Assessing the Evolution of Meteorological Seasons and Climate Changes Using Hierarchical Clustering

In this paper, we analyze global climate change patterns using unsupervised machine learning techniques, specifically hierarchical clustering, which is a less known method for time series analysis. Focusing on Oujda, a Moroccan semiarid city as a case study, temperature and precipitation data from its weather station are clustered to identify distinct weather states. The primary objective is to investigate the efficiency of using hierarchical clustering on time series datasets and more specifically on climate time series to identify clusters that shows the climate changes and the shifts in transitions between meteorological seasons over time. Our study shows that this class of machine learning methods can give decent quality clustering for time series data and thus it helps discovering relevant patterns among it. Furthermore, our study is a new and additional evidence for climate change worldwide which is based on unsupervised machine learning.