| ENSO机理及其预测研究 |
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| 引用本文： | 李崇银,穆穆,周广庆,等.ENSO机理及其预测研究[J].大气科学,2008,32(4):761-781. |
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| 作者姓名： | 李崇银 穆穆 周广庆 等 |
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| 作者单位： | 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,北京,100029 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)
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中国科学院知识创新工程项目 |
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| 摘 要： | 资料分析研究表明ENSO(El Ni?o和La Ni?a)实际上是热带太平洋次表层海温距平的循环，而次表层海温距平的循环是赤道西太平洋异常纬向风所驱动的，赤道西太平洋的异常纬向风又主要由异常东亚冬季风所激发。因此可以将ENSO的机理视为主要是由东亚季风异常造成的赤道西太平洋异常纬向风所驱动的热带太平洋次表层海温距平的循环。同时分析还表明，热带西太平洋大气季节内振荡(ISO)的明显年际变化，作为一种外部强迫，对ENSO循环起着十分重要的作用；El Ni?o的发生同大气ISO的明显系统性东传有关。资料分析也表明，El Ni?o持续时间的长短与大气环流异常有密切关系。 用非线性最优化方法研究El Ni?o－南方涛动(ENSO)事件的可预报性问题，揭示了最容易发展成ENSO事件的初始距平模态，即条件非线性最优扰动(CNOP)型初始距平；找出能够导致显著春季可预报性障碍(SPB)，且对ENSO预报结果有最大影响的一类初始误差——CNOP型初始误差，进而探讨耦合过程的非线性在SPB研究中的重要作用，提出了关于ENSO事件发生SPB的一种可能机制；用CNOP方法揭示了ENSO强度的不对称现象，探讨ENSO不对称性的年代际变化问题，提出ENSO不对称性年代际变化的一种机制；建立了关于ENSO可预报性的最大可预报时间下界、最大预报误差上界和最大允许初始误差下界的三类可预报性问题，分别从三个方面揭示ENSO事件的春季可预报性障碍现象，比较有效地量化了模式ENSO事件的可预报性。 利用中国科学院大气物理研究所地球流体力学数值模拟国家重点实验室的ENSO预测系统，研究了海洋资料同化在ENSO预测中的应用，该系统可以同时对温、盐剖面资料和卫星高度计资料进行同化。并且在模式中采用次表层上卷海温的非局地参数化方法，可有效地改进ENSO模拟水平。采用集合卡曼滤波(Ensemble Kalman Filter，EnKF)同化方法以及在集合资料同化中“平衡的”多变量模式误差扰动方法为集合预报提供更加精确和协调的初始场，ENSO预报技巧得到提高。
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| 关 键 词： | ENSO 机理 可预报性 预测 |
Mechanism and Prediction Studies of the ENSO |
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| LI Chongyin,MU Mu,ZHOU Guangqing and et al.Mechanism and Prediction Studies of the ENSO[J].Chinese Journal of Atmospheric Sciences,2008,32(4):761-781. |
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| Authors: | LI Chongyin MU Mu ZHOU Guangqing and |
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| Abstract: | A new theory on the ENSO cycle is advanced in this study:the ENSO is an interannual cycle of SOTA in the tropical Pacific driven by zonal wind anomaly over the equatorial western Pacific which is caused mainly by anomalous East Asian winter monsoon.El Nio(La Nia)or ENSO is really the subsurface ocean temperature anomalies(SOTA)in the tropical Pacific.The interannual cycle of SOTA in the tropical Pacific is driven by zonal wind anomaly over the equatorial western Pacific which is caused mainly by anomalous East Asian winter monsoon/The strongest signal of interannual variations of the tropical atmospheric ISO(intraseasonal oscillation)lies in the western Pacific.As an external forcing,the interannual variations of the ISO play an important role in the ENSO cycle.The occurrence of El Nio(La Nia)is related to the eastward(westward)propagation of ISO.The circulation anomalies are strongly related to the lasting time of El Nio events.Nonlinear optimization method is used to explore the predictability problems for ENSO events.Significant results are obtained.Conditional nonlinear optimal perturbation(CNOP)acts as the initial anomaly pattern that evolves into the ENSO event most probably.And the CNOP-type error superimposed on the ENSO event causes a significant "spring predictability barrier"(SPB)and has the largest negative effect on the prediction.A possible mechanism for SPB is provided to explain SPB,which suggests the role of nonlinearity in SPB.Besides,by using the CNOP approach,the ENSO amplitude asymmetry is addressed by nonlinearity.The decadal change of ENSO asymmetry is also revealed and an explanation is given to address the mechanism.Finally,the lower bound of the maximum predicable time,the upper bound of maximum prediction error,and the lower bound of maximum allowable initial error for ENSO events are established with three different nonlinear optimization problems,which reveal the SPB for ENSO events from three different perspectives.Furthermore,an ENSO prediction system is developed.In that system,a three-dimensional variational ocean data assimilation system is used.This is capable of assimilating in situ sea water temperature and salinity observations and satellite altimetry data.An empirical parameterization of subsurface entrainment temperature in the coupled model may effectively improve ENSO simulation.An ensemble Kalman filter data assimilation system is implemented to provide the initial ensemble.And balanced multivariate model error is used in the ensemble Kalman filter data assimilation.ENSO prediction is improved well by those ensemble forecasts. |
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| Keywords: | ENSO mechanism predictability prediction |
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