Oil spill risk assessment based on ocean model ensemble prediction system

Abstract

Accidental oil spills at sea can have severe environmental impact on the marineenvironment. To help quantify the risk of petroleum activities, relevant oilspill scenarios are simulated ahead of time, to form a picture of possibleoutcomes, and to estimate needs for response equipment. Formally, anEnvironmental Risk Assessment (ERA) may be carried out, in which risk isquantified by estimating the environmental consequences of different outcomes,weighted by the probabilities of those outcomes. Probabilities in ERA are commonly determined by ensemble simulations with anoil spill trajectory model. Long time series of environmental data are producedfor the relevant area, and the oil spill scenario is simulated repeatedly atdifferent intervals within the environmental data set. Due to differences inwind, current and other environmental parameters, the outcome of a scenariowill be different each time, and each simulation in the ensemble constitutes asample from the space of possible outcomes. In this work, we run ensembles with the OSCAR oil spill model, using half ayear of data from 24 different ensemble members of an ocean model EPS (EnsemblePrediction System) setup for the Barents Sea. We demonstrate that in additionto the variation in outcomes from running simulations at different times, wealso get variation across the 24 different realisations of the environmentaldata. Assuming that each of the ensemble members are equally likely guesses atthe ocean state, the use of the EPS data as input to the oil spill simulationsallow us to explore a larger range of possible outcomes of the oil spill. The use of EPS in weather forecasting is already common practice, and availableto the public through ranges of uncertainty in weather apps. Given that thetransport of oil at the sea surface is to a large degree controlled by thewind, the use of EPS data in operational oil spill modelling of ongoing eventsis already possible. Making use of such data can help predict significant, butperhaps unlikely events, such as catastrophic oiling of sensitive beaches.