The formation and fate of internal waves in the South China Sea

Journal name:
Nature
Volume:
521,
Pages:
65–69
Date published:
DOI:
doi:10.1038/nature14399
Received
Accepted
Published online

Internal gravity waves, the subsurface analogue of the familiar surface gravity waves that break on beaches, are ubiquitous in the ocean. Because of their strong vertical and horizontal currents, and the turbulent mixing caused by their breaking, they affect a panoply of ocean processes, such as the supply of nutrients for photosynthesis1, sediment and pollutant transport2 and acoustic transmission3; they also pose hazards for man-made structures in the ocean4. Generated primarily by the wind and the tides, internal waves can travel thousands of kilometres from their sources before breaking5, making it challenging to observe them and to include them in numerical climate models, which are sensitive to their effects6, 7. For over a decade, studies8, 9, 10, 11 have targeted the South China Sea, where the oceans’ most powerful known internal waves are generated in the Luzon Strait and steepen dramatically as they propagate west. Confusion has persisted regarding their mechanism of generation, variability and energy budget, however, owing to the lack of in situ data from the Luzon Strait, where extreme flow conditions make measurements difficult. Here we use new observations and numerical models to (1) show that the waves begin as sinusoidal disturbances rather than arising from sharp hydraulic phenomena, (2) reveal the existence of >200-metre-high breaking internal waves in the region of generation that give rise to turbulence levels >10,000 times that in the open ocean, (3) determine that the Kuroshio western boundary current noticeably refracts the internal wave field emanating from the Luzon Strait, and (4) demonstrate a factor-of-two agreement between modelled and observed energy fluxes, which allows us to produce an observationally supported energy budget of the region. Together, these findings give a cradle-to-grave picture of internal waves on a basin scale, which will support further improvements of their representation in numerical climate predictions.

At a glance

Figures

  1. Overview of internal waves in the South China Sea.
    Figure 1: Overview of internal waves in the South China Sea.

    a, Vertical displacement of ocean layers near 200 m depth from the far-field numerical simulation. Orange and blue indicate upward and downward displacements, respectively. b, A collage of synthetic aperture radar images taken on 12 August 2009, 04 August 2009 and 25 April 2005. Wave fronts are visible because they focus surface waves, increasing sea surface roughness. Red and blue correspond to greater and less surface roughness, respectively. c, Instrumentation deployed during IWISE. The Kuroshio Current is sketched schematically. PIES, pressure inverted echo sounder. d, Time series (5–20 August 2011) of depth-averaged tidal current in Luzon Strait over a spring/neap cycle, showing the presence of once-daily (diurnal, D1) and twice-daily (semidiurnal, D2) frequencies. Inset, globe showing location of Luzon Strait and South China Sea.

  2. Near-field processes in the Luzon Strait.
    Figure 2: Near-field processes in the Luzon Strait.

    a, Time-mean total energy flux from the near-field numerical model (white arrows) and field measurements (coloured arrows). LADCP, lowered acoustic Doppler current profiler measurements (see Methods). b, c, Snapshots from the two-dimensional model showing internal wave breaking at the location indicated in a by the green star, corresponding to times T0 and T1 indicated in f. Colours and lines in b and c indicate east–west velocity and density contours, respectively. d, Corresponding field measurements at the location of the vertical dashed line in b and c. e, Depth-averaged dissipation rate computed from Thorpe scales. f, Depth-integrated eastward tidal transport, showing the times T0, T1 of the frames in b and c.

  3. Internal wave energy fluxes in the South China Sea.
    Figure 3: Internal wave energy fluxes in the South China Sea.

    a, b, Semidiurnal (a) and diurnal (b) energy flux from the far-field model. c, Energy flux along 21° N. Arrows in c indicate integrated energy fluxes (numbers on arrows are fluxes in kilowatts per metre) at 21° N in the semidiurnal and diurnal internal tides and in the solitary or nonlinear internal waves (NLIW). Flux values at 120° E are from the near-field model; flux and dissipation values at 115.19° E, 117.25° E and 117.895° E are from observations (see Methods). d, Bathymetry along 21° N. The processes of generation, breaking, propagation, steepening and dissipation are shown schematically.

  4. The Kuroshio and its impact on wave propagation.
    Figure 4: The Kuroshio and its impact on wave propagation.

    a, Observed (green) and modelled (grey) Kuroshio flow during June–August 2011 in the Luzon Strait region. The meshes are modelled phase lines of internal waves during February 2006 (red) and February 2011 (blue). KS model indicates the NRL Kuroshio model (see Methods). b, c, Measured wave displacement at the locations shown in a. Waves were observed year-round at the southern station in 2011 (c), but not at the northern station in 2006 (b), when the Kuroshio Current deflected the internal wave paths southward (a, red).

  5. Comparison of observed and model energy flux.
    Extended Data Fig. 1: Comparison of observed and model energy flux.

    Left, Scatter plot of flux magnitude F from observations (x axis) and far-field (UAF; University of Alaska Fairbanks) model (y axis). Error bars are ±20% for observed values and ±10% for model values (see Methods). Right, As for the left panel, but for direction θ; error bars are ±30°. See source data and ref. 15 for station locations. Black, semi-diurnal; red, diurnal.

Tables

  1. Conversion and radiated flux integrated over the region 19[deg] to 21.5[deg] N, 120[deg] to 122.5[deg] E.
    Extended Data Table 1: Conversion and radiated flux integrated over the region 19° to 21.5° N, 120° to 122.5° E.

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Author information

  1. Present address: School of Earth and Ocean Sciences, University of Victoria, British Columbia V8P 5C, Canada.

    • David M. Farmer

Affiliations

  1. Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92037, USA

    • Matthew H. Alford,
    • Jennifer A. MacKinnon,
    • Luca R. Centuroni,
    • T. M. Shaun Johnston,
    • Ruth Musgrave,
    • Robert Pinkel &
    • Daniel L. Rudnick
  2. University of Washington, Seattle, Washington 98105, USA

    • Matthew H. Alford,
    • Ren-Chieh Lien,
    • Andrew I. Pickering &
    • Luc Rainville
  3. Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA

    • Thomas Peacock &
    • Andrew I. Pickering
  4. Oregon State University, Corvallis, Oregon 97370, USA

    • Jonathan D. Nash &
    • James N. Moum
  5. University of Southern Mississippi, Stennis Space Center, Mississippi 39529, USA

    • Maarten C. Buijsman
  6. University of Maryland, Cambridge, Maryland 21613, USA

    • Shenn-Yu Chao
  7. Institute of Oceanography, National Taiwan University, Taipei 10617, Taiwan

    • Ming-Huei Chang,
    • Sen Jan,
    • Joe Wang,
    • Yiing J. Yang &
    • Tswen-Yung (David) Tang
  8. University of Rhode Island, Rhode Island 02882, USA

    • David M. Farmer
  9. Stanford University, Stanford, California 94305, USA

    • Oliver B. Fringer
  10. National Sun-Yat Sen University, Kaohsiung 80424, Taiwan

    • Ke-Hsien Fu,
    • I-Huan Lee &
    • Yu-Huai Wang
  11. Naval Research Laboratories (NRL), Stennis Space Center, Mississippi 39529, USA

    • Patrick C. Gallacher &
    • Dong S. Ko
  12. University of Miami, Miami, Florida 33149, USA

    • Hans C. Graber
  13. Woods Hole Oceanographic Institution, Falmouth, Massachusetts 02543, USA

    • Karl R. Helfrich &
    • Louis C. St Laurent
  14. Florida Institute of Technology, Melbourne, Florida 32901, USA

    • Steven M. Jachec
  15. Global Ocean Associates, Alexandria, Virginia 22310, USA

    • Christopher R. Jackson
  16. University of Victoria, Victoria, British Columbia V8W 3P6, Canada

    • Jody M. Klymak
  17. Princeton University, New Jersey 08542, USA

    • Sonya Legg
  18. Institut de Mécanique des Fluides de Toulouse, Toulouse 31400, France

    • Matthieu J. Mercier
  19. Korea Institute of Ocean Science and Technology, Ansan 426–744, South Korea

    • Jae-Hun Park
  20. Soliton Ocean Services, Carmel, California 93924, USA

    • Steven R. Ramp
  21. University of California San Diego, La Jolla, California 92037, USA

    • Sutanu Sarkar
  22. University of North Carolina, Chapel Hill, North Carolina 25599, USA

    • Alberto Scotti
  23. University of Alaska at Fairbanks, Fairbanks, Alaska 99775, USA

    • Harper L. Simmons
  24. Colorado State University, Fort Collins, Colorado 80523, USA

    • Subhas K. Venayagamoorthy
  25. Office of Naval Research, Arlington, Virginia, USA

    • Theresa Paluszkiewicz

Contributions

All authors contributed to the paper in multiple ways. Primary writing: M.H.A., T. Peacock, J.A.M. & J.D.N. Synthesis and overall coordination: T. Paluszkiewicz & T.-Y.T. Energy flux calculations: M.H.A. & A.I.P. Energy budget calculation: M.H.A., M.C.B., M.-H.C., R.-C.L., J.M.K. & L.C.S.L. Near-field moorings and calculations: M.H.A., A.I.P., L.R., J.D.N., J.N.M. & M.-H.C. Far-field moorings and calculations: L.R.C., M.-H.C., R.-C.L., S.R.R., Y.J.Y. & T.-Y.T. Near-field CTD measurements (Fig. 2d): R.P. & R.M. Near-field lowered acoustic Doppler current profiler measurements: M.H.A., J.D.N., J.A.M., L.R., H.L.S., A.I.P & R.M. Pressure inverted echo sounder measurements: D.M.F., J.-H.P., Y.J.Y. & M.H.A. Microstructure measurements: L.C.S.L., K.-H.F., H.L.S. & Y.-H.W. Remote sensing: C.R.J. & H.C.G. Theory: K.R.H. & D.M.F. Glider measurements: T.M.S.J. & D.L.R. Regional contextualization and logistical support: S.-Y.C., I-H.L., S.R.R., J.W., Y.J.Y. & T.-Y.T. Far-field modelling: S.J. & H.L.S. Two-dimensional modelling: J.M.K., S.S., S.M.J., A.S., R.M. & K.V. Near-field modelling: M.C.B., O.B.F., S.L. & S.M.J. Kuroshio modelling: P.C.G., S.J. & D.S.K. Laboratory measurements: T. Peacock & M.J.M.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

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Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Comparison of observed and model energy flux. (221 KB)

    Left, Scatter plot of flux magnitude F from observations (x axis) and far-field (UAF; University of Alaska Fairbanks) model (y axis). Error bars are ±20% for observed values and ±10% for model values (see Methods). Right, As for the left panel, but for direction θ; error bars are ±30°. See source data and ref. 15 for station locations. Black, semi-diurnal; red, diurnal.

Extended Data Tables

  1. Extended Data Table 1: Conversion and radiated flux integrated over the region 19° to 21.5° N, 120° to 122.5° E. (35 KB)

Additional data