Quantitative assessment of two oil-in-ice surface drift algorithms

Abstract

The ongoing reduction in extent and thickness of sea ice in the Arctic might result in an increase of oil spill risk due to the expansion of shipping activity and oil exploration shift towards higher latitudes. This work assessed the response of two oil-in-ice surface drift models implemented in an open-source Lagrangian framework. By considering two numerical modeling experiments, our main finding indicates that the drift models provide fairly similar outputs when forced by the same input. It was also found that using higher resolution ice-ocean model does not imply better results. We highlight the role of sea ice in the spread, direction and distance traveled by the oil. The skill metric seems to be sensitive to the drift location, and drift model re-initialization is required to avoid forecast deterioration and ensure the accurate tracking of oil slicks in real operations.