Voeikov Main Geophysical Observatory (MGO)

• AMIP representatives: Dr. Valentin Meleshko, Dr. Vadim Matyugin.
• Mail address: Voeikov Main Geophysical Observatory, 7 Karbyshev Str., 194021 St. Petersburg, Russia
• Phone numbers: +7(812)247-4390, +7(812)247-8668
• Fax number: +7(812)247-8661
• Internet e-mail addresses: meleshko@main.mgo.rssi.ru ,  matyugin@main.mgo.rssi.ru
• Internet World Wide Web Address: http://www.mgo.rssi.ru

 

End of Contact Information

• Start time  00Z 1 January 1979.
• Stop  time  00Z 1 January 1996.

The AMIP II specifications are approximated as follows: the obliquity and the longitude of perihelion depend on date according to Montenbruck and Pfleger (1993). Their average values are correspondingly 23.441 and 102.7 degrees. The eccentricity is 0.016715.

• Relevant references:

Montenbruck, O. and T. Pfleger, 1993: Astronomija s personalneim kompjuterom. Izdatelstvo Mir, Moskva, 280 pp. (English translation, 1994: Astronomy on the Personal Computer. Springer Verlag, Heidelberg, 2nd edition).

As recommended for AMIP II, a realistic calendar with leap years in 1980, 1984, 1988, 1992, and 1996 is used.
 

• As specified for AMIP II, the solar constant is 1365 Wm^-2 (with both seasonal and diurnal cycles present).

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

• The monthly ozone concentration is the recommended zonal mean climatology of Wang et al. (1995) interpolated to the model grid.

•  Global vertical profiles for methane and nitrous oxide are specified from U.S. Standard Model Atmosphere Profiles (cf. AFGL 1986). In the lower and middle troposphere, the concentrations are 1700 ppbv for methane, and 320 ppbv for nitrous oxide. 

• Global vertical profiles for CFC-11 and CFC-12 are specified after Fabian (1989). In the lower and middle troposphere, the concentrations are 190 pptv for CFC-11, and 350 pptv CFC-12.  

• The aerosol concentration is specified separately for continental and maritime points as a background value, without seasonal variation, after WCRP (1986). See also Chemistry.

• Relevant references:

Wang, W.-C., X.-Z Liang, M.P. Dudek, D. Pollard, S.L. Thompson, 1995: Atmospheric ozone as a climate gas. Atm. Res., 37, 247-256.

AFGL, 1986: AFGL Atmospheric Constituent Profiles (0-120 km). AFGL-TR-86-0110, Environmental Research Papers, 954.

Fabian, P., 1989: Proposed Reference Models for CO2 and Halogenated Hydrocarbons. In: Reference Models of Trace Species. MAP Handbook, 31, 99-108.

WCRP, 1986: A preliminary cloudless standard atmosphere for radiation computation. WCP, 112, World Meteorological Organization, 62 pp.

The AMIP II sea surface temperature (SST) and sea ice boundary conditions are those derived by Taylor et al. (1997)  from observational data of Fiorino (1997).

• As recommended, these boundary conditions, obtained from PCMDI, are spatially interpolated at the model's horizontal resolution and temporally interpolated to daily values, so as to preserve monthly means.

Fractional values of sea ice less than 0.05 are set to zero.  If sea ice is present,  the AMIP II SSTs are not used for calculating the radiative and turbulent fluxes from the underlying ocean; instead, a value of -1.8 deg C (the melting temperature of sea ice) is prescribed.
 

• The model orography is derived from the AMIP-recommended U.S. Navy 10' x 10' data set, and its global-average height is 237.204 m. The raw data are averaged over the model grid boxes. A special filter (cf. Hoskins 1980) is also applied. The orography is truncated at the model's spectral T30 horizontal resolution . Sub-gridscale orographic variances, which impact the parameterization of gravity wave drag, are also computed from the raw data.

• The land-sea mask  is derived from the U.S. Navy data set. Each grid box of the mask is expressed as a percentage of land.   Fractional values of land less than 0.05 are set to zero and fractional values of land more than 0.95 are set to unity.

• Relevant references:

Hoskins, B.J., 1980: Representation of the earth's topography using spherical harmonics. Mon. Wea. Rev., 108, 111-115.

The global-average value of model surface pressure is 982.15 hPa. This value, which corresponds to the dry + wet mass of the atmosphere remains unchanged throughout the model run.
 

Procedure for spin-up of the model to quasi-equilibrium at the nominal starting time of 00Z 1 January 1979.

• The MGOAM2 model was run from 00Z 1 February 1978 to 00Z 1 January 1979 using additional spinup SSTs provided by PCMDI.

• The initial dump used for the start of this spinup was an operational analysis for 00Z 3 January 1987 (i.e. from a 14-level, horizontal resolution model analysis).

• The snow depth was taken from model climatology for  1 February 1978,  and then was allowed to evolve during the spinup.

• The start conditions for soil moisture/temperature(s) for 1 February 1978 were taken from the model climatology.

The AMIP II experiment was run on a PC 600MHz Pentium 3  using 1 processor, under a version of LINUX.

To simulate 1 day, the AMIP II experiment required about 2.6 minutes.
 

• Temperature tendency due to total diabatic heating.
  
• Temperature tendencies due to short-wave and long-wave radiation.
• Temperature tendency due to moist convection.
• Temperature tendency due to dry convectionav.
• Temperature tendency due to large-scale/stratiform precipitation.
• Total moisture tendency due to diabatic processes.

• Calculation of cloud properties:
• cloud water/ice (if applicable): available at model levels.
• extinction coefficient (cloud optical thickness/layer depth): not available.
• cloud emittance: not available.

• Calculation of surface variables (following the procedure of Hess and McAvaney, 1995):
• 10 m winds
• 2m specific humidity
• 2m temperature

• Calculation of mean sea-level pressure (following the ECMWF algorithm, (Trenberth et al., 1993)).

• Calculation of clear-sky radiation and cloud radiative forcing (following the procedure of Potter et al., 1992).
  

• Calculation of potential vorticity: not available.

• Calculation of planetary boundary layer height: not available.

• Relevant references

Boer, G.J., 1986: A comparison of mass and energy budgets from two FGGE datasets and a GCM., Mon. Wea. Rev., 114, 885-902.

Hess, G. D., R.A. Colman and B.J. McAvaney, 1995: On computing screen temperatures, humidities and anemometer-height winds in large-scale models.  Aust. Met. Mag., 44, 139-145.

Potter, G. L., J. M. Slingo, J.-J. Morcrette, and L. Corsetti, 1992: A modeling perspective on cloud radiative forcing. J. Geophys. Res, 97, 20,507-20,518.

Trenberth, K. E., J. C. Berry and L. E. Buja, 1993: Vertical Interpolation and Truncation of Model-Coordinate Data. NCAR Tech.Note. NCAR/TN-396+STR, 54 pp.

Monthly means are calculated by accumulating model diagnostics (at every time step or every 6 hours, depending on  AMIP II Guidelines) over each day of the calendar month, then dividing by the number of days in that month.

• Algorithm for interpolation of standard output variables to 17 WMO pressure surfaces: Model-level dynamical variables and their products fields are interpolated to the WMO pressure surfaces following Trenberth et al., 1993 and then  monthly averages computed. Because of their highly nonlinear character, model-level cloud variables are not interpolated to the WMO pressure surfaces.

• Algorithm for treatment of variables at pressure surfaces below ground: ECMWF algorithm, (Trenberth et al., 1993).

• Relevant references:

 Trenberth, K. E., J. C. Berry and L. E. Buja, 1993: Vertical Interpolation and Truncation of Model-Coordinate Data. NCAR  Tech.Note. NCAR/TN-396+STR, 54 pp.

• The output data were supplied to PCMDI in netCDF format, organized according to the LATS data structure.

MGO MGOAM2 (T30L14) 2001
 

The model is descended from MGO AMIP92 (T30L14), used in AMIP I.
 

http://www-pcmdi.llnl.gov/projects/modeldoc/amip/01toc.html

Key documentation of model features is provided by  Shneerov at al. 2001, Shneerov et al. 1999 and Shneerov at al. 1997 and related reference citations.

• Relevant references

Shneerov, B. E., V. P. Meleshko, A.P. Sokolov, D. A. Sheinin, V. A. Lyubanskaya, P. V. Sporyshev, V. A. Matyugin, V. M. Kattsov, V. A. Govorkova and T. V. Pavlova, 1997: MGO Global Atmosphere General Circulation and Upper Layer Ocean Model.Trudy GGO (MGO Proc.), No 544, 3-123 (in Russian).

Shneerov, B. E., V. P. Meleshko, P. V. Sporyshev,  V. A. Matyugin, T. V. Pavlova, V.M. Gavrilina and V.A. Govorkova, 1999: MGO Atmospheric Global Circulation Model: Current state. Trudy GGO (MGO Proc.), No 547, 15-36 (in Russian).

Shneerov, B. E., V. P. Meleshko, V. A. Matyugin, P. V. Sporyshev, T. V. Pavlova, S. V. Vavulin, I. M. Shkol'nik, V. A. Zubov, V. M. Gavrilina and V. A. Govorkova, 2001: The up-tu-date version of the MGO global model of general circulation of the atmosphere (version MGO-2). Trudy GGO (MGO Proc.), No 550, 3-43 (in Russian).

• Representation of horizontal diffusion:

Del⁴ horizontal diffusion is applied to vorticity, divergence, potential temperature and specific humidity on sigma surfaces for all spectral wave numbers (cf. Laursen and Eliasen 1989).

• Representation of vertical diffusion above the surface layer:

Stability-dependent vertical diffusion of atmospheric momentum, temperature and specific humidity is modeled (cf. Louis 1979)

• Relevant references:                  

Laursen L. and E. Eliasen, 1989: On the effects of the damping mechanisms in an atmospheric general circulation model. Tellus, V. 41A., pp. 385-400.        

Louis, J.-F., 1979: A parametric model of vertical eddy fluxes in the atmosphere. Bound. Layer Meteor, 17, 187-202.

• Radiatively active constituents, in addition to water vapor/clouds, include carbon dioxide, ozone, methane, nitrous oxide, CFC-11, CFC-12, and aerosols.  See also Radiative Boundary Conditions.
  

• The shortwave fluxes are computed in two subranges: ultraviolet/visible (0.2 - 0.75 microns, 4 spectral bands), and near-infrared (0.75 - 4.0 microns, 12 spectral bands). In the UV/visible, absorption by ozone is treated by the two-stream delta-Eddington approximation. In the near-infrared, absorption by water vapor in 12 bands and by carbon dioxide in 6 bands is determined using a Goody statistical band model (cf. Rozanov and Frolkis 1988). Rayleigh scattering by air molecules, and scattering/absorption by a climatological background aerosol and cloud droplets in both subranges are accounted for using the delta-Eddington approximation (cf. Shneerov et al. 1997 and 2001 for details). See also Chemistry and Radiative Boundary Conditions.
• The longwave fluxes are computed in 9 spectral bands with the following boundaries (units of m^-1): 0-28000; 28000-48000; 48000-58000; 58000-76000 (excluding 64000-70000); 64000-70000; 76000-98000; 98000-106000; 106000-130000; and 130000-220000. The absorption by water vapor, carbon dioxide, ozone, trace gases, and background aerosol (see Chemistry) are taken into account. The gas absorption computation is based on the Goody statistical band model, and its parameters depend on temperature (cf. Karol 1986). Water vapor continuum absorption is calculated by the method of Roberts et al. (1976).

• In the shortwave, cloud optical properties (optical depth, single-scattering albedo and asymmetry factor) are parameterized separately for water and ice. For water, the cloud optical prorerties are functions of condensed water content and effective droplet radius according to Slingo (1989). The effective radius of cloud droplets is fixed at 10 microns. For ice, the cloud optical prorerties are linked to cloud ice/water path and local temperature according to Platt (1989), and Platt and Harshvardhan (1988). For mixed phase clouds, a smooth transition from water to ice clouds is assumed. Clouds are treated as graybodies in the longwave. Cloud emissivity depends on cloud ice/water path. Random overlapping is assumed for the clouds located in different layers. Cf. Shneerov et al. (1997, 2001) for further details. See also Cloud Formation. 

• Relevant references:

Shneerov, B. E., V. P. Meleshko, A.P. Sokolov, D. A. Sheinin, V. A. Lyubanskaya, P. V. Sporyshev, V. A. Matyugin, V. M. Kattsov, V. A. Govorkova and T. V. Pavlova, 1997: MGO Global Atmosphere General Circulation and Upper Layer Ocean Model.Trudy GGO (MGO Proc.), No 544, 3-123 (in Russian).

Shneerov, B. E., V. P. Meleshko, V. A. Matyugin, P. V. Sporyshev, T. V. Pavlova, S. V. Vavulin, I. M. Shkol'nik, V. A. Zubov, V. M. Gavrilina and V. A. Govorkova, 2001: The up-tu-date version of the MGO global model of general circulation of the atmosphere (version MGO-2). Trudy GGO (MGO Proc.), No 550, 3-43 (in Russian).

Rozanov, E.V. and Frolkis V.A., 1988: Method of radiation fluxes calculation in the near infrared range. Trudy GGO (MGO Proc.), No. 516, 61-71 (in Russian).

Karol, I.L. (ed)., 1986: Radiative-Photochemical Atmospheric Models. Gidrometeoizdat, Leningrad, 192 pp (in Russian).

Roberts, R.E., J.A. Selby, and L.M. Biberman, 1976: Infrared continuum absorption by atmospheric water vapor in the 8-12 micron window.

Convection

• A Tiedtke mass flux scheme (Tiedtke, 1989) is used for simulation of deep, shallow and mid-level convection. It is assumed that cumulus clouds embedded in the large-scale environment, have a common cloud base but different heights of tops due to different entrainment and detrainment rates. They are defined by upward and downward mass fluxes and by their thermal properties as dry static energy, moisture and cloud water content. Penetrative convection takes place  when a deep layer of conditional instability and large-scale moisture convergence occur. 

• Shallow convection is largely through surface evaporation as the contributions from large-scale convergence are small or negative. Midlevel convection occurs in the free atmosphere due to advection in the warm sectors of extratropical cyclones. The entrainment/detrainment rates are 0.0001 m^-1  for penetrative and midlevel convection, and 0.0003 m^-1 for shallow convection.

• Relevant references:

Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon.Wea.Rev.,117,1779-1800.

Cloud Formation

• Cloud prediction scheme is based on the algorithm originally suggested by Slingo (1987). Cloud fraction is determined from diagnostic relations. Within each layer of the middle and upper troposphere, cloud fraction depends on the relative humidity, whose threshold values are a function of height. In the low troposphere, the cloud fraction depends on the relative humidity, vertical motion and static stability (cf. Hack et al. 1993). The convective cloud fraction depends on convective precipitation rate. Cf. Shneerov et al. (2001) for further details.

• The cloud ice/water path for ice and mixed phase clouds is determined from the assumption that moist air rises adiabatically in the cloud layer (cf. Betts and Harshvardhan 1987). For the water clouds, the path is determined from empirical relations based on the analysis of satellite observation separately for oceans and land (cf. Tselioudis et al. 1992). Cf. Shneerov et al. (1997, 2001) for further details. See also Radiation.

• Relevant references:

Slingo, J.M., 1987: The development and verification of a cloud prediction model for the ECMWF model. Quart. J. Roy. Meteor. Soc., 113, 899-927.

Hack, J.J., B.A. Boville, B.P. Briegleb, J.T. Kiehl, P.J. Rash and D.L. Williamson, 1993: Description of the NCAR Community Climate Model (CCM2). Tech. Note NCAR/TN-382+STR. Nat. Center for Atmos. Res., Boulder, Colorado, 108 pp.

Betts, A.K. and Hardshvardhan, 1987: Thermodynamic constraint on the cloud liquid water feedback in climate models. J. Geophys. Res., 92, 8483-8485.

Tselioudis, G., W.B. Rossow and D. Rind, 1992: Global patterns of cloud optical thickness variation with temperature. J. Climate, 5, 1484-1495.

Precipitation

• Large-scale precipitation forms when the local relative humidity exceeds 100 percent. Convective precipitation depends on a parameter which specifies conversion rate of cloud droplets into rain droplets. The parameter is currently set to 0.0002. Subsequent evaporation of precipitation is not simulated. See also Snow Cover.

 

Planetary Boundary Layer

Sea Ice

Snow Cover

•  Precipitation may fall as snow if the temperature at the lowest atmospheric level less than 273.16 K. Snow depth is determined from the prognostic value of snow mass and density, assumed to be 200 kg/m3. Snow melts if the ground temperature exceeds 273.16 K
• Fractional snow coverage of a grid square is given by the ratio of snow depth to a critical water-equivalent depth (0.025 m), or is set to unity if the snow depth exceeds this critical value.
• Sublimation of snow does not contribute to the total surface evaporative flux. Snow cover affects the surface albedo of land and sea ice , (see Surface Characteristics) as well as the heat capacity/conductivity  (see Land Surface Processes) of the soil. See also Surface Characteristics.
  

Surface Characteristics

• Surface types include land, ocean, sea ice, and permanent land ice. Different surface types may coexist in a grid box. Every snow-free gridbox over land is conceptually divided in three fractions: fraction coverd by interception reservoir, dry vegetation fraction, dry bare soil fraction (cf. Viterbo and Beljaars,1995).  Geographically varying fields of vegetation parameters (vegetation fraction, forest index, leaf area index) are prescribed after Claussen et al. (1994).

• The roughness length over land is a prescribed function of orography and vegetation type (cf. Louis 1984). Over ice surfaces, the roughness length is uniformly specified as 0.01 m. Over the oceans, the dependence of roughness length on surface wind stress follows Charnock (1955), with a dimensionless factor of 0.018 (cf. Ariel and Murashova 1981). For a surface wind speed <1 m/s, a minimum ocean roughness is determined from an asymptotic relationship (cf. Zilitinkevich 1970).

• Annual mean surface albedos are prescribed for bare soil and vegetation after data of Hagemann et al. (1999). The albedo of snow-covered land depends on the annual mean background albedo, the liquid snow equivalent, and the maximum snow albedo. The surface albedo of open ocean is a function of solar zenith angle according to Briegleb et al. (1986). The albedo of glacial ice on Greenland and Antarctica is 0.80, while the albedo of sea ice is a function of surface temperature (cf. Wilson and Mitchell 1987). 

• Longwave emissivity is prescribed as unity (blackbody emission) for all surfaces.

Relevant references

Hageman, S., Botzet, M., Dumenil, L. and Machenhauer, B., 1999: Derivation of global GCM boundary conditions from 1 km land use satellite data. MPI Report No.289, 1-34.

Briegleb, B.P., Minnis, P., Ramanathan, V., and Harrison, E., 1986: Comparison of regional clear-sky albedos inferred from satellite observations and model computations. J. Climate Appl. Meteor., Vol.25, 214-226.

Claussen M., Lohmann U., Roeckner E., 1994: A global data set of land-surface parameters.
Max-Planck-Institut fur Meteorologie. Report No.135, 23 p.

Viterbo, P. and A.C.M.Beljaars. 1995: An improved land surface parameterization scheme in the ECMWF model and its validation. J. Climate, Vol. 8. P. 2716-2748.

Louis, J.F. (ed.), 1984: ECMWF forecast model physical parameterisation. Research Manual No. 3, European Centre for Medium-Range Weather Forecasts, Reading, England.

Charnock, H., 1955: Wind stress on a water surface. Quart. J. Roy. Meteor. Soc., 81, 639-640.

Ariel, N.Z., A.V. Murashova, 1981: Computing the adjusted nomograms for resistance coefficients of heat and moisture exchange above the sea. Trudy GGO (MGO Proc.). No 454, 9-23 (in Russian).

Zilitinkevich, S.S., 1970: Dynamics of the Atmospheric Boundary Layer. Gidrometeoizdat, Leningrad, 292 pp (in Russian).

Wilson, M.F., and A. Henderson-Sellers, 1985: A global archive of land cover and soils data sets for use in general circulation models. Int. J. Climatology, 5, 119-143.

Wilson, C.A., and J.F.B. Mitchell, 1987: A doubled CO2 climate sensitivity experiment with a global climate model including a simple ocean. J. Geophys. Res., 92, 13315-13343.

Surface Fluxes

Land Surface Processes

• Land-surface processes are simulated following Shneerov at al. (2001).
  

• Soil temperature is computed using a four-layer model having the following layer thicknesses 0.1, 0.25, 0.65, and 2.0m, from top to bottom. Heating of the soil is computed from a heat conductivity equation with the net surface heat fluxes as the upper boundary condition and zero heat flux assumed at the lower boundary of the bottom soil layer. The thermodynamic properties of the soil are assumed to be spatially uniform. The thermodynamic properties of the upper layer depend on snow depth. When snow melts, the surface temperature remains constant (273.16K) until all snow disappears.

• Soil hydrology is based on a finite difference form of water diffusivity equation with the same vertical discretization as for soil thermodynamics. The hydraulic diffusivity is a function of soil water content (cf. Viterbo and Beljaars,1995). Soil moisture changes due to infiltrated precipitation and snowmelt, evapotranspiration and direct evaporation. Both surface runoff and subsurface runoff are also considered. Surface runoff depends on rain or snowmelt rate, soil moisture content of upper layer and soil water storage capacity. Drainage from each layer depends on its water content (cf. Dümenil and Todini (1992)). The water drained from upper layer goes to the next (lower) layer and increases the water content of the latter. Drainage from the lowest layer is computed assuming that its water content cannot exceed the field capacity (cf. Meleshko et al. 1991).

• Precipitation interception by the canopy, evapotranspiration from the dry vegetation are included (cf. Viterbo and Beljaars,1995). The rooting depth for transpiration is assumed to be 1 meter for all vegetation types except forest, where rooting depth is assumed to be 3 meter.Transpiration is limited by aerodynamic and canopy resistance, that depends on photosynthetically active radiation (PAR) and soil moisture availability (cf. Sellers et al., 1986).Geographical distribution of soil water-holding capacity is prescribed after Patterson (1990)
  

Relevant references

Shneerov, B. E., V. P. Meleshko, V. A. Matyugin, P. V. Sporyshev, T. V. Pavlova, S. V. Vavulin, I. M. Shkol'nik, V. A. Zubov, V. M. Gavrilina and V. A. Govorkova, 2001: The up-tu-date version of the MGO global model of general circulation of the atmosphere (version MGO-2). Trudy GGO (MGO Proc.), No 550, 3-43 (in Russian).

Meleshko, V.P., A.P. Sokolov, D.A. Sheinin, V.A. Lyubanskaya, P.V. Sporyshev, V.A. Matyugin, B.E. Shneerov, V.A. Govorkova, and V.M. Kattsov, 1991: An atmospheric general circulation/mixed layer ocean model for climate studies and long range weather forecasts. Meteorologia i Hydrologia, 5. (In Russian, with English translation also available.)

Claussen M., Lohmann U., Roeckner E., 1994: A global data set of land-surface parameters.
Max-Planck-Institut fur Meteorologie. Report No.135, 23 p.

Patterson K.A.,1990: A global distribution of total and total-available soil wa-ter-holding capacities
M.S. thesis. Dept. of Geography, University of Delaware. 119 p.

Viterbo, P. and A.C.M.Beljaars. 1995: An improved land surface parameterization scheme in the ECMWF model and its validation. J. Climate, Vol. 8. P. 2716-2748.

Dümenil, L., Todini E.,1992: A rainfall-runoff scheme for use in the Hamburg climate model / Ed.O'Kane. Advances in theoretical hydrology, a tribute to James Dooge, V.1 of European Geophysical Society on Hydrological Sciences. Elsevier, Amsterdam. P. 129-157

Roeckner E., K.Arpe, L.Bengtsson, M.Christoph, M.Claussen, L.Dumenil, M.Esch,
M.Giorgetta, U.Schlese, U.Schulzweida. 1996: The Atmospheric General Circulation Model ECHAM-4: Model Description and Simulation of Present-day Climate. Report MPI No. 218. 90 p.

Sellers, P.J., Y. Mintz, Y. C. Sud, and A. Dalcher, 1986: A simple Biosphere Model (SiB)
for use within general circulation models. J. Atmos. Sci., 43, 505-531.

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Last update 19 December 2003