By Antonio Navarra (auth.), Hans von Storch, Antonio Navarra (eds.)

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**Additional resources for Analysis of Climate Variability: Applications of Statistical Techniques**

**Sample text**

Thus, in this case, TD may be seen as a physically useful time scale, namely a "persistence time scale" (but see the dependency on the time step discussed below). 8) returns times TD < Llt, even though prob ability statements for any two states with an even time lag are identical to probabilities of an AR(p) process with an AR-coefficient 10'1. Thus the number TD makes sense as a characteristic time scale when dealing with red noise processes. But for many high er order AR(p)processes the number TD does not reflect a useful physical information.

4. 8-proeess is 9 days or 21 days - if we sampie the process on ce a day or onee every 21 days. = = = = = = = We eonelude that the absolute value of the deeorrelation time is of questionable information al value. However, the relative values obtained from several time series sampled with the same time inerement are useful to infer whether the system has in some eomponents a longer memory than in others. 5-eurve, are mostly useless. 4: Misleading Names 21 We have seen that the name "Decorrelation Time" is not based on physical reasoning but on strictly mathematical grounds.

10 between monthly anomalies in zonally-averaged SST and turbulent heat ßux in the Atlantic during the period 1949-1979. The anomalies were derived from the COADS data set, and a second order trend removed from the anomaly time series to remove some of the artificial trends introduced by the changes in the wind measurement methods. In the extratropical regions, the atmospheric anomalies lead the oceanic ones, but the slightly anti-symmetric shape of the cross-correlation functions and the sm aller peak when the ocean leads by one month suggests that the turbulent heat ßux not only contributes to generating the SST anomalies, but also acts as a negative feedback.