Disclosing Climate Change Patterns Using an Adaptive Markov Chain Pattern Detection Method

This paper proposes an adaptive Markov chain pattern detection (AMCPD) method for disclosing the climate change patterns of Singapore through meteorological data mining. Meteorological variables, including daily mean temperature, mean dew point temperature, mean visibility, mean wind speed, maximum sustained wind speed, maximum temperature and minimum temperature are simultaneously considered for identifying climate change patterns in this study. The results depict various weather patterns from 1962 to 2011 in Singapore, based on the records of the Changi Meteorological Station. Different scenarios with varied cluster thresholds are employed for testing the sensitivity of the proposed method. The robustness of the proposed method is demonstrated by the results. It is observed from the results that the early weather patterns that were present from the 1960s disappear consistently across models. Changes in temporal weather patterns suggest long-term changes to the climate of Singapore which may be attributed in part to urban development, and global climate change on a larger scale. Our climate change pattern detection algorithm is proven to be of potential use for climatic and meteorological research as well as research focusing on temporal trends in weather and the consequent changes.