Over What Area Did the Oil and Gas Spread During the 2010 Deepwater Horizon Oil Spill?

Abstract

The 2010 Deepwater Horizon (DWH) oil spill in the Gulf of Mexico resulted in the collection of a vast amount of situ and remotely sensed data that can be used to determine the spatiotemporal extent of the oil spill and test advances in oil spill models, verifying their utility for future operational use. This article summarizes observations of hydrocarbon dispersion collected at the surface and at depth and our current understanding of the factors that affect the dispersion, as well as our improved ability to model and predict oil and gas transport. As a direct result of studying the area where oil and gas spread during the DWH oil spill, our forecasting capabilities have been greatly enhanced. State-of-the-art oil spill models now include the ability to simulate the rise of a buoyant plume of oil from sources at the seabed to the surface. A number of efforts have focused on improving our understanding of the influences of the near-surface oceanic layer and the atmospheric boundary layer on oil spill dispersion, including the effects of waves. In the future, oil spill modeling routines will likely be included in Earth system modeling environments, which will link physical models (hydrodynamic, surface wave, and atmospheric) with marine sediment and biogeochemical components.

Full text

CITATION
Özgökmen, T.M., E.P. Chassignet, C.N. Dawson, D. Dukhovskoy, G. Jacobs, J. Ledwell,
O. Garcia-Pineda, I.R. MacDonald, S.L. Morey, M.J. Olascoaga, A.C. Poje, M. Reed,
and J. Skancke. 2016. Over what area did the oil and gas spread during the 2010
Deepwater Horizon oil spill? Oceanography 29(3):96–107, http://dx.doi.org/10.5670/
oceanog.2016.74.
DOI
http://dx.doi.org/10.5670/oceanog.2016.74
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Oceanography | Vol.29, No.3
96
GoMRI: DEEPWATER HORIZON OIL SPILL AND ECOSYSTEM SCIENCE
Over What Area Did the Oil
and Gas Spread During the 2010
Deepwater Horizon Oil Spill?
By Tamay M. Özgökmen,
Eric P. Chassignet,
Clint N. Dawson,
Dmitry Dukhovskoy, Gregg Jacobs,
James Ledwell,
Oscar Garcia-Pineda,
Ian R. MacDonald, Steven L. Morey,
Maria Josefina Olascoaga,
Andrew C. Poje, Mark Reed,
and Jørgen Skancke
Oceanography | Vol.29, No.3
96

Oceanography | September 2016 97
INTRODUCTION
e 2010 Deepwater Horizon (DWH) oil
spill in the Gulf of Mexico (GoM) under-
scored the need for an immediate and
informed response at the onset of such
a disaster. It is imperative to be able to
quickly answer questions such as: Where
will the oil go? How fast will it get there?
How much oil will be transported? e
answers help determine the allocation of
limited response resources and ultimately
the socioeconomic and environmental
impacts of a spill. e benet of predic-
tive capability during events such as an
oil spill is analogous to the forecasting of
any natural disaster—it allows individu-
als, entire communities, and emergency
planners to take necessary measures to
respond. e need for this capability, par-
ticularly with regard to potential oil spills,
is urgent because of the ongoing construc-
tion of deepwater rigs. We require a much
better understanding of the spatially and
temporally varying transport pathways
between these rigs and the coastline than
we had during the DWH oil spill.
is article has two main goals:
(1) to summarize the area over which
the DWH oil spill spread, and (2) to
highlight the progress made, since the
2010 event, in understanding the pro-
cesses responsible for the spreading of
released hydrocarbons and in forecasting
hydrocarbon dispersion.
OBSERVATIONS OF AN OIL SPILL
Assessment of oating oil distribution
and magnitude is necessary for quanti-
fying the extent of an oil spill and pro-
viding accurate initial conditions to oil
spill prediction models. Because it is
not always practical to conduct exten-
sive in situ measurements in the aer-
math of a spill, assessments rely heavily
on remote- sensing data analysis. Relevant
remote-sensing techniques include opti-
cal, microwave, and radar sensors set
up on aircra and satellites (Leifer etal.,
2012). Of these, synthetic aperture radar
(SAR) has proven its ability to detect
oating oil for response and assessment of
oil spills over 30 years of operational use
(Holt, 2004). SAR data are particularly
useful during an oil spill event because
oil spills (and the resulting movement
of hydrocarbons) continue 24/7, without
regard for day or night visibility. However,
SAR imagery may be limited by certain
weather conditions (Garcia-Pineda etal.,
2009). Satellite imagery in the visible and
near infrared (NIR) has also been widely
used to delineate oil slicks in the ocean
(Hu et al., 2003). Recently, the wider
availability of medium-resolution (250m
and 300 m) MODIS and MERIS data
made it also possible to use these wide-
swath (2,330 km and 1,150 km, respec-
tively) satellite instruments for cost-
eective spill monitoring in near-real
time. Airborne remote sensing is another
very useful technique, as it provides
higher temporal and spatial resolution
than satellite remote sensing; however, it
is not as cost-eective. It provides only a
partial overview of the aected areas, and
it can be slow to process and distribute.
e geographic source of the DWH
discharge was essentially constant during
the 87 days of ow, but physical details of
the release points underwent substantial
changes as responders gradually regained
well control. e critical shi was amputa-
tion of the fallen risers on June 2–3. Prior
to this action, discharges were dispersed
among several points of failure along the
fallen pipes; aer, the entire discharge
escaped from a single point atop the dys-
functional blowout preventer. Although
the gross ow rate then increased, recap-
ture of oil and treatment with dispersants
reduced the net discharge until installa-
tion of the riser stack on July 15 ended
all releases (Lehr et al., 2010; McNutt
etal., 2012). erefore, the two periods,
April20 to June 1 and June 2 to July15,
oered signicantly dierent condi-
tions, which potentially aected the sub-
sequent distribution and fate of the oil.
Remote-sensing data provided a means
for tracking a critical component of this
discharge—movement of oil across the
ocean surface. It is this component of the
oil that generated contaminated marine
snow (Passow, 2014), injured mesophotic
corals (Silva et al., 2016; Etnoyer etal.,
2016), and oiled over 2,100 km of the
Gulf Coast (Nixon etal., 2016).
SAR imaging of surface oil commenced
on April 24 and continued at high capacity
ABSTRACT. e 2010 Deepwater Horizon (DWH) oil spill in the Gulf of Mexico
resulted in the collection of a vast amount of situ and remotely sensed data that can
be used to determine the spatiotemporal extent of the oil spill and test advances in oil
spill models, verifying their utility for future operational use. is article summarizes
observations of hydrocarbon dispersion collected at the surface and at depth and our
current understanding of the factors that aect the dispersion, as well as our improved
ability to model and predict oil and gas transport. As a direct result of studying the
area where oil and gas spread during the DWH oil spill, our forecasting capabilities
have been greatly enhanced. State-of-the-art oil spill models now include the ability to
simulate the rise of a buoyant plume of oil from sources at the seabed to the surface.
A number of eorts have focused on improving our understanding of the inuences
of the near-surface oceanic layer and the atmospheric boundary layer on oil spill
dispersion, including the eects of waves. In the future, oil spill modeling routines will
likely be included in Earth system modeling environments, which will link physical
models (hydrodynamic, surface wave, and atmospheric) with marine sediment and
biogeochemical components.
OPPOSITE. Dye release during the Surfzone
Coastal Oil Pathway Experiment (SCOPE) as
captured from a tethered balloon. The dye was
released outside of the surf zone, but did not
make land fall during the four hours of aerial
observation because of processes involved in
the interaction of the surf zone with the inner
shelf, as well as a 2 m thick buoyant flow released
from a tidal inlet. These processes influence
which coastlines will be most impacted by oil
spills. Photo credit: Guillaume Novelli

Oceanography | Vol.29, No.3
98
through August 3, aer which oating oil
was no longer detected. MacDonald etal.
(2015) analyzed 166 SAR images collected
during this period; they used Texture
Classifying Neural Network Algorithm
(TCNNA) routines (Garcia-Pineda etal.,
2009) to delineate areas of water covered
by thin (~1 μm) oil and Oil Emulsion
Detection Algorithm (OEDA) routines
(Garcia-Pineda et al., 2013) to detect
much smaller areas of thick (~70 μm) oil.
Interpolation among the images pro-
duced a continuous time series of grid-
ded values for oating oil and oil emul-
sion (m3 km–2) in 5 × 5 km cells across
the impacted region (MacDonald etal.,
2015). e surface oil covered a large and
dynamically amorphous region that was
focused over the release point but contin-
uously driven into dierent distribution
patterns over a 149,000 km2 area of the
northeastern Gulf under changing wind
and current eects. Figure1 (upper panel)
shows the average values in these cells for
April 24 to August 3. Analysis of the daily
aggregated values shows two prominent
features of the surface oil. First, the mag-
nitude of oil was highly sensitive to wind
speeds; throughout the emergency, sur-
face oil that was visible to SAR decreased
sharply when winds exceeded about
5 m s–1 and then gradually increased
when winds subsided (Figure 1, lower
FIGURE1. (upper panel) Distribution and average volume of surface oil (m3 km–2) from Deepwater Horizon
(DWH) discharge, gridded at 5 × 5 km scale across a cumulative footprint of 149,000 km2, April 24–
August 3, 2010. Data were derived from 169 synthetic aperture radar (SAR) images acquired during this inter-
val and processed using Texture Classifying Neural Network Algorithm (TCNNA) and Oil Emulsion Detection
Algorithm (OEDA) techniques. (lower panel) Time series of DWH discharge plotted with surface oil and aver-
age wind speeds. Release magnitudes show best daily estimates of oil escaping from the damaged well.
Discharge subtracts the oil recovered from the gross release, while treatment further subtracts oil burned
and dispersed by aerial and subsea applications of Corexit at maximum ecacy. Response events poten-
tially aected the spread of surface oil: (a) Macondo well blowout occurs. (b) DWH drill rig sinks and release
begins. (c) Aerial dispersant application begins. (d) Containment dome attempt fails, and burning of surface oil
begins. (e)Subsea dispersant campaign begins (May 5). (f) Flaring of recovered oil begins. (g) Top kill attempt.
(h) The riser is cut from the blowout preventer, and direct injection of subsea dispersant begins. (i) Hurricane
Alex makes landfall. (j) Capping of the stack closure stops release of oil. (k) Tropical Storm Bonnie makes land-
fall. (l) The well is killed by static backfill. From MacDonald (2015) and MacDonald etal. (2015)
55,000
45,000
35,000
25,000
15,000
5,000
–5,000
10
9
8
7
6
5
4
3
2
1
0
Volume (m3) and Area (km2)
Wind Speed (m s–1)
Apr 18
Apr 28
May 8
May 18
May 28
Jun 7
Jul 7
Jun 17
Jul 17
Jun 27
Jul 27
Aug 27
Release
Discharge
Untreated Residual
Surface Oil Area
Surface Oil Volume
Response Events
Average Wind

Oceanography | September 2016 99
panel). Second, there is a state change in
the geographic concentration and distri-
bution of surface oil when the pre- and
post-riser removal periods are compared.
In summary, the total detected volume of
oil decreased by 21% aer riser removal.
However, probably due to increased
treatments with Corexit (a dispersant),
the ocean area over which the remain-
ing oil was dispersed increased by 49%
(Figure1, lower panel). At face value, this
result is consistent with the ecacy of
response eorts to reduce surface oil by
recapture and burning operations (Lehr
etal., 2010) and with the subsea applica-
tion of dispersant. is benet has to be
weighed against increased exposure of
planktonic larvae and pelagic organisms
to oil, which can produce deleterious
eects to developing sh even at very low
concentrations (Incardona etal., 2014).
FACTORS AFFECTING
HYDROCARBON DISPERSION
IN THE ENVIRONMENT
In order to model the area over which the
DWH oil and gas spread, it is necessary
to have a basic understanding of the fac-
tors that aect hydrocarbon dispersion
in the environment. Figure 2 shows the
complexity of the physical processes that
govern particle transport in the aermath
of a deepwater oil or gas spill. Initially,
the DWH spill was produced by the
high-pressure eux of a hot, multiphase
mixture of oil and gas at several sites
in the broken riser pipe. Containment
eorts involved cutting the riser pipe to
isolate the release to a single, nominally
0.5 m diameter, source (McNutt et al.,
2011) and application of chemical dis-
persants in eorts to minimize the size
and therefore maximize the subsurface
mixing of oil droplets. A multiphase tur-
bulent jet issuing from the source rap-
idly transitions to a multiphase turbu-
lent plume that mixes with ambient uid
by entrainment processes. e buoy-
ancy uxes associated with the DWH
spill are extremely large—the oil buoy-
ancy anomaly alone was equivalent to a
heat ux of 1 GW m–2 (1 GW = 109 W;
Reddy etal., 2012), with the accompany-
ing gases providing anomalies ve times
larger. Such buoyancy uxes, two orders
of magnitude larger than those of deep
ocean thermal vents (Speer and Marshall,
1995), and greater still than those associ-
ated with cold air outbreaks at the ocean
surface, imply that the resulting plume
does not simply passively advect through
the rotating, stratied water column,
but is instead capable of driving local
dynamic processes.
Turbulent levels at the source, along
with the application of chemical disper-
sants, minimized the mean size of oil
droplets, eectively reducing the oil slip
velocity relative to seawater and increas-
ing the droplet rise time. Given the ambi-
ent environmental stratication and
the levels of turbulence generated by
the extreme buoyancy uxes associated
with the spill, the resulting plume was
expected to be characterized by multi-
ple lateral intrusion levels, where down-
dras of negatively buoyant ambient uid
suppress the rise of positively buoyant
oil and gas (Asaeda and Imberger, 1993;
Socolofsky and Adams, 2005). Discrete
subsurface maxima of constituent hydro-
carbon concentrations were observed
in the aermath of the incident (Reddy
etal., 2012; Spier etal., 2013).
When hydrocarbons do eventually
reach the surface, they are strongly inu-
enced by air-sea forcing, and there are
several identiable stages of transport,
including (1) surface dispersion under
the action of mixed layer dynamics,
FIGURE2. Schematic depiction of transport processes in a subsurface spill.

Oceanography | Vol.29, No.3
100
mesoscale currents, wind, and waves,
including tropical storm conditions;
(2) release of gas into the atmospheric
boundary layer by air-sea interaction pro-
cesses through the burning of surface oil;
(3) transport of gas in the atmosphere;
and (4) transport to the coast across the
inner shelf and surf zone (Figure2).
An aerial photograph taken during
the DWH event (Figure3, upper panel)
shows a striking example of how the com-
plex interactions between the atmosphere
and the ocean shape the oil distribution
along the boundary of these large sys-
tems. Figure 3 (lower panel) illustrates
a general classication of transport pro-
cesses near the ocean’s surface. At scales of
1 m to 100m, and 1 s to a few hours, fully
three-dimensional turbulent processes
dominate the boundary layer dynamics.
At scales of 100 m to 10 km, and O(1) day,
the so-called submesoscale processes crit-
ically impact transport and mixing in the
upper ocean, modify mixed-layer strati-
cation, and dominate relative dispersion
of near-surface material (Capet et al.,
2008a,b; Zhong et al., 2012, Özgökmen
etal., 2012a,b). Stokes dri from surface
waves and Ekman transport from wind
stress combine to form the near-surface
current that advects oil. e depth of this
current is controlled by boundary layer
turbulence, including Langmuir circu-
lations, that are driven by air-sea uxes
and surface waves. Surface convergences
above the Langmuir downwelling zones
concentrate oil into along-wind streaks,
as do larger-scale convergences at fronts.
Frontal submesoscale eddies can move oil
across these fronts. e vertical velocities
in the boundary layer and at the fronts
mix oil into the boundary layer and below
it. ese processes combine to distribute
material concentrations in a very dier-
ent manner than expected when consid-
ering only the mesoscale ows (10 km to
100 km, and days to months, for exam-
ple,a Loop Current eddy in the Gulf of
Mexico). us, the impacts of processes
over a wide range of spatial and tempo-
ral scales on the eventual oil distribution
must also be taken into account when
responding to an oil spill.
EXPERIMENTAL STUDIES
OF OIL AND GAS TRANSPORT
PROCESSES
Since the DWH oil spill, a great deal
of research has been undertaken to
understand the dynamics of the pro-
cesses behind the transport of hydro-
carbons released in the marine environ-
ment. Here, we review some of these
FIGURE3. (upper panel) Aerial photo of surface oil during the Deepwater Horizon spill (reproduced through an
agreement with D. Beltra). (lower panel) Illustration of surface ocean transport processes.

Oceanography | September 2016 101
experimental studies of mechanisms rel-
evant to transport of hydrocarbons at the
ocean surface and at depth in the north-
ern Gulf of Mexico.
Surface Dispersion Experiments
As discussed in the previous section,
the surface extent and movement of the
DWH oil spill resulted from interaction
of motions at dierent scales. During May
2010, a few weeks into the spill, the core
of the Loop Current was located about
150 km south of the oil spill site, too far
to directly aect the spreading of the oil.
However, mesoscale cyclonic eddies on
the edge of the Loop Current did sub-
stantively aect the spreading of the oil
as they controlled the development of a
large nger in the oil slick, referred to as
a “tiger tail,” as well as the accumulation
of oil on the northeastern side of the spill
site during May–June 2010 (Olascoaga
and Haller, 2012; Olascoaga etal., 2013).
Intense southeast winds associated with
Hurricane Alex, which developed in late
June, eventually caused a reduction of
the surface oil extent at the end of June
and the beginning of July (Figure1, lower
panel), as oil was driven onshore and
mixed underwater (Goni etal., 2015).
Interactions between dierent scales of
motion, namely submesoscales and meso-
scales, may have played an important role
in the dispersion of the spilled oil during
the DWH event, as revealed by satellite
images. Observations suciently dense
to permit extraction of material patterns
on multiple scales are limited. To ll this
void, the Grand LAgrangian Deployment
(GLAD) experiment (Figure 4 upper
panel) was conducted in the summer of
2012. GLAD was the largest synoptic sur-
face drier deployment in oceanogra-
phy to date, with 317 Lagrangian instru-
ments launched in clusters in DeSoto
Canyon, the location of the DWH spill,
over 10 days. Conditions sampled over
the subsequent six months ranged from
calm to extreme (Hurricane Isaac). While
dynamics at submesoscales (100 m to
10km) are well dened by recent research
(Capet et al., 2008a,b; Fox-Kemper
and Ferrari, 2008; D’Asaro et al., 2011;
Mensa etal., 2013), the investigation of
their eects on material transport by the
ocean has been mostly based on model-
ing (Poje etal., 2010; Haza et al., 2012;
Özgökmen etal., 2012a,b) because obser-
vations are still very rare (Shcherbina
etal., 2013). Also, the details of the estab-
lishment, maintenance, and energet-
ics of such features in the GoM remain
unclear. Lagrangian experiments are cur-
rently the most accurate way to quantify
the net eect of all ow scales on ocean
transport. e intensive drier deploy-
ments in the GLAD experiment revealed
submesoscale dispersion during the sum-
mer in DeSoto Canyon (Poje etal., 2014)
and mesoscale-dominated dispersion
in the interior of the Gulf (Olascoaga
etal., 2013). GLAD observations allowed
quantication of the amount of scale-
dependent dispersion that is missing in
current operational circulation models
and satellite altimeter-derived velocity
FIGURE 4. Grand LAgrangian Deployment (GLAD) drifter trajectories three
months after release near the Deepwater Horizon region, superimposed on
satellite sea surface temperature. Navy Coastal Ocean Model (NCOM) sim-
ulation for SCOPE, resolving frontal structures trapping and transporting sur-
face particles (shown in white) in comparison to real drifters (black circles).
Most modeled and real drifters aligned along fronts, implying a critical role for
coastal fronts in trapping and transporting surface material.
86.8°W 86.6°W
21.6
20.8
20.0
19.2
18.4
17.6
16.8
16.0
30.4°N
30.3°N
30.2°N
30.1°N
30°N
26°N
28°N
24°N
22°N
94°W 86°W90°W 82°W92°W 84°W88°W 80°W

Oceanography | Vol.29, No.3
102
elds. Subsequently, GLAD observations
have been used to assess and improve
predictions from models and satellite-
altimeter data sets (Carrier et al., 2014;
Jacobs et al., 2014; Berta et al., 2015;
Coelho etal., 2015).
e Surfzone Coastal Oil Pathway
Experiment (SCOPE; Figure 4 lower
panel and title page photo) was con-
ducted in December 2013 to measure
the inner shelf and surf zone processes
responsible for the “last mile” of oil trans-
port. e intensive three-week campaign
consisted of a cross-shore array of xed
instrumentation to measure background
wind, waves, currents, and water proper-
ties from 10 m water depth to the shore-
line; Lagrangian observations (180 GPS-
equipped surface driers, uorescent
dye); and moving-vessel measuring plat-
forms (small vessels, wave runners, and
unmanned subaqueous and aerial vehi-
cles). One of the primary ndings during
SCOPE was that surface convergence
zones, created by freshwater fronts from
estuaries by tidal exchange, appear to
control the distribution of surface mate-
rial near the coast (Figure4 lower panel;
Hugenard etal., 2015).
Deep Dispersion Experiments
In late July 2012, a passive tracer was
released near the site of the DWH erup-
tion (Ledwell etal., 2016). Tracer disper-
sion was studied through August 2013
to quantify the fate of material acciden-
tally or naturally released along the West
Florida slope. e tracer, deployed near
the depth of the DWH plume that was
found near 1,100 m depth by Camilli
et al. (2010) moved westward, follow-
ing isobaths at rst, and then dispersed
over much of the northern Gulf (see
Figure5; Ledwell etal., 2016). Mixing of
the tracer, both across and along density
surfaces, was greatly enhanced by ener-
getic ows over the ridges and salt domes
of the West Florida slope. Hurricane
Isaac, which passed over the site about
a month aer the tracer release, gener-
ated particularly strong currents along
the slope. Homogenization of the tracer
along isopycnal surfaces by stirring and
small-scale mixing was much more rapid
than in the open ocean thermocline.
Nevertheless, streakiness of the tracer dis-
tribution persisted over the whole period,
though it steadily declined. Peak concen-
trations fell to 10–8 of the concentration
in the initial plume aer 12 months. A
numerical simulation of the tracer disper-
sion, conducted at North Carolina State
University using the South Atlantic Bight
and Gulf of Mexico (SABGOM) general
circulation model, reproduced fairly well
the statistics that are important to envi-
ronmental impact, such as changes with
time and spatial autocorrelation of con-
centrations (Ledwell etal., 2016).
MODELING AND PREDICTING
OIL AND GAS TRANSPORT
Model predictions of the evolution of an
oil spill in the ocean are typically per-
formed by computing the movement
of large numbers of simulated discrete
“particles,” each representing a volume of
oil or related constituents. Oil spill mod-
els vary in dimensional complexity, simu-
lating (1) only the movement of oil oat-
ing on the surface, a two-dimensional
computation; (2) the three-dimensional
movement of oil in the water column,
allowing for oil to submerge and resur-
face; or (3) the full life cycle of hydro-
carbons released from a subsurface blow-
out through a buoyant plume to the
surface, with dissolution of some compo-
nents into subsurface layers. Models also
incorporate dierent levels of sophis-
tication to simulate various constitu-
ents of the hydrocarbons being released
and their modication through chemi-
cal alteration, emulsication, and biolog-
ical activity (processes oen collectively
termed “weathering”), as well as response
activities such as skimming, burning, and
application of surfactants.
Surface Oil Drift Modeling
A decades-old methodology for modeling
an oil spill is to advect simulated particles
in a velocity eld that is some function
of the surface current and near-surface
82°W84°W86°W88°W90°W92°W94°W96°W98°W
21°N
22°N
23°N
24°N
25°N
26°N
27°N
28°N
29°N
30°N
31°N
Column Integral (nmol m–2)
0.00 0.05 0.10 0.15 0.20 0.25 0.30
FIGURE5. Distribution of the tracer 12 months after it was released near the site of the
DWH rupture. The sampling stations are indicated by circles, colored with the column inte-
gral of tracer found. The background color is a smoothed map of tracer distribution based
on these sampling stations. The isobaths are plotted every 500 m.

Oceanography | September 2016 103
wind. An oen used method has been to
add to the ocean surface current vector
an additional velocity vector that is some
fraction of the wind speed (oen 3.5%,
the so-called “3.5% rule”) in magnitude
directed at some clockwise rotation from
the wind direction. ese methods have
evolved from using a constant 20° clock-
wise rotation (Smith et al., 1982) to
wind-speed dependent rotation angles
(Samuels etal., 1982). ese approaches
were developed to account for processes,
such as Ekman and Langmuir dynamics,
that are unresolved near the surface in
ocean circulation models. Comparison
of forecasts from these types of oil spill
models forced by mesoscale eddy-
resolving ocean model currents and
winds from operational weather mod-
els to drogued and oil-following dri-
ers (Reed etal., 1988) have been disap-
pointingly low (Price etal., 2006). Recent
advances in numerical models now per-
mit horizontal resolutions as ne as 20m
to 50 m on the coast and 1 km in the
deep water. Since the DWH event, fore-
casting advancements can be attributed
to both increased capability in numeri-
cal models and a better understanding of
the processes controlling the oil disper-
sion, specically those due to ocean cur-
rents and the impact of near- surface pro-
cesses such as Stokes dri and Langmuir
circulation (Le Héna et al., 2012;
Curcic etal., 2016).
In addition to the basic geostrophic
deepwater dynamics that played a major
role in dispersing the oil during the DWH
event (Walker etal., 2011; e.g.,the Loop
Current eddy and associated peripheral
cyclones as discussed above), Ekman
dri, in particular, was a signicant factor
(Liu etal., 2014). is was demonstrated
by computing trajectories calculated from
geostrophic currents determined from
sea surface height maps with and without
an Ekman dri added. Current trajecto-
ries were compared to driers released
during the DWH event to demonstrate
improved prediction with Ekman dri.
Numerical models with sucient ver-
tical resolution represent the Ekman
dri, and additional parameterizations of
Stokes dri and Langmuir eects can fur-
ther improve prediction skills (Le Héna
et al., 2012). e importance of consid-
ering the near-surface wind-driven pro-
cesses was evident from retrospective
model studies of the DWH event. e
generally southerly winds that occurred
throughout that time period were shown
to have helped prevent oil distribution
beyond the GoM. Without the eects of
the wind dri, simulations show that oil
would likely have reached the Straits of
Florida by the middle of May 2010. In
addition, the wind dri altered the distri-
bution of oil along the coastline, sparing
Florida signicantly greater impact from
oil coming ashore. e Mississippi River
outow was also shown to have impacted
the DWH oil transport (Kourafalou and
Androulidakis, 2013).
Oil Spill Predictive Modeling
Oil spill models, such as the General
NOAA Operational Modeling Environ-
ment (GNOME) used operationally
during the DWH event, were primarily
computations of surface trajectories of
oil-simulating particles. ough GNOME
has the ability to simulate weathering
eects, it was run operationally during
the DWH spill simply as a conservative
particle advection model with random
diusion (MacFadyen et al., 2011). For
forecasting purposes, the model was ini-
tialized with the location of the surface
slick daily as determined from aircra
and satellite observations, and it was run
forced by currents and winds from ocean
and weather model forecasts. Multiple
ocean current and wind forecast products
permitted ensembles of predictions to be
run. Dierences in the individual ensem-
ble members highlight the substantial
uncertainty in oil spill trajectory forecasts
that arises from the uncertainty in wind
and ocean current forcing (MacFadyen
etal., 2011, their Figure5).
Operational oil spill forecasts during
the DWH spill were performed on short
(72-hour) time horizons using particle
trajectory models that did not include
detailed oil weathering eects. However,
these eects are crucial to the accuracy
of long-term predictions of the total
area to be aected by an oil spill or the
amount of oil arriving on shorelines. As
an example, a computation performed
by the National Center for Atmospheric
Research simulated the movement of a
passive tracer released from the DWH
site over several months in order to pro-
vide an estimate of the envelope for pos-
sible oil dispersal scenarios. e simu-
lation showed oil exiting the GoM and
owing northward along the Atlantic
coast with the Gulf Stream and eastward
through the Atlantic becoming progres-
sively diluted with distance (Klemas,
2010). No indication of the presence
of hydrocarbons from the DWH has
been found this far from the source in
the Atlantic, though; we note that these
model scenarios did not include weath-
ering eects leading to the dissipation
of oil. In contrast, a series of simulations
run with a simple oil spill particle advec-
tion model that accounts for weather-
ing of oil, parameterized by random
removal of oil particles based on a pre-
scribed half-life, was in good agreement
with SAR-derived maps of oil coverage
during the DWH time period (Figure1).
Objective comparisons between simu-
lated time-composited oil coverage and
that derived from SAR data show that the
simulated coverage of oil best agrees with
the SAR-observed oil coverage when oil
is removed from the model with a half-
life between three and six days (Morey
etal., 2011; Dukhovskoy etal., 2015).
One of the consistent points revealed
and reinforced by the research is that scar-
city of observations is a critical factor lim-
iting predictive skill (Mariano etal., 2011).
Satellite altimeters typically provide only
one to two ground tracks daily, and even
using the three satellites available during
DWH, forecast skill was strongly aected.
Work supported by the Gulf of Mexico
Research Initiative (GoMRI) brought
a range of targeted observational capa-
bilities to the GoM. Perhaps one of the
most promising was drier observations,

Oceanography | Vol.29, No.3
104
which can be employed at low cost and
persist in an area of interest. Results of
assimilating the GLAD drier observa-
tions indicate signicant advancement in
dri trajectory forecasting (Carrier etal.,
2014; Muscarella etal., 2015). Evaluation
of the impact of specic observations can
be performed using Observation System
Simulation Experiments (OSSE), which
has long been a basis for building sup-
port for meteorological instruments.
Correctly conguring OSSE is challeng-
ing, yet there are recent examples of
ocean applications (Halliwell etal., 2015).
Even as observations are added and mod-
els advance, it is important to remember
that errors will persist at some level. e
methods for forecasting state errors for
the ocean are typically through ensem-
bles. Wei et al. (2014) showed that the
small errors in ocean state, which imply
small errors in the positions of ocean
eddy features, lead to large uncertainties
in the forecast dri trajectory.
e problem of forecasting parti-
cle trajectories is much more challeng-
ing than that posed in traditional ocean
prediction, where the primary focus has
been on predicting mesoscale veloc-
ity and density elds. Recent advance-
ments in modeling particle trajectories
have been made by correcting the back-
ground ow eld with observed trajecto-
ries. Coelho et al. (2015) demonstrated
an ensemble approach that combines the
forecasts from dierent forecast systems,
weighted to provide an optimal forecast,
while Berta et al. (2015) used a back-
ground geostrophic velocity eld from
sea surface height and observed veloci-
ties to construct an optimal forecast tra-
jectory. Such approaches oer advan-
tages over traditional data assimilation
systems, as dynamical balances between
variables are not required. Advancement
from the predictive capability prior to
the DWH event can be illustrated by
comparing the work of Price etal. (2006)
to more recent studies. Price etal. (2006)
found position errors between ocean-
following driers and predictions to be
78 km RMS aer three days. Berta etal.
(2015) and Yaremchuk et al. (2013),
using more recent model congurations
with the more extensive observations
collected since 2010, have shown error
levels are about 45 km RMS aer three
days. e addition of drier trajectories
to correct the background currents for
the forecasts further reduced the error
levels by half.
Deep-Sea Plume Modeling
Deepwater blowout plumes, such as
those produced following the DWH
accident, are characterized by extreme
buoyancy uxes produced by an evolv-
ing multiphase mixture of oil and gas
at temperatures far above that of the
ambient seawater. e resulting plumes
are not passively mixed with the envi-
ronmental uid, but instead dynami-
cally alter the local ow eld. While of
primary importance for remediation and
response eorts, accurate prediction of
how much and where the euent will
reach the surface, and the observed dis-
tribution of pollutant constituents within
the water column (Reddy et al., 2012;
Spier etal., 2013), poses a unique mod-
eling challenge due to a broad range of
physical and chemical processes occur-
ring on disparate spatial and temporal
scales. Modeling responses to the DWH
incident have advanced along two inter-
connected lines. Predictive spill mod-
els, allowing detailed parameterization
of droplet and bubble size distributions
as well as thermochemistry, are typi-
cally based on Eulerian integral formu-
lations of the near-eld hydrodynamics
and Lagrangian evolution of gas bubbles
and oil droplets in the ow above the
intrusion level (Adcro etal., 2010; Yapa
et al., 2012). Results from an industry-
sponsored intercomparison of such
models, which also allow for the param-
eterized eects of dispersant application
at the source, are detailed in Socolofsky
et al. (2015). In addition, the unique
characteristics of the DWH incident
have prompted research into funda-
mental aspects of the hydrodynamics of
multiphase plumes in stratied, rotating
environments. While classical integral
model predictions of primary trapping
heights are in general agreement with
observations of hydrocarbon concentra-
tion maxima in the vertical (Socolofsky
et al., 2011), questions persist about
the existence of secondary intrusion
layers and observations of concentra-
tion maxima at heights much closer to
the spill site. In order to begin to address
these questions, detailed turbulence-
resolving simulations of mixed buoy-
ancy source, multiphase plumes using
both Eulerian-Eulerian (Fabregat et al.,
2015) and Eulerian-Lagrangian formu-
lations (Fraga et al., 2016) have been
conducted. Dierential turbulent mix-
ing of mixed buoyancy sources is capa-
ble of both signicantly reducing the
vertical extent of thermal buoyancy and
producing turbulence- driven secondary
intrusions of ne oil droplets above the
main intrusion level, even in the com-
plete absence of any relative velocity
between oil and water phases (Fabregat
Tomàs et al., in press). More dramati-
cally, turbulence-resolving multiphase
plume simulations have revealed the
strong eect of system rotation on over-
all mixing and entrainment intrusion
heights. As Figure6 shows, Earth’s rota-
tion induces global, anticyclonic preces-
sion of the plume, greatly increasing the
turbulence in the intrusion layer, lead-
ing to a signicant reduction in the over-
all height of the plume and a signicant
increase in the thickness of any intrusion
layers (Fabregat Tomàs etal., in press).
DISCUSSION
From analysis of observational data and
modeling exercises during and following
the DWH oil spill, it is clear that uncer-
tainties in hydrodynamic/atmospheric
forcing, model initialization, parame-
terization of unresolved processes, and
weathering processes are key areas that
need more study in order to improve
the ability to predict the fate of an oil
spill. Indeed, quantication of the uncer-
tainty of oil spill model simulations aris-
ing from the dierent factors has been a

Oceanography | September 2016 105
particularly active area of research since
the DWH event (Gonçalves etal., 2016).
Fundamentally, improvements in ocean
and atmospheric model prediction will
have profound impacts on the ability to
forecast oil spills, even with no improve-
ments to the most advanced oil spill
models themselves. However, signicant
eorts have been undertaken by the oil
spill research community to implement
advances in the physical, chemical, and
even biological dynamics of models to
improve forecasting ability. State-of-the-
art oil spill models now include the ability
to simulate the rise of oil through a buoy-
ant plume from sources at the seabed
to the surface. As accuracy in forecast-
ing the three-dimensional ocean veloc-
ity eld improves, simulating the sur-
facing of oil in this manner can address
the uncertainty associated with initial-
ization of the distribution of surface oil.
Consideration of the three-dimensional
movement of oil also permits prediction
of the spreading of oil through subsurface
plumes, which was suggested by lim-
ited in situ sampling and model particle
advection simulations to have occurred
during the DWH spill (Camilli et al.,
2010; Weisberg etal., 2011).
Downscaling from the ocean model
upper-layer velocity, which may rep-
resent the average velocity over a layer
several meters thick, to the true surface
velocity that moves oating oil, has tra-
ditionally been parameterized using
simple methods of adjusting the upper-
layer currents for local winds. A num-
ber of eorts have focused on improving
our understanding of the near-surface
oceanic layer and atmospheric bound-
ary layer, including the inuence of waves
(Le Héna etal., 2012; Clark etal., 2016)
and the modication of wind-forced
motions by the inuence of oating oil on
ocean surface roughness and temperature
(Zheng etal., 2013).
Perhaps the most advanced recent
improvement in oil spill modeling is that
we have a better understanding of the
size of droplets formed in the turbulent
plume above the wellhead. During the
spill itself, no model was able to predict
the droplet size distribution, which dic-
tates rise times, dissolution, and biodeg-
radation, and therefore the ultimate fate
of the oil. Following the spill, experi-
mental work with down-scaled blow-
outs in laboratory settings led to a greatly
improved model for droplet size forma-
tion (Johansen et al., 2013; Brandvik
etal., 2013), which has subsequently been
adopted in most state-of-the-art oil spill
models (Socolofsky et al., 2015). ere
is good reason to believe that the impact
of the DWH spill will continue to make
its mark on oil spill model development
in the years to come. One legacy of the
DWH oil spill has been the collection of
a vast amount of data, both in situ and
remotely sensed, that can now be used to
test advances in oil spill models and ver-
ify their utility for future operational use
(https://data.gulfresearchinitiative.org).
Future enhancements will likely be
inclusion of oil spill modeling routines
in Earth system modeling environments,
which will link physical models (hydro-
dynamic, surface wave, and atmospheric)
with marine sediment and biogeochemi-
cal components. is coupled Earth sys-
tem modeling framework will be used
to simulate the interaction of oil with
its environment through sedimentation
and biodegradation processes. ough
advances are being made in this direc-
tion, transitioning the research into
demonstrated improvements for oper-
ational forecasting use will require the
commitment of institutions funding basic
research in oil spill modeling.
FIGURE 6. Eects of system rotation on the instantaneous oil volume fraction for a subsurface multiphase (thermal, oil, gas bubbles) blow-
out plume at inlet buoyancy flux and (linear) stratification approximating those of the Deepwater Horizon accident (Fabregat Tomàs et al., in
press). (left panel) With ambient rotation. (right panel) Without rotation. Note the deviation from the vertical with rotation. Horizontal and vertical
axes are in meters.
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80
40
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60
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Oceanography | Vol.29, No.3
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ACKNOWLEDGMENTS
This research was made possible by a grant from
BP/The Gulf of Mexico Research Initiative to the
CARTHE and Deep-C Consortia, and by contract
M12PC00003 from the Bureau of Ocean Energy
Management (BOEM). We would like to acknowledge
Alex Fabregat for Figure2, Eric D’Asaro for the lower
panel of Figure3, and Edward Ryan for the upper
panel of Figure4.
AUTHORS
Tamay M. Özgökmen (tozgokmen@rsmas.miami.
edu) is Professor, Department of Ocean Sciences,
Rosenstiel School of Marine and Atmospheric
Science (RSMAS), University of Miami, Miami, FL,
USA. Eric P. Chassignet is Director, Center for
Ocean-Atmospheric Prediction Studies (COAPS),
and Professor, Department of Earth, Ocean
& Atmospheric Science (EOAS), Florida State
University, Tallahassee, FL, USA. Clint N. Dawson
is Professor and Head, Institute for Computational
Engineering and Sciences, University of Texas
Austin, Austin, TX, USA. Dmitry Dukhovskoy is
Associate Research Scientist, COAPS, Florida State
University, Tallahassee, FL, USA. Gregg Jacobs
is Head, Ocean Dynamics and Prediction Branch,
Naval Research Laboratory, Stennis Space Center,
MS, USA. James Ledwell is Senior Scientist, Woods
Hole Oceanographic Institution, Woods Hole, MA,
USA. Oscar Garcia-Pineda is Director, WaterMapping
LLC, Tallahassee, FL, USA. Ian R. MacDonald is
Professor, EOAS, Florida State University, Tallahassee,
FL, USA. Steven L. Morey is Research Scientist,
COAPS, Florida State University, Tallahassee,
FL, USA. Maria Josefina Olascoaga is Associate
Professor, RSMAS, University of Miami, Miami, FL,
USA. Andrew C. Poje is Professor, Department of
Mathematics, City University of New York, New York,
NY, USA. Mark Reed is Senior Research Scientist,
SINTEF, Trondheim, Norway. Jørgen Skancke is
Master of Science, SINTEF, Trondheim, Norway.
ARTICLE CITATION
Özgökmen, T.M., E.P. Chassignet, C.N. Dawson,
D. Dukhovskoy, G. Jacobs, J. Ledwell,
O. Garcia-Pineda, I.R. MacDonald, S.L. Morey,
M.J. Olascoaga, A.C. Poje, M. Reed, and
J. Skancke. 2016. Over what area did the oil
and gas spread during the 2010 Deepwater
Horizon oil spill? Oceanography 29(3):96–107,
http://dx.doi.org/10.5670/oceanog.2016.74.