Understanding global teleconnections to surface air temperatures in Japan based on a new climate classification

Abstract

Utilizing the self‐organizing map (SOM), a type of artificial neural network, a new classification of the climate of Japan is proposed. The SOM is applied on the monthly mean surface air temperature (SAT) anomalies, extracted from 762 stations. Considering the strong seasonal cycle in mid‐latitudes, the classification is performed for two distinct seasons, boreal winter and boreal summer. Applied on monthly average temperature, to capture the seasonal signal, the SOM is an easily implementable interesting tool to (a) objectively capture the patterns present in the input data, and (b) identify the source of interannual variability, which is crucial for power demand forecasting. While modulated by local conditions, SAT in Japan is mainly influenced by large‐scale circulation. It is found in this study that stronger relationships exist for tropics with southern regions and for extra‐tropics with northern regions, in the seasonally oriented teleconnection patterns. In winter, the regions are organized along a north–south orientation, with a secondary west–east orientation in the central part of the country. This organization is a function of the strength of the link with the tropical Pacific and Indian Oceans sea‐surface temperatures. Changes in the speed of the westerly jet, together with the modulation of the Western Pacific (WP)‐like and Eurasian (EU)‐like patterns, are also important for SAT in Japan in winter. In summer, the main organization follows a west–east orientation. The Pacific Japan (PJ)‐like pattern plays an important role in the geographical distribution of the SAT, together with the existence of mid‐latitude and subtropical wave trains, like the Silk Road (SR)‐like pattern.

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