Assessment of MJO Predictability for Boreal Winter with Various Statistical and Dynamical Models

Abstract

The predictability of intraseasonal variation in the tropics is assessed in the present study by using various statistical and dynamical models with rigorous and fair measurements. For a fair comparison, the real-time multivariate Madden?Julian oscillation (MJO) (RMM) index, proposed by Wheeler and Hendon, is used as a predictand for all models. The statistical models include the models based on a multilinear regression, a wavelet analysis, and a singular spectrum analysis (SSA). The prediction limits (correlation skill of 0.5) of statistical models for RMM1 (RMM2) index are at days 16?17 (14?15) for the multiregression model, whereas they are at days 8?10 (9?12) for the wavelet- and SSA-based models. The poor predictability of the wavelet and SSA models is related to the tapering problem for a half-length of the time window before the initial condition. To assess the dynamical predictability, long-term serial prediction experiments with a prediction interval of every 5 days are carried out with Seoul National University (SNU) AGCM and coupled general circulation model (CGCM) for 26 (1980?2005) boreal winters. The prediction limits of RMM1 and RMM2 occur at around 20 days for both AGCM and CGCM. These results demonstrate that the skills of dynamical models used in this study are better than those of the three statistical predictions. The dynamical and statistical predictions are combined using a multimodel ensemble method. The combination provides a superior skill to any of the statistical and dynamical predictions, with a prediction limit of 22?24 days. The dependencies of prediction skill on the initial phase and amplitude of the MJO are also investigated.