Update of global maps of Alisov’s climate classification

Abstract

Abstract Alisov’s climate classification was proposed in 1954, and it focuses on the January–July changes in large-scale air mass zones and their fronts. In this study, 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 climate classification for the first time in almost 70 years and applies data-driven machine learning to establishing a standard for genetic climate classification.