Cascade of Internal Wave Energy Catalyzed by Eddy‐Topography Interactions in the Deep South China Sea

Hu, Qi; Huang, Xiaodong; Zhang, Zhiwei; Zhang, Xiaojiang; Xu, Xiaoyang; Sun, Hui; Zhou, Chun; Zhao, Wei; Tian, Jiwei

doi:10.1029/2019gl086510

Cited by 21 publications

(18 citation statements)

References 68 publications

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“…In this study, we follow the analysis of the mooring data presented in Hu et al. (2020) and extend it to focus on the interaction of the subinertial flow with the two bumps and remote wave and turbulent energy propagation and dissipation. The reader is referred to Hu et al.…”

Section: Flow Response To Topography In Observationsmentioning

confidence: 99%

“…Subinertial flows interacting with topography might not be the only mechanism to sustain the observed enhanced near-inertial and high-frequency flow response. Hu et al (2020) also discussed the role of wind and tides for the wave and turbulent energy generation in this region. They estimated the energy input into near-inertial waves by wind and found no correlation with the near-inertial and high-frequency response observed in this region.…”

Section: Near-inertial and High-frequency Flow Responsementioning

confidence: 99%

“…Hu et al. (2020) also discussed the role of wind and tides for the wave and turbulent energy generation in this region. They estimated the energy input into near‐inertial waves by wind and found no correlation with the near‐inertial and high‐frequency response observed in this region.…”

Section: Flow Response To Topography In Observationsmentioning

confidence: 99%

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Non‐Local Energy Dissipation of Lee Waves and Turbulence in the South China Sea

2022

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Breaking of internal lee waves has been shown to drive enhanced turbulence and mixing in regions where strong bottom flows interact with rough topography. However, theoretical predictions for the energy conversion from the bottom flows into lee waves differ from the corresponding turbulent energy dissipation rates observed locally at the wave generation sites. Recent idealized numerical simulations suggest that the discrepancy may be attributed to non‐local wave breaking and dissipation effects: when a mean flow impinges on rough topography and generates lee waves and turbulence, a significant fraction of the generated energy gets advected downstream of the generation site and dissipates remotely. Here, we present a case study for the non‐local lee wave energy dissipation in the South China Sea by using a combination of in situ mooring observations of the bottom flow interacting with two topographic features and corresponding numerical simulations. The results show that most of the near‐inertial and high‐frequency response observed at the mooring site is not locally generated, but rather is produced by the interaction of the subinertial flow with the topographic feature upstream. The wave and turbulent energy detected at the mooring site can be enhanced by an order of magnitude compared to the energy of the locally generated motions. The simulations confirm that up to 70% of the enhanced energy is advected into the region by the subinertial flow, while the rest is radiated into the region as waves. Implication of our results for mixing observations and ocean model parameterizations are discussed.

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Section: Flow Response To Topography In Observationsmentioning

confidence: 99%

Section: Near-inertial and High-frequency Flow Responsementioning

confidence: 99%

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Non‐Local Energy Dissipation of Lee Waves and Turbulence in the South China Sea

2022

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“…Wind forcing is the predominant generation mechanism of NIWs, and the global power input from wind to NIWs is estimated to be 0.3-1.5 TW [1][2][3][4], which is comparable to the converted energy from surface tide to internal tides [5] and the work done by wind on the general circulation [6]. The tidal forcing, loss of stability of large-scale circulation, and eddy-topography interactions are also potential forcing mechanics of NIWs [7][8][9]. NIWs appear as a prominent peak in an internal wave spectrum, leading to intense vertical shear, and are thought to be a potential energy source for oceanic internal mixing to maintain the abyssal stratification [2][3][4]7].…”

Section: Introductionmentioning

confidence: 99%

Variation and Episodes of Near-Inertial Internal Waves on the Continental Slope of the Southeastern East China Sea

Yang

Hou

2021

JMSE

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Based on in situ observations, six episodes of near-inertial internal waves (NIWs) were detected on the East China Sea (ECS) continental slope, and the mechanisms and characteristics of them were examined. The generation mechanisms of the observed NIWs included typhoon, wind burst, lateral propagation, and energy transfer from low-frequency flow. The depth-integrated near-inertial kinetic energy (NIKE) showed no significant seasonal variation, and the annual mean NIKE and near-inertial currents were 400 J/m2 and 3.50 cm/s, respectively. Downward propagation of NIKE was evident in the small wavenumber band according to the rotary vertical wavenumber spectra. The NIKE was subsurface-intensified, and the near-inertial vertical shear reached 0.01 s−1. The vertical phase speeds of the NIWs ranged from 5 to 19 m/h. The frequencies of the NIWs were mostly red-shifted, however, blue-shift also existed. One episode had both blue- and red-shifted frequencies vertically, and had both upward and downward propagating vertical phase speeds. The e-folding times of the observed NIWs ranged from 4 to 11 days, which were influenced by successive wind bursts and background vorticity. On the left-hand side of Kuroshio, the background vorticity is usually positive; however, the NIWs were almost red-shifted, which resulted from the Doppler shift of the Kuroshio.

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“…Many geophysical processes in marine environments are influenced by seafloor bathymetry. Some examples are ocean tides (Egbert & Ray, 2001), tsunamis (Mofjeld et al, 2001), ocean circulation (Gille et al, 2004), internal waves (Hu et al, 2020), and turbulent mixing (Kunze & Smith, 2004). The propagation of ocean surface waves is strongly affected by bathymetry when depths are comparable to their wavelengths.…”

Section: Introductionmentioning

confidence: 99%

Modeling Uncertainties of Bathymetry Predicted With Satellite Altimetry Data and Application to Tsunami Hazard Assessments

et al. 2020

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Models of bathymetry derived from satellite radar altimetry are essential for modeling many marine processes. They are affected by uncertainties which require quantification. We propose an uncertainty model that assumes errors are caused by the lack of high-wavenumber content within the altimetry data. The model is then applied to a tsunami hazard assessment. We build a bathymetry uncertainty model for northern Chile. Statistical properties of the altimetry-predicted bathymetry error are obtained using multibeam data. We find that a Von Karman correlation function and a Laplacian marginal distribution can be used to define an uncertainty model based on a random field. We also propose a method for generating synthetic bathymetry samples conditional to shipboard measurements. The method is further extended to account for interpolation uncertainties, when bathymetry data resolution is finer than ∼10 km. We illustrate the usefulness of the method by quantifying the bathymetry-induced uncertainty of a tsunami hazard estimate. We demonstrate that tsunami leading wave predictions at middle/near field tide gauges and buoys are insensitive to bathymetry uncertainties in Chile. This result implies that tsunami early warning approaches can take full advantage of altimetry-predicted bathymetry in numerical simulations. Finally, we evaluate the feasibility of modeling uncertainties in regions without multibeam data by assessing the bathymetry error statistics of 15 globally distributed regions. We find that a general Von Karman correlation and a Laplacian marginal distribution can serve as a first-order approximation. The standard deviation of the uncertainty random field model varies regionally and is estimated from a proposed scaling law.

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Cascade of Internal Wave Energy Catalyzed by Eddy‐Topography Interactions in the Deep South China Sea

Cited by 21 publications

References 68 publications

Non‐Local Energy Dissipation of Lee Waves and Turbulence in the South China Sea

Non‐Local Energy Dissipation of Lee Waves and Turbulence in the South China Sea

Variation and Episodes of Near-Inertial Internal Waves on the Continental Slope of the Southeastern East China Sea

Modeling Uncertainties of Bathymetry Predicted With Satellite Altimetry Data and Application to Tsunami Hazard Assessments

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