Comparison of the simulated upper-ocean vertical structure using 1-dimensional mixed-layer models

Abstract. Atmospheric fluxes influence the momentum and scalar properties in the upper-cean. Buoyancy fluxes result in a diurnal variability in the sea-surface temperature (SST), whereas the wind stress forms near-inertial currents in the mixed layer (ML). In this study, we investigate the contrasts between the simulated SST and the vertical structure of the temperature and shear by three different mixing models: the PWP bulk mixed-layer model, the KPP non-local boundary layer model and the κ−ϵ local mixing model. We choose two upper-ocean datasets for our studies, namely the SWAPP (1990) and the MLML (1991). The SWAPP dataset shows the presence of strong near-inertial shear below the ML and negligible near-inertial shear within the ML. The MLML dataset shows a negligible rise in the SST during the first 22 day mixing phase, which is followed by a steep rise by 6 °C during the subsequent 75 day restratification phase.

Comparison with the SWAPP dataset shows that the KPP and κ−ϵ models form strong shear near the surface due to weak eddy viscosities, thus producing a thin shear layer over the entire range of frequencies in the wind stress. At the ML base, the models form an inertial and a diurnal maximum. The inertial maximum extends over a substantial range of depths, and is continuous for the κ−ϵ model but discontinuous for the KPP and PWP models.

Comparison with the MLML dataset reveals that the KPP yields the largest SST amplitude over a 24-hour diurnal cycle, and is followed by the κ−ϵ and PWP. However, the net warming of SST at the end of the diurnal cycle is stronger for the PWP compared to κ−ϵ and KPP. The PWP also forms stronger temperature gradients at the ML base compared to κ−ϵ and KPP. Over multiple diurnal cycles, the shallowing and deepening of the mixed layer results in multiple sharp temperature gradients in PWP, thus forming a serrated vertical profile that remains unaffected during the restratification phase of the MLML dataset.