How Predictability Depends on the Nature of Uncertainty in Initial Conditions in a Coupled Model of ENSO

Abstract

The predictability of any complex, inhomogeneous system depends critically on the definition of analysis and forecast errors. A simple and efficient singular vector analysis is used to study the predictability of a coupled model of El Niño?Southern Oscillation (ENSO). Error growth is found to depend critically on the desired properties of the forecast errors (?where and what one wants to predict?), as well as on the properties of the analysis error (?what information is available for that prediction?) and choice of optimization time. The time evolution of singular values and singular vectors shows 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 approximation to the analysis error covariance to define the relative importance of errors in different variables gives very different results to the more frequently used ?energy norm,? and indicates a much larger role for sea surface temperature information in seasonal (3?6-month timescale) predictability. Seasonal variations in the predictability of the coupled model are also investigated, addressing in particular the question of whether seasonal variations in the dominant singular values (the ?spring predictability barrier?) may be largely due to the seasonality in the variance of SST anomalies.