TY - JOUR
T1 - Improvement of the Ocean Mixed Layer Model via Large-Eddy Simulation and Inverse Estimation
AU - Choi, Yeonju
AU - Noh, Yign
AU - Hirose, Naoki
AU - Song, Hajoon
N1 - Funding Information:
This work was supported by the Korea Meteorological Administration Research and Development Program under Grant KMI2021-01512 and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020R1A4A1016537). This work was carried out by utilizing the supercomputer system at the National Center for Meteorological Supercomputer of the Korea Meteorological Administration.
Funding Information:
Acknowledgments. This work was supported by the Korea Meteorological Administration Research and Development Program under Grant KMI2021-01512 and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020R1A4A1016537). This work was carried out by utilizing the supercomputer system at the National Center for Meteorological Supercomputer of the Korea Meteorological Administration.
Publisher Copyright:
© The Authors.
PY - 2022/10
Y1 - 2022/10
N2 - The ocean mixed layer model (OMLM) is improved using the large-eddy simulation (LES) and the inverse estimation method. A comparison of OMLM (Noh model) and LES results reveals that underestimation of the turbulent kinetic energy (TKE) flux in the OMLM causes a negative bias of the mixed layer depth (MLD) during convection, when the wind stress is weak or the latitude is high. It is further found that the entrainment layer thickness is underestimated. The effects of alternative approaches of parameterizations in the OMLM, such as nonlocal mixing, length scales, Prandtl number, and TKE flux, are examined with an aim to reduce the bias. Simultaneous optimizations of empirical constants in the various versions of Noh model with different parameterization options are then carried out via an iterative Green’s function approach with LES data as constraining data. An improved OMLM is obtained, which reflects various new features, including the enhanced TKE flux, and the new model is found to improve the performance in all cases, namely, wind-mixing, surface heating, and surface cooling cases. The effect of the OMLM grid resolution on the optimal empirical constants is also investigated.
AB - The ocean mixed layer model (OMLM) is improved using the large-eddy simulation (LES) and the inverse estimation method. A comparison of OMLM (Noh model) and LES results reveals that underestimation of the turbulent kinetic energy (TKE) flux in the OMLM causes a negative bias of the mixed layer depth (MLD) during convection, when the wind stress is weak or the latitude is high. It is further found that the entrainment layer thickness is underestimated. The effects of alternative approaches of parameterizations in the OMLM, such as nonlocal mixing, length scales, Prandtl number, and TKE flux, are examined with an aim to reduce the bias. Simultaneous optimizations of empirical constants in the various versions of Noh model with different parameterization options are then carried out via an iterative Green’s function approach with LES data as constraining data. An improved OMLM is obtained, which reflects various new features, including the enhanced TKE flux, and the new model is found to improve the performance in all cases, namely, wind-mixing, surface heating, and surface cooling cases. The effect of the OMLM grid resolution on the optimal empirical constants is also investigated.
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U2 - 10.1175/JTECH-D-21-0157.1
DO - 10.1175/JTECH-D-21-0157.1
M3 - Article
AN - SCOPUS:85139511562
SN - 0739-0572
VL - 39
SP - 1483
EP - 1498
JO - Journal of Atmospheric and Oceanic Technology
JF - Journal of Atmospheric and Oceanic Technology
IS - 10
ER -