ClimateChange Assessment Guide.pdf - University of Waterloo

Guide for Assessment of Hydrologic Effects of Climate Change in Ontario30easily or conclusively tested, and many downscalingapproaches do not yield a sufficiently wide array ofparameters at short time intervals (i.e., hourly) necessaryfor hydrologic modelling. Downscaling methods can becomputationally intense and software applications rangefrom inexpensive and straightforward to expensiveand complex in nature. The following sections providedescriptions of the major downscaling approaches toconstructing climate sets for climate change impactassessment.4.3.1 Statistical - RegressionStatistical regression models, as a downscaling method,are conceptually simple approaches to climate scenariogeneration. In this approach, resolved behaviour atthe global scale is linked to climate in the study areathrough statistical relationships. These tools rely uponpredictors selected from large scale climate modelling(i.e., GCM) output (e.g. upper atmosphere water-vapourcontent, barometric pressure or geopotential thickness).Predictors must be well selected for the strength of theirinfluence on important local scale predictands such asair temperature, precipitation, wind speed and radiation.The importance of predictands varies with hydrologicmodels. The assumption of stationarity, that theserelationships will hold true into the future with climatechange, is crucial to the validity of this approach.Various methods exist among the many statisticalregression models for establishing the relationshipsbetween predictors and predictands including multipleregression, linear and non-linear correlation analysis(i.e., artificial neural networks), and canonical correlationanalysis. Statistical methods and application issues arediscussed in detail in the Fourth Assessment Reportof the IPCC (Carter et al., 2007) and other guidancedocuments focussed on statistical downscaling methods(Wilby et al., 2004; Willows and Connell, 2003).With statistical regression, the climate time series aregenerated using the regression relationships establishedbetween the large scale predictors and the local scalepredictands. Predictors for current conditions areavailable either from GCM runs covering the currenttime period or from reanalysis data. Reanalysis data areatmospheric information that are created from observedweather maps and are a retrospective of what hashappened. For the future, the climate changed seriesof GCM-based predictors are used to generate climatechanged predictands. If the relationship betweenpredictor and predictand still hold in the future, then theclimate generated will accurately reflect the changedclimate conditions.The challenge in applying statistical regression typestatistical downscaling methods is to find predictorsthat both have a strong statistical relationship with localscale predictands, and are physically related to thepredictor they address. For example, when the objectiveis to simulate local precipitation one should focus onatmospheric water-content variables in the suite ofpotential predictors. It is possible to identify apparentcorrelations between predictors and predictands that arecoincidental and will, therefore, not provide a good basisfor projecting future conditions.When various statistical models were compared inone study (Wilby and Wigley, 1997) results variedconsiderably between methods, especially forprecipitation. It was concluded that additionalatmospheric predictors such as water-content relatedvariables were needed to effectively characterizeatmospheric saturation. Wilby et al. (2004) have notedthat the choice of predictors is critical in statisticaldownscaling methods especially when simulatingprecipitation.In general, statistical regression type models underpredict extreme event intensity, duration and frequency.This is primarily due to the bias in statistical fittingprocedures towards the more commonly occurring meanconditions. This problem is lessened where very longmeteorological records exist, wherein extremes are wellrepresented.The advantages of the statistical – regression approachinclude (Gachon et al., 2005; Goodess et al., 2003; Wilbyet al., 2004):• Ease of application as user friendly software(i.e., SDSM) is available and methods are welldocumented;• Applicable to any site or region with observationaldata, so resulting climates are site- specific to localweather station records;• Long time series and multiple scenarios can begenerated leading to a better understanding ofextremes and uncertainty;• May overcome certain GCM scale biases;