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Modelling oil thickness in the OSCAR oil spill model: improvements for natural entrainment by breaking waves

DescriptionThe purpose of particle based Lagrangian oil spill models is to predict the trans- port and fate of oil spilled on the ocean. In this paper we describe why it is important to describe well oil and emulsion thickness in oil spill modelling, with a particular focus on its role in modelling entrainment. Oil thickness is challenging to measure comprehensively both in the field and in the lab, which makes it harder to build simple correlation models for oil thickness in the same way as has been done for other properties, such as viscosity and emulsion water content. The variability of thickness is high, where the value of can vary more than 10000-fold, from sub-micrometer to multiple centimeters, both in space and time. This implies that it can significantly affect the processes that take thickness as a numerical input, such as evaporation, dissolution, and emulsification. For natural entrainment, thickness can be used as a length-scale for the calculation of the size of droplets generated by breaking waves. State-of-the-art oil spill models contain these processes but may differ in how they calculate thickness and use it. To calculate thickness in particle- based models, one can define and discretize the surface particle distribution volume density function and find thickness as the function volume over some discretized area. However, this faces the challenge that oil is not uniformly spread out by the area spanned by the particles in the oil spill model. Using the area spanned out by the particles will most likely lead to underestimating oil thickness, especially on longer timescales. In this work, we investigate the challenge of estimating oil thickness from particles in Lagrangian oil spill models. We find that by introducing an estimate of the oil-covered vs open-water area associated with each particle, one can arrive at a way of calculating oil thickness that avoids underestimating this value even on longer timescales.

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