Global Warming in Japanese Cities from 1960 to 2019 Using Machine Learning

In this study, we investigated the variations in warming between Japanese cities for 1960-1989, and 1990-2019 using principal component analysis (PCA) and k-means clustering. The precipitation and sunshine hours exhibited opposite tendencies in the PCA results. It was found that 1960M and 1990M had a correlation (r = 0.51). The 1960M and 1990M are the mean temperature anomalies in Japanese cities for 1960-1989 and 1990-2019, respectively. There was a strong correlation between temperature and precipitation (r = 0.62). There was an inverse correlation between 1960M and sunshine hours (r = ？0.25), but a correlation between 1990M and sunshine hours (r = 0.11). Sunshine hours had less effect on the 1960M but more impact on the 1990M. The k-means clustering for 1960M and 1990M can be classified into four types: high 1960M and high 1990M, which indicates that global warming is progressing rapidly (Sapporo, Tokyo, Kyoto, Osaka, Fukuoka, Nagasaki), low 1960M and low 1990M, global warming is progressing slowly (Nemuro, Ishinomaki, Yamagata, Niigata, Fushiki, Nagano, Karuizawa, Mito, Suwa, Iida, Hamada, Miyazaki, Naha), low 1960M and high 1990M, global warming has accelerated since 1990 (Utsunomiya, Kofu, Okayama, Hiroshima), and normal 1960M and normal 1990M, the rate of warming is normal among the 38 cities (Asahikawa, Aomori, Akita, Kanazawa, Maebashi, Matsumoto, Yokohama, Gifu, Nagoya, Hamamatsu, Kochi, Kagoshima). Higher annual temperatures were correlated with higher annual precipitation according to the k-means clustering of temperature and precipitation. Two of the four categories consisted of places with high annual temperatures and high precipitation (Fushiki, Kanazawa, Kochi, Miyazaki, Kagoshima, Naha, Ishigakijima), and places with low annual temperatures and low precipitation (Asahikawa, Nemuro, Sapporo, Karuizawa).