IOCAS-IR  > 海洋环流与波动重点实验室
黑潮延伸体双模态对温盐的影响及其转变的可预报性研究
耿雨
学位类型博士
导师穆穆
2020-05-20
学位授予单位中国科学院大学
学位授予地点中国科学院海洋研究所
学位名称理学博士
学位专业物理海洋学
关键词黑潮延伸体 混合层温盐 可预报性 条件非线性最优扰动 目标观测
摘要

   黑潮延伸体(Kuroshio ExtensionKE)在年代际尺度上具有典型的双模态特征:稳定模态和不稳定模态。考虑到黑潮延伸体不同模态下海水盐度、温度异常变化对大气环流、海洋水团、鱼类生存等方面的重要影响,本文首先利用观测和再分析资料探究黑潮延伸体双模态(年代际变化)对其周围区域海水盐度、温度异常季节变化的影响。

   通过分析发现黑潮延伸体年代际变化对黑潮-亲潮交汇(Kuroshio-Oyashio ConfluenceKOC)区域混合层盐度、温度异常季节变化均有重要的调制作用:两者在黑潮延伸体两种模态下季节变化趋势类似,在稳定模态下先增后减,在不稳定模态下先减后增。然而,导致两者季节变化的因素存在差异。对混合层盐度异常季节变化起重要作用的是海洋平流过程,其次是大气强迫(淡水通量);对温度异常季节变化贡献显著的是海洋动力过程,包括海洋平流和夹卷过程,大气强迫(热通量)对其起负反馈作用,但效果并不明显。

   另一方面,由于黑潮延伸体双模态在北太平洋气候系统中的重要作用,本文采用涡分辨率的区域海洋模式(Regional Ocean Modeling SystemROMS)和条件非线性最优扰动(conditional nonlinear optimal perturbationCNOP)方法开展黑潮延伸体模态转变的可预报性和目标观测研究。

   本文利用ROMS模式成功模拟出符合观测的黑潮延伸体模态转变过程,以此为基础建立非线性优化系统,并获得黑潮延伸体从稳定模态转变为不稳定模态过程中的最快增长初始误差(CNOP型初始误差)。结果表明,这种初始误差主要位于黑潮延伸体上游区域上层500米。此外,针对误差的维持和发展,本文从涡度和能量的角度考察了黑潮延伸体模态转变过程中的误差增长机制。研究发现在线性平流的作用下,误差从日本东南部逐渐东移至黑潮延伸体第一个准静止弯曲附近,非线性平流则减缓了误差东移的速度,导致误差在黑潮延伸体第一个准静止弯曲附近长期停留,使其有充分的时间通过正压不稳定过程从背景场吸收能量,最终造成误差快速地增长,使得黑潮延伸体的强度被高估。

   进一步,本文基于最快增长初始误差的空间结构识别出黑潮延伸体模态转变过程的目标观测敏感区。分别从初始误差的空间位置和空间结构这两个方面开展敏感性试验,验证了预报黑潮延伸体模态转变过程时所识别敏感区的有效性以及敏感区内CNOP型初始误差空间结构的重要性。此后,借助观测系统模拟试验(Observing System Simulation ExperimentOSSE)对目标观测效果进行理论评估,结果表明在敏感区内“实施”目标观测较为合适,能够有效地减小预报不确定性,较大程度地改善黑潮延伸体模态转变的预报效果。

其他摘要

   The Kuroshio Extension (KE) exhibits the typical bimodal states –– a stable state and an unstable state –– on the decadal time scales. The anomalous temperature and salinity variations in the different Kuroshio Extension (KE) states have significant effects on the atmospheric circulation, mode water and survival of fish. Therefore, in this dissertation, observation and reanalysis data are used to explore the effects of the KE bimodal states (the KE decadal variations) on the seasonal changes of anomalous temperature and salinity in the surrounding region of the KE.

   Our analysis reveals that decadal variations of the KE play an important modulation role in the seasonal changes of the mixed-layer salinity anomaly (MLSA) and mixed-layer temperature anomaly (MLTA) in the Kuroshio-Oyashio Confluence Region (KOCR). Both variables have similar seasonal variation trend under the KE bimodal states. In the stable state of the KE, they increase first and then decrease, while in the unstable state, they change in the opposite trend. However, the factors causing their respective seasonal variations are different. For the MLSA, the contribution of ocean advection is primary and that of the atmospheric forcing (freshwater flux) is secondary. For the MLTA, the ocean dynamic processes, including ocean advection and entrainment process, have a significant impact on its seasonal variations, while the atmospheric forcing (heat flux) has a negative feedback, but not obvious.

    Due to the important role of the KE bimodal states in the North Pacific climate system, we adopt an eddy-resolving Regional Ocean Modeling System (ROMS) and the conditional nonlinear optimal perturbation (CNOP) method to investigate the predictability of the KE transition process and conduct its target observation.

    In this dissertation, the KE state transition is successfully simulated using ROMS model, and the simulation is consistent with the observation in time. Based on the results of the simulation, a nonlinear optimization system is established to obtain the fastest-growing initial errors (CNOP-type initial errors) in the KE transition from the stable to the unstable state. Our results indicate that the large values of this kind of initial errors are mainly located in the upstream of the KE over the upper 500 m. Besides, from the viewpoint of the relative vorticity and eddy-energy, the dynamic mechanism of the error growth is explored in terms of the error maintenance and development during the KE transition period. The results indicate that the errors are transported eastward from the south of Japan to the KE first quasi-stationary meander by linear advection. And the nonlinear advection slows down the moving speed of these errors, which lead to the long-term existence of them in the KE first quasi-stationary meander. In this situation, errors have enough time to absorb energy from the background field through barotropic energy conversion continuously. Ultimately, errors grow rapidly and the KE strength is overestimated.

   Furthermore, based on the spatial structure of the fastest-growing initial errors, the sensitive areas of target observation of the KE state transition are identified. The sensitivity experiments in terms of the spatial locations and patterns of the initial errors are conducted. And the validity of the sensitive areas and the importance of the structures of the CNOP-type initial errors in this area are verified in the prediction of the KE state transition. Moreover, the target observation improvement is evaluated theoretically with the help of Observing System Simulation Experiment (OSSE). The results show that the target observation is relatively suitable when it is implemented in the sensitive area, which can effectively reduce the prediction uncertainty and improve the predicted results of the KE state transition to a larger extent.

学科领域海洋物理学
学科门类理学::海洋科学
页数112
语种中文
目录

1  绪论..................................................................................... 1

1.1  选题背景和研究意义...................................................................................... 1

1.2  研究现状和研究进展...................................................................................... 5

1.2.1  黑潮延伸体区域温度、盐度的变化及变化机制................................... 5

1.2.2  黑潮延伸体模态转变的物理机制........................................................... 6

1.2.3  黑潮延伸体模态转变的可预报性研究................................................... 7

1.3  研究内容和章节安排...................................................................................... 8

2  黑潮延伸体双模态对黑潮-亲潮交汇区温盐的影响.......... 11

2.1  引言................................................................................................................ 11

2.2  方法和数据.................................................................................................... 12

2.2.1  混合层盐度、温度异常收支方程......................................................... 12

2.2.2  数据介绍................................................................................................. 14

2.3  黑潮延伸体双模态对黑潮-亲潮交汇区温盐异常季节变化的调制.......... 15

2.3.1  黑潮延伸体双模态对黑潮-亲潮交汇区盐度异常季节变化的影响... 18

2.3.2  黑潮延伸体双模态对黑潮亲潮交汇区温度异常季节变化的影响... 28

2.4  小结和讨论.................................................................................................... 36

3  可预报性研究中的数值模式和方法介绍........................... 39

3.1  ROMS模式.................................................................................................... 39

3.1.1  模式简介................................................................................................. 39

3.1.2  模式控制方程......................................................................................... 40

3.1.3  模式坐标系统......................................................................................... 44

3.1.4  模式网格................................................................................................. 44

3.2  模式设置........................................................................................................ 46

3.3  模式模拟结果................................................................................................ 48

3.3.1  黑潮延伸体基本特征及其年代际变化................................................. 48

3.3.2  地形对黑潮延伸体年代际变化的影响................................................. 52

3.4  方法介绍........................................................................................................ 55

3.4.1  研究方法................................................................................................. 55

3.4.2  非线性优化系统的建立......................................................................... 56

3.5  小结................................................................................................................ 59

4  黑潮延伸体模态转变预报中的最快增长初始误差............ 61

4.1  引言................................................................................................................ 61

4.2  最快增长初始误差的求解............................................................................ 61

4.3  最快增长初始误差及其发展........................................................................ 65

4.3.1  空间结构和影响..................................................................................... 65

4.3.2  误差发展................................................................................................. 69

4.3.3  误差增长机制......................................................................................... 74

4.4  小结和讨论.................................................................................................... 77

5  黑潮延伸体模态转变的目标观测研究............................... 79

5.1  引言................................................................................................................ 79

5.2  敏感区的识别................................................................................................ 80

5.3  敏感性验证.................................................................................................... 81

5.4  观测系统模拟试验........................................................................................ 85

5.5  小结和讨论.................................................................................................... 89

6  总结与讨论........................................................................ 91

6.1  本文的主要结论............................................................................................ 91

6.2  本文的创新点................................................................................................ 93

6.3  工作展望和讨论............................................................................................ 93

参考文献........................................................................................... 95

  ............................................................................................. 109

作者简历及攻读学位期间发表的学术论文与研究成果.................. 111

文献类型学位论文
条目标识符http://ir.qdio.ac.cn/handle/337002/164706
专题海洋环流与波动重点实验室
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耿雨. 黑潮延伸体双模态对温盐的影响及其转变的可预报性研究[D]. 中国科学院海洋研究所. 中国科学院大学,2020.
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