FY 2024

The K-profile parameterization augmented by Deep Neural Networks (KPP_DNN) in the General Ocean Turbulence Model (GOTM)

Yuan, J., J.-H. Liang, E.P. Chassignet, O. Zavala-Romero, X. Wan, and M.F. Cronin

J. Adv. Model. Earth Syst., 16(9), e2024MS004405, doi: 10.1029/2024MS004405, View open access article at AGU/Wiley (external link) (2024)

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.

Plain Language Summary. The uppermost tens of meters of the ocean, known as the ocean surface boundary layer, are rich in intricate and chaotic fine-scale (cm to 100s m) ocean currents referred to as turbulence. These currents, spanning from centimeters to hundreds of meters, play pivotal roles in shaping the oceanic environment and influencing Earth's climate dynamics. Despite their significance, simulating these fine-scale ocean currents remains beyond the capabilities of current and foreseeable supercomputing resources. Consequently, simplified formulas derived from fundamental principles are commonly employed to approximate these currents in ocean and climate models. However, these approximations still cannot cover all types of choppy currents and uncertainties in these approximations represent a substantial source of bias in contemporary ocean and climate modeling endeavors. In this study, we enhance one of the prevalent physics-based approximations of fine-scale turbulent currents using machine learning techniques. Our tests show that integrating machine learning in physics-based approximation is stable and efficient and is suitable for use in ocean and climate models.