A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland

• Published in: Sustainability
• Main Authors: Ross, J.B., Bigg, J.R., Zhao, Y., Hanna, E.
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI 2021
• Subjects: F840 Physical Geography; Newfoundland; North Atlantic
• Online Access: https://eprints.lincoln.ac.uk/id/eprint/45798/; https://eprints.lincoln.ac.uk/id/eprint/45798/1/sustainability-13-07705-v2%20%281%29.pdf; https://doi.org/10.3390/su13147705

Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, reliable predictions of the severity of a season several months in advance would be useful for planning monitoring strategies and also for shipping companies in designing optimal routes across the North Atlantic for specific years. A seasonal forecast model of the build-up of seasonal iceberg numbers has recently become available, beginning to enable this longer-term planning of marine operations. Here we discuss extension of this control systems model to include more recent years within the trial ensemble sample set and also increasing the number of measures of the iceberg season that are considered within the forecast. These new measures include the seasonal iceberg total, the rate of change of the seasonal increase, the number of peaks in iceberg numbers experienced within a given season, and the timing of the peak(s). They are predicted by a range of machine learning tools. The skill levels of the new measures are tested, as is the impact of the extensions to the existing seasonal forecast model. We present a forecast for the 2021 iceberg season, predicting a medium iceberg year.