** Progress in Earth and Planetary Science is the official journal of the Japan Geoscience Union, published in collaboration with its 50 society members.
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Atmospheric and hydrospheric sciences
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Update of global maps of Alisov's climate classification
Ryu Shimabukuro, Tomohiko Tomita, Ken-ichi Fukui
Alisov's climate classification、Air mass、Front、Data clustering by machine learning
The classical Alisov's climate classification (Alisov 1954).
The renewed Alisov's climate classification in this study. The renewed Alisov’s climate classification based on clustering in this study accurately captures intense mid-latitude rain bands (white contours) as winter polar fronts (green and yellow boundaries) with baroclinic instability.
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.
