Application of synoptic patterns to the definition of seasons in the Republic of Korea

Abstract

In this study, we applied the classification of synoptic patterns to define seasons. Principal component analysis (PCA) and k‐means clustering were employed to classify the synoptic patterns. In this analysis, seven synoptic patterns were classified, corresponding to two patterns exhibiting winter, spring, and autumn‐like patterns each and one representing summer‐like patterns. The climate characteristics of each synoptic pattern for summer, spring, and autumn were clearly different from each other. The two winter patterns were considered as one season. Seasons were defined based on the occurrence frequency of each synoptic pattern, using the 11‐day window size. Accordingly, the year was divided into first spring, second spring, summer, first autumn, second autumn, and winter. The onset dates of the corresponding seasons were March 8, April 15, June 6, September 8, October 23, and November 29, respectively. In the changing trend of the seasons calculated by year, the start date of first spring was earlier by approximately 2.0 days per decade, and second autumn was delayed by 1.7 days per decade. In addition, the length of first autumn increased by 2.9 days per decade, whereas the length of winter decreased by 3.3 days per decade. In this study, a new method that had not previously been applied in Korea was used to define seasons by considering various climate variables. Compared to previous studies, the seasonal definition method used in this study is objective and reflects comprehensive climate characteristics by considering diverse variables. Furthermore, the proposed methodology for defining the seasons for each year is applicable to the study of changes in seasonal onsets.