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Modelling snow
Structure of the snowpack
The density and temperature of snow is not usually uniform throughout the snowpack. Density at all snow levels is related to how much air is trapped, the ice or water content, and also linked to the temperature of the snow itself. The upper snow layer, especially if of fresh snow, is largely uncompressed and has relatively low density. Lower layers in the snowpack generally have greater density due to compaction by the snow above.
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In snow-free areas, there is a ready exchange of heat, moisture and momentum between the atmosphere and underlying surface. Snow-covered regions have reduced heat conductivity, higher surface albedo, and reduced roughness compared to areas without snow. Thus for snowy areas there is an effective thermal, hydrological, and mechanical decoupling between the overlying atmosphere and underlying soil.
Skin temperature of snow and the 2m temperature
The skin temperature of the upper snow layer is governed by the balance between:
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Forecasts of 2m air temperature use the skin temperature of the snow as if it were at the model ground surface rather than at the elevation of the snow surface. This may lead to errors in derivation of 2m temperatures forecast in cases of deep snow.
The multi-layer snow model
The IFS multi-layer snow model uses up to five layers to represent the snowpack and the complex heat fluxes and interactions between them. It represents the vertical structure and evolution of snow temperature, snow mass, density, and liquid water content in each layer. The energy flux at the snowpack surface is the balance of the upward and downward energy fluxes, including the effect of any snow evaporation. Albedo and surface fluxes vary according to the snow extent, depth and ground coverage (with account taken of trees in areas of forest), and age of the snow. Heat flux from the underlying ground is also incorporated. The fluxes are illustrated and explained in Fig2A.1.4.4-1, Fig2A.1.4.4-2, Fig2A.1.4.4-3.
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- for snow depth <12.5cm only one snow layer is modelled (Fig2A.1.4.4-1). Note: Partial cover of the 'tile' is assumed if snow depth is less than 10cm.
- for snow on glaciers, land-ice or sea-ice only one snow layer is modelled.
- for snow depth >12.5cm the number of snow layers varies up to a maximum of 5 layers according to the total snow depth (Fig2A.1.4.4-2).
- permanent snow is defined as snow water equivalent >= 10m with 5 snow layers (Fig2A.1.4.4-3).
Fig2A.1.4.4-1: Schematic representation of the multi-layer snow scheme. Shallow snow layer. Snow depth <12.5cm. (Note: Snow depth < 10cm implies only a partial cover of snow). Snow on glaciers, land-ice and sea-ice is processed as a single layer.
Fig2A.1.4.4-2: Schematic representation of the multi-layer snow scheme. Deep snow. Snow depth >27.5cm. Any additional snow accumulation is added into the fourth snow layer in order to preserve the characteristics and thermal flux qualities of thinner layers at base and top of the snowpack. For snow depths between 12.5cm and 27.5cm additional snow is added proportionately to the layers as they are introduced.
Fig2A.1.4.4-3: Schematic representation of the multi-layer snow scheme for permanent snow (e.g. Greenland, Antarctica). Snow depth is defined as ≥10m. Any additional snow accumulation is added into the fifth snow layer. This is to preserve the characteristics and thermal flux qualities of thinner layers at the top of the snowpack.
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Table2.1.4.4-1: List of symbols for parameters shown in Fig2A.1.4.4-1, Fig2A.1.4.4-2, Fig2A.1.4.4-3.
Vertical discretisation over complex terrain areas
A different algorithm is applied to define the snow layers in regions of complex or mountainous terrain where snow depth >25 cm. These layers are thicker than used for a snowpack with same depth over a flat region. For example, in complex terrain an 85cm deep snowpack is discretised with layer depths: 16.00cm, 17.25cm, 17.25cm, 17.25cm, 17.25cm.
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Ground height data from internationally available datasets at 1km resolution are interpolated to a spectral representation at model resolution. Also included are standard deviations of mean height, slopes, and direction of unresolved orography. However, in rugged mountainous areas smoothing misses important detail and mountain peaks may be under-represented and narrow valleys may not be represented at all.
Permanent snow areas
In permanent snow areas (e.g. Greenland, Antarctica) a fixed snow layering it is used. The top four layers of the five have a constant depths of 50 cm. Any additional snow accumulation is added into the bottom layer (Fig2A.1.4.4-3). Glaciers are handled differently
Sea-ice
Representation of snow on top of sea ice or ice on lakes is introduced in Cy50r1 in May 2026. Coupling between the sea ice model to the atmosphere allows sea-ice and variable ice thickness to be represented in the atmospheric forecast model. Snow cover on ice acts to increase its persistence by increasing the albedo and reducing the heat flux into the modelled ice. Reduces the warm bias seen in winter over the ice surface, especially in cloud free situations. Thin sea-ice or lake-ice covered by thin snow grows or melts much faster than does thick ice with deep snow.
Glaciers
Representation of glaciers is introduced in Cy50r1 in May 2026 by use of a new land-ice tile within HTESSEL. This allows representation of the proportion of land-ice or glaciers that partly (or wholly) cover the grid box. The parametrisation scheme uses four layers of equal thickness on top of the soil column. There is an exchange of energy and water fluxes between the ice and the atmosphere and also at the bottom where the soil (ground) temperature is used as boundary condition and to compute the ice basal heat flux.
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The new glacier scheme replaces the binary glacier mask (either wholly covering a grid square or not) used in Cy49 and earlier.
Fig2A.1.4.4-4: Schematic representation of the multi-layer ice scheme for glacier (e.g. Greenland, Antarctica), and-ice and sea-ice representation in the sea-ice scheme SI3. There are four layers of ice in the model. Any accumulation of snow is added to the surface and the snow is modelled as a single layer.
Fig2A.1.4.4-5: Glacier and permanent ice cover is predominantly in the coloured areas. These are predominantly in Greenland and other Arctic mountainous island areas. Permanent snow is located in the Himalayan region. Isolated locations are in the Alpine region, Caucasus mountains and southern Norway. Elsewhere the grid squares can have partial ice or glacier cover and this is taken care of by a glacier/land-ice tile in HTESSEL. Many coastal regions of Greenland, Ellesmere Island and Baffin Island have less permanent snow and ice. Snowmelt and warming of exposed rock allows better modelling of temperature and precipitation changes.
Snow Indication
Snow depth
The model snow depth changes when fresh snow falls, or when snow on the ground melts, evaporates or is compressed. The response in dry periods at different altitudes is shown in Fig2A.1.4.4-9.
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The current snow scheme tends to melt snow too slowly.
Snow cover
Snow cover is diagnosed from the water equivalent of the modelled snow:
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The albedo of snow in forested areas is given by a look-up table depending on (high) vegetation type (Table 2.1.4.4-2). The albedo of exposed snow decays with time between 0.85 for fresh white snow to 0.5 for older snow. It is reset to 0.85 after large snowfall events.
Table2.1.4.4-2:Mean values of Northern Hemisphere five-year (2000-2004) broad band surface albedo (in the presence of snow) aggregated by high vegetation type.
Data assimilation of snow on the ground
Snow cover, snow depth and snow compaction affect all IFS atmospheric forecast models. It is important the IFS monitors actual values and updates the background fields accordingly. Any discrepancy will cause errors in the forecast as several physical properties of snow influence:
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At high levels (altitudes >1500m) IMS data is not used and observations of snow depth are sparse or non-existent. In these cases snow depth prediction depends only upon the short range IFS evolution. Thus there can be little or no decrease in snow water equivalent (if it remains cold enough), though there might be an increase after further forecast or actual snowfalls. Snow depths may also reduce because the density of the snow tends to increase with time through compaction in the model (and also in reality). Snow depths in such regions rise in response to forecast snowfall but may not decrease sufficiently at other times (See example in Fig2A.1.4.4-6 and Fig2A.1.4.4-10).
Interpreting snow forecast information
Some considerations should be made before issuing forecasts when interpreting model output of snow.
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Fig2A.1.4.4-6:. Weather station at Røldalsfjellet (Norway). The temperature sensor is mounted at 5m above the ground (left picture). This allows sufficient clearance beneath the sensor with high snow accumulation (right picture). Photos:MET Norway.
Fig2A.1.4.4-7: Snow depth (cm) and sea-ice cover (%) at the resolution of Ensemble Control Forecast. DT 12UTC 07 Feb 2023 T+00. Note frozen lakes (e.g. NW Russia, north Caspian Sea, Uzbekistan) are also plotted as "sea ice". FLake represents or generates ice on coastal or inland water.
Fig2A.1.4.4-8: Conversion of background and forecast snow water equivalents to snow cover. Forecast snow water equivalents of 10cm or greater are considered as associated with full cover of snow on the ground. Snow water equivalent of 5cm is considered to be associated with half cover.
Fig2A.1.4.4-9: Conversion of IMS information into an estimate of snow water equivalent for data assimilation. IMS delivers binary information on the presence of snow for each grid cell but does not give information on snow depth.
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IMS strongly impacts upon any updates to the background snow depth field. Only if both IMS and background fields indicate snow is the IMS information not used.
Fig2A.1.4.4-10: Forecast snow water equivalent at high level stations (blue) and low level stations (red) during the winter of 2019/20.
- At low levels background fields are updated using IMS data and numerous observations of snow depth. Forecasts show a gradual decrease in snow water equivalent during a dry period.
- At high levels observations are more sparse and IMS data is not used (>1500m). Background fields rely on earlier snow depth forecasts. Forecasts show constant snow water equivalent during a dry period.
Additional sources of information
(Note: In older material there may be references to issues that have subsequently been addressed)
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