JMA Coupled General Circulation Model     Dynamical Model
Japan Meteorological Agency
Tokyo, Japan

JMA's El Nino forecast model is a coupled atmosphere-ocean model that consists of an atmospheric general circulation model (AGCM) and an ocean general circulation model (OGCM).

Flux exchanges between AGCM and OGCM are done every 24 hours. Climatic drift is suppressed by flux correction. Initial data for ocean are derived from the ODAS. Initial data for the atmosphere are produced by the global analysis of the NWP. The outlook of NINO.3 SST deviations is derived from the ensemble forecast of 12 members based on the LAF (Lagged Averaged Forecast) method. The forecast frequency of each member is 6 times per month, and lags of the members range from about 5 to 60 days.

Historically, JMA has operated a Coupled ocean-atmosphere General Circulation Model (JMA-CGCM01) for the prediction of ENSO since 1999. Model Output Statistics (MOS) correction with statistical correlation of the model outputs is adapted to improve Region B SST forecast. JMA put into operation a new Coupled ocean-atmosphere General Circulation Model (JMA-CGCM02) named "Kookai2003" in July 2003. This model revised the physical process in the Atmospheric General Circulation Model (AGCM) and introduced a new Ocean Data Assimilation System (ODAS). The ENSO forecasts of JMA-CGCM02 show better performance. The improvement is more evident within shorter lead time until seven to eight months.

The SST index region that the JMA ENSO forecast model targets is the NINO3 region. The JMA monitors SSTs in the three original NINO regions (NINO1+2, NINO3, and NINO4), and also a region called "NINO.WEST", located 0°-15°N, 130°E-150°E.

Browse additional information about the ENSO forecast model.

A current diagnosis of the ENSO situation is provided here.

Current ENSO forecasts can be browsed at here.

Contact: elnino@hq.kishou.go.jp

References:
Japan Meteorological Agency, 2002: Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO numerical weather prediction progress report.
Bloom, S.C., L. L. Tacks, A. M. daSilva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256-1271.
Derber, J. C. and A. Rosati, 1989: A global oceanic data assimilation technique. J. Phys. Oceanogr., 19, 1333-1347.