Preliminary Study of Reconstruction of a Dynamic System Using an One-Dimensional Time Series

This paper concerns the reconstruction of a dynamic system based on phase space continuation of monthly mean temperature 1D time series and the assumption that the equation for the time-varying evolution of phase-space state variables contains linear and nonlinear quadratic terms, followed by the fitting of the dataset subjected to continua-tion so as to get, by the least square method, the coefficients of the terms, of which those of greater variance contribu-tion are retained for use. Results show that the obtained low-order system may be used to describe nonlinear proper-ties of the short range climate variation shown by monthly mean temperature series.