Downscaling statistical information: a statistical approach

If the shape of mathematical curves describing local weather statistics are systematically influenced by large-scale conditions and geographical factors, then it may be possible to downscale this kind of information directly. Such curves may include probability density functions (pdfs) for daily temperature/precipitation or intensity-duration-frequency (IDF) curves for estimating return values of intense sub-daily rainfall. Downscaling the shape of such curves may be referred to as ‘downscaling climate’ if we regard ‘local climate’ as the statistical description of various weather parameters. This approach is distinct from the more traditional approach ‘downscaling weather’, where one seeks to estimate particular local states for instance on a day-by-day basis. We present work on downscaling the shapes of pdfs and IDFs involving large multi-model ensembles for the application in climate change adaptation efforts. Our efforts also include an evaluation of both methodology and the global climate models' (GCMs) ability to reproduce observed large-scale climatic variability in terms of the salient spatio-temporal covariance structure. We emphasise that it’s important to combine different strategies for downscaling, e.g. regional climate models (RCMs) and empirical-statistical downscaling (ESD) that are based on different assumptions, for getting robust future regional climate projections.