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Fig2.1.4.4-3: Schematic representation of the multi-layer snow scheme for permanent snow (e.g. Greenland, Antarctica) and for glaciers.  Snow depth is defined as ≥10m.  Any additional snow accumulation is added into the fifth snow layer in order to preserve the characteristics and thermal flux qualities of thinner layers at the top of the snowpack.  

Table2.1.4.4-1: List of symbols for parameters shown in Fig2.1.4.4-1, Fig2.1.4.4-2, Fig2.1.4.4-3.

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In permanent snow areas (e.g. Greenland, Antarctica and ,  and glaciers) a fixed snow layering it is used. The top four layers (counting from the one in contact with the atmosphere) have a constant depths of 50 cm, whereas any additional snow accumulation is added into the bottom layer (Fig2.1.4.4-3 and Fig2.1.4.4-4). 

Sea ice:

There is no representation of snow on top of sea ice or ice on lakes.  Snow cover on ice acts to increase its persistence by increasing the albedo and reducing the heat flux into the modelled ice.  Thin sea ice or lake ice covered by thin snow grows or melts much faster than does thick ice with deep snow.  

Snow depth

The snow depth in the model 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 Fig2.1.4.4-8. 

Fresh snowfall is added to the top layer, with a new snow density depending on air temperature and wind speed.  Melted snow is removed from the top layer.  The snow mass is then redistributed across the different layers but relatively shallow layers of snow are maintained at the top and at the base so that the atmosphere/snow and soil/snow heat fluxes can be best modelled. 

Liquid water from rainfall onto snow or melting percolates downwards and can refreeze on a different level, releasing latent heat.

Snow depth water equivalent is the sum of frozen and liquid water within the snowpack.  Snow density considers meltwater refreezing, so the density will vary but the snow water equivalent should not change.

The snow depth of each layer is calculated by snow depth water equivalent divided by snow density for each layer.  

Snow depth is computed using:

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Fig2.1.4.4-4: Permanent snow and glacier grid points. 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.  Many coastal regions of Greenland, Ellesmere Island and Baffin Island have less permanent snow allowing snowmelt and warming of the rock to allow modelling of temperature and precipitation changes. 

Sea ice:

There is no representation of snow on top of sea ice or ice on lakes.  Snow cover on ice acts to increase its persistence by increasing the albedo and reducing the heat flux into the modelled ice.  Thin sea ice or lake ice covered by thin snow grows or melts much faster than does thick ice with deep snow.  

Snow depth

The snow depth in the model 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 Fig2.1.4.4-8. 

Fresh snowfall is added to the top layer, with a new snow density depending on air temperature and wind speed.  Both convective and dynamic snowfall is considered to be homogenous over the grid box.  Melted snow is removed from the top layer.  The snow mass is then redistributed across the different layers but relatively shallow layers of snow are maintained at the top and at the base so that the atmosphere/snow and soil/snow heat fluxes can be best modelled. 

Liquid water from rainfall onto snow or melting percolates downwards and can refreeze on a different level, releasing latent heat.  If snow already exists on the ground then freezing rain and ice pellets are accounted for as rainfall that has frozen.

Snow depth water equivalent is the sum of frozen and liquid water within the snowpack.  Snow density considers meltwater refreezing, so the density will vary but the snow water equivalent should not change.

The snow depth of each layer is calculated by snow depth water equivalent divided by snow density for each layer.  

Snow depth is computed using:

• the liquid water equivalent of snow lying on the ground
• the average density of the snow layer
• the liquid water equivalent of snow lying on the ground
• the average density of the snow layer (typically lower for uncompacted fresh snow, higher for compacted old snow).

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• the snow depth is defined as 10m of water equivalent.
• there is a fixed snow density for all 5 layers.
• the depth of each of the upper four layers is 0.50m; fresh snowfall is added the bottom layer (i.e. layer 5 is used as an accumulation layer). See Fig2.. See Fig2.1.4.4-3.

It is common for snow depth to be extremely high at grid points within these areas of permanent snow .  

Snow cover

Snow cover is diagnosed from the water equivalent of the modelled snow: 

• Total snow cover is assumed where snow depth is diagnosed as >10cm.  Only snow or forest snow "tiles" are used by HTESSEL.
• Partial snow cover is assumed where snow depth is diagnosed as <10cm.  A snow water equivalent of 6cm is considered to be associated with 60% snow cover (Fig2.1.4.4-6).   Other "tiles" which describe the location are used by HTESSEL in addition to the snow or forest snow "tiles".

Snow is not intercepted by a tree canopy and will accumulate on the ground.  Snow does not accumulate on sea ice or lake ice. 

The albedo of snow in forested areas is given by a look-up table depending on (high) vegetation type (Table 2.1.4.4-

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It is common for snow depth to be extremely high at grid points within these areas of permanent snow .  

Snow cover

Snow cover is diagnosed from the water equivalent of the modelled snow: 

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1).  The albedo of exposed snow decays with time between 0.85 for fresh white snow to 0.5 for .  It is reset to 0.85 after large snowfall events.

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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 for 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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Snowfields are initialized every day at 00UTC from continuous offline data.  

Snow data assimilation at ECMWF relies on:

• an Optimal Interpolation method which adjusts the model-analysed snow water equivalent and snow density prognostic variables.
• conventional measurements of snow depth (from SYNOP and other national networks) with additional national snow depth observations, particularly in Europe and North America.  These are generally an important and reliable source of information.  However, snow depth observations from many other regions of the world remain unavailable to IFS.  Thick hoar frost (which can look like a dusting of snow) can be incorrectly reported as very shallow snow.  This can be assimilated by the model despite no supporting evidence from other sources.  
• snow extent data from the NOAA/NESDIS Interactive Multi-sensor Snow and Ice Mapping System (IMS).  This combines satellite visible and microwave data with weather station reports to give snow cover information and sea ice extent over the northern hemisphere at 4km resolution.  There is some manual intervention and quality control.  The IMS product only shows where at least 50% of the grid cell is covered by snow and is converted to snow depths using relationships shown in  Fig2.1.4.4-6 7 and Fig2.1.4.4-78.  IMS data is not currently used by the IFS at altitudes above 1500m.

Incorrect analyses and forecasts of snow are possible:

• in data sparse areas and the representativeness of observations.
• after a prolonged period without observations.
• at altitudes above 1500m.
• near glaciers.  Glaciers are considered as very deep snow rather than ice - this can cause nearby correct observations to be rejected.
• at some high-latitude grid points in the model it is common for snow depth to be extremely high and may not be assimilated.

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 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 Fig2.1.4.4-4, 5 and Fig2.1.4.4-89).

Lake ice and sea ice do not have snow cover in the model.      

Fig2.1.4.4-45:. Weather station at Røldalsfjellet (Norway).  The temperature sensor is mounted at 5m above the ground (left picture) to allow sufficient clearance beneath the sensor with high snow accumulation (right picture). Photos:MET Norway.

Fig2.1.4.4-56: Snow depth (cm) and sea-ice cover (%) in the high resolution forecast (HRES).  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.  

Fig2.1.4.4-67: 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.

Fig2.1.4.4-78: 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.

Fig2.1.4.4-89: Forecast snow water equivalent at high level stations (blue) and low level stations (red) during the winter of 2019/20.

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• Snow temperature considerations

  • Variation in the surface reflectance (snow-albedo) can influence surface heat flux and skin temperatures (by 1°C-4°C).  Fresh (white) snow has high albedo reflecting much of the incoming radiation.  Dirty or older (greyer) snow absorbs more radiation with greater heat flux into the snowpack.  The sun's elevation at high latitudes is limited (and non-existent in winter) which reduces the availability of solar radiation to the snow surface.  
  • Snow surfaces are likely to melt a little more readily in forests as the heat flux at the snow/atmosphere interface is rather larger than with exposed snow.
  • Phase changes can cause a delay in warming during melting or sublimation of snow.  In IFS, airborne snow tends to sublimate much more readily than the undisturbed snow on the ground.
  • If ground surface temperatures are above 0°C, shallow surface snow often takes too long to melt.  This can have an adverse impact on albedo and radiation fluxes.
  • Thermal properties of the snow can cause heat and moisture transfers to be effectively de-coupled.  Snow, especially new dry snow, is a good thermal insulator.
  • Snow depths may reduce gradually because the density of the snow has increased through compaction in the model (and also in reality) as the days progress.

  • Forest snow night time temperatures fall too low.  Even if the forest is dominant, the vertical interpolation to evaluate T2m is done as for an exposed snow tile (because verifying SYNOP stations are always in a clearing).  In reality, forest generatedturbulence maintains turbulent exchange over the clearing and prevents extreme cooling.

  • Forecast 2m temperatures over deep snow:
    • have good agreement with observations between −15°C and 0°C.
    • tend to be too warm by around 3-5°C compared to observations when T2m <-15°C.   Large night time errors of forecast temperatures, even by as much as 10°C too warm, are more likely under clear skies, even when this has been correctly simulated by the model. 
    • have a relatively constant cold bias during the day of ~1.5°C compared to observations.
    • the amplitude of the forecast diurnal cycle of T2m underestimates the amplitude of the observed diurnal cycle by between ~10% to 30%.  Forecast minima tend to be warmer and daytime maxima colder than observations. 
    • verification of temperatures can be difficult.  This is due to variations in the height that temperature observations are made.  Some countries and locations:
      • maintain the sensors 2m above the snow surface, adjusted after every fall of snow.
      • have sensors higher than 2m above the ground to ensure measurement of air temperature throughout the year even after large accumulations of snow.  High snow depths in late winter mean a short distance between snow surface and the sensor, while the sensor will be in a greater distance than usual to the ground surface during the warm period of the year.  See Fig2.1.4.4-45. 

• Snow depth and coverage considerations

  • The smooth nature of the snow surface can cause momentum fluxes to be decoupled and winds increase in the absence of friction.  
  • Strong winds can alter snow depth and snow compaction.  Transport of snow can bring areas of drifting with snow compaction and associated increase in density.  This can be particularly effective for polar snow, where snow temperature is extremely low throughout the winter and compaction due to other processes is limited.  Conversely, strong winds can carry away dry surface snow and reduce snow depth in exposed areas.  The user should consider this effect in periods of strong winds or in generally windy regions.
  • Bias in snow depths:
    • Short-range snow depth forecasts, when compared with independent observations, on average show high quality but with a slight overestimation of snow depth in the background and analysis fields.
    • There is a tendency towards underestimation of snow depth in central Eurasia implying either melting or compaction is overestimated for these forested areas.

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