Statistical Forecast Model for Ice-Related Events in the Arctic

Abstract

This paper presents a statistical ice event forecast model for the Arctic based on Fourier transforms and a mathematical filter. The results indicate that this model compares very well with both a multiple regression model and a human-made forecast. There seems to be a direct link between the period associated with the dominant spectral peak of the Fourier transform and the ease with which the date of events, such as fractures, bergy water, or open water, can be forecast. While useful for the normal timing of events, at this time, none of the current forecast models can predict events that occur before or beyond the usual or historical dates, which poses a forecast problem in the Arctic.