Spatial distribution of turbulent mixing in the upper ocean of the South China Sea

Shang, Xiaodong; Liang, Cheng; Chen, Guiying

doi:10.5194/os-13-503-2017

Cited by 30 publications

(15 citation statements)

References 59 publications

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“…Our understanding of turbulent mixing in the SCS has been improved in the past years. Studies of spatial structure and temporal variability of turbulent mixing in the SCS have been carried out successively 4,6–9 . These studies revealed that enhanced mixing with diapycnal diffusivity of 10 −3 m 2 /s, being two orders of magnitude larger than that in the Pacific Ocean, occurs in the deep SCS and it is highly variable in both space and time.…”

Section: Introductionmentioning

confidence: 99%

Dissipation of mesoscale eddies and its contribution to mixing in the northern South China Sea

Yang

Nikurashin

Sasaki

et al. 2019

Sci Rep

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It is reported that turbulent mixing is enhanced in the South China Sea (SCS), and it is highly variable in both space and time. Generation and breaking of internal tides has been identified as the main process to drive turbulent mixing in the SCS, while the contributions from other processes are not clear enough. Here we investigate the potential contribution from mesoscale eddies to turbulent mixing in the SCS using a high resolution numerical simulation. Our results show that mesoscale eddies in the SCS effectively dissipate over complex rough topography and indicate that the generation of submesoscale motions and lee waves are two pathways for the transfer of mesoscale eddy energy down to small dissipation scales. The energy loss from mesoscale eddies near the Xisha Islands is estimated to be sufficient to sustain turbulent kinetic energy dissipation rate of O (10−8) W/kg. This study suggests an alternative and potentially efficient mechanism to internal tides for the local maintenance of turbulent mixing in the SCS.

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Section: Introductionmentioning

confidence: 99%

Dissipation of mesoscale eddies and its contribution to mixing in the northern South China Sea

Yang

Nikurashin

Sasaki

et al. 2019

Sci Rep

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“…Shang et al . 22 found that MG parameterization can be applied in the upper ocean of the South China Sea.…”

Section: Introductionmentioning

confidence: 99%

Assessment of fine-scale parameterizations at low latitudes of the North Pacific

Liang

Shang

et al. 2018

Sci Rep

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Fine-scale parameterizations based on shear and stratification are widely used to study the intensity and spatial distribution of turbulent diapycnal mixing in the ocean. Two well-known fine-scale parameterizations, Gregg–Henyey–Polzin (GHP) parameterization and MacKinnon–Gregg (MG) parameterization, are assessed with the full-depth microstructure data obtained in the North Pacific. The GHP parameterization commonly used in the open ocean succeeds in reproducing the dissipation rates over smooth topography but fails to predict the turbulence over rough topography. Failure of GHP parameterization over rough topography is attributed to the deviation of internal wave spectrum from the Garrett–Munk (GM) spectrum. The internal wave field over rough topography is characterized by energetic intermediate-scale and small-scale internal waves that are not described well by the GM model. The MG parameterization that is widely used in coastal environments is found to be successful in reproducing the dissipation rates over both smooth and rough topographies. The efficacy of GHP and MG parameterizations in evaluating the dissipation rates has been assessed. The result indicates that MG parameterization predicts the magnitude and variability of the dissipation rates better than the GHP parameterization.

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“…The methods we use are the non-linear regression and iterative methods commonly used in the study of Natural science [17][18][19][20] . Compared with the Susceptible-Infected-Removed (SIR) and Susceptible-Exposed-Infectious-Recovered (SEIR) models used in epidemiology, our method is simple, accurate, and reliable.…”

Section: Resultsmentioning

confidence: 99%

A simple transmission dynamics model for predicting the evolution of COVID-19 under control measures in China

Shang

Yang

Chen

et al. 2020

Preprint

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Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modeling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91,253, and the maximum number of beds required for hospitalized patients would be 62,794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resourceallocation and planning in the health sector while providing a theoretical basis forgovernments to respond to future global health crises or epidemics.

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Spatial distribution of turbulent mixing in the upper ocean of the South China Sea

Cited by 30 publications

References 59 publications

Dissipation of mesoscale eddies and its contribution to mixing in the northern South China Sea

Dissipation of mesoscale eddies and its contribution to mixing in the northern South China Sea

Assessment of fine-scale parameterizations at low latitudes of the North Pacific

A simple transmission dynamics model for predicting the evolution of COVID-19 under control measures in China

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