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Dylan Schlichting

and 2 more

In this work, the impacts of spurious numerical salinity mixing ($\mathcal{M}_{num}$) on the larger-scale flow and tracer fields are characterized using idealized simulations. The idealized model is motivated by realistic simulations of the Texas-Louisiana shelf and features oscillatory near-inertial wind forcing. $\mathcal{M}_{num}$ can exceed the physical mixing from the turbulence closure ($\mathcal{M}_{phy}$) in frontal zones and within the mixed layer. This suggests simulated mixing processes in frontal zones may be driven largely by $\mathcal{M}_{num}$. Near-inertial alongshore wind stress amplitude is varied to identify a base case that maximizes the ratio of $\mathcal{M}_{num}$ to $\mathcal{M}_{phy}$. We then we test the sensitivity of the base case with three tracer advection schemes (MPDATA, U3HC4, and HSIMT) and conduct ensemble runs with perturbed bathymetry. Instability growth is evaluated with several analysis methods: volume-integrated eddy kinetic energy ($EKE$) and available potential energy ($APE$), surface and bottom isohaline variability, and alongshore-averaged salinity sections. While all schemes have similar total mixing, HSIMT simulations have over double the volume-integrated $\mathcal{M}_{num}$ and 20\% less $\mathcal{M}_{phy}$ relative to other schemes, which suppresses the release of $APE$ and reduces the $EKE$ by roughly 25\%. HSIMT instabilities are confined shoreward relative to the other schemes. This results in reduced isohaline variability and steeper isopycnals, evidence that enhanced numerical mixing suppresses instability growth.

Qiyu Xiao

and 5 more

The Surface Water and Ocean Topography (SWOT) satellite is expected to observe the sea surface height (SSH) down to scales of ∼10-15 kilometers. While SWOT will reveal submesoscale SSH patterns that have never before been observed on global scales, how to extract the corresponding velocity fields and underlying dynamics from this data presents a new challenge. At these soon-to-be-observed scales, geostrophic balance is not sufficiently accurate, and the SSH will contain strong signals from inertial gravity waves — two problems that make estimating surface velocities non-trivial. Here we show that a data-driven approach can be used to estimate the surface flow, particularly the kinematic signatures of smaller scales flows, from SSH observations, and that it performs significantly better than directly using the geostrophic relationship. We use a Convolution Neural Network (CNN) trained on submesoscale-permitting high-resolution simulations to test the possibility of reconstructing surface vorticity, strain, and divergence from snapshots of SSH. By evaluating success using pointwise accuracy and vorticity-strain joint distributions, we show that the CNN works well when inertial gravity wave amplitudes are weak. When the wave amplitudes are strong, the model may produce distorted results; however, an appropriate choice of loss function can help filter waves from the divergence field, making divergence a surprisingly reliable field to reconstruct in this case. We also show that when applying the CNN model to realistic simulations, pretraining a CNN model with simpler simulation data improves the performance and convergence, indicating a possible path forward for estimating real flow statistics with limited observations.