Variations of the Kuroshio in the Luzon Strait Revealed by EOF Analysis of Repeated XBT Data and Sea‐Level Anomalies

Yu, Long; Zhu, Xiaohua; Guo, Xinyu; Ji, Fei; Li, Zhiyuan

doi:10.1029/2020jc016849

Cited by 8 publications

(4 citation statements)

References 51 publications

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“…A small distance indicates the similarity between two water parcels. The definition of distance can be utilized for the purpose of tracing water masses, which has been applied in the Antarctic Intermediate Water (Huang et al., 2021), Indian Ocean Dipole (Chen et al., 2023), as well as water masses surrounding the LS (Gao et al., 2020; Long et al., 2021). Additionally, the method has been applied to identify the pathway of densest overflow in the Nordic Seas in the upper 2,000 m (Huang et al., 2020), showing the applicability of the method in the deep ocean with water intrusion.…”

Section: Methodsmentioning

confidence: 99%

“…While the Kuroshio intrusion in the upper LS is relatively well‐documented due to abundant observations from satellites, moorings, and research voyages (Long et al., 2021; Nan et al., 2015; Sun et al., 2020; Zhong et al., 2021 and references therein), transport in the middle and deep layers has received less attention. Snapshots of temperature ( T ) and salinity ( S ) profiles from voyages across the strait provide evidence of outflow (inflow) in the middle (deep) layer (Shen et al., 2022; Zhou et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

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Water Exchange Through the Upper and Middle Luzon Strait Using the Sigma–Pi Diagram

Zheng,

Zhu

2024

JGR Oceans

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Water exchange in the Luzon Strait (LS) is critical for layered circulation in the South China Sea (SCS); however, observational evidence of sandwich‐like water exchange is scarce. This study presents a comprehensive analysis of the meridional and zonal spatial patterns of water exchange in the upper and middle LS, along with its seasonal variations in a sigma–pi diagram using Argo profiles. As observed viewed from the perspective of SCS, inflow and outflow occur in the upper and middle layers, respectively. Upper‐layer Kuroshio intrudes into the SCS primarily in the northern and central regions of the LS, extending along the continental shelf into the inner SCS. A significant middle‐layer eastward outflow is evident at 26.7–27.56 kg/m3 (500–1,500 m) in the northern part of the strait, extending to 123°E. The Kuroshio intrusion intensifies during the winter, whereas the middle‐layer outflow is most pronounced in the autumn.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Water Exchange Through the Upper and Middle Luzon Strait Using the Sigma–Pi Diagram

Zheng,

Zhu

2024

JGR Oceans

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“…On the other hand, if multiple 3D SSFs {X t ∈ R M ×N ×I } T t=1 (e.g., from different seasons) are available, the joint learning of basis functions has potential to realize long-term effective representation 38,39 , since more SSF variations (e.g., from different seasons) are taken tant role in changing the ocean dynamics of a semi-closed ocean system 2 .…”

Section: Learning Basis Functions From Multiple 3d Ssfsmentioning

confidence: 99%

Tensor-based Basis Function Learning for Three-dimensional Sound Speed Fields

Liu¹,

Ji²,

Zhao³

et al. 2022

Preprint

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Basis function learning is the stepping stone towards effective three-dimensional (3D) sound speed field (SSF) inversion for various acoustic signal processing tasks, including ocean acoustic tomography, underwater target localization/tracking, and underwater communications. Classical basis functions include the empirical orthogonal functions (EOFs), Fourier basis functions, and their combinations. The unsupervised machine learning method, e.g., the K-SVD algorithm, has recently tapped into the basis function design, showing better representation performance than the EOFs. black However, existing methods do not consider basis function learning approaches that treat 3D SSF data as a third-order tensor}, and thus cannot fully utilize the 3D interactions/correlations therein. To circumvent such a drawback, basis function learning is linked to tensor decomposition in this paper, which is the primary drive for recent multi-dimensional data mining. In particular, a tensor-based basis function learning framework is proposed, which can include the classical basis functions (using EOFs and/or Fourier basis functions) as its special cases. This provides a unified tensor perspective for understanding and representing 3D SSFs. Numerical results using the South China Sea 3D SSF data have demonstrated the excellent performance of the tensor-based basis functions.

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“…Given that the LST is largely determined by the zonal flow across the strait, many observational efforts have been made to measure the zonal flow in the Luzon Strait over past decades (e.g., Chen & Huang, 1996; Liang et al., 2008; Liao et al., 2008; Long et al., 2021; Qu et al., 2006; Tian et al., 2006; Zhang et al., 2015). These observations showed that the zonal flow through the Luzon Strait have a three‐layer structure in the vertical direction.…”

Section: Introductionmentioning

confidence: 99%

Novel Insights Into the Zonal Flow and Transport in the Luzon Strait Based on Long‐Term Mooring Observations

et al. 2023

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The zonal flow and volume transport in the Luzon Strait play a crucial role in modulating the circulation, heat and material balance, and biogeochemical processes in the South China Sea (SCS), but the quantitative values remain unclear due to lack of long‐term direct observations. Here, this knowledge gap is bridged by analyzing 4‐year velocity measurements from a mooring array along 119.9°E. Based on the novel data, the mean value of the upper‐layer Luzon Strait transport (i.e., LST_up) is estimated at −4.54 ± 1.69 Sv. Seasonally, the westward LST_up attains its peak and trough in January and June with values of −6.80 ± 1.46 Sv and −2.59 ± 0.76 Sv, respectively. At the interannual time scale, the LST_up was strongest in 2016–2017 but weakest in 2017–2018. Further analysis suggested that local winds and the combination of local winds and upstream Kuroshio transport are likely the dominant modulation mechanisms for its seasonal and interannual variations, respectively. In the middle layer, a quasi‐steady cyclonic flow structure is identified and the volume transport is therefore trivial. We further found that seasonal variation of the middle‐layer transport is dominated by the eastward flow of this cyclonic structure. Corresponding to the gravest‐mode response in the Luzon Strait, seasonal‐to‐interannual variations of this middle‐layer eastward flow are nearly in‐phase with the upper‐layer westward flow. Overall, the above observational results provide a benchmark for the flow and transport in the Luzon Strait which can help understand the dynamics of the SCS circulation and validate and improve regional numerical simulations.

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Variations of the Kuroshio in the Luzon Strait Revealed by EOF Analysis of Repeated XBT Data and Sea‐Level Anomalies

Cited by 8 publications

References 51 publications

Water Exchange Through the Upper and Middle Luzon Strait Using the Sigma–Pi Diagram

Water Exchange Through the Upper and Middle Luzon Strait Using the Sigma–Pi Diagram

Tensor-based Basis Function Learning for Three-dimensional Sound Speed Fields

Novel Insights Into the Zonal Flow and Transport in the Luzon Strait Based on Long‐Term Mooring Observations

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