Model Information for IPCC AR4 contribution

Meteorological Research Institute

10 Febrary 2005


Ⅰ. Model identity:

  1. Institution, sponsoring agency, country
    Meteorological Research Institute, Japan Meteorological Agency, Japan
  2. Model name
    MRI-CGCM2.3.2 (TAR = MRI-CGCM2.0)
  3. Vintage (i.e., year that model version was first used in a published application)
    2003
  4. General published references and web pages
    Yukimoto et al. (2001)
    http://www.mri-jma.go.jp/Dep/cl/cl4/publications/yukimoto_pap2001.pdf
    The above document is for the TAR version (MRI-CGCM2.0).
  5. References that document changes over the last ~5 years (i.e., since the IPCC TAR) in the coupled model or its components.
    The major changes for the current version are documented in Yukimoto and Noda (2002) for the former version (MRI-CGCM2.2). The changes for the current version (MRI-CGCM2.3.2) from the former version, which are small, have not published yet. It is in preparation for submission to a journal.
  6. IPCC model version's global climate sensitivity (KW-1m2) to increase in CO2
    The climate sensitivity is 0.86 K/(Wm–2), which is estimated from a slab ocean 2xCO2 equilibrium experiment.



Ⅱ.Component model characteristics (of current IPCC model version)

  1. Atmosphere
    General published documentation of the atmospheric component:
    Shibata et al. (1999)
    http://www.mri-jma.go.jp/Dep/cl/cl4/publications/shibata_pap1999.pdf
    1. resolution
      - horizontal resolution: T42 (approx. 2.8 degrees)
    2. numerical scheme/
      - numerical scheme: spectral transform method
      - time-stepping scheme: leap-frog, semi-implicit with Asselin time filter

      - model top: 0.4 hPa
      - vertical coordinate: sigma-pressure hybrid, 30 layers (16 layers above 200 hPa, 5 layers below 850 hPa)
    3. list of prognostic variables
      - velocity (velocity potential and streamfunction)
      - temperature

      - specific humidity
    4. major parameterizations
    1. clouds
      diagnostic clouds based on function of relative humidity
      Noda et al. (2001), Yukimoto and Noda (2002)
    2. convection
      Prognostic Arakawa-Schubert
      Randall and Pan (1993)
    3. boundary layer
      turbulent closure level 2
      Mellor and Yamada (1974)
    4. SW, LW radiation
      SW: delta-two-stream
      Shibata and Uchiyama (1992)
      LW: Multi-parameter random model
      Shibata and Aoki (1989)
    5. any special handling of wind and temperature at top of model
      Rayleigh friction with hyperbolic tangent profile
      Shibata et al. (1999)


  2. Ocean
    1. resolution
      longitude: 2.5 degrees
      latitude: 2.0 degree (poleward of 12S and 12N) ~ 0.5 (4S-4N)
    2. numerical scheme/
      - numerical scheme/grid: Arakawa B-grid
      - advection scheme: central differencing
      - time-stepping scheme: leap-frog and Matsuno scheme
      - vertical coordinate: z-coordinate
      - surface condition: regid lid
      - freshwater flux: virtual salt flux
    3. list of prognostic variables and tracers
      - velocities (eastward and northward)
      - temperature
      - salinity
    4. parameterizations
    1. eddy parameterization
      Laplacian-type horizontal diffusion
      + GM paramererization (Gent and McWilliams, 1990)
    2. bottom boundary layer treatment and/or sill overflow treatment
      Not included.
    3. mixed-layer treatment
      turbulent closure level 2
      Mellor and Yamada (1974)
      Mellor and Durbin (1975)
    4. sunlight penetration
      Sunlight penetrates with a constant decaying rate of 10 meters e-folding depth.
    5. tidal mixing
      Not included.
    6. river mouth mixing
      Larger vertical diffusivities in the upper 30m are applied at the Amazon’s river mouth.
    7. mixing isolated seas with the ocean
      Not included. (There is no isolated seas.)
    8. treatment of North Pole "singularity" (filtering, pole rotation, artificial island?)
      Fourier filtering at northward of 82N

  3. sea ice
    1. horizontal resolution, number of layers, number of thickness categories
      - horizontal resolution: 2.5 degrees (longitude) x 2.0 degrees (latitude)
      - number of layers: 1 layer
      - no thickness categories
    2. numerical scheme/grid, including advection scheme, time-stepping scheme
      - numerical scheme/grid: Arakawa B-grid
      - time-stepping scheme: forward differencing
    3. list of prognostic variables
      - thickness
      - compactness
    4. completeness
      Thickness and compactness (leads) are advected by a function of ocean surface current. Diffusion is also applied for thickness and compactness. Rheology is not included. Thermo-dynamical processes of snow on sea ice are treated.
    5. treatment of salinity in ice
      Salinity of sea ice is fixed at a constant value (4 psu).
    6. brine rejection treatment
      No brine rejection from sea ice is treated, since the salinity of sea ice is constant. Salinity flux into ocean is accounted only for frazil ice formation and melt/freeze of sea ice.
    7. treatment of the North Pole "singularity"
      There is no special treatment of the North Pole for sea ice, though Fourier filtering is applied for ocean


  4. land / ice sheets
    1. resolution number of layers for heat and water
      - horizontal resolution: T42 Gaussian grid (~300km, same as the atmosphere)
      - number of layers: 3 layers (for both heat and water)
    2. treatment of frozen soil and permafrost
      Frozen soil water is treated as a prognostic variable. Where the soil layer is year-round frozen can be defined as permafrost.
    3. treatment of surface runoff and river routing scheme
      River routing scheme is included. Surface runoff occurs when the precipitation rate is larger than the maximum (saturated) infiltration ratio. Surface runoff is discharged into ocean at the river mouth via the river routing.
    4. treatment of snow cover on land
      One snow layer. The energy and water mass are conserved in the thermo-dynamical processes of snow.
    5. description of water storage model and drainage
      Prognostic surface temperature and water storage for four major lakes are modeled, but drainage from the lakes is not treated.
      The drainage from the bottom soil layer is included, and the amount is evaluated from the vertical gradient of hydraulic potential. The drainage water is also discharged through the river route.
    6. surface albedo scheme
      The soil skin albedo depends on the vegetation type. The snow skin albedo depends on the snow temperature.  In case that the amount of snow is small, the partial snow is considered in the estimation of the skin albedo. The radiation transfer between canopy top and skin is calculated, then albedo for the surface or canopy top is evaluated.
    7. vegetation treatment
      Canopy and grass. There are 13 vegetation types, and the vegetation parameters depend on the type and month of the year.
    8. list of prognostic variables
      Temperature of canopy, skin(including grass), 3 soil layers. The moisture of canopy, grass, 3 soil layers.
    9. ice sheet characteristics
      The ice sheet is one of the vegetation types. It is treated as white soil. On the “soil”, the snow accumulates and melts. When the mass of the snow is more than some threshold (10 m), the mass is passed to the river routing model, and it is immediately discharged as iceberg to the ocean. In any case, the handling of the heat and water fluxes is the same as those over snow layer of other vegetation type.


  5. coupling details
  1. frequency of coupling
    24 hours
  2. Are heat and water conserved by coupling scheme?
    Yes.
  3. list of variables passed between components:
    1. atmosphere – ocean
      - heat flux (sensible heat + latent heat + long wave radiation)
      - solar radiative flux
      - freshwater mass flux
      - wind stress (eastward and northward)
    2. atmosphere – land
      - sensible heat flux
      - latent heat flux
      - long wave radiation
      - solar radiation
      - precipitation
      - evaporation/sublimation
      - wind stress (eastward and northward)
    3. land – ocean
      - freshwater mass flux (river discharge)
      - ice mass flux (iceberg discharge)
    4. sea ice – ocean
      - heat flux
      - salt flux
      - frazil ice mass
      - ocean surface velocities (eastward and northward)
    5. sea ice – atmosphere
      - sensible heat flux
      - latent heat flux
      - long wave radiation
      - solar radiation
      - precipitation
      - evaporation/sublimation
      - wind stress (eastward and northward)

  4. Flux adjustment
    Monthly climatological flux adjustment for heat, water and momentum (only 12S-12N) is used.


References

Gent, P. R. and J. C. McWilliams, 1990:  Isopycnal mixing in ocean circulation models.  J. Phys. Oceanogr., 20, 150-155.

Mellor, G. L. and P. A. Durbin, 1975:  The structure and dynamics of the ocean surface mixed layer.  J. Phys. Oceanogr., 5, 718-728.
Mellor, G. L. and T. Yamada, 1974:  A hierarchy of turbulence closure models for planetary boundary layers.  J. Atmos. Sci., 31, 1791-1806.
Mellor, G. L. and T. Yamada, 1982:  Development of a turbulence closure model for geophysical fluid problems.  Rev. Geophys. Space Phys., 20, 851-875.
Noda, A., S. Yukimoto, S. Maeda, T. Uchiyama, K. Shibata, and  S. Yamaki, 2001: A new Meteorological Research Institute coupled GCM (MRI-CGCM2). – Transient response to greenhouse gas and aerosol scenarios –.  CGER’s Supercomputer Monograph Report, 7, 63pp, NIES, Japan.
Randall, D. and D.-M. Pan, 1993:  Implementation of the Arakawa-Schubert cumulus parameterization with a prognostic closure. Meteorological Monograph/The representation of cumulus convection in numerical models, 46, 145-150.
Shibata, K. and T. Aoki, 1989:  An infrared radiative scheme for the numerical models of weather and climate.  J. Geophys. Res., 94, 14923-14943.
Shibata, K. and A. Uchiyama, 1992:  Accuracy of the delta-four-stream approximation in inhomogeneous scattering atmospheres.  J. Meteor. Soc. Japan, 70, 1097-1109.
>Shibata, K., H. Yoshimura, M. Ohizumi, M. Hosaka, and M. Sugi, 1999: A simulation of troposphere, stratosphere and mesosphere with an MRI/JMA98 GCM. Pap. Meteor. Geophys, 50, 15-53.
Yukimoto, S., and A. Noda, 2002: Improvements of the Meteorological Research Institute global ocean-atmosphere coupled GCM (MRI-CGCM2) and its climate sensitivity. CGER’s Supercomputer Activity Report, 10, 37-44, NIES, Japan.
Yukimoto, S., A. Noda, A. Kitoh, M. Sugi, Y. Kitamura, M. Hosaka, K. Shibata, S. Maeda, and T. Uchiyama, 2001: The new Meteorological Research Institute coupled GCM (MRI-CGCM2). – Model climate and variability –.  Pap. Meteor. Geophys., 51, 47-88.

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