**Model Information of Potential Use to the IPCC Lead Authors and the
AR4.**

**BCCR-BCM2.0**

15 June 2005

# Model identity:

A.  Institution, sponsoring agency, country

> Bjerknes Centre for Climate Research (BCCR), Univ. of Bergen, Norway

B.  Model name (and names of component atmospheric, ocean, sea ice, etc.
    models)

> Bergen Climate Model (BCM) Version 2
>
> Atmosphere/land: ARPEGE-CLIMAT Version 3
>
> Ocean/sea ice: NERSC largely modified version of MICOM Version 2.8,
> with NERSC Sea Ice Model

C.  Vintage (i.e., year that model version was first used in a published
    application)

> 2005

D.  General published references and web pages

> [www.bcm.uib.no](http://www.bcm.uib.no/)
>
> www.bjerknes.uib.no

E.  References that document changes over the last \~5 years (i.e.,
    since the IPCC TAR) in the coupled model or its components. We are
    specifically looking for references that document changes in some
    aspect(s) of model performance.

> Not member of IPCC TAR

F.  IPCC model version\'s global climate sensitivity (KW^-1^m^2^) to
    increase in CO~2~ and how it was determined (slab ocean expt.,
    transient expt\--Gregory method, ±2K Cess expt., etc.)

> Not available yet

G.  Contacts (name and email addresses), as appropriate, for:

    1.  coupled model

> Asgeir Sorteberg
> ([asgeir.sorteberg@bjerknes.uib.no](mailto:asger.sorteberg@bjerknes.uib.no))
>
> Helge Drange (<helge.drange@nersc.no>)

2.  atmosphere

> Nils Gunnar Kvamstø (<nilsg@gfi.uib.no>)
>
> Asgeir Sorteberg
> ([asgeir.sorteberg@bjerknes.uib.no](mailto:asger.sorteberg@bjerknes.uib.no))
>
> Ina Thorstensen Kindem (<ina@gfi.uib.no>)
>
> Frode Flatøy (frode@gfi.uib.no)

3.  ocean

> Helge Drange (<helge.drange@nersc.no>)
>
> Tore Furevik (<tore@gfi.uib.no>)
>
> Mats Bentsen (mats.bentsen@nersc.no)

4.  sea ice

> Helge Drange (<helge.drange@nersc.no>)
>
> Mats Bentsen (mats.bentsen@nersc.no)

5.  land surface

> Nils Gunnar Kvamstø (<nilsg@gfi.uib.no>)
>
> Asgeir Sorteberg
> ([asgeir.sorteberg@bjerknes.uib.no](mailto:asger.sorteberg@bjerknes.uib.no))

6.  vegetation

> Nils Gunnar Kvamstø (<nilsg@gfi.uib.no>)
>
> Asgeir Sorteberg
> ([asgeir.sorteberg@bjerknes.uib.no](mailto:asger.sorteberg@bjerknes.uib.no))

7.  other?

# Besides atmosphere, ocean, sea ice, and prescription of land/vegetated surface, what can be included (interactively) and was it active in the model version that produced output stored in the PCMDI database?

A.  atmospheric chemistry?

No

B.  interactive biogeochemistry?

> No

C.  what aerosols and are indirect effects modeled?

> The distributions of marine, desertic, urban aerosols, sulfate
> aerosols are specified. Marine and desertic aerosols are constant in
> all experiments. Urban aerosols vary according to estimates between
> 1850 and 2000. Sulfate aerosols are specified in all experiments
> according to Boucher and Pham (2002) data, see
> <http://www-loa.univ-lille1.fr/~boucher/sres/> for more details. Note
> that only the direct effect of anthropogenic sulfate aerosols was
> taken into account.

D.  dynamic vegetation?

> No

E.  ice-sheets?

> Fixed

# List the community based projects (e.g., AMIP, C4MIP, PMIP, PILPS, etc.) that your modeling group has participated in and indicate if your model results from each project should carry over to the current (IPCC) version of your model in the PCMDI database.

AMIP (older version)

CMIP (older version)

PMIP 6k BP simulation with former version of ARPEGE-Climat (older
version)

PILPS with the ISBA land surface scheme (same as in the IPCC)

# Component model characteristics (of current IPCC model version):

A.  Atmosphere

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1.  resolution

> T63L31: A triangular truncation T63 with "linear" reduced Gaussian
> grid equivalent to T42 quadratic grid (2.8 °). See: Hortal and
> Simmonds (1991) for description of the linear reduced grid.

2.  numerical scheme/grid (advective and time-stepping schemes; model
    top; vertical coordinate and number of layers above 200 hPa and
    below 850 hPa)

> Semi-lagrangian semi-implicit time integration with 30 min time-step,
> 3 hour time-step for radiative transfer; top layer 10 hPa, progressive
> hybrid sigma-pressure vertical coordinate with 31 layers.

3.  list of prognostic variables (be sure to include, as appropriate,
    liquid water, chemical species, ice, etc.). Model output variable
    names are not needed, just a generic descriptive name (e.g.,
    temperature, northward and eastward wind components, etc.)

4.  name, terse descriptions, and references (journal articles, web
    pages) for all major parameterizations. Include, as appropriate,
    descriptions of:

    a.  Clouds:\
        - statistical cloud scheme for stratiform clouds based on Ricard
        and Royer (1993). Convective cloud cover based on the mass-flux
        transport

    b.  Convection\
        - mass-flux convective scheme with Kuo-type closure based on
        Bougeault (1985) boundary layer based on Louis et al. (1982)
        with modifications by Mascart et al. (1995). SW, LW radiation
        based on Fouquart and Morcrette parameterizations implemented in
        a former version of the ECMWF model (Morcrette JJ, 1990;
        Morcrette JJ, 1991)

    c.  any special handling of wind and temperature at top of model:\
        - relaxation of temperature, linear (Rayleigh) friction for wind

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B.  Ocean

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1.  resolution

> The model has 35 vertical layers and approximately square horizontal
> grid cells with 1.5° grid spacing along the equator. Near the equator
> the meridional grid spacing is gradually decreased to 0.5° at the
> equator.

2.  numerical scheme/grid, including advection scheme, time-stepping
    scheme, vertical coordinate, free surface or rigid lid, virtual salt
    flux or freshwater flux

> Numerical grid: Arakawa C-grid.
>
> Momentum equation: PV-conserving (Sadourny 1975).
>
> Layer thickness advection: Flux corrected transport (Zalesak 1979).
>
> Tracer advection: MPDATA (Smolarkiwicz, 1984).
>
> Time-stepping: Leapfrog with Asselin time filtering.
>
> Vertical coordinate: Isopycnic interior layers with slab surface mixed
> layer.
>
> Free surface: Yes.
>
> Uses virtual salt flux.

3.  list of prognostic variables and tracers

> Prognostic variables: Horizontal velocity components, layer thickness,
> temperature and salinity.

4.  name, terse descriptions, and references (journal articles, web
    pages) for all parameterizations. Include, as appropriate,
    descriptions of:

    a.  eddy parameterization

> Horizontal:
>
> Tracer: Laplacian diffusion with diffusivity proportional to grid
> spacing.
>
> Momentum: Combination of deformation dependent eddy viscosity
> (Smagorinsky 1963) and Laplacian diffusion with diffusivity
> proportional to grid spacing.
>
> Layer interfaces: Biharmonic diffusion with diffusivity proportional
> to grid spacing qubed.
>
> Vertical:
>
> Background mixing with diffusivity dependent on stability (Gargett
> 1984)
>
> Shear instability mixing (Large et at. 1994)
>
> Gravity current entrainment (Turner 1986)
>
> Momentum mixing with Prandtl number 10.

b.  bottom boundary layer treatment and/or sill overflow treatment

> Bottom friction scaled with near bottom velocity squared.

c.  mixed-layer treatment

> Mixed layer depth modified according to the mixed layer turbulence
> parameterization of Gaspar (1988).

d.  sunlight penetration

> Uses parameters for red light surface absorbtion and blue light
> extinction assuming Jerlov type 1 water.

e.  tidal mixing

> NA

f.  river mouth mixing

> NA

g.  mixing isolated seas with the ocean

> NA

h.  treatment of North Pole \"singularity\" (filtering, pole rotation,
    artificial island?)

> There are no grid singularities in the ocean domain. Grid
> singularities are placed over Antarctica and Siberia.

C.  sea ice

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1.  horizontal resolution, number of layers, number of thickness
    categories

> Grid consists of 4 grid cells pr ocean grid cell.
>
> One ice layer and one snow layer.
>
> Single thickness category.

2.  numerical scheme/grid, including advection scheme, time-stepping
    scheme,

> Numerical grid: Arakawa B-grid.
>
> Momentum equation: Daily updated steady state balance of forces.
>
> Advection of concentration, ice thickness, snow and ice age using
> 3^rd^ order weighted essentially non-oscillatory scheme (Jiang and
> Shu, 1996) with 2^nd^ order Runge-Kutta time discretization.

3.  list of prognostic variables

> Prognostic variables: Velocity, concentration, ice thickness, snow
> thickness, ice age.

4.  completeness (dynamics? rheology? leads? snow treatment on sea ice)

> Rheology: Viscous-plastic (Hibler III, 1979) with modifications by
> Harder (1996).
>
> Minimum lead fraction: 0.5%.
>
> Assumes linear temperature profile in ice and snow.
>
> Salinity dependent freezing temperature.
>
> Ocean heat flux parameterized according to Maykut and McPhee (1995).
>
> Snow is converted to ice due to aging and when flooded.
>
> Minimum ice thickness of 0.5 m (Arctic)/0.3 m (Antarctic) at initial
> freezing.

5.  treatment of salinity in ice

> Assumes constant salinity of 6 psu in sea ice.

6.  brine rejection treatment

> Brine salt flux absorbed in mixed layer.

7.  treatment of the North Pole \"singularity\" (filtering, pole
    rotation, artificial island?)

> There are no grid singularities in the ocean domain. Grid
> singularities are placed over Antarctica and Siberia.

D.  land / ice sheets (some of the following may be omitted if
    information is clearly included in cited references.

> The land surface scheme ISBA (Interactions Soil Biosphere Atmosphere)
> developed initially by Noilhan and Planton (1989) has been updated and
> described by Mahfouf et al. (1995).

1.  resolution (tiling?), number of layers for heat and water

> Hor. Res.: same as atmospheric grid. 4 soil layers for heat and 2 for
> water

2.  treatment of frozen soil and permafrost

> Yes

3.  treatment of surface runoff and river routing scheme

> The TRIP dataset (Oki and Sud, 1998) has been used to connect land
> grid cells to ocean runoff discharge coast grid cells. Runoff is
> stored in a reservoir which is drained with an e-folding time scale of
> 7 days.

4.  treatment of snow cover on land

> Prognostic scheme calculating: water equivalent in snowpack, snow
> surface albedo and snowpack density (Douville et al., 1995a, 1995b)

5.  description of water storage model and drainage

> Force-restore approach
>
> Deep drainage as a relaxation toward field capacity + a residual
> subgrid drainage below field capacity
>
> VIC-type subgrid runoff

6.  surface albedo scheme

> Ocean:
>
> Constant albedo of 0.065 for diffusive light, zenith angle dependent
> albedo for direct light using a functional fit to observations of
> Payne (1972).
>
> Sea ice:
>
> 0.85 with snow, 0.75 with melting snow, ice thickness dependent albedo
> for bare ice with maximum albedo of 0.70 for frozen ice and 0.60 for
> melting ice.
>
> Land:
>
> snowfree albedo from ECOCLIMAP (Masson et al. 2003)
>
> prognostic snow albedo (Douville et al. 1995a)
>
> diagnostic snow fraction as a function of snow mass, vegetation
> roughness length and subgrid orography (Douville et al. 1995a and
> 1995b)

7.  vegetation treatment (canopy?)

> prescribed vegetation map and prescribed phenology from ECOCLIMAP
> (Masson et al. 2003)
>
> no carbon cycle

8.  list of prognostic variables:

> 4-layer soil temperatures
>
> 2-layer soil hydrology (liquid and solid)
>
> 1-layer snow hydrology (prognostic snow mass, snow albedo and snow
> density)
>
> 1 interception reservoir (liquid water on the canopy)

9.  ice sheet characteristics (How are snow cover, ice melting, ice
    accumulation, ice dynamics handled? How are the heat and water
    fluxes handled when the ice sheet is melting?)

> Ice sheet are represented simply by prescribing initially a huge snow
> amount and applying to it the same snow parameterization as over the
> land surface.
>
> Ice dynamics is not included

E.  coupling details

> Coupling between atmosphere, ocean and sea ice models is done through
> the OASIS 2.2 coupler developed at CERFACS. Note that the sea ice
> model is included in the ocean model. For a reference of the coupling
> software, see Terray et al. (1998).

1.  frequency of coupling

> Once per day.

2.  Are heat and water conserved by coupling scheme?

> Yes.

3.  list of variables passed between components:

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a.  atmosphere -- ocean

> Solar heat flux, non-solar heat flux, derivative of non-solar heat
> flux with respect to surface temperature, evaporation, liquid
> precipitation, solid precipitation, zonal wind stress, meridional wind
> stress, cloud cover

b.  atmosphere -- land

> Internal treatment in atmosphere model.

c.  land -- ocean

> Runoff.

d.  sea ice -- ocean

> Internal treatment in ocean/sea ice model

e.  sea ice -- atmosphere

> Surface temperature, ice concentration, albedo.

f.  ocean -- atmosphere

> Surface temperature, albedo, surface velocities.

4.  Flux adjustment? (heat?, water?, momentum?, annual?, monthly?).

> None.
>
> References:
>
> Bougeault, P. (1985): A simple parameterization of the large-scale
> effects of cumulus convection, Mon. Wea. Rev. 113, 2108-2121
>
> Déqué M., Dreveton, C., Braun, A., and Cariolle, D. (1994): The
> ARPEGE/IFS atmosphere model : A contribution to the French community
> climate modelling. Climate Dyn., 10, 249-266.
>
> Déqué, M. and J.P. Piedelievre (1995): High resolution climate
> simulation over Europe. Climate Dyn., 11, 321-339.
>
> Douville H, Royer JF, Mahfouf JF (1995a): A new snow parameterization
> for the Meteo-France climate model, Part I: Validation in stand-alone
> experiments. *Climate Dyn*, **12**, 21-35.
>
> Douville H, Royer JF, Mahfouf JF (1995b): A new snow parameterization
> for the Meteo-France climate model, Part II: Validation Validation in
> a 3D GCM experiment. *Climate Dyn*, **12**, 37-52.
>
> Gargett, A. E., 1984: Vertical eddy diffusivity in the ocean interior.
> J. Mar. Res., **42**, 359--393.

Gaspar, P., 1988: Modeling the seasonal cycle of the upper ocean. J.
Phys. Oceanogr., **18**, 161--180.

> Harder, M., 1996: Dynamik, Rauhigkeit und Alter des Meereises in der
> Arktis. Ph.D. thesis, Alfred-Wegener-Institut f¨ur Polar- und
> Meeresforschung, Bremerhaven, Germany. 124 pp.
>
> Hibler III, W. D., 1979: A dynamic thermodynamic sea ice model. J.
> Phys. Oceanogr., **9**, 815--846.
>
> Hortal A. and Simmonds A. J., 1991: Use of reduced Gaussian grids in
> spectreal models. Mon. Wea. Rev, **119**, 1057-1074
>
> Jiang, G.-S. and C.-W. Shu, 1996: Efficient implementation of weighted
> ENO schemes. J. Comput. Phys., **126**, 202--228.
>
> Large,W. G., J. C. McWilliams, and S. C. Doney, 1994: Oceanic vertical
> mixing: A review and a model with a nonlocal boundary layer
> parameterization. Rev. Geophys., **32**, 363--403.
>
> Mahfouf, J.-F. et al. (1995): The land surface scheme ISBA within the
> Météo-France climate model ARPEGE. Part I: Implementation and
> preliminary results. J. Climate 8, 2039-2057
>
> Mascart P., Noilhan, J. and Giordani, H. (1995): A modified
> parameterization of flux profile relationships in the surface layer
> using different roughness length values for heat and momentum. Bound.
> Layer Met. 72, 331-344
>
> Masson, V. et al. (2003) : A global database of land surface
> parameters at 1-km resolution in meteorological and climate models, J.
> Climate, 16, 1261-1282
>
> Maykut, G. A. and M. G. McPhee, 1995: Solar heating of the Arctic
> mixed layer. J. Geophys. Res., **100**(C12), 24,691--24,703.
>
> Morcrette, J.-J. (1990): Impact of changes to the radiation transfer
> parameterizations plus cloud optical properties in the ECMWF model.
> Mon. Wea. Rev., 118, 847-873
>
> Morcrette, J.-J. (1991): Radiation and cloud radiative properties in
> the European Centre for Medium Range Forecasts forecasting system, J.
> Geophys. Res. 95, 9121-9132
>
> Louis et al. (1982): A short history of the operational
> PBL-parameterization at ECMWF. In: ECMWF Workshop on Planetary
> Boundary Layer Parameterization, ECMWF, Reading, pp 59-80
>
> Noilhan, J. and Planton, S. (1989): A simple parameterization of land
> surface processes for meteorological models, Mon. Wea. Rev. 117,
> 536-549
>
> Oki, T. and Y. C. Sud, 1998: Design of Total Runoff Integrating
> Pathways (TRIP) - A global river channel network. Earth Interactions,
> **2**.
>
> Payne, R. E., 1972: Albedo of the Sea Surface. J. Atmos. Sci., **29**,
> 959--970.
>
> Ricard, J.-L. and Royer, J.-F. (1993): A statistical cloud scheme for
> use in a AGCM, Ann. Geophysicae 11, 1095-1115.
>
> Royer, J.-F., D. Cariolle, F. Chauvin, M. Déqué, H. Douville, R.M. Hu,
> S. Planton, A. Rascol, J.-L. Ricard, D. Salas y Mélia, F. Sevault, P.
> Simon, S. Somot, S. Tytéca, L. Terray, S. Valcke (2002): Simulation
> des changements climatiques au cours du 21-ième siècle incluant
> l\'ozone stratosphérique. C. R. Geophys., 334, 147-154.
>
> Sadourny, R., 1975: The dynamics of finite-difference models of the
> shallow-water equations. J. Atmos. Sci., **32**, 680--689.
>
> Smagorinsky, J. S., 1963: General circulation experiments with the
> primitive equations. I: The basic experiment. Mon. Wea. Rev., **91**,
> 99--164.
>
> Smolarkiewicz, P. K., 1984: A fully multidimensional positive definite
> advection transport algorithm with small implicit diffusion. J.
> Comput. Phys., **54**, 325--362.
>
> Turner, J. S., 1986: Turbulent entrainment: the development of the
> entrainment assumption, and its application to geophysical flows. J.
> Fluid. Mech., **173**, 431--471.
>
> Zalesak, S. T., 1979: Fully multidimensional flux-corrected transport
> algorithms for fluids. J. Comput. Phys., **31**, 335--362.

# Simulation Details (report separately for each IPCC simulation contributed to database at PCMDI):

**Picntrl/Run_1**

> This preindustrial control simulation was initialized from an ocean at
> rest with temperature and salinity profiles specified from Levitus
> (1982). The BCM was then integrated for 80 year with preindustrial
> 1860 greenhouse gases concentrations as a spin-up. After this spin-up
> period results have been stored from years 1850 to 2100 (250 years)

**20C3M/Run_1**

This simulation was initialized from January/1850 of the Picntrl/Run_1
simulation

> Solar forcing was set at 1368 W/m2 and no solar or volcanic
> variability were included. The greenhouse gases annual global
> concentrations were specified based on observations as specified in
> the ENSEMBLES project webpage
> (http://www.cnrm.meteo.fr/ensembles/public/results/results.html).
> Sulfate aerosols are specified according to Boucher and Pham (2002)
> data (<http://www-loa.univ-lille1.fr/~boucher/sres/>). Results are
> given for the years 1850 to 2000

**SRESA2/Run_1**

This simulation was initialized from January/2000 of the 20C3M/Run_1
simulation.

> The greenhouse gases annual global concentrations were specified based
> on scenario SRES A2 as specified in the ENSEMBLES project webpage
> (<http://www.cnrm.meteo.fr/ensembles/public/results/results.html>).
> Sulfate aerosols are specified according to Boucher and Pham (2002)
> data (<http://www-loa.univ-lille1.fr/~boucher/sres/>). Results are
> given for years 2000 to 2100.

**SRESA1B/Run_1**

> This simulation was initialized from January/2000 of the 20C3M/Run_1
> simulation. The greenhouse gases annual global concentrations were
> specified based on scenario SRES A1B as specified in the ENSEMBLES
> project webpage
> (http://www.cnrm.meteo.fr/ensembles/public/results/results.html).
> Sulfate aerosols are specified according to Boucher and Pham (2002)
> data (<http://www-loa.univ-lille1.fr/~boucher/sres/>). Results are
> given for years 2000 to 2300.

**SRESB1/Run_1**

This simulation was initialized from January/2000 of the 20C3M/Run_1
simulation. The greenhouse gases annual global concentrations were
specified based on scenario SRES B1 as specified in the ENSEMBLES
project webpage
(http://www.cnrm.meteo.fr/ensembles/public/results/results.html).
Sulfate aerosols are specified according to Boucher and Pham (2002) data
(<http://www-loa.univ-lille1.fr/~boucher/sres/>). Results are given for
years 2000 to 2300.

**1%to2x/Run_1**

This simulation was initialized from January/2000 of the PIcntrl/Run_1
simulation. This initial state corresponds to year 1860 CO2
concentrations. The concentrations of greenhouse gases are held constant
at preindustrial levels, except for CO2, which increases from its
preindustrial level (286 ppm) at the rate of 1% per year, until the
initial concentration is doubled. From the time of doubling, the
concentrations of all radiative forcing are held constant for 10 years.
Results are given for years 2000 to 2080.