Simulating Oil Spill Evolution in Water and Sea Ice in the Beaufort Sea

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1 Proceedings of the Twenty-third (2013) International Offshore and Polar Engineering Anchorage, Alaska, USA, June 30 July 5, 2013 Copyright 2013 by the International Society of Offshore and Polar Engineers (ISOPE) ISBN (Set); ISSN (Set) Simulating Oil Spill Evolution in Water and Sea Ice in the Beaufort Sea Shannon H. Nudds, Adam Drozdowski, Youyu Lu and Simon Prinsenberg Fisheries and Ocean Canada, Bedford Institute of Oceanography Dartmouth, NS, Canada ABSTRACT The fate of a potential oil spill from an active lease location in the Beaufort Sea is simulated based on the solution of a high-resolution (1/18 th degree), pan-arctic ocean and sea ice model. Experiments were set up to analyze the fate of the spill on a seasonal basis, in both the ocean surface and the sea ice, and to test the model sensitivity to various forcing and diffusivity coefficients. In general, spills from this location move toward the Canadian shore, except for during the fall when the ocean currents shift and carry the oil west toward Alaska. During the winter, the combination of high ice concentration and low diffusivity of the oil results in a relatively contained spill. A spill would be most detrimental during the summer months when, even with a lower diffusion coefficient, oil in ice spreads over almost as much area as oil in water. This work also highlights the importance of highresolution modelling for near-shore spills, the use of realistic forcing, and the necessity for more knowledge on how oil behaves under Arctic conditions. KEY WORDS: oil spills; simulations; Arctic; Beaufort Sea; highresolution; ice-ocean; modelling. INTRODUCTION There is an increasing demand for fossil fuels worldwide. As the Arctic ice coverage continues to decrease and the Arctic becomes more accessible, there has been an increase in exploration of the Arctic for oil mining purposes, particularly in the Beaufort Sea. Figure 1 shows a map taken from the 2011 Northern Oil and Gas Annual Report (NOGB, 2012) with areas that have been licensed to oil companies for exploration and production in the Beaufort Sea. Oil spill trajectory models are an essential tool for risk assessment. The primary task of a spill trajectory model is to predict where the oil is most likely to go based on information about the ocean currents, winds and other environmental variables. The model can be used to assess the risk associated with drilling in a particular location, and in the event of a spill, can aid in planning response measures by providing information on where the oil is likely to go and estimates on how long it could take to reach an area of interest. Fig. 1 Areas licensed to oil and gas exploration and production in the Beaufort Sea. Taken from the 2011 Northern Oil and Gas Annual Report (Northern Oil and Gas Branch, 2012). The red 'X' indicates the location of the simulated spill in this study. The Arctic environment introduces a new level of complexity when it comes to simulating spill trajectories. The presence of ice not only adds to the complexity of the ocean model, but knowledge of how oil behaves in ice is required to realistically parameterize diffusion processes. Satellite observations of ice allow for validation of the sea ice aspects of a model, but sparse observations of the ocean currents makes it difficult to assess the model performance. The limited ability to monitor an Arctic spill and the increased time required for spill response increases the necessity to have accurate models. Several ice-ocean models of the Arctic Ocean exist, however, few have the resolution and capability to provide the complex information required for computing trajectories. Here we present results of a simulated oil spill from one of the 1066

2 exploration leases in the Beaufort Sea using the solution of a highresolution Arctic ocean and sea ice model, ARC118, and an offline particle tracking scheme, BOLT. The spills are simulated in both water and ice, and analyzed on a seasonal basis. Sensitivity tests were done using various rates of oil diffusivity and various annual forcing datasets. MODEL DESCRIPTION The ocean and sea ice model ARC118 is a 1/18 th degree pan-arctic ocean and sea ice model. The model domain covers the entire Arctic Ocean, the Canadian Arctic Archipelago, and Hudson Bay, with open boundaries through Bering Strait and the North Atlantic (Fig. 2). It is based on version 2.3 of NEMO (Nucleus for European Modelling of the Ocean; which includes an ocean component OPA and a sea-ice component, LIM2. NEMO is widely used by government and university labs in Canada, and has been adopted by several Canadian Government departments for the development of a global coupled atmosphere-ice-ocean forecasting system for weather and ice-ocean forecasts. The model grid is created by shifting poles to the equator such that the grid spacing is relatively uniform over the entire domain (~6 km). This also avoids errors involved in having a singularity in the grid at the North Pole. For the Beaufort Sea region, the model grid spacing is < 6 km. High resolution is essential for realistic trajectory modelling. Not only is the model able to reproduce more realistic changes in sea ice and ocean conditions, but it allows the model to simulate small scale features required for particle trajectories. Figure 3a shows the September mean surface circulation from a 1/6 th degree pan-arctic model. Figure 3b shows the same simulation from the high-resolution model used for this study, illustrating the improved representation of near-shore small scale circulation features. The model was initialized with the January climatology of temperature, salinity, and sea ice conditions. The model was spun-up for 10 years using normal-year forcing of the Common Ocean-ice Reference Experiments (CORE2; Large and Yeager, 2008) to reach an equilibrium state. The model is able to reproduce realistic surface circulation for the Beaufort Sea with the correct seasonal variability. In the summer, the Alaska Coastal Current (generated from inflowing Pacific water through the Bering Strait) continues eastward as a coastal jet along the coast of Alaska, opposite the flow of the Beaufort Gyre. In the winter, it can weaken considerably and even partially reverse (described by Pickart, 2004). This seasonal variability is reproduced in the model simulations (Fig. 3b-c). A common problem with Arctic ocean and sea ice models is that the ice moves too fast (compared to buoy and satellite ice drift products), often by a factor of 10 (Martin and Gerdes, 2007). To simulate realistic oil spill trajectories in ice, it is necessary to reproduce realistic ice drift speeds. We were able to control the velocity of the ice by reducing the air-ice drag coefficient. A value of 0.6e-3 was used which is within the range (0.5e-3 to 5.2e-3) of air-ice drag coefficients determined observationally by Prinsenberg and Peterson (2002). Figure 4 shows the modelled ice drift compared to that calculated from the daily velocities of the buoys from the International Arctic Buoy Program (IABP; Ortmeyer and Rigor, 2004). Fig 2. Model domain and bathymetry. a) c) Fig 3. Surface currents in the Beaufort Sea for September from a low resolution (1/6 th degree) model (a) and a high resolution (1/18 th degree) model (. March surface currents from the high resolution model are also shown (c). The colour bar indicates the magnitude of the current velocity in m/s. Vectors are plotted every second grid cell. 1067

3 a) and Clites, Advection is performed in 1 st order or the 4 th order Runge-Kutta scheme. Sub-grid diffusion processes are parameterized through random kicks with a variable diffusion coefficient, A h. As BOLT is in early stages of development, there are many aspects of oil spills that are not considered such as evaporation, weathering, and emulsification. However, for the purpose of this study, looking at the large scale seasonal trajectories of the spills, the particle tracking scheme with the inclusion of a diffusion coefficient provides a good approximation of a spill trajectory. Figure 1 shows the location of the simulated oil spill (in exploration lease EL446 at 136 W, N). Particles were released at the beginning of each season (JFM, AMJ, JAS, and OND) and advected with daily output of both the ocean surface and ice velocity fields. The spills were continuous (particles released once a day) for the first 10 days and tracked for 3 months (90 days). The following plots show the final distribution of the oil spill evolution 90 days from the start of the 10 day release of the oil. SENSITIVITY TESTS The simulated spill trajectories are dependent on the parameterization and inputs to the model. Here we test the sensitivity of the results to the diffusion coefficient and the atmospheric forcing fields. Model sensitivity to A h Applying a diffusion coefficient to the particles allows you to simulate spreading of the oil independent of the ocean current; ie. how much will the oil spread if the water or ice velocity is 0 m/s? The rate at which oil spreads is dependent on the environment and the type of oil. In general, oil will spread more quickly in warm, turbulent, ice free water than in cold, calm, ice infested water. The model was run for both summer and winter seasons with three different values of A h to test the model sensitivity, and to see if this sensitivity varied seasonally in water or ice. Fig. 4 Modelled (black) and observed (red) ice drift in a) March and September of The oil spill model BOLT (Bedford Institute of Oceanography Ocean Lagrangian Tracking) is an offline particle tracking scheme currently configured to work with NEMO output fields (developed from BBLT3D; Drozdowski, 2009). The program takes in an arbitrary initial distribution of oil packets and computes trajectories following a 3D ocean velocity field (or 2D ice field if oil is frozen in ice). Velocity fields are interpolated linearly in space and time. Near the coast, the code uses nearest neighbour extrapolation onto land following Bennet Figure 5a shows snapshots of the spill using various values of A h in the water during the Summer. A value of 1 m 2 /s results in the particles being carried along shore with very little spreading, essentially following a single streamline. Increasing A h to 10 m 2 /s causes the particles to spread across current which then allows them to get entrained by adjacent currents and advected more toward the coast. Further increasing A h to 20 m 2 /s does not result in much of an increase in spreading offshore (as the variability in the current field is low), but results in more particles being advected another 200 km or so into the Amundsen Gulf. Similar patterns, although on a smaller scale, are seen in the spill trajectories in the water during the winter (not shown). During the summer, the ice in the Beaufort Sea retreats from the coast resulting in an ice field that is highly mobile. Ice concentration around the spill site is < 50%. In regions of low ice concentration, the ocean surface currents are the primary force driving the ice drift. Thus the results of the Summer sensitivity tests with oil in ice are similar to those of oil in water-- very little spreading with A h < 1 m 2 /s (Fig. 5. During the winter, ice concentration in the Beaufort Sea is closer to 100% and essentially static. In this case there is such little advection that the spreading of the oil is entirely dependent on the diffusion coefficient (Fig. 5c). These results highlight the importance of using an appropriate value for the diffusion coefficient. This requires extensive knowledge of oil 1068

4 spreading behaviour under Arctic conditions. a) concentration of each season (see Table 1). For comparison, OILmap (a commercially available oil spill trajectory model, widely used in ice free waters), typically uses 1-3 m 2 /s for low energy waters such as enclosed estuaries, 5-10 m 2 /s for medium energy water, and >10 m 2 /s for highly energetic ocean environments (OILmap user manual, available online at Table 1. Seasonal values of A h used for the diffusivity of the oil in water and in ice. Relative Temperature Relative Ice Cover A h in water (m 2 /s) A h in ice (m 2 /s) Winter Low High Spring Med Med 10 1 Summer High Low Fall Med Med 10 1 Model sensitivity to forcing a) c) Fig. 5 Distribution of the simulated oil spill after 90 days with various values of A h in the water during the Summer (a), and in the ice during the Summer ( and Winter (c). The black 'X' marks the location of the spill source. Background shading and grayscale indicate the modelled ice concentration. For the final oil spill simulations, values of A h were chosen in a qualitative manner based on relative water temperature and ice Fig. 6 Distribution of the simulated oil spill after 90 days during the Spring of 1998, 1999, 2000 and from climatology in a) water, and ice. The black 'X' marks the location of the spill source. Background shading and grayscale indicate the modelled ice concentration. The model uses five main atmospheric forcings: air temperature, humidity, precipitation, radiation, and wind. Out of these, the wind (or wind-stress) has the largest influence on the ocean surface circulation 1069

5 a) Banks Island Beaufort Sea Amundsen Gulf c) d) Fig. 7 Snapshot of simulated oil spills in water (red) and in ice (blue) for a) Winter, Spring, c) Summer, and d) Fall. The black X marks the location of the spill source. The background shading and grayscale indicate the modelled ice concentration. and the ice drift and thus is likely to have the largest effect on the spill trajectory. Forcing the model with climatology (also referred to as normal year forcing) simulates an average year and thus does not depict any interannual variability. However, any one year can vary significantly from an average year. Storms, which are only represented in a statistical fashion and therefore not representative of real storms in the climatological wind field, could have a significant influence on a spill trajectory. The model sensitivity to forcing was examined by comparing spill trajectories using model output from a climatological run with those using output from model runs which used interannually varying forcing for 1998, 1999, and All forcing fields are from CORE2. The spill trajectories differed significantly between different model year runs in all seasons. Figure 6 shows the results of the Spring simulations as an example. Simulations of oil in water showed little-tono overlap in the final spill distributions for different model years. For oil in ice, the trajectories were shorter but the variability was greater, particularly in the spring when the model shows no overlap in the final spill distributions. The different trajectories highlight the importance of using real forcing for the model and spill simulations. The Beaufort Sea is a highly variable area and although climatological simulation are useful for showing where, on average, the oil is likely to go, for risk analysis and spill response, realistic forcing is essential. An ensemble of spill simulations with various model years, like the ones done here, provide an estimate of the errors associated with the climatological forcing used for this study. RESULTS Here we show seasonal results of oil spill simulations using output from a climatology run, using the values of A h listed in Table 1. Figure 7 (a-d) shows the snapshots of the spill at the end of each season: Winter, Spring, Summer and Fall, respectively. There are three very general statements that can be made from these 1070

6 results. First, generally spills from this site move toward the Canadian coast. The exception is in the Fall when the mean current around the spill site changes direction and carries oil west, toward Alaska. Second, the spill trajectories, in both water and ice, are smallest during the winter and largest during the summer. And third, as expected, the trajectories of oil in ice are consistently shorter than those in the water. However, during the summer when the ice concentration is minimal, oil in ice spreads over a significantly large area than during any other season, comparable to that of the oil in water. Although these results strongly suggest that the trajectories of oil in ice are correlated to the ice concentration, it is not clear that the presence of ice directly influences the trajectories of the oil in water. Our simulations show that during the spring, the Beaufort Sea is, for the most part, ice covered, and becomes ice free near the end of the season. In the Fall, it is the opposite-- mostly ice free and becomes ice covered again near the end of the season. However, the resulting spill size for these two seasons is similar. Considering that the value of A h used in the Spring and Fall is the same, the results imply that the ice coverage has little influence on these trajectories. Another important result is that the ice and water trajectories are not colocated. This is important when considering the ability to track oil in ice using remote sensing, and the inability to track oil in water under ice. These results show that you could not assume that where you observe oil in ice, that the oil in water is directly below. Lastly, according to these simulations, it is not likely that an oil spill from this location will reach the shore within 3 months. A spill during the Summer present the highest risk as some oil gets advected into the Amundsen Gulf, but the majority of the oil remains in the Beaufort Sea, traveling north ~100 km off the coast of Banks Island. CONCLUSIONS An oil spill in the Beaufort Sea was simulated based on the solution of a high-resolution pan-arctic model, and a new particle tracking scheme, BOLT. The spill was tracked in both the water and ice. Results were analyzed on a seasonal basis. The model sensitivity to the diffusion coefficient and forcing were tested. Results of the sensitivity tests showed that the chosen value of A h can make a significant difference to the spill trajectory. Cross-current spread subsequently leads to increased advection. This was more evident closer to shore where the current field variability increases. Thus, it is likely that the model sensitivity to A h is dependent on the model resolution. More effort should be put into quantifying this value. Testing the model sensitivity to forcing showed that there is a lot of variability in the spill trajectories, especially for oil in ice, around the climatological mean. For risk analysis and spill response, realistic forcing is essential. The model simulations show that the ice edge passes through the spill site twice a year-- once in the spring as the ice retreats, and once in the fall as it expands back toward the shore. This alone increases the risk associated with oil production at this site. It is clear that the presence of ice keeps an oil spill relatively contained. As the amount of Arctic sea-ice continues to decrease (both in area and thickness), we can expect more open water conditions in the Beaufort Sea in the near future, and thus greater distribution of the oil. However, it is likely that the response to a spill during the summer months (or in ice free waters) would be more effective, so although the spill is less contained but it may be reached quicker than a spill in ice infested water. In addition, ice coverage is not only decreasing but becoming more unpredictable which further increases the risk involved in oil exploration in this area, and decreases confidence in the predicted model trajectories. This type of model can be used to estimate spill trajectories of oil in ice and water, but the results should be taken as such, estimates. More effort is required to determine appropriate diffusion coefficients of oil in Arctic conditions. Increasing model resolution will not necessarily improve the model results as Arctic forcing fields are based on sparse observations. Access to daily forcing fields would improve predicted trajectories in the case of a spill. REFERENCES Bennet, JR, and Clites, AH (1987). 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