Submesoscale dispersion in the vicinity of the Deepwater Horizon spill

Abstract

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) ...

Submesoscale Water-Mass Spectra in the Sargasso Sea

“…Additional submesoscale shearing must be present on these scales. This conclusion has also been reached from drifter studies (Lumpkin and Elipot 2010;Poje et al 2014) and primitive equation numerical simulations (Capet et al 2008a,b;Tulloch et al 2011). While quasigeostrophic models find a k 1 spectrum at high wavenumbers (Smith and Ferrari 2009), primitive equation numerical simulations reproduce the observed k 0 spectrum (Fig.…”

“…An observed spectrum redder than the k 1 Batchelor spectrum suggests that there is significant horizontal shear on length scales smaller than the Rossby deformation scale accelerating the cascade to smaller scales. Drifter pair spreading also finds significant submesoscale shear (Lumpkin and Elipot 2010;Poje et al 2014). …”

Investigating the Eddy Diffusivity Concept in the Coastal Ocean

“…Diffusivity can also be estimated based on two-particle (or relative) statistics (LaCasce and Ohlmann 2003;Poje et al 2014) as well as inferred from the observed or numerically simulated tracer distributions (Sundermeyer and Ledwell 2001;Nakamura 1996Nakamura , 2001Abernathey and Marshall 2013). Different methods have different advantages and challenges specific for each technique.…”

Multi-Iteration Approach to Studying Tracer Spreading Using Drifter Data

“…Note also that if one is interested in a small-scale rather than basinwide spreading of tracer, then the multi-iteration technique could just as easily be applied to study transport pathways from a smaller and more localized source of contamination, provided additional regional drifter datasets are available, such as, for example, Coastal Ocean Dynamics Experiment (CODE) drifters in the Cape Cod Bay and Maine coastal areas (Manning et al 2009) or the recent deployment of drifters in the Gulf of Mexico (Olascoaga et al 2013;Poje et al 2014; Beron-Vera and LaCasce 2016; see also https://www.oceannews.com/news/2016/04/13/ one-thousand-drifters-and-one-future-satellite-in-thegulf-of-mexico). The multi-iteration procedure is essentially equivalent to stitching together segments from different trajectories passing through the same geographical location to augment the drifter dataset and then using this extended dataset for computing the probability and travel time maps.…”