Wave–turbulence interaction-induced vertical mixing and its effects in ocean and climate models

Qiao, Fangli; Yuan, Yue; Deng, Jia; Dai, Dejun; Song, Zhenya

doi:10.1098/rsta.2015.0201

Cited by 76 publications

(83 citation statements)

References 46 publications

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“…Field data have revealed that this kind of interaction is phase‐locking, i.e., the wave‐turbulence interaction happens in the trough of surface wave [ Qiao et al . ]. In (5),

α

is constant and set to 1.0,

E true(\vec{k} true)

represents the wave number spectrum and can be calculated from a numerical wave model,

ω

is wave angular frequency,

k

is wave number, and

z

is depth.…”

Section: The Physical Processes We Explorementioning

confidence: 99%

Sensitivity of typhoon modeling to surface waves and rainfall

et al. 2017

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Improving intensity simulation and forecast of tropical cyclones has always been a challenge, although in recent years the track forecasts have been remarkably improved. In this study, we explore the sensitivity of typhoon simulation to three physical processes using a fully coupled atmosphere‐ocean‐wave model. Two storms, a strong and a weak one, have been chosen. The effects of wave breaking induced sea spray, ocean vertical mixing associated with nonbreaking surface waves, and sea surface cooling due to intense rainfall are assessed by means of a set of numerical experiments. The results show and confirm that sea spray leads to an increase of typhoon intensity by enhancing the air‐sea heat flux, while nonbreaking wave‐induced vertical mixing and rainfall lead to a decrease. Each process can be relevant, depending on wind and wave conditions. These can vary dramatically when typhoons interact with not sufficiently well‐defined coastal areas, typically an archipelago. Compared with the control runs, when all the three physical processes are considered, the (absolute) difference between the modeled sea level pressure and best track data is reduced from 26.05 to 0.70 hPa for typhoon Haiyan, and from −9.42 to −8.67 hPa for typhoon Jebi. We have found a steady overestimate of the dimensions of the typhoons. We have verified an extreme sensitivity to the initial conditions, especially when small differences in the typhoon track may imply different relevance of the physical processes, like the ones we have considered, governing the evolution of the storm.

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“…Field data have revealed that this kind of interaction is phase‐locking, i.e., the wave‐turbulence interaction happens in the trough of surface wave [ Qiao et al . ]. In (5),

α

is constant and set to 1.0,

E true(\vec{k} true)

represents the wave number spectrum and can be calculated from a numerical wave model,

ω

is wave angular frequency,

k

is wave number, and

z

is depth.…”

Section: The Physical Processes We Explorementioning

confidence: 99%

Sensitivity of typhoon modeling to surface waves and rainfall

et al. 2017

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“…This method is a general data‐driven method without any a priori basis functions, and the signal is decomposed based on the local characteristics of the data. It has been successfully applied to the eddy‐covariance analysis of air–sea interface turbulent fluxes (Wang et al ), ADV data processing (Qiao et al ), and geophysical studies (Huang and Wu )…”

Section: Materials and Proceduresmentioning

confidence: 99%

“…Qiao et al () showed that the EMD can effectively remove the noise in the ADV data, in which the ADV vertical velocity was decomposed into 12 IMFs, and components 1 and 2 were treated as noise because they have characteristics of white noise. In our ADV datasets, the first few components of IMFs are also associated with Doppler noise and high‐frequency contamination.…”

Section: Materials and Proceduresmentioning

confidence: 99%

“…Lumley and Terray () proposed that the advection velocity U should be replaced by u w in the frequency‐wavenumber transformation when the former is smaller than the latter. The EMD can be used to separate approximately the wave motions from the turbulence (Qiao et al ). As a whole, U is larger than or close to u w at the sampling depth during these bursts (Fig.…”

Section: Assessmentmentioning

confidence: 99%

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Calculation of turbulent dissipation rate with acoustic Doppler velocimeter

Huang

Guo

et al. 2018

Limnology & Ocean Methods

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Turbulent dissipation rates are calculated from an acoustic Doppler velocimeter by fitting the measured wavenumber spectrum to a universal turbulence spectrum. A combination of the Butterworth filter and empirical model decomposition is employed to reduce Doppler noise and high‐frequency fluctuations. Different from the classical inertial subrange dissipation method fitting to the Kolmogorov − 5/3 slope, we propose the method here which can make longer available spectrum bands. We analyzed 408 bursts with turbulent dissipation rates ranging from 10−8 to 10−5 W kg−1 as measured in the coastal ocean of the South China Sea and found for all of these bursts, features of the clean spectrum can be resolved to the dissipation range of turbulence, in which about 15% bursts can be resolved to the Kolmogorov wavenumber, and 51% to 1/2 Kolmogorov wavenumber. Comparisons of this method with the inertial subrange method indicated that its estimated turbulent dissipation rates were somewhat smaller than those from the inertial subrange method in most of the bursts. Their ratios had a mean value of 0.77, which is typical for oceanic turbulence measurements.

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“…The authors for the articles in this theme issue stem from many different fields, including applied mathematics (with articles on new theoretical developments by Cicone et al [1]; Daubechies et al [2]; Hou & Shi [3]; Joliffe & Cadima [4]; and Mallat [5]), physics (Meignen et al [6]), physiology (Yeh et al [7]), general science and data analysis (Huang et al [8]), meteorology and climate (Qiao et al [9] and Wu et al [10]) and biomedical research (Hemakom et al [11]). …”

mentioning

confidence: 99%