A seasonal classification system interpolating biomes within the year for improved temporal aggregation

Abstract
We present a seasonal classification system to improve the temporal framing of comparative scientific analysis. Research often uses yearly aggregates to understand inherently seasonal phenomena like harvests, monsoons, and droughts. This obscures important trends across time and differences through space by including redundant data. Our classification system allows for a more targeted approach. We split global land into four principal climate zones: desert, arctic and high montane, tropical, and temperate. A cluster analysis with zone-specific variables and weighting splits each month of the year into discrete seasons based on the monthly climate. We expect the data will be able to answer global comparative analysis questions like: are global winters less icy than before? Are wildfires more frequent now in the dry season? How severe are monsoon season flooding events? This is a natural extension of the historical concept of biomes, made possible by recent advances in climate data availability and artificial intelligence.