Pattern Recognition Through Empirical Mode Decomposition for Temperature Time Series Between 1986 and 2019 in Mexico City Downtown for Global Warming Assessment

Abstract

Global warming is a real threat for the survival of life on Earth in the following 80 years. The effects of Global Warming are particularly harmful for inhabitants of very saturated urban settlements, which is the case of Mexico City. In this work, we analyse temperature time series from Mexico City Downtown, taken hourly between 1986 and 2019. The gaps in the time series were interpolated through the kriging method. Then, temporal tendencies and main frequencies were obtained through Empirical Mode Decomposition. The first frequency mode reveals a clear increasing tendency driven by Global Warming, which for 2019 was of 0.72 C above a 30-year baseline period mean between 1986 and 2016. Furthermore, the shorter periods identified in the first intrinsic mode functions are likely driven by the solar activity periods. It remains to find the origin of the smallest identified periods in the time series (<0.36 years).