@article{RomanoThielKurthsetal.2005,
  author    = {Romano, Maria Carmen and Thiel, M. and Kurths, J{\"u}rgen and Kiss, Istvan Z. and Hudson, J. L.},
  title     = {Detection of synchronization for non-phase-coherent and non-stationary data},
  issn      = {0295-5075},
  year      = {2005},
  abstract  = {We present a new method to detect phase as well as generalized synchronization in a wide class of complex systems. It is based on the recurrences of the system's trajectory to the neighborhood of a former state in phase space. We illustrate the applicability of the algorithm for the paradigmatic chaotic Rossler system in the funnel regime and for noisy data, where other methods to detect phase synchronization fail. Furthermore, we demonstrate for electrochemical experiments that the method can easily detect phase and generalized synchronization in non-phase- coherent and even non-stationary time series},
  language  = {en}
}
@article{vonBlohRomanoThiel2005,
  author    = {von Bloh, Werner and Romano, Maria Carmen and Thiel, Marco},
  title     = {Long-term predictability of mean daily temperature data},
  issn      = {1023-5809},
  year      = {2005},
  abstract  = {We quantify the long-term predictability of global mean daily temperature data by means of the Renyi entropy of second order K-2. We are interested in the yearly amplitude fluctuations of the temperature. Hence, the data are low- pass filtered. The obtained oscillatory signal has a more or less constant frequency, depending on the geographical coordinates, but its amplitude fluctuates irregularly. Our estimate of K-2 quantifies the complexity of these amplitude fluctuations. We compare the results obtained for the CRU data set (interpolated measured temperature in the years 1901- 2003 with 0.5 degrees resolution, Mitchell et al., 2005(1)) with the ones obtained for the temperature data from a coupled ocean-atmosphere global circulation model (AOGCM, calculated at DKRZ). Furthermore, we compare the results obtained by means of K-2 with the linear variance of the temperature data},
  language  = {en}
}