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Statistics of bubble plumes generated by breaking surface waves
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  • Morteza Derakhti,
  • Jim Thomson,
  • Christopher S Bassett,
  • Mika Petteri Malila,
  • James T. Kirby
Morteza Derakhti
University of Washington

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Jim Thomson
University of Washington
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Christopher S Bassett
University of Washington
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Mika Petteri Malila
Norwegian Meteorological Institute
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James T. Kirby
University of Delaware
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Abstract

We examine the dependence of the penetration depth and fractional surface area (e.g., whitecap coverage) of bubble plumes generated by breaking surface waves on various wind and wave parameters over a wide range of sea state conditions in the North Pacific Ocean, including storms with sustained winds up to 22 ms$^{-1}$ and significant wave heights up to 10 m. Observations include arrays of freely drifting SWIFT buoys together with shipboard wind and optical video systems, which enabled concurrent high-resolution measurements of wind, waves, bubble plumes, and turbulence. We estimate bubble plume penetration depth from echograms that extend to more than 30 m depth in a surface-following reference frame collected by downward-looking echosounders integrated onboard the buoys. Our observations indicate that the mean and maximum bubble plume penetration depths exceed 10 m and 30 m beneath the surface at high winds, respectively, with a plume residence time of many wave periods. Bubble plume depths are well correlated with wind speeds, spectral wave steepness, and whitecap coverage. Plume depths scaled by total significant wave height are strongly linearly correlated with the inverse of wave age.
Plume depths scaled by either wind sea or total significant wave height vary non-monotonically with increasing wind speeds. Dependencies of the combined observations on various non-dimensional predictors used for whitecap coverage estimation are also explored. This study provides first field evidence of a direct relation between bubble plume penetration depth and whitecap coverage, suggesting that the volume of bubble plumes could be estimated by remote sensing.
24 Feb 2023Submitted to ESS Open Archive
27 Feb 2023Published in ESS Open Archive