Atmospheric physics is a vital part of a weather forecast model and is often referred to as the physical parametrization. Our research focuses on how to represent unresolved physical processes in the atmosphere, such as radiation, clouds and subgrid turbulent motions.
The physical processes associated with radiative transfer, convection, clouds, surface exchange, turbulent mixing, subgrid-scale orographic drag and non-orographic gravity wave drag have a strong impact on the large-scale flow of the atmosphere. However, these mechanisms are often active at scales smaller than the resolved scales of the model grid. Parametrization schemes are then necessary in order to properly describe the impact of these subgrid-scale mechanisms on the large-scale flow of the atmosphere. In other words the ensemble effect of the subgrid-scale processes has to be formulated in terms of the resolved gridscale variables. Furthermore, forecast weather parameters, such as two-metre temperature, precipitation and cloud cover, are computed by the physical parametrization part of the model.
The radiation scheme performs computations of the short-wave and long-wave radiative fluxes using the predicted values of temperature, humidity, cloud, and monthly-mean climatologies for aerosols and the main trace gases (CO2, O3, CH4, N2O, CFCl3 and CF2Cl2). The radiation code is based on the Rapid Radiation Transfer Model (RRTM, Mlawer et al. 1997; Iacono et al. 2008). Cloud-radiation interactions are taken into account in detail by using the values of cloud fraction and liquid, ice and snow water contents from the cloud scheme using the McICA (Monte Carlo Independent Column Approximation) method (McRad, Morcrette et al. 2008). The solution of the radiative transfer equations to obtain the fluxes is computationally very expensive, so depending on the model configuration, full radiation calculations are performed on a reduced (coarser) radiation grid and/or on a reduced time frequency. The results are then interpolated back to the original grid. However, the short-wave fluxes are updated at every grid point and timestep using values of the solar zenith angle.
- Iacono, M., Delamere, J., Mlawer, E., Shephard, M., Clough, S. and Collins, W. (2008). Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113D, 13103.
- Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. and Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102D, 16663-16682.
- Morcrette, J.-J., Barker, H., Cole, J., Iacono, M. and Pincus, R. (2008). Impact of a new radiation package, McRad, in the ECMWF Integrated Forecasting System. Mon. Wea. Rev., 136, 4773-4798.
The moist convection scheme is based on the mass-flux approach and represents deep (including congestus), shallow and mid-level (elevated moist layers) convection. The distinction between deep and shallow convection is made on the basis of the cloud depth (< 200 hPa for shallow). For deep convection the mass-flux is determined by assuming that convection removes Convective Available Potential Energy (CAPE) over a given time scale. The intensity of shallow convection is based on the budget of the moist static energy, i.e. the convective flux at cloud base equals the contribution of all other physical processes when integrated over the subcloud layer. Finally, mid-level convection can occur for elevated moist layers, and its mass flux is set according to the large-scale vertical velocity. The scheme, originally described in Tiedtke (1989), has evolved over time and amongst many changes includes a modified entrainment formulation leading to an improved representation of tropical variability of convection (Bechtold et al. 2008), and a modified CAPE closure leading to a significantly improved diurnal cycle of convection (Bechtold et al. 2014).
- Bechtold, P., Koehler, M., Jung, T., Leutbecher, M., Rodwell, M., Vitart, F. and Balsamo, G. (2008). Advances in predicting atmospheric variability with the ECMWF model: From synoptic to decadal time-scales. Q. J. R. Meteorol. Soc., 134, 1337-1351.
- Bechtold, P., N. Semane, P. Lopez, J.-P. Chaboureau, A. Beljaars, and N. Bormann (2014). Representing equilibrium and non-equilibrium convection in large-scale models. J. Atmos. Sci.
- Tiedtke, M. (1989). A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779-1800.
Clouds and stratiform precipitation
Clouds and large-scale precipitation are parametrized with a number of prognostic equations for cloud liquid, cloud ice, rain and snow water contents and a sub-grid fractional cloud cover. The cloud scheme represents the sources and sinks of cloud and precipitation due to the major generation and destruction processes, including cloud formation by detrainment from cumulus convection, condensation, deposition, evaporation, collection, melting and freezing. The scheme is based on (Tiedtke 1993) but with an enhanced representation of mixed-phase clouds and prognostic precipitation (Forbes and Tompkins 2011; Forbes et al. 2011). Supersaturation with respect to ice is commonly observed in the upper troposphere and is also represented in the parametrization (Tompkins et al. 2007).
- Forbes, R. M. and Tompkins, A. M. (2011). An improved representation of cloud and precipitation. ECMWF Newsletter No. 129, pp. 13-18.
- Forbes, R. M., Tompkins, A. M. and Untch, A. (2011). A new prognostic bulk microphysics scheme for the IFS. ECMWF Tech. Memo. No. 649.
- Tiedtke, M. (1993). Representation of clouds in large-scale models. Mon. Wea. Rev., 121, 3040-3061.
- Tompkins, A. M., Gierens, K. and Radel, G. (2007). Ice supersaturation in the ECMWF integrated forecast system. Q. J. R. Meteorol. Soc., 133, 53-63.
The surface parametrization scheme represents the surface fluxes of energy and water and corresponding sub-surface quantities. The scheme is based on a tiled approach (TESSEL) representing different sub-grid surface types for vegetation, bare soil, snow and open water. Each tile has its own properties defining separate heat and water fluxes used in an energy balance equation which is solved for the tile skin temperature. Four soil layers are represented as well as snow mass and density. The evaporative fluxes consider separately the fractional contributions from snow cover, wet and dry vegetation and bare soil. An interception layer collects water from precipitation and dew fall, and infiltration and run-off are represented depending on soil texture and subgrid orography (HTESSEL, Balsamo et al. 2009). A carbon cycle is included and land-atmosphere exchanges of carbon dioxide are parametrized to respond to diurnal and synoptic variations in the water and energy cycles (CHTESSEL, Boussetta et al. 2012).
- Balsamo, G., Viterbo, P., Beljaars, A., van den Hurk, B., Hirschi, M., Betts, A. K. and Scipal, K. (2009). A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the Integrated Forecast System. J. Hydrometeorol., 10, 623-643.
- Boussetta, S., Balsamo, G., Beljaars, A., Agusti-Panareda, A., Calvet, J., Jacobs, C., van den Hurk, B., Viterbo, P., Lafont, S., Dutra, E., Jarlan, L., Balzarolo, M., Papale, D. and van der Werf, G. (2012). Natural land carbon dioxide exchanges in the ECMWF Integrated Forecasting System: Implementation and offline validation. ECMWF Tech. Memo. No. 675.
The turbulent diffusion scheme represents the vertical exchange of heat, momentum and moisture through sub-grid scale turbulence. The vertical turbulent transport is treated differently in the surface layer and above. In the surface layer, the turbulence fluxes are computed using a first order K-diffusion closure based on the Monin-Obukhov (MO) similarity theory. Above the surface layer a K-diffusion turbulence closure is used everywhere, except for unstable boundary layers where an Eddy-Diffusivity Mass-Flux (EDMF) framework is applied, to represent the non-local boundary layer eddy fluxes (Koehler et al. 2011). The scheme is written in moist conserved variables (liquid static energy and total water) and predicts total water variance. A total water distribution function is used to convert from the moist conserved variables to the prognostic cloud variables (liquid/ice water content and cloud fraction), but only for the treatment of stratocumulus. Convective clouds are treated separately by the shallow convection scheme.
- Koehler, M., Ahlgrimm, M. and Beljaars, A. (2011). Unified treatment of dry convective and stratocumulus-topped boundary layers in the ECMWF model. Q. J. R. Meteorol. Soc., 137, 43-57.
The effects of unresolved orography on the atmospheric flow are parametrized as a sink of momentum (drag). The turbulent diffusion scheme includes a parametrization in the lower atmosphere to represent the turbulent orographic form drag induced by small scale (< 5 km) orography (Beljaars et al. 2004). In addition, in stably stratified flow, the orographic drag parametrization represents the effects of low-level blocking due to unresolved orography (blocked flow drag) and the absorption and/or reflection of vertically propagating gravity waves (gravity wave drag) on the momentum budget (Lott and Miller 1997).
- Beljaars, A. C. M., Brown, A. R. and Wood, N. (2004). A new parametrization of turbulent orographic form drag. Q. J. R. Meteorol. Soc., 130, 1327-1347.
- Lott, F. and Miller, M. J. (1997). A new subgrid-scale orographic drag parametrization: Its formulation and testing. Q. J. R. Meteorol. Soc., 123, 101-127.
Non-orographic gravity wave drag
The non-orographic gravity wave drag parametrization accounts for the effects of unresolved non-orographic gravity waves. These waves are generated in nature by processes like deep convection, frontal disturbances, and shear zones. Propagating upward from the troposphere the waves break in the middle atmosphere, comprising the stratosphere and the mesosphere, where they exert a strong drag on the flow. The parametrization uses a globally uniform wave spectrum, and propagates it vertically through changing horizontal winds and air density, thereby representing the wave breaking effects due to critical level filtering and non-linear dissipation. A description of the scheme and its effects on the middle atmosphere circulation can be found in Orr et al. (2010).
- Orr, A., Bechtold, P., Scinocca, J. F., Ern, M. and Janiskova, M. (2010). Improved middle atmosphere climate and forecasts in the ECMWF model through a non-orographic gravity wave drag parametrization. J. Climate, 23, 5905-5926.