Physical parametrizations

In global atmospheric models, subgrid-scale parametrisations describe the effect of unresolved processes on the resolved scale (e.g. surface exchange, convection, gravity wave drag, vertical diffusion), but also describe diabatic effects such as radiation and water phase changes. The IFS has a comprehensive package of sub-grid parametrization schemes representing radiative transfer, convection, clouds, surface exchange, turbulent mixing, sub-grid-scale orographic drag and non-orographic gravity wave drag.

At the sea surface, the IFS has a two-way coupling to the WAve Model (WAM, Hasselmann et al., 1988). The coupling with WAM is performed every time step whereby WAM is forced by IFS 10-metre wind speeds while the IFS is forced by surface roughness over sea.

The operational IFS version also includes the NEMO ocean model, though this is not available in OpenIFS.

Radiation

The radiation spectrum is divided into a long-wave part (thermal infrared) and a short-wave part (solar radiation).  The radiation scheme performs computations of the short-wave and long-wave radiative fluxes using the predicted values of temperature, humidity, multi-layer clouds, surface long-wave emissivity and short-wave albedo, and monthly-mean climatologies for aerosols and the main trace gases (CO₂, O₃, CH₄, N₂O, CFCl₃ and CF₂Cl₂).  The cloud-radiation interaction is dealt with in considerable detail using the values of cloud fraction, an assumed multi-layer cloud overlap, and liquid, ice and snow water contents from the cloud scheme.  Solving the radiative transfer equations to obtain the fluxes is computationally expensive.  So, depending on the model configuration, full radiation calculations are performed on a reduced (coarser) grid and on a reduced time frequency (about 6-10 times fewer points and at intervals of 1 hour for HRES and 3 hours for other model configurations).  Additionally, the short-wave fluxes are updated at every grid point and time-step using solar radiation values modified by path length through the model atmosphere due to the varying solar zenith angle.  The fluxes are then interpolated back to the original grid.  However, a more efficient and computationally economic radiation scheme has been introduced (in Cycle 43R3) benefiting from reduced noise and more accurate long-wave radiation transfer calculations.  The new scheme, ecRad, is 30%-35% faster than the old one

Coastal temperatures can be affected by incorrect allocation of radiation flux.  Surface radiative fluxes computed over the ocean may incorrectly be used over adjacent land where the surface temperature (‘skin temperature’) and surface albedo differ greatly from those at sea.  This can lead to large near-surface temperature errors at coastal land points. To combat this, surface long-wave and shortwave fluxes are updated at every model time step and grid point according to the local skin temperature and albedo.

• ecRAD home page.
• Read more on the radiation scheme or for a brief overview of Radiation within the IFS atmospheric physics (navigate to "Radiation").
• See the article "reducing surface temperature errors at coastlines".

Convection

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).The convection scheme does not predict individual convective clouds, only their physical effect on the surrounding atmosphere in terms of latent heat release, precipitation and the associated transport of moisture and momentum. The scheme differentiates between deep, shallow and mid-level convection but only one type of convection can occur at any given grid point at any one time. In the current configuration of the convection scheme within IFS, while the effects of convection (changes to the temperature or humidity) drift downwind, any (convective) precipitation that is developed is considered to remain within the column and fall vertically downwards instantaneously (i.e. taking zero time to reach the surface). This means that showers are not advected with the wind during their life-cycle. The effect is particularly evident in wintertime when the showers developed over the sea do not penetrate beyond the coast while in reality active showers move well inland. The errors are greater in colder airmasses producing wintry precipitation because snowflakes fall more slowly than raindrops and thus advect further in the wind before reaching the ground.

For a measure of convection intensity the Convective Available Potential Energy (CAPE) is evaluated from the IFS model atmosphere. At any given grid point the convection scheme inspects the temperature and humidity structure progressively from the surface to 300hPa and if there exists a level of free convection (LFC) it evaluates the CAPE. Entrainment of surrounding air is not considered and thus the CAPE is likely to be a slight overestimate. The technique currently in use for estimating CAPE allows for the discovery of elevated instability, even at night, despite low-level stability. Convective Inhibition (CIN) is assessed from the IFS model atmosphere in a similar way. CAPE and CIN are computed and provided as (MARS) output parameters in order to help the user assess the likelihood of severe convective storms. CAPE-shear is a combination of bulk shear (vector wind shear in the lowest 6km of the atmosphere) and CAPE and is used to identify areas of potentially extreme convection.

An increase in the amount of super-cooled liquid water held by the convection scheme at colder temperatures (down to -38C) was introduced in mid 2017 in Cycle 43R3, to improve the development of convective precipitation. New ways of forecasting the degree of sub-grid variability in precipitation totals have also been developed (Point Rainfall). Future updates to the IFS may allow some of the convective precipitation (mainly as snow) to be advected downstream into adjoining grid boxes.

Additional Sources of Information

(Note: In older material there may be references to issues that have subsequently been addressed)

• Read more on the Convective Scheme in the atmospheric physics page (scroll down to "Convection") or in "Breakthrough in Forecasting Convection".
• Read more on atmospheric moist convection.
• Watch a comprehensive lecture on model physics (30sec delay before video begins).  The convection parameterisation scheme is considered in the lecture between 36min45sec and 40min45sec.  Penetration inland of showers is considered in the lecture between 45min20sec and 46min50sec.
• Watch a comprehensive lecture on model clouds and precipitation (25sec delay before video begins).

Clouds

Stratiform cloud processes

Turbulent diffusion

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.

Orographic drag

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).

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).