Approaches to the simulation of regional climate change: A review

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Abstract
The increasing demand by the scientific community, policy makers, and the public for realistic projections of possible regional impacts of future climate changes has rendered the issue of regional climate simulation critically important. The problem of projecting regional climate changes can be identified as that of representing effects of atmospheric forcings on two different spatial scales: large‐scale forcings, i.e., forcings which modify the general circulation and determine the sequence of weather events which characterize the climate regime of a given region (for example, greenhouse gas abundance), and mesoscale forcings, i.e., forcings which modify the local circulations, thereby regulating the regional distribution of climatic variables (for example, complex mountainous systems). General circulation models (GCMs) are the main tools available today for climate simulation. However, they are run and will likely be run for the next several years at resolutions which are too coarse to adequately describe mesoscale forcings and yield accurate regional climate detail. This paper presents a review of these approaches. They can be divided in three broad categories: (1) Purely empirical approaches, in which the forcings are not explicitly accounted for, but regional climate scenarios are constructed by using instrumental data records or paleoclimatic analogues; (2) semiempirical approaches, in which GCMs are used to describe the atmospheric response to large‐scale forcings of relevance to climate changes, and empirical techniques account for the effect of mesoscale forcings; and (3) modeling approaches, in which mesoscale forcings are described by increasing the model resolution only over areas of interest. Since they are computationally inexpensive, empirical and semiempirical techniques have been so far more widely used. Their application to regional climate change projection is, however, limited by their own empiricism and by the availability of data sets of adequate quality. More recently, a nested GCM‐limited area model methodology for regional climate simulation has been developed, with encouraging preliminary results. As it is physically, rather than empirically, based, the nested modeling framework has a wide range of applications.