For some applications, the regional information provided by AOGCMs might suffice (Section 10.2.1), while in other cases regionalisation techniques are needed in order to enhance the regional information provided by coupled AOGCMs. The “added value” expected of a regionalisation technique essentially depends on the specific problem of interest. Examples in which regionalisation tools can enhance the AOGCM information include the simulation of the spatial structure of temperature and precipitation in areas of complex topography and land-use distribution, the description of regional and local atmospheric circulations (e.g., narrow jet cores, mesoscale convective systems, sea-breeze type circulations, tropical storms) and the representation of processes at high frequency temporal scales (e.g., precipitation frequency and intensity distributions, surface wind variability, monsoon front onset and transition times).

The basic principles behind the regionalisation methods identified here are discussed in Section 10.2.2, high resolution and variable resolution AGCM experiments; Section 10.2.3, RCMs; and Section 10.2.4, empirical/statistical and statistical/dynamical models. The general philosophy behind regionalisation techniques is to use input data from AOGCMs to produce more detailed regional information. By design, many of these techniques are not intended to strongly modify the planetary scale circulations produced by the forcing AOGCM. This ensures consistency with the AOGCM simulation and facilitates the interpretation of the additional detail as due to the increase in resolution. However, high and variable resolution AGCMs, as well as RCMs with sufficiently large domains, can yield significant modification of the large-scale circulations, often leading to an improved simulation of them. This would tend to increase confidence in the simulations, but the implications of inconsistencies with the AOGCM forcing fields would need to be considered carefully in the interpretation of the climate change information.

Note that RCMs and statistical models are often referred to as “downscaling” tools of AOGCM information. The concept of “downscaling” implies that the regional climate is conditioned but not completely determined by the larger scale state. In fact, regional states associated with similar larger scale states may vary substantially (e.g., Starr, 1942; Roebber and Bosart, 1998).

The majority of climate change impact studies have made use of climate change information provided by transient runs with coupled AOGCMs without any further regionalisation processing. The primary reason for this is the ready availability of this information, which is global in nature and is routinely stored by major laboratories. Data can easily be drawn from the full range of currently available AOGCM experiments of the various modelling centres for any region of the world and uncertainty due to inter-model (or inter-run) differences can thus be evaluated (e.g., Hulme and Brown, 1998). In addition, data can be obtained for a large range of variables down to short (sub-daily) time-scales.

From the theoretical viewpoint, the main advantage of obtaining regional climate information directly from AOGCMs is the knowledge that internal physical consistency is maintained. The feedback resulting from climate change in a particular region on planetary scale climate and the climate of other regions is allowed for through physical and dynamical processes in the model. This may be an important consideration when the simulation of regional climate or climate change is compared across regions.

The limitations of AOGCM regional information are, however, well known. By definition, coupled AOGCMs cannot provide direct information at scales smaller than their resolution (order of several hundred kilometres), neither can AOGCMs capture the detailed effects of forcings acting at sub-grid scales (unless parametrized). Biases in the climate simulation at the AOGCM resolution can thus be introduced by the absence of sub-grid scale variations in forcing. As an example, a narrow (sub-grid scale) mountain range can be responsible for rain shadow effects at the broader scale. Many important aspects of the climate of a region (e.g., climatic means in areas of complex topography or extreme weather systems such as tropical cyclones) can only be directly simulated at much finer resolution than that of current AOGCMs. Analysis relevant to these aspects is undertaken with AOGCM output, but various qualifications need to be considered in the interpretation of the results. Past analyses have indicated that even at their smallest resolvable scales, which still fall under our definition of regional, AOGCMs have substantial problems in reproducing present day climate characteristics. The minimum skilful scale of a model is of several grid lengths, since these are necessary to describe the smallest wavelengths in the model and since numerical truncation errors are most severe for the smallest resolved spatial scales. Furthermore, non-linear interactions are poorly represented for those scales closest to the truncation of a model because of the damping by dissipation terms and because only the contribution of larger scale (and not smaller scale) eddies is accounted for (e.g., von Storch, 1995).

Advantages and disadvantages of using AOGCM information in impact studies can weigh-up differently depending on the region and variables of interest. For example, in instances for which sub-grid scale variation is weak (e.g., for mean sea level pressure) the practical advantages of using direct AOGCM data may predominate (see also Chapter 13). However, even if resolution factors limit the feasibility of using regional information from coupled AOGCMs for impact work, AOGCMs are the starting point of any regionalisation technique presently used. Therefore, it is of utmost importance that AOGCMs show good performance in simulating circulation and climatic features affecting regional climates, such as jet streams and storm tracks. Indeed, most indications are that, in this regard, the AOGCM performance is generally improving, because of both increased resolution and improvements in the representation of physical processes (see Chapter 8).

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