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.

Additional sources of information:

• 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".

Aerosols & greenhouse gases

The IFS considers the effects of several greenhouse gases and aerosol species that impact forecasts via their interaction with short-wave and long-wave radiation, which can heat or cool the atmosphere and the surface. Aside from water vapour, the greenhouse gases considered are carbon dioxide, ozone, methane, nitrous oxide and four CFC compounds. The aerosol types considered are sea salt, desert dust, organic matter, black carbon, and ammonium sulphate. In the case of sea salt and dust, three different sized particles are represented. Several of the aerosol species are hydrophyilic, which means that they swell as the relative humidity increases. This makes the aerosol more optically thick and can act to reduce the visibility in the model in humid conditions.

A prerequisite to a reliable treatment of the interaction of greenhouse gases and aerosols with radiation is that their global distribution is well represented. There are two different configurations of the IFS that are used operationally, and which predict their global distribution in different ways:

• CAMS forecasts (IFS only). In the model configuration used to produce air-quality forecasts for the Copernicus Atmosphere Monitoring Service (CAMS), including the CAMS reanalysis, gases and aerosols are represented by prognostic variables. This means that they are advected around with the model winds, and source and sink processes are represented. In the case of gases, fluxes from anthropogenic sources, vegetation, wetlands and ocean are all taken into account, as well as chemical reactions between gases. In the case of natural aerosol, the dependence of surface emissions on wind speed is represented (e.g. dust raised by wind and sea salt from breaking waves). Anthropogenic aerosol emissions such as those from urban sources and biomass burning make use of a database of surface sources.  In addition to aerosol particles being advected by the mean wind, they are also affected by vertical diffusion and convective lofting, sedimentation, dry deposition, and wet deposition by large-scale and convective precipitation.
• All other forecasts (OpenIFS/IFS): Since prognostic variables are computationally expensive, in all other configurations of the IFS (HRES, ENS, extended-range and seasonal forecasts, and ECMWF Reanalysis products), gases and aerosols are represented by a monthly-mean climatology. In the case of the different greenhouse gases, the climatology varies with month, latitude and height, but not longitude.  In the case of the various aerosol species, the climatology varies with month, latitude and longitude, but the vertical structure of the aerosol mass mixing ratio follows a simple exponential decrease with height.  For both gases and aerosols, the climatology has been derived from the CAMS reanalysis but with a slight tuning to account for known deficiencies in certain locations of the globe.  Note that before the introduction of IFS Cycle 43R3 in July 2017, an older aerosol climatology was used that considered fewer aerosol species and did not include the dependence of optical properties on relative humidity.  Atmospheric chemistry is not yet included in the IFS.  Users should also be aware aerosol is not considered in the cloud microphysics (e.g. condensation nuclei) within the operational IFS - this may be important for weather is worth stressing although the impact is difficult to assess.

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

• Stratiform Cloud Processes and Precipitation
  • Additional Sources of Information

Stratiform Cloud Processes and Precipitation

In the IFS Stratiform (or Large-scale) Cloud scheme there are five prognostic cloud variables plus water vapour (yellow boxes in Fig2.1.5.3-1) and the modelling of changes of state or precipitation development is represented by various microphysical processes.  The large scale water process is the development of precipitation in the stratiform cloud scheme.  Separate cloud ice and cloud water variables allow the representation of supercooled liquid water and mixed-phase processes, commonly observed in clouds.  All cloud and precipitation variables are advected by the wind.

The precipitation can be either rain or snow or a mix of the two.  Once precipitation is generated it will fall and during descent it will either:

• grow if it enters lower layers of cloud or
• start to evaporate in sub-saturated air.

This process can occur several times before reaching the surface.

The descent of stratiform precipitation follows a pathway according to IFS wind speeds and particulate fall-speeds, so rain and snow precipitation particles can transfer between grid boxes as they descend in the IFS.  This "precipitation drift" is much more pronounced for snow because it has a much slower fall-speed than rain.

Convective precipitation is not treated in the same way (see convective cloud process and precipitation).

 Fig2.1.5.3-1:  Schematic of processes and interactions within the Stratiform Cloud Scheme.  Note: Precipitation is only produced as rain or snow.  Precipitation from stratiform cloud is referred to as "large-scale" precipitation.

Additional Sources of Information

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

• See more information on considerations when interpreting large scale precipitation.
• Read more on clouds and stratiform precipitation in the atmospheric physics page (scroll to "Clouds and Stratiform Precipitation").
• Watch a comprehensive lecture on model clouds and precipitation.

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).

Surface fields and fluxes

Sea-surface temperature (SST) and ice concentration

Sea-surface temperature are initialised using analyses received daily from the Met Office (OSTIA, 5 km resolution).

Snow coverage and snow depth

Snow water equivalent and snow temperature are prognostic variables of the forecasting system.  They are important parameters as they affect all forecast types (medium-range, extended-range and seasonal) and because several fundamental physical properties of snow modulate the energy/water exchanges between the surface and the atmosphere:

• Surface reflectance: impacts upon albedo (snow-albedo feedback).
• Thermal properties: impacts can include the effective de-coupling of heat and moisture transfers.  Snow, especially new dry snow, is a good thermal insulator.
• Phase changes: one impact is a delay in warming during the melt period.

Snow depth is computed using the liquid water equivalent of snow lying on the ground, and the average density of that snow layer (typically lower for fresh snow, higher for old snow due to compaction). Snow depth changes in the model when fresh snow falls or when snow on the ground melts, evaporates or is compressed. At some high-latitude or ‘glacial’ grid points in the model it is common for snow depth to be extremely high. There is no representation of snow on top of sea ice.  Partial snow cover is assumed where snow depth is diagnosed as less than 10cm; total cover is assumed where it is diagnosed as greater than 10cm. Albedo and surface fluxes vary according to the diagnosed snow extent, depth and ground coverage (with account taken also of snow on trees). Snow drifting is not accounted for in the IFS. In windy periods/regions this can play a large (but unrepresented) role in depleting snow depth, with airborne snow tending to sublimate much more readily than does the IFS' equivalent of this (i.e. its undisturbed snow on the ground).

Note that snow depth also depends upon snow density. Snow depths may reduce because the density of the snow is tending to increase through compaction in the model (and also in reality) as the days progress.

Additional sources of information

• Read more on the model snow depth and sea ice and model sea-surface temperature.
• Read more on snow data assimilation or land surface monitoring by satellite (LDAS).

Soil temperatures and soil moisture

Soil temperature and soil water content are prognostic variables of the forecasting system and need to be initialised at each analysis cycle.  However, there is a lack of directly measured observational data of these parameters.  The ECMWF soil moisture, soil temperature and snow temperature analyses rely in particular on an a priori analysis of the model screen level (2m) variables based on the assimilation of SYNOP reports of relative humidity and temperature at screen-level (2m). Surface skin temperature and moisture values are derived from the expected air temperature and moisture structure in the lowest 2m together with energy fluxes (from HTESSEL).  The 2m temperature and humidity are diagnostic parameters of the model, so their analysis only has an indirect effect on atmosphere through the soil and snow variables. 

Soil moisture is a measure of the water content within the ground and is commonly expressed as a percentage of the water that the ground could hold when fully saturated.  Its evaluation and prediction is important as soil moisture governs the efficiency of evapotranspiration from vegetation – too little available moisture and plants die (below the Permanent Wilting Point, PWP) but with increasing soil moisture evapotranspiration efficiency rises to a maximum and plants can flourish best (Field Capacity, CAP).  The soil does not have to be saturated for this to happen, and indeed, as soil moisture increases beyond this, the intrinsic efficiency of plant evapotranspiration will stay the same.  For each soil type and location there is a pre-defined value of the ability to hold moisture and this is used to assess the impact of model rainfall.  The HTESSEL system includes allowance for water capture by interception of precipitation and dew fall, and at the same time, there are infiltration and run-off schemes that take account of soil texture and the standard deviation of sub-grid scale orography.

Note: Cycle 45r1 released in June 2018 introduced a soil ice dependency and amendments to the soil thermal conductivity formulation.

Albedo

Albedo is the fraction of solar energy (shortwave radiation) reflected from the earth back into space; lower values mean more radiation is absorbed at the earth's surface.  Its value depends upon the characteristics of the underlying surface.  In particular, reflectivity is high (and correspondingly a high albedo) where snow cover or sea ice is present.  The albedo varies throughout the forecast period and is determined at analysis and forecast times time via a combination of the background monthly climate, satellite measurements, and observed and forecast surface fields (notably snow depth and extent). Snow cover or ice sheets that are produced by the model also modify the values throughout the forecast.  However, it should be noted that the snow-free land albedo is taken from a climatology from the MODIS satellite and thus is slow to change through the forecast period.  Also albedo is not modified by the diurnal variation in solar zenith angle and surfaces are assumed to be horizontal so no account is taken of land orientation or slope.  It is important to consider these aspects and their effects when appraising model forecasts (e.g. 2m temperatures, or possible shower development, which in certain conditions in mountainous areas could be more favoured over slopes facing the sun).

Read more on Albedo.