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62 COST Action 719 Final report
II.2.6.3 Dynamical Downscaling (RCM)
As the name implies, a regional climate model does not attempt to simulate the entire globe
but only a portion thereof. Regional models use the same laws of physics described in terms
of mathematical equations as do global models. The technique of nested region climate
modelling consists of using output from global model simulations to provide initial conditions
and time-dependent lateral meteorological boundary conditions to drive limited area model
simulation for selected time slices of the global model run (Dickinson et al. 1989, Giorgi et al.
1990, 1991). This technique is essentially originated from numerical weather prediction,
however the RCM is adapted for climate time scales and thus compromises have been made
between horizontal and temporal resolution. For the commonly used 30 year time slices (1961
– 1990 and 2071 – 2100) they reach a horizontal resolution of about 20 – 50 km.
The nested regional climate modelling techniques can not only be used to downscale GCMs
but also to dynamical downscale reanalysis such as the ERA40. Such runs are called ‘perfect
boundary condition runs’ and are among other applications used to test the performance of the
RCM.
The main theoretical weakness of RCMs is that systematic errors of the GCM are handed
down to the RCM. Furthermore, depending on the domain size and the resolution, RCM
simulations can be as computationally demanding as GCMs are.
The RCM is the right choice if changes in variability and extremes are required for the impact
input and if the simulation is significantly more realistic at high resolution or even only
available at high resolution. Furthermore, the use of RCM is preferable for regional and local
impact assessment especially in complex topography, where coast lines are important and in
region with highly heterogeneous land surface cover.
The physical modelling approach is mentioned here because it also has future possibilities
within climatological monitoring. There also exist some applications where output fields from
such models form the first guess of a field for objective interpolations (e.g. the Swedish
mesoscale analysis system MESAN (Häggmark et al., 1997)).