Abrupt Transitions in Submesoscale Structure in Southern Drake Passage: Glider Observations and Model Results

Viglione, Giuliana; Thompson, Andrew F.; Flexas, María del Mar; Sprintall, Janet; Swart, Sebastiaan

doi:10.1175/jpo-d-17-0192.1

Cited by 48 publications

(84 citation statements)

References 69 publications

(83 reference statements)

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“…Exceptions to these results will be found within strong current systems. Indeed, western boundary currents such as the Kuroshio and Gulf Stream (D'Asaro et al, ; Thomas et al, ), equatorial regions (Marchesiello et al, ; Holmes et al, ), and ACC of the Southern Ocean (Adams et al, ; du Plessis et al, ; Viglione et al, ) are associated with strong mean flows that generate ocean fronts, filaments, and eddies characterized by considerable lateral density gradients. In such cases, wind‐ and buoyancy‐forced SI will be particularly intense and the dissipation due to submesoscales, ϵ SM , might rival that of surface processes, ϵ SF .…”

Section: Discussion: Implications For Upper Ocean Energy Budgetsmentioning

confidence: 99%

The Contribution of Surface and Submesoscale Processes to Turbulence in the Open Ocean Surface Boundary Layer

Buckingham

Lucas

Belcher

et al. 2019

J Adv Model Earth Syst

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The ocean surface boundary layer is a critical interface across which momentum, heat, and trace gases are exchanged between the oceans and atmosphere. Surface processes (winds, waves, and buoyancy forcing) are known to contribute significantly to fluxes within this layer. Recently, studies have suggested that submesoscale processes, which occur at small scales (0.1-10 km, hours to days) and therefore are not yet represented in most ocean models, may play critical roles in these turbulent exchanges. While observational support for such phenomena has been demonstrated in the vicinity of strong current systems and littoral regions, relatively few observations exist in the open-ocean environment to warrant representation in Earth system models. We use novel observations and simulations to quantify the contributions of surface and submesoscale processes to turbulent kinetic energy (TKE) dissipation in the open-ocean surface boundary layer. Our observations are derived from moorings in the North Atlantic, December 2012 to April 2013, and are complemented by atmospheric reanalysis. We develop a conceptual framework for dissipation rates due to surface and submesoscale processes. Using this framework and comparing with observed dissipation rates, we find that surface processes dominate TKE dissipation. A parameterization for symmetric instability is consistent with this result. We next employ simulations from an ocean front-resolving model to reestablish that dissipation due to surface processes exceeds that of submesoscale processes by 1-2 orders of magnitude. Together, these results suggest submesoscale processes do not dramatically modify vertical TKE budgets, though such dynamics may be climatically important owing to their ability to remove energy from the ocean. Key Points:• We present a multimonth record of OSBL turbulence in the open ocean • The contribution of surface and submesoscale processes is examined • Dissipation rates due to surface processes dominate those of submesoscale processes Supporting Information:• Supporting Information S1 • Movie S1 Figure 1. (a) Surface and (b) submesoscale processes believed to be the dominant mechanisms for turbulence generation in the open-ocean environment. These are the expectations at the OSMOSIS observation site. (a) Winds drive waves and currents in the upper ocean. This creates turbulence through the effects of breaking waves, current shear, and Langmuir motions caused by the interaction of the Stokes shear with the background vorticity field.Similarly, buoyancy loss at the ocean surface reduces vertical stratification and permits upright convection (i.e., gravitational instability). (b) Stirring and straining by the mesoscale eddy field generates pronounced lateral gradients in density. Winds oriented downfront (i.e., in the direction of the geostrophic shear at the front) transport dense water into areas of less dense (more buoyant) waters. Termed the Ekman buoyancy flux, B e , this flux reduces vertical stratification and admits symmetric instability (SI) within t...

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Section: Discussion: Implications For Upper Ocean Energy Budgetsmentioning

confidence: 99%

The Contribution of Surface and Submesoscale Processes to Turbulence in the Open Ocean Surface Boundary Layer

Buckingham

Lucas

Belcher

et al. 2019

J Adv Model Earth Syst

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“…(), with N 2 = ∂b / ∂z as the vertical stratification and

ζ = f + \nabla \times u_{g} \cdot \overset{k}{true}

as the vertical component of the absolute vorticity of the geostrophic flow. This approach has been used by previous studies to assess the susceptibility of the flow to submesoscale instabilities (e.g., Naveira Garabato et al., ; Ramachandran et al., ; Thompson et al., ; Viglione et al., ). The instability criterion for GI, SI, and CI may be expressed as the following: (i)For unstable vertical stratification (i.e., N 2 < 0), GI is expected to dominate when −180°

< ϕ_{R i_{B}} < -

135°, and a hybrid GI/SI will occur when −135°

< ϕ_{R i_{B}} < -

90° . (ii)For stable stratification (i.e., N 2 > 0) and cyclonic vertical vorticity, SI is predicted to develop when −90°

< ϕ_{R i_{B}} < ϕ_{C}

, with ϕ C <−45°. (iii)For stable stratification (i.e., N 2 > 0) and anticyclonic vertical vorticity, SI is expected for −90°

< ϕ_{R i_{B}} < -

45° with ϕ C >−45°, and a hybrid SI/CI is predicted when −45°

< ϕ_{R i_{B}} < ϕ_{C}

. …”

Section: Methodsmentioning

confidence: 99%

“…as the vertical component of the absolute vorticity of the geostrophic flow. This approach has been used by previous studies to assess the susceptibility of the flow to submesoscale instabilities (e.g., Naveira Ramachandran et al, 2018;Thompson et al, 2016;Viglione et al, 2018). The instability criterion for GI, SI, and CI may be expressed as the following:…”

Section: Categorizing Instability Typesmentioning

confidence: 99%

Wind‐Forced Symmetric Instability at a Transient Mid‐Ocean Front

Garabato

Martin

et al. 2019

Geophysical Research Letters

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Mooring and glider observations and a high‐resolution satellite sea surface temperature image reveal features of a transient submesoscale front in a typical mid‐ocean region of the Northeast Atlantic. Analysis of the observations suggests that the front is forced by downfront winds and undergoes symmetric instability, resulting in elevated upper‐ocean kinetic energy, restratification, and turbulent dissipation. The instability is triggered as downfront winds act on weak upper‐ocean vertical stratification and strong lateral stratification produced by mesoscale frontogenesis. The instability's estimated rate of kinetic energy extraction from the front accounts for the difference between the measured rate of turbulent dissipation and the predicted contribution from one‐dimensional scalings of buoyancy‐ and wind‐driven turbulence, indicating that the instability underpins the enhanced dissipation. These results provide direct evidence of the occurrence of symmetric instability in a quiescent open‐ocean environment and highlight the need to represent the instability's restratification and dissipative effects in climate‐scale ocean models.

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“…Two-dimensional interpolation is perhaps a better approach when, for example, comparing gradients over shorter time-scales. Past studies have used a MATLAB implementation of objective mapping (objmap in the MATLAB toolbox 7 , Thompson et al, 2016;Todd et al, 2016;Viglione et al, 2018;du Plessis et al, 2019). GliderTools offers a Pythonic implementation of the MATLAB objective interpolation function (gt.interp_obj).…”

Section: Vertical Binning and Two-dimensional Interpolationmentioning

confidence: 99%

“…For the calculation of vertical and horizontal (temporal and spatial) gradients of physical properties in the ocean, GliderTools performs optimal interpolation/objective mapping to the raw data, providing data gridded to a monotonically increasing grid. This approach is particularly useful for studies investigating the evolution of submeso-to mesoscale processes (Thompson et al, 2016;Todd et al, 2016;Viglione et al, 2018;du Plessis et al, 2019) as it negates the bias in the calculation which may arise due to non-uniform horizontal gridding. Note this does not remove the challenge of distinguishing variations in the given property as spatial or temporal, or some combination of these, but provides the user with robust method of along-track interpolation which is currently only available in a MATLAB implementation.…”

Section: Recommendations For Processing and Data Managementmentioning

confidence: 99%