Autonomous Underwater Vehicles (AUVs) have a future as effective platforms for multi-disciplinary science research and monitoring in the polar oceans. However, operation under ice may involve significant risk to the vehicle. A risk assessment and management process that balances the risk appetite of the responsible owner with the reliability of the vehicle and the probability of loss has been proposed. A critical step in the process of assessing risk is based on expert judgment of the fault history of the vehicle, and what affect faults or incidents have on the probability of loss. However, this subjective expert judgment is sensitive to the nature of sea ice cover. In contrast to the simple, yet high risk, case of operation under an ice shelf, sea ice offers a complex risk environment. Furthermore, the risk is modified by the characteristics of the support vessel, especially its ice-breaking capability. We explore how the ASPeCt sea ice characterization protocol and probability distributions of ice thickness and concentration can be used within a rigorous process to quantify risk given a range of sea ice conditions and with ships of differing ice capabilities. A solution founded on a Bayesian Belief Network approach is proposed, where the results of the expert judgment elicitation is taken as a reference. The design of the network topology captures the causal effects of the environment separately on the vehicle and on the ship, and combines these to produce the output. Complementary expert knowledge is included within the conditional probability tables of the Bayesian Belief Network. Using expert judgment on the fault history of the Autosub3 vehicle and sea ice data gathered in the Arctic and Antarctic by its predecessor, Autosub2, examples are provided of how risk is modified by the sea ice environment.
