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• the land-surface model (HTESSEL).  
• the lakes and coastal waters model (FLake).  This also provides information on water temperature changes and the development and decay of areas of ice.  The forecasts of ice cover impact upon heat flux and albedo.
• the Wave Model (ECWAM).
• the Dynamic Ocean Model (NEMO).  This includes a program (LIM2) which provides information on the development, decay and movement of areas of sea ice.  The forecasts of ice cover impact upon heat flux and albedo.
  
  

Fig211.AFig2.1.1-1: Sub-grid scale parameterised processes in the ECMWF model – Surface to Stratosphere.

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Basic prognostic equations are efficiently processed in spectral space and need only a relatively small proportion of computer time required for a forecast.  But many processes are computed in grid point space (e.g. rainfall) and this requires a larger, but relatively modest, proportion of computer time.   Processes in grid-point and in spectral space, and in the associated spectral transforms, are broadly similar in computer time.  However, the necessary transpositions between spectral and grid point spaces are a significant computing overhead.  Semi-lagrangian computations also take up an important proportion of processing time.

Fig211.BFig2.1.1-2: A schematic pie-chart showing approximate proportions of computer processing time during execution of an atmospheric model forecast based on T799 (25km resolution on regular grid) and 91 levels.  The current resolution for ENS is T_co1279 (9km resolution) and 137 levels.   Parameterised physical processes consume about 30% of computer processing time.  Computations in grid point space and spectral space together take about 20% of computer processing time while rather more time (~27%) is taken in transposing data from one space to the other.

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