Marine cold-air outbreaks (MCAOs) create conditions for hazardous maritime mesocyclones (polar lows) posing risks to marine infrastructure. For marine management, skilful predictions of MCAOs would be highly beneficial. For this reason, we investigate (a) the ability of a seasonal prediction system to predict MCAOs and (b) the possibilities to improve predictions through large-scale causal drivers. Our results show that the seasonal ensemble predictions have high prediction skill for MCAOs over the Nordic Seas for about 20 days starting from November initial conditions. To study causal drivers of MCAOs, we utilize a causal effect network approach applied to the atmospheric reanalysis ERA-Interim and identify local sea surface temperature and atmospheric circulation patterns over Scandinavia as valuable predictors. Prediction skill for MCAOs is further improved up to 40 days by including MCAO predictors in the analysis.

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Oil spill contingency planning for a marine environment requires a thorough understanding of metocean conditions that can be expected within the planning area. Oil spill response systems have different resilience, or viability, towards the physical environment encountered at sea. Hence it is important to know the operational limitations of response systems, and how metocean factors may impact on operations. The Norwegian Coastal Administration (NCA) contracted DNV GL to perform an Oil Spill Response Viability analysis to quantify the window of opportunities for defined oil spill response systems. A Response Viability Analysis (RVA) estimates the percentage of time that metocean conditions may be favorable, marginal, or not favorable for defined oil spill response systems. A 10×10 km metocean dataset was established covering Norwegian waters, including a 10-year data series with relevant parameters; wind speed, wave height, horizontal visibility, daylight/darkness, wind chill, structural icing and sea ice concentration. NCA identified 12 relevant response systems for the analysis, and established individual operational limitations for included parameters. The limits defined the response conditions in three categories; 1) favorable conditions, 2) marginal conditions and 3) unfavorable conditions. The analysis was conducted for 3-hour time steps in each grid cell in the study area using a custom code identifying it as favorable, marginal or unfavorable for each of the response systems. The results were implemented in a web-based tool to make the large amount of data produced by the analysis easily available to the user. Key information in the tool is preprocessed maps showing monthly distribution of each response category for each response system as percentage of time. By clicking on the maps, more detailed information is available for each grid cell. This includes a histogram with monthly viability for the chosen response system. All the response systems are internally ranked by the highest viability for the chosen month, and the limiting factor (if any) is displayed automatically. Additional features in the tool include a map showing potential change of wave height due to future climate changes, based from another study. The web-tool also includes typical map-tools as well as metadata.