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ClimateChange Assessment Guide.pdf - University of Waterloo

ClimateChange Assessment Guide.pdf - University of Waterloo

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<strong>Guide</strong> for <strong>Assessment</strong> <strong>of</strong> Hydrologic Effects <strong>of</strong> Climate Change in Ontario34Table 4.1 Summary <strong>of</strong> general advantages and disadvantages <strong>of</strong> GCM-change field, synthetic, analogue anddownscaled climates for use in assessmentsTechnique Description Advantages DisadvantagesGCM-change fieldSynthetic andAnalogue ScenariosStatisticalDownscaling withRegressionWeatherGeneratorsRegional ClimateModels• Simulated climate at coarsegrid spacing• Use to develop monthly(seasonal or other) changefields• Provides data used instatistical downscaling• Wide variety <strong>of</strong> GCMsavailable• Artificial or based on spatialor temporal transposition <strong>of</strong>climates• Climates based uponstatistical relationshipsbetween climate variablesand atmospheric predictors• Weather data generatedbased on statistical attributes<strong>of</strong> local climate and GCMvariables• Higher resolution physicallybased models <strong>of</strong>tenembedded in GCMs• Local physiography and waterbodies are included• Several GCMs have beenapplied to long-termsimulations for family <strong>of</strong>emission scenarios• Comprehensive physicallybased tools• Several variables available• Good agreement across GCMson temperature change• Can be realistic if well informed• Complete sets <strong>of</strong> variablesrelevant to hydrology may bereadily available• Supports examination <strong>of</strong>sensitivity and vulnerability• High spatial and temporalresolution if observational dataavailable• Several variables available• Ease <strong>of</strong> application, thus manylong-term scenarios can betested• Good skill demonstrated atsimulating temperature• High spatial and temporalresolution if observational dataavailable• Several variables available• Ease <strong>of</strong> application, thus manylong-term scenarios can betested• Good skill demonstrated atsimulating temperature• Good skill at simulating periods<strong>of</strong> drought and extendedprecipitation• High spatial and temporalresolution output for severalvariables• Improved extreme eventsimulation over GCMs• Physically based and consistentwith GCMs• May account for feedback fromanthropogenic or natural systems• Coarse spatial resolution,unrepresentative <strong>of</strong> local climate• Extreme events generally occurat a smaller scale than GCMscales, not represented• Modest agreement across GCMson precipitation• Cannot be used to changeclimate variability• Not related to SRES scenarios• Usefulness limited to smallerchanges• Analogues for enhanced GHGconditions likely not available• May be physically implausible• Assumes empirical relationshipswill hold as climates change• May perpetuate GCM biases• Generally underestimatesextremes• Some models do not simulatemonthly variability well• Poor skill in simulatingprecipitation extremes• Neighbouring stations notcorrelated• Observational data to buildstatistical relationships notavailable in all areas• Assumes empirical relationshipswill hold as climates change• May perpetuate GCM biases• Generally underestimatesextremes• Some models do not simulatemonthly variability well• Poor skill in simulatingprecipitation extremes• Neighbouring stations notcorrelated• Observational data not availablein all areas• Few model runs and GHGemission scenarios available• May perpetuate GCM biases• Complex model systemsprecludes non-expert use• Simulation periods are typicallyshort, thus fewer extremes arerepresented

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