Upgrade of Coupled ocean-atmosphere General Circulation Model (JMA-CGCM02)


  1. Introduction

  2. Outline of JMA-CGCM02

  3. Hindcast experiments of JMA-CGCM02

  4. MOS forecast with JMA-CGCM02


  1. Introduction

    The Japan Meteorological Agency (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. This article describes the changes of specification of the new model, the results of hindcast experiments and the verification of MOS products applied to the new model output.


  2. Outline of JMA-CGCM02

    JMA-CGCM02 includes the following main three changes:

    • The atmospheric part is a lower resolution version (T42) of the current three-month prediction model (GSM0103) in operation since March 2001. Compared with the former AGCM, the vertical resolution is enhanced and the cumulus convection and radiation schemes are revised. Cloud water content becomes a prognostic variable.
    • The oceanic part is identical to the former Ocean General Circulation Model (OGCM) only except slight change in the vertical mixing parameterization. A new ODAS is put into operation in early 2003. A three dimensional variational analysis scheme based on Derber and Rosati (1989) is introduced. The nudging scheme is replaced by an incremental analysis update scheme (Bloom et al., 1996). Salinity and sea surface height data are newly assimilated in addition to temperature.
    • The flux adjustment amount of momentum and heat flux is newly derived with the observed SST variations.

    [Table] Main specification changes of JMA-CGCM02

    Atmospheric General Circulation Model
    Former model
    (T42L21 GSM8911)
    New Model
    (T42L40 GSM0103)
    Vertical resolution
    21 levels (model top: 10hPa)
    40 levels (model top: 0.4hPa)
    Cumulus convection parameterization
    Kuo scheme
    Prognostic Arakawa-Schubert scheme
    Cloud water content
    Diagnostic
    Prognostic variable
    Radiation process
    Solar, Infrared
    Solar, Infrared, direct aerosol effect

    Ocean Data Assimilation System
    (OGCM : 2.5º (lon.) x 0.5 - 2º (lat.), L20)
    Former model
    New Model
    Analysis scheme
    Two-dimensional optimum interpolation method
    Three-dimensional variational method
    Assimilation scheme
    Nudging
    Incremental Analysis Update
    Assimilated data
    Temperature
    Temperature, Salinity, Sea surface height
    Analysis interval
    5-day
    1-day


  3. Hindcast experiments of JMA-CGCM02

    A set of 119 Hindcast experiments of 17-month prediction from January 1985 to February 2000 was performed. Figure.1 shows all of the predicted sea surface temperature (SST) deviations from 1961-1990 means for Region B (4ºN-4ºS, 150ºW-90ºW) since 1988 together with the observed one. Due to bad qualities of the initials forecasts starting from 1985 to 1987 are poor and thus omitted. JMA-CGCM02 properly predicts the variations of Region B SST. In particular, the occurrence of the 1997/98 El Niño event is well predicted with more than six months in advance though the predicted amplitude is a little bit smaller.

    Figure.1 SST deviations from 1961-1990 mean for Region B from January 1988 to February 2000. Thick red line indicates observation and thin lines 17-month forecasts of MA-CGCM02.

    Figure.2 shows anomaly correlation coefficient and root mean square error (RMSE) of Region B SST of hindcast of the new model (88 experiments on January 1989 - January 2000) and the former model (116 experiments on February 1989 - November 2000). Anomaly correlation coefficient for the new model is improved by about 0.05-0.3 compared with the former model. As of RMSE, the skill of the new model exceeds that of persistent forecast after two-three months lead time, while that of the former model does after five-six months. Performance of the new model is improved, especially within shorter lead time until seven to eight months.

    Figure.2 Anomaly correlation coefficient (top) and RMSE (bottom) of the new model and the former model
    Red line indicates forecasts of the new model and blue indicates those of the former model. Green and red line indicate persistent and climatological forecasts, respectively.


  4. MOS forecast with JMA-CGCM02

    The verification of MOS product should be done using independent samples. Due to lack of independent samples, however, enhancement of the forecast skill by MOS is verified using the dependent samples. Figure 3 shows anomaly correlation coefficient and RMSE (minus bias) of MOS forecast and model forecast of the new model (96 samples; top figure) and the former model (132 samples; bottom figure) since 1988. MOS correction works more effectively in the new model than in the former model because of the enhanced model performance. Anomaly correlation coefficient of the new MOS forecast is larger than 0.8 until nine to ten months lead time, while that of the former MOS forecast until seven to eight months. Thus, it can be expected that new MOS forecast improves predictability even in independent forecasts.

    New model


    Former model
    Figure 3 Anomaly correlation coefficient and RMSE of MOS forecast and model forecast of the new model (top) and the former model (bottom)
    Red and blue line indicate anomaly correlation coefficient of MOS products and model outputs, respectively. Pink and light blue line indicate RMSE of MOS products and model outputs, respectively.


Refereneces

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.
Japan Meteorological Agency, 2002: Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO numerical weather prediction progress report.


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