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.
Original language | English |
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Pages (from-to) | 471-479 |
Number of pages | 9 |
Journal | Nonlinear Processes in Geophysics |
Volume | 12 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2005 |
Keywords
- recurrence plot
- climate change
- attractors
- dynamics
- Pacific
- weather
- model
- flow