Long-term predictability of mean daily temperature data

• Main Authors: W. von Bloh, M. C. Romano
• Format: Article
• Language: English
• Published: Copernicus Publications 2005-01-01
• Series: Nonlinear Processes in Geophysics
• Online Access: http://www.nonlin-processes-geophys.net/12/471/2005/npg-12-471-2005.pdf

We quantify the long-term predictability of global mean daily temperature data by means of the R&#233;nyi entropy of second order <i>K₂</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>K₂</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>K₂</i> with the linear variance of the temperature data.