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Estuaries, as connectors between land and ocean, have complex interactions of river and tidal flows that affect the transport of buoyant materials like floating plastics, oil spills, organic matter, and larvae. This study investigates surface-trapped buoyant particle transport in estuaries by using idealized and realistic numerical simulations along with a theoretical model. While river discharge and estuarine exchange flow are usually expected to export buoyant particles to the ocean over subtidal timescales, this study reveals a ubiquitous physical transport mechanism that causes retention of buoyant particles in estuaries. Tidally varying surface convergence fronts affect the aggregation of buoyant particles, and the coupling between particle aggregation and oscillatory tidal currents leads to landward transport at subtidal timescales. Landward transport and retention of buoyant particles is greater in small estuaries, while large estuaries tend to export buoyant particles to the ocean. A dimensionless width parameter incorporating the tidal radian frequency and lateral velocity distinguishes small and large estuaries at a transitional value of around 1. Additionally, higher river flow tends to shift estuaries toward seaward transport and export of buoyant particles. These findings provide insights into understanding the distribution of buoyant materials in estuaries and predicting their fate in the land–sea exchange processes.

 

Recent years have seen a surge in interest for leveraging neural networks to parameterize small-scale or fast processes in climate and turbulence models. In this short paper, we point out two fundamental issues in this endeavor. The first concerns the difficulties neural networks may experience in capturing rare events due to limitations in how data is sampled. The second arises from the inherent multiscale nature of these systems. They combine high-frequency components (like inertia-gravity waves) with slower, evolving processes (geostrophic motion). This multiscale nature creates a significant hurdle for neural network closures. To illustrate these challenges, we focus on the atmospheric 1980 Lorenz model, a simplified version of the Primitive Equations that drive climate models. This model serves as a compelling example because it captures the essence of these difficulties.

 

A general, variational approach to derive low-order reduced models from possibly non-autonomous systems is presented. The approach is based on the concept of optimal parameterizing manifold (OPM) that substitutes more classical notions of invariant or slow manifolds when the breakdown of “slaving” occurs, i.e., when the unresolved variables cannot be expressed as an exact functional of the resolved ones anymore. The OPM provides, within a given class of parameterizations of the unresolved variables, the manifold that averages out optimally these variables as conditioned on the resolved ones. The class of parameterizations retained here is that of continuous deformations of parameterizations rigorously valid near the onset of instability. These deformations are produced through the integration of auxiliary backward–forward systems built from the model’s equations and lead to analytic formulas for parameterizations. In this modus operandi, the backward integration time is the key parameter to select per scale/variable to parameterize in order to derive the relevant parameterizations which are doomed to be no longer exact away from instability onset due to the breakdown of slaving typically encountered, e.g., for chaotic regimes. The selection criterion is then made through data-informed minimization of a least-square parameterization defect. It is thus shown through optimization of the backward integration time per scale/variable to parameterize, that skilled OPM reduced systems can be derived for predicting with accuracy higher-order critical transitions or catastrophic tipping phenomena, while training our parameterization formulas for regimes prior to these transitions takes place.

 

Recent high-resolution large-eddy simulations (LES) of a stable atmospheric boundary layer (SBL) with mesh sizes N=(5123,10243,20483) or mesh spacings ▵=(0.78,0.39,0.2) m are analyzed. The LES solutions are judged to be converged based on the good collapse of vertical profiles of mean winds, temperature, and low-order turbulence moments, i.e., fluxes and variances, with increasing N. The largest discrepancy is in the stably stratified region above the low-level jet. Subfilter-scale (SFS) motions are extracted from the LES with N=20483 and are compared to sonic anemometer fields from the horizontal array turbulence study (HATS) and its sequel over the ocean (OHATS). The results from the simulation and observations are compared using the dimensionless resolution ratio Λw/▵f where ▵f is the filter width and Λw is a characteristic scale of the energy-containing eddies in vertical velocity. The SFS motions from the observations and LES span the ranges 0.1<Λw/▵f<20 and are in good agreement. The small, medium, and large range of Λw/▵f correspond to Reynolds-averaged Navier–Stokes (RANS), the gray zone (a.k.a. “Terra Incognita”), and fine-resolution LES. The gray zone cuts across the peak in the energy spectrum and then flux parameterizations need to be adaptive and account for partially resolved flux but also “stochastic” flux fluctuations that represent the turbulent correlation between the fluctuating rate of strain and SFS flux tensors. LES data with mesh 20483 will be made available to the research community through the web and tools provided by the Johns Hopkins University Turbulence Database.

 

Surface gravity wave effects on currents (WEC) cause the emergence of Langmuir cells (LCs) in a suite of high horizontal resolution (Δx= 30 m), realistic oceanic simulations in the open ocean of central California. During large wave events, LCs develop widely but inhomogeneously, with larger vertical velocities in a deeper mixed layer. They interact with extant submesoscale currents. A 550-m horizontal spatial filter separates the signals of LCs and of submesoscale and larger-scale currents. The LCs have a strong velocity variance with small density gradient variance, while submesoscale currents are large in both. Using coarse graining, we show that WEC induces a forward cascade of kinetic energy in the upper ocean up to at least a 5-km scale. This is due to strong positive vertical Reynolds stress (in both the Eulerian and the Stokes drift energy production terms) at all resolved scales in the WEC solutions, associated with large vertical velocities. The spatial filter elucidates the role of LCs in generating the shear production on the vertical scale of Stokes drift (10 m), while submesoscale currents affect both the horizontal and vertical energy fluxes throughout the mixed layer (50–80 m). There is a slightly weaker forward cascade associated with nonhydrostatic LCs (by 13% in average) than in the hydrostatic case, but overall the simulation differences are small. A vertical mixing schemeK-profile parameterization (KPP) partially augmented by Langmuir turbulence yields wider LCs, which can lead to lower surface velocity gradients compared to solutions using the standard KPP scheme.

 

Abstract Glacial fjord circulation modulates the connection between marine-terminating glaciers and the ocean currents offshore. These fjords exhibit a complex 3D circulation with overturning and horizontal recirculation components, which are both primarily driven by water mass transformation at the head of the fjord via subglacial discharge plumes and distributed meltwater plumes. However, little is known about the 3D circulation in realistic fjord geometries. In this study, we present high-resolution numerical simulations of three glacial fjords (Ilulissat, Sermilik, and Kangerdlugssuaq), which exhibit along-fjord overturning circulations similar to previous studies. However, one important new phenomenon that deviates from previous results is the emergence of multiple standing eddies in each of the simulated fjords, as a result of realistic fjord geometries. These standing eddies are long-lived, take months to spin up, and prefer locations over the widest regions of deep-water fjords, with some that periodically merge with other eddies. The residence time of Lagrangian particles within these eddies are significantly larger than waters outside of the eddies. These eddies are most significant for two reasons: 1) they account for a majority of the vorticity dissipation required to balance the vorticity generated by discharge and meltwater plume entrainment and act to spin down the overall recirculation and 2) if the eddies prefer locations near the ice face, their azimuthal velocities can significantly increase melt rates. Therefore, the existence of standing eddies is an important factor to consider in glacial fjord circulation and melt rates and should be taken into account in models and observations.