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CHAPTER 3 Arctic Oil Spill Response Research Large-scale work on oil spills in sea ice began in the early 1970s in Canada and the United States with the Beaufort Sea Project and the Outer Continental Shelf Environmental Assessment Program, respectively (e.g., Lewis, 1976). In both programs, laboratory and tank research was carried out, but the Beaufort Sea Project also involved the first significant field release of oil under growing sea ice during the winter season. More recently, in 2007-2010 an Oil in Ice Joint Industry Program44 (JIP) was managed by the Foundation for Scientific and Industrial Research (SINTEF). This large research effort was sponsored by six companies. It included a wide range of laboratory, tank, and field tests, including two cruises in the Norwegian Barents Sea where oil was deliberately released to assess weathering, burning, herding agents, skimmers, and in situ burning (ISB) (Sørstrøm et al., 2010). In 2012, an Arctic Oil Spill Response Technology JIP,45 with nine participating companies, launched a range of research projects on all aspects of responding to oil spills in the Arctic. This is the largest research program of its kind and is scheduled to continue through 2015 (Mullin, 2012). This chapter describes the current state of oil spill response research. It begins with a discussion of a risk-based framework for thinking about oil spill impacts and then provides a summary of the state of knowledge governing our understanding of expected oil behavior in ice. The chapter then moves through a description of various oil spill response options and promising new concepts. RISK-BASED FRAMEWORK The committee chose to look at oil spill response in a risk-based framework. A classical risk matrix would depict the risk of an oil spill as a function of its probability (or likelihood) versus the magnitude of its impact (Figure 3.1). Therefore, there are two ways to reduce the risk related to oil spills—reduce the probability of an event, or reduce the magnitude of the impact. Environmental risks include not only low-probability, high-impact events like the Deepwater Horizon oil spill, but also small oil spill events with greater likelihoods. The probability of a spill is related to the type and condition of a vessel, pipeline, rig, or storage facility; the accuracy and availability of maps and charts; season, weather conditions, and presence or absence of ice; the behaviors, decisions, and levels of experience of key personnel; and the availability of infrastructure to support spill response (availability of a capping stack, for example). Spill impacts will be related to the amount and type of oil released; meteorological, oceanographic, and geologic conditions, including ice characteristics and cover; the degree of interaction between spilled oil and valued ecosystem elements; and the availability of response infrastructure and trained personnel. The choice and efficacy of oil spill response activities (commonly referred to as “countermeasures”) will also affect the magnitude of the impacts. 44 See http://www.sintef.no/Projectweb/JIP-Oil-In-Ice/. 45 See http://www.arcticresponsetechnology.org/. 61 PREPUBLICATION COPY

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62 Responding to Oil Spill in the U.S Arctic Mar Environ g ls S. rine nment Figure 3.1 An oil spill risk matrix with low-probability/low-impact events in the l 1 h lower left corner (low risk) an nd high-probaability/high-imp events in the upper right corner (high r pact t risk). Risk incr departs from the reases as one d lower left corner of the matrix. Scenario related to di c m os ifferent oil spil response eve are mapped onto the risk ll ents d matrix; nummbers correspo to scenario presented in Chapters 4 an 5. Source: C ond os nd Committee. Risk scenario were deve R os eloped to hig ghlight differrent facets of Arctic oil s f spill respons se. Although no formal risk assessm h r ments were un ndertaken, sc cenarios wer developed for discuss re d sion purposes and to prov illustrati vide ions of the different type of events t could po d es that otentially result in an Arc oil spill response. Th is similar to processe followed i a variety o reports (e. ctic r his r es in of .g., Arctic Co ouncil, 2009 2013). Sce 9; enarios are ch haracterized as having r d relatively low to high w probabiliities—based on exposure frequency and relativ risk levels e, y, ve s—and are m mapped onto the risk matr shown in Figure 3.1. Impact level are charac rix ls cterized as reelatively low to high, ba w ased on potent oil spill volume or quantity and the ability of resources t reach and respond to t tial v q to d the spill. Sce enarios are briefly descri ibed below, with full dis cussions fou in Chapt 4 and 5. w und ters Scenarios 1 and 4—a passenger cruis or research ship accide and a ba a se h ent arge separateed from its tow—are determined to be of higher probability due to frequ seasona operations t r y uent al s, often in shallow, nea s arshore water The other scenarios— large oil t rs. r —a tanker accid (Scenari 2), dent io a bulk caarrier driven ashore (Scennario 3), a su ubsea pipelin break (Sc ne cenario 5), a well blowou ut (Scenario 6), and lan o nd-based oil tank spills (S t Scenario 7)— though to be less l —are ht likely events due s to existin containme and prev ng ent vention systems, includin establishe navigation routes for b ng ed n bulk ore carrie Large ta ers. anker acciden subsea pipeline brea and well blowouts a considere to nts, p aks, l are ed have relaatively high impacts, prim i marily becau of large s use spill volume and the rem es moteness of spill f PREPUB BLICATION C OPY

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Arctic Oil Spill Response Research 63 locations. The structural failure of a land-based oil tank would be relatively low impact because of the limited spill size, existing spill response equipment, and the ability to reach the site with existing onshore resources. OIL PROPERTIES Crude oil is composed of a complex mixture of paraffinic, naphthenic, and aromatic hydrocarbons. Oils can differ from each other in a variety of ways, including density and sulfur content. The physical and chemical properties of an oil are not static but can vary between regions, within wells at the same location, and even within a given well over time (EPA, 2011). Key oil properties in cold water environments include measures of the American Petroleum Institute (API) gravity46 (an indicator of relative density in comparison to water), pour point (the temperature at which a fluid ceases to readily flow), and viscosity. As temperature decreases, viscosity increases and the possibility of going below the pour point becomes more likely. These properties are often considered in early stages of an oil spill response because they usually help define the most effective response options. There are four standard groupings of oil types (ITOPF, 2013). Group I oils, which include diesel fuel, are non-persistent—they dissipate rapidly through evaporation and natural dispersion within a few hours and are unlikely to form emulsions, in which water droplets become entrained in the oil through mixing. Group II and III oils will partially dissipate, losing up to 40% of their volume through evaporation. These oils are likely to increase in volume due to their tendency to form viscous water-in-oil emulsions. This also leads to a lack of natural dispersion, especially in Group III oils. Group IV oils have low volatility and are highly viscous. They are highly persistent and are unlikely to evaporate or disperse (ITOPF, 2013). The properties of a fresh oil may change with time, as the petroleum reservoir changes during production. Because of this, a given set of measurements to characterize a fresh oil represents a snapshot in time that may need to be updated. Nevertheless, the classification of oils into specific groups allows broad understanding of how they will behave under different environmental circumstances. The development of biofuels has somewhat complicated this scheme, as they represent a class of materials that does not readily fit into the categories developed for petroleum products. While the viscosity and relative density of biofuels may be similar to crude oils and petroleum- based fuels, other properties may be quite different, especially as they relate to effectiveness of oil spill response methods. Ethanol from plant sources such as corn, sugar beets, or sugar cane is quite different from crude oil-based fuels because of its infinite solubility in water. In the event of a spill, ethanol would be more akin to a chemical spill rather than an oil spill. Another common form of biofuel is biodiesel, which may be made from either plant- or animal-based materials—animal fats such as tallow and lard; plant oils such as corn, canola, sunflower, and rapeseed; and recycled grease and used cooking oils. Recently, the U.S. Air Force and Purdue University have focused on biofuel for jet aircraft use derived from the Camelina plant species.47 46 API gravity is measured in degrees, and is calculated using the following equation: API gravity = (141.5 / SG) - 131.5, where SG is the specific gravity of the petroleum liquid at 60°F. 47 See http://www.purdue.edu/newsroom/releases/2013/Q3/purdue-jet-to-fly-to-international-air-show-powered-by- biofuel.html. PREPUBLICATION COPY

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64 Responding to Oil Spills in the U.S. Arctic Marine Environment Biodiesel generally has higher viscosity, flash point, and pour point compared to petroleum-derived diesel, with similar specific gravity (NREL, 2009). Unlike conventional diesel, biodiesel may be suitable for mechanical collection in the event of a spill because of its higher flashpoint and pour point, especially in colder environments. However, its response to dispersants may be quite different from that of crude oil-derived products because the biodiesel’s range of molecular components is narrower. North Slope Oil Properties Oil properties and characteristics for specific fields are provided to regulatory agencies by industry when applying for exploration drilling and development permits. For exploration drilling permits, oil properties are estimated from other oils that have been discovered in the region (see Figure 1.2 for locations of many of fields mentioned in this section). Light (high API gravity or low density) crude oils with 32-57° API gravity and very low sulfur contents (0.0- 0.2%) have been encountered in Chukchi Sea wells (Burger, Popcorn, and Klondike). These petroleum hydrocarbons are similar to oils found at the Umiat, Alpine, Tarn, and related fields in northern Alaska. In the Beaufort Sea, several of the oil occurrences in nearshore wells like Liberty are related to the medium-gravity (25-30° API), high-sulfur (1.0-2.0%) Prudhoe Bay and Kuparuk oils of northern Alaska. Farther offshore, oil occurrences in the Beaufort Sea (Northstar, Kuvlum) are similar to the high-API gravity (30-40°), low-sulfur (0.0-0.5%) oils known from the northernmost Arctic National Wildlife Refuge (described by Lillis et al., 1999). The offshore Hammerhead oil field (25 km north of Point Thomson) features an anomalously heavy crude oil (19° API) that has been altered by bacterial degradation, but is probably also related to the offshore Beaufort oils (Banet, 1994; Curiale, 1995; Lillis et al., 1999). The Bureau of Ocean Energy Management (BOEM) has also identified 97 suspected natural oil seeps, 80 percent of which are in the Chukchi Sea, in unpublished data collected by industry in the 1980s and 1990s. These seep data could provide suggestions of where additional sampling or analysis might be targeted. However, the available seep data are too elementary for biomarker fingerprinting of oil types and making correlations to oils encountered in exploratory wells or oil fields (presentation by Kirk Sherwood, BOEM, March 2013). OIL WEATHERING While properties described in the above section can characterize a crude oil at a particular point in time, weathering after being spilled can change its overall chemical and physical properties. Weathering involves processes that are typically experienced in an open ocean environment—evaporation, dissolution, dispersion, oxidation, emulsification, biodegradation, and sedimentation (Sørstrøm et al., 2010). In the case of a subsea oil spill, surface weathering processes may not be significant if oil does not reach the surface. Key factors that impact weathering include air and water temperatures; the presence of waves, currents, and wind; exposure to sunlight; the presence of ice or snow; and the presence of natural sediments (Figure 3.2). Changes in an oil due to weathering affect spill response options and oil interactions with organisms and ecosystems. PREPUBLICATION COPY

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Arctic Oi Spill Resp il ponse Resear rch 65 Figure 3.2 Environmenta processes tha affect oil beh 2 al at havior and wea athering in ope water and in ice. Source: en n Modified from Daling et al., 1990, A. Allen. f A Oil characteri O istics will also determine to what ext e tent differen weathering processes w nt g will be signifi ficant. Some processes af ffect oil quic ckly (e.g., ev vaporation in a warm env n vironment), while oth may tak longer (e.g biodegrad hers ke g., dation).48 Evvaporation an dissolutio cause the loss nd on of the lig ghtest chemic compoun in oil; th cal nds hese smaller- -molecule, m more volatile compounds e s contribut to lower viscosity, low density, and greater s te v wer solubility. T Their loss fro an oil slic om ck can have a significan effect on th bulk char nt he racteristics o any remain of ning floating oil. g Evaporation is the most rapid weathering process It accounts for the loss of 20-50% of E i s. s s many cru oils, 75% or more of some refine petroleum products, b only 10% or less of ude % f ed m but % residual fuel oils (NR 2003, 20 f RC, 005). The ev vaporative lo of a light oil under th different ice oss hree t coverage levels (open water, 30% ice covera and 90% ice coverag at variou current an e n % age, % ge) us nd wave hei ight conditio with diffe ons ferent air tem mperatures (− to about −5°C) was studied by −15 t Brandvik and Faksne (2009). They reported that evapo k ess T orative loss w estimate to be 30% for was ed % open wat 25% for the lighter ic coverage, and 19% fo the heavie ice covera due to ter, ce , or er age, differenc in oil film thicknesse ces m es. Another impo A ortant proces is emulsif ss fication. As o resides on the water’s surface, there is oil n s a general tendency fo it to incorp l or porate water and form an oil-in-wate emulsion. The additio of r er . on mixing energy from waves can accelerate the process. As emulsifica w a e ation occurs, there is an increase in volume, viscosity, and water cont v tent, each of which can i f influence the efficiency of e response options. For example, ISB loses its applicability once the w r y water content of the emul t lsion 48 ITOPF online, 2012; see http://www o s w.itopf.com/mar rine-spills/fate/ e/weathering-pr rocess/. PREPUB BLICATION C OPY

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66 Responding to Oil Spills in the U.S. Arctic Marine Environment begins to exceed 30-50%. Additionally, the use of dispersants may become less effective once the emulsion viscosity exceeds a threshold, although this will differ from one crude oil to another (Daling et al., 1990; Federal Interagency Solutions Group, 2010).49 Photo-oxidation is an additional process that contributes to the degradation and transformation of crude oil compounds after release to the environment (Garrett et al., 1998; Dutta and Harayama, 2000; Prince et al., 2003b). Light intensity near the water’s surface will be lower at northern latitudes due to a low angle of incidence, and the region has a wide range of daylight hours based on the season. Due to longer exposure times during the summer months, photo-oxidation may be a much more important process for oil degradation in the Arctic than in more temperate climates ( Serova, 1992; Ivanov et al., 2005). It should also be noted that not all of the chemicals that make up crude oil biodegrade at the same rate. Higher-molecular weight components, including polycyclic aromatic hydrocarbons, cyclic alkanes, and naphtheno-aromatic hydrocarbons, may persist for some time. While weathering trends (Table 3.1) provide some generalities, each real-world case will be different. Weathering may alter the potential toxicity of crude oil components through processes such as evaporation and dissolution of low-molecular weight components, while photo-oxidation near the water surface could create products not initially present in the oil. There are a number of modeling tools available to assist in weathering predictions,50 although the underlying assumptions and potential limitations of models and the information from which they operate need to be understood and questioned when necessary. In general, evaporation rates take into account compositional details of specific crude oils. In the event that a crude oil released into the environment is not known completely, the use of another with similar characteristics may be used to gain an approximation of its behavior.51 49 Laboratory assessment of emulsions that were formed during the Macondo Well release indicated that they were susceptible to dispersion at reasonably low dispersant-to-oil ratios. See, for example, work done by SINTEF and documented within: Oil Budget Calculator, Deepwater Horizon Technical Documentation: A Report by the Federal Interagency Solutions Group, Oil Budget Calculator Science and Engineering Team, November 2010. 50 For example, see the following models: ADIOS2™, developed by the National Oceanic and Atmospheric Administration (NOAA) (Jones, 1997); the Type A model developed by ASA (Reed et al., 1989; French et al., 1996); the OWM model developed by SINTEF (Reed, Singsaas et al., 2001); the OSCAR model developed by SINTEF (1995, 1999). 51 Environment Canada developed an online database of crude oil properties that is still useful when attempting to determine weathering characteristics: http://www.etc-cte.ec.gc.ca/databases/Oilproperties/oil_prop_e.html. PREPUBLICATION COPY

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Arctic Oil Spill Response Research 67 Table 3.1 Chemical and Physical Changes to Crude Oil From Weathering. MW stands for molecular weight. Property Potential Change upon Weathering Cause Oil viscosity Increase (by an order of magnitude) Loss of low-molecular-weight (low- MW) components from evaporation or dissolution Increase (several orders of magnitude) Formation of water-in-oil emulsions (mousse) Oil specific gravity Increase Loss of low-MW components from evaporation or dissolution Oil slick volume Decrease Loss of low-MW components from evaporation or dissolution; loss of mass due to entrainment of oil droplets due to breaking waves Increase (by magnitude factor of 3 to 5) Formation of emulsions (mousse) Potential toxicity Decrease Loss of low-MW components from evaporation or dissolution Increase Formation of photo-oxidation products near water surface OIL BEHAVIOR IN ICE The presence of sea ice, discussed in Chapter 2, affects oil weathering processes and the overall behavior of oil in Arctic waters. In many cases, processes that affect traditional oil behavior in open water like evaporation, emulsification, and natural dispersion are slowed down or eliminated for extended periods of time. Laboratory, basin, and field experiments on oil behavior and weathering under Arctic conditions have been conducted independently in Canada and Norway (Sørstrom et al., 1994, 2010; Brandvik and Faksness, 2009; Buist et al., 2009). Dickins (2011) summarized the behavior of oil in ice, derived from these findings and from direct observations from large-scale field trials dating back to 1972 (McMinn, 1972; NORCOR Engineering & Research Ltd., 1975; Dickins and Buist, 1981; Nelson and Allen, 1982; Buist and Dickins, 1987; Sørstrøm et al., 1994; Brandvik et al., 2006; Dickins et al., 2008; Sørstrøm et al., 2010). Figure 3.2 shows a schematic of different potential oil-ice interactions. Weathering Oil spilled during freeze-up will be affected by evaporation, dissolution, emulsification, and natural dispersion to some degree. Most oil spilled during the freeze-up period will remain on the surface of the ice or will migrate up, where it will be affected by evaporation. The evaporation rate is partially controlled by oil slick thickness—in freezing conditions, thicker oil slicks will evaporate more slowly than slicks in open water. Cold temperatures reduce evaporation rates, as would snow forming a thin film on or covering the oil. Even when covered with snow, oil on an ice surface will lose approximately the same amount to evaporation as it would on water in more temperate waters (Buist et al., 2009). PREPUBLICATION COPY

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68 Responding to Oil Spills in the U.S. Arctic Marine Environment Brandvik and Faksness (2009) reported that the formation of water-in-oil emulsions under Arctic conditions at a test site inside the ice pack, far removed from the effects of ocean swell, was significantly reduced. The presence of ice effectively dampens both wind wave activity and swell (depending on distance from the pack edge; e.g., Wadhams et al., 1988), increases the thickness of the oil slick, and reduces its surface area. In contrast, within the marginal ice zone at the edge of the pack, there is enough wave action to grind individual floes together and accelerate emulsification. This process was observed by Buist et al. (2009) and Payne et al. (1991) when they did a tank experiment with crude oil spilled in ice in an induced wave environment. Movement and Drift Rates Oil trapped within pack ice tends to move with the ice, which is in turn driven by currents and wind; oil in more open drift ice will be less strictly herded by the ice. Under-ice currents in most Arctic nearshore areas are not strong enough to spread the oil far beyond its initial contact with the ice. A 20 cm/s (~0.5 kn) current is needed to initiate and sustain movement of oil under the surface of the oil (Buist et al., 2009). Spreading Spreading behavior is one of the properties that is most different between oil spills in ice compared to open water. Oil spilled in the presence of ice is often naturally contained within a relatively small area, which has mostly positive implications for response and recovery options (Buist and Dickins, 1987). Table 3.2 compares the predicted areas and thicknesses covered by a 1,600 m3 (10,000 barrels [bbl]) crude oil spill on open water, under ice, and on smooth sea ice with and without snow. The table is intended for illustrative purposes to demonstrate the dramatic difference in contaminated areas between oil on water, under ice, and on ice. Under solid midwinter ice, the maximum pool depth varies based on the depth of under-ice depressions, and the contaminated area is determined by both the volume of those depressions and the ways they fill with oil. Table 3.2 An illustration of the impact of ice and snow on the spreading rates of oil, using as an example a 1,600 m3 (10,000 bbl) crude oil spill. The maximum pool depth under solid midwinter ice is determined by the depth of the under-ice depressions, which become deeper as the ice increases and deforms over winter. The final area is determined by both the volume of under-ice depressions and how they fill with oil. Source: SL Ross et al., 2010. Under Solid Open Water On Smooth Ice Midwinter Ice Ice Snow Final average oil thickness 0.016 40 to 90 3 40 (mm) Final area 10,000 7 to 70 50 4 (ha) The spread of oil is reduced by ice and snow, with resulting oil slicks that are much thicker than those in open water (Dickins, 2011). In practice, an oil slick on open water will spread to cover areas with different equilibrium thickness after some time has elapsed. The majority of the oil is contained within a relatively small, thick patch, while the rest spreads out as PREPUBLICATION COPY

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Arctic Oil Spill Response Research 69 a thin film or sheen over a much larger area. In contrast, the maximum contaminated areas under or on ice are predicted to be hundreds to thousands of times smaller. This can be a critical difference when looking at the potential for wildlife exposure, as thin films on the surface create significant risks to waterfowl. The degree of natural oil containment provided by close pack ice depends on ice concentration and other variables. Generally, the presence of 6/10 ice concentration leads to a slick that is less than half the area of the same oil volume in open water. However, in open drift ice at concentrations less than 6/10, spreading rates approach those for open water (Buist and Dickins, 1987). Encapsulation, Migration, and Release Oil density and turbulence tend to govern how much oil becomes incorporated into growing ice, while the oil viscosity is a factor in how it will break down (Dickins, 2011). Heavier fuel oil particles can remain suspended at depth in slush ice; this was observed in the 1979 Kurdistan tanker oil spill between Nova Scotia and Newfoundland (Vandermeulen and Buckley, 1985). Oil spilled under new ice will likely become encapsulated within 12 to 24 hours (Dickins et al., 1975; Dickins and Buist, 1999; Brandvik et al., 2006). New ice grows beneath the spilled oil, trapping the oil between layers of ice and isolating it from the ocean. Oil may migrate to the surface under thin new ice (less than ~10 cm thick) but will be trapped as the ice solidifies (Dickins, 2011). While lighter fuel oils may surface quickly, heavy fuel oils may be suspended in slush ice. Oil spilled under ice in late winter is unlikely to become encapsulated due to the slowdown in the rate of ice growth. In the spring, the trapped oil migrates vertically though brine channels in the ice sheet (Dickins et al., 1975; Brandvik et al., 2006; Dickins, 2011). In an experiment that was part of the 1974-1975 Beaufort Sea Project, over 80% of spilled oil migrated to the ice surface and floated on melt pools by early summer (NORCOR Engineering & Research Ltd., 1975). The oil appeared as essentially fresh crude. Once the oil was exposed to the atmosphere, evaporation occurred at rates similar to spills in southern locations (NORCOR Engineering & Research Ltd., 1975). Oil that is distributed as fine droplets under the ice may migrate more slowly to the surface, a situation that could occur during a subsea blowout with a large gas volume (Dickins and Buist, 1981). In those cases, oil may not be exposed at the surface until the ice begins to melt (Dickins, 2011). As ice melts, encapsulated or surface oil can end up in the water, forming thin oil films before it is naturally dispersed due to wave action. There are two areas where the knowledge of oil behavior is limited, due to a lack of field observations. The first is the behavior of oil spilled under multiyear ice. The only field experiment designed to explore this behavior provided limited answers (Comfort and Purves, 1982). The second involves processes governing the interaction of oil with new and developing ice during and following freeze-up. This has been addressed by a tank study that was funded through the European Union (Wilkinson et al., 2014). Developing knowledge of how to prepare for and respond to the possibility of Arctic oil spills is linked to the ability to safely conduct deliberate field-scale oil releases. A number of successful projects over the past four decades have demonstrated that having the ability to conduct deliberate and controlled releases of oil into the marine environment provides an important opportunity to advance the state of knowledge in all aspects of Arctic spill response. PREPUBLICATION COPY

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70 Responding to Oil Spills in the U.S. Arctic Marine Environment When done carefully, these field releases have few or no discernible negative environmental impacts. Important spinoffs from field trials include the opportunity for training of oil spill responders and public consultation to transfer understanding and knowledge. Over the past 15 years, it has become much harder to obtain the necessary permits in the United States to conduct deliberate oil releases (Dickins, 2011). In the absence of any clear process to permit such work, scientists and engineers have looked towards other countries such as Norway, which has consistently supported this type of research by permitting spills in ice when clear research needs, methods, and goals, combined with responsible cleanup and monitoring plans, have been established (e.g., Sørstrøm et al., 1994, 2010). However, conditions are not completely equivalent to those encountered in the U.S. Arctic. Carefully planned field releases in areas potentially impacted by exploration, such as Alaska, Greenland, Canada, and Russia, could improve evaluation of new response strategies and understanding of oil-in-ice interactions. Dickins (2011) summarized the benefits of previous experimental field releases for research purposes. While there is broad support among industry and other stakeholders, there have been no successful permits to conduct a deliberate field release in U.S. waters since the early 1990s. There has been some interest from the federal government as well. The Interagency Coordinating Committee on Oil Pollution Research has been in discussion with the Environmental Protection Agency and other federal agencies, with an aim to improve the permitting process for deliberate oil releases for experimental purposes (Eric Miller, personal communication, 2013). In 2013, the Bureau of Safety and Environmental Enforcement (BSEE) issued a solicitation for a joint industry project to assess the need for a field release of oil, dispersants, and natural gas in the U.S. outer continental shelf, with the possibility of an actual release. OIL SPILL SOURCES AND VOLUMES Three potential point sources for oil spills in the U.S. Arctic are oil and gas wells and pipelines, ships (large oil tankers, bulk carriers, and fuel barges), and land-based municipal fuel storage tanks. The following section discusses likely spill volumes in each of these cases. If a blowout were to occur in an exploratory well in the Chukchi or Beaufort Seas, the possible rate of uncontrolled flow would be dependent on a number of variables. These include the reservoir characteristics (e.g., porosity, permeability, pressure, temperature, oil viscosity, oil gas content, and compressibility), wellbore configuration, and the ambient pressure at the blowout discharge point at the seafloor or at the rig. BOEM models “very large oil spills” (VLOSs) and “worst case discharges” using a finite-difference simulator, incorporating data from exploration wells, onshore and near-shore exploration and production wells, likely well designs, known or estimated properties of potential reservoir oils, and geophysical data from seismic surveys (presentation by Kirk Sherwood, BOEM, March 2013). VLOSs represent extreme (improbable, but geologically possible) scenarios that are intended to support assessments with very high environmental impacts. The models estimate daily discharge rates but also report the cumulative amount of oil that could be discharged over the estimated period of time required to drill a relief well (presentation by Kirk Sherwood, BOEM, March 2013). The results of two VLOS models are presented in Table 3.3. In practice, a successful well-capping operation could halt or significantly slow the flow of oil into the marine environment earlier than the time needed to drill a relief well. PREPUBLICATION COPY

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Arctic Oil Spill Response Research 71 Many types of ships have recently been utilizing the Arctic marine environment, including government vessels and icebreakers, container ships, general cargo ships, bulk carriers, tanker ships, passenger ships, tugs and barges, fishing vessels, and vessels related to oil and gas exploration (Arctic Council, 2009). A record of transits through the Northern Sea Route in Table 3.3. Beaufort and Chukchi Sea VLOS volumes, modeled by BOEM for purposes of environmental assessment. Note: The relief well period for the Beaufort Sea assumes that ice conditions prevent access to the well throughout the winter. Source: presentation by Kirk Sherwood, BOEM, March 2013. Model Worst Case Oil Discharge at Relief Well Oil Discharge Model Oil Discharge Day 30 (bbls) Period at End of Relief Gravity (° API) (bbls/day) (Required by (days) Well Period Regulations) BOEM Beaufort VLOS 69,271 1,140,655 300 3,922, 903 26 BOEM Chukchi 61,672 1,148,300 46 1,552,400 35 VLOS* * BOEM (2011). 201352 indicates that some tanker ships carried over 800,000 bbl of oil as cargo, although smaller ships carried as little as 35,000 bbl of diesel fuel cargo. These numbers illustrate the broad range in volume of potential spills from cargo ships, which does not include the fuel oil they carry aboard. In the U.S. Arctic, doubled-hulled barges that provide fuel resupply for Alaskan villages can carry over 6,000 bbl of oil cargo.53 The villages store oil, diesel, and gasoline supplies for home and business heating, aviation fuel, and industrial needs for mining and oil and gas production. Because there are long periods between resupply due to sea and river ice, significant volumes of fuel may be stored in relatively close proximity to the shoreline. Examples include large storage facilities at the Red Dog Mine’s Delong Mountain Terminal and tank farms in the community of Barrow. ARCTIC OIL SPILL COUNTERMEASURES Arctic response strategies can leverage the natural behavior of oil in, on, and under ice. For instance, ice can bar the spread of oil, reducing spreading rates and leading to smaller contaminated areas (Sørstrøm et al., 1994, 2010; Potter et al., 2012); due to encapsulation or a lack of weathering, oil remains fresher for a longer time; and ice-covered areas generally have less severe wind and sea conditions. In spite of the documented effects of climate change leading to later freeze-ups, greater extent of northerly ice edge retreat, and longer summer open water seasons, the Chukchi and Beaufort Sea coastlines are still buffered from oil spilled offshore by a fringe of fast ice for eight to nine months of the year. However, Arctic conditions impose many challenges for oil spill response—low temperatures and extended periods of darkness in the winter; oil that is encapsulated under ice or trapped in ridges and leads; oil spreading due to sea ice drift and surface currents; reduced effectiveness of conventional containment and recovery systems in measurable ice concentrations; and issues of life and safety of responders. The following section reviews the state of knowledge and recent advances regarding key response countermeasures and tools for oil removal under Arctic conditions: biodegradation 52 See http://www.arctic-lio.com/docs/nsr/transits/Transits_2013_final.pdf. 53 See http://www.marinelink.com/news/ecofriendly-operate335711.aspx. PREPUBLICATION COPY

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86 Responding to Oil Spills in the U.S. Arctic Marine Environment non-governmental organization community maintain that even when used in conjunction, mechanical recovery, ISB, and biodegradation and dispersants may still only remove a small fraction of spilled oil (Goodyear and Beach, 2012; presentations by Stan Senner, Ocean Conservancy; Peter Van Tuyn, the Pew Charitable Trusts; and Chris Krenz, Oceana, June 2013). Furthermore, these groups call for rigorous testing under a range of Arctic conditions before using these techniques during a major spill. Detection and Monitoring To mount an effective response, it is critical to know where spilled oil is at any given time. Finding and mapping oil in ice, even under favorable weather and light conditions, is far from straightforward. The lack of significant waves in the presence of ice complicates the use of marine or satellite radar systems, both of which depend on differences in surface roughness as a means of detection. With adverse weather or darkness, obtaining consistently reliable detection and mapping of contaminated areas becomes challenging and requires a mix of remote sensors operating in different parts of the electromagnetic spectrum. Assessments of remote sensing system capabilities for oil spills in ice have been forced to draw upon practical detection experiences of spills in open water environments (Coolbaugh, 2008; Dickins and Andersen, 2009; Fingas and Brown, 2011). A wide range of sensor types have been tested for use in spill detection in ice. These include analytical, bench, and basin tests and field trials using a wide range of sensor types— acoustics, radar, ultraviolet fluorescence, infrared (IR), gamma ray, microwave radiometer, resonance scattering theory, gas sniffers, and ground penetrating radar (GPR) (e.g., Dickins, 2000; Goodman, 2008). Beginning in 2004, projects sponsored by the Minerals Management Service and industry (including the SINTEF Oil in Ice JIP) evaluated and tested a variety of sensors currently used to detect oil on open water to evaluate their potential for detecting oil in ice. In addition to the sensors above, these projects looked at side-looking airborne radar, synthetic aperture radar (SAR) satellites, forward-looking infrared (FLIR), trained dogs, and sonar (Bradford et al., 2010; Dickins et al., 2010). The current Arctic Response Technology JIP (2012-2015) is examining a range of these and other airborne, surface, and subsurface technologies in order to assign priorities for future development and testing (Puestow et al., 2013; Wilkinson et al., 2013). Table 3.4 compares the capabilities of different sensors for remote sensing of oil spills in ice according to the platform and the oil/ice configuration over a range of ice environments (Dickins and Andersen, 2009). SINTEF JIP field experiments in 2008 and 2009 evaluated some of these technologies in close pack ice. Expected capabilities of different systems are based on information gathered during those experiments and from results of previous trials, not necessarily in the Arctic. Dickins and Andersen (2009) concluded that current airborne systems are useful for detecting and mapping large spills in open ice but have much less potential as ice increases. Many of the non-radar sensors on airborne systems do not work well under Arctic conditions of darkness, cloudiness, fogginess, and rain for much of the year. A quantum leap in all-weather capability was realized in the late 1990s with the advent of commercially available, high- resolution SAR satellite systems, which are unaffected by darkness or cloud cover and can now resolve targets of a few meters (e.g., Radarsat, ERS-1, TerraSAR-X, COSMO-Skymed). First- generation SAR satellites mapped several large marine oil spills, including the Prestige, PREPUBLICATION COPY

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Arctic Oil Spill Response Research 87 Nakodka, and Sea Empress (Hodgins et al., 1996; Lunel et al., 1997). The ability of SAR satellites to detect and map oils slicks in the ocean with moderate wind conditions is likely to be practical for well-defined oil spills that spread in very open to open pack ice, where capillary waves can develop on the surface (Babiker et al., 2010). The Deepwater Horizon oil spill provided an opportunity to utilize many of the latest detection technologies. Leifer et al. (2012) summarized how passive and active satellite and airborne marine remote sensing were applied to the spill. Slick thickness and oil-to-water emulsion ratios are key spill response parameters for containment and cleanup. These parameters were derived for thick (greater than 0.1 mm) slicks detected by the Airborne Visible/Infrared Imaging Spectrometer satellite. The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data allowed for detection of the total slick and was used to produce maps of oil thickness. Airborne and SAR provided synoptic data under all-sky conditions; however, SAR typically is not able to discriminate between thick oil slicks and thin sheens (0.1 mm or less). The Jet Propulsion Laboratory’s Uninhabited Aerial Vehicle SAR’s56 higher spatial resolution and signal-to-noise ratio led to better pattern discrimination. 56 See http://uavsar.jpl.nasa.gov/. PREPUBLICATION COPY

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88 Responding to Oil Spills in the U.S. Arctic Marine Environment Table 3.4 Overview of Remote Sensing Systems for Oil in Ice Detection. An asterisk (*) denotes sensors blocked by dark/cloud/fog/precipitation. Source: Dickins and Andersen, 2009. Platform Ice Surface AUV Shipborne Airborne Satellite Marine SLA Sensor Dogs GPR Sonar FLIR GPR Visible UV FLIR SAR Radar R OIL ON ICE Exposed on cold Not Likely Not applicable Not applicable Not likely Likely* Likely Likely* Not likely* Likely* Not likely ice surface likely Exposed on Not Likely Not applicable Not applicable Possible Likely* Likely* Possible* Likely* Possible Not likely spring melt pools likely Buried under Not Likely Likely Not applicable Not applicable Not likely* Likely Not likely* Not likely* Not likely* Not likely snow likely OIL UNDER ICE Smooth fast ice Not Possible Likely Likely Not applicable Not applicable Likely Not applicable Not applicable Not applicable Not likely likely Deformed pack Not Possible Possible Likely Not applicable Not applicable Possible Not applicable Not applicable Not applicable Not likely ice likely OIL IN ICE Discrete Not encapsulated Possible Likely Not likely Not applicable Not applicable Likely Not applicable Not applicable Not applicable Not likely likely layer Diffuse vertical Not Possible Possible Not likely Not applicable Not applicable Possible Not applicable Not applicable Not applicable Not likely saturation likely OIL BETWEEN ICE FLOES 1 to 3/10 Not Not applicable Not applicable Not likely Likely Likely* Likely* Likely* Likely* Likely Likely concentration likely 4 to 6/10 Not Not likely Not applicable Not likely Possible Likely* Likely* Possible* Likely* Possible Possible concentration likely 7 to 9/10 Not Not Possible Not applicable Not likely Not likely Likely* Likely* Not likely Likely* Not likely concentration likely likely PREPUBLICATION COPY

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Oil Properties and Arctic Oil Spill Countermeasures 89 At present, there is a lack of hard data to confirm theoretical assumptions about the performance of most remote sensing systems in a particular oil-in-ice scenario (GPR, discussed below, is one exception). SAR satellite imagery may be of use for detecting oil slicks in ice but will be dependent on factors such as the size of the spill, ice floe size and concentration, and wind speed. The main value of radar imagery in an Arctic spill incident lies in its ability to document changing ice conditions near a spill, which provides a valuable tactical tool for response (API, 2012). False positives or negatives are a concern with SAR imagery, because other conditions can create smooth surface areas that resemble oil (e.g., grease ice), while atmospheric phenomena can hide potential oil slicks (e.g., rain cells). An important national issue is that the United States does not presently have its own commercial SAR satellite mission, so international partnerships are necessary. Access to images from international satellites typically only lasts about three days, which could be an issue for oil spill response (presentation by Nettie LaBelle-Hamer, University of Alaska Fairbanks, March 19, 2013). Classified SAR satellites from the National Geospatial-Intelligence Agency can be used for monitoring natural disasters and oil spills, but the information is only available to those people with appropriate clearances. However, derivative maps can be made using the data. Having emergency responders able to access necessary SAR data is of great importance for an integrated response. In addition to rapidly developing remote sensing technologies, there will always be a need for well-trained observers flying in helicopters and fixed-wing aircraft to map oiled areas and to transmit critical information to response crews. Spotter aircraft were essential to the success of individual ISB operations during Deepwater Horizon (Allen et al., 2011). Comprehensive tracking and long-term monitoring of oil released in ice require assimilating field data, plotting real-time observations, and integrating this information with forecasting tools such as weather models, ice drift algorithms, and oil spreading and weathering models. Commercially available ice-strengthened Global Positioning System (GPS) beacons and buoys have, for many years, been tracking ice movements during an entire winter season throughout the polar basin (Vaudrey and Dickins, 1996). Tracking oil spills accurately in a moving ice cover involves deploying large numbers of beacons at regular intervals on the ice as oil moves away from the spill source, which can then be used to direct air and marine responders toward the spill. Closely spaced GPS beacons can follow the evolving pattern of spill fragmentation and divergence as the pack expands and contracts (Hirvi et al., 1987; Weingartner et al., 1998). There are also subsurface Lagrangian floats that can operate in ice-covered waters and be acoustically tracked while under sea ice, with capabilities including subsurface profiles down to 2000 m water depth. These will be deployed under the Office of Naval Research Departmental Research Initiative Marginal Ice Zone Program.57 In the absence of sea ice or in reduced sea ice cover, the floats surface and send water column profiles and subsurface position data via satellite; two-way communications allow the float’s drift depth and/or profiling frequency to be changed when the float surfaces. Promising New Concepts in Detection and Monitoring A number of systems, including GPR, are capable of both airborne and surface operation. Tank and field experiments from 2004-2006 showed that surface-based, commercially available GPR can detect and map oil sheens as thin as 1-3 cm underneath 1 m or more of solid ice or 57 See http://www.apl.washington.edu/project/project.php?id=miz. PREPUBLICATION COPY

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90 Responding to Oil Spills in the U.S. Arctic Marine Environment trapped as layers within ice (Dickins et al., 2008). In 2008, the same radar suspended beneath a helicopter traveling at speeds up to 20 kn and altitude up to 20 m successfully detected a thin layer of crude oil buried under hard-packed snow (Bradford et al., 2010). A prototype frequency- modulated continuous-wave radar designed to detect oil trapped under solid ice from a low- flying helicopter was developed in 2011-2012, with plans to test it as part of a new Arctic Response Technology JIP research program beginning in 2014. More recently, consideration is being given to utilizing nuclear magnetic resonance as a potential means to detect oil trapped under or in ice in the future (Nedwed et al., 2008). Although further testing is needed to evaluate the practicality and effectiveness of an operational system, nuclear magnetic resonance has been successfully used to locate groundwater on multiple occasions. IR systems (alone or in conjunction with high-speed marine radar and low-light-level video) can also be used from the surface, low-flying helicopters, aircraft (tracking high- resolution FLIR), or vessels. In 2009 SINTEF JIP tests, a basic uncooled hand-held IR sensor was able to discriminate between oil, open water, snow, and oil-free ice floes during daytime (Dickins et al., 2010). These experiments confirmed prior offshore releases in pack ice, in which IR differentiated warmer oil from cold water and ice (Singsaas et al., 1994). Depending on ice conditions (e.g., floe size, thickness, stability), a variety of remote sensing systems that operate directly from the ice surface or from a nearby vessel could be deployed. As part of the SINTEF JIP, trained dogs on the ice tracked and located small oil spills buried under snow from a distance of 5 km and also determined the approximate dimensions of a larger oil spill (Brandvik and Buvik, 2009). X-band marine radar has been used to detect slicks at sea in large-scale trials, and may be able to detect oil slicks in open ice (Dickins and Andersen, 2009). Integrated systems that combine high-resolution FLIR and low-light cameras are now routinely deployed on response vessels in Norway as the SECurus system.58 Unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) already have the capability of carrying useful sensor packages over long distances (albeit at slow speed) for Arctic oil spill surveillance (Wadhams et al., 2006). Both single- and multi-beam sonar sensors successfully detected and mapped oiled boundaries and thicknesses under ice in a recent basin test at the U.S. Army Cold Regions Research and Engineering Laboratory (Wilkinson et al., 2014). A September 2013 exercise aboard the USCG Healey field tested UAVs, AUVs, and Arctic skimmers for response capabilities (USCG, 2013b). Further testing of different UAV sensors for oil spilled in ice is planned for 2014 and is being sponsored by the European Union and Arctic Response Technology JIP. Arctic nations such as Canada, Iceland, Finland, Denmark, Norway, and Sweden operate dedicated pollution surveillance aircraft. Canada dedicates one of its aircraft (a DHC-7) to Arctic missions. From 1983 to 2013, the USCG operated a fleet of Falcon Jets for maritime surveillance; most of these obsolete HU-25s have been decommissioned, with complete phase- out by 2014.59 They are being replaced by the HC-144A Ocean Sentries, which entered service in 2009 (there are currently15). The search radars in USCG fixed-wing aircraft have a SAR setting that can be used for oil spill detection. There was some limited evaluation during Deepwater Horizon, but no rigorous testing has been performed and no testing with ice has yet occurred. The fixed-wing aircraft and helicopters have an electro-optical/IR system that may be 58 See www.aptomar.com. 59 See http://www.uscg.mil/hq/cg7/cg711/hu25.asp. PREPUBLICATION COPY

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Arctic Oil Spill Response Research 91 useful in some Arctic conditions (Kurt Hansen, USCG, personal communication, February 3, 2014). A key aspect of the future effectiveness of remote sensing systems is the ability to integrate different datasets into a useful real-time or near-real-time product that responders can use with minimal interpretation. While there has been considerable progress on multispectral data fusion applied to pollution surveillance aircraft, an equivalent capability for Arctic spill surveillance has yet to be developed (Baschek, 2007). Lessons learned from Deepwater Horizon (Leifer et al., 2012) could be applied to optimizing future Arctic systems for detecting oil in ice. Trajectory Modeling Several reviews of oil spill modeling technology are available (Huang, 1983; Spaulding, 1988; ASCE, 1996; Reed et al., 1999; Yapa et al., 2006; Drozdowski et al., 2011). Both earlier and more recent reviews recognize the challenges associated with oil spills in Arctic conditions: the presence of sea ice, cold and darkness in the winter, sparse observational networks for meteorological and oceanographic conditions, and a limited ability to respond to and monitor the physical and chemical evolution of a spill. Oil in ice behaves in complex ways. Challenges associated with modeling the physics of ice growth, movement, and deformation on scales of meters or tens of meters are magnified when the details of oil behavior are incorporated. Background literature exists on oil-ice interaction studies over the past 40 years. Dickins and Fleet (1992) and SL Ross et al. (2010) provide overviews of key studies. More recent work has largely focused on oil in and under ice (Yapa and Belaskas, 1993; El-Tahan and Venkatesh, 1994; Yapa and Weerasuriya, 1997), but mostly relies on small-scale, short-term laboratory studies. Ice lead dynamics that govern the spreading of oil in the field tend to not be included in these solutions, a significant shortcoming. Understanding the effect of Arctic conditions on oil spill behavior and fate has increased significantly over the past decade (e.g., Gjøsteen et al., 2003; Faksness, 2007; Dickins et al., 2008; Brandvik and Faksness, 2009; Buist et al., 2009; Brandvik et al., 2010). Much of this knowledge has been garnered during mesoscale and field experiments (e.g., Singsaas et al., 1994; Buist et al., 2007; Sørstrøm et al., 2010), which show that in ice leads, evaporation, dispersion, and emulsification are slowed. The primary factors in determining observed weathering rates appear to be temperature, wave damping, and the presence of ice. Despite these advances, there continues to be a need for additional Arctic data and for incorporation of these data into comprehensive oil spill models (Holland-Bartels and Pierce, 2011). A key problem in achieving such integration lies in a limited ability to model ice behavior at appropriate (meters) spatial scales. Forecasting (Reed and Aamo,1994) and hindcasting (Johansen and Skognes, 1995) demonstrate the issues that can be encountered when oil-ice interaction models are used in the field. The limited capabilities of modeling ice behavior at scales of 1-10 m also significantly restrict the extent to which advances in oil spill modeling can be used. Ice coverage is dynamic and can change rapidly, with significant implications for oil weathering and transport. Gjøsteen (2004) produced a model for spreading of oil in irregularly shaped simulated ice fields. Russian work in this field exists but is often not published in English, restricting its use and citation (e.g., Ovsienko et al., 1999). Both Gjøsteen and Ovsienko have developed spreading models that account for spreading of oil among ice floes. Incorporation into numerical models of these advances, as well as increased understanding of oil weathering processes in the presence of sea ice (e.g., Brandvik et al., 2004, 2005, 2010; Dickins et al., 2008; Faksness, 2007; Brandvik PREPUBLICATION COPY

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92 Responding to Oil Spills in the U.S. Arctic Marine Environment and Faksness, 2009; Buist et al., 2009), have been hindered by the interdisciplinary nature of the problem. Integration of knowledge of oil behavior and fates, ice cover, and hydrodynamic models is necessary for further significant advances in oil-ice interaction modeling. Achieving higher spatial resolution using existing classical sea ice models (e.g., Hibler, 1979) is not sufficient, as robust oil spill models will need more detailed representations of sea ice (e.g., ice floe sizes, ice porosity, ice drift, ridging and growth rates, under-ice roughness). Oil spill models can then compute and retain fractions of oil on, in, and under ice floes, and allow for dynamic partitioning in both space and time. Advancement in this direction is needed for both sea ice and oil spill models, although neither is likely to be able to reliably perform at this level of detail in the near future. The Arctic Oil Spill Response Technology JIP has recently initiated a research effort to improve oil spill trajectory modeling capability within the Arctic, with plans to develop new sea ice models related to ice dynamics. The models will be evaluated at regional scales using a high- resolution, coupled ice-ocean model, and a new type of model will be developed to simulate sea ice dynamics in the marginal ice zone. All of the products developed during the JIP will be integrated into established oil spill trajectory models (Mullin, 2012). BOEM currently uses a model called the Oil Spill Risk Analysis Model,60 which simulates the possible paths of oil spills through modeled ocean currents, winds, and sea ice. In March 2011, a workshop was held with the goal of improving BOEM oil spill models (SAIC, 2011). A main recommendation was the need for assimilating new oceanic, atmospheric, and sea ice data collected in the Arctic environment. Another was the requirement for validation and sensitivity testing of the model through integration with observational studies—for example, under programs such as the Forum for Arctic Ocean Modeling and Observational Synthesis.61 NOAA’s Office of Response and Restoration's Emergency Response Division also employs an oil spill trajectory model, which is effectively a statistical model that is run many times to determine an uncertainty boundary where oil may be found. The General NOAA Operational Modeling Environment62 predicts possible trajectories of oil in water for given environmental forcing. An Arctic module exists which contains information on tides, currents, bathymetry, and coastlines in the Arctic region, but it is unclear whether sea ice is factored into the module. In 2013, BSEE and NOAA entered into an interagency agreement to adapt NOAA’s current models to better account for Arctic conditions. Their efforts are intended to complement the work being done by others, including the Arctic Oil Spill Response Technology JIP. Promising New Concepts in Trajectory Modeling Promising advances in sea ice modeling in the past decade include detailed models of brine-channel formation and drainage by Petrich et al. (2006, 2013). This approach allows for incorporation of oil into brine channels as well as bulk oil freezing into ice (Faksness and Brandvik, 2008; Faksness et al., 2011). Hopkins (1996) and Hopkins and Daly (2003) have developed a discrete element approach to modeling sea ice that allows for variably sized ice floes. These two advances together permit a parameterization of oil-ice interactions at a 60 See http://www.boem.gov/Environmental-Stewardship/Environmental-Assessment/Oil-Spill-Modeling/Oil-Spill- Risk-Analysis-Model-%28OSRAM%29.aspx; accessed April 29, 2013. 61 See http://www.whoi.edu/projects/famos/. 62 See http://response.restoration.noaa.gov/oil-and-chemical-spills/oil-spills/response-tools/gnome.html. PREPUBLICATION COPY

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Arctic Oil Spill Response Research 93 conceptual resolution that is significantly closer to reality than was previously possible. Wilkinson et al. (2007) have demonstrated the possibility of modeling the flow of oil under sea ice based on the topography of the under-ice surface. The authors relied on an AUV to map the under-ice contours, an approach that is probably not realistic over a long time frame and a dynamic ice field. Future ice models will need to produce an estimate of under-ice roughness if the spreading process is to be adequately represented. Other advances in oil spill modeling are occurring, although they are driven mostly by the Deepwater Horizon rather than issues associated with the Arctic (e.g., the Gulf of Mexico Research Initiative63). There is a strong focus on underwater near-field plume modeling, including the effectiveness of dispersant injection at the wellhead. Uncertainty as Related to Numerical Tools for Decision Support Numerical simulation models of coupled atmospheric, sea ice, and ocean physics are key components of today's weather, ice, and ocean current forecasting operations. The success of these tools in providing accurate forecasts is defined by the degree to which model results reflect reality. This, in turn, depends on an understanding of the underlying processes, building this understanding into the models, and on available computational power. Simulation tools are also used for planning and training, support during a spill response, and evaluation of potential and actual environmental effects after an accident. These tools bring additional physical, chemical, and biological processes into the equation, along with their associated uncertainties. Reliable forecast tools depend heavily on the assimilation of real-world observations because numerical models are by definition simplified representations of reality, relevant processes and their linkages are incompletely understood, and there are computational limitations on the ability to represent these processes over the full range of physical and temporal scales. The ability to supply observational data will remain a key element in reducing this uncertainty. For weather forecasts, such observations typically include wind speed, air temperature, humidity, and other environmental variables that are measured regularly at numerous locations on the Earth's surface and in the overlying atmosphere. The density of similar measurements in the oceans and at the air-ocean interface is much less, but has increased significantly over the past two to three decades, particularly at lower latitudes. Such measurements are scarcer in the Arctic, where the complexity of the physical relationships is increased by seasonal growth, drift, and decay of sea ice. Adding spilled oil increases this complexity. Simulation models are powerful integrators of complex interacting processes and events, and can be useful tools for decision support in both planning and executing emergency response actions. Statistical and ensemble analyses are two standard approaches to quantify the uncertainty of model results. Statistical modeling is typically used in planning for a possible future event, when details of the event are not known beforehand. In oil spill contingency planning, for example, the timing, durations, and magnitudes of possible releases from a given production facility are unknown but can be estimated within certain bounds. One can then run a large number of simulations sampling from ranges of start times, release rates, and durations to produce probability maps for a variety of endpoints—for example, oil arrival time, the amount of oil on the sea surface, or the amount of oil along shorelines. Results can be used to support planning that protects priority resources. The probability of an impact on a given resource, weighted by 63 See http://gulfresearchinitiative.org/. PREPUBLICATION COPY

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94 Responding to Oil Spills in the U.S. Arctic Marine Environment the probable magnitude of the impacts, supplies measures of environmental risk that can in turn supply criteria for electing alternative response options. Ensemble modeling simulates a given event using multiple models, then interprets the variability of the resulting outputs as a measure of intrinsic model-related uncertainty. This approach was used by both circulation and trajectory models during the Deepwater Horizon oil spill (e.g., Chang et al., 2011; Marianoa et al., 2011). It is also commonly applied in climate change modeling (e.g., Djalalova et al., 2010) and is most useful when there are multiple available models of similar complexity and focus. Uncertainty in oil spill trajectory forecasting for open water conditions has been a recognized challenge for decades (e.g., Galt, 1994) and will probably be greater in the presence of sea ice (Eicken, 2013). Trajectory analyses for decision support will need to account for this uncertainty in ways that are useful to response practitioners. Overlaying the trajectory forecast and the associated envelope of uncertainty on natural resource maps provides a graphical basis for decision making and reflects the present state of the art (e.g., NOAA, 2012). CHAPTER CONCLUSIONS AND RECOMMENDATIONS Conclusion: Arctic oil spill research and development needs for improved decision support include:  Determining and verifying biodegradation rates for hydrocarbons in offshore environments, in order to establish potential and capacities for natural attenuation or recovery and to determine which strategies can accelerate oil biodegradation;  Improving technologies for dispersant application and induction of turbulence for oil spills in ice;  Evaluating the toxicity of dispersants and chemically dispersed oil on key Arctic marine species, with appropriate experimental design and incorporation of real-world conditions and concentrations;  Identifying and understanding ecosystem responses associated with changes in microbial biomass and species alterations;  Further understanding of biogeochemical cycles, including particulate matter–petroleum chemical interactions;  Improving ignition methods for in situ burning, focused specifically on the Arctic  Mapping the usefulness of chemical herders at different spatial scales, oil types, and weathered states, and in conjunction with other response options such as in situ burning;  Understanding the limitations of mechanical recovery in both open water and ice, which will improve decision making regarding possible implementation of other response strategies;  Investing in under-ice detection and response strategies, including remote sensing technology that will reliably detect oil in different ice conditions;  Integrating remote sensing and observational techniques for detecting and tracking ice and oil, including unmanned aerial vehicles, autonomous underwater vehicles, SAR, and drifting buoys;  Additional research into the physics of oil incorporation into developing ice  Establishing robust operational U.S. Arctic meteorological-ocean-ice and oil spill trajectory forecasting models for contingency planning and response support; PREPUBLICATION COPY

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Arctic Oil Spill Response Research 95  Testing and evaluating risk-benefit decision processes, including NEBA, for use in the Arctic; and  Summarizing relevant ongoing and planned research worldwide to achieve synergy and avoid unnecessary duplication. Conclusion: Though much is known about the oil behavior and response technologies in ice- covered environments, there are areas where additional research is needed to make informed decisions about the most effective response strategies for different Arctic situations. In addition, there is a need to validate current and emerging oil spill response technologies on operational scales under realistic environmental conditions. Conclusion: A systematic program of carefully planned and controlled field experiments that release oil in the U.S. Arctic is needed to advance understanding of oil behavior and response options. Recommendation: A comprehensive, collaborative, long-term Arctic oil spill research and development program needs to be established. The program should focus on understanding oil spill behavior in the Arctic marine environment, including the relationship between oil and sea ice formation, transport, and fate. It should include assessment of oil spill response technologies and logistics, improvements to forecasting models and associated data needs, and controlled field releases under realistic conditions for research purposes. Industry, academic, government, non-governmental, grassroots, and international efforts should be integrated into the program, with a focus on peer review and transparency. An interagency permit approval process that will enable researchers to plan and execute deliberate releases in U.S. waters is also needed. Recommendation: Priorities for oil spill research should leverage existing joint agreements and be addressed through a comprehensive, coordinated effort that links industry, government, academia, international and local experts, and non-governmental organizations. The Interagency Coordinating Committee on Oil Pollution Research, which is tasked to coordinate oil spill research and development among agencies and other partners, should lead the effort. Conclusion: The oil spill response toolbox requires flexibility to evaluate and apply multiple response options, whether on their own or concurrently. No single technique will apply in all situations. Recommendation: Dispersant pre-approval in Alaska should be based on sound science, including research on fates and effects of chemically dispersed oil in the Arctic environment, experiments using oils that are representative of those in the Arctic, toxicity tests of chemically dispersed oil at realistic concentrations and exposures, and the use of representative microbial and lower-trophic benthic and pelagic Arctic species at appropriate temperatures and salinities. PREPUBLICATION COPY

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96 Responding to Oil Spills in the U.S. Arctic Marine Environment Conclusion: Well-defined and tested decision processes for oil spill countermeasure deployment are critical to expedite review and approval. Decision processes need to include rapid research on countermeasures and be exercised regularly. PREPUBLICATION COPY