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热带大气ISO在几种再分析资料中的对比分析
引用本文:丁莉,李清泉,刘芸芸.热带大气ISO在几种再分析资料中的对比分析[J].应用气象学报,2013,24(3):314-322.
作者姓名:丁莉  李清泉  刘芸芸
作者单位:1.中国气象科学研究院,北京 100081
基金项目:国家重点基础研究发展计划(2013CB430202),国家高技术研究发展计划(2010AA012404),公益性行业(气象)科研专项(GYHY 201106022),国家自然科学基金项目(41175065,41005037)〖ZK)〗
摘    要:利用NCEP/NCAR, NCEP/DOE和ERA40 3套再分析资料的逐日200 hPa纬向风数据,选取1961—1990年、1971—2000年和1981—2010年3种不同气候态,对比分析了3种气候态下热带大气季节内振荡 (ISO) 的基本气候特征及其在不同再分析资料中的异同。研究表明:1981—2010年气候态下,热带大气ISO冬春强、夏秋弱的年循环特征更加明显,东传短波能量增强,起始北传时间偏晚。NCEP/NCAR与NCEP/DOE资料所表征的热带大气ISO在空间分布、强度和能量传播方面的一致性较好。NCEP/NCAR资料反映的热带大气ISO强度在热带印度洋和热带西太平洋地区较ERA40资料偏弱,在赤道东太平洋地区较ERA40资料偏强;ERA40资料反映的热带大气ISO强度在12月—次年3月中旬较NCEP/NCAR资料偏强,而在3月中旬—11月偏弱;ERA40资料反映的热带大气ISO振荡位相较NCEP/NCAR资料超前10 d左右;NCEP/NCAR资料反映的东传谱能量弱于ERA40资料,西传能量强于ERA40资料;7月中旬,NCEP/NCAR资料反映的东亚地区大气ISO经向北传较ERA40资料偏晚。

关 键 词:热带大气ISO    气候态    再分析资料
收稿时间:2012-10-02

The Tropical Atmospheric Intra-seasonal Oscillation with Different Reanalysis Data
Affiliation:1.Chinese Academy of Meteorological Sciences, Beijing 1000812.Laboratory for Climate Studies, National Climate Center, CMA, Beijing 100081
Abstract:Characteristics of the tropical intra-seasonal oscillation are analyzed using three kinds of data (NCEP/NCAR reanalysis data, NCEP/DOE reanalysis data and ECMWF/ERA40 reanalysis data) in three climate states of 1961—1991, 1971—2000 and 1981—2010. There is a significant 30—60 d oscillation period of 200 hPa zonal wind in different climate states of different reanalysis data. Different intra-seasonal oscillation characteristics can be found in different data and in different climate states. It is found that in 1981—2010 climate state compared with the other two climate states (1971—2000 and 1981—2010), annual cycle characteristics of the tropical intra-seasonal oscillation are more pronounced, strong in winter-spring and weak in summer-autumn. The tropical intra-seasonal oscillation is stronger in the tropical Indian Ocean and the tropical western Pacific Ocean, also its active areas extend eastwards and become larger. The eastward propagating energy of the tropical intra-seasonal oscillation centers more in 1—3 waves, while the westward propagating energy becomes weaker. The tropical intra-seasonal oscillation starts northward propagating later. It propagates southward strongly which spreads to the Southern Hemisphere in the beginning of May. NCEP/NCAR reanalysis data and NCEP/DOE reanalysis data match well in the annual cycle characteristics of tropical intra-seasonal oscillation, intensity and energy propagation. While NCEP/NCAR reanalysis data and ERA40 reanalysis data have some distinctions: The tropical intra-seasonal oscillation periods center in 20—100 d and its peak value is 55 d of NCEP/NCAR reanalysis data, while its oscillation periods are 20—60 d and its peak value is 50 d of ERA40 reanalysis data. As to the periods of 80—100 d, the oscillation pattern of ERA40 reanalysis data is stronger than that of NCEP/DOE reanalysis data. The tropical intra-seasonal oscillation variance contribution proportion of NCEP/NCAR reanalysis data is a little less than that of ERA40 reanalysis data in the tropical Indian Ocean and the tropical western Pacific Ocean, while stronger than that of ERA40 reanalysis data in the Eastern Equatorial Pacific Ocean. From December to the middle of March, the tropical intra-seasonal oscillation intensity of ECMWF data is stronger than that of NCEP/NCAR reanalysis data. While from the middle of March to November, the tropical intra-seasonal oscillation intensity of ERA40 reanalysis data is weaker than that of NCEP/NCAR reanalysis data; the tropical intra-seasonal oscillation phase of ERA40 reanalysis data is about 10 days ahead of NCEP/NCAR reanalysis data. The eastward propagating energy of the tropical intra-seasonal oscillation of NCEP/NCAR reanalysis data is weaker than that of ERA40 reanalysis data, while the westward propagating energy is stronger than that of ERA40 reanalysis data. In the middle of July, the northward propagating of NCEP/NCAR data is weaker than that of ERA40 reanalysis data.
Keywords:tropical intra seasonal oscillation  climate state  reanalysis data
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