• Abernathey, R. P., I. Cerovecki, P. R. Holland, E. Newsom, M. Mazloff, and L. D. Talley, 2016: Water-mass transformation by sea ice in the upper branch of the Southern Ocean overturning. Nat. Geosci., 9, 596601, https://doi.org/10.1038/ngeo2749.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, R., J.-J. Wang, M. Sapiano, G. Huffman, D. Bolvin, E. Nelkin, and NOAA CDR Program, 2017: Global Precipitation Climatology Project (GPCP) Climate Data Record (CDR), Version 1.3 (Daily). NOAA National Centers for Environmental Information, accessed 10 February 2019, https://doi.org/10.7289/V5RX998Z.

    • Search Google Scholar
    • Export Citation
  • Agrawal, Y. C., E. A. Terray, M. A. Donelan, P. A. Hwang, A. J. Williams, W. M. Drennan, K. K. Kahma, and S. A. Krtaigorodskii, 1992: Enhanced dissipation of kinetic energy beneath surface waves. Nature, 359, 219220, https://doi.org/10.1038/359219a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Belcher, S. E., and Coauthors, 2012: A global perspective on Langmuir turbulence in the ocean surface boundary layer. Geophys. Res. Lett., 39, L18605, https://doi.org/10.1029/2012GL052932.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clayson, C. A., J. Brown, and NOAA CDR Program, 2016a: NOAA Climate Data Record (CDR) of sea surface temperature - WHOI, Version 2. NOAA National Climatic Data Center, accessed 20 March 2020, https://doi.org/10.7289/V5FB510W.

    • Search Google Scholar
    • Export Citation
  • Clayson, C. A., J., Brown, and NOAA CDR Program, 2016b: NOAA Climate Data Record (CDR) of ocean near surface atmospheric properties, version 2. NOAA National Center for Environmental Information, accessed 20 March 2020, https://doi.org/10.7289/V55T3HH0.

    • Search Google Scholar
    • Export Citation
  • Clayson, C. A., J., Brown, and NOAA CDR Program, 2016c: NOAA Climate Data Record (CDR) of ocean heat fluxes, version 2. NOAA National Center for Environmental Information, accessed 20 March 2020, https://doi.org/10.7289/V59K4885.

    • Search Google Scholar
    • Export Citation
  • Craig, P. D., and M. L. Banner, 1994: Modeling wave-enhanced turbulence in the ocean surface layer. J. Phys. Oceanogr., 24, 25462559, https://doi.org/10.1175/1520-0485(1994)024<2546:MWETIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Craik, A. D., 1988: Wave Interactions and Fluid Flows. Cambridge University Press, 322 pp.

  • D’Asaro, E. A., 2014: Turbulence in the upper-ocean mixed layer. Annu. Rev. Mar. Sci., 6, 101115, https://doi.org/10.1146/annurev-marine-010213-135138.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • D’Asaro, E. A., J. Thomson, A. Y. Shcherbina, R. R. Harcourt, M. F. Cronin, M. A. Hemer, and B. Fox-Kemper, 2014: Quantifying upper ocean turbulence driven by surface waves. Geophys. Res. Lett., 41, 102107, https://doi.org/10.1002/2013GL058193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Danabasoglu, G., S. C. Bates, B. P. Briegleb, S. R. Jayne, M. Jochum, W. G. Large, S. Peacock, and S. G. Yeager, 2012: The CCSM4 Ocean Component. J. Climate, 25, 13611389, https://doi.org/10.1175/JCLI-D-11-00091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derakhti, M., J. Thomson, and J. T. Kirby, 2020: Sparse sampling of intermittent turbulence generated by breaking surface waves. J. Phys. Oceanogr., 50, 867885, https://doi.org/10.1175/JPO-D-19-0138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dohan, K., and R. E. Davis, 2011: Mixing in the transition layer during two storm events. J. Phys. Oceanogr., 41, 4266, https://doi.org/10.1175/2010JPO4253.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, S., J. Sprintall, S. T. Gille, and L. Talley, 2008: Southern Ocean mixed-layer depth from Argo float profiles. J. Geophys. Res., 113, C06013, https://doi.org/10.1029/2006JC004051.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du Plessis, M., S. Swart, I. J. Ansorge, A. Mahadevan, and A. F. Thompson, 2019: Southern Ocean seasonal restratification delayed by submesoscale wind–front interactions. J. Phys. Oceanogr., 49, 10351053, https://doi.org/10.1175/JPO-D-18-0136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Edson, J. B., and Coauthors, 2013: On the exchange of momentum over the open ocean. J. Phys. Oceanogr., 43, 15891610, https://doi.org/10.1175/JPO-D-12-0173.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fer, I., A. K. Peterson, and J. E. Ullgren, 2014: Microstructure measurements from an underwater glider in the turbulent Faroe bank channel overflow. J. Atmos. Oceanic Technol., 31, 11281150, https://doi.org/10.1175/JTECH-D-13-00221.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferris, L., 2022: Across-scale energy transfer in the Southern Ocean. Ph.D. dissertation, Dept. of Physical Sciences, Virginia Institute of Marine Science, William and Mary, 190 pp.

    • Search Google Scholar
    • Export Citation
  • Ferris, L., D. Gong, T. Ijichi, S. T. Merrifield, J. Shapiro, and L. St. Laurent, 2020: Turbulent Dissipation Rate and Mixing Variations in the Polar Front of the Southern Ocean. Ocean Sciences Meeting 2020, San Diego, CA, Amer. Geophys. Union, PS51A-04, https://agu.confex.com/agu/osm20/meetingapp.cgi/Paper/648069.

    • Search Google Scholar
    • Export Citation
  • Fox-Kemper, B., L. Johnson, and F. Qiao, 2022: Ocean near-surface layers. Ocean Mixing: Drivers, Mechanisms and Impacts, M. Meredith and A. N. Garabato, Eds., Elsevier, 6594, https://doi.org/10.1016/B978-0-12-821512-8.00011-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gemmrich, J. R., T. D. Mudge, and V. D. Polonichko, 1994: On the energy input from wind to surface waves. J. Phys. Oceanogr., 24, 24132417, https://doi.org/10.1175/1520-0485(1994)024<2413:OTEIFW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerbi, G. P., J. H. Trowbridge, E. A. Terray, A. J. Plueddemann, and T. Kukulka, 2009: Observations of turbulence in the ocean surface boundary layer: Energetics and transport. J. Phys. Oceanogr., 39, 10771096, https://doi.org/10.1175/2008JPO4044.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hwang, P. A., 2009: Estimating the effective energy transfer velocity at air-sea interface. J. Geophys. Res., 114, C11011, https://doi.org/10.1029/2009JC005497.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kantha, L. H., and C. A. Clayson, 2004: On the effect of surface gravity waves on mixing in the oceanic mixed layer. Ocean Modell., 6, 101124, https://doi.org/10.1016/S1463-5003(02)00062-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Large, W. G., J. C. McWilliams, and S. C. Doney, 1994: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization. Rev. Geophys., 32, 363403, https://doi.org/10.1029/94RG01872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Large, W. G., E. G. Patton, A. K. DuVivier, P. P. Sullivan, and L. Romero, 2019: Similarity theory in the surface layer of large-eddy simulations of the wind-, wave-, and buoyancy-forced Southern Ocean. J. Phys. Oceanogr., 49, 21652187, https://doi.org/10.1175/JPO-D-18-0066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Q., and Coauthors, 2019: Comparing ocean surface boundary vertical mixing schemes including Langmuir turbulence. J. Adv. Model., 11, 35453592, https://doi.org/10.1029/2019MS001810.

    • Search Google Scholar
    • Export Citation
  • Lombardo, C. P., and M. C. Gregg, 1989: Similarity scaling of viscous and thermal dissipation in a convecting surface boundary layer. J. Geophys. Res., 94, 62736284, https://doi.org/10.1029/JC094iC05p06273.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacKinnon, J., L. St. Laurent, and A. C. Naveira Garabato, 2013: Diapycnal mixing processes in the ocean interior. Ocean Circulation and Climate: A 21st Century Perspective, 2nd ed. G. Siedler et al. Eds., Academic Press, 159184.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McWilliams, J. C., P. P. Sullivan, and C. H. Moeng, 1997: Langmuir turbulence in the ocean. J. Fluid Mech., 334, 130, https://doi.org/10.1017/S0022112096004375.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McWilliams, J. C., E. Huckle, J. Liang, and P. P. Sullivan, 2014: Langmuir turbulence in swell. J. Phys. Oceanogr., 44, 870890, https://doi.org/10.1175/JPO-D-13-0122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merckelbach, L., A. Berger, G. Krahmann, M. Dengler, and J. R. Carpenter, 2019: A dynamic flight model for Slocum gliders and implications for turbulence microstructure measurements. J. Atmos. Oceanic Technol., 36, 281296, https://doi.org/10.1175/JTECH-D-18-0168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merrifield, S. T., 2016: Mechanisms for enhanced turbulence in the Drake Passage region of the Southern Ocean. Ph.D. dissertation, Massachusetts Institute of Technology, 134 pp.

    • Search Google Scholar
    • Export Citation
  • Merrifield, S. T., L. S. Laurent, B. Owens, A. M. Thurnherr, and J. M. Toole, 2016: Enhanced diapycnal diffusivity in intrusive regions of the Drake Passage. J. Phys. Oceanogr., 46, 13091321, https://doi.org/10.1175/JPO-D-15-0068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monin, A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Tr. Geofiz. Inst., Akad. Nauk SSSR, 24, 163187.

    • Search Google Scholar
    • Export Citation
  • Moum, J. N., 2021: Variations in ocean mixing from seconds to years. Annu. Rev. Mar. Sci., 13, 201226, https://doi.org/10.1146/annurev-marine-031920-122846.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sallée, J.-B., K. Speer, S. Rintoul, and S. Wijffels, 2010: Southern ocean thermocline ventilation. J. Phys. Oceanogr., 40, 509529, https://doi.org/10.1175/2009JPO4291.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, W. H. F., and D. T. Sandwell, 1997: Global sea floor topography from satellite altimetry and ship depth soundings. Science, 277, 19561962, https://doi.org/10.1126/science.277.5334.1956.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • St. Laurent, L., and S. Merrifield, 2017: Measurements of near-surface turbulence and mixing from autonomous ocean gliders. Oceanography, 30, 116125, https://doi.org/10.5670/oceanog.2017.231.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • St. Laurent, L., A. C. Naveira Garabato, J. R. Ledwell, A. M. Thurnherr, J. M. Toole, and A. J. Watson, 2012: Turbulence and diapycnal mixing in drake passage. J. Phys. Oceanogr., 42, 21432152, https://doi.org/10.1175/JPO-D-12-027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sullivan, P. P., J. C. McWilliams, and W. K. Melville, 2007: Surface gravity wave effects in the oceanic boundary layer: Large-eddy simulation with vortex force and stochastic breakers. J. Fluid Mech., 593, 405452, https://doi.org/10.1017/S002211200700897X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sutherland, G., B. Ward, and K. H. Christensen, 2013: Wave-turbulence scaling in the ocean mixed layer. Ocean Sci., 9, 597608, https://doi.org/10.5194/os-9-597-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sutherland, G., K. H. Christensen, and B. Ward, 2014: Evaluating Langmuir turbulence parameterizations in the ocean surface boundary layer. J. Geophys. Res. Oceans, 119, 18991910, https://doi.org/10.1002/2013JC009537.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sutherland, G., L. Marié, G. Reverdin, K. H. Christensen, G. Broström, and B. Ward, 2016: Enhanced turbulence associated with the diurnal jet in the ocean surface boundary layer. J. Phys. Oceanogr., 46, 30513067, https://doi.org/10.1175/JPO-D-15-0172.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Talley, L. D., 1996: Antarctic intermediate water in the South Atlantic. The South Atlantic. G. Wefer et al., Eds., Springer, 219238.

  • Terray, E. A., M. A. Donelan, Y. C. Agrawal, W. M. Drennan, K. K. Kahma, A. J. Williams, P. A. Hwan, and S. A. Kitaigorodskii, 1996: Estimates of kinetic energy dissipation under breaking waves. J. Phys. Oceanogr., 26, 792807, https://doi.org/10.1175/1520-0485(1996)026<0792:EOKEDU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thomson, J., M. S. Schwendeman, S. F. Zippel, S. Moghimi, J. Gemmrich, and W. E. Rogers, 2016: Wave-breaking turbulence in the ocean surface layer. J. Phys. Oceanogr., 46, 18571870, https://doi.org/10.1175/JPO-D-15-0130.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thorpe, S. A., 2005: The Turbulent Ocean. Cambridge University Press, 439 pp.

  • Umlauf, L., H. Burchard, and K. Bolding, 2005: General Ocean Turbulence Model: Source code documentation. Baltic Sea Research Institute Warnemünde Tech. Rep. 63, 346 pp.

    • Search Google Scholar
    • Export Citation
  • Waterhouse, A. F., and Coauthors, 2014: Global patterns of diapycnal mixing from measurements of the turbulent dissipation rate. J. Phys. Oceanogr., 44, 18541872, https://doi.org/10.1175/JPO-D-13-0104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., J. Scott, R. Hoffman, M. Leidner, R. Atlas, and J. Ardizzone, 2015: Remote Sensing Systems Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, version 2.0. Remote Sensing Systems, accessed 24 January 2019, www.remss.com/measurements/ccmp.

    • Search Google Scholar
    • Export Citation
  • Zippel, S. F., and Coauthors, 2021: Moored turbulence measurements using pulse-coherent Doppler sonar. J. Atmos. Oceanic Technol, 38, 16211639, https://doi.org/10.1175/JTECH-D-21-0005.1.

    • Search Google Scholar
    • Export Citation
  • View in gallery
    Fig. 1.

    AUSSOM glider observations (2017/18). (a) Glider track with CCMPv2 winds along glider track, plotted over Operational Mercator currents (blue to yellow) from 5 Dec 2017. (b) CTD and TKE observations with Smith and Sandwell (1997) bathymetry. Vertical lines and crosses correspond to mission events. The Subantarctic Front (SAF), Polar Front (PF), and Southern ACC Front (SACCF) are labeled in white.

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    Fig. 2.

    Mixing sections. Showing (a) turbulent dissipation with actively mixing layer (AML) depth, (b) buoyancy frequency with mixed layer depth (MLD), (c) diffusivity with AML and MLD, and (d) a case of AML identification using log-linear fit.

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    Fig. 3.

    Monin–Obukhov length scale (LMO) in comparison to the actively mixing layer (AML). Note the y axis has been limited from 50 to −250 m to focus on those Monin–Obukhov length scales in proximity to the AML depth. There are 385 additional points above 50 m (representing stabilizing buoyancy forcing) and 141 additional points below −250 m (representing destabilizing-but-inconsequential buoyancy forcing).

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    Fig. 4.

    Boundary layer similarity scaling (BLS) showing (a) observed turbulent dissipation with actively mixing layer (AML) depth, (b) direct meteorological forcing of near-surface turbulence including the full and wind-only estimate of u*, (c) turbulent dissipation estimated using BLS, and (d) depth-integrated energy levels (units of flux) for each observed (green) and derived (blue and black) profile. Note that estimated turbulent dissipation section derived from COARE (wind and buoyancy flux) and CCMP (wind) are visually identical such that only the latter is shown.

  • View in gallery
    Fig. 5.

    One centered interval of BLS, (left) time-averaging (over inertial period) turbulent dissipation and (right) bias expressed as ratio of ϵBLS to measured ϵ in depth space. Hereafter time averaging is performed in AML-normalized (dimensionless) depth space. Averaged profiles are bold.

  • View in gallery
    Fig. 6.

    Bias of BLS showing (a) friction velocity u* and wind-sea significant wave height Hs over glider, (b) availability of microstructure and associated BLS profiles for 14.5-h temporal average, (c) BLS bias in the normalized AML, and (d) observed turbulent dissipation rates depth normalized by the AML. Also showing (e) wind direction as a function of time, with 0° and 90° indicating wind toward the north and east, respectively, and total wind speed (m s−1) as color axis.

  • View in gallery
    Fig. 7.

    Histograms for bias in the (a) near-surface (blue) and deeper (red) AML. (b) Full dataset polynomial fit (dotted black) with 95% confidence intervals (dotted magenta). Additionally, polynomial fits for the mean (solid black) and standard deviation (solid magenta) of BLS bias are computed using a moving vertical window.

  • View in gallery
    Fig. 8.

    Histograms for BLS separated by water friction velocity (u*) and position within the AML. (a)–(c) A bias section, near-surface histogram, and deeper AML histogram for low-wind conditions. (d)–(f) As in (a)–(c), but for high-wind conditions. Near-surface and deeper regimes are separated by z^=0.1 as in Fig. 7a.

  • View in gallery
    Fig. 9.

    Histograms for BLS separated by wave steepness (H/L) and position within the AML. (a)–(c) A bias section, near-surface histogram, and deeper AML histogram for low-wind conditions. (d)–(f) As in (a)–(c), but for high-steepness conditions. Near-surface and deeper regimes are separated by z^=0.1 as in Fig. 7a.

  • View in gallery
    Fig. 10.

    Histograms for BLS separated by whether conditions are conducive or not conducive to Langmuir circulation, as well as position within the AML. (a)–(c) A bias section, near-surface histogram, and deeper AML histogram for low-wind conditions. (d)–(f) As in (a)–(c), but for Langmuir-stable conditions. Near-surface and deeper regimes are separated by z^=0.1 in Fig. 7a.

  • View in gallery
    Fig. 11.

    Near-surface hydrography showing (a) friction velocity and significant wind wave height, (b) turbulent Langmuir number and wave steepness (H/L), (c) shallow density with isopycnal contours at 0.07 kg m−3 intervals, and (d) stratification with AML and MLD depth normalized by MLD.

  • View in gallery
    Fig. 12.

    Boundary layer structure. (a) Cartoon schematic from Gerbi et al. (2009) and (b) the same figure constructed using AUSSOM glider microstructure dissipation measurements, wind-sea significant wave height Hs, and wind forcing F1=u*3Gt, where Gt is an empirical function of approximate wave age. Scatter (blue) includes 1 in 5 profiles for clarity.

  • View in gallery
    Fig. 13.

    Depth-integrated TKE in comparison to inputs of TKE from wind estimated by alternative scalings; Gt is an empirical function of wave age (Terray et al. 1996) which ranges from 37 to 182 for our dataset. Wave age is calculated using an approximation of data published in Edson et al. (2013), given by u*a/cp ≈ 0.004U10 − 0.003 (J. B. Edson 2020, personal communication). Effective energy transfer velocity ce ≈ 0.148U10 + 1.11 is calculated after Hwang (2009) and is generally 1.5–3 m s−1 in the ocean.

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Shear Turbulence in the High-Wind Southern Ocean Using Direct Measurements

Laur FerrisaVirginia Institute of Marine Science, William and Mary, Gloucester Point, Virginia
eApplied Physics Laboratory, University of Washington, Seattle, Washington

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Donglai GongaVirginia Institute of Marine Science, William and Mary, Gloucester Point, Virginia

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Carol Anne ClaysonbWoods Hole Oceanographic Institution, Woods Hole, Massachusetts

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Sophia MerrifieldcScripps Institution of Oceanography, University of California, San Diego, La Jolla, California

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Emily L. ShroyerdOffice of Naval Research, Washington, D.C.

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Madison SmitheApplied Physics Laboratory, University of Washington, Seattle, Washington

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Louis St. LaurenteApplied Physics Laboratory, University of Washington, Seattle, Washington

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Abstract

The ocean surface boundary layer is a gateway of energy transfer into the ocean. Wind-driven shear and meteorologically forced convection inject turbulent kinetic energy into the surface boundary layer, mixing the upper ocean and transforming its density structure. In the absence of direct observations or the capability to resolve subgrid-scale 3D turbulence in operational ocean models, the oceanography community relies on surface boundary layer similarity scalings (BLS) of shear and convective turbulence to represent this mixing. Despite their importance, near-surface mixing processes (and ubiquitous BLS representations of these processes) have been undersampled in high-energy forcing regimes such as the Southern Ocean. With the maturing of autonomous sampling platforms, there is now an opportunity to collect high-resolution spatial and temporal measurements in the full range of forcing conditions. Here, we characterize near-surface turbulence under strong wind forcing using the first long-duration glider microstructure survey of the Southern Ocean. We leverage these data to show that the measured turbulence is significantly higher than standard shear-convective BLS in the shallower parts of the surface boundary layer and lower than standard shear-convective BLS in the deeper parts of the surface boundary layer; the latter of which is not easily explained by present wave-effect literature. Consistent with the CBLAST (Coupled Boundary Layers and Air Sea Transfer) low winds experiment, this bias has the largest magnitude and spread in the shallowest 10% of the actively mixing layer under low-wind and breaking wave conditions, when relatively low levels of turbulent kinetic energy (TKE) in surface regime are easily biased by wave events.

Significance Statement

Wind blows across the ocean, turbulently mixing the water close to the surface and altering its properties. Without the ability to measure turbulence in remote locations, oceanographers use approximations called boundary layer scalings (BLS) to estimate the amount of turbulence caused by the wind. We compared turbulence measured by an underwater robot to turbulence estimated from wind speed to determine how well BLS performs in stormy places. We found that in both calm and stormy conditions, estimates are 10 times too small closest to the surface and 10 times too large deeper within the turbulently mixed surface ocean.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher's Note: This article was revised on 14 October 2022 to correct typographical errors that occured in the last sentence of the Significance Statement, and in the last sentence of the paragraph in which Eq. (9) appears.

Corresponding author: Laur Ferris, lferris@apl.washington.edu

Abstract

The ocean surface boundary layer is a gateway of energy transfer into the ocean. Wind-driven shear and meteorologically forced convection inject turbulent kinetic energy into the surface boundary layer, mixing the upper ocean and transforming its density structure. In the absence of direct observations or the capability to resolve subgrid-scale 3D turbulence in operational ocean models, the oceanography community relies on surface boundary layer similarity scalings (BLS) of shear and convective turbulence to represent this mixing. Despite their importance, near-surface mixing processes (and ubiquitous BLS representations of these processes) have been undersampled in high-energy forcing regimes such as the Southern Ocean. With the maturing of autonomous sampling platforms, there is now an opportunity to collect high-resolution spatial and temporal measurements in the full range of forcing conditions. Here, we characterize near-surface turbulence under strong wind forcing using the first long-duration glider microstructure survey of the Southern Ocean. We leverage these data to show that the measured turbulence is significantly higher than standard shear-convective BLS in the shallower parts of the surface boundary layer and lower than standard shear-convective BLS in the deeper parts of the surface boundary layer; the latter of which is not easily explained by present wave-effect literature. Consistent with the CBLAST (Coupled Boundary Layers and Air Sea Transfer) low winds experiment, this bias has the largest magnitude and spread in the shallowest 10% of the actively mixing layer under low-wind and breaking wave conditions, when relatively low levels of turbulent kinetic energy (TKE) in surface regime are easily biased by wave events.

Significance Statement

Wind blows across the ocean, turbulently mixing the water close to the surface and altering its properties. Without the ability to measure turbulence in remote locations, oceanographers use approximations called boundary layer scalings (BLS) to estimate the amount of turbulence caused by the wind. We compared turbulence measured by an underwater robot to turbulence estimated from wind speed to determine how well BLS performs in stormy places. We found that in both calm and stormy conditions, estimates are 10 times too small closest to the surface and 10 times too large deeper within the turbulently mixed surface ocean.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher's Note: This article was revised on 14 October 2022 to correct typographical errors that occured in the last sentence of the Significance Statement, and in the last sentence of the paragraph in which Eq. (9) appears.

Corresponding author: Laur Ferris, lferris@apl.washington.edu

1. Introduction

The surface boundary layer is the gateway for heat, momentum, and gas transfer between the atmosphere and interior ocean. Turbulent kinetic energy (TKE) injected into the upper-ocean boundary layer, together with the surface buoyancy flux, directly affects the depth of mixing, controls water mass transformation, and mixes water to increase potential energy of the upper-ocean structure (at the expense of TKE). As the only sector of the global ocean that connects all three major ocean basins through the meridional overturning circulation (MOC), the Southern Ocean is an especially important site of water mass transformation. Buoyancy forcing through air–sea exchange and interior mixing driven by internal waves transforms North Atlantic Deep Water (NADW) first into Subantarctic Mode Water (SAMW) and eventually into Antarctic Intermediate Water (AAIW) (Abernathey et al. 2016). The Scotia Sea east of the Drake Passage is believed to be a critical site of SAMW and AAIW modification and subduction (Talley 1996; Sallée et al. 2010), but little is known about the formation of these water masses. Despite its importance, mixing processes in the Southern Ocean have been undersampled, largely due to its remote location and severe conditions.

An autonomous profiling glider program called Autonomous Sampling of Southern Ocean Mixing (AUSSOM) was conducted in the Drake Passage region between the end of austral winter and the beginning of austral spring in 2017/18. AUSSOM represents the first extended glider deployment in the Drake Passage region of the ACC (Fig. 1) and is the longest continuous glider microstructure record ever collected. Unlike shipboard methods, gliders remain deployed for months at a time sampling though all sea states; thus, it is a first opportunity to understand turbulent dissipation rate and mixing variations in the Polar Front (PF) of the Southern Ocean though a full range of atmospheric forcing conditions. The high spatial resolution and temporal extent of this dataset is also an opportunity to understand the performance of boundary layer similarity scaling (BLS) through the full range of meteorological forcing.

Fig. 1.
Fig. 1.

AUSSOM glider observations (2017/18). (a) Glider track with CCMPv2 winds along glider track, plotted over Operational Mercator currents (blue to yellow) from 5 Dec 2017. (b) CTD and TKE observations with Smith and Sandwell (1997) bathymetry. Vertical lines and crosses correspond to mission events. The Subantarctic Front (SAF), Polar Front (PF), and Southern ACC Front (SACCF) are labeled in white.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

Much of the energy for turbulent mixing is injected into the surface mixed layer by a combination of buoyancy flux (convection), wind-driven shear flow, and wind-forced surface gravity waves (wave breaking and Langmuir circulation) (Mackinnon et al. 2013). Due to the inherent challenges of observation and representation of turbulence, the community relies on similarity scaling to estimate surface boundary layer turbulence in a variety of observational, analytical, and modeling pursuits; operational models such as HYCOM and ROMS utilize similarity scaling embedded in K-profile parameterization (KPP) mixing algorithms (Monin and Obukov 1954; Large et al. 1994). Turbulence parameterizations based on law-of-the-wall BLS are common in models (Umlauf et al. 2005), as well as analytical and observational studies. BLS leverages fundamental results for fluid behavior at a boundary to estimate the turbulent dissipation caused by shear and convection and is a simplification of the full TKE budget. The TKE budget, assuming the ocean is locally in steady state such that TKE per unit volume is constant, can be described in horizontally homogenous form (m2 s−3) by
0=1ρozwp+ν2z2kzwkuwdUdzϵgρ0ρw,
where (u, υ, w) are turbulent velocity components, k = (⟨u2⟩ + ⟨υ2⟩ + ⟨w2⟩)/2 is the TKE, ⟨_⟩ denotes an average, p is the pressure fluctuation, ν is kinematic viscosity, dU/dz is vertical shear of the mean flow velocity, g is gravity, and ρ = −ρ0αT′ − βS′ is the density fluctuation due to temperature and salinity fluctuations T′ and S′. The terms on the right-hand side are 1) pressure-driven divergence of vertical kinetic energy flux, 2) viscous divergence of vertical kinetic energy flux, 3) vertical turbulent transport, 4) shear production, 5) dissipation rate, and 6) buoyancy production. Just outside the region closest to the boundary where viscous effects dominate (the viscous sublayer), there exists1 a logarithmic layer (or inertial sublayer) in which the turbulence budget is typically approximated as a first-order balance between shear production, dissipation, and buoyancy (terms 4–6). However, there are several issues with this simplification, which neglects wave effects and boundary sources; in the real surface boundary layer, there is addition production due to Stokes shear, and neither variety of shear is necessarily aligned with the momentum flux 〈uw〉. Several past works have explored these additions (Agrawal et al. 1992; Sutherland et al. 2013, 2014, 2016; D’Asaro et al. 2014; Fox-Kemper et al. 2022).
To develop BLS, the transfer of horizontal momentum in the x direction by fluctuations in vertical velocity is assumed to be constant (Thorpe 2005) and dominated by fluctuations in velocity rather than density such that Reynolds stress in the logarithmic layer is also assumed to be constant, τ/ρo = −ρouw〉. This shear-dominated simplification of Eq. (1) is
ϵ=(τ/ρo)dU/dz.
Friction velocity (of the water at the boundary) is given by u*=(τ/ρo), where τ is wind stress and ρo is water density at the surface. When assuming that viscous effects are negligible in the logarithmic layer, dimensional grounds demand dU/dz = ϕ(z)u*/kz, where k = 0.41 is von Kármán’s constant (Thorpe 2005). By assuming a perfect logarithmic layer and constant shear, the similarity structure function2 is taken to be ϕ(z) ≡ 1. Substituting into Eq. (2) gives the principal equation for BLS of shear turbulence
ϵ=u*3kz.

In the presence of convection induced by buoyancy flux, Eq. (3) is adapted to include the effects of buoyancy flux (Jb). One such adaptation (Lombardo and Gregg 1989) based on similarity scaling of the atmospheric boundary layer is given in Table 1, where buoyancy production is represented as a constant function of surface flux cJb = −gρw〉, where c is a constant between 0 and 1. It is defined using the ratio of the Monin–Obukhov length scale LMO = −u*3/(kJb) (the depth at which the effects of wind-driven shear are equivalent to convection in turbulent flows) and the actively mixing layer (AML, the vertical extent of active turbulence, given in negative meters). Here LMO, which is negative in destabilizing conditions, describes the scale inside of which turbulence generated by wind-driven shear dominates that generated by convection. If the AML is much less than LMO or LMO > 0, it is a wind-dominated regime and convection is neglected. If the AML is significantly smaller than the LMO, it is a convection-dominated regime and wind is neglected. Lombardo and Gregg (1989) tested BLS during mild-to-moderate winds, focusing on times with when the ocean steadily lost buoyancy to the atmosphere such that convection significantly contributed to dissipation.

Table 1

Boundary layer similarity scaling (Lombardo and Gregg 1989). Showing piecewise equation used to estimate TKE dissipation rate (ϵ) from water friction velocity (u*) and buoyancy flux (Jb), with number of microstructure profiles described by each regime.

Table 1

Observations of turbulent dissipation are globally sparse (Waterhouse et al. 2014). The Southern Ocean has been noted as a location believed to exhibit large biases in mixed layer depth in climate models (e.g., CESM, CCSM; Danabasoglu et al. 2012). Here, we describe direct observation of boundary layer turbulence from AUSSOM using a framework of boundary layer scalings derived from wind and buoyancy forcing. This study focuses on the surface AML and its parameterization across the full range of wind forcing (up 20 m s−1 or ∼40 kt), and it is the first step in a larger effort to combine BLS with satellite data products to provide a time-varying estimate of upper-ocean mixing in the Southern Ocean. Understanding the physical processes and associated parameterizations for turbulent mixing in the surface mixed layer is critical for 1) understanding energy transfer into the mixed layer, 2) improving OSBL flux schemes embedded in circulation models, and 3) expanding turbulence estimations to satellite remote sensing platforms.

2. Methods

a. Glider observations

A Teledyne Webb Research Slocum glider equipped with a Rockland Scientific MicroRider was used to collect a 6-week record of upper-ocean turbulence spanning 800 km from the Shackleton Fracture Zone to the Falkland Plateau (Fig. 1). This glider-based methodology of measuring turbulence is well documented in published literature (Fer et al. 2014; St Laurent and Merrifield 2017; Zippel et al. 2021). The glider was deployed at 58°S, 64°W at the southern edge of the PF on 16 November 2017 from the R/V Laurence M. Gould, sampled for 60 days until 12 January 2018 when sensing was disabled to preserve the battery, and was recovered near Port Stanley, Falkland Islands, on 5 February 2018. The dataset is one of the largest microstructure datasets ever collected, totaling over 3028 CTD profiles and 932 microstructure profiles from 0 to 350 m (totaling approximately 300 000 m of microstructure profiles in 60 days). For context, DIMES (Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean) collected 800 000 m of profiles over 5 years, 8 cruises, and 1 year of ship time. It is likely the most ever microstructure collected by a single instrument system.

The MicroRider, a glider-based sensor package for making direct turbulence measurements, was used for AUSSOM. In general, direct measurement of turbulence from a free-fall platform assumes 3D isotropy, which allows viscous dissipation (ϵ) of turbulent kinetic energy to be approximated by
ϵ=(152)ν(du/dz)2,
where z′ is the coordinate aligned with the shear probes, u′ is the water velocity component normal to z′, and du′/dz′ = (∂u′/∂t)(∂t/∂z′) = (∂u′/∂t)/V. Here ν is the molecular kinematic viscosity of water (∼1 × 10−6 m2 s−1) and ∂u′/∂t are velocity fluctuations measured by the shear probes. When using any package, velocity of the instrument through the water (V) is required to calculate turbulent dissipation. Glider microstructure differs from free-fall microstructure in that the velocity of shear probes through the water is not the same as its fall rate. It is possible to calculate vertical glider speed using a flight model (Merckelbach et al. 2019), but the pressure-derived vertical velocity W is sufficiently accurate for this application (Fer et al. 2014). The velocity (m s−1) of the glider through the water V = W/sin(ϕ + α) is calculated using the vertical component of velocity, W, and glide angle, where glide angle is the sum of pitch angle (ϕ) and the angle of attack (α) (St. Laurent and Merrifield 2017). Vertical eddy diffusivity of density Kρ=Γϵ/N2¯ is estimated using measured turbulent dissipation rate (ϵ), buoyancy frequency (N2) calculated from CTD using adiabatic leveling, and an assumed efficiency factor of Γ = 0.2. We explore the collected dataset using the framework of shear-convective BLS: we implement BLS, compare to glider microstructure, and explore the differences between observed turbulence and BLS estimates of turbulence in the high-wind Southern Ocean.

b. Boundary layer similarity scaling

In the absence of direct meteorological measurements, we harness satellite data for records of meteorological forcing, which are required for buoyancy flux calculations. The buoyancy flux is calculated using
Jb=g[αρcpQtot+βSo(EP)],
where α and β are the expansion coefficients for heat and salinity, cp is the specific heat of seawater, Qtot is the total heat flux [SeaFlux CDR dataset (Clayson et al. 2016c)], So is sea surface salinity (Copernicus product Global SSS/SSD L4 Processor V1.1), E is the evaporation rate (SeaFlux CDR), and P is precipitation (GPCP V1.3 Daily Rainfall). Friction velocity (u*) is computed from surface radiation flux (CERES_SYN1deg_Ed4A), winds (CCMPv2), near-surface specific humidity (SeaFlux CDR), near-surface temperature (SeaFlux CDR), and SST (SeaFlux Ocean CDR) using the COARE Met Flux Algorithm v3.5 (Edson et al. 2013). Wind-sea significant wave height (Hs) is obtained from the Copernicus Global Ocean Waves Reanalysis (WAVERYS) multiyear product, which is a global wave reanalysis on a 1/5° grid, at a 3-hourly temporal resolution. Wave steepness (H/L) is calculated from the WAVERYS product (1/5° grid, at a 3-hourly temporal resolution) using wind wave mean period (T) and significant wave height from wind and swells (H) using L = 2πH/gT2, and the turbulent Langmuir number is calculated Lat = (u*/us0)1/2 where us0 is surface velocity of Stokes drift. Direct wave observations are not available.

The determination of AML depth and mixed layer depth (MLD) are demonstrated by Fig. 2. Whereas the AML is defined by elevated turbulent dissipation, the MLD is defined by homogenous density. AML identification is completed for 932 microstructure profiles using a simple algorithm. The steps for each microstructure profile are to 1) find the depth at which a log-linear fit of surface (upper 100 m) ϵ falls to an empirically determined background ϵ = 10−8 W kg−1 (Fig. 2d); 2) discard obviously wrong fits (∼0.75% of profiles) using automatic checks, and 3) interpolate good AML depths. A critical step in the process is excluding enhanced turbulence at depth that is unrelated to direct surface (wind or buoyancy) forcing; restricting polynomial fitting to the upper 100 m—empirically selected to focus on surface-forced turbulence—avoids mixing events that are unrelated to surface boundary layer physics (e.g., internal wave and forward cascade). While the polynomial coefficients are determined from ϵ data in the upper 100 m, the resulting fit is allowed to extend below this depth. The result is a working AML depth dataset that avoids deep (e.g., internal wave related) mixing (Fig. 2a). MLD is from glider CTD using a surface-density difference criterion of Δρ = 0.03 kg m−3 and ΔT = 0.2°C, where the two estimates of MLD are compared for sensitivity and shallower estimate is generally used (Dong et al. 2008). AML depth can change on a faster (∼20 m h−1) time scale than the MLD; turbulence of the AML works to homogenize the water column, producing a mixed layer.

Fig. 2.
Fig. 2.

Mixing sections. Showing (a) turbulent dissipation with actively mixing layer (AML) depth, (b) buoyancy frequency with mixed layer depth (MLD), (c) diffusivity with AML and MLD, and (d) a case of AML identification using log-linear fit.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

Upon inspection (Fig. 3) it is clear that our study is wind dominated (with less than 1% of cases invoking buoyancy flux into BLS) such that we can neglect convection. Two versions of boundary layer similarity scaling (BLS) are implemented (Fig. 4). The standard version (using COARE variables) applies the full wind and buoyancy flux scaling (Table 1) using u* and Jb. We also implemented a simplified version of BLS using solely u* interpolated from CCMPv2, the easily accessible wind product available from Remote Sensing Systems (http://www.remss.com/). With close agreement relative to the biases, the reader may consider for themself (Figs. 4b,d) when it might be appropriate to just use the simplified version of BLS in wind-dominated situations. However, the rest of our paper uses the standard version of BLS.

Fig. 3.
Fig. 3.

Monin–Obukhov length scale (LMO) in comparison to the actively mixing layer (AML). Note the y axis has been limited from 50 to −250 m to focus on those Monin–Obukhov length scales in proximity to the AML depth. There are 385 additional points above 50 m (representing stabilizing buoyancy forcing) and 141 additional points below −250 m (representing destabilizing-but-inconsequential buoyancy forcing).

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

Fig. 4.
Fig. 4.

Boundary layer similarity scaling (BLS) showing (a) observed turbulent dissipation with actively mixing layer (AML) depth, (b) direct meteorological forcing of near-surface turbulence including the full and wind-only estimate of u*, (c) turbulent dissipation estimated using BLS, and (d) depth-integrated energy levels (units of flux) for each observed (green) and derived (blue and black) profile. Note that estimated turbulent dissipation section derived from COARE (wind and buoyancy flux) and CCMP (wind) are visually identical such that only the latter is shown.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

Individual microstructure and synchronous BLS profiles were also integrated [Eq. (6)] to obtain the dissipated power associated with the observations and scaled estimates (Fig. 4d):
Φ=zAMLzminρϵdz.

Turbulence observations and estimates were temporally averaged prior to calculating the observed bias in BLS, log10(ϵBLS/ϵ). Because the time scale of mixing events is shorter than the temporal resolution (6 h) of the CCMPv2 wind data, individual microstructure profiles must be averaged over some time scale (long enough that the wind product adequately represents mean turbulent dissipation but short enough to capture changing conditions) to produce useful comparison to wind-based BLS profiles. A 14.5-h (inertial period) averaging interval is used. Surface boundary layer turbulence is normalized by the AML depth and temporally averaged with adjacent profiles using 30 vertical bins. Finally, polynomial fits are used to document the structure of the observed bias.

3. Results

a. Comparison to turbulence estimates of standard shear-convective BLS

Overall, glider survey revealed interesting subsurface physics, observing elevated turbulent dissipation rates (ϵ = 10−7 W kg−1) for the entire duration for which the glider sampled the core of the Polar Front (Fig. 1b). Glider CTD observed some salt fingering and double diffusive staircases north of the PF (consistent with Merrifield et al. 2016) and sporadic diffusive/oscillatory convection (Ferris et al. 2020). Subsurface phenomena are examined in Ferris (2022). Convection forced by buoyancy flux played a minimal role in forcing the AML during the study (Fig. 3), with buoyancy rarely removed from the upper ocean and energy for near-surface mixing predominately supplied by wind stress. An analysis of time-averaged microstructure and turbulence profiles estimated using boundary layer scaling (Lombardo and Gregg 1989) demonstrates that the BLS turbulent dissipation in the shallowest depths is higher than predicted by the BLS paradigm and turbulent dissipation deeper within the AML is lower than predicted by BLS (see Fig. 5 for an example profile), consistent with Merrifield’s (2016) bulk analysis of tow-yo VMP transects from DIMES US5.

Fig. 5.
Fig. 5.

One centered interval of BLS, (left) time-averaging (over inertial period) turbulent dissipation and (right) bias expressed as ratio of ϵBLS to measured ϵ in depth space. Hereafter time averaging is performed in AML-normalized (dimensionless) depth space. Averaged profiles are bold.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

A section of this bias is shown in Fig. 6c, with blue hues (red hues) indicating underprediction (overprediction) of turbulence in the surface boundary layer, with underprediction of near-surface TKE dissipation rates by up to four orders of magnitude. The vertical extent of this underprediction varies in depth, with three strong events lagging 2–3 days after intense storms; these will be revisited in section 4. Several cases do not have the characteristic bias profile (Fig. 6b, bright red hues at the surface), which are associated with either instances in which there are fewer than five microstructure profiles available within the 14.5-h interval for averaging (21 and 30 November), or profiles overlying the continental rise or shelf (after 22 December). Few profiles available for averaging is an obvious factor in inconsistent BLS bias due to higher statistical uncertainty [see Moum (2021) for a recent review]. The similarity of observed turbulence to BLS does not depend on whether wind inflection (wind speed increasing or decreasing), nor proximity to Polar Front.

Fig. 6.
Fig. 6.

Bias of BLS showing (a) friction velocity u* and wind-sea significant wave height Hs over glider, (b) availability of microstructure and associated BLS profiles for 14.5-h temporal average, (c) BLS bias in the normalized AML, and (d) observed turbulent dissipation rates depth normalized by the AML. Also showing (e) wind direction as a function of time, with 0° and 90° indicating wind toward the north and east, respectively, and total wind speed (m s−1) as color axis.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

Before documenting the character and magnitude of the bias (Fig. 6c), we place several restrictions on the data. Profiles are excluded (181 profiles or ∼19.4%) because 1) the profile does not have a recognizable AML, 2) the profile is over the continental rise or shelf and thus likely contaminated by elevated bottom boundary layer mixing, or 3) there are no measurements in an entire vertical bin of a temporal average. Profiles are normalized by AML depth and measurements beyond the AML are omitted from analysis. After quality control, bias is quantified in two ways (Fig. 7b): a full dataset polynomial fit, and polynomial fit for the mean μ(z) = ln(ϵBLS/ϵ) and the standard deviation σ(z) of the depth-dependent probability distribution function (PDF) fit to individual PDFs computed from a moving vertical window. Polynomial fits ξ(z^) of ln(ϵBLS/ϵ) and its standard deviation are given by
ξ(z^)=ξ5z^5+ξ4z^4+ξ3z^3+ξ2z^2+ξ1z^+ξ0,
where z^=z/AML is the magnitude of the distance from the surface and coefficients are provided in Table 2. We used a fifth-degree polynomial because it best described this particular dataset, but do not suggest there is a physical reason that future adaptations to BLS should take this form. Depth regimes for Fig. 7a are partitioned by the zero crossing of bias polynomials (Fig. 7b), z^=0.1, which is the same regardless of whether bias is defined log10(ϵBLS/ϵ) or ln(ϵBLS/ϵ). The near-surface AML exhibits a larger standard deviation in bias (Fig. 7a) than the deeper AML, suggesting wave dynamics at the air–sea interface is a significant factor.
Fig. 7.
Fig. 7.

Histograms for bias in the (a) near-surface (blue) and deeper (red) AML. (b) Full dataset polynomial fit (dotted black) with 95% confidence intervals (dotted magenta). Additionally, polynomial fits for the mean (solid black) and standard deviation (solid magenta) of BLS bias are computed using a moving vertical window.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

Table 2

Coefficients for bias polynomials [Eq. (7)] given in Fig. 7.

Table 2

b. Controls on bias in two depth regimes

Next, we examine controls on the normalized bias (Figs. 810) including friction velocity (u*), wind-sea significant wave height (Hs), and turbulent Langmuir number (Lat). Parameters u* and Hs mirror each other such that they are a reasonable proxy for one another. We separate turbulence estimates into a near-surface regime and a deeper regime as in Fig. 7a. We observe that wind speed (Fig. 8) has an inverse effect on the magnitude of near-surface underestimation (μ = −0.574 low wind versus μ = 0.099 high wind), with larger biases in low-wind conditions, but a direct effect on the magnitude of deep overestimation (μ = 0.345 low wind versus μ = 0.491 high wind). Wave breaking (Fig. 9) has a direct effect on the magnitude of near-surface underestimation (μ = −0.091 nonbreaking versus μ = −0.563 breaking waves), with larger biases in breaking wave conditions, but an inverse effect on the magnitude of deep overestimation (μ = 0.451 nonbreaking versus μ = 0.354 breaking waves). Conditions conducive to Langmuir circulation (Fig. 10) have a direct effect on the magnitude and sign of near-surface underestimation (μ = 0.146 Langmuir inactive versus μ = −0.396 Langmuir active) and an inverse effect on the deep overestimation (μ = 0.517 Langmuir inactive versus μ = 0.377 Langmuir active). Langmuir circulation is unlikely the principle physical process at work (out of those unrepresented by BLS) because Langmuir circulation would be expected redistribute turbulence from the near-surface to the deeper AML, causing a tendency toward overestimation in the near-surface and underestimation at depth (the opposite of what we observed).

Fig. 8.
Fig. 8.

Histograms for BLS separated by water friction velocity (u*) and position within the AML. (a)–(c) A bias section, near-surface histogram, and deeper AML histogram for low-wind conditions. (d)–(f) As in (a)–(c), but for high-wind conditions. Near-surface and deeper regimes are separated by z^=0.1 as in Fig. 7a.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

Fig. 9.
Fig. 9.

Histograms for BLS separated by wave steepness (H/L) and position within the AML. (a)–(c) A bias section, near-surface histogram, and deeper AML histogram for low-wind conditions. (d)–(f) As in (a)–(c), but for high-steepness conditions. Near-surface and deeper regimes are separated by z^=0.1 as in Fig. 7a.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

Fig. 10.
Fig. 10.

Histograms for BLS separated by whether conditions are conducive or not conducive to Langmuir circulation, as well as position within the AML. (a)–(c) A bias section, near-surface histogram, and deeper AML histogram for low-wind conditions. (d)–(f) As in (a)–(c), but for Langmuir-stable conditions. Near-surface and deeper regimes are separated by z^=0.1 in Fig. 7a.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

c. Relationship of mixed layer development and the Polar Front

We observe an interesting relationship between frontal hydrography and shallow mixing (Fig. 11). The glider crossed into the PF on 28 November, marking a sharp reduction in salinity (Fig. 1b) and mixed layer depth (Fig. 2b). This is associated with a transition in the relationship between MLD and AML (Fig. 11d). Before the PF the AML rarely develops beyond the mixed layer; TKE erodes the base of MLD, mixing away this interface. But beyond the PF in the cold, fresh Southern Ocean waters, the AML routinely develops beyond the MLD; there is with little compliance from the mixed layer itself (Fig. 2). This could be due to greater stratification resisting mixed layer deepening (despite churning by TKE), or intense lateral density gradients within the PF core creating stability and preventing convection. The relationship between water masses and the AML:MLD ratio is complicated by seasonal transition from winter to summer, increasing stratification of the upper Southern Ocean, similar to that observed by du Plessis et al. (2019). A deepening of isopycnals occurs during the 6 and 12 December storm events (Fig. 11), as well as following Langmuir-circulation-favorable conditions on 17 December. The influence of both the Polar Front and seasonal transition on mixing dynamics are worthy of future investigation.

Fig. 11.
Fig. 11.

Near-surface hydrography showing (a) friction velocity and significant wind wave height, (b) turbulent Langmuir number and wave steepness (H/L), (c) shallow density with isopycnal contours at 0.07 kg m−3 intervals, and (d) stratification with AML and MLD depth normalized by MLD.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

4. Discussion

a. Influence of waves

Throughout AUSSOM buoyancy flux played a minimal role in deepening the AML in the Drake Passage and Scotia Sea region (usually extracting energy and reducing its development), with energy for near-surface mixing supplied almost solely by wind stress. Focusing our discussion on shear production, BLS likely underpredicts energy input into the near-surface ocean because it does include surface gravity wave breaking and/or TKE from alternative sources in the observed Southern Ocean environment. Our observations suggest BLS (Lombardo and Gregg 1989) of shear turbulence in the Southern Ocean exhibit a systematic bias, underestimating (overestimating) turbulent dissipation rates in the shallower (deeper) parts of the surface boundary layer. The magnitude of the near-surface underestimate is greatest when wind is mild (Fig. 8) and waves are breaking (Fig. 9). This is not surprising; the Lombardo and Gregg (1989) form of BLS is a rigid-boundary theory and assumes a TKE budget dominated by shear production, buoyancy production, and dissipation [Eq. (1)]. Contrary to the rigid-boundary paradigm, surface gravity waves are known to alter boundary layer structure within several significant wave heights of the surface (Agrawal et al. 1992), and our observations are not the first for which waves cause a departure from BLS theory. Gerbi et al. (2009) used a model and observations from the Coupled Boundary Layers and Air–Sea Transfer (CBLAST) low-winds experiment to find production alone was unable to balance dissipation [as in Eq. (1)] in the wave-affected surface layer, which lies above the logarithmic layer (Terray et al. 1996).

We have reproduced the Gerbi et al. (2009) finding in Fig. 12 using our measurements, which should produce identical statistics to those computed using an Eulerian platform (Derakhti et al. 2020). The inclusion of a transport term (representing wave breaking, nonlinear wave–turbulence interactions, and Langmuir turbulence) improved the model, though contributions of Langmuir turbulence were found unimportant relative to wave breaking (like our results, Figs. 9 and 10). Fox-Kemper et al. (2022) note that systematic inconsistencies arise when wind waves deviate in direction from the wind stress itself or propagate from a nonlocal generation site, which is worth mentioning given that our dataset contains a prominent wave presence. In the real ocean surface boundary layer, there is addition production due to Stokes drift (Us) (McWilliams et al. 1997; Belcher et al. 2012) and shear is not necessarily aligned with the momentum flux 〈uw〉 (McWilliams et al. 2014) such that more updated representation of Eq. (1) with terms 1–3 neglected is
0=uwdUdzF(US)F(z/LMO)cosAuwdUSdzcosBϵgρ0ρw+WBP,
where angles A and B are the wind direction relative to the shear terms. In conditions where ϵ is strongly driven by the wave field and Stokes drift, surface gravity waves reduce the shear by a function of the Stokes drift F(US) (Large, et al. 2019) and there is wave breaking production (WBP) such that Reynolds stress is realistically a decaying function of depth F(z). The angles A and B are rarely both near zero, and A can exceed 90° in some real ocean conditions due to varying wind direction, such that energy is extracted. In the absence of background velocity shear, we cannot evaluate the leftmost term of Eq. (8) but evaluate uw(dUs/dz)cosB to demonstrate the importance of Stokes shear production, with dUs/dz estimated from Craik (1988) using
Us(z)4π2a2λTe4πz/λ,
where c = gT/(2π), T is the wave period of the spectral peak, and a is the amplitude of the primary swell. Near-surface underestimation (bias in the shallowest 10% of the AML) reduces from μ = −0.554 to μ = −0.450 overall (as in Fig. 7a), μ = −0.574 to μ = −0.517 in low-wind conditions and μ = 0.099 to μ = 0.133 in high wind conditions (as in Fig. 8), μ = −0.091 to μ = −0.048 in nonbreaking and μ = −0.563 and μ = −0.510 in breaking wave conditions (as in Fig. 9), and μ = −0.396 to μ = −0.344 in Langmuir active and μ = 0.146 to μ = 0.177 in Langmuir active conditions (as in Fig. 10); representing an improvement in all cases except high wind and Langmuir inactive conditions, when bias is positive to begin with. The deep overestimation worsens from μ = 0.960 to μ = 1.111 overall and in all cases, but this is unsurprising because we did not account for vertical decay due to F(US). Notably, however, this representation does not explain the bias in our dataset. Deep understimation events (Fig. 6c) would worsen if overall positive bias were made to accommodate 〈uw〉 decreasing from the surface as in Eq. (8).
Fig. 12.
Fig. 12.

Boundary layer structure. (a) Cartoon schematic from Gerbi et al. (2009) and (b) the same figure constructed using AUSSOM glider microstructure dissipation measurements, wind-sea significant wave height Hs, and wind forcing F1=u*3Gt, where Gt is an empirical function of approximate wave age. Scatter (blue) includes 1 in 5 profiles for clarity.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

Numerical modeling literature has aimed to understand the implications of breaking surface waves and Langmuir turbulence, which are not included in wall-bounded (standard shear-convective BLS) turbulence parameterizations and subgrid mixing schemes unless explicitly added (e.g., Kantha and Clayson 2004). Belcher et al. (2012) concluded surface wave-forced Langmuir turbulence should be a dominant TKE source in the Southern Ocean, and several observational studies (D’Asaro et al. 2014; Sutherland et al. 2014) corroborate the importance of Langmuir circulation in turbulence generation. While the inclusion of Langmuir turbulence parameterization schemes in ocean general circulation models (OGCMs) produces mixed layers of 2%–25% deeper in extratropical, weak-convection regions such as the austral summer Southern Ocean (Li et al. 2019), it is unclear to what extent Langmuir turbulence is mechanistically responsible for deeper mixed layers in the real ocean (D’Asaro 2014). Sullivan et al. (2007) used large eddy simulation to find that the wave age cp/u*a (where cp is phase speed of the spectral peak and u*a is air friction velocity) impacted the near-surface mixing, with younger wave groups and higher wind speeds exhibiting a larger positive feedback with Langmuir turbulence and increasing near-surface dissipation. To explore scalings leveraging these wave characteristics, we tested two alternative scalings in comparison to depth-integrated TKE (Fig. 13) including ones based on wave age (F1=Gtu*3, Craig and Banner 1994) and wave-range effective speed (F2=ceu*2, Gemmrich et al. 1994). Thomson et al. (2016) tested these scalings (albeit without consideration of buoyancy flux) and found that F2 marginally had the best agreement with observations. Our data (Fig. 13) are inconsistent with their result; we find both scalings produce inflated energy levels relative to Eq. (3). Even with bias, BLS performs one to two orders of magnitude better than alternative scalings based on wave age or wave-range effective speed.

Fig. 13.
Fig. 13.

Depth-integrated TKE in comparison to inputs of TKE from wind estimated by alternative scalings; Gt is an empirical function of wave age (Terray et al. 1996) which ranges from 37 to 182 for our dataset. Wave age is calculated using an approximation of data published in Edson et al. (2013), given by u*a/cp ≈ 0.004U10 − 0.003 (J. B. Edson 2020, personal communication). Effective energy transfer velocity ce ≈ 0.148U10 + 1.11 is calculated after Hwang (2009) and is generally 1.5–3 m s−1 in the ocean.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0015.1

Surface gravity wave breaking in the high-wind Southern Ocean environment violates a key assumption of BLS (that shear stress in the logarithmic layer is constant function of wind-imparted stress). However, this physical explanation alone is insufficient because near-surface bias is more severe during the mildest winds. While presence of nonlocally generated swell could be a factor, it is also possible that contributions from surface gravity waves are persistent but only noticeable in low-wind cases due to lower levels of TKE dissipation. The near-surface underestimation and deeper-AML overestimation is inherently coupled; energy lost in the near-surface will not reach the deeper AML, resulting in lower levels of turbulent dissipation than predicted. Near-surface underestimation by BLS is worse when there are breaking wave conditions and low wind-driven shear (Figs. 8 and 9), but the opposite effect is not seen in the deeper histograms suggesting there must be other physical processes at work.

b. Influence of sources other than breaking waves

A second physical explanation is that sources of turbulent kinetic energy (TKE) other than wind-driven shear significantly contribute to observed turbulent dissipation. Lombardo and Gregg (1989) assume energy injection into the dissipative scale is accomplished by direct meteorological forcing (wind-driven shear and convection) in the surface boundary layer, but other processes such as Langmuir driven turbulence (discussed in section 4a), shear instabilities, and submesoscale instabilities could be active in an intense wind-sheared frontal zone. As stated in section 4a, our dataset does not support a dominant role of Langmuir-driven turbulence; generation and redistribution of TKE by Langmuir circulation cannot be the only additional source of TKE because the presence or absence of this mechanism does not explain deep underestimation events. Rather, it favors alternative (other than wind-driven) mechanisms such in Sutherland et al. (2016), who observed a wind-driven jet in the subtropical Atlantic during the SPURS (Salinity Processes in the Upper Ocean Regional Study) to find that diurnal increase in stratification restricts vertical diffusion of wind stress and depth of momentum flux, increasing near-surface shear instability (an additional source of near-surface TKE). Mixing in the Antarctic Circumpolar Current (ACC) might be further complicated by the numerous other processes turbulently transforming the upper ocean, such as internal wave driven mixing (St. Laurent et al. 2012) and double diffusion (Merrifield et al. 2016). It should be emphasized that this second explanation is not a complete explanation by itself because it does not account for the lack of observed turbulence at depth in Figs. 710.

c. Impact of storms

We revisit the cause of the deeper underestimation events (Fig. 6c); restated, patches of elevated observed turbulence (blue hues) extending deeper into the AML which were not captured by BLS. There is some second-order dynamical effect; the time scale of this effect is much longer than the inertial period (∼14.5 h), and time scale for a storm system to pass the glider is less than one day. Glider depth-averaged current is comparable to ACC velocity (from Operational Mercator, Fig. 1) extracted along the track of the glider such that the platform is effectively Lagrangian; it is not the case that ACC velocity is advecting/distorting patches of turbulence (associated with wave breaking) faster than the glider such that they appear lagged in the turbulence record. A plausible physical mechanism explaining these deeper underestimation events is described in Dohan and Davis (2011); who observed a storm to excite near-inertial oscillations and currents (with their own additional shear), causing elevated mixing for 3 days after the storm itself. Wind direction turned with the direction of inertial rotation such that it resonantly excited the oscillations, and we similarly see a wind direction turn in the direction of inertial rotation (Fig. 6e) during storm events. An important question is why the TKE contribution shear instability in inertial currents would appear in BLS bias log10(ϵBLS/ϵ) as a delayed underestimation event and not immediately. During the storm itself, the calculation of bias would be heavily buffered by the wind-forced shear turbulence, such that the secondary component would perhaps not become noticeable until the wind relented and only the current shear remained. As wind subsides, the contribution of mixing due to current shear would subside, both ϵ and ϵBLS become smaller, and this additional contribution becomes more noticeable. We speculate that this mechanism could similarly create a delayed TKE contribution from the storms, though this cannot be confirmed with the available data.

5. Conclusions

We tested boundary layer scalings (BLS) from satellite data against direct measurements of TKE dissipation rate from a glider. We found that BLS underestimates turbulent dissipation in the near-surface and overestimates turbulent dissipation below the near-surface, consistent with Merrifield (2016). The structure of this bias is consistent across wind speeds in the lower 90% of the AML, but strongly contingent on wind speed in the upper 10% of the AML. In the near-surface AML, underestimation by BLS is larger in low-wind conditions, breaking wave conditions, or when Langmuir circulation is active; however, in the deep AML, differences across-wind and wave conditions are much less statistically significant. Explanations for this systematic bias are that 1) the rigid-boundary paradigm does not account for surface gravity wave breaking and momentum loss in the high-wind Southern Ocean environment; 2) sources of TKE other than wind-driven shear and buoyancy flux are contributing to dissipation, and furthermore, Langmuir circulation alone cannot explain deep underestimation events; and 3) deep underestimation events are due to additional shear caused by storm-forced inertial currents (see Dohan and Davis 2011). Despite these shortcomings, we found that BLS still outperforms alternative scalings (Craig and Banner 1994; Gemmrich et al. 1994) based on wave age or wave-range effective speed, motivating its further development. We built on the observational work of Lombardo and Gregg (1989) by showing a wind-dominated regime is characterized by significant momentum loss, alternative TKE sources, and significantly greater turbulent dissipation in the near-surface than predicted by BLS. Representing the physical processes responsible for this near-surface TKE dissipation is critical for understanding mixed layer dynamics and water mass transformation when wind-driven shear dominates convection in the global ocean. AUSSOM tested boundary layer scaling in high-wind, nonconvective conditions, but future investigations covering the full wind and buoyancy forcing parameter space are needed; especially involving cases where both wind-driven shear and buoyancy loss are significant.

1

In reality, this “logarithmic layer” is logarithmic only when shear production exactly balances dissipation.

2

In the presence of penetrating radiation and Stokes shear production, similarity structure functions are no longer governed by the same systems of equations as in Monin–Obukhov theory. An excellent review of this topic is provided by Fox-Kemper et al. (2022).

Acknowledgments.

This paper is VIMS Contribution 4103. Computational resources were provided by the VIMS Ocean-Atmosphere and Climate Change Research Fund. AUSSOM was supported by the OCE Division of the National Science Foundation (1558639). We thank the captain and crew of the R/V Gould for their excellent support during the field program, and Justin Shapiro for the recovery mission. We also thank the editor and two anonymous reviewers whose invaluable feedback greatly improved the paper. This study has been conducted using E.U. Copernicus Marine Service Information. CCMP Version-2.0 vector wind analyses are produced by Remote Sensing Systems.

Data availability statement.

All glider data used in the paper will be shared at microstructure.ucsd.edu, the NSF-funded Microstructure Database. The COARE 3.5 algorithm and its associated data products are publicly available. This includes surface radiation data from the CERES project (https://ceres.larc.nasa.gov/products-info.php?product=SYN1deg), CCMPv2 winds (www.remss.com; Wentz et al. 2015), near-surface temperature and humidity from SeaFlux CDR (https://www.ncdc.noaa.gov/cdr/atmospheric/ocean-near-surface-atmospheric-properties; Clayson et al. 2016b), sea surface temperature from SeaFlux Ocean CDR (https://www.ncdc.noaa.gov/cdr/oceanic/sea-surface-temperature-whoi; Clayson et al. 2016a), GPCP V1.3 daily rainfall product (https://www.ncdc.noaa.gov/cdr/atmospheric/precipitation-gpcp-daily; Adler et al. 2017), and the aforementioned CMEMS products.

REFERENCES

  • Abernathey, R. P., I. Cerovecki, P. R. Holland, E. Newsom, M. Mazloff, and L. D. Talley, 2016: Water-mass transformation by sea ice in the upper branch of the Southern Ocean overturning. Nat. Geosci., 9, 596601, https://doi.org/10.1038/ngeo2749.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, R., J.-J. Wang, M. Sapiano, G. Huffman, D. Bolvin, E. Nelkin, and NOAA CDR Program, 2017: Global Precipitation Climatology Project (GPCP) Climate Data Record (CDR), Version 1.3 (Daily). NOAA National Centers for Environmental Information, accessed 10 February 2019, https://doi.org/10.7289/V5RX998Z.

    • Search Google Scholar
    • Export Citation
  • Agrawal, Y. C., E. A. Terray, M. A. Donelan, P. A. Hwang, A. J. Williams, W. M. Drennan, K. K. Kahma, and S. A. Krtaigorodskii, 1992: Enhanced dissipation of kinetic energy beneath surface waves. Nature, 359, 219220, https://doi.org/10.1038/359219a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Belcher, S. E., and Coauthors, 2012: A global perspective on Langmuir turbulence in the ocean surface boundary layer. Geophys. Res. Lett., 39, L18605, https://doi.org/10.1029/2012GL052932.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clayson, C. A., J. Brown, and NOAA CDR Program, 2016a: NOAA Climate Data Record (CDR) of sea surface temperature - WHOI, Version 2. NOAA National Climatic Data Center, accessed 20 March 2020, https://doi.org/10.7289/V5FB510W.

    • Search Google Scholar
    • Export Citation
  • Clayson, C. A., J., Brown, and NOAA CDR Program, 2016b: NOAA Climate Data Record (CDR) of ocean near surface atmospheric properties, version 2. NOAA National Center for Environmental Information, accessed 20 March 2020, https://doi.org/10.7289/V55T3HH0.

    • Search Google Scholar
    • Export Citation
  • Clayson, C. A., J., Brown, and NOAA CDR Program, 2016c: NOAA Climate Data Record (CDR) of ocean heat fluxes, version 2. NOAA National Center for Environmental Information, accessed 20 March 2020, https://doi.org/10.7289/V59K4885.

    • Search Google Scholar
    • Export Citation
  • Craig, P. D., and M. L. Banner, 1994: Modeling wave-enhanced turbulence in the ocean surface layer. J. Phys. Oceanogr., 24, 25462559, https://doi.org/10.1175/1520-0485(1994)024<2546:MWETIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Craik, A. D., 1988: Wave Interactions and Fluid Flows. Cambridge University Press, 322 pp.

  • D’Asaro, E. A., 2014: Turbulence in the upper-ocean mixed layer. Annu. Rev. Mar. Sci., 6, 101115, https://doi.org/10.1146/annurev-marine-010213-135138.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • D’Asaro, E. A., J. Thomson, A. Y. Shcherbina, R. R. Harcourt, M. F. Cronin, M. A. Hemer, and B. Fox-Kemper, 2014: Quantifying upper ocean turbulence driven by surface waves. Geophys. Res. Lett., 41, 102107, https://doi.org/10.1002/2013GL058193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Danabasoglu, G., S. C. Bates, B. P. Briegleb, S. R. Jayne, M. Jochum, W. G. Large, S. Peacock, and S. G. Yeager, 2012: The CCSM4 Ocean Component. J. Climate, 25, 13611389, https://doi.org/10.1175/JCLI-D-11-00091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derakhti, M., J. Thomson, and J. T. Kirby, 2020: Sparse sampling of intermittent turbulence generated by breaking surface waves. J. Phys. Oceanogr., 50, 867885, https://doi.org/10.1175/JPO-D-19-0138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dohan, K., and R. E. Davis, 2011: Mixing in the transition layer during two storm events. J. Phys. Oceanogr., 41, 4266, https://doi.org/10.1175/2010JPO4253.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, S., J. Sprintall, S. T. Gille, and L. Talley, 2008: Southern Ocean mixed-layer depth from Argo float profiles. J. Geophys. Res., 113, C06013, https://doi.org/10.1029/2006JC004051.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du Plessis, M., S. Swart, I. J. Ansorge, A. Mahadevan, and A. F. Thompson, 2019: Southern Ocean seasonal restratification delayed by submesoscale wind–front interactions. J. Phys. Oceanogr., 49, 10351053, https://doi.org/10.1175/JPO-D-18-0136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Edson, J. B., and Coauthors, 2013: On the exchange of momentum over the open ocean. J. Phys. Oceanogr., 43, 15891610, https://doi.org/10.1175/JPO-D-12-0173.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fer, I., A. K. Peterson, and J. E. Ullgren, 2014: Microstructure measurements from an underwater glider in the turbulent Faroe bank channel overflow. J. Atmos. Oceanic Technol., 31, 11281150, https://doi.org/10.1175/JTECH-D-13-00221.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferris, L., 2022: Across-scale energy transfer in the Southern Ocean. Ph.D. dissertation, Dept. of Physical Sciences, Virginia Institute of Marine Science, William and Mary, 190 pp.

    • Search Google Scholar
    • Export Citation
  • Ferris, L., D. Gong, T. Ijichi, S. T. Merrifield, J. Shapiro, and L. St. Laurent, 2020: Turbulent Dissipation Rate and Mixing Variations in the Polar Front of the Southern Ocean. Ocean Sciences Meeting 2020, San Diego, CA, Amer. Geophys. Union, PS51A-04, https://agu.confex.com/agu/osm20/meetingapp.cgi/Paper/648069.

    • Search Google Scholar
    • Export Citation
  • Fox-Kemper, B., L. Johnson, and F. Qiao, 2022: Ocean near-surface layers. Ocean Mixing: Drivers, Mechanisms and Impacts, M. Meredith and A. N. Garabato, Eds., Elsevier, 6594, https://doi.org/10.1016/B978-0-12-821512-8.00011-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gemmrich, J. R., T. D. Mudge, and V. D. Polonichko, 1994: On the energy input from wind to surface waves. J. Phys. Oceanogr., 24, 24132417, https://doi.org/10.1175/1520-0485(1994)024<2413:OTEIFW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerbi, G. P., J. H. Trowbridge, E. A. Terray, A. J. Plueddemann, and T. Kukulka, 2009: Observations of turbulence in the ocean surface boundary layer: Energetics and transport. J. Phys. Oceanogr., 39, 10771096, https://doi.org/10.1175/2008JPO4044.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hwang, P. A., 2009: Estimating the effective energy transfer velocity at air-sea interface. J. Geophys. Res., 114, C11011, https://doi.org/10.1029/2009JC005497.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kantha, L. H., and C. A. Clayson, 2004: On the effect of surface gravity waves on mixing in the oceanic mixed layer. Ocean Modell., 6, 101124, https://doi.org/10.1016/S1463-5003(02)00062-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Large, W. G., J. C. McWilliams, and S. C. Doney, 1994: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization. Rev. Geophys., 32, 363403, https://doi.org/10.1029/94RG01872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Large, W. G., E. G. Patton, A. K. DuVivier, P. P. Sullivan, and L. Romero, 2019: Similarity theory in the surface layer of large-eddy simulations of the wind-, wave-, and buoyancy-forced Southern Ocean. J. Phys. Oceanogr., 49, 21652187, https://doi.org/10.1175/JPO-D-18-0066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Q., and Coauthors, 2019: Comparing ocean surface boundary vertical mixing schemes including Langmuir turbulence. J. Adv. Model., 11, 35453592, https://doi.org/10.1029/2019MS001810.

    • Search Google Scholar
    • Export Citation
  • Lombardo, C. P., and M. C. Gregg, 1989: Similarity scaling of viscous and thermal dissipation in a convecting surface boundary layer. J. Geophys. Res., 94, 62736284, https://doi.org/10.1029/JC094iC05p06273.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacKinnon, J., L. St. Laurent, and A. C. Naveira Garabato, 2013: Diapycnal mixing processes in the ocean interior. Ocean Circulation and Climate: A 21st Century Perspective, 2nd ed. G. Siedler et al. Eds., Academic Press, 159184.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McWilliams, J. C., P. P. Sullivan, and C. H. Moeng, 1997: Langmuir turbulence in the ocean. J. Fluid Mech., 334, 130, https://doi.org/10.1017/S0022112096004375.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McWilliams, J. C., E. Huckle, J. Liang, and P. P. Sullivan, 2014: Langmuir turbulence in swell. J. Phys. Oceanogr., 44, 870890, https://doi.org/10.1175/JPO-D-13-0122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merckelbach, L., A. Berger, G. Krahmann, M. Dengler, and J. R. Carpenter, 2019: A dynamic flight model for Slocum gliders and implications for turbulence microstructure measurements. J. Atmos. Oceanic Technol., 36, 281296, https://doi.org/10.1175/JTECH-D-18-0168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merrifield, S. T., 2016: Mechanisms for enhanced turbulence in the Drake Passage region of the Southern Ocean. Ph.D. dissertation, Massachusetts Institute of Technology, 134 pp.

    • Search Google Scholar
    • Export Citation
  • Merrifield, S. T., L. S. Laurent, B. Owens, A. M. Thurnherr, and J. M. Toole, 2016: Enhanced diapycnal diffusivity in intrusive regions of the Drake Passage. J. Phys. Oceanogr., 46, 13091321, https://doi.org/10.1175/JPO-D-15-0068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monin, A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Tr. Geofiz. Inst., Akad. Nauk SSSR, 24, 163187.

    • Search Google Scholar
    • Export Citation
  • Moum, J. N., 2021: Variations in ocean mixing from seconds to years. Annu. Rev. Mar. Sci., 13, 201226, https://doi.org/10.1146/annurev-marine-031920-122846.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sallée, J.-B., K. Speer, S. Rintoul, and S. Wijffels, 2010: Southern ocean thermocline ventilation. J. Phys. Oceanogr., 40, 509529, https://doi.org/10.1175/2009JPO4291.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, W. H. F., and D. T. Sandwell, 1997: Global sea floor topography from satellite altimetry and ship depth soundings. Science, 277, 19561962, https://doi.org/10.1126/science.277.5334.1956.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • St. Laurent, L., and S. Merrifield, 2017: Measurements of near-surface turbulence and mixing from autonomous ocean gliders. Oceanography, 30, 116125, https://doi.org/10.5670/oceanog.2017.231.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • St. Laurent, L., A. C. Naveira Garabato, J. R. Ledwell, A. M. Thurnherr, J. M. Toole, and A. J. Watson, 2012: Turbulence and diapycnal mixing in drake passage. J. Phys. Oceanogr., 42, 21432152, https://doi.org/10.1175/JPO-D-12-027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sullivan, P. P., J. C. McWilliams, and W. K. Melville, 2007: Surface gravity wave effects in the oceanic boundary layer: Large-eddy simulation with vortex force and stochastic breakers. J. Fluid Mech., 593, 405452, https://doi.org/10.1017/S002211200700897X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sutherland, G., B. Ward, and K. H. Christensen, 2013: Wave-turbulence scaling in the ocean mixed layer. Ocean Sci., 9, 597608, https://doi.org/10.5194/os-9-597-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sutherland, G., K. H. Christensen, and B. Ward, 2014: Evaluating Langmuir turbulence parameterizations in the ocean surface boundary layer. J. Geophys. Res. Oceans, 119, 18991910, https://doi.org/10.1002/2013JC009537.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sutherland, G., L. Marié, G. Reverdin, K. H. Christensen, G. Broström, and B. Ward, 2016: Enhanced turbulence associated with the diurnal jet in the ocean surface boundary layer. J. Phys. Oceanogr., 46, 30513067, https://doi.org/10.1175/JPO-D-15-0172.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Talley, L. D., 1996: Antarctic intermediate water in the South Atlantic. The South Atlantic. G. Wefer et al., Eds., Springer, 219238.

  • Terray, E. A., M. A. Donelan, Y. C. Agrawal, W. M. Drennan, K. K. Kahma, A. J. Williams, P. A. Hwan, and S. A. Kitaigorodskii, 1996: Estimates of kinetic energy dissipation under breaking waves. J. Phys. Oceanogr., 26, 792807, https://doi.org/10.1175/1520-0485(1996)026<0792:EOKEDU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thomson, J., M. S. Schwendeman, S. F. Zippel, S. Moghimi, J. Gemmrich, and W. E. Rogers, 2016: Wave-breaking turbulence in the ocean surface layer. J. Phys. Oceanogr., 46, 18571870, https://doi.org/10.1175/JPO-D-15-0130.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thorpe, S. A., 2005: The Turbulent Ocean. Cambridge University Press, 439 pp.

  • Umlauf, L., H. Burchard, and K. Bolding, 2005: General Ocean Turbulence Model: Source code documentation. Baltic Sea Research Institute Warnemünde Tech. Rep. 63, 346 pp.

    • Search Google Scholar
    • Export Citation
  • Waterhouse, A. F., and Coauthors, 2014: Global patterns of diapycnal mixing from measurements of the turbulent dissipation rate. J. Phys. Oceanogr., 44, 18541872, https://doi.org/10.1175/JPO-D-13-0104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., J. Scott, R. Hoffman, M. Leidner, R. Atlas, and J. Ardizzone, 2015: Remote Sensing Systems Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, version 2.0. Remote Sensing Systems, accessed 24 January 2019, www.remss.com/measurements/ccmp.

    • Search Google Scholar
    • Export Citation
  • Zippel, S. F., and Coauthors, 2021: Moored turbulence measurements using pulse-coherent Doppler sonar. J. Atmos. Oceanic Technol, 38, 16211639, https://doi.org/10.1175/JTECH-D-21-0005.1.

    • Search Google Scholar
    • Export Citation
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