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首页> 外文期刊>Advances in Atmospheric Sciences >Applications of Conditional Nonlinear Optimal Perturbation in Predictability Study and Sensitivity Analysis of Weather and Climate
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Applications of Conditional Nonlinear Optimal Perturbation in Predictability Study and Sensitivity Analysis of Weather and Climate

机译：条件非线性最优摄动在天气和气候可预报性研究和敏感性分析中的应用

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Considering the limitation of the linear theory of singular vector (SV), the authors and their collaborators proposed conditional nonlinear optimal perturbation (CNOP) and then applied it in the predictability study and the sensitivity analysis of weather and climate system. To celebrate the 20th anniversary of Chinese National Committee for World Climate Research Programme (WCRP), this paper is devoted to reviewing the main results of these studies. First, CNOP represents the initial perturbation that has largest nonlinear evolution at prediction time, which is different from linear singular vector (LSV) for the large magnitude of initial perturbation or/and the long optimization time interval. Second, CNOP, rather than linear singular vector (LSV), represents the initial anomaly that evolves into ENSO events most probably. It is also the CNOP that induces the most prominent seasonal variation of error growth for ENSO predictability; furthermore, CNOP was applied to investigate the decadal variability of ENSO asymmetry. It is demonstrated that the changing nonlinearity causes the change of ENSO asymmetry. Third, in the studies of the sensitivity and stability of ocean's thermohaline circulation (THC), the nonlinear asymmetric response of THC to finite amplitude of initial perturbations was revealed by CNOP. Through this approach the passive mechanism of decadal variation of THC was demonstrated; Also the authors studies the instability and sensitivity analysis of grassland ecosystem by using CNOP and show the mechanism of the transitions between the grassland and desert states. Finally, a detailed discussion on the results obtained by CNOP suggests the applicability of CNOP in predictability studies and sensitivity analysis.
机译：考虑到奇异矢量（SV）线性理论的局限性，作者及其合作者提出了条件非线性最优摄动（CNOP），然后将其应用于天气和气候系统的可预测性研究和敏感性分析。为庆祝中国国家世界气候研究委员会（WCRP）成立20周年，本文专门回顾了这些研究的主要成果。首先，CNOP表示在预测时间具有最大非线性演化的初始扰动，这与线性奇异矢量（LSV）的初始扰动幅度大或优化时间间隔长有关。其次，CNOP而非线性奇异矢量（LSV）代表了最有可能演变为ENSO事件的初始异常。也是CNOP引起误差增长最显着的季节性变化，以提高ENSO的可预测性。此外，CNOP被用于研究ENSO不对称性的年代际变化。结果表明，不断变化的非线性会引起ENSO不对称性的变化。第三，在海洋热盐环流（THC）的敏感性和稳定性研究中，CNOP揭示了THC对初始扰动有限幅度的非线性不对称响应。通过这种方法证明了THC年代际变化的被动机制。此外，作者还使用CNOP研究了草地生态系统的不稳定性和敏感性分析，并说明了草地与荒漠状态之间转换的机制。最后，对CNOP获得的结果的详细讨论表明CNOP在可预测性研究和敏感性分析中的适用性。

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