Twentieth International Conference on Environmental, Cultural, Economic & Social Sustainability

Abstract

One of the profound effects produced by climate change is shifting the seasons in terms of both duration and start/end dates. It is important for sustainable management to detect and predict any such seasonal changes because even a slight air temperature variation may trigger earlier-than-usual timing of plant phenology, animal migration, and other ecological, environmental, economic, and social implications. In this study, we use meteorological data recorded in Toronto, Ontario, Canada over the past 69 years (1953-2022) to explore the relationship between climate variables and seasonal shifts. Applying a combination of statistical and machine learning algorithms, a novel methodology is suggested for analyzing and visualizing seasonal clusters and trends. The outputs of this research can inform policy- and decision-makers on more effective climate adaption and mitigation strategies.