摘要
基于 Kaplan等重建的 1856—2001年全球海表水温距平(SSTA)资料,用小波变换分析了热带太平洋SSTA的气候变率,对热带太平洋SSTA分别进行2—8、8—30和30—100a带通滤波,然后进行EOF分解。结果发现,ENSO模态具有5a左右的年际变化和15a左右的年代际变化2种显著周期,当二者位相相同时,ENSO事件加强,当二者位相相反时,ENSO事件减弱,当年际变化不明显时,显著的年代际变化也可单独导致ENSO事件;热带太平洋SSTA气候态变率以西太平洋暖池和赤道两侧的热带中东太平洋明显海温同号异常为主要特征,具60a左右的周期,其相位变化与气候跃变及Dl Nino 事件的类型有密切联系;长期增温倾向加大了El Nino事件的振幅。文章最后指出,ENSO事件强度变化是由年际、年代际和气候态等3类模态变率共同作用的结果,在ENSO预报模式中考虑并引入年代际和气候态变化对ENSO循环的影响,是提高ENSO预测水平的有效途径之一。
Based on the global SSTA data from 1856 to 2001 reconstructed by Kaplan et
al, the climate variability of the tropical Pacific SSTA was analyzed by the Morlet wave-
let transform. The results indicated that SSTA in the tropical Pacific are constituted by
interannual and interdecadal climatic state variability, as well as the linear trend. SSTA
based on 2--8, 8 -- 30 and 30 -- 100 year band filter was then analyzed by EOF respec-
tively. It was found that the first EOFs of both interannual and interdecadal components
are typical ENSO pattern. The time series of the first EOF mode approximately have
two significant periods, i. e. about 5-year and 15-year periods, corresponding to the in-
terannual and interdecadal variabilities respectively, which means, when they are in
phase, strong ENSO events tend to occur; when they are out of phase, the occurrence
of strong ENSO events is lessened; and either of them at peak condition can also induce
an ENSO event alone. The first mode of the climatic state change of the tropical Pacific
SSTA has obvious anomalies over the western tropical Pacific and the both sides of equa-
torial eastern Pacific, and it has close relations to the global climate jumps and the pat-
tern of El Nino events. Therefore, the influence of the interdecadal variability and cli-
matic state change of the tropical Pacific SSTA on the ENSO cycle must be considered
and should be included in the ENSO models, which is one of the best ways to improve
ENSO prediction level.
出处
《热带海洋学报》
CAS
CSCD
北大核心
2003年第4期1-9,共9页
Journal of Tropical Oceanography
基金
国家重点基础研究发展规划项目(G1998040900)
中国科学院知识创新工程项目(KZCX2-SW-210)
国家自然科学基金(49906003)