| 北太平洋海温的聚类客观分型及其在厄尔尼诺监测预测中的应用 |
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| 引用本文： | 王咏亮,宋家喜.北太平洋海温的聚类客观分型及其在厄尔尼诺监测预测中的应用[J].海洋学报,1999,21(4):35-42. |
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| 作者姓名： | 王咏亮 宋家喜 |
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| 作者单位： | 国家海洋环境预报中心, 北京 100081 |
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| 摘 要： | 本文通过聚类分析将北太平洋月平均△SST场客观地分成10型,这10个型具有以赤道中、东太平洋距平变化为主体的整体变化特征,它能按ENSO现象归纳为厄尔尼诺类和拉尼娜类、两大类各包含了典型场、非典型场及其3个过渡场等5个型.典型场具有良好持续性,它们正确地拟合了1951～1996年之间发生的厄尔尼诺和拉尼娜过程.两大典型场在半年之内不会相互转换,而非典型场则是典型场在加强或减弱过程中的一个补充阶段.过渡型的持续性较差,且同类之间过渡型相互转换的可能性较小.两大类型之间转换的主要过渡型分别为3型和9型.通过当前海温场所属的型并配合指标的变化,可望提前1～2个月预报出厄尔尼诺过程的开始和减弱.
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| 关 键 词： | ENSO 客观分析 预测 |
| 收稿时间： | 9/3/1998 12:00:00 AM |
| 修稿时间： | 1999/1/18 0:00:00 |
Objective cluster grouping to SST of the North Pacific and itsapplication in monitor and prediction of El Niňo |
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| Wang Yongliang and Song Jiaxi.Objective cluster grouping to SST of the North Pacific and itsapplication in monitor and prediction of El Niňo[J].Acta Oceanologica Sinica (in Chinese),1999,21(4):35-42. |
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| Authors: | Wang Yongliang and Song Jiaxi |
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| Institution: | National Marine Environmental Forecasting Center, Satate Oceanic Administration, Beijing 100081 |
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| Abstract: | By use of cluster analysis in this paper,the monthly averaged SST of the North Pacific is objectively gouged into ten types that have the totally changing characteristics mainly with the changing artanalies of the Mid-East Equatorial Pacific.We can group them into El Niňo-type and La Niňa-type according to the ENSO pherxmerxm.These two types include typical field,non-typical field and three transitional fields.The typical one has the good persistence and fits correctly the EI Nino and La Niňa occurring from 1951 to 1996.Within half a year,there is not inter-conversion between the two typical fields;however,the non-typical field compensates to typical one when it enhances or weakens.The transitional type persists poorly.It is rarely possible for the same transitional types to inter-converse.The main transitional types for conversing are Type 3 and Type 9.By using the type the presern SST field belongs to plus the change of index,it is expelled to predict the beginning and the declination of El Niňo from 1 to 2 months in advance. |
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| Keywords: | ENSO objective grouping forecast |
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