The structure and dynamic nature of daily temperature time series of Khorramabad synoptic station

Air temperature is considered as one of the most important criteria for detecting fluctuations or changes in the climate system, which appears as a fractal phenomenon by adopting a self-similar structure in multiple time scales; Therefore, the temperature signal has a complex and non-linear structure that shows a very complex behavior in different time. In this study, the dynamic nature of daily temperature time series (1401-1329) of Khorramabad synoptic station was identified using Hurst scale-range analysis (R/S) and detrended fluctuation analysis (DFA2). The results of (R/S) show that there are sinusoidal fluctuations with annual and seasonal scales in the time series of air temperature. Also, three points of cross in the signal of temperature fluctuations were identified, and these fluctuations have different trends and structures around these points. On the other hand, the DFA2 results revealed that the scale profile and time series correlation have been changed due to seasonal trends. In this regard, the seasonal detrending of the data revealed that the fluctuation function increases proportionally with the increase of the time scale, which indicates the fractal nature of the temperature signal. Also, the Hurst exponent was obtained as 0.77, which indicates the existence of correlation and long-term memory in the daily air temperature signal of Khorramabad.