Abstract
Bayesian change-point analysis is applied to detect a change-point in the occurrences of tropical night (TN) days in the 50-year time series data for five major cities in Republic of Korea. A TN day is simply defined as a day when the daily minimum temperature is greater than 25∘C. A Bayesian analysis is performed for detecting a change-point at an unknown time point in the TN day frequency time series, which is modeled by an independent Poisson random variable. The results showed that a single change occurred around 1993 for three cities (Seoul, Incheon, and Daegu). However, when we excluded the extraordinary year, 1994, a single change occurred around 1993 only in Seoul and Daegu. The average number of TN days in Seoul and Daegu increased significantly, by more than 150%, after the change-point year. The abrupt increase in TN day frequency in two cities over Republic of Korea around 1993 may be related to the significant decadal change in the East Asian summer monsoon around the mid 1990s and to rapid urbanization.
Highlights
Extreme weather and climate events have wide-ranging impacts on human society as well as on biophysical systems [1,2,3]
A Bayesian analysis is presented for the detection of a change-point at an unknown time point in the tropical night (TN) days, which is modeled by an independent Poisson random variable
The frequency and intensity of tropical night (TN) days or warm night days have significantly increased due to enhanced warming and rapid urbanization in Republic of Korea
Summary
Extreme weather and climate events have wide-ranging impacts on human society as well as on biophysical systems [1,2,3]. Owing to their potential of causing damage to life and property, extreme weather events, such as a heat wave, tropical night (TN), and extreme rainfall events, have drawn increased attention in recent decades [4,5,6,7,8,9,10,11,12,13]. It is well known that a significant increase in the annual occurrence of warm nights or TN days arises because of a greater increase in daily minimum temperature than that in the maximum temperature, which is caused by the urban heat island effect [21,22,23,24]
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