Improving prediction of two ENSO types using a multi-model ensemble based on stepwise pattern projection model

Abstract

This study focuses on improving prediction of the two types of ENSO by combining multi-model ensemble (MME) with a statistical error correction method that is based on a stepwise pattern projection and applied to all models before doing the MME. We evaluate such a combinational approach using five dynamical model datasets from the North American Multi-model Ensemble (NMME) project for the period of 1982–2010. The prediction skills of the proposed MME show an improvement over most tropical Pacific regions. With regard to the two ENSO types, improvements in prediction skills of the proposed MME are particularly evident for the Niño indices for short lead time. The differences between the Eastern Pacific and Central Pacific ENSO types are more pronounced in the corrected forecasts compared with the uncorrected ones. The zonal center position of sea surface temperature anomalies for the corrected MME is closer to the observed than that for the uncorrected MME. The results indicate that reducing prediction errors of each model member by a good correction method before applying the MME method can provide an effective way for empirically improving forecasts of the two ENSO types.

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