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Jennifer A. MacKinnon
,
Zhongxiang Zhao
,
Caitlin B. Whalen
,
Amy F. Waterhouse
,
David S. Trossman
,
Oliver M. Sun
,
Louis C. St. Laurent
,
Harper L. Simmons
,
Kurt Polzin
,
Robert Pinkel
,
Andrew Pickering
,
Nancy J. Norton
,
Jonathan D. Nash
,
Ruth Musgrave
,
Lynne M. Merchant
,
Angelique V. Melet
,
Benjamin Mater
,
Sonya Legg
,
William G. Large
,
Eric Kunze
,
Jody M. Klymak
,
Markus Jochum
,
Steven R. Jayne
,
Robert W. Hallberg
,
Stephen M. Griffies
,
Steve Diggs
,
Gokhan Danabasoglu
,
Eric P. Chassignet
,
Maarten C. Buijsman
,
Frank O. Bryan
,
Bruce P. Briegleb
,
Andrew Barna
,
Brian K. Arbic
,
Joseph K. Ansong
, and
Matthew H. Alford

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 spatiotemporal 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 5 years and under the auspices of U.S. Climate Variability and Predictability Program (CLIVAR), a National Science Foundation (NSF)- and National Oceanic and Atmospheric Administration (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.

Open access