Nonlinear Analysis of Time Series of Maximum Daily Temperature in Kerman Station Based on Chaos Theory

To identify the dynamism of any system, it is required that its nonlinear behavior is identified on the basis of some specific algorithms, such as chaos theory study. Recognition of behavior of one parameter such as temperature, which is a key component of any climate theoretical model, is critically important. In this research, the behavior of a 25-year time series of daily maximum temperature in Kerman station as one of climate parameters using dynamically nonlinear were analyzed. Accordingly, the parameters required for the reconstruction of phase space, time delay and embedding dimension were calculated as 82 days and 7, respectively. The results showed that there was chaos in the time series. So, correlation dimension and maximum Lyapunov exponent were obtained to be 2.78 and 0.0149, respectively.