A Comparative Study of Vertical Mixing Schemes in Modeling the Bay of Bengal Dynamics. Issue 8 (5th August 2022) - Tirodkar, Siddhesh; Murtugudde, Raghu; Behera, Manasa R.; Balasubramanian, Sridhar
Abstract: The choice of vertical mixing scheme in ocean models plays an important role in modeling the surface and subsurface circulation and the vertical structure. This work performs a comparative study between K‐profile parametrization (KPP) and k ‐ ϵ mixing schemes for a regional domain in the Bay of Bengal (BoB) using the Modular Ocean Model version 5 (MOM5). It is observed that sea surface temperature (SST) and the mixed layer depth (MLD) show significant improvement with the k ‐ ϵ mixing scheme. Energetic analysis shows that changes in the viscous dissipation and turbulent buoyancy flux are the primary reason for improvement with k ‐ ϵ . The overestimation of viscous dissipation in the KPP scheme is corrected by k ‐ ϵ, resulting in a deeper mixed layer closer to observations. The tendency of buoyancy flux to retain stability in the water column also results in a better representation of SST in k ‐ ϵ . Overall, we conclude that the k ‐ ϵ mixing scheme works better for the BoB region. Plain Language Summary: A comparison of two different vertical mixing schemes, namely K‐profile parametrization (KPP) and k ‐ ϵ, is performed in the Bay of Bengal region using the Modular Ocean Model. Basic ocean properties such as the sea surface temperature (SST), sea surface salinity (SST), and mixed layer depth (MLD) are computed and analyzed using these two mixing schemes. The results show that k ‐ ϵ is a superior mixing scheme for representing the circulation and vertical structure. The characterization of eddy kinetic energy, buoyancy flux, and dissipation rate reveals the physics behind improvements brought about by using the k ‐ ϵ scheme. Our study suggests that k ‐ ϵ is a better model for representing the Bay of Bengal dynamics. Key Points: k ‐ ϵ vertical mixing scheme gives better results for surface and subsurface ocean circulation compared to K‐profile parameterization (KPP) SST and MLD biases show significant reduction with k ‐ ϵ mixing scheme Improved representation of turbulent buoyancy flux and viscous dissipation is the primary reason for the superior performance of k ‐ ϵ mixing scheme