Statistical modeling, forecasting and time series analysis of birch phenology in Montreal, Canada

Abstract

The aim of this study was to analyse birch pollen time series observed in Montreal (Canada) in order to understand the link between inter-annual variability of phenology and environmental factors and to build predictive models for the upcoming pollen season. Modeling phenology is challenging, especially in Canada, where phenological observations are rare. Nevertheless, understanding phenology is required for scientific applications (e.g. inputs to numerical models of pollen dispersion) but also to help allergy sufferers to better prepare their medication and avoidance strategies before the start of the pollen season. We used multivariate statistical regression to analyse and predict phenology. The predictors were drawn from a large basin (over 60) of potential environmental predictors including meteorological data and global climatic indices such NAO (North Atlantic Oscillation index) and ENSO/MEI (Multivariate Enso Index). Results of this paper are summarized as follows: (1) an accurate forecast for the upcoming season starting date of the birch pollen season was obtained (showing low bias and total forecast error of about 4 days in Montreal), (2) NAO and ENSO/MEI indices were found to be well correlated (i.e. 44% of the variance explained) with birch phenology, (3) a long-term trend of 2.6 days per decade (p < 0.1) towards longer season duration was found for the length of the birch pollen season in Montreal. Finally, perturbations of the quasi-biennial cycle of birch were observed in the pollen data during the pollen season following the Great Ice Storm of 1998 which affected south-eastern Canada.