| Literature DB >> 30270923 |
Jennifer A MacKinnon1, Matthew H Alford1, Joseph K Ansong2, Brian K Arbic2, Andrew Barna1, Bruce P Briegleb3, Frank O Bryan3, Maarten C Buijsman4, Eric P Chassignet5, Gokhan Danabasoglu3, Steve Diggs1, Stephen M Griffies6, Robert W Hallberg6, Steven R Jayne7, Markus Jochum8, Jody M Klymak9, Eric Kunze10, William G Large3, Sonya Legg11, Benjamin Mater11, Angelique V Melet12, Lynne M Merchant1, Ruth Musgrave13, Jonathan D Nash14, Nancy J Norton3, Andrew Pickering14, Robert Pinkel1, Kurt Polzin7, Harper L Simmons15, Louis C St Laurent7, Oliver M Sun7, David S Trossman16, Amy F Waterhouse1, Caitlin B Whalen17, Zhongxiang Zhao17.
Abstract
Diapycnal mixing plays a primary role in the thermodynamic balance of the ocean and, consequently, in oceanic heat and carbon uptake and storage. Though observed mixing rates are on average consistent with values required by inverse models, recent attention has focused on the dramatic spatial variability, spanning several orders of magnitude, of mixing rates in both the upper and deep ocean. Away from ocean boundaries, the spatio-temporal patterns of mixing are largely driven by the geography of generation, propagation and dissipation of internal waves, which supply much of the power for turbulent mixing. Over the last five years and under the auspices of US CLIVAR, a NSF- and NOAA-supported Climate Process Team has been engaged in developing, implementing and testing dynamics-based parameterizations for internal-wave driven turbulent mixing in global ocean models. The work has primarily focused on turbulence 1) near sites of internal tide generation, 2) in the upper ocean related to wind-generated near inertial motions, 3) due to internal lee waves generated by low-frequency mesoscale flows over topography, and 4) at ocean margins. Here we review recent progress, describe the tools developed, and discuss future directions.Entities:
Year: 2017 PMID: 30270923 PMCID: PMC6157636 DOI: 10.1175/BAMS-D-16-0030.1
Source DB: PubMed Journal: Bull Am Meteorol Soc ISSN: 0003-0007 Impact factor: 8.766