Seasonal climate predictions for marine risk assessment in the Barents Sea

• Published in: Climate Services
• Main Authors: Polkova, I., Schaffer, L., Aarnes, Ø., Baehr, J.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2022
• Subjects: Barents Sea
• Online Access: https://publications.hereon.de/id/50521; https://publications.hzg.de/id/50521; https://doi.org/10.1016/j.cliser.2022.100291

We investigate the potential for using seasonal climate predictions for marine risk assessment in the Barents Sea. Marine risk is based on the diagnostic of the probability of extreme climate conditions and their consequences to identify time and regions vulnerable to climate hazards. The information on marine risk is operationally provided by Det Norske Veritas (DNV) to support sustainable and safe marine activities. So far, the probability of climate conditions was based on historical observations. Here, we implement predicted probabilities from an ensemble of seasonal predictions provided by the German Meteorological Service. We analyze predicted probabilities for summer for three indicators from the DNV’s marine service: two of them represent meteorological properties such as wind speed and 2-meter temperature. The third indicator, the wind chill index (WCI), is a combination of the previous two. The prediction skill, the “trust level” for predictions, is assessed over 1990–2017 and suggests that 2-meter temperature has the highest skill followed by WCI and wind speed. In addition, we use a real-time prediction to represent an actual application example. The maps of the real-time predicted probabilities show that large areas of the Barents Sea represent favorable conditions for marine operations considering low likelihood for WCI above 1000 and T2m below 0 in July and August. Wind speed is poorly predicted beyond the first forecast month. We describe the workflow for an application of seasonal predictions for the DNV’s marine service as well as lessons learned for similar applications involving risk assessment.