integrated Global Radiosonde Archive (IGRA) global sounding observations over China from 1989 to 2008. Three sets of reliability and accuracy studies were carried out: the first evaluated the spatial distributions of summer mean values of the several high-variables represented by all reanalysis mentioned above. The second aimed to assess the performance of the inter-annual variation of high-variables described by reanalysis. The third compared the similarities of empirical orthogonal function (EOF) modes between observations and reanalysis. It was found that the mean values of geopotential height and temperature in each reanalysis dataset are consistent with the observations, but the wind fields, especially the meridional wind, are not. Besides, the reanalysis products do a bad job in revealing the inter-annual variation of meridional wind. The results of EOF analysis imply that all reanalysis datasets exhibit better performance in depicting the temporal and spatial distributions of geopotential height and temperature than other variables, especially the wind fields;MERRA performs specific humidity better than other reanalysis products. Generally, NCEP/NCAR, NCEP/DOE and NCEP/CFSR products are not as good as JRA-25, ERA-Interim and MERRA.Based on the study, we recognized the differences of each reanalysis in describing the characteristics of upper-level variables. In this way, more desirable alternative will be made when considering which reanalysis will be used in climate change research. However, most of these reanalysis do a bad work in revealing the wind field, which may be caused by the wind rotation. Hence, more studies are needed on the ability of reanalysis in representing wind field in the future. Besides, only upper-level variables have been compared, the research on surface variables, such as surface temperature and precipitation, can be added in the next step. Nowadays, the field of regional climate modeling is booming due to enormous demands for prediction of future regional climate change, downscaling seasonal prediction, and for use in a variety of regional climate applications. It is, therefore, imperative to better understand the strengths, deficiencies, and limitations as well as the sources of uncertainties that can occur with regional climate model (RCM) simulations. As we know, running a RCM needs lateral boundary (LB) forcing fields and initial conditions. Hence, different LB forcing fields and initial conditions may cause different simulation results. In the future, the uncertainties of RCMs caused by different driving reanalysis datasets will be researched. |
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| Keywords: | Reanalysis datasets Sounding observations Upper-level variables Reliability |
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