Abstract

Proposed in 1954, Alisov’s climate classification (CC) focuses on climatic changes observed in January–July in large-scale air mass zones and their fronts. Herein, data clustering by machine learning was applied to global reanalysis data to quantitatively and objectively determine air mass zones, which were then used to classify the global climate. The differences in air mass zones between two half-year seasons were used to determine climatic zones, which were then subdivided into continental or maritime climatic regions or according to east–west climatic differences. This study renews Alisov’s CC for the first time in almost 70 years and employs data-driven machine learning to establish a standard for causal CC based on air masses.