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On the basis of the latest greenhouse warming experiment performed with the Max-Planck Institut coupled atmosphere/isopycnal ocean model (ECHAM4/OPYC) it is shown that not only the climate mean but also the statistics of higher-order statistical moments respond sensitively to greenhouse warming. In particular the El Nino-Southern Oscillation (ENSO) cycle obtains more energy, and a tendency toward 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 warm- ing simulation. The results provide a rough estimate of the timescale over which human-caused changes in the statistics of ENSO can be expected to exceed the level of natural variability. These results reveal in particular that recent observed changes of ENSO variability are consistent with the null hypothesis of natural climate variability. Furthermore, an information theoretical approach is adopted to investigate possible influences of global warm- ing on ENSO predictability.

Detecting the Nonstationary Response of ENSO to Greenhouse Warming