How predictability depends on the nature of uncertainty in initial conditions in a coupled model of ENSO

The predictability of any complex, inhomogeneous system depends critically on the definition of analysis and forecast errors. A simple and efficient s ingular vector analysis is used to study the predictability of a coupled mo del of El Nino-Southern Oscillation (ENSO). Error growth is found to depend critically on the desired properties of the forecast errors ("where and wh at one wants to predict"), as well as on the properties of the analysis err or ("what information is available for that prediction") and choice of opti mization time. The time evolution of singular values and singular vectors s hows that the predictability of the coupled model is clearly related to the seasonal cycle and to the phase of ENSO, It is found that the use of an ap proximation to the analysis error covariance to define the relative importa nce of errors in different variables gives very different results to the mo re frequently used "energy norm," and indicates a much larger role for sea surface temperature information in seasonal (3-6-month timescale) predictab ility. Seasonal variations in the predictability of the coupled model are a lso investigated, addressing in particular the question of whether seasonal variations in the dominant singular values (the "spring predictability bar rier") may be largely due to the seasonality in the variance of SST anomali es.