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| Titel |
Long-term predictability of mean daily temperature data |
| VerfasserIn |
W. Bloh, M. C. Romano, M. Thiel |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 12, no. 4 ; Nr. 12, no. 4 (2005-05-13), S.471-479 |
| Datensatznummer |
250010672
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| Publikation (Nr.) |
 copernicus.org/npg-12-471-2005.pdf |
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| Zusammenfassung |
| 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. |
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