On the basis of the latest greenhouse warming experiment performed with the coupled general circulation model ECHAM4/OPYC it is shown that not only the climate mean but also the statistics of higher order statistical moments responds sensitively to greenhouse warming. In particular the ENSO cycle obtains more energy and a tendency towards cold events can be observed. These statistical changes are superimposed on an overall warming trend.
It is suggested that this information can be used in order to refine climate change detection via the optimal fingerprinting strategy. An optimal spectral fingerprint is developed on the basis of linear perturbation theory of wavelet variances. In order to elucidate the potential of higher order statistical moments in the climate change detection context the optimal spectral fingerprint technique is applied to the ECHAM4/OPYC greenhouse warming simulation.
The results give a rough estimation of when human-caused changes in the statistics of ENSO can be expected to exceed the natural variability level. Our results reveal in particular that recent observed changes of ENSO variability are consistent with the null hypothesis of natural climate variability