Data‐Driven Identification of Turbulent Oceanic Mixing From Observational Microstructure Data

Couchman, Miles; Wynne‐Cattanach, Bethan L.; Alford, Matthew H.; Caulfield, C. P.; Kerswell, Rich R.; MacKinnon, Jennifer A.; Voet, Gunnar

doi:10.1029/2021gl094978

Cited by 9 publications

(25 citation statements)

References 40 publications

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“…In agreement with the analysis of oceanographic data by Couchman et al. (2021), figure 2( d ) demonstrates that although the majority of the domain indeed appears to be well characterized by the canonical flux coefficient , significantly elevated is associated with the most extreme events in , events that are not reflected by a corresponding local increase in . Given the current relative sparsity of measurements within the ocean, mixing parametrizations may thus be biased towards the most commonly measured events, which are not necessarily the most significant.…”

Section: Discussionsupporting

confidence: 90%

“…Comparing the right panels of figures 2( e ) and 2( f ) reveals that is far more dominated by extreme events than , in agreement with the analysis of oceanographic data by Couchman et al. (2021). For instance, when each tail contains volume, the stable (blue) and unstable (red) tails each contain approximately of the total in the domain, but and of the total , respectively.…”

Section: Pointwise Statistics Conditioned On Local Density Gradientsupporting

confidence: 87%

“…By considering local correlations between direct ocean measurements of and , Couchman et al. (2021) further emphasized the importance of extreme events, finding that while the majority of the sampled domain was characterized by the canonical flux coefficient , isolated mixing events containing the largest were not reflected by a corresponding local increase in , yielding a dramatic increase in .…”

Section: Introductionmentioning

confidence: 99%

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Mixing across stable density interfaces in forced stratified turbulence

2023

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Understanding how turbulence enhances irreversible scalar mixing in density-stratified fluids is a central problem in geophysical fluid dynamics. While isotropic overturning regions are commonly the focus of mixing analyses, we here investigate whether significant mixing may arise in anisotropic statically stable regions of the flow. Focusing on a single forced direct numerical simulation of stratified turbulence, we analyse spatial correlations between the vertical density gradient $\partial \rho /\partial z$ and the dissipation rates of kinetic energy $\epsilon$ and scalar variance $\chi$ , the latter quantifying scalar mixing. The domain is characterized by relatively well-mixed density layers separated by sharp stable interfaces that are correlated with high vertical shear. While static instability is most prevalent within the mixed layers, much of the scalar mixing is localized to the intervening interfaces, a phenomenon not apparent if considering local static instability or $\epsilon$ alone. While the majority of the domain is characterized by the canonical flux coefficient $\varGamma \equiv \chi /\epsilon =0.2$ , often assumed in ocean mixing parametrizations, extreme values of $\chi$ within the statically stable interfaces, associated with elevated $\varGamma$ , strongly skew the bulk statistics. Our findings suggest that current parametrizations of turbulent mixing may be biased by undersampling, such that the most common, but not necessarily the most significant, mixing events are overweighted. Having focused here on a single simulation of stratified turbulence, it is hoped that our results motivate a broader investigation into the role played by stable density interfaces in mixing, across a wider range of parameters and forcing schemes representative of ocean turbulence.

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Section: Discussionsupporting

confidence: 90%

Section: Pointwise Statistics Conditioned On Local Density Gradientsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Mixing across stable density interfaces in forced stratified turbulence

2023

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“…A cutoff of T z > 1 × 10 −4 °C m −1 was used in the calculation of ϵ χ (Alford & Pinkel, 2000). While ϵ and ϵ χ may differ due to both physical or sampling-related reasons (Couchman et al, 2021;Gregg et al, 2018;Taylor et al, 2019) agreement between ϵ and ϵ χ computed from MMP data (not shown) was generally within a factor of two.…”

Section: Fastctdmentioning

confidence: 98%

Measurements of Turbulence Generated by Wake Eddies Near a Steep Headland

et al. 2022

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Currents impinging on islands, headlands and submarine ridges can lead to the generation of vortices and internal waves downstream (e.g., Chang et al., 2013;Edwards et al., 2004). These processes and the eventual transition to turbulence lead to the loss of kinetic energy and momentum in the wake and weaken the incident currents Wolanski et al., 1984). The vertical transport generated by mixing in the wake has been seen to increase chlorophyll concentrations and may also enhance biological productivity in the region (e.g., Gove et al., 2016;Heywood et al., 1990). It is therefore important to understand the turbulence and mixing generated in the lee of topographic features such as islands and headlands.This study was motivated by observations collected around Velasco Reef, a promontory at the northern end of the Pacific archipelago of Palau. The complex topography at this location, consisting of both a steep headland and a deep water ridge, leads to the formation of both wake eddies (MacKinnon et al., 2019) and lee waves (Voet et al., 2020). We are interested in understanding how these wake processes may head to turbulence downstream of the headland.Previous observations of turbulence at headlands and islands have often focused on measurements in the immediate vicinity of topographic obstacles to quantify the turbulent drag leading to flow separation (e.g., Edwards et al., 2004;MacKinnon et al., 2019). The few observational studies that have collected measurements of turbulent

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“…The wealth of data available from DNS may be readily utilised by machine learning methods, which have become a popular choice for reduced order modelling of fluid flows due to their inherent ability to capture complex spatio-temporal dynamics [2,12,14] and reveal insights into flow physics [5,3]. Recently, practical uncertainty estimates available from probabilistic neural networks have been gaining increasing popularity in geophysical and environmental applications [9,1].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows

Lewin¹,

Kops²,

Portwood³

et al. 2021

Preprint

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Motivated by oceanographic observational datasets, we propose a probabilistic neural network (PNN) model for calculating turbulent energy dissipation rates from vertical columns of velocity and density gradients in density stratified turbulent flows. We train and test the model on high-resolution simulations of decaying turbulence designed to emulate geophysical conditions similar to those found in the ocean. The PNN model outperforms a baseline theoretical model widely used to compute dissipation rates from oceanographic observations of vertical shear, being more robust in capturing the tails of the output distributions at multiple different time points during turbulent decay. A differential sensitivity analysis indicates that this improvement may be attributed to the ability of the network to capture additional underlying physics introduced by density gradients in the flow.

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Data‐Driven Identification of Turbulent Oceanic Mixing From Observational Microstructure Data

Cited by 9 publications

References 40 publications

Mixing across stable density interfaces in forced stratified turbulence

Mixing across stable density interfaces in forced stratified turbulence

Measurements of Turbulence Generated by Wake Eddies Near a Steep Headland

Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows

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