Although computing power has substantially increased during the last few years, the horizontal resolution of present coupled GCMs is still too coarse to capture the effects of local and regional forcings in areas of complex surface physiography and to provide information suitable for many impact assessment studies. Since IPCC (1992), significant progress has been achieved in the development and testing of statistical downscaling and regional modeling techniques for the generation of high-resolution regional climate information from coarse-resolution GCM simulations.
Statistical downscaling is a two-step process basically consisting of i) development of statistical relationships between local climate variables (e.g., surface air temperature and precipitation) and large-scale predictors, and ii) application of such relationships to the output of GCM experiments to simulate local climate characteristics. A range of statistical downscaling models have been developed (IPCC 1996, WG I), mostly for U.S., European, and Japanese locations where better data for model calibration are available. The main progress achieved in the last few years has been the extension of many downscaling models from monthly and seasonal to daily time scales, which allows the production of data more suitable for a broader set of impact assessment models (e.g., agriculture or hydrologic models).
When optimally calibrated, statistical downscaling models have been quite successful in reproducing different statistics of local surface climatology (IPCC 1996, WG I). Limited applications of statistical downscaling models to the generation of climate change scenarios has occurred showing that in complex physiographic settings local temperature and precipitation change scenarios generated using downscaling methods were significantly different from, and had a finer spatial scale structure than, those directly interpolated from the driving GCMs (IPCC 1996, WG I).
The (one-way) nested modeling technique has been increasingly applied to climate change studies in the last few years. This technique consists of using output from GCM simulations to provide initial and driving lateral meteorological boundary conditions for high-resolution Regional Climate Model (RegCM) simulations, with no feedback from the RegCM to the driving GCM. Hence, a regional increase in resolution can be attained through the use of nested RegCMs to account for sub-GCM grid-scale forcings. The most relevant advance in nested regional climate modeling activities was the production of continuous RegCM multi-year climate simulations. Previous regional climate change scenarios were mostly produced using samples of month-long simulations (IPCC 1996, WG I). The primary improvement represented by continuous long-term simulations consists of equilibration of model climate with surface hydrology and simulation of the full seasonal cycle for use in impact models. In addition, the capability of producing long-term runs facilitates the coupling of RegCMs to other regional process models, such as lake models, dynamical sea ice models, and possibly regional ocean (or coastal) and ecosystem models.
Continuous month- or season-long to multi-year experiments for present-day conditions with RegCMs driven either by analyses of observations or by GCMs were generated for regions in North America, Asia, Europe, Australia, and Africa. Equilibrium regional climate change scenarios due to doubled CO2 concentration were produced for the continental U.S., Tasmania, Eastern Asia, and Europe. None of these experiments included the effects of atmospheric aerosols.
In the experiments mentioned above, the model horizontal grid point spacing varied in the range of 15 to 125 km and the length of runs from 1 month to 10 years. The main results of the validation and present-day climate experiments with RegCMs can be summarized in the following points:
When applied to the production of climate change scenarios, nested model experiments showed the following (IPCC 1996, WG I):
Finally, of relevance for the simulation of regional climate change is the development of a variable-resolution global model technique, whereby the model resolution gradually increases over the region of interest.
Other reports in this collection