Sea spray icing is considered a major environmental challenge and a critical risk element for industrial operations in the Arctic Ocean. Although some studies on modelling and estimation of spray-icing rate have been carried out (e.g., RIGICE04 and ICEMOD models), it is shown that such models suffer from some unrealistic modelling assumptions and limited verification. Moreover, there is limited research on the prediction of icing rates in the long term, as well as climatological information on spray icing for long-term risk-based decisions in Arctic offshore industrial applications. A major challenge for such long-term predictions is the changing pattern of spray-icing factors including meteorological and oceanographic parameters due to global warming and sea-ice-edge retreat, which is not tackled in available studies. In this work, a newly-developed model known as MINCOG, is adapted to study and analyse the ice accretion rates on Arctic offshore vessels by employing high-resolution reanalysis data such as “NOrwegian ReAnalysis 10km” data set (NORA10) (Reistad et al., 2011) of meteorological and oceanographic parameters. Moreover, machine-learning algorithms will be used for predicting icing rates and its influencing factors in the long term by accounting for the decadal changes in the climate patterns, sea-ice retreat impact on spray icing in areas near the ice edge, and inter-dependencies of meteorological and oceanographic parameters. The developed mathematical framework will then be employed to develop the climatology of spray icing in the Norwegian Arctic waters. Finally, a spray icing risk indicator will be modelled and proposed for Arctic offshore applications including logistics operations.
Project manager: Truls Bakkejord Ræder
Project code: 500 1032019