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) ...

Mesoscale oceanic eddies are routinely detected from instantaneous velocities derived from satellite altimetry data. While simple to implement, this approach often gives spurious results and hides true material transport. Here it is shown how geodesic transport theory, a recently developed technique from nonlinear dynamical systems, uncovers eddies objectively. Applying this theory to altimetry-derived velocities in the South Atlantic reveals, for the first time, Agulhas rings that preserve their material coherence for several months, while ring candidates yielded by other approaches tend to disperse or leak within weeks. These findings suggest that available velocity-based estimates for the Agulhas leakage, as well as for its impact on ocean circulation and climate, need revision.

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[1] We demonstrate the feasibility of using dynamical systems tools to unambiguously identify mesoscale oceanic eddies from surface ocean currents derived using climatological hydrography and altimetry. Specifically, our analysis is based on extracting Lagrangian coherent structures (LCSs) from finite-time Lyapunov exponent (FTLE) fields. The FTLE fields reveal with unprecedented detail an intricate tangle of LCSs, which are hidden in ocean surface topography maps but sometimes are apparent in ocean color images. These LCSs delineate fluid domains with very different advective properties, and thus their detection provides an objective (i.e., frame-independent) means of identifying eddy boundaries. The importance of considering LCSs in quantifying transport by eddies is highlighted. Such a quantification does not rely on the common assumption-which is shown to be generally not valid-that transport is largely effected by the trapping and subsequent translation of water slugs inside eddies defined as the regions enclosed by sea height (streamfunction) contours within which rotation dominates over strain. LCSs are calculated for the whole globe and compared with satellite-tracked drogue drifter trajectories within a selected region of the South Atlantic. Citation: Beron-Vera, F. J., M. J.Olascoaga, and G. J. Goni (2008), Oceanic mesoscale eddies as revealed by Lagrangian coherent structures, Geophys. Res. Lett., 35, L12603,

The lack of reliable forecasts for the spread of oceanic and atmospheric contamination hinders the effective protection of the ecosystem, society, and the economy from the fallouts of environmental disasters. The consequences can be dire, as evidenced by the Deepwater Horizon oil spill in the Gulf of Mexico in 2010. We present a methodology to predict major short-term changes in environmental contamination patterns, such as oil spills in the ocean and ash clouds in the atmosphere. Our approach is based on new mathematical results on the objective (frame-independent) identification of key material surfaces that drive tracer mixing in unsteady, finite-time flow data. Some of these material surfaces, known as Lagrangian coherent structures (LCSs), turn out to admit highly attracting cores that lead to inevitable material instabilities even under future uncertainties or unexpected perturbations to the observed flow. These LCS cores have the potential to forecast imminent shape changes in the contamination pattern, even before the instability builds up and brings large masses of water or air into motion. Exploiting this potential, the LCS-core analysis developed here provides a model-independent forecasting scheme that relies only on already observed or validated flow velocities at the time the prediction is made. We use this methodology to obtain highprecision forecasts of two major instabilities that occurred in the shape of the Deepwater Horizon oil spill. This is achieved using simulated surface currents preceding the prediction times and assuming that the oil behaves as a passive tracer. In April 2010, a blowout caused an explosion on the Deepwater Horizon (DWH) mobile offshore oil rig near the Mississippi River's mouth in the Gulf of Mexico. The resulting fire could not be extinguished and the drilling rig sank shortly after, leaving the oil well gushing at the sea floor. Before the well was capped in mid-July, an estimated 4 million barrels of oil escaped (1), causing the largest accidental marine oil spill in the history of the petroleum industry. Beyond the enormous ecological damage, the spill resulted in an estimated loss of over a billion dollars for the tourism industry alone.In this environmental disaster, uncertainties in the spread of the pollutant plume had severe financial implications. Mass cancellations devastated the tourism industry along the Southwest Florida coastline, which was never actually reached by the DWH oil spill. Beyond the measurable cost, the lack of reliable forecasts for the spread of contamination hindered effective countermeasures and led to suboptimal resource allocation by decision makers.Precise longer-term forecasts for the underlying ocean flow have not been within reach because of the same inherent sensitivities and uncertainties that affect weather-forecasting models. Shorter-term predictions of ocean currents are more accurate, but the relevant details of such predictions typically depend on the models and initial conditions on which they are based.In this paper, we pro...