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• its age (the model facilitates slow, natural compression),
• melting (small amounts of snow on the ground tend to take too long to melt, even if the temperature of the overlying air is well above 0°C).
• interception (of rain)
• addition of new snow.

However, at present, there is no mechanism to deal with density variations in the vertical within the snowpack.  This has an impact on energy fluxes which in turn has potential to adversely affect the forecasts of 2m temperature.   For example, when new low density snow falls onto old dense snow, the atmosphere may be "re-insulated" from a ground heat source, allowing 2m temperatures to drop lower in reality than in the model.  In practice this particular problem will be exaggerated by temperature sensors ending up closer to the snow surface when snow has fallen (assuming they are not elevated manually).

Currently snow is modelled as a single layer of snow which allows too much heat to be transferred up from the underlying ground.  A by a multi-layer snow scheme under development will allow allowing more realistic heat transfer. 

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Fig9.2.1.3: The snow depth in the vicinity of Murmansk is shown as a shade of green (5-10cm).   A snow depth of 10cm (actual snow depth, not water equivalent ) is the threshold for the IFS to assume the entire grid box fully snow covered (snow cover fraction = 1 ).  Thus a difference around this threshold value can change the tile partitioning and thus snow coverage may not be uniform or continuous over the grid box.  The snow-free tiles would have less insulation from the soil underneath so maintaining the average skin temperature to higher temperature compared to a fully snow-covered grid box.  This can potentially impact the 2-metre temperature computation.

There is a significant difference between the observed (black) and forecast T+72 HRES forecast (red) temperature structure at Murmansk (location shown by the arrow).  The observed structure is much colder than that forecast, and in this case, surface snow cover appears to have been critical to the forecast.  The observed temperature structure could be due to stronger radiative cooling due to more extensive and/or deeper snow cover than is indicated in the IFS snow depth chart .  

Also, notably, at 12UTC the observed structure (black line) shows more cloud in reality than forecast and yet is still a lot colder. 

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Land surface characteristics (soil moisture, leaf area index) have an impact upon temperature forecasts.    Significant differences in temperature can occur over a short distance where there is a sharp change of surface characteristics.   This can influence the location and development of subsequent convection.

Fig9.2.1.5: An example of incorrect assessment of heat and moisture fluxes (temperatures - diagram on left; dewpoints - diagram on right), at Cordoba 12 June 2017: HRES forecast temperatures and dewpoints (red) and observed temperatures and dewpoints (black).  HRES has under-estimated the maximum temperatures by some 3ºC.  

The left panel shows that during this very hot spell the maximum temperature, on 12th, was under-predicted by 3ºC. This may be due to unrepresented local factors, such as urbanisation, though on the other hand the signal is also typical of what we often see during extreme summer heatwaves.  This bias is a subject of current research; it may be symptomatic of an IFS inability to generate the superadiabatic near surface layers that one sometimes sees on radiosonde ascents.

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Soil moisture and temperature is modelled in four soil levels but there is a considerable lack of real-time observations of soil condition and moisture content.  Nevertheless heat and moisture fluxes have an impact on model surface and 2m temperature and moisture. 

HRES mean and the ENS The ensemble mean values of soil moisture slightly overestimate the diurnal cycle of soil temperature:

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