Monthly ENSO Forecast Skill and Lagged Ensemble Size

Abstract

The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real‐time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real‐time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8–10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities.

Key Points

• Burst, lagged, and weighted lagged ensemble skill indistinguishable from that of the optimal lagged ensemble

• Current MSE of the NCEP operational ENSO forecasts is close to that estimated for the infinite ensemble

• Parametric method used to find optimal lagged ensemble size for real‐time CFSv2 forecast of Niño 3.4 index