Bureau of Meteorology Research Centre: Model BMRC BMRC2.3 (R31 L9) 1990

## Bureau of Meteorology Research Centre: Model BMRC BMRC2.3 (R31 L9) 1990

### AMIP Representative(s)

Dr. Bryant McAvaney, Bureau of Meteorology Research Centre, Box 1289K, GPO Melbourne, Victoria 3001, Australia; Phone: +61-3-9669-4000; Fax: +61-3-9669-4660; e-mail: bma@bom.gov.au; WWW URL: http://www.bom.gov.au/bmrc/climdyn/staff/bma/bma.shtml

### Model Designation

BMRC BMRC2.3 (R31 L9) 1990

### Model Lineage

The BMRC model is a descendant of a spectral general circulation model first developed in the 1970s (cf. Bourke et al. 1977 [1], and McAvaney et al. 1978 [2]).

### Model Documentation

Key documentation of the BMRC model is provided by Bourke (1988) [3], Hart et al. (1988 [4], 1990 [5]), Colman and McAvaney (1991) [6], McAvaney et al. (1991) [7], and Rikus (1991) [8]. The model configuration for the AMIP experiment is described by McAvaney and Colman (1993) [9].

## Numerical/Computational Properties

### Horizontal Representation

Spectral (spherical harmonic basis functions) with transformation to a Gaussian grid for calculation of nonlinear quantities and physics.

### Horizontal Resolution

Spectral rhomboidal 31 (R31), roughly equivalent to 2.8 x 3.8 degrees latitude-longitude.

### Vertical Domain

Surface to about 9 hPa. For a surface pressure of 1000 hPa, the lowest atmospheric level is at about 991 hPa.

### Vertical Representation

Conservative finite differences in sigma coordinates.

### Vertical Resolution

There are nine unevenly spaced sigma levels. For a surface pressure of 1000 hPa, 3 levels are below 800 hPa and 3 are above 200 hPa.

### Computer/Operating System

The AMIP simulation was run on a Cray Y/MP 2E computer using 1 processor in a UNICOS 6.1.6 environment.

### Computational Performance

For the AMIP, about 3 minutes Cray Y/MP computation time per simulation day.

### Initialization

For the AMIP simulation, the model atmosphere is initialized from the ECMWF III-B analysis for 12 UT of 1 January 1979, with nonlinear normal mode initialization operative. Soil moisture is initialized from the January data of Mintz and Serafini (1989) [10]. Snow cover is initialized from the albedo data of Hummel and Reck (1979) [11]: albedos greater than 40 percent define areas of seasonal snow with initial depth of 5 m; in areas of permanent snow (i.e., Antarctica and Greenland) the initial depth is set to 250 m.

### Time Integration Scheme(s)

A semi-implicit scheme with an Asselin (1972) [12] frequency filter is combined with a split implicit scheme for the vertical diffusion component of the model physics. A time step of 15 minutes is used for both dynamics and physics, except that full calculations of radiative fluxes and heating rates are done once every 3 hours.

### Smoothing/Filling

Orography is smoothed (see Orography). Filling of negative atmospheric moisture values is performed by a combination of local horizontal and vertical borrowing, and global borrowing following the method of Royer (1986) [13]. A mass adjustment scheme is also used to prevent a slow drift in surface pressure during long integrations. Cf. McAvaney et al. (1991) [7] for further details.

### Sampling Frequency

For the AMIP simulation, the model history is written every 6 hours. (However, fields such as convective and total precipitation are accumulated over a 24-hour period; caution should therefore be exercised in interpreting such fields at subintervals of a day.)

## Dynamical/Physical Properties

### Atmospheric Dynamics

Primitive-equation dynamics are expressed in terms of vorticity, divergence, temperature, surface pressure, and specific humidity.

### Diffusion

• Linear second-order (del-squared) horizontal diffusion is applied for wave numbers n > 31 in the upper part of the spectral rhomboid, with a first-order sigma coordinate correction applied near topography.

• Stability dependent vertical diffusion after Louis (1979) [14] is only applied for sigma levels > 0.5 in stable layers, but it operates in all unstable layers with no separate removal of dry superadiabats, and with a minimum wind speed difference of 1 m/s assumed between model levels.

### Gravity-wave Drag

Momentum transports associated with gravity waves are simulated by the method of Palmer et al. (1986) [15], using directionally dependent subgrid-scale orographic variances. Surface stress due to gravity waves excited by stably stratified flow over irregular terrain is calculated from linear theory and dimensional considerations. 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.

### 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

Carbon dioxide is assumed to be well mixed at the AMIP-prescribed concentration of 345 ppm. Zonally averaged seasonal mean ozone distributions are prescribed from the data of Dopplick (1974) [16], with linear interpolation for intermediate times. No other trace gases or aerosols are present, but the radiative effects of water vapor are included (see Radiation).

• Shortwave Rayleigh scattering and absorption in ultraviolet (< 0.35 micron) and visible (0.5-0.7 micron) spectral bands by ozone, and in the near-infrared (0.7-4.0 microns) by water vapor and carbon dioxide follow the method of Lacis and Hansen (1974) [17]. Pressure corrections and multiple reflections between clouds are treated. The radiative effects of aerosols are not included directly.

• Longwave radiation follows the simplified exchange method of Fels and Schwarzkopf (1975) [18] and Schwarzkopf and Fels (1991) [19], with slight modifications. (The parent code is compared against benchmark computations by Fels et al. 1991 [20].) Longwave calculations follow the broad-band emissivity approximation in 8 spectral intervals (with wavenumber boundaries at 0, 1.6 x 10^4, 5.6 x 10^4, 8.0 x 10^4, 9.0 x 10^4, 9.9 x 10^4, 1.07 x 10^5, 1.20 x 10^5, and 2.20 x 10^5 m^-1). Another 14 bands are accounted for in the cooling-to-space corrections. Included in the calculations are Fels and Schwarzkopf (1981) [35] transmission coefficients for carbon dioxide, the water vapor continuum of Roberts et al. (1976) [21], and the effects of water-carbon dioxide overlap and of a Voigt line-shape correction. , and the effects of water-carbon dioxide overlap and of a Voigt line-shape correction.

• The treatment of cloud-radiative interactions is as described by Rikus (1991) [8] and McAvaney et al. (1991) [7]. Shortwave cloud reflectivity/absorptivity is prescribed for ultraviolet-visible and near-infrared spectral bands and depends only on the height class of the cloud (see Cloud Formation). In the longwave, all clouds are assumed to behave as blackbodies (emissivity of 1). For purposes of the radiation calculations, all clouds are assumed to be randomly overlapped in the vertical.

### Convection

• Deep convection is simulated by a variation of the method of Kuo (1974) [22] that includes modifications of Anthes (1977) [23]. Penetrative convection is assumed to occur only in the presence of conditionally unstable layers in the vertical and large-scale net moisture convergence. The convective cloud base is assumed to be at the first level (maximum sigma = 0.926) above the planetary boundary layer (PBL) which is conditionally unstable. The convective cloud is assumed to dissolve instantaneously through lateral mixing, thereby imparting heat and moisture to the environment. In a vertical column the total moisture available from convergence is divided between a fraction b that moistens the environment and the remainder (1 - b) that contributes to the latent heating (rainfall) rate. In the Anthes modification of the Kuo scheme, the moistening parameter b is determined as a cubic function of the ratio of the mean relative humidity of the cloud layer to a prescribed critical relative humidity threshold value; if the cloud relative humidity is less than the threshold, b is set to 1 (no heating of the environment).

• Simulation of shallow convection is parameterized in terms of the model's vertical diffusion scheme, following the method of Tiedtke (1983 [24], 1988 [25]).

### Cloud Formation

Stratiform cloud formation is based on the relative humidity diagnostic form of Slingo (1987) [26]. Clouds are of 3 height classes: high (sigma levels 0.189-0.336), middle (sigma levels 0.500-0.664), and low (sigma levels 0.811-0.926). The fractional amount of each type of cloud is determined from a quadratic function of the difference between the maximum relative humidity of the sigma layer and a threshold relative humidity that varies with sigma level; for high and low cloud the threshold is 60 percent humidity, while for middle cloud it is 50 percent. In addition, following Rikus (1991) [8], low cloud forms when the relative humidity at the lowest atmospheric level (sigma = 0.991) exceeds 60 percent, and is capped by strong static stability in the layer immediately above (i.e., a temperature inversion is present). In this case, the amount of low cloud increases with the strength of the inversion. (See Convection for the treatment of convective cloud and Radiation for cloud-radiative interactions.)

### Precipitation

Precipitation from large-scale condensation occurs if the relative humidity exceeds 100 percent. The convective precipitation rate is determined from the variable moistening parameter b in the Anthes (1977) [23] modification of the Kuo (1974) [22] convection scheme (see Convection). No evaporation of precipitation is simulated. See also Snow Cover.

### Planetary Boundary Layer

The height of the PBL is assumed to be that of the lowest prognostic vertical level (sigma = 0.991). Winds, temperatures, and humidities for calculation of turbulent eddy surface fluxes from bulk formulae are taken to be the same values as those at this lowest atmospheric level (see Surface Fluxes). See also Diffusion and Surface Characteristics.

### Orography

Orography from a 1 x 1-degree U.S. Navy dataset is grid-point smoothed using a Cressman (1959) [27] area-averaged weighting function with a radius of influence of 3 degrees for the spectral R31 model resolution (cf. Bourke 1988) [1].

### Ocean

AMIP monthly sea surface temperature fields are prescribed, with values updated every 5 days by linear interpolation.

### Sea Ice

Monthly AMIP sea ice extents are prescribed via a Cressman (1959) [27] weighting function with a 3-degree radius of influence; these monthly ice extents are updated by interpolation every 5 days. The thickness of the sea ice is held fixed at 1 m for the Antarctic region and 2 m for the Arctic. Snow is permitted to accumulate or to melt on the ice surface, but there is no conversion of snow to ice. The surface temperature of the sea ice/snow is determined from a heat balance calculation (see Surface Fluxes) with inclusion of a conduction term from the ocean (at a fixed temperature of 271 K) below the ice.

### Snow Cover

When the weighted average of the air temperature at the lowest two levels (sigma = 0.991 and 0.926) falls below 273.16 K, precipitation falls to the surface as snow. Prognostic snow mass with accumulation and melting over both land and sea ice is modeled. Snow cover affects the surface albedo and the surface roughness (see Surface Characteristics), but there is no explicit allowance for the effects of fractional snow cover. Melting of snow, which occurs when the surface temperature exceeds 0 degrees C, contributes to soil moisture (see Land Surface Processes), but sublimation of snow is not calculated as part of the surface evaporative flux (see Surface Fluxes).

### Surface Characteristics

• Distinguished surface types include ocean, land, land ice, and sea ice, and the presence of snow cover is also accounted for on the latter three surfaces. Soil or vegetation types are not distinguished.

• The roughness length over oceans is determined from the surface wind stress, following Charnock (1955) [28], with a coefficient of 0.0185 assigned after Wu (1982) [29]; the ocean roughness is constrained to a minimum value of 1.5 x 10^-5 m. Roughness lengths are prescribed uniform values over sea ice (0.001 m) and land surfaces (0.168 m), but the presence of snow cover changes the roughness to a new (fixed) value.

• Over oceans, the surface albedo depends on solar zenith angle, following Payne (1972)[30]. Seasonal climatological surface albedos of Hummel and Reck (1979) [11] are prescribed over land. The surface albedos of sea ice and snow-covered land follow the temperature-ramp formulation of Petzold (1977) [31], with different values of albedo limits and a lower temperature range for sea ice and snow, as described by Colman and McAvaney (1992). [32]

• Longwave emissivity is set to unity for all surfaces (i.e., blackbody emission is assumed).

### Surface Fluxes

• Surface solar absorption is determined from surface albedos, and longwave emission from the Planck equation with prescribed constant surface emissivity of 1.0 (see Surface Characteristics).

• The surface turbulent eddy fluxes of momentum, heat, and moisture follow Monin-Obukhov similarity theory, and are formulated in terms of bulk formulae with stability-dependent drag/transfer coefficients determined as in Louis (1979) [14]. The momentum flux is given by the product of the air density, a neutral drag coefficient, wind speed and wind vector at the lowest prognostic level (sigma = 0.991), and a transfer function that depends on roughness length (see Surface Characteristics) and stability (bulk Richardson number). Surface wind speed is constrained to a minimum of 1 m/s. The flux of sensible heat is given by a product of a neutral exchange coefficient, the wind speed at the lowest prognostic level, the difference in temperatures between the ground and the first prognostic atmospheric level, and a modified form of the transfer function for unstable conditions (cf. Louis 1979) [14].

• The flux of surface moisture is given by a product of the same transfer coefficient and stability function as for sensible heat, an evapotranspiration efficiency beta, and the difference between the specific humidity at the first prognostic level and the saturation specific humidity at the surface temperature and pressure. For calm conditions over the oceans, evaporation also is enhanced following the approximation of Miller et al. (1992) [33] for the transfer coefficient. Over oceans, sea ice, and snow, beta is prescribed to be unity; over land, beta is a function of the ratio of soil moisture to a constant field capacity (see Land Surface Processes).

### Land Surface Processes

• Soil temperature is computed from heat storage in two layers with a climatological temperature specified in a deeper layer. The upper boundary condition is the surface energy balance (see Surface Fluxes). The heat conductivity of soil is fixed under all conditions.

• Prognostic soil moisture is represented by a single-layer "bucket" model with uniform field capacity of 0.15 m after Manabe and Holloway (1975). [34]. Both precipitation and snowmelt contribute to soil moisture. The evapotranspiration efficiency beta (see Surface Fluxes) is a function of the ratio of soil moisture to the field capacity. Runoff occurs implicitly if this ratio exceeds unity.