SKILL ASSESSMENT FOR ENSO USING ENSEMBLE PREDICTION

A crucial component of any prediction system is the ability to estimat e the predictive skill of a forecast so that the degree of confidence that can be placed in an individual forecast can be assessed. In this paper we have used ensemble prediction techniques to develop a means o f estimating a priori the predictive skill of forecasts of El Nino Sou thern Oscillation (ENSO) using an intermediate coupled ocean-atmospher e model. Each member of an ensemble forecast is perturbed using either noise forcing fields or perturbations that are known to increase the low-frequency variability of the coupled model. These are respectively the so-called stochastic optimals and optimal perturbations of the co upled system; they are added to the model to mimic the presence of ini tial-condition errors and high-frequency stochastic noise and their ef fect on the predictability of the coupled system in the tropics. By pe rforming ensemble predictions in hindcast mode, we have identified a u sable relation between the skill of a model hindcast and the spread of the ensemble measured relative to some control hindcast. The practica l nature of ensemble prediction is demonstrated by computing the relat ionship between model skill and the spread of an ensemble from hindcas ts of ENSO for the period 1972-86, and then comparing the actual hindc ast skills for the period 1987-93 with those suggested by the skill-sp read relation from the preceding period. The relationship that we find between the model skill and spread of an ensemble appears to be robus t in the sense that it is relatively insensitive to variations in the ensemble prediction procedure and to changes in some model parameters. However, crucial to the success of the ensemble predictions is the us e of perturbations in the ensembles that are known to increase the low -frequency variability of the coupled model. These perturbations can e fficiently probe the probability density function of possible coupled- model states. If randomly chosen perturbations are used in the ensembl es, however, no practical relation between model skill and the spread of an ensemble emerges. The relationship identified here, between mode l skill and the spread of an ensemble prediction, offers a practical m eans of estimating the confidence that we can place in future forecast s of ENSO using the same coupled model.