Nonlinear dynamics of mean daily temperature and dewpoint time series at Babolsar, Iran, 1961–2005

Chaos theory and its applications are at the cutting edge of investigations in environmental sciences (e.g. Environmental Physics, Biometeorology and Atmospheric Sciences among many others). However, we still need to take advantage of some recent computational efforts for nonlinear investigations of empirical meteorological variables. The objectives of this work were (i) to quantify the nonlinear variability of observed time series of mean daily temperature and dewpoint records collected at Babolsar, Iran and (ii) to stimulate the use of recent computational tools for investigating environmental data. Positive Lyapunov exponents were found for both time series (λmax = 0.0174 and λmax = 0.0169 for mean daily air temperature and dewpoint temperature, respectively). In addition, determinism tests showed that both time series masked strong determinist components (|κ→|=0.721 and |κ→|=0.693 for mean daily temperature and dewpoint, respectively). That nonlinear test could be of use in future research for making decisions in applying deterministic, stochastic or combinations of both models with climate data. There were found interesting evidences on the existence of empirical attractors in univariate climate time series as expressed by reconstructing both phase spaces.