Long-term predictability of mean daily temperature data

W. von Bloh, M. C. Romano, Marco Thiel

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


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 languageEnglish
Pages (from-to)471-479
Number of pages9
JournalNonlinear Processes in Geophysics
Issue number4
Publication statusPublished - 2005


  • recurrence plot
  • climate change
  • attractors
  • dynamics
  • Pacific
  • weather
  • model
  • flow


Dive into the research topics of 'Long-term predictability of mean daily temperature data'. Together they form a unique fingerprint.

Cite this