Nonlinear Processes in Geophysics www.nonlin-processes-geophys.net 1023-5809 1607-7946 12 4 2005 10.5194/npg-12-471-2005 http://www.nonlin-processes-geophys.net/12/471/2005/ http://www.nonlin-processes-geophys.net/12/471/2005/npg-12-471-2005.html http://www.nonlin-processes-geophys.net/12/471/2005/npg-12-471-2005.pdf 471 479 2005-05-13 Long-term predictability of mean daily temperature data W. von Bloh M. C. Romano M. Thiel Potsdam Institute for Climate Impact Research, PO Box 60 12 03, 14412 Potsdam, Germany Institut für Physik, Universität Potsdam, Am Neuen Palais 10, 14469 Potsdam We quantify the long-term predictability of global mean daily temperature data by means of the R&#233;nyi entropy of second order <i>K<sub>2</sub></i>. 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 <i>K<sub>2</sub></i> 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&deg; 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 <i>K<sub>2</sub></i> with the linear variance of the temperature data.