Turbulent and numerical mixing in a salt wedge estuary: Dependence on grid resolution, bottom roughness, and turbulence closure

The Connecticut River is a tidal salt wedge estuary, where advection of sharp salinity gradients through channel constrictions and over steeply sloping bathymetry leads to spatially heterogeneous stratification and mixing. A 3‐D unstructured grid finite‐volume hydrodynamic model (FVCOM) was evaluated against shipboard and moored observations, and mixing by both the turbulent closure and numerical diffusion were calculated. Excessive numerical mixing in regions with strong velocities, sharp salinity gradients, and steep bathymetry reduced model skill for salinity. Model calibration was improved by optimizing both the bottom roughness (z0), based on comparison with the barotropic tidal propagation, and the mixing threshold in the turbulence closure (steady state Richardson number, Rist), based on comparison with salinity. Whereas a large body of evidence supports a value of Rist ∼ 0.25, model skill for salinity improved with Rist ∼ 0.1. With Rist = 0.25, numerical mixing contributed about 1/2 the total mixing, while with Rist = 0.10 it accounted for ∼2/3, but salinity structure was more accurately reproduced. The combined contributions of numerical and turbulent mixing were quantitatively consistent with high‐resolution measurements of turbulent mixing. A coarser grid had increased numerical mixing, requiring further reductions in turbulent mixing and greater bed friction to optimize skill. The optimal Rist for the fine grid case was closer to 0.25 than for the coarse grid, suggesting that additional grid refinement might correspond with Rist approaching the theoretical limit. Numerical mixing is rarely assessed in realistic models, but comparisons with high‐resolution observations in this study suggest it is an important factor. Key Points Numerical mixing is a major part of the total mixing in a high‐resolution model of a highly stratified estuary with steep bathymetry Model skill is improved by reducing the turbulent mixing to compensate for excessive numerical mixing High‐resolution observations identify model errors and show that numerical mixing corresponds with regions of observed turbulent mixing