Long-term predictability of mean daily temperature data

W. von Bloh (1) , M. C. Romano (2) , M. Thiel

1 PIK - Potsdam Institute for Climate Impact Research
2 Institut für Physik

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.

Domaines: Cosmologie et astrophysique extra-galactique [astro-ph.CO]; Astrophysique [astro-ph]; Sciences de la Terre

W. von Bloh, M. C. Romano, M. Thiel. Long-term predictability of mean daily temperature data. Nonlinear Processes in Geophysics, 2005, 12 (4), pp.471-479. ⟨hal-00302597⟩