Diapycnal advection by double diffusion and turbulence in the ocean

Laurent, Louis C. St.

doi:10.1575/1912/4697

Cited by 7 publications

(3 citation statements)

References 61 publications

(83 reference statements)

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“…Wave generation is strongly dependent on the horizontal and vertical scales inherent in the bottom topography. Using the classical formulation for lee waves [e.g., Cushman ‐ Roisin , 1994], St. Laurent [1999] argued that horizontal scales ranging from 0.1 to 2.5 km generate internal waves when forced by steady flows. Features typical of abyssal hill morphology (e.g., 50 m height over 1 km horizontal scale) will generate a significant vertical internal wave energy flux.…”

Section: Introductionmentioning

confidence: 99%

Global prediction of abyssal hill root‐mean‐square heights from small‐scale altimetric gravity variability

Goff

2010

J. Geophys. Res.

125

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[1] Abyssal hills, which are pervasive landforms on the seafloor of the Earth's oceans, represent a potential tectonic record of the history of mid-ocean ridge spreading. However, the most detailed global maps of the seafloor, derived from the satellite altimetry-based gravity field, cannot be used to deterministically characterize such small-scale (<10 km) morphology. Nevertheless, the small-scale variability of the gravity field can be related to the statistical properties of abyssal hill morphology using the upward continuation formulation. In this paper, I construct a global prediction of abyssal hill root-mean-square (rms) heights from the small-scale variability of the altimetric gravity field. The abyssal hill-related component of the gravity field is derived by first masking distinct features, such as seamounts, mid-ocean ridges, and continental margins, and then applying a newly designed adaptive directional filter algorithm to remove fracture zone/discontinuity fabric. A noise field is derived empirically by correlating the rms variability of the small-scale gravity field to the altimetric noise field in regions of very low relief, and the noise variance is subtracted from the small-scale gravity variance. Suites of synthetically derived, abyssal hill formed gravity fields are generated as a function of water depth, basement rms heights, and sediment thickness and used to predict abyssal hill seafloor rms heights from corrected small-scale gravity rms height. The resulting global prediction of abyssal hill rms heights is validated qualitatively by comparing against expected variations in abyssal hill morphology and quantitatively by comparing against actual measurements of rms heights. Although there is scatter, the prediction appears unbiased.Citation: Goff, J. A. (2010), Global prediction of abyssal hill root-mean-square heights from small-scale altimetric gravity variability,

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Section: Introductionmentioning

confidence: 99%

Global prediction of abyssal hill root‐mean‐square heights from small‐scale altimetric gravity variability

Goff

2010

J. Geophys. Res.

125

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“…Dynamically, vertical gradients of the diapycnal diffusivity dictate the strength of diapycnal advection w via the buoyancy equation [ St. Laurent , 1999]:

w \approx - N^{- 2} \partial_{z} J_{b} = \partial_{z} K_{ρ} + \frac{K_{ρ}}{N^{2}} \partial_{z} N^{2}

where J b = − K ρ N 2 is the buoyancy flux. In turn, the vertical gradient of diapycnal advection influences the geostrophic vorticity balance.…”

Section: Comparison Of Jsl01 and Rdm To Microstructure Surveysmentioning

confidence: 62%

Spatially heterogeneous diapycnal mixing in the abyssal ocean: A comparison of two parameterizations to observations

Decloedt

Luther

2012

J. Geophys. Res.

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[1] The spatial distributions of the diapycnal diffusivity predicted by two abyssal mixing schemes are compared to each other and to observational estimates based on microstructure surveys and large-scale hydrographic inversions. The parameterizations considered are the tidal mixing scheme by Jayne, St. Laurent and co-authors (JSL01) and the Roughness Diffusivity Model (RDM) by Decloedt and Luther. Comparison to microstructure surveys shows that both parameterizations are conservative in estimating the vertical extent to which bottom-intensified mixing penetrates into the stratified water column. In particular, the JSL01 exponential vertical structure function with fixed scale height decays to background values much nearer topography than observed. JSL01 and RDM yield dramatically different horizontal spatial distributions of diapycnal diffusivity, which would lead to quite different circulations in OGCMs, yet they produce similar basin-averaged diffusivity profiles. Both parameterizations are shown to yield smaller basin-mean diffusivity profiles than hydrographic inverse estimates for the major ocean basins, by factors ranging from 3 up to over an order of magnitude. The canonical 10 À4 m 2 s À1 abyssal diffusivity is reached by the parameterizations only at depths below 3 km. Power consumption by diapycnal mixing below 1 km of depth, between roughly 32 S and 48 N, for the RDM and JSL01 parameterizations is 0.40 TW & 0.28 TW, respectively. The results presented here suggest that present-day mixing parameterizations significantly underestimate abyssal mixing. In conjunction with other recently published studies, a plausible interpretation is that parameterizing the dissipation of bottom-generated internal waves is not sufficient to approximate the global spatial distribution of diapycnal mixing in the abyssal ocean.Citation: Decloedt, T., and D. S. Luther (2012), Spatially heterogeneous diapycnal mixing in the abyssal ocean: A comparison of two parameterizations to observations,

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“…The initial motivation for studying the SSH noise process stems primarily from ongoing efforts [ Goff and Smith , 2003; Goff et al , 2004] to infer the roughness properties of the seafloor from the statistical fabric of the altimetric gravity field. Small‐scale seafloor roughness is a critical factor in mesoscale ocean mixing [ Polzin et al , 1997; St. Laurent and Thurnherr , 2007], tidal dissipation [ Egbert and Ray , 2003] the formation of internal waves in the deep ocean [ St. Laurent , 1999]. However, global ocean bathymetric data sets, including the predicted bathymetry from satellite altimetry data, are largely deficient at the smaller scales (∼1 km) needed to model these oceanographic processes [ Smith and Sandwell , 1997].…”

Section: Introductionmentioning

confidence: 99%

Statistical characterization of Geosat altimetry noise: Dependence on environmental parameters

Goff

2009

Geochem Geophys Geosyst

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[1] Sea surface height (SSH) measurements from the Geosat satellite altimetry mission are analyzed to statistically characterize their noise process, which is defined here as the small-scale (<50 km), timevarying component of the altimetry data. This analysis is one component of a larger effort to extract meaningful information about seafloor roughness from the small-scale fabric of the altimetric gravity field. Both small-scale seafloor topography (e.g., abyssal hills) and SSH noise significantly influence the measured gravity fabric. Hence, a global, quantitative understanding of SSH noise is required to infer what portion of the gravity fabric is associated with the true geoid. It is also anticipated that the SSH noise analysis will prove useful in designing improvements to altimetry processing algorithms. The SSH data are first processed by differencing nearest-neighbor tracks, to minimize the static component of variability, and then by high-pass filtering, to remove variability associated with mesoscale oceanic circulation. Autocovariance analysis reveals that SSH noise can be decomposed into uncorrelated (white noise) and correlated, semiperiodic components. Three parameters are measured which characterize the variance of both components and the correlation length of the correlated component. The global distribution of uncorrelated noise variance is closely correlated to significant wave heights, directly linking the uncertainty of the altimeter return time to the roughness of the sea surface. The variance and correlation length of the correlated component of noise can be associated with three separate environmental parameters: sea ice, precipitation rates, and upper atmospheric variability associated with the subtropical jet stream. As widely differing as these environmental processes are, it is hypothesized that features like sea ice, rain storms, and atmospheric turbulence can cause sudden perturbations in the return time of the altimetry signal, creating decaying oscillations associated with the onboard tracking algorithms.

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Diapycnal advection by double diffusion and turbulence in the ocean

Cited by 7 publications

References 61 publications

Global prediction of abyssal hill root‐mean‐square heights from small‐scale altimetric gravity variability

Global prediction of abyssal hill root‐mean‐square heights from small‐scale altimetric gravity variability

Spatially heterogeneous diapycnal mixing in the abyssal ocean: A comparison of two parameterizations to observations

Statistical characterization of Geosat altimetry noise: Dependence on environmental parameters

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