A framework for evaluating simulated mixed layer depth biases as applied to MPAS-Ocean

Modeled oceanic mixed layer depth biases that are large in space and time have been well documented by previous work. While these biases are well known, the root cause is not well understood. Processes that influence stratification (e.g. vertical mixing and surface forcing) are most likely responsible for the observed biases. Here we analyze KPP simulated mixed layer depths in smaller, more constrained experiments, under a wide range of forcing conditions. These tests utilize analytic solutions, observations, and large eddy simulation (LES) as the reference solutions. This framework is applied to the Model for Prediction Across Scales-Ocean (MPAS-Ocean). MPAS-Ocean utilizes a horizontally unstructured mesh based on spherical centroidal voronoi tessellations, which allows for variability of horizontal resolution within a domain. In these tests KPP is chosen to represent vertical mixing in MPAS-Ocean. Results show that under a wide array of simulations, the simulated vertical heat and salt fluxes using KPP with standard parameters are too strong. Further, the simulated fluxes are strongly dependent on model resolution near the boundary layer. This framework is used to better constrain KPP parameters to improve the simulated stratification, and hence mixed layer depths, in MPAS-Ocean.