Japan Meteorological Agency: Model JMA GSM (TL959 L60) 20031216-MRICLIM

Contact Information

Model Abstract

Experimental Implementation

Model Output Description

Detail Model Characteristics

Contact Information

Modeling Group

Model Abstract

The model used in this study is an early version of the next generation of the global atmospheric model of Japan Meteorological Agency (JMA). MRI and JMA are in collaboration to develop this model for the use of both climate simulations and weather predictions. The model is based on the global forecasting model of JMA (JMA-GSM0103), upon which many modifications have been implemented. Detail descriptions about JMA-GSM0103 is available on Section 4.2 of Outline of the Operational Numerical Weather Prediction at the Japan Meteorological Agency (JMA, 2002).

Based on JMA-GSM0103, modifications described below are implemented into the model.

First, a vertically conservative semi-Lagrangian scheme (Yoshimura and Matsumura 2003), a new quasiconservative semi-Lagrangian scheme, has been developed and introduced for time integrations. Furthermore, a two-time-level semi-Lagrangian scheme has been introduced instead of a three-time-level scheme, which provides a doubling of efficiency in principle (Temperton et al. 2001; Hortal 2002; Yoshimura and Matsumura 2005). These improvements of efficiency enable us to perform high-resolution, long-term integrations. Prognostic variables have been changed from vorticity and divergence to zonal and meridional wind components with the introduction of the semi-Lagrangian scheme (Ritchie et al. 1995).

Second, some physical process schemes are improved. Cumulus parameterization scheme is improved to include the entrainment and detrainment effects between the cloud top and cloud base in convective downdraft instead of reevapolation of convective precipitation (Nakagawa and Shimpo 2004). Cloud ice fall scheme based on an analytically integrated solution by Rotstayn (1997) is introduced (Kawai 2003) instead of rather simple parameterization in which cloud ice falls only into the next layer or to the ground. A new stratocumulus parameterization scheme is introduced following a simple and classical one proposed by Slingo (1987) with some modifications (Kawai 2004). Cloud is formed in the model when there is inversion at the top of boundary layer and mixing layer is formed near the sea surface.

The radiation scheme and the land surface scheme developed for the climate model of Meteorological Research Institute has been introduced to the model. The radiation scheme is based on MRI/JMA98 GCM (Shibata et al. 1999). The treatment of land surface has been improved from SiB (Simple Biosphere model), mainly in soil and snow schemes. In the soil scheme, the 3 layers for the soil water equation are shared with the heat budget equation and the phase changes of water are included, so that the water and energy can conserve in the soil layers. It also has the 4th layer as a heat buffer. In the snow scheme, the number of snow layers varies up to 3 depending on the snow amount, and the heat and water fluxes are calculated. Snow albedo depends on the snow age (Aoki et al. 2003).

Our simulations were performed at a triangular truncation 959 with linear Gaussian grid (TL959) in the horizontal, in which the transform grid uses 1920 x 960 grid cells, corresponding to the grid size of 20 km. The model uses 60 levels in the vertical with the model top at 0.1hPa. If we use an Eularian scheme of the same horizontal resolution, we need a time step less than about 1 minute to satisfy the CFL criterion. But the time step we use in this study is 6 minutes since we use a semi-Lagrangian scheme so the time step is not constrained by the criterion.

Experimental Implementation

Simulation Period

1. a present climate simulation using the observed climatological sea surface temperature (SST) as boundary conditions (10 years; named as "AJ")
2. a global warming simulation forced by climatological SST plus anomalies around the year of 2090 obtained from atmosphere-ocean coupled model simulations (10 years; named as "AK")

Ocean Surface Boundary Conditions

Radiative Boundary Conditions

Spinup/Initialization

Earth Orbital Parameters

Calendar

Orography/Land-Sea Mask

Atmospheric Mass

Computer/Operating System

Computational Performance

Model Output Description

Calculation of Standard Output Variables

• Method for calculation of percentage time that a pressure surface is below ground: a one (1) is set when a pressure surface is below ground and a zero (0) is set when a pressure surface is above ground. The percentage time is calculated by accumulating this value at every time step.

• The total moisture tendency includes that due to moist convection, large-scale precipitation and vertical diffusion.

• Method for calculation of cloud properties:  The sum of cloud water and ice is stored (see Cloud and Large-scale Precipitation), but the extinction coefficient (cloud optical thickness/layer depth) and cloud emittance are not.

• Method for calculation of surface variables:  When the specified height of canopy top, which depends on vegetation types, exceeds  the defined height of the surface variable (10 m for wind, 2 m for specific humidity and temperature), values in the canopy space are assigned to the surface quantities. Otherwise, on the assumption of a logarithmic distribution in neutral stability, the surface variables are interpolated between the values at the lowest atmospheric level (at 995 hPa for a surface pressure of 1000 hPa) and the canopy space values.

• The recommended method of Trenberth et al. (1993) is used for calculation of mean sea-level pressure.

• The recommended method of Potter et al.(1992) is used for calculation of clear-sky radiation and cloud radiative forcing.

• Potential vorticity is not calculated.

• The planetary boundary layer (PBL) height is not explicitly computed.

Sampling Procedures

Interpolation Procedures

• Variables are interpolated to constant pressure surfaces at every time step, (see Time Integration Scheme(s)) and then are time-averaged to obtain monthly mean data.

• Variables on pressure surfaces below ground are not calculated.

Output Data Structure/Format/Compression

Model Characteristics

Model Lineage

Numerical/Computational Properties

Horizontal Representation

Horizontal Resolution

Spectral triangular 959 (linear grid; TL959), in which the transform grid uses 1920 x 960 grid cells, corresponding to the grid size of 20 km.

Vertical Representation

Vertical Resolution

Time Integration Scheme(s)

• A two-time-level semi-implicit semi-Lagrangian scheme is used for the time integration (cf. Yoshimura 2003; Yoshimura 2005).

• A fixed time step length ( 6 minutes ) is used for dynamics and physics, except for radiation/cloud calculations.

• Shortwave radiation and cloud properties are recalculated hourly, and longwave radiation every 1 hour (but with corrections made at every time step for diurnal variations in the shortwave fluxes and in the surface upward longwave fluxes).

 

Smoothing/Filling

• Orography is truncated at the model's spectral TL959 horizontal resolution. (see Orography/Land-Sea Mask)

• Spurious negative values of atmospheric specific humidity (due to truncation errors in the discretized moisture equation) are reset to zero

Dynamical/Physical Properties

Equations of State

Diffusion

• The representation of horizontal diffusion is as follows:

• A linear fourth-order (del**4) horizontal diffusion is applied to vorticity, divergence and temperature on the hybrid sigma-pressure surfaces in spectral space.

 
• In order to reduce spurious mixing along steep mountain slopes, a first-order correction to approximate diffusion on constant pressure surfaces is also applied to the temperature equations.

 
• Diffusion coefficients are chosen so that the enstrophy power spectrum coincides with that expected from two-dimensional turbulence theory.

• The representation of vertical diffusion is as follows:

• Within the PBL (see Planetary Boundary Layer), a stability-dependent local formulation of the vertical diffusion of momentum, heat and moisture follows the level-2 turbulence closure scheme of Mellor and Yamada (1974) [14].

 
• The vertically variable diffusion coefficient depends on stability (bulk Richardson number) after Blackadar (1962) [15], following standard mixing-length theory.

Gravity Wave Drag

• Orographic gravity wave drag is parameterized by two schemes that differ mainly in the vertical partitioning of the momentum drag, depending on the wavelength of the gravity waves (cf. Iwasaki et al. (1989) [18] for further details).

• Longwaves (wavelengths >100 km) are assumed to exert drag mainly in the stratosphere (type A scheme), and shortwaves (wavelengths approximately 10 km) only in the troposphere (type B scheme). In both schemes,  gravity wave stress is a function of atmospheric density, wind, the Brunt-Vaisalla frequency and subgrid-scale orographic variance obtained from the 5'x5' U.S. Navy data set (see Orography/Land-Sea Mask). For the type B scheme, orographic variance is calculated as an average difference of maximum and minimum heights within each 5'x5' grid box.

• Since the momentum drag (stress) due to shortwave gravity waves decreases with height as a result of nonhydrostatic effects (cf. Wurtele et al. 1987 [17]), the type B scheme assumes the wave stress to be quadratic in pressure and to vanish near the tropopause.  In the type A scheme, the vertical structure of the momentum stress induced by gravity waves is simulated after a modified Palmer et al. (1986) [16] amplitude saturation hypothesis.

Chemistry

• The carbon dioxide concentration is the AMIP II-specified value of 348 ppmv.

• Monthly averaged zonal ozone distributions are specified from data of McPeters et al.(1984).

• Radiative effects of water vapor, but not of aerosols, are also included (see Radiation).

Radiation

• A delta-two-stream approximation is used to represent short wave radiation. Spectral intervals, the data for k-distribution and absorption cross section and optical parameters for  clouds are taken from Briegleb (1992). The absorption gases and bands are ozone (O₃) ultraviolet and visible region (8 intervals in the 0.2-0.7 micron band), water vapor near-infrared region (7 intervals in the 0.5-5.0 micron band), carbon dioxide (CO₂, 2.7 and 4.3 micron) and O₂ (A and B bands). Delta-two-stream calculation for transmission and reflection are implemented by the discrete ordinate method (Shibata and Uchiyama, 1992).
• The  multi-parameter random model (Shibata and Aoki, 1989) is used to represent long-wave radiation. Four spectral intervals (20-550, 550-800, 800-1200 and 1200-2200 cm^-1) and five gases are treated. Water vapor is treated in all the intervals with continuum absorption, while carbon dioxide (CO₂,15 micron) and ozone (O₃, 9.6 micron) absorption are included in the second and third spectral intervals, respectively. In addition nitrous oxide (N₂O, 7.8 micron) and methane (CH₄,7.6 micron) absorption are incorporated in the fourth spectral interval. Full- and half-level temperatures are calculated from the prognostic layer-mean temperature profile (cf. Shibata and Uchiyama, 1994) and a two-grid noise suppression scheme is included in the integration of transmission function (Shibata, 1989). In the longwave, cloud is treated as a gray body, with optical depth proportional to the cloud water path.
• Aerosol

Convection

• The cumulus convection scheme proposed by Arakawa and Schubert (1974) is implemented. The vertical profile of the upward mass flux is assumed to be a linear function of height, as proposed by Moorthi and Suarez (1992). The mass flux at the cloud base is determined by solving a prognostic equation (Randall and Pan 1993; Pan and Randall 1998).
• Cumulus parameterization scheme is improved to include the entrainment and detrainment effects between the cloud top and cloud base in convective downdraft instead of reevapolation of convective precipitation (Nakagawa and Shimpo 2004). This reduces cooling bias in the tropical lower troposphere of the model as cooling by the reevapolation is reduced.

Cloud and Large-scale Precipitation

• Clouds are prognostically determined in a similar fashion to that of Smith (1990). A simple statistical approach proposed by Sommeria and Deardorff (1977) is adopted for the calculation of the cloud amount and the cloud water content. The parameterization of the conversion rate from cloud water to precipitation follows the scheme proposed by Sundqvist (1978).
• Cloud ice fall scheme based on an analytically integrated solution by Rotstayn (1997) is introduced (Kawai 2003) instead of rather simple parameterization in which cloud ice falls only into the next layer or to the ground. The prognostic cloud scheme is modified to reduce dependence of precipitation on the integration time step. In order to represent subtropical marine stratocumulus off the west coasts of the continents, a new stratocumulus parameterization scheme is introduced following a simple and classical one proposed by Slingo (1987) with some modifications (Kawai 2004). Cloud is formed in the model when there is inversion at the top of boundary layer and mixing layer is formed near the sea surface.

Planetary Boundary Layer

• Above the surface layer, the Mellor and Yamada (1974) [14] level-2 turbulence closure scheme is used to determine effects of vertical diffusion of heat, momentum, and moisture. The PBL top is not explicitly determined.

Sea Ice

• The AMIP II monthly sea ice extents and concentrations are used as the model's sea ice, with intermediate daily values determined by linear interpolation (see Ocean Surface Boundary Conditions).  Because the model does not account for fractional sea ice, a threshold concentration of 55 % is used to determine whether a grid cell is covered by sea ice.

• Sea ice is assumed to have a constant depth of 2 m, and the ocean temperature below the ice is specified to be that for sea ice formation (about -2 degrees C). The surface temperature of the ice is prognostically determined by the force-restore method of Deardorff (1978) [38]. The forcing includes the net energy balance of surface heat fluxes as  well as the conduction heating from the ocean below.  Snow does not accumulate on sea ice.

Snow Cover

• Precipitation may fall as snow if the temperature at the lowest atmospheric level is less than the freezing point temperature of water (273.16 K). Snow depth (measured in meters of equivalent liquid water) is prognostically determined from a budget equation that accounts for accumulation (allowed only on land surfaces) and melting. Snow melt (which contributes to soil moisture) may occur if either the ground temperature exceeds the freezing point or the heat quantity of rainfall on snow exceeds the latent heat of fusion.

• A snow density value of 0.2 is used for conversion of snow mass to snow depth (water equivalent). Fractional snow coverage of a grid square without vegetation is given by the ratio of snow depth to a critical water-equivalent depth (0.02 m), or is set to unity if the snow depth exceeds this critical value. In a vegetation-covered grid square, critical depth varies with vegetation type and coverage.

• Sublimation of snow does not contribute to the total surface evaporative flux. Roughness of the surface decreases with increasing snow depth, the minimum value being 5 % of the snow-free value.  Snow cover also alters the surface albedo and the heat capacity/conductivity of the soil.  See also Surface Characteristics.

Surface Characteristics

• In the model, surface types are distinguished as ocean, sea ice, continental ice, bare ground (desert, half desert, tundra) and  vegetated ground, where the vegetation types of the Simple Biosphere (SiB) model of Sellers et al. (1986) [39] are specified at monthly intervals.

• Over open ocean,  roughness lengths for the surface momentum flux are determined from variable surface wind stress after the method of Charnock (1955) [40], while those for surface heat and moisture fluxes are specified as a constant 1.52 x 10^-4 m (cf. Kondo 1975 [41]). Roughness length over sea ice is prescribed as a uniform 1 x 10^-3 m.  Spatially varying roughness lengths over land vary monthly according to the seasonal changes in vegetation (cf. Dorman and Sellers 1989 [42]) and are altered by snow cover.

• Wavelength-independent surface albedos over open ocean are prescribed to be 0.12347 for the direct-beam (with sun overhead) and 0.0419 for the diffuse-beam component of radiation; the direct-beam albedo varies with the solar zenith angle. Albedo of sea ice (independent of the solar zenith angle) is a constant 0.80 for the UV/visible and 0.40 for the near-infrared spectral intervals. Over land, albedo is a function of the solar zenith angle and is independent of the soil moisture; it is specified separately for the UV/visible and near-infrared spectral intervals. Snow-free land albedos vary monthly according to the seasonal changes in vegetation (cf. Dorman and Sellers 1989 [42]). Following Sellers et al. (1986) [39], land albedo is altered by snow cover; it is an average of the background albedo and the snow albedo, weighted by the fractional snow coverage. Snow albedo (maximum 0.80 for UV/visible and 0.40 for near-infrared) varies depending on the snow mass temperature (see Snow Cover).

• Longwave emissivity is prescribed to be unity (blackbody emission) for those sufaces without vegetation. There is graybody longwave emission (emissivity less than unity) from the vegetated surfaces.

Surface Fluxes

• For the treatment of surface radiative fluxes, albedo and longwave emissivity are determined for the distinguished surface types (see Surface Characteristics).

• Treatment of turbulent eddy fluxes of surface momentum, heat and moisture follows the Monin-Obukhov similarity theory implemented as bulk formulae. The required values of wind, temperature and humidity are taken to be the values at the lowest atmospheric level. Over land, surface moisture flux includes evapotranspiration from the dry vegetation (reflecting the presence of stomatal and canopy resistances) as well as direct evaporation from the wet canopy and bare soil. See also Land Surface Processes.

• Following Louis et al. (1981) [43], turbulent drag coefficients depend on the vertical stability and surface roughness (see Surface Characteristics). Whenever surface temperature and humidity are determined over land, the surface roughness of grassland is applied to calculations for all surface types.

Land Surface Processes

• The treatment of land surface has been improved from SiB (Simple Biosphere model), mainly in soil and snow schemes. In the soil scheme, the 3 layers for the soil water equation are shared with the heat budget equation and the phase changes of water are included, so that the water and energy can be conserved in the soil layers. It also has the 4th layer as a heat buffer. In the snow scheme, the number of snow layers varies up to 3 depending on the snow amount, and the heat and water fluxes are calculated. Snow albedo depends on the snow age (Aoki et al. 2003).

Parameter Tunings

All the settings in the physical parameterizations were 'tuned' for the original version of the model with 300 to 60 km mesh resolutions. If the settings were applied to 20 km mesh resolution without any modification, many problems arose from characteristics depending on resolution. For instance, 1) amount of global average precipitation increased, 2) temperature at tropical upper troposphere became higher, and 3) cloud amount decreased as the horizontal resolution got higher. In addition to these resolution dependences, resolution independent characteristics of the model which did not need to be considered at lower resolutions became conspicuous; convection was obviously less organized in meso-beta scale than observation and frequency of tropical cyclones generation was less than observation. Therefore, some parameterizations of sub-grid scale physical processes were adjusted in order to reduce these biases. We tried several sets of the adjustments described below but we could not do systematic parameter sweep experiments due to constraint of computation resources and time schedule.

Inhomogeneity of field variables (e.g., temperature, water vapor, etc.) in a certain large (say, 300 km) area would increases with higher resolution, even though the area-mean values do not change. Evaporation is therefore increases since it is a function of square of wind speed. So we estimate 10% less evaporation in the TL959 model than in the other resolution models. The amount of precipitation, however, is not changed so much since negative feedback works against the modification.

On the other hand, as the resolution becomes finer, deviation from grid-mean value becomes smaller than in a coarser resolution. Therefore, assumed sub-grid variance of water vapor is set to be 10% smaller in the cloud scheme of the TL959 model. This modification decreases the over-estimated condensation and prevents instability from dissolving too fast, resulting in promoting organization of convection. This is effective also in decreasing the resolution dependence of global average precipitation.

Detrainment of cloud water at the top of cumulus convection is increased. Transformation speed from cloud water to precipitation is decreased in the cloud scheme. These are implemented in order that cumulus and layer cloud increase and resolution dependence decreases. These are also effective in decreasing the amount of global average precipitation. Values of parameters are selected so that the radiation balance is consistent with observations.

Among a number of modifications implemented in the physical processes of the TL959 model, the most effective one for improving the representation of tropical cyclones is to decrease the vertical transport of horizontal momentum in the convection scheme. The ensemble effect of the convective momentum transport is generally downgradient and acts to reduce the vertical wind shear of tropical cyclones. When a convectivescale pressure gradient force (Wu and Yanai 1993; Gregory et al. 1999) is not included in the convection scheme, the downgradient momentum transport is over-estimated, which weakens tropical cyclones excessively. Therefore, as a simple approximation of the effect of the pressure gradient force, we reduce the estimation of the effect of the momentum transport by 60 percent, resulting in more realistic organization of tropical cyclones.

The surface roughness length over the ocean is set to be larger in order to enhance thermal interaction between sea surface and boundary layer. This also improves the representation of tropical cyclones. Gravity wave drag coefficient for short waves is increased in order to control excessive developments of extratropical cyclones.

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