Past studies of added value afforded by RCMs have proceeded by separating spatial scales into those resolved by driving GCMs and those additional scales permitted by the higher resolution RCM, by using either Fourier (Denis et al., 2002) or digital (Feser, 2006) filtering, or with a perfect-prognosis approach based on aggregation (Di Luca et al., 2011). These approaches have all shown limitations.
We propose to approach the quantification of added value from a different perspective based on an understanding of the physical mechanisms that contribute to temperature and precipitation extremes. Our working assumption is that for each region of Canada, the most significant local weather and climate phenomena (such as freezing rain, downslope windstorms, valley drainage winds) can be related to characteristic signatures in large-scale atmospheric circulation or specific environmental precursors related to recent past anomalies (such as a wet or dry previous season). Monitoring the presence of such signatures in GCM climate simulations would allow focussing the efforts on specific episodes for performing expensive very high-resolution simulations of the anticipated significant phenomena with RCMs, in order to identify and best exploit the added value afforded by the use of high-resolution in dynamical downscaling.
This Theme will try to answer pressing questions such as: How will the intensity of future storms and their trajectories be modified under a warmer climate? What will be the consequences of such changes for local climate conditions? What modelling tools are required to adequately represent these physical processes?