基于条件非线性最优扰动方法和IOCAS ICM的ENSO可预报性研究

IOCAS-IR > 海洋环流与波动重点实验室

	基于条件非线性最优扰动方法和IOCAS ICM的ENSO可预报性研究
	陶灵江
学位类型	硕士
导师	张荣华
	2017-05
学位授予单位	中国科学院大学
学位授予地点	北京
学位专业	环境工程
关键词	中等复杂程度的海气耦合模式（icm）条件非线性最优扰动（cnop）方法 El Niño模拟和预测初始场误差模式参数误差
摘要	厄尔尼诺（El Niño）是热带中东太平洋海温异常增暖的海气耦合现象，它的发生往往造成全球性的自然气候灾害，因而备受国际社会和学术界的高度关注。近几十年来的不断深入研究加深了对其动力过程的理解以及数值模拟和预报，但El Niño实时预报仍然存在着很大的不确定性。通常，初始场误差和模式误差被认为是导致El Niño预报不准确的主要原因。本文基于IOCAS ICM（中国科学院海洋研究所简化的海气耦合模式），利用条件非线性最优扰动（CNOP）方法，考察初始场误差和参数误差以及其联合效应对El Niño可预报性的影响，并进一步考察去除CNOP相关的敏感区的初始场误差对预报改进的效果，最后给出改进模式的一些建议。本论文研究的主要内容和结论如下：（1）考察了ICM对热带太平洋海表温度（SST）的模拟和预报能力。表明ICM能够准确模拟出具有4年准周期振荡的ENSO现象，在冬季振幅达到极值。ICM在中太平洋海区具有较高的预报技巧，但同其他模式一样，在跨春季预报时，其预报技巧迅速降低，具有较强的春季预报障碍（SPB）现象。（2）揭示了模式中造成对El Niño预报最大不确定性的最优初始场误差（CNOP-I）时空特征。指出SST和海表高度（SL）的CNOP-I空间结构与预报初始时刻所在季节有关，这些初始场误差会产生类似Bjerknes正反馈机制以及温跃层反馈机制，从而产生类似La Niña模态的误差演变过程。考察CNOP-I引起的季节性误差增长，表现出较强的SPB现象。针对CNOP-I误差极值的局地性特征，揭示了ICM对ENSO预报的敏感区主要在中西太平洋表层以及东太平洋次表层，这些为目标观测提供了理论指导。特别地，考虑到随季节变化的CNOP-I，暗示ICM的敏感区也随季节变化，从而建议采用随季节而变的适应性观测来改善预报模式初始场，会更有效提高模式对El Niño的预报能力，并能够减弱SPB现象。（3）考察了CNOP-I相关的目标观测对El Niño预测技巧的提高程度。通过理想观测系统模拟试验。发现，相对于其他地区，若在赤道中太平洋地区增加观测，能够更有效地提高ICM对ENSO的预报技巧，可以使预报误差减小25%左右；其次较为有效的地区为东太平洋。此外，也指出，仅仅去除某一区域的初始场误差，会使其与初始场不匹配，从而甚至会使预报技巧降低。进一步，实施CNOP-I相关的观测网，考察去除这些敏感区的初始场误差对预报改善的效果，发现随季节变动的观测网更有利于抑制预报误差的发展：例如在中太平海区增加观测基础上，若在4-10月份补充赤道东太平洋的观测，会进一步提高预报技巧，改进预报效果达到62%以上。同时，这种CNOP-I相关敏感区的观测网能够有效削弱ENSO预报的SPB现象，而去除非敏感区的误差并不会削弱SPB现象甚至会加强SPB。因而对于目标观测要充分考虑敏感区的季节性变化，才能最大化预报技巧的提高，进一步证实了CNOP-I确定的敏感区对ENSO预报的重要意义。（4）用CNOP方法探讨了模式误差对El Niño预报的影响，如海气耦合相对系数（）和温跃层反馈系数（）的模式参数误差以及其初始场误差共同作用所造成的El Niño预报不确定性。揭示了最优模式参数误差（CNOP-P）空间结构与El Niño事件本身有关，同样其所造成的误差增长也有很大的不确定性，在某些情况产生类似La Niña模态的误差增长，也会出现El Niño模态的误差增长。但是，CNOP-P误差分布也有一定的局地性：误差集中在中太平洋，误差则集中在东太平洋冷舌区。这种误差分布会使模式产生较强的Bjerknes正反馈和次表层对表层的热力强迫效应，从而使得ICM模拟结果偏离真实海洋热力状况。进一步，考察了在模式误差和初始场误差同时存在（C-CNOPs）的情况下，其对预报误差发展的上限。指出，预报误差的发展主要取决于初始场误差，季节性的C-CNOPs引起的误差演变与CNOP-I相似，而模式参数误差引发的增强的Bjerknes正反馈和温跃层效应会进一步扩大误差的发展。值得注意的是，C-CNOPs也能产生类似SPB现象，所引起的季节性误差增长远大于CNOP-P和CNOP-I以及CNOP-P和CNOP-I的线性组合（CNOP-I+CNOP-P）；这表明SPB一方面可能由于特定类型的初始场误差造成，同时模式的不确定性也会对ENSO预报技巧的季节性变化产生影响，而且当模式误差和初始场误差同时存在的情况下，ICM对El Niño的预报更容易出现SPB现象，从而造成预报技巧降低。（5）提出了基于IOCAS ICM改进ENSO预报技巧的新思路。在考察CNOP-P或者C-CNOPs参数误差的空间特征时，表现出明显的局地特性：海气系统相对耦合系数（）误差集中在中太平洋，次表层夹卷温度反馈系数（）误差则集中在东太平洋冷舌区。换句话说，El Niño模拟对中太平洋的海洋和大气的耦合关系特别敏感；同时，东太平洋冷舌区的次表层对表层的热力强迫过程也对用ICM模拟及预报ENSO有着重要的意义。因此为了提高ICM对El Niño的准确模拟及预报，除了提供更为精确的初始场外，对于中太平洋海洋与大气间相互作用以及东太平洋冷舌区域次表层对表层的热力强迫作用必须给予很好的处理，尤其是它们在数值模式中的表征和参数化方面。
其他摘要	The El Niño, an ocean-atmosphere coupled phenomenon characterized by an abnormal warming in the central and eastern tropical Pacific, has been much focused on because of its effects on natural disasters around the world. In recent decades, continued in-depth studies of El Niño events have deepened our understanding of its dynamics, modelings and predictions. However, significant uncertainties still exist in real-time ENSO predictions. Generally, the prediction uncertainties are mainly attributed to errors in the initial conditions and numerical models. In this study, based on an intermediate coupled model (ICM), the conditional nonlinear optimal perturbation (CNOP) approach was employed to study the optimal initial condition and model parameter errors and also their combinded effects on largest error growth in the El Niño predictions. Then, we investgated the extent to which ENSO predictions can be improved by removing CNOP-related initial condition errors in the ICM. Lastly, some suggestions with respect to model improvement are presented. The main contents and conlusions are summaried as follows: (1) The simulative and predictive skills of the tropical Pacific SST were investigated with the ICM. The ICM can successfully depict a dominant four-year oscillation period of ENSO cycle and phase locking. The high prediction skill region is located in the central and eastern equatorial Pacific. Similar to other ENSO models, the “spring predictability barrier” (SPB) is also strong in the ICM. (2) The optimal initial condition errors (as represented by CNOP-I) in sea surface temperature anomalies (SSTAs) and sea level anomalies (SLAs) were obtained with seasonal variation. The CNOP-induced perturbations tend to evolve into the La Niña mode beacused it casuses the Bjerknes-like positive feedback and thermocline feedback. The CNOP-I was found to induce the SPB phenomenon. Based on the characteristic distributions of the CNOP-I, it implies that the upper layer in the central equatorial Pacific and subsurface in the eastern Pacific are the most sensitive areas for El Niño prediction in the ICM. Given the season-dependence of the CNOP-I, targeted observing strategies are suggested to be implemented seasonally. (3) The extent to which ENSO predictions can be improved by removing CNOP-related initial condition errors in the ICM was investigated. Observing system simulation experiments (OSSEs) indicate that additional obsevations in the central equatorial Pacific are more effective for improvement of prediction skills than in other areas: The forecast errors can be reduced by 25%. It is worth noting that removing initial errors in certain areas may worsen the prediction due to the imbalance of initial fields. Further, CNOP-I-related targeted observation strategies are employed. It was found that seasonal varying observational network can effectively limt the prediction error growth: On the premise of the central Pacific observations, an additional observation in the easern Pacific during April to October can futher improve the forecast skills by more than 62%. Particullarly, CNOP-I-related targeted obsevation can weaken the SPB phenomenon and vice versa. (4) The roles playe by model parameter errors [relative ocean-atmosphere coupling coefficient ( ) and thermocline effect ( )], initial condition errors and their optimal combination in ENSO prediction uncertainties were investigated. It reveals that the prediction errors induced by CNOP-P, which are found to be depedent on El Niño itself, show great uncertainties. However, despite all that, CNOP-type parameter errors have localized distributions: the component errors are mainly located in the central equatorial Pacific, whereas component errors are mainly located in the eastern Pacific cold tongue region. CNOP-P can strengthen the Bjerknes feedback and subsurface thermal forcing to surface, so that the prediction results are deviated from the reference ENSO events. Furthermore, the optimal combinations of parameter and initial condition errors (C-CNOPs) were calculated. Seasonal C-CNOPs-induced error evolutions are similar to those of CNOP-I but have larger amplitude for their intensified Bjerknes feedback and thermocline feedback. Addtionally, more significant SPB phenomena are induced by C-CNOPs than by CNOP-P, CNOP-I or even CNOP-P+CNOP-I (the simple combination of the CNOP-I and CNOP-P). It indicates that the coexisting initial and model errors are more likely to lead to a significant SPB phenomenon, thus contaminating the prediction results. (5) Revealing a new way to improve the ENSO prediction skill on model perspective. The parameter errors derived frome C-CNOPs or CNOP-P dominate a few areas: errors are concentrated on the central equatorial Pacific, and errors are mainly in the eastern Pacific cold tongue region. That is to say, the El Niño simulations and predictions are significantly sensitive to the ocean-atmosphere coupling in the central Pacific and thermocline effect in the eastern Pacific. Therefore, except for providing accurate observations, improving these two dynamic representions in the central and eastern Pacific, especially their parameterization in numerical model, can more effectively enhance the El Niño prediction skills.
学科领域	物理海洋学
语种	中文
文献类型	学位论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/136558
专题	海洋环流与波动重点实验室
作者单位	1.中国科学院海洋研究所 2.中国科学院大学
推荐引用方式 GB/T 7714	陶灵江. 基于条件非线性最优扰动方法和IOCAS ICM的ENSO可预报性研究[D]. 北京. 中国科学院大学,2017.

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