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Fig9.2.1.3-3: An example of possible connection between IFS forecast 2m temperature (here using forecast EFI as a proxy) and forecast soil moisture content.  EFIs  EFI lower than -0.8 suggest suggests extreme low temperatures compared to the model M-Climate and appear to be associated with with high soil moisture content.  During the 10 day period, surface ground moisture appears to limit the amount of diurnal surface warming.  The reasons IFS are showing the  soil is forecast to dry out and the 2m temperature is forecast to become unexceptional.

The reasons are not clear why IFS shows temperature and moisture differences and uncertainties in analysis and forecasts are not clear.  Possible causes and correlations: 

• There is a dry bias during the dry season and first guess 2m temperatures can be too cold.  During the analysis process, soil moisture increments attempt to better represent 2m humidity.  However, this results in excessive soil moisture near the surface despite no precipitation.  This moisture in IFS can then incorrectly percolate to lower layers or even runoff on the surface.
• Surface ground moisture limits the amount of diurnal surface warming . The soil is forecast to dry out during the 10 days and the 2m temperature is forecast to become unexceptional.Vegetation could be greener and more lush after previous wet period, and nighttime cooling leading to lower 2m temperature and higher 2m relative humidity being being observed and forecast.The observed temperature
• Vegetation could be colder than the first guess and thus in the analysis cause wetting of the model soil (despite no precipitation) and a higher relative humidity.Evapotranspiration lush and green after previous wet period and/or evapotranspiration could be mis-represented.
• Diurnal influxes of moist sea air may replenish moisture and/or not be well forecast either in depth or penetration.
• Insufficient information on atmospheric structure due to the the lack of radiosonde . 

Users should assess the analysis of temperature and moisture against observations in the area against observations, and modify forecasts accordingly.

Summary of soil temperature errors:

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