Session G39: Geophysical Fluid Dynamics Ocean I

Cross-Shore Structure of Alongshore Flow over a Coral Reef Shelf

Tidally driven alongshore flow over a reef shelf on O'ahu, Hawai'i is examined using spatial velocity measurements from an autonomous underwater vehicle (AUV) along with time series observations of the alongshore pressure gradient. Depth-averaged velocities are reconstructed from AUV-based velocity observations as a function of cross-shore distance assuming tidal periodicity. Ensemble averages of the alongshore pressure gradient and velocities versus tidal phase from multiple AUV surveys reveal characteristics akin to an oscillatory boundary layer, with the nearshore flow leading the offshore flow and with a corresponding attenuation in velocity magnitude in shallower regions. Analysis of the depth-averaged alongshore momentum balance indicates that the cross-shore structure and evolution of the tidal boundary layer is well described by a balance between local acceleration, barotropic pressure gradient, and bottom drag. This primary balance allows estimation of the drag coefficient over depths spanning from 25 to 5 m. Estimates agree with analysis of time series data and compare favorably with drag coefficients estimated from AUV-based roughness mapping. Roughness data suggest that larger scales, at wavelengths comparable to the depth, play a more significant role than smaller meter-scale roughness.   

Predicting orientation of coastal-zone Langmuir cells influenced by misaligned current, wind and wave forcing

Multi-scale physical processes that involve the interaction between winds, waves, and mean currents regulate turbulence in the upper ocean boundary layer (OBL). Among them, Langmuir turbulence is one such process. In coastal settings, Langmuir turbulence is subjected to additional shear from mean currents. Thus, such wind-wave-mean current parameter space and the system response to their disequilibrium becomes an important study for improved parameterizations of OBLs with wave effects. As such, this investigation considers idealized cases of wind-wave-mean current misalignment and attempts to predict the resultant orientation of coastal Langmuir cells. An \textit{a priori }predictive model based on mean Lagrangian shear direction is formulated and direct comparisons have been performed successfully with a thorough list of large-eddy simulation results of the Craik-Leibovich equations. The prognostic model as well as the numerical results depict that the resultant cells maintain coherency (although, diminishes with increasing obliquity between the underlying forces) and aligns at an intermediate angle to the range of imposed forces. This was further justified with theoretical developments, which involved analysis of the vorticity transport equation to assess the terms responsible for the sustenance of streamwise vorticity.   

Study of surface wave effect on the turbulence underneath using wave-phase-resolved simulation

The Eulerian orbital motions and the Lagrangian motions of the water surface waves introduce different time scales to the turbulence underneath. In this study, the turbulence dynamics under progressive surface waves are studied using wave-phase-resolved simulations. Compared to the traditional wave-phase-averaged approach of modelling the wave-turbulence interaction, the fast turbulent motions with time scales similar to the waves are directly resolved. Based on the simulation data, we find that the coherent turbulence structures and turbulence statistics are wave-phase dependent. The mechanisms of the wave-phase variation of the turbulence are analyzed in the wave-phase-resolved frame and it is found that the variation is due to the periodic stretching and tilting of the wave orbital straining. The correlations between the wave phase and turbulence statistics, such as the vorticity and Reynolds stresses, are further quantified and their wave-phase-averaged contribution to the wave-turbulence interactions are modelled.   

Phase-Resolved Ocean Waves Prediction via Machine Learning

Phase-resolved prediction of ocean waves is one of the most important outstanding problems in ocean science and engineering. With an accurate prediction of ocean surface height, extreme events such as rogue waves can be braced for, or entirely avoided (e.g. via rerouting of ocean vessels); ocean wave harnessing devices can tune up in real-time to take the most energy out of incoming waves; and the final destination of passive floating particles (e.g. pollutants) can be precisely determined. The problem, however, is very complex because equations that govern the waves evolution are nonlinear; hence even inferring wave components that make up a given surface is already a difficult task. Here we show that a Convolutional Recurrent Neural Network (CRNN) has a strong potential to efficiently make real-time prediction of nonlinear non-breaking ocean waves. We use extensive direct simulation of nonlinear ocean waves to train and then test our proposed CRNN-based methodology. Each input node in our CRNN is composed of the discretized surface elevation in a given spatial domain at a specific time. The network takes a time-window of input nodes, and outputs the spatiotemporal prediction of surface waves. Accuracy, reliability and limitations of the proposed methodology are discussed.   

Machine Learning for Trajectory Prediction in Geophysical Flows

Echo state network based machine learning (ML) is applied to two elementary models of ocean circulation: the well-known double-gyre stream function model with time-variable forcing and a one-layer quasi-geostrophic (QG) basin model. These models are used to generate time-dependent two-dimensional stream function fields, from which flow maps, trajectories and ensembles of trajectories are computed, assuming ideal particles. Varied physical model parameters allow sampling of a wide range of dynamical behaviors corresponding to diverse geophysical flow regimes; the QG PDE model can realize Munk, Stommel or strongly nonlinear time-dependent solutions. We evaluate the effectiveness and fidelity of our machine learning approach in capturing the characteristics of trajectories, both directly and indirectly, via stream function field prediction. We asses the predictive power of ML models against other predictive and descriptive models of QG flows, including finite time Lyapunov exponent, or FTLE, accounting for the role of physical and numerical parameters on our results.   

Predictability of ROMS-Ocean State Ocean Model using Information Theory

The Ocean State Ocean Model (OSOM) is an implementation of the Regional Ocean Modeling System (ROMS) covering Rhode Island waterways which includes the Narragansett Bay, Mt. Hope Bay, and nearby regions including the shelf circulation from Long Island to Nantucket. Our focus is on modeling the physical aspects of the Bay in order to build a forecast and prediction system. Perturbed ensemble simulations with altered initial condition parameters (temperature, salinity) are combined with concepts from Information Theory to quantify the predictability of the OSOM forecast system. Predictability provides a theoretical estimate of the potential forecasting capabilities of the model in the form of prediction time scales and enhances readily estimable timescales such as the freshwater flushing timescale. The predictability of the OSOM model is around 10-40 days, varying by perturbation parameters and season.   

Uncertainty quantification of trajectory clustering in ocean ensemble forecasts

Identifying coherent structures in unsteady flows helps differentiate flow regions based on material transport. Partitioning flows into regions that minimally mix with their surroundings in the oceans, for instance, can assist search-and-rescue planning by reducing the search domain. One partitioning method is the spectral clustering of trajectories, which maximizes within-cluster similarities while minimizing between-cluster similarities. For ocean applications, however, in addition to the complex dynamics, there are several sources of uncertainty: model initialization and parameters, limited knowledge of the processes, boundary conditions, and forcing terms. Therefore, when applied to ocean forecasting, the clustering method should analyze multiple realizations, identify robust features, and quantify the uncertainty. We present an investigation of the sensitivity of the spectral clustering method to uncertain parametrization and noise through application to simulations of an analytic geostrophic flow model. We then apply this approach to an operational coastal ocean forecast and compare the results with observational drifter data from a field study.   

Geophysical turbulence dominated by inertia-gravity waves

Recent evidence from both oceanic observations and global-scale ocean model simulations indicate the existence of regions where low-mode internal tidal energy dominates over that of the geostrophic balanced flow. Inspired by these findings, we examine the effect of the first vertical mode inertia-gravity waves on the dynamics of balanced flow using an idealized model obtained by truncating the hydrostatic Boussinesq equations on to the barotropic and the first baroclinic mode. On investigating the wave–balance turbulence phenomenology using freely evolving numerical simulations, we find that the waves continuously transfer energy to the balanced flow in regimes where the balanced-to-wave energy ratio is small, thereby generating small-scale features in the balanced fields. We examine the detailed energy transfer pathways in wave-dominated flows and thereby develop a generalized small Rossby number geophysical turbulence phenomenology, with the two-mode (barotropic and one baroclinic mode) quasi-geostrophic turbulence phenomenology being a subset of it. The present work therefore shows that inertia–gravity waves would form an integral part of the geophysical turbulence phenomenology in regions where balanced flow is weaker than gravity waves.