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Abstract We present improvements in the modeling of the vertical wavenumber spectrum of the internal gravity wave continuum in high‐resolution regional ocean simulations. We focus on model sensitivities to mixing parameters and comparisons to McLane moored profiler observations in a Pacific region near the Hawaiian Ridge, which features strong semidiurnal tidal beams. In these simulations, the modeled continuum exhibits high sensitivity to the background mixing components of the K‐Profile Parameterization (KPP) vertical mixing scheme. Without the KPP background mixing, stronger vertical gradients in velocity are sustained in the simulations and the modeled kinetic energy and shear spectral slopes are significantly closer to the observations. The improved representation of internal wave dynamics in these simulations makes them suitable for improving ocean mixing estimates and for the interpretation of satellite missions such as the Surface Water and Ocean Topography mission.
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Breaking Internal Waves and Ocean Diapycnal Diffusivity in a High‐Resolution Regional Ocean Model: Evidence of a Wave‐Turbulence Cascade
Abstract It is generally understood that the origin of ocean diapycnal diffusivity is primarily associated with the stratified turbulence produced by breaking internal (gravity) waves (IW). However, it requires significant effort to verify diffusivity values in ocean general circulation models in any particular geographical region of the ocean due to the scarcity of microstructure measurements. Recent analyses of downscaled IW fields from an internal‐wave‐admitting global ocean simulation into higher‐resolution regional configurations northwest of Hawaii have demonstrated a much‐improved fit of the simulated IW spectra to the in‐situ profiler measurements such as the Garrett‐Munk (GM) spectrum. Here, we employ this dynamically downscaled ocean simulation to directly analyze the nature of the IW‐breaking and the wave‐turbulence cascade in this region. We employ a modified version of the Kappa Profile Parameterization (KPP) to infer what the horizontally averaged vertical profile of diapycnal diffusivity should be, and compare this to the background profile that would be employed in the ocean component of a low‐resolution coupled climate model such as the Community Earth System Model (CESM) of the US National Center for Atmospheric Research (NCAR). In pursuing this goal, we also demonstrate that the wavefield in the high‐resolution regional domain is dominated by a well‐resolved spectrum of low‐mode IWs that are predictable by solving an appropriate eigenvalue problem for stratified flow. We finally suggest a new tentative approach to improve the KPP parameterization.
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- Award ID(s):
- 2241495
- PAR ID:
- 10572877
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Oceans
- Volume:
- 129
- Issue:
- 6
- ISSN:
- 2169-9275
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract This study utilizes Deep Neural Networks (DNN) to improve the K‐Profile Parameterization (KPP) for the vertical mixing effects in the ocean's surface boundary layer turbulence. The deep neural networks were trained using 11‐year turbulence‐resolving solutions, obtained by running a large eddy simulation model for Ocean Station Papa, to predict the turbulence velocity scale coefficient and unresolved shear coefficient in the KPP. The DNN‐augmented KPP schemes (KPP_DNN) have been implemented in the General Ocean Turbulence Model (GOTM). The KPP_DNN is stable for long‐term integration and more efficient than existing variants of KPP schemes with wave effects. Three different KPP_DNN schemes, each differing in their input and output variables, have been developed and trained. The performance of models utilizing the KPP_DNN schemes is compared to those employing traditional deterministic first‐order and second‐moment closure turbulent mixing parameterizations. Solution comparisons indicate that the simulated mixed layer becomes cooler and deeper when wave effects are included in parameterizations, aligning closer with observations. In the KPP framework, the velocity scale of unresolved shear, which is used to calculate ocean surface boundary layer depth, has a greater impact on the simulated mixed layer than the magnitude of diffusivity does. In the KPP_DNN, unresolved shear depends not only on wave forcing, but also on the mixed layer depth and buoyancy forcing.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1029/2023JC020509
