Regional Climate Models
An alternative to downscaling using statistical techniques is the use of a regional climate models (RCM). These numerical models are similar to global climate models, but are of higher resolution and therefore contain a better representation of, for example, the underlying topography within the model domain and, depending on the model resolution, may also be able to resolve some of the atmospheric processes which are parameterized in a global climate model.
The general approach is to 'nest' an RCM within the 'driving' global climate model so that the high resolution model simulates the climate features and physical processes in much greater detail for a limited area of the globe, whilst drawing information about initial conditions, time-dependent lateral meteorological conditions and surface boundary conditions from the GCM. Most nesting techniques are one-way, i.e. there is no feedback from the RCM simulation to the driving GCM. The global model simulates the response of the global circulation to large-scale forcings, whilst the RCM accounts for sub-GCM grid scale forcings, such as complex topographical features and land cover inhomogeneity, in a physically-based way and thus enhances the simulations of atmospheric and climatic variables at finer spatial scales. However, the RCM is susceptible to any systematic errors in the driving fields provided by the global models, and these may be exacerbated in the RCM thus resulting in a poor simulation of the regional climate. High frequency, i.e. 12 or 6 hourly, time-dependent GCM fields are required to provide the boundary conditions for the RCM; these are generally not routinely stored by global climate modellers, and so there needs to be careful co-ordination between the global and regional climate modelling groups in order to ensure that the appropriate data are available. Also, RCM simulations may be computationally demanding, depending on the domain size and resolution, and this has limited the length of many experiments.
A Canadian RCM (CRCM) has been developed through the collaboration of a modelling team at the Université du Québec à Montréal and the CCCma global climate modelling team in Victoria. CRCM has been used in various simulations of current and future climate for western Canada (Laprise et al., 1998; Laprise et al., 2003), and more recently over all Canada (Plummer et al., 2006), both at a spatial resolution of 45km. An earlier version of CRCM was used in doubled CO2 studies (and with IS92a), but more recently with new versions a number of time slice experiments have been undertaken for 1961-2000, and 2040-2069 (SRES-A2 scenario only). In these latter experiments, this emission scenario includes the effects of sulphate aerosols with the corresponding increase in CO2 concentration.
As shown in Figure 1, increased regional detail in topography is apparent over Canada in the CRCM, as would be expected as a result of the improved resolution from a coarse scale Canadian GCM. Monthly CRCM output is available from the CCCma web site and daily or sub-daily outputs are available for research purposes only, i.e. restrictive access for research projects within EC and other members (see conditions of access in the registration section). For these datasets, see the Download Data section. For more information about the Canadian RCM see the CRCM.
Figure 1: Comparison of detail in the orography patterns (in m) over Canada as used in the CGCM1 grid (on the top) and in the CRCM 45 km-grid (on the bottom). (Source: Barrow et al., 2004.) (For more recent maps and results from the CRCM see the Visualization section).
As suggested in the guidelines edited by Mearns et al. (2003), the main strengths and weaknesses of dynamical models are:
Advantages
- These techniques may be able to provide more realistic scenarios of climate change at regional scale than the direct application of GCM-derived scenarios
- Provides very highly resolved information (spatial and temporal)
- Information is derived from physically-based models
- Many variables available
- Better representation of some weather extremes than in GCMs
Disadvantages
- Computationally expensive, and thus few multiple scenarios available
- Lack of two-way nesting may raise concern regarding completeness
- Dependent on (usually biased) inputs from driving AOGCM
- Few time windows are available.
See the Downscaling Tools section for further information about downscaling tools which are generally available.
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References
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