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With the observational wind data and the Zebiak-Cane model, the impact of Madden-Julian Oscillation(MJO) as external forcing on El Nino–Southern Oscillation(ENSO) predictability is studied. The observational data are analyzed with Continuous Wavelet Transform(CWT) and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation(CNOP) method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nino 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error affects much greater than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.
The observational wind data and the Zebiak-Cane model, the impact of Madden-Julian Oscillation (MJO) as external forcing on El Nino-Southern Oscillation (ENSO) predictability is studied. The observational data are analyzed with Continuous Wavelet Transform (CWT) and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation (CNOP) method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nino 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error muchased much more than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical found ation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.