ClimateChange Assessment Guide.pdf - University of Waterloo

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Guide for Assessment of Hydrologic Effects of Climate Change in Ontario80Scenarios can be sorted according to annual simulatedchanges in air temperature and total precipitation. Thescenarios associated with the percentiles of interestin the Percentile Method (Section 6.4.3.2) can bedetermined from these sorted groupings.The meteorological inputs to the hydrology model canbe adjusted to reflect the simulated change fields on amonth by month basis for each scenario selected. Thesemonthly change fields represent average changes in theclimate parameter and are applied to each value in theinput data time series. This process changes the meanvalues for the time series but will not affect the variabilityor frequency of occurrence of extreme conditions. It is,therefore, not suitable for assessments that focus onpeak flow and flooding issues.While most hydrologic models and climate changeimpact assessments focus on the effects of airtemperature and precipitation, other meteorologicalparameters may be required. Models such as HSP-F canuse solar radiation and wind speed to simulate snowpackbehaviour and to modify evapotranspiration. Hydrologicmodels are not as sensitive to wind and solar radiationas they are to temperature or precipitation; therefore,this aspect of future climates is of lesser importance. Forthis reason, the GCM change field method is generallyadequate for adjusting these parameters for use inclimate change impact assessments. This is also true inassessments involving downscaled climates.Assessment of climate impacts is discussed in Section6.6. Examples of studies conducted in Ontario using thechange field method and typical change field values arediscussed in Section 5.1.6.5.3 Statistical Downscaling MethodVarious statistical downscaling models are currentlyavailable for climate downscaling. The StatisticalDownscaling Model (SDSM) is one tool that is availableon the Environment Canada website (www.cccsn.ca). Thismodel was developed in the UK by Drs. R. Wilby and C.Dawson. It is a hybrid of a stochastic weather generatorand a regression-based downscaling method, packagedwith a user friendly Windows interface. The modelallows users to generate single site scenarios of dailyweather variables (i.e., air temperature and precipitation)for current and future climates. SDSM helps the useridentify large scale predictors (i.e., mean sea levelpressure, surface vorticity or specific humidity) thatexplain much of the climate variability at a site. Statisticalmodels are built using these predictors to estimate thepredictand values. Predictands are the local climatevariables of interest (i.e., temperature and precipitation).As predictors tend to change in the future, so toowill the simulated predictands, through the statisticalrelationships determined by the model.Guidance:To examine climate variability, at least onestatistical downscaling model scenario should beapplied to develop future climates. The GCMemissionscenario used in the climate simulationshould match one of the scenarios chosen for thesuite of GCM-emission scenarios, if possible.The SDSM application process requires predictor timeseries for current and future conditions. For currentconditions, predictor time series are created from theGCM runs, available at GCM grid points using the NCEP(National Centre for Environmental Prediction) model.NCEP is a reanalysis of recent weather that creates aretrospective accounting of the atmospheric conditionsbased on observed data. In effect, the NCEP runs fillin values for a wide variety of predictors by modellingexisting and recent conditions. A large number of NCEPvariables are available for all of North America for theperiod 1961 to 1990.GCM-based runs that extend well into the future(typically to 2100) provide the predictor time series forfuture emission scenarios, for various time horizons(i.e., 2050s, 2070s etc.). The local climate predictandsare generated using the same statistical relationshipsbetween the predictors and predictands developed forcurrent conditions.To acquire a downloaded copy of SDSM with the user’smanual as well as a brief summary of the modellingprocess, visit the Environment Canada website, www.CCCSN.ca. Click Downscaling Tools on the left handside of the screen and select SDSM from the drop downmenu. Review the summary version of model applicationand refer to the full version of the manual for detaileddescriptions and a well documented example for
Climate Change Assessment81reference. The overall process is shown in a flow chart inthe SDSM manual (Figure 2.1; Wilby and Dawson, 2007).Prior to modelling, data must be acquired and preparedfor downscaling. Required data includes the localobserved air temperature and precipitation as well aspredictor files downloaded from the CCCSN website.Observational data is used to calibrate and validate thehydrologic model developed for the assessment. Thisdataset should contain observational data from the localmeteorological station for a recent time period. Longertime periods (i.e., > 30 years) provide a more robustmodel. Observed air temperature and precipitationin daily format is required. Air temperature shouldbe represented as daily minimum and maximum airtemperature for the site to be downscaled. Precipitationshould be provided as daily total precipitation. Datashould be formatted as single columns of data (no timevalues) in a spreadsheet or spreadsheets as indicated inthe SDSM manual.Predictor datasets are downloaded from the CCCSNwebsite for available scenarios.• Click Download Data on the left hand side of thescreen.• Select Statistical Downscaling Input from the dropdown menu.• Select HadCM3 Predictors.The upcoming screen indicates that three datasetsare available. These include:1. NCEP (reanalysis) data covering 1961 to 2001. Thisrepresents the current conditions.2. Scenario A2(A) from the AR4 emission scenario runs(1961 to 2099).3. Scenario B2(A) from the AR4 emission scenario runs(1961 to 2099).• Click on the map of North America and fill in studyarea latitude and longitude. Note that longitude canbe entered as negative degrees (i.e., degrees west ofGreenwich) or as degrees east of Greenwich. Ontariolongitudes range from about -75 to -95 degrees westor 285 to 265 degrees east.• Click Get Data and a zip file will be available fordownload to your computer. The zip file containsthree folders, the NCEP and two future scenariodatasets. Datasets are formatted as required by SDSMand contain 23 predictors.Follow a similar process to download CGCM3 predictors.In this case the user must register on the Data AccessIntegration (DAI) system. This site appears when CGCM3predictors is selected. Once access is granted witha password, the user will be able to access the list ofavailable zipped files. These include:• NCEP data for the 1961 to 2001 period;• Scenario A2 for 1961 to 2000 and 2001 to 2100; and• Scenario A1B for the period 2001 to 2100.With the CGCM3, 25 predictors are available in thepredictor dataset for use in developing statisticalrelationships with predictors. To avoid confusion, theuser should carefully follow directions relating to settingup directories for data archives, model input/output andother documentation.The SDSM manual and summary document identifiesseven steps in the SDSM modelling procedure. Each ofthese is described briefly here:1. Setup. Download model, setup and install the modeland create directories for input and output files asrecommended.2. Quality control. This step checks observed predictandfiles for missing data or suspect values and provides acapability to transform data. Transformations may benecessary for data which is not normally distributed(i.e., precipitation). Transformations can createnormalized datasets that can be modelled moreaccurately.3. Screen variables. This process is aimed at selectingthe predictors with the strongest interrelationshipswith the predictands, temperature and precipitation.The Analyze step provides the user with a tableindicating percentage of variance explained byeach predictor-predictand pair by month, season orannual period. A correlation matrix is available thatindicates the inter-variable correlations and partialcorrelations with the predictands. Scatter plots arealso provided to help determine if transformationsare needed and whether outliers may be affectingresults. The user needs to select the best and mostpromising predictors for each predictand in this stage.The manual should be followed closely at this step asthis is the most critical aspect of developing reliablestatistical models for the predictands.4. Calibrate model. The statistical downscaling modelsfor the predictands are developed at this stage.
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