ClimateChange Assessment Guide.pdf - University of Waterloo

Estimating Future Local Climates29climate parameters at high spatial and temporalresolution that are not normally available from a GCMscenario output.Spatial analogues are effective educational tools; theycan be graphically communicated to the public andpolicy makers. Analogues are also useful tools forvalidating system response and vulnerability to extremeevents (de Loë and Berg, 2006), as the scenarios mayforce a simulated response that lies well outside therange of conditions normally experienced in the studyarea (Mearns et al., 2001; Willows and Connell, 2003).The disadvantages of analogue scenarios are that thechanges affected are not related to GHG concentrations;the physical differences between the study area andspatial analogue areas are not considered (i.e., physicallyimplausible); and climates for two areas are not likelyconsistent across the range of climate parameters(Feenstra et al., 1998). Temporal analogues are physicallyplausible; however, it is unlikely that a wide range of highresolution climate parameters are available, or that thedegree of future climate change can be closely reflectedwith historical information.While useful as educational tools and for testingan area’s sensitivity to change, analogues are notrecommended for climate change impact assessmentas they are not plausible with respect to future GHGconcentrations, nor do they provide a consistent set ofhigh resolution and locally realistic climate parameters.4.3 DownscalingThe most promising areas of future climate predictionfor climate change impact assessment involve the useof GCM output downscaled to reflect local conditions.Two fundamentally different approaches exist: statisticaldownscaling (SD) and dynamical downscaling (DD).While many variations on statistical downscaling exist, ingeneral, they rely on empirical relationships and statisticsto generate climate time series on a site specific basis.With dynamical downscaling finer resolution physicallybased regional climate models (RCMs) are embeddedwithin the GCMs. Regional climate models can be setupto represent large regions (e.g., North America, Europe)and to simulate climates that reflect local conditions (i.e.,topography and large water bodies). Both approachesincorporate local climate factors; either statistically orphysically.With both downscaling approaches, the GCM scenarioruns are used to develop future climates. With statisticaldownscaling, the underlying statistical relationships orcharacteristics determined for current climate conditionsare adjusted to reflect the changed climate projectedby the GCMs. RCMs are embedded directly withinthe GCMs and take all climate changes as boundaryconditions from the GCM.Downscaled scenarios reflect future global GHGconcentrations and atmospheric-oceanographic forcingfunctions along with local physical and climatologicalcharacteristics. These techniques have been used toyield plausible future climates at much higher spatialresolution than the GCM outputs.The underlying assumption with both statistical anddynamical downscaling is that the local climate (thepredictands) is affected by large scale climate patterns(the predictors) and modified by local or regionalfeatures. These relationships are assumed to hold truein the future with changed climates (i.e., stationarity)(de Loë and Berg, 2006). Examples of pridictors include:mean sea level pressure; 500 hPa geopotential height(height of the 500 hPa pressure surface in the freeatmosphere); near surface relative humidity; relativehumidity at 500 hPa height; vorticity (measure of therotation of the air); and the zonal velocity component(velocity component along a line of latitude (e.g.,east-west). Examples of predictands include: dailytemperature ranges, precipitation, wind speed, sunshineduration, and solar radiation. The choice of predictors iscritical to the downscaling process and the following keyassumptions apply (Giorgi et al., 2001; Wilby et al., 2004):• The modelled climate predictors are realisticallyrepresented within the GCMs;• The relationship between the predictors and otherclimate variables are valid into the future; and• The degree of change, as measured by the chosenpredictors, fully reflects the changing climatethroughout the period of interest.The common advantages of downscaling methodsare that they are physically plausible and consistentwith GCM output; they provide high resolution climateinformation; and they address weather extremes (i.e.,variance) and prevailing weather patterns (i.e., means).The main disadvantages of downscaling methods arethat the primary underlying assumptions cannot be