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Long-term predictability of mean daily temperature data

  • 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

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Metadaten
Author:Werner von Bloh, Maria Carmen Romano, Marco Thiel
ISSN:1023-5809
Document Type:Article
Language:English
Year of first Publication:2005
Year of Completion:2005
Release Date:2017/03/24
Source:Nonlinear Processes in Geophysics. - ISSN 1023-5809. - 12 (2005), 4, S. 471 - 479
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie
Peer Review:Referiert
Publication Way:Open Access
Institution name at the time of publication:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik