Low-order stochastic model and "past-noise forecasting" of the Madden-Julian Oscillation

This paper presents a predictability study of the Madden-Julian Oscillation (MJO) that relies on combining empirical model reduction (EMR) with the "past-noise forecasting" (PNF) method. EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity, seasonality and serial correlation in the estimated noise, while PNF constructs an ensemble of forecasts that accounts for interactions between (i) high-frequency variability (noise), estimated here by EMR, and (ii) the low-frequency mode of MJO, as captured by singular spectrum analysis (SSA). A key result is that - compared to an EMR ensemble driven by generic white noise - PNF is able to considerably improve prediction of MJO phase. When forecasts are initiated from weak MJO conditions, the useful skill is of up to 30 days. PNF also significantly improves MJO prediction skill for forecasts that start over the Indian Ocean. Key Points Nonlinear stochastic MJO model with memory effects derived from RMM indices PNF method significantly improves MJO prediction PNF skill is comparable with skill reported for a dynamical multi-model ensemble ©2013. American Geophysical Union. All Rights Reserved