Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander
Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander
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DOI：
10.1007/s00376-011-0199-0
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发表时间：
2012-01
影响因子：
5.8
通讯作者：
Qiang Wang;M. Mu;H. Dijkstra
中科院分区：
文献类型：
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作者：
Qiang Wang;M. Mu;H. Dijkstra
利用简化重力正压浅水模式模拟了黑潮路径的变化。结果表明，该模型能够捕捉到这些路径变化的基本特征。我们使用一个模拟的模型作为参考状态，并研究了模型参数的误差对预测的过渡到黑潮大弯曲（KLM）状态的条件非线性最优参数扰动（HOCP-P）方法的影响。由于其相对较大的不确定性，三个模型参数被认为是：界面摩擦系数，风应力幅值，和侧摩擦系数。我们独立地确定了针对这三个参数中的每一个优化的hocP-Ps，并且我们使用光谱投影梯度2（SPG 2）算法同时优化了所有三个参数。同样，在初始条件的误差所造成的影响进行了检查，使用条件非线性最优初始扰动（hocP-I）方法。hocP-I和hocP-P都可能导致KLM在240天的提前期内的显著预测误差。但hocP-I引起的预报误差大于hocP-P引起的预报误差。研究结果表明，初始条件误差对KLM预报的影响不仅大于模型参数误差，而且后者也不容忽视。因此，为了提高KLM在该模型中的预测能力，应首先改善初始条件，模型参数应使用最佳估计。
A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method. Because of their relatively large uncertainties, three model parameters were considered: the interfacial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.