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The multi-layer snow model has a fairly realistic representation of the vertical density and temperature profiles of the snowpack which allows a good representation of its thermal properties.  

The model represents:

• thermal coupling across the snow-atmosphere interface and within the thin top snow layer.  This allows a realistic representation of sporadic surface snow melt and is especially important for warmer sites with wet snow.
• variations in snow density with depth in thick and cold snowpacks.
• percolation, freezing and melting of water which has an effect upon the transfer, release and absorption of heat in each layer and on the surface.
• heat fluxes across the snow-soil interface and within the thin bottom snow layer.  This allows a representation of changes in permafrost.   

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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.  

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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).

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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.  

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The albedo of snow in forested areas is given by a look-up table depending on (high) vegetation type (Table 2.1.4.4-1).  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.

Table2.1.4.4-12:Mean values of Northern Hemisphere five-year (2000-2004)  broad band surface albedo (in the presence of snow) aggregated by high vegetation type.

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• 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.

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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 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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