2012, Oceanography 25(2):150–159, http://dx.doi.org/10.5670/oceanog.2012.50
Jody M. Klymak | School of Earth and Ocean Sciences, University of Victoria, Canada
Sonya Legg | Atmospheric and Ocean Sciences Program, Princeton University, Princeton, NJ, USA
Matthew H. Alford | Applied Physics Laboratory, and School of Oceanography, University of Washington, Seattle, WA, USA
Maarten Buijsman | Atmospheric and Ocean Sciences Program, Princeton University, Princeton, NJ, USA
Robert Pinkel | Marine Physics Laboratory, Scripps Institution of Oceanography, La Jolla, CA, USA
Jonathan D. Nash | College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA
Internal waves are often observed to break close to the seafloor topography that generates them, or from which they scatter. This breaking is often spectacular, with turbulent structures observed hundreds of meters above the seafloor, and driving turbulence dissipations and mixing up to 10,000 times open-ocean levels. This article provides an overview of efforts to observe and understand this turbulence, and to parameterize it near steep "supercritical" topography (i.e., topography that is steeper than internal wave energy characteristics). Using numerical models, we demonstrate that arrested lee waves are an important turbulence-producing phenomenon. Analogous to hydraulic jumps in water flowing over an obstacle in a stream, these waves are formed and then break during each tidal cycle. Similar lee waves are also observed in the atmosphere and in shallow fjords, but in those cases, their wavelengths are of similar scale to the topography, whereas in the ocean, they are small compared to the water depth and obstacle size. The simulations indicate that these nonlinear lee waves propagate against the generating flow (usually the tide) and are arrested because they have the same phase speed as the oncoming flow. This characteristic allows estimation of their size a priori and, using a linear model of internal tide generation, computation of how much energy they trap and turn into turbulence. This approach yields an accurate parameterization of mixing in numerical models, and these models are being used to guide a new generation of observations.
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