Estimating dissipation rates associated with double diffusion

Author Posting. © American Geophysical Union, 2021. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 48(15), (2021): e2021GL092779, https://doi.org/10.1029/2021GL0927...

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Bibliographic Details
Published in:Geophysical Research Letters
Main Authors: Middleton, Leo, Fine, Elizabeth C., MacKinnon, Jennifer A., Alford, Matthew H., Taylor, John R.
Format: Article in Journal/Newspaper
Language:unknown
Published: American Geophysical Union 2021
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Online Access:https://hdl.handle.net/1912/27740
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Summary:Author Posting. © American Geophysical Union, 2021. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 48(15), (2021): e2021GL092779, https://doi.org/10.1029/2021GL092779. Double diffusion refers to a variety of turbulent processes in which potential energy is released into kinetic energy, made possible in the ocean by the difference in molecular diffusivities between salinity and temperature. Here, we present a new method for estimating the kinetic energy dissipation rates forced by double-diffusive convection using temperature and salinity data alone. The method estimates the up-gradient diapycnal buoyancy flux associated with double diffusion, which is hypothesized to balance the dissipation rate. To calculate the temperature and salinity gradients on small scales we apply a canonical scaling for compensated thermohaline variance (or ‘spice’) on sub-measurement scales with a fixed buoyancy gradient. Our predicted dissipation rates compare favorably with microstructure measurements collected in the Chukchi Sea. Fine et al. (2018), https://doi.org/10.1175/jpo-d-18-0028.1, showed that dissipation rates provide good estimates for heat fluxes in this region. Finally, we show the method maintains predictive skill when applied to a sub-sampling of the Conductivity, Temperature, Depth (CTD) data. This work was supported by the Natural Environment Research Council (grant number NE/L002507/1).