Articles | Volume 12, issue 4
Nonlin. Processes Geophys., 12, 471–479, 2005
https://doi.org/10.5194/npg-12-471-2005

Special issue: Nonlinear analysis of multivariate geoscientific data - advanced...

Nonlin. Processes Geophys., 12, 471–479, 2005
https://doi.org/10.5194/npg-12-471-2005

  13 May 2005

13 May 2005

Long-term predictability of mean daily temperature data

W. von Bloh1, M. C. Romano2, and M. Thiel W. von Bloh et al.
  • 1Potsdam Institute for Climate Impact Research, PO Box 60 12 03, 14412 Potsdam, Germany
  • 2Institut für Physik, Universität Potsdam, Am Neuen Palais 10, 14469 Potsdam

Abstract. We quantify the long-term predictability of global mean daily temperature data by means of the Rényi entropy of second order K2. 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 K2 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° resolution, Mitchell et al., 2005)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 K2 with the linear variance of the temperature data.