Climate Change 2001:
Working Group I: The Scientific Basis
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10.2.3 Regional Climate Models (RCMs)

The nested regional climate modelling technique consists of using initial conditions, time-dependent lateral meteorological conditions and surface boundary conditions to drive high-resolution RCMs. The driving data is derived from GCMs (or analyses of observations) and can include GHG and aerosol forcing. A variation of this technique is to also force the large-scale component of the RCM solution throughout the entire domain (e.g., Kida et al., 1991; Cocke and LaRow, 2000; von Storch et al., 2000)

To date, this technique has been used only in one-way mode, i.e., with no feedback from the RCM simulation to the driving GCM. The basic strategy is, thus, to use the global model to simulate the response of the global circulation to large-scale forcings and the RCM to (a) account for sub-GCM grid scale forcings (e.g., complex topographical features and land cover inhomogeneity) in a physically-based way; and (b) enhance the simulation of atmospheric circulations and climatic variables at fine spatial scales.

The nested regional modelling technique essentially originated from numerical weather prediction, and the use of RCMs for climate application was pioneered by Dickinson et al. (1989) and Giorgi (1990). RCMs are now used in a wide range of climate applications, from palaeoclimate (Hostetler et al., 1994, 2000) to anthropogenic climate change studies (Section 10.5). They can provide high resolution (up to 10 to 20 km or less) and multi-decadal simulations and are capable of describing climate feedback mechanisms acting at the regional scale. A number of widely used limited area modelling systems have been adapted to, or developed for, climate application. More recently, RCMs have begun to couple atmospheric models with other climate process models, such as hydrology, ocean, sea-ice, chemistry/aerosol and land-biosphere models.

Two main theoretical limitations of this technique are the effects of systematic errors in the driving fields provided by global models; and lack of two-way interactions between regional and global climate (with the caveats discussed in Section 10.2.2 for variable resolution models). Practically, for a given application, consideration needs to be given to the choice of physics parametrizations, model domain size and resolution, technique for assimilation of large-scale meteorological conditions, and internal variability due to non-linear dynamics not associated with the boundary forcing (e.g., Giorgi and Mearns, 1991, 1999; Ji and Vernekar 1997). Depending on the domain size and resolution, RCM simulations can be computationally demanding, which has limited the length of many experiments to date. Finally, GCM fields are not routinely stored at high temporal frequency (6-hourly or higher), as required for RCM boundary conditions, and thus careful co-ordination between global and regional modellers is needed in order to perform RCM experiments.

10.2.4 Empirical/Statistical and Statistical/Dynamical Downscaling

Statistical downscaling is based on the view that regional climate may be thought of as being conditioned by two factors: the large-scale climatic state, and regional/local physiographic features (e.g., topography, land-sea distribution and land use; von Storch, 1995, 1999a). From this viewpoint, regional or local climate information is derived by first determining a statistical model which relates large-scale climate variables (or "predictors") to regional and local variables (or "predictands"). Then the predictors from an AOGCM simulation are fed into this statistical model to estimate the corresponding local and regional climate characteristics.

A range of statistical downscaling models, from regressions to neural networks and analogues, has been developed for regions where sufficiently good data sets are available for model calibration. In a particular type of statistical downscaling method, called statistical-dynamical downscaling (see Section, output of atmospheric mesoscale models is used in statistical relationships. Statistical downscaling techniques have their roots in synoptic climatology (Growetterlagen; e.g., Baur et al., 1944; Lamb, 1972) and numerical weather prediction (Klein and Glahn, 1974), but they are also currently used for a wide range of climate applications, from historical reconstruction (e.g., Appenzeller et al., 1998; Luterbacher et al., 1999), to regional climate change problems (see Section 10.6). A number of review papers have dealt with downscaling concepts, prospects and limitations: von Storch (1995); Hewitson and Crane (1996); Wilby and Wigley (1997); Zorita and von Storch (1997); Gyalistras et al. (1998); Murphy (1999,2000).

One of the primary advantages of these techniques is that they are computationally inexpensive, and thus can easily be applied to output from different GCM experiments. Another advantage is that they can be used to provide local information, which can be most needed in many climate change impact applications. The applications of downscaling techniques vary widely with respect to regions, spatial and temporal scales, type of predictors and predictands, and climate statistics (see Section 10.6). In addition, empirical downscaling methods often offer a framework for testing the ability of physical models to simulate the empirically found links between large-scale and small-scale climate (Busuioc et al., 1999; Murphy, 1999; Osborn et al., 1999; von Storch et al., 1993; Noguer, 1994).

The major theoretical weakness of statistical downscaling methods is that their basic assumption is not verifiable, i.e., that the statistical relationships developed for present day climate also hold under the different forcing conditions of possible future climates. In addition, data with which to develop relationships may not be readily available in remote regions or regions with complex topography. Another caveat is that these empirically-based techniques cannot account for possible systematic changes in regional forcing conditions or feedback processes. The possibility of tailoring the statistical model to the requested regional or local information is a distinct advantage. However, it has the drawback that a systematic assessment of the uncertainty of this type of technique, as well as a comparison with other techniques, is difficult and may need to be carried out on a case-by-case basis.

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