Revisiting Earth’s Seasonality using Machine Learning Models

Earth’s seasonality profoundly influences nearly every aspect of life on our planet. It plays a key role in driving vegetation cycles and shaping wildlife behavior. Seasonality also impacts human life significantly, affecting health, mood, social dynamics, and cultural patterns. Despite its importance, seasonality is still traditionally defined by astronomical seasons—equal-length divisions applied uniformly across the Earth. Although this division is simple and intuitive, it overlooks crucial seasonal patterns influenced by atmospheric weather. In this study, we propose a data-driven approach to redefining seasons using objective clustering. We develop an algorithm that segments various meteorological factors into meaningful seasonal clusters. Building on this algorithm, we objectively define seasons for each region globally and analyze the effects of Climate Change on these clusters. We find that seasonality is driven by different meteorological factors in different regions on Earth. Additionally, we observe that Climate Change has significantly altered the duration and onset of Earth’s seasons.