## Meteorological Research Institute: Model MRI GCM-II (4x5 L15) 1993

### AMIP Representative(s)

Dr. Akio Kitoh, Meteorological Research Institute, 1-1, Nagamine, Tsukuba-shi, Ibaraki-ken, 305 Japan; Phone: +81-298-53-8594; Fax: +81-298-55-2552; e-mail: kitoh@mri-jma.go.jp; World Wide Web URL: http://www.mri-jma.go.jp/Welcome.html### Model Designation

MRI GCM-II (4x5 L15) 1993### Model Lineage

The MRI model is derived from an earlier version of the University of California at Los Angeles (UCLA) model (cf. Arakawa and Mintz 1974 [1] and Arakawa and Lamb 1977 [2]). Subsequent modifications include increases in vertical resolution, and changes in parameterizations of gravity-wave drag, atmospheric radiation, convection, surface characteristics, and land surface processes.### Model Documentation

Documentation for an earlier five-level version of the MRI model is provided by Tokioka et al. (1984) [3]. Descriptions of subsequent modifications are given by Yagai and Tokioka (1987) [4], Yagai and Yamazaki (1988) [5], Kitoh et al. (1988) [6], Tokioka et al. (1988) [7], Noda and Tokioka (1989) [8], and Shibata and Aoki (1989) [9]. Results from the AMIP simulation by the current MRI model are discussed by Kitoh et al. (1995) [10].## Numerical/Computational Properties

### Horizontal Representation

Finite differences on a C-grid with conservation of mass, momentum, energy, and potential enstrophy (cf. Arakawa and Lamb 1977) [2].### Horizontal Resolution

4 x 5-degree latitude-longitude grid.### Vertical Domain

Surface to 1 hPa (with the highest prognostic level at 1.39 hPa). For a surface pressure of 1000 hPa, the lowest atmospheric level is at a pressure of about 912 hPa. In addition, the depth of the boundary layer is a prognostic variable--see Planetary Boundary Layer.### Vertical Representation

Finite differences in hybrid coordinates. Above 100 hPa log-pressure coordinates are used, and below 100 hPa modified sigma coordinates: sigma = (P - PI)/(PS - PI), where P and PS are atmospheric and surface pressure, respectively, and PI is a constant 100 hPa. The vertical differencing scheme is after Tokioka (1978) [11].### Vertical Resolution

15 hybrid levels. For a surface pressure of 1000 hPa, 1 level is below 800 hPa and 9 levels are above 200 hPa. See also Vertical Representation.### Computer/Operating System

The AMIP simulation was run on a HITAC S-810/10 using a single processor in the VOS3 environment.### Computational Performance

For the AMIP experiment, about 6 minutes of HITAC S-810/10 computation time per simulated day.### Initialization

For the AMIP simulation, a one-month "warm-up" period precedes the formal start on 1 January 1979. The model atmosphere, soil moisture, and snow cover/depth are initialized from a previous model solution for 1 December; then the model is integrated to a simulated 1 January state.### Time Integration Scheme(s)

The model is integrated by the leapfrog scheme with a time step of 6 minutes, and with a Matsuno step (an Euler forward integration followed by a backward integration) inserted hourly. At the forward stage of the Matsuno step, diabatic and dissipative terms, sources and sinks in atmospheric water vapor, and the change in the depth of the prognostic planetary boundary layer (PBL) are calculated. The PBL entrainment rate and the turbulent fluxes at the PBL top and at the surface are solved iteratively by backward implicit differencing (see Planetary Boundary Layer and Surface Fluxes). Shortwave and longwave radiation are computed hourly, but with transmission functions for longwave fluxes calculated every 3 hours.### Smoothing/Filling

Orography is area-averaged (see Orography). A longitudinal smoothing of the zonal pressure gradient and of the zonal and meridional mass flux is also performed in high latitudes (cf. Tokioka et al. 1984) [3]. A precise determination of the horizontal flux of atmosphere moisture and use of vertical interpolation at half-levels prevents the occurrence of negative humidity values (cf. Tokioka et al. 1984) [3], thereby avoiding the need for moisture filling.### Sampling Frequency

For the AMIP simulation, the model history is written every 6 hours.## Dynamical/Physical Properties

### Atmospheric Dynamics

Primitive-equation dynamics are expressed in flux form in terms of u and v winds, temperature, specific humidity, and surface pressure.### Diffusion

- Nonlinear horizontal diffusion is applied only to momentum (cf. Holloway and Manabe 1971)
[12].
- Vertical diffusion is not applied above the well-mixed PBL (see Planetary Boundary Layer). However, momentum is redistributed vertically by cumulus convection (see Convection).

### Gravity-wave Drag

Orographic gravity-wave drag is simulated following Palmer et al. (1986) [13] with quantitative adjustments described by Yagai and Yamazaki (1988) [4]. The dependence of the surface momentum flux on the surface wind direction is considered (see Orography on the computation of the required subgrid-scale orographic covariances). Surface stress due to gravity waves excited by stably stratified flow over irregular terrain is calculated from linear theory and dimensional considerations. The gravity-wave stress is a function of atmospheric density, low-level wind, and the Brunt-Vaisalla frequency. The vertical structure of the momentum flux induced by gravity waves is calculated from a local wave Richardson number, which describes the onset of turbulence due to convective instability and the turbulent breakdown approaching a critical level. The gravity-wave generation factor kappa is set to 6.0 x 10^-6 m^-1.### Solar Constant/Cycles

The solar constant is the AMIP-prescribed value of 1365 W/(m^2). Both seasonal and diurnal cycles in solar forcing are simulated.### Chemistry

The carbon dioxide concentration is the AMIP-prescribed value of 345 ppm. Zonal profiles of ozone concentration are prescribed monthly from data of McPeters et al. (1984) [14]. The radiative effects of water vapor are also treated, but not those of aerosols (see Radiation).### Radiation

- Shortwave Rayleigh scattering and absorption in ultraviolet (wavelengths
less than 0.35 micron) and visible (wavelengths 0.5 to 0.7 micron) spectral
bands by ozone, and in the near-infrared (wavelengths 0.7 to 4.0 microns) by
water vapor and carbon dioxide, follows the method of Lacis and Hansen (1974)
[15]. Pressure corrections and multiple
reflections between randomly overlapped partial clouds and the surface are
treated.
- Longwave calculations are based on the multiparameter random model of
Shibata and Aoki (1989)
[9] applied in four spectral regions (with boundaries at
2.0 x 10^3, 5.5 x 10^4, 8.0 x 10^4, 1.2 x 10^5, and 2.2 x 10^5 m^-1).
Absorption bands of carbon dioxide, ozone, and water vapor are included.
Continuum absorption of water vapor is treated after the method of Clough et al. (1980)
[16], but with the temperature
dependency used by Roberts et al. (1976)
[17].
Mean diffuse transmittances in each region are determined from line-by-line
calculations approximated by an exponential function including 10 or 12
parameters. Transmittances through inhomogeneous atmospheres are computed by a modified Godson (1953)
[18] method.
- In the shortwave, clouds are treated by a delta Eddington approximation with prescribed single-scattering albedo and asymmetry factor, and with cloud optical depth a function of height. In the longwave, clouds behave as blackbodies, except for high (above 400 hPa) clouds, whose emissivity is related to shortwave optical depth. For purposes of the radiation calculations, all clouds are assumed to be randomly overlapped in the vertical. See also Cloud Formation.

### Convection

- Simulation of cumulus convection is by a modified Arakawa and Schubert (1974)
[19] scheme, as implemented by Lord
(1978)
[20], Lord and Arakawa (1980)
[21], and Lord et al. (1982)
[22]. Convective mass fluxes are assumed to
originate in the PBL (see Planetary Boundary Layer); these are predicted
from mutually interacting cumulus subensembles, which have different
entrainment rates and levels of neutral buoyancy that define the tops of the
clouds and their associated convective updrafts. In turn, the convective mass
fluxes feed back on the large-scale fields of temperature (through latent
heating and compensating subsidence), moisture (through precipitation and
detrainment), and momentum (through cumulus friction). The effects on cloud
buoyancy of phase changes from water to ice and the drying and cooling effects
of convective-scale downdrafts on the environment are not included explicitly.
The Arakawa-Schubert scheme is modified to impose an additional constraint
between the minimum entrainment rate and the prognostic depth of the PBL (cf.
Tokioka et al. 1988)
[7].
- The mass flux for each cumulus subensemble is predicted from an integral
equation that includes a positive-definite work function (defined by the
tendency of cumulus kinetic energy for the subensemble) and a negative-definite
kernel which expresses the effects of other subensembles on this work function.
The predicted mass fluxes are optimal solutions of this integral equation under
the constraint that the rate of generation of conditional convective
instability by the large-scale environment is balanced by the rate at which the
cumulus subensembles suppress this instability via large-scale feedbacks (cf.
Lord et al. 1982)
[22]. The mass fluxes are computed by the "exact direct method,"
which guarantees an exact solution within roundoff errors.
- If the lapse rate becomes dry convectively unstable at any level, moisture and enthalpy are redistributed vertically. In addition, a moist convective adjustment simulates midlevel convection originating above the PBL. When the lapse rate exceeds moist adiabatic under supersaturated conditions, mass is mixed such that either the lapse rate is restored to moist adiabatic, or the supersaturation is eliminated by formation of convective precipitation. Cf. Tokioka et al. (1984) [3] for further details.

### Cloud Formation

- Five types of cloud are simulated: penetrative and midlevel convective
cloud, cirrus anvil cloud, large-scale condensation cloud, and PBL stratus
cloud. The PBL stratus is the only cloud type that does not interact with
radiation.
- The penetrative and midlevel convective cloud are associated,
respectively, with the cumulus convection and moist convective adjustment
schemes (see Convection). Convective clouds in a vertical column are
assumed to overlap randomly, with a total cloud fraction of 0.3. Cirrus anvil
cloud forms if cumulus convection penetrates to levels above 400 hPa, and
large-scale condensation cloud is present if the local relative humidity of a
layer is at least 100 percent. The latter two cloud types cover the grid box
(cloud fraction = 1).
- Stratus cloud capping the PBL forms if the specific humidity at the PBL top is greater than saturation (see Planetary Boundary Layer). The base of the cloud is determined from the vertical distribution of temperature and specific humidity in the PBL. See also Radiation for treatment of cloud-radiative interactions.

### Precipitation

Large-scale precipitation results from condensation under supersaturated conditions. Precipitation from cumulus convection originating in the PBL is simulated by the Arakawa-Schubert (1974) [19] scheme, and precipitation from midlevel convection by a moist adjustment process (see Convection). Both large-scale and convective precipitation may evaporate in falling through lower layers; the amount of evaporation is equal to that required to saturate each of these layers in turn.### Planetary Boundary Layer

- The PBL is parameterized as a well-mixed layer after Randall (1976)
[23]. The depth and mean structure of the PBL are
prognostic functions of the surface momentum, heat, and moisture fluxes (see
Surface Fluxes), as well as horizontal mass convergence, entrainment,
and cumulus mass fluxes determined by the cumulus convection scheme (see
Convection). PBL u-v winds, temperature, and specific humidity are
predicted. Discontinuities in atmospheric variables that may exist at the PBL
top because of the absence of vertical diffusion in the free atmosphere (see
Diffusion) are also predicted. In addition, PBL stratus cloud is
produced under saturated conditions (see Cloud Formation), and its
evaporation by drier entrained air is treated.
- The turbulence kinetic energy (TKE) also is determined. (TKE is generated by wind shear and convective buoyancy fluxes, and is depleted by surface dissipation, by work done against the free atmosphere, and by newly turbulent air that is entrained into the PBL.) Because of the mutual dependence of the entrainment rate and the turbulent fluxes at the PBL top and at the surface, these quantities are solved iteratively under the assumption that the generation of TKE balances its dissipation. See also Surface Characteristics and Time Integration Scheme(s).

### Orography

Orography, obtained from UCLA, is area averaged over each 4 x 5-degree model grid box. Orographic covariances required for the parameterization of gravity-wave drag (see Gravity-wave Drag) are computed from the U.S. Navy 10-minute resolution topography dataset (cf. Joseph 1980) [24].### Ocean

AMIP monthly sea surface temperature fields are prescribed, with daily values determined by linear interpolation.### Sea Ice

- AMIP monthly sea ice extents are prescribed. Sea ice concentration
fraction (ranging between 0 and 1) for each grid box is prescribed monthly by
averaging over corresponding weekly U.S. Navy and National Oceanic and
Atmospheric Administration (NOAA) Joint Ice Center data for 1979 to 1988. The
spatially variable thickness of sea ice is given by the local concentration
fraction multiplied by 3.0 meters in the Northern Hemisphere and by 0.5 meter
in the Southern Hemisphere. Daily values of the above quantities are determined
by linear interpolation.
- The ice surface temperature is predicted from the net flux of energy (see Surface Fluxes), including a subsurface conduction heat flux that is proportional to the difference between the ice surface temperature and that prescribed (271.3 K) for the ocean below. Snow may accumulate on sea ice or melt if the snow surface temperature exceeds 0 degrees C (see Snow Cover). Snow also alters the heat capacity/conductivity of the ice, but the heat capacity of snow is independent of depth.

### Snow Cover

If the surface air temperature is <0 degrees C, precipitation falls as snow. Snow may accumulate on both land and sea ice, with complete coverage of a grid box assumed (i.e., there is no fractional snow coverage). Prognostic snow mass is depleted both by snowmelt (which contributes to soil moisture) and by sublimation (which contributes to surface evaporation). Snow melts if its surface temperature exceeds 0 degrees C. Snow cover affects both surface albedo and the thermal properties of the surface. See also Sea Ice, Surface Characteristics, Surface Fluxes, and Land Surface Processes.### Surface Characteristics

- The surface type is defined as open ocean, sea ice, glacial ice, lake, or
land. (Lakes are treated as mixed layers of depth 10 m, with prognostic
temperatures.)
- The surface roughness length is a fixed value over oceans (2 x 10^-4 m),
sea ice (1 x 10^-4 m), glacial ice (5 x 10^-3 m), and land (0.45 m); the
surface drag coefficient over land is a function of orographic variances,
however (see Orography and Surface Fluxes).
- Surface albedos depend on solar zenith angle (cf. Paltridge and Platt
1976)
[25], but not on spectral interval. The
ocean albedo is a maximum of 0.07. The albedo of bare sea ice ranges between a
maximum of 0.50 to 0.64 as a function of surface temperature; the albedo of
snow-covered sea ice is a maximum of 0.70, depending on snow depth.
Snow-covered glacial ice albedos range between 0.70 and 0.85. Snow-free land
albedos are obtained from the data of Matthews (1983)
[26]; with snow cover, the land albedo ranges
from its snow-free value to a maximum between 0.60 to 0.70, depending on
topographic height (see Orography). On all surfaces, the albedo of
melting snow is 0.60, and that of frost is 0.30.
- Longwave emissivity is prescribed as unity (blackbody emission) for all surfaces.

### Surface Fluxes

- Surface solar absorption is determined from albedos, and longwave emission
from the Planck equation with emissivity of 1.0 (see
Surface Characteristics).
- Turbulent eddy fluxes of momentum, heat, and moisture are parameterized as
bulk formulae with drag and transfer coefficients that depend on vertical
stability (bulk Richardson number) and the (locally variable) depth of the PBL
normalized by the surface roughness length, following Deardorff (1972)
[27]. The drag coefficient over land is also
increased as a function of orographic variances (see Orography and cf.
Yagai and Tokioka 1987
[4]). The surface atmospheric values of wind, dry static
energy, and humidity required for the bulk formulae are taken to be those
predicted in the PBL (see Planetary Boundary Layer). Because of the
mutual dependence of the turbulent fluxes at the surface and at the PBL top,
these (as well as the PBL entrainment rate) are solved by mutual iteration. See
also Surface Characteristics and Time Integration Scheme(s).
- The surface moisture flux depends also on an evapotranspiration efficiency factor beta, which is set to unity over ocean and ice surfaces and in areas of continental dew formation; otherwise, over land, beta is a function of soil moisture (see Land Surface Processes).

### Land Surface Processes

- Land-surface parameterizations follow Katayama (1978)
[28], with subsequent modifications in the
treatment of soil moisture described by Kitoh et al. (1988)
[6]. Soil heat and
moisture move along the gradients of temperature and wetness, respectively. The
thermodynamic and hydrologic effects of a vegetation canopy are not explicitly
modeled, however.
- The temperature of bare and snow-covered land is predicted in four layers,
and that of glacial ice in a single-layer. The upper boundary condition is the
net balance of surface energy fluxes (see Surface Fluxes), while there
is zero heat transfer at the lower boundary. Heat exchange associated with the
freezing or melting of soil moisture and interstitial ice is taken into
account. Snow cover and soil moisture/ice also affect the heat
capacity/conductivity of the surface, but the heat capacity of snow is assumed
to be independent of its depth.
- Soil moisture (in both liquid and frozen form) is predicted in four layers (with bottom boundaries at 0.10, 0.50, 1.50, and 10.0 m below the surface); it is augmented by precipitation and snow/ice melt, and is depleted by surface evaporation and runoff. The evapotranspiration efficiency beta (see Surface Fluxes) is set to unity if the fractional soil moisture (the ratio of soil moisture to the field capacity, assumed to be 20 percent of the volume of a soil column) is at least 0.5; beta is set to twice the fractional soil moisture otherwise. When all of the moisture in a soil layer is completely frozen (freezing begins when the layer temperature falls below 0 degrees C), no deeper penetration of moisture is allowed. Runoff occurs in any layer that is either saturated or completely frozen.

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Last update April 19, 1996. For further information, contact: Tom Phillips (phillips@tworks.llnl.gov )

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