ECMWF Coupled Model Documentation

The atmospheric component is the ECMWF NWP IFS model version 23r4. The dynamical framework is based on the spectral method with a truncation of T95. In the physical space it uses a linear reduced Gaussian grid, with a horizontal spacing of 1.8° (about 200 km resolution). There are 40 sigma levels in the vertical. The model has a two-time level semi-Lagrangian advection scheme (Ritchie et al., 1995) and runs with a time step of one hour. It includes a stochastic representation of the indeterminacy of parametrizations (Buizza et al., 1999), also known as "stochastic physics". A complete documentation for the the 23r4 cycle is available following this link. In short, the model uses the rapid radiative transfer model for longwave radiation (Morcrette et al, 1998); the shortwave radiation scheme developed in Fouquart (1987); a tiling scheme for the surface boundary with 8 different tiles, a subgrid-scale orographic drag scheme (Lott and Miller, 1996); a bulk mass flux scheme for cumulus convection (Tiedtke, 1989) considering deep, shallow and mid-level convection, taking also into account convective momentum transport; a set of prognostic equations for cloud liquid water/ice and cloud fraction and diagnostic relations for precipitation to describe cloud and large-scale precipitation processes (Tiedtke, 1993); and a separate treatment of precipitation into cloudy and clear-sky contributions (Jakob and Klein, 1999) with maximum overlap (Jakob and Klein, 2000). It also includes a tiling scheme for the surface boundary with up to 6 different tiles over land and a comprehensive surface scheme with four layers in the soil (Viterbo and Beljaars, 1995; van den Hurk et al., 2000). More detailed information about the physical parametrizations can be found in Gregory et al. (2000).

The ocean model is based on the HOPE general ocean circulation model (Wolff et al., 1997). The zonal grid spacing is 1.4° in the zonal direction, while the meridional spacing ranges from 0.3° near the equator (within 10° around the Equator) smoothly increasing polewards up to 1.4° close to 30°. It has 29 vertical levels (15 of these in the top 200 m). The vertical mixing parameterization follows Peters et al. (1988). It uses a time step of one hour and an explicit barotropic solver with a time step of 15 seconds (Killworth et al., 1991). The sea-ice in the model solution is strongly relaxed to prescribed values, consisting of the initial sea-ice anomaly damped on a 60-day time scale towards the SSMI climatology.

The coupling between ocean and atmosphere is done using a version of OASIS (Terray et al., 1995). The models exchange fields once per day, the atmosphere being driven with SST and sea-ice, and the ocean being driven with heat, momentum and moisture fluxes. Solar radiation, needed to calculate the effect of penetrative radiation within the upper layers of the ocean, is passed separately from the other heat fluxes.

Prescribed atmospheric fluxes from ERA40 force the ocean model to generate ocean initial conditions. A strong relaxation to observed SSTs (Reynolds 2D-Var SST) is also present. A univariate statistical interpolation method (Smith et al., 1991), with a 10 day data window is used to assimilate ocean temperature data (Alves et al., 2002). Salinity is changed beneath the surface layer in order to preserve the T-S relationship (Troccoli et al., 2001). There is a weak relaxation to Levitus98 subsurface climatological salinity and temperature. There is also a geostrophic correction applied to velocity following a density change (Burgers et al., 2002).

The ocean model was initialised from an ocean at rest with January climatological temperature and salinity, and spun up for 4 years forced with monthly mean climatological wind stress, before starting the forcing with ERA40.

Alves, O., M. Balmaseda, D. Anderson and T. Stockdale, 2002. Sensitivity of dynamical seasonal forecasts to ocean initial conditions. ECWMF Technical Note No. 369.

Buizza, R., M. Miller and T. N.Palmer, 1999. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 2887-2908.

Burgers, G., M. Balmaseda, F. Vossepoel, G. J. van Oldenborgh and P. J. va Leeuwen, 2002. Balanced ocean-data assimilation near the equator. J. Phys. Oceanogr., in press.

Fouquart, Y., 1987. Radiative transfer in climate modeling. NATO Advanced Study Institute on Physically-Based Modeling and Simulation of Climate and Climate Changes, Erice, Sicily, 11-23 May 1986, M.E. Schlesinger, Ed., 223-283.

Gregory, D., J. J. Morcrette, C. Jakob, A. C. M. Beljaars and T. Stockdale, 2000. Revision of convection, radiation and cloud schemes in the ECMWF Integrated Froecasting System. Q. J. Roy. Meteor. Soc., 126, 1685-1710.

Janssen, P. A. E. M., J. D. Doyle, J. Bidlot, B. Hansen, L. Isaksen and P. Viterbo, 2002. Impact and feedback of ocean waves on the atmosphere. in Advances in Fluid Mechanics, Atmosphere-Ocean Interactions, Vol. I, WITpress, Ed. W.Perrie. 155-197.

Jakob, C. and S. A. Klein, 1999. The role of vertically varying clod fraction in the parametrization of microphysical processes in the ECMWF model. Quart. J. Roy. Meteor. Soc., 125, 941-965.

Jakob, C. and S. A. Klein, 2000. A parametrization of the effects of cloud and precipitation overlap for use in general circulation models. Quart. J. Roy. Meteor. Soc., 126, 2525-2544.

Killworth, P. D., D. Stainforth, D. J. Webb, and S. M. Paterson, 1991. The development of a free-surface Bryan-Cox-Semtner ocean model. J. Phys. Oceanogr., 21, 1333-1348.

Lott, F. and Miller, M. J., 1996. A new subgrid-scale orographic drag parametrization: Its formulation and testing, Q. J. R. Meteorol. Soc., 123, 101-127.

Morcrette, J. J., S. A. Clough, E. J. Mlawer and M. J. Iacono, 1998. Impact of a validated radiative transfer scheme, RRTM, on the ECMWF model climate and 10-day forecasts. ECMWF Technical Memo No. 252, 47 pp.

Peters H., M. C. Gregg and J. M. Tool, 1988. On the parameterization of Equatorial turbulence. J. Geophys. Res., 93,1199-1219.

Ritchie, H., C. Temperton, A. J. Simmons, M. Hortal, T. Davies, D. Dent and M. Hamrud, 1995. Implementation of the semi-Lagrangian method in a high resolution version of the ECMWF forecast model. Mon. Wea. Rev., 123, 489-514.

Smith, N., J. Blomley and G. Meyers, 1991. A univariate statistical interpolation scheme for subsurface thermal analyses in the tropical oceans. Prog in Oceanography, 28,219-256.

Terray, L., E. Sevault, E. Guilyardi and O. Thual, 1995. OASIS 2.0, user's guide and reference manual. Technical report. [Available from CERFACS, France].

Tiedtke, M., 1993. Representation of clouds in large-scale models. Mon. Weather Rev., 121, 3040-3061.

Troccoli, A. M. Balmaseda, J. Sigschneider, J. Vialard and D. Anderson, 2002. Salinity adjustments in the presence of temperature data assimilation. Mon. Wea. Rev., 130, 89-102.

van den Hurk, B.J.J.M., P.Viterbo, A.C.M. Beljaars and A.K. Betts, 2000: Offline validation of the ERA40 surface scheme. ECMWF Tech Memo 295, 42 pp.

Viterbo, P., and A.C.M. Beljaars, 1995: An improved land surface parametrization scheme in the ECMWF model and its validation. J. Climate, 8, 1061 2716-2748.

Wolff, J. E., E. Maier-Reimer and S. Legutzke, 1997. The Hamburg Ocean Primitive Equation Model. Deutsches Klimarechenznetrum, Hamburg, Technical Report No. 13.