Coupled Ocean-Atmosphere General Circulation Model (JMA/MRI-CGCM)


  1. Introduction

  2. Outline of JMA/MRI-CGCM

  3. Outline of the ensemble method

  4. Performance of JMA/MRI-CGCM

  5. Products

  6. The upgrade history of the El Niño prediction model


  1. Introduction

    The Japan Meteorological Agency (JMA) operates a Coupled ocean-atmosphere General Circulation Model (CGCM) as an El Niño prediction tool.

    Operational information of the El Niño outlook has been produced with the new prediction system since March 2010. The new system improved the calculation method for initial perturbations, with perturbations for the atmosphere being introduced in addition to those for the ocean. Moreover, the ensemble size for the calculation of ensemble statistics was increased from 30 to 51.

    Since this improvement, the CGCM has also been applied to seasonal weather forecasts.


  2. Outline of JMA/MRI-CGCM

    The oceanic part of the model is known as MRI.COM (Ishikawa et al., 2005), and is identical to the ocean general circulation model used in the Multivariate Ocean Variational Estimation/Meteorological Research Institute Community Ocean Model-Global (MOVE/MRI.COM-G) system. The atmospheric component is a lower-resolution version of the Global Spectral Model (GSM0603) used by JMA for operational numerical weather prediction (JMA, 2007). To improve the features of heat, momentum and fresh water fluxes on the sea surface, several physical parameterizations in the atmospheric component were modified from those in the higher-resolution model.

    Coupling between the ocean and the atmosphere is performed every hour. The oceanic component of the CGCM supplies sea surface temperatures (SST) to its atmospheric component, while the atmospheric component provides hourly-mean heat, momentum and freshwater fluxes to the ocean component. Simulated fields in a coupled model are liable to approach the model climate state, which differs substantially from the averaged state of the real ocean and atmosphere. In order to suppress this climate drift, adjustment is made to both the heat and momentum fluxes. The adjustment amounts are statistically calculated for each initial month and for each lead time from hindcast experiments: the momentum-flux adjustment is estimated as the difference between JRA-25 analysis and atmospheric model prediction, while the heat-flux adjustment is estimated from the thermal amount required for relaxation of the prediction to the analysis of SSTs. The specifications of the CGCM are summarized in Table 1.

    Table 1. Specifications of the El Niño Prediction Model (JMA/MRI-CGCM)
    Atmospheric component
    Basic equation
    Primitive
    Domain
    Global
    Resolution
    TL95, 40 vertical levels
    Cumulus convection
    Prognostic Arakawa-Schubert scheme
    Land surface process
    Simple Biosphere (SiB)
    Planetary boundary layer
    Mellor & Yamada Level 2
    Oceanic component
    Basic equation
    Primitive, free surface
    Domain
    Global except the Arctic Ocean
    Resolution
    1.0º (lon) x 1.0º (lat),
    (1.0º (lon) x 0.3º (lat) near equator)
    50 vertical levels
    Vertical diffusion
    Noh and Kim (1999)
    Coupling
    Frequency
    Every hour
    Flux correction
    Momentum and heat flux



  3. Outline of the ensemble method

    The Ensemble Prediction System (EPS) with the CGCM for El Niño prediction adopts a combination of the initial perturbation method and the Lagged Average Forecasting (LAF) method. Nine members are run every five days, and the EPS consists of fifty-one members for the latest six initial dates.

    Initial perturbations are generated both for the atmosphere and for the ocean. The results of analysis by the JMA Climate Data Assimilation System (JCDAS) are adopted as the atmospheric initial conditions for the CGCM. Atmospheric initial perturbations are obtained using the Breeding of Growing Modes (BGM) method. Oceanic initial perturbations are estimated through the ocean data assimilation sys tem (MOVE/MRI.COM-G) forced with surface heat and momentum fluxes in the atmospheric initial perturbation fields.

    Figure 1 Schema of the aggregation in the Ensemble Prediction System with the CGCM for El Niño prediction
  4. Performance of JMA/MRI-CGCM

    A set of 348 hindcast experiments of 15-month prediction from January 1979 to December 2007 was performed. Figure 2 shows all predicted sea surface temperature (SST) deviations from the 1971 - 2000 means for the NINO.3 region (5ºN - 5ºS, 150ºW - 90ºW) since 1979 together with the observed values. JMA/MRI-CGCM appropriately predicts most cases with large deviations for the NINO.3 region. In particular, the occurrence of the 1997/98 El Niño event is well predicted more than six months in advance, although the predicted amplitude is a little smaller than the observation.

    Figure 2 SST deviations from the 1971 - 2000 average for the NINO.3 region from January 1978 to January 2010. The red line indicates observed values, and the black lines show the 13-month predictions of JMA/MRI-CGCM.

    Figure 3 shows the performance of JMA/MRI-CGCM in terms of anomaly correlations and root mean square errors (RMSE) for NINO.3 SST anomalies for the 348 hindcast experiments from 1979 to 2007. The anomaly correlations for persistence prediction and the RMSEs for persistence and climatology predictions are shown for reference. The anomaly correlations for the predictions with 8- and 12-month lead times are nearly 0.6 and over 0.3, respectively. It is shown that the model performs well up to a 9-month lead time in terms of RMSE compared to climatological and persistence predictions.


    Figure 3 The performance of the El Niño Prediction Model in terms of anomaly correlation (upper) and root mean square error (lower) for the period from 1979 to 2007.
    Asterisks (red), open squares (green) and solid squares (blue) denote the model's prediction, persistence prediction and climatological prediction, respectively.


  5. Products

    Based on CGCM predictions, JMA issues a six-month ENSO outlook in its Monthly El Niño Monitoring Report. Figure 4 shows a sample of a chart provided in the report, indicating a time series of monthly SST deviations for the NINO.3 region. The red line with closed circles shows the observed SST deviation values available at the time of issuance, and the yellow boxes show the prediction based on the average of the 51 prediction members, from which systematic biases are subtracted and whose systematic errors of deviation amplitude are adjusted. The boxes indicate the range within which the SST deviation is predicted to fall with a probability of 70%. This chart is available through JMA's Distributed Database (http://ddb.kishou.go.jp) and the Tokyo Climate Center website (http://ds.data.jma.go.jp/tcc/tcc/index.html).

    NINO.3 SST Outlook by the El Niño prediction model


    Figure 4 Outlook of SST deviations for the NINO.3 region by the El Niño prediction model (JMA/MRI-CGCM).
    The figure indicates a time series of monthly SST deviations for NINO.3 (5ºN - 5ºS, 150ºW - 90ºW) region. The red line with closed circles shows the observed SST deviations, and the yellow boxes show values for the next six months as forecast by the El Niño prediction model. Each box denotes the range within which the SST deviation is predicted to fall with a probability of 70%.


  6. The upgrade history of the El Niño prediction model

    Operationally-used since  
    Aug 1998 Coupled ocean-atmosphere General Circulation Model (CGCM) put into operation as an El Niño prediction tool.
    Jul 2003 Atmospheric component and flux adjustment method improved.
    Jun 2005 Frequency of prediction model integration increased (from every 15 days to every 5 days).
    Mar 2008 El Niño prediction model replaced with new version featuring higher oceanic and atmospheric resolutions.
    - Atmospheric component changed to lower-resolution version (TL95L40) of the atmospheric global spectral model (GSM) in operation for numerical weather prediction.
    - Oceanic component made identical to that of ocean general circulation model (MRI.COM) described in ocean data assimilation system (MOVE/MRI.COM-G).
    Mar 2009 Perturbations for ocean introduced.
    Ensemble size increased from 12 to 30.
    Mar 2010 Perturbations for atmosphere introduced in addition to those for ocean.
    Ensemble size increased from 30 to 51.
    CGCM application extended to seasonal weather forecasts.


References

Ishikawa, I., H. Tsujino, M. Hirabara, H. Nakano, T. Yasuda, and H. Ishizaki, 2005: Meteorological Research Institute Community Ocean Model (MRI.COM) manual. Technical Reports of the Meteorological Research Institute, 47, 189pp. (In Japanese)

Japan Meteorological Agency, 2007: Outline of operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO numerical weather prediction progress report.

Noh, Y., and H.-J. Kim, 1999: Simulation of temperature and turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851-875.


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