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Abstract The Surface Water and Ocean Topography (SWOT) satellite is expected to observe sea surface height (SSH) down to scales approaching ∼15 km, revealing submesoscale patterns that have never before been observed on global scales. Features at these soon‐to‐be‐observed scales, however, are expected to be significantly influenced by internal gravity waves, fronts, and other ageostrophic processes, presenting a serious challenge for estimating surface velocities from SWOT observations. Here we show that a data‐driven approach can be used to estimate the surface flow, particularly the kinematic signatures of smaller scale flows, from SSH observations, and that it performs significantly better than using the geostrophic relationship. We use a Convolutional 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‐divergence joint distributions, we show that the CNN works well when inertial gravity wave amplitudes are relatively weak. When the wave amplitudes are strong, reconstructions of vorticity and strain are less accurate; however, we find that the CNN naturally filters the wave‐divergence, making divergence a surprisingly reliable field to reconstruct. We also show that when applied to realistic simulations, a CNN model pretrained with simpler simulation data performs well, indicating a possible path forward for estimating real flow statistics with limited observations.

Abstract The thickness‐weighted average (TWA) framework, which treats the residual‐mean flow as the prognostic variable, provides a clear theoretical formulation of the eddy feedback onto the residual‐mean flow. The averaging operator involved in the TWA framework, although in theory being an ensemble mean, in practice has often been approximated by a temporal mean. Here, we analyze an ensemble of North Atlantic simulations at mesoscale‐permitting resolution (1/12°). We therefore recognize means and eddies in terms of ensemble means and fluctuations about those means. The ensemble dimension being orthogonal to the temporal and spatial dimensions negates the necessity for an arbitrary temporal or spatial scale in defining the eddies. Eddy‐mean flow feedbacks are encapsulated in the Eliassen‐Palm (E‐P) flux tensor and its convergence indicates that eddy momentum fluxes dominate in the separated Gulf Stream. The eddies can be interpreted to contribute to the zonal meandering of the Gulf Stream and a northward migration of it in the meridional direction. Downstream of the separated Gulf Stream in the North Atlantic Current region, the interfacial form stress convergence becomes leading order in the E‐P flux convergence.

Abstract Biological productivity in the Southern Ocean is limited by iron availability. Previous studies of iron supply have focused on mixed‐layer entrainment and diapycnal fluxes. However, the Southern Ocean is a region highly energetic mesoscale and submesoscale turbulence. Here we investigate the role of eddies in supplying iron to the euphotic zone, using a flat‐bottom zonally re‐entrant model, configured to represent the Antarctic Circumpolar Current region, that is coupled to a biogeochemical model with a realistic seasonal cycle. Eddies are admitted or suppressed by changing the model's horizontal resolution. We utilize cross spectral analysis and the generalized Omega equation to temporally and spatially decompose the vertical transport attributable to mesoscale and submesoscale motions. Our results suggest that the mesoscale vertical fluxes provide a first‐order pathway for transporting iron across the mixing‐layer base, where diapycnal mixing is weak, and must be included in modeling the open‐Southern Ocean iron budget.

Abstract We investigate the role of small‐scale, high‐frequency motions on lateral transport in the ocean, by using velocity fields and particle trajectories from an ocean general circulation model (MITgcm‐llc4320) that permits submesoscale flows, inertia‐gravity waves, and tides. Temporal averaging/filtering removes most of the submesoscale turbulence, inertia‐gravity waves, and tides, resulting in a largely geostrophic flow, with a rapid drop‐off in energy at scales smaller than the mesoscales. We advect two types of Lagrangian particles: (a) 2‐D particles (surface restricted) and (b) 3‐D particles (advected in full three dimensions) with the filtered and unfiltered velocities and calculate Lagrangian diagnostics. At large length/time scales, Lagrangian diffusivity is comparable for filtered and unfiltered velocities, while at short scales, unfiltered velocities disperse particles much faster. We also calculate diagnostics of Lagrangian coherent structures:rotationally coherent Lagrangian vortices detected from closed contours of the Lagrangian‐averaged vorticity deviation and material transport barriers formed by ridges of maximum finite‐time Lyapunov exponent. For temporally filtered velocities, we observe strong material coherence, which breaks down when the level of temporal filtering is reduced/removed, due to vigorous small‐scale mixing. In addition, for the lowest temporal resolution, the 3‐D particles experience very little vertical motion, suggesting that degrading temporal resolution greatly reduces vertical advection by high‐frequency motions. Our study suggests that Lagrangian diagnostics based on satellite‐derived surface geostrophic velocity fields, even with higher spatial resolutions as in the upcoming Surface Water and Ocean Topography mission, may overestimate the presence of mesoscale coherent structures and underestimate dispersion.