| The spring predictability barrier in the El Nino-Southern Oscillation (ENSO) is investigated using both statistical and dynamical models. The predictability barrier appears as a drop-off in the persistence of ENSO indices and as a decrease in the forecast skill of coupled ocean-atmosphere models.; Statistical models are used to determine the cause of the persistence barrier in time series, while Monte Carlo perturbation experiments are used to determine the effects of random error growth on model forecast skill. It is shown that in both data and models the barrier occurs because the boreal spring is the transition time from one climate state to another, when the "signal-to-noise" of the system is lowest and the system is most susceptible to perturbations. The strength of the predictability barrier depends on the degree of phase-locking of the ENSO to the annual cycle.; The phase-locking to the annual cycle, as well as the ENSO variance, is shown to vary on interdecadal time scales. During 1890-1920 and 1960-1990 the ENSO variance was high, while during 1920-1950 the ENSO variance was low. The persistence barrier strength is shown to decrease after 1977, and it is suggested that this decrease is related to the increased forecast skill recently seen in coupled ocean-atmosphere models.; Finally, using wavelet analysis, the interdecadal variability in ENSO is shown to be correlated with changes in Indian summer monsoon strength. The theory of wavelet analysis is described and statistical significance and confidence levels for the wavelet power spectrum are derived. |
