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Jones, Ian; Volkov, Yu. A.; Toba, Yoshiaki; Larsen, Søren Ejling; Huang, Norden E.

doi:10.1017/cbo9780511552076.002

Cited by 7 publications

(12 citation statements)

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“…The z 0 complexity hinders the straightforward modeling of the MASL as a wall‐bounded shear flow (Prandtl, ):

U false/ u * = \frac{1}{κ} \log ((), \frac{u_{*} z}{ν}) + C,

where κ is Von Kármán's constant, C is an integration constant, z is height into the constant flux layer, ν is kinematic viscosity, and u * is the shear velocity (

τ \equiv ρ u_{*}^{2}

). This rare, analytical fluid mechanics solution (Bradshaw & Huang, ) presumes the flow and surface are in statistical equilibrium (Hultmark et al, ; Jones et al, ). Over the ocean, the stress‐relevant roughness ( z 0 ∝ ν / u * ) is primarily carried by gravity capillary waves (Hwang, ; Laxague et al, ), which have short relaxation times relative to local forcing changes (Zhang et al, ).…”

Section: Introductionmentioning

confidence: 99%

Interactions Between Nonlinear Internal Ocean Waves and the Atmosphere

Ortiz‐Suslow

Wang

Kalogiros

et al. 2019

Geophysical Research Letters

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The heterogeneity in surface roughness caused by transient, nonlinear internal ocean waves is readily observed in coastal waters. However, the quantifiable impact this heterogeneity has on the marine atmospheric surface layer has not been documented. A comprehensive data set collected from a unique ocean platform provided a novel opportunity to investigate the interaction between this internal ocean process and the atmosphere. Relative to the background atmospheric flow, the presence of internal waves drove wind velocity and stress variance. Furthermore, it is shown that the wind gradient adjusts across individual wave fronts, setting up localized shear that enhanced the air‐sea momentum flux over the internal wave packet. This process was largely mechanical, though secondary impacts on the bulk humidity variance and gradient were observed. This study provides the first quantitative analysis of this phenomenon and provides insights into submesoscale air‐sea interactions over a transient, internal ocean feature.

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“…The z 0 complexity hinders the straightforward modeling of the MASL as a wall‐bounded shear flow (Prandtl, ):

U false/ u * = \frac{1}{κ} \log ((), \frac{u_{*} z}{ν}) + C,

where κ is Von Kármán's constant, C is an integration constant, z is height into the constant flux layer, ν is kinematic viscosity, and u * is the shear velocity (

τ \equiv ρ u_{*}^{2}

Section: Introductionmentioning

confidence: 99%

Interactions Between Nonlinear Internal Ocean Waves and the Atmosphere

Ortiz‐Suslow

Wang

Kalogiros

et al. 2019

Geophysical Research Letters

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“…Jones and Toba (2001) and Donelan et al. (2012) assumed Re = 0.11 for the near‐surface laminar air flow.…”

Section: Methodsmentioning

confidence: 99%

“…The total wind stress consists of two components: the form stress τ f caused by pressure difference between the windward and leeward sides of the wave and the viscous (skin) stress τ v caused by the drag in the viscous sublayer (Donelan et al., 2012; Jones & Toba, 2001; Makin et al., 1995; Monin & Yaglom, 1971). As the aerodynamic sheltering is specifically a consequence of the wave form, τ v must be subtracted from the measured stress such that

{\tau }_{f}=\tau -{\tau }_{v}={\rho }_{a}\sqrt{{\left(-\overline{{u}^{\prime }{w}^{\prime }}\right)}^{2}+{\left(-\overline{{v}^{\prime }{w}^{\prime }}\right)}^{2}}-{\tau }_{v}

…”

Section: Methodsmentioning

confidence: 99%

“…To obtain τ v , we assume the smooth law of wall with a pure laminar flow immediately adjacent to the water surface where (Donelan et al., 2012; Jones & Toba, 2001)

U(z)=\frac{{u}_{\ast \nu }}{\kappa }\mathrm{ln}\frac{z}{{z}_{0\nu }}

where u * ν and z 0 ν are the viscous friction velocity and roughness lengths, respectively, and are related through the viscous Reynolds number and the kinematic viscosity ν :

\mathrm{R}\mathrm{e}=\frac{{z}_{0\nu }{u}_{\ast \nu }}{\nu }

…”

Section: Methodsmentioning

confidence: 99%

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Laboratory Wave and Stress Measurements Quantify the Aerodynamic Sheltering in Extreme Winds

et al. 2023

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Wave growth is governed to first-order by momentum flux from the wind, which in turn feeds back to affect the exchange of momentum from wind to the ocean surface and subsurface (Grare et al., 2013). Under wind forcing, smaller waves on the leeward side of larger waves are sheltered from the wind. Better understanding and quantifying the role of sheltering on wave growth under wind forcing remains elusive. The sensitivity of sheltering to different background waves, especially under extreme wind forcing, is also not well understood. This is important because tropical cyclones are known to have complex wind-wave states in different quadrants of the storm (Wright et al., 2001).One of the most popular theories that incorporate sheltering is the Jeffreys's sheltering (JS) hypothesis (Jeffreys, 1924(Jeffreys, , 1925. The JS hypothesis is based on the geometric form of the wavy surface: when wind blows over the water surface, the waves induce a flow separation on the leeward side of the wave and the air flow detaches from the wave surface and creates a low-pressure area. Thus, the lee of wave is sheltered by the windward side of the wave.Two wave growth theories alternative to JS were proposed by Phillips (1957) andMiles (1959). Phillips (1957) argued that the initial wave growth relies on a resonance between surface waves and the pressure fluctuations in the wind. The wind has a turbulent component-composed of an ensemble of eddies-as it blows over the water surface. The pressure disturbances associated with those eddies generate ripples on the water surface. As waves grow higher, the resonance plays a lesser role in wave growth. Thus, Philips theory is relevant only for

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“…The zonal and meridional surface vector wind stresses are calculated from the surface vector wind data, as

τ = ρ_{a} C_{D} | U_{10} | {bold-italicU}_{10}

(Smith, 1988; Trenberth et al., 1990) with

ρ_{a}

is the air density,

{bold-italicU}_{10}

is the wind speed at 10 m, and

C_{D}

is the drag coefficient (Jones & Toba, 2001). The SLA, SST, and surface wind data contain the same grid information.…”

Section: Methodsmentioning

confidence: 99%

Reconstruction of Three‐Dimensional Temperature and Salinity Fields From Satellite Observations

et al. 2021

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Observation of the ocean is crucial to the studies of ocean dynamics, climate change, and biogeochemical cycle. However, current oceanic observations are patently insufficient, because the in situ observations are of difficulty and high cost while the satellite remote‐sensed measurements are mainly the sea surface data. To make up for the shortage of ocean interior data and make full use of the abundant satellite data, here we develop a data‐driven deep learning model to estimate ocean subsurface and interior variables from satellite‐observed sea surface data. Exclusively and simply using satellite data, three‐dimensional ocean temperature and salinity fields are successfully reconstructed, which are at 26 level depths from 0 to 2,000 m. We further design a scheme to increase the horizontal resolution from 1° to 1/4°, which is higher than the Argo gridded data. Estimations from our model are accurate, reliable, and stable for a wide range of research areas and periods. Dynamic height fields that are derived from the estimated temperature and salinity, as well as the associated ocean geostrophic flows, are also calculated and analyzed, which indicates the potentials of our model for reconstructing the ocean circulation fields as well. This study enriches oceanic observations with respect to vertical dimension and horizontal resolution, which can largely make up for the paucity of the subsurface and deep ocean observation, both before and during Argo era. This work also provides some new foundations for and insights into geoscience and climate change fields.

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Overview

Cited by 7 publications

References 0 publications

Interactions Between Nonlinear Internal Ocean Waves and the Atmosphere

Interactions Between Nonlinear Internal Ocean Waves and the Atmosphere

Laboratory Wave and Stress Measurements Quantify the Aerodynamic Sheltering in Extreme Winds

Reconstruction of Three‐Dimensional Temperature and Salinity Fields From Satellite Observations

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