Reliable forecasts for the dispersion of oceanic contamination are important for coastal ecosystems, society, and the economy as evidenced by the Deepwater Horizon oil spill in the Gulf of Mexico in 2010 and the Fukushima nuclear plant incident in the Pacific Ocean in 2011. Accurate prediction of pollutant pathways and concentrations at the ocean surface requires understanding ocean dynamics over a broad range of spatial scales. Fundamental questions concerning the structure of the velocity field at the submesoscales (100 m to tens of kilometers, hours to days) remain unresolved due to a lack of synoptic measurements at these scales. Using high-frequency position data provided by the near-simultaneous release of hundreds of accurately tracked surface drifters, we study the structure of submesoscale surface velocity fluctuations in the Northern Gulf of Mexico. Observed two-point statistics confirm the accuracy of classic turbulence scaling laws at 200-m to 50-km scales and clearly indicate that dispersion at the submesoscales is local, driven predominantly by energetic submesoscale fluctuations. The results demonstrate the feasibility and utility of deploying large clusters of drifting instruments to provide synoptic observations of spatial variability of the ocean surface velocity field. Our findings allow quantification of the submesoscale-driven dispersion missing in current operational circulation models and satellite altimeter-derived velocity fields.T he Deepwater Horizon (DwH) incident was the largest accidental oil spill into marine waters in history with some 4.4 million barrels released into the DeSoto Canyon of the northern Gulf of Mexico (GoM) from a subsurface pipe over ∼84 d in the spring and summer of 2010 (1). Primary scientific questions, with immediate practical implications, arising from such catastrophic pollutant injection events are the path, speed, and spreading rate of the pollutant patch. Accurate prediction requires knowledge of the ocean flow field at all relevant temporal and spatial scales. Whereas ocean general circulation models were widely used during and after the DwH incident (2-6), such models only capture the main mesoscale processes (spatial scale larger than 10 km) in the GoM. The main factors controlling surface dispersion in the DeSoto Canyon region remain unclear. The region lies between the mesoscale eddy-driven deep water GoM (7) and the winddriven shelf (8) while also being subject to the buoyancy input of the Mississippi River plume during the spring and summer months (9). Images provided by the large amounts of surface oil produced in the DwH incident revealed a rich array of flow patterns (10) showing organization of surface oil not only by mesoscale straining into the loop current "Eddy Franklin," but also by submesoscale processes. Such processes operate at spatial scales and involve physics not currently captured in operational circulation models. Submesoscale motions, where they exist, can directly influence the local transport of biogeochemical tracers (11, 12) ...

Application of recent geometric tools for Lagrangian coherent structures (LCS) shows that material attraction in geostrophic velocities derived from altimetry data imposed an important constraint to the motion of drifters from the Grand Lagrangian Deployment (GLAD) in the Gulf of Mexico. This material attraction is largely transparent to traditional Eulerian analysis. Attracting LCS acted as approximate centerpieces for mesoscale patterns formed by the drifters. Persistently attracting LCS cores emerged 1 week before the development of a filament resembling the “tiger tail” of the Deepwater Horizon oil slick, thereby anticipating its formation. Our results suggest that the mesoscale circulation plays a significant role in shaping near‐surface transport in the Gulf of Mexico.

Although model simulations that assimilated the radar observations produced currents that were in general agreement with the pattern seen in the radar data, Lewis et al. [1998] expressed concern that errors in the radar data could cause problems in the simulations. Horizontal divergences calculated from the radar data showed unrealistically large magnitudes, changes in sign from time step to time step, and little coherence between adjacent grid cells. These horizontal divergence patterns, when assimilated into the model, would tend to produce unrealistic sea level differences of the order of meters at adjacent grid cells separated by 2.8 km. Lewis et al. [1998] concluded that additional processing of the radar data would be useful in order to minimize such effects. This issue is one of the subjects of this paper.The experiences of Lewis et al. [1998] are likely to be repeated by other coastal zone modelers. This is because in recent years there has been a dramatic increase in the capability to provide high-resolution space and time data of estuarine and coastal regions. HF radar data are but one example. Others are synthetic aperture radars, Lagrangian drifters, new generation passive remote-sensing platforms, and a variety of towed instrumentation suites that provide fine-scale information on the density and velocity fields along ship tracks. These developments have been matched by equally dramatic increases in computational capabilities. Consequently, oceanographers now routinely access both observational and computational capabilities unimagined even a few years ago. 3425

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The life cycle of large anticyclonic rings in the Gulf of Mexico (GOM) is widely described by pinch off from the Loop Current, migration across the Gulf and eventual spin down along the western slope. Extensive observational and modeling efforts provide a relatively consistent picture of rings pinching off from the Loop Current and of complex interaction between anticyclones and cyclones driven by bathymetry along the western and northwestern shelf. The observational record for Loop Current rings (LCRs) during the intermediate period of westward translation is less clear. A number of studies recognize distinct anomalies in LCR characteristics in deep water as the rings enter the western Gulf near 92-94W. These include abrupt changes in the geometry of observed drifter trajectories and derived eddy parameter fits as well as changes in both ring translation speeds and the estimated rate of ring decay. Such observations are consistent with intense interaction and mass exchange between the rings and other coherent mesoscale features known to be present in the western Gulf. We test the hypothesis that interactions with the ambient mesoscale field can lead to rapid loss of coherence of some LCRs well before they reach the 'eddy graveyard' in the western Gulf. We use the data-assimilating, eddyresolving numerical GOM model described by Kantha et al. (2005) to assess the fates of readily identified LCRs Fourchon, Juggernaut, and Millenium during the period July 1998 to August 2001. Lagrangian metrics, including relative dispersion of small drifter clusters seeded in the ring cores, analysis of evolving blobs seeded in the ring cores, and finite-scale Lyapunov exponents, are used to track model ring evolution. These metrics clearly show that interactions with existing mesoscale cyclones and anticyclones caused Fourchon and Juggernaut to break up near 92W on advective time scales. In addition, Millenium also experienced an intense deformation, stirring, and mixing episode near 92W. Blob studies showed that the core fluid of Millenium was ultimately dispersed over much of the western basin. Our results show that some LCRs may break up through interactions with existing western Gulf cyclones and anticyclones prior to reaching the western slope.

Clusters of material at the ocean surface have been frequently observed. Such accumulations of material play an important role in a variety of applications, from biology to pollution mitigation. Identifying where clusters will form can aid in locating, for example, hotspots of biological activity or regions of high pollutant concentration. Here cluster strength is introduced as a new metric for defining clusters when all particle positions are known. To diagnose regions likely to contain clusters without the need to integrate millions of particle trajectories, we propose to use dilation, which quantifies area changes of Lagrangian patches. Material deformation is decomposed into dilation and area‐preserving stretch processes to refine previous approaches based on finite‐time Lyapunov exponents (FTLE) by splitting the FTLE into fundamental kinematic properties. The concepts are developed theoretically and illustrated in the context of a state‐of‐the‐art data‐assimilating predictive ocean model of the Gulf of Mexico. Regions of dilation less than one are shown to be much more likely (6 times more likely in the given example) to be visited by particles than those of dilation greater than one. While the relationship is nonlinear, dilation and cluster strength exhibit a fairly good correlation. In contrast, both stretch and Eulerian divergence are found to be uncorrelated with cluster strength. Thus, dilation maps can be used as guides for identifying cluster locations, while saving some of the computational cost of trajectory integrations.

Eulerian velocity fields are derived from 300 drifters released in the Gulf of Mexico by The Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) during the summer 2012 Grand Lagrangian Deployment (GLAD) experiment. These data are directly assimilated into the Navy Coastal Ocean Model (NCOM) four-dimensional variational data assimilation (4DVAR) analysis system in a series of experiments to investigate their impact on the model circulation. The NCOM-4DVAR is a newly developed tool for data analysis, formulated for weak-constraint data assimilation based on the indirect representer method. The assimilation experiments take advantage of this velocity data along with other available data sources from in situ and satellite measurements of surface and subsurface temperature and salinity. Three different experiments are done: (i) A nonassimilative NCOM free run, (ii) an assimilative NCOM run that utilizes temperature and salinity observations, and (iii) an assimilative NCOM run that uses temperature and salinity observations as well as the GLAD velocity observations. The resulting analyses and subsequent forecasts are compared to assimilated and future GLAD velocity and temperature/salinity observations to determine the performance of each experiment and the impact of the GLAD data on the analysis and the forecast. It is shown that the NCOM-4DVAR is able to fit the observations not only in the analysis step, but also in the subsequent forecast. It is also found that the GLAD velocity data greatly improves the characterization of the circulation, with the forecast showing a better fit to future GLAD observations than those experiments without the velocity data included.

Ageostrophic ocean processes such as frontogenesis, submesoscale mixed‐layer instabilities, shelf break fronts, and topographic interactions on the continental shelf produce surface‐divergent flows that affect buoyant material over time. This study examines the ocean processes leading to clustering, i.e., the increase of material density over time, on the ocean surface. The time series of divergence along a material trajectory, the Lagrangian divergence (LD), is the flow property driving clustering. To understand the impacts of various ocean processes on LD, numerical ocean model simulations at different resolutions are analyzed. Although the relevant processes differ, patterns in clustering evolution from the deep ocean and the continental shelf bear similarities. Smaller‐scale ocean features are associated with stronger surface divergence, and the surface material clustering is initially dominated by these features. Over time, the effect of these small‐scale features becomes bounded, as material traverses small‐scale regions of both positive and negative divergence. Lower‐frequency flow phenomena, however, continue the clustering. As a result, clustering evolves from initial small‐scale to larger‐scale patterns.