Long-term predictability of mean daily temperature data
We quantify the long-term predictability of global mean daily temperature data by means of the Rényi 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 ob...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2005-01-01
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| Series: | Nonlinear Processes in Geophysics |
| Online Access: | http://www.nonlin-processes-geophys.net/12/471/2005/npg-12-471-2005.pdf |
| Summary: | We quantify the long-term predictability of global mean daily temperature data by means of the Rényi 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° 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. |
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| ISSN: | 1023-5809 1607-7946 |