Wavenumber Spectrum in the Gulf Stream from Shipboard ADCP Observations and Comparison with Altimetry Measurements

Wang, Dong Ping; Flagg, Charles N.; Donohue, Kathleen; Rossby, H. Thomas

doi:10.1175/2009jpo4330.1

Cited by 64 publications

(82 citation statements)

References 16 publications

(12 reference statements)

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“…That discussion (e.g. Stammer, 1997;Le Traon et al, 2008;Wang et al, 2010), however, has largely ignored the internal-wave field emphasized in earlier works (e.g. Katz, 1973).…”

Section: Theoretical Predictions For Submesoscale Wavenumber Spectramentioning

confidence: 97%

“…The potential energy spectra were instead reported to scale close to P k ∼ k −2 in both mixed layer and thermocline. Wang et al (2010) analyzed a long-term data set of mixed-layer velocities and temperatures in the Gulf Stream region, along a transect between New York Harbor and Bermuda. They found that the energy spectra fall off approximately like K k ∼ P k ∼ k −3 on scales between 250 km and 20 km, consistent with interior QG turbulence.…”

Section: Mixed-layer Flowsmentioning

confidence: 99%

“…To answer these questions, we analyze two in-situ data sets in two different dynamical regimes: the Oleander data set (Wang et al, 2010) in the Gulf Stream region and the Spice data set (Ferrari and Rudnick, 2000) in the eastern subtropical North Pacific ( Fig. 2.2).…”

Section: Introductionmentioning

confidence: 99%

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Submesoscale turbulence in the upper ocean

Callies¹

2016

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Submesoscale flows, current systems 1-100 km in horizontal extent, are increasingly coming into focus as an important component of upper-ocean dynamics. A range of processes have been proposed to energize submesoscale flows, but which process dominates in reality must be determined observationally. We diagnose from observed flow statistics that in the thermocline the dynamics in the submesoscale range transition from geostrophic turbulence at large scales to inertia-gravity waves at small scales, with the transition scale depending dramatically on geographic location. A similar transition is shown to occur in the atmosphere, suggesting intriguing similarities between atmospheric and oceanic dynamics. We furthermore diagnose from upper-ocean observations a seasonal cycle in submesoscale turbulence: fronts and currents are more energetic in the deep wintertime mixed layer than in the summertime seasonal thermocline. This seasonal cycle hints at the importance of baroclinic mixed layer instabilities in energizing submesoscale turbulence in winter. To better understand this energization, three aspects of the dynamics of baroclinic mixed layer instabilities are investigated. First, we formulate a quasigeostrophic model that describes the linear and nonlinear evolution of these instabilities. The simple model reproduces the observed wintertime distribution of energy across scales and depth, suggesting it captures the essence of how the submesoscale range is energized in winter. Second, we investigate how baroclinic instabilities are affected by convection, which is generated by atmospheric forcing and dominates the mixed layer dynamics at small scales. It is found that baroclinic instabilities are remarkably resilient to the presence of convection and develop even when rapid overturns keep the mixed layer unstratified. Third, we discuss the restratification induced by baroclinic mixed layer instabilities. We show that the rate of restratification depends on characteristics of the baroclinic eddies themselves, a dependence not captured by a previously proposed parameterization. These insights sharpen our understanding of submesoscale dynamics and can help focus future inquiry into whether and how submesoscale flows influence the ocean's role in climate.Thesis supervisor: Raffaele Ferrari Title: Breene M. Kerr Professor of Oceanography

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“…That discussion (e.g. Stammer, 1997;Le Traon et al, 2008;Wang et al, 2010), however, has largely ignored the internal-wave field emphasized in earlier works (e.g. Katz, 1973).…”

Section: Theoretical Predictions For Submesoscale Wavenumber Spectramentioning

confidence: 97%

Section: Mixed-layer Flowsmentioning

confidence: 99%

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Submesoscale turbulence in the upper ocean

Callies¹

2016

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“…Spectra are based on the Discrete Fourier Transform (DFT) determined from the Fast Fourier Transform (FFT) (see Sch16 or Wang et al [15], who also used the DFT to analyze TEX spectra). The FFT requires equally spaced, gap-free data, i.e., gaps, if they exist in the original series, must be filled and the data must be interpolated to equal spacing, if not already equally spaced, prior to applying the FFT algorithm.…”

Section: The Spectral Approachmentioning

confidence: 99%

Determining the Pixel-to-Pixel Uncertainty in Satellite-Derived SST Fields

Cornillon

Boussidi

et al. 2017

Remote Sensing

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Abstract:The primary measure of the quality of sea surface temperature (SST) fields obtained from satellite-borne infrared sensors has been the bias and variance of matchups with co-located in-situ values. Because such matchups tend to be widely separated, these bias and variance estimates are not necessarily a good measure of small scale (several pixels) gradients in these fields because one of the primary contributors to the uncertainty in satellite retrievals is atmospheric contamination, which tends to have large spatial scales compared with the pixel separation of infrared sensors. Hence, there is not a good measure to use in selecting SST fields appropriate for the study of submesoscale processes and, in particular, of processes associated with near-surface fronts, both of which have recently seen a rapid increase in interest. In this study, two methods are examined to address this problem, one based on spectra of the SST data and the other on their variograms. To evaluate the methods, instrument noise was estimated in Level-2 Visible-Infrared Imager-Radiometer Suite (VIIRS) and Advanced Very High Resolution Radiometer (AVHRR) SST fields of the Sargasso Sea. The two methods provided very nearly identical results for AVHRR: along-scan values of approximately 0.18 K for both day and night and along-track values of 0.21 K for day and night. By contrast, the instrument noise estimated for VIIRS varied by method, scan geometry and day-night. Specifically, daytime, along-scan (along-track), spectral estimates were found to be approximately 0.05 K (0.08 K) and the corresponding nighttime values of 0.02 K (0.03 K). Daytime estimates based on the variogram were found to be 0.08 K (0.10 K) with the corresponding nighttime values of 0.04 K (0.06 K). Taken together, AVHRR instrument noise is significantly larger than VIIRS instrument noise, along-track noise is larger than along-scan noise and daytime levels are higher than nighttime levels. Given the similarity of results and the less stringent preprocessing requirements, the variogram is the preferred method, although there is a suggestion that this approach overestimates the noise for high quality data in dynamically quiet regions. Finally, simulations of the impact of noise on the determination of SST gradients show that on average the gradient magnitude for typical ocean gradients will be accurately estimated with VIIRS but substantially overestimated with AVHRR.

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“…Although the constant interior PV assumption seems to be too strong [La- Is has also been argued that the SQG may be a better framework for the upper ocean balanced dynamics since the slope of (along-track) altimeter-derived SSH wavenumber spectra at mesoscales seem to be more consistent with the slope predicted by SQG turbulence theory [e.g., Le Traon et al, 2008] than with the slope predicted by the classical QG turbulence theory and previously used to rationalize SSH observations [e.g., Stammer, 1997]. However, altimeter-derived spectra slopes do not seem to match those few estimates from in-situ observations [Wang et al, 2010], likely owing to noise contamination in altimeter measurements even at mesoscales [e.g., Fu, 2011, 2012].…”

Section: Introductionmentioning

confidence: 84%

Energetics, baroclinic instability and models of vertical structure in the Brazil Current region (22S-28S)

Rocha¹

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Wavenumber Spectrum in the Gulf Stream from Shipboard ADCP Observations and Comparison with Altimetry Measurements

Cited by 64 publications

References 16 publications

Submesoscale turbulence in the upper ocean

Submesoscale turbulence in the upper ocean

Determining the Pixel-to-Pixel Uncertainty in Satellite-Derived SST Fields

Energetics, baroclinic instability and models of vertical structure in the Brazil Current region (22S-28S)

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