localized climate projections for waterloo region...ii for waterloo region, projections for a number...
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Localized Climate Projections for Waterloo Region
FINALREPORT–December2015
Preparedby:
Preparedfor:
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Coverphotos:
• Top:TherooftopoftheSeagramLoftsprovidesabird’seyeviewofadeadlythunderstormrollinginoverUptownWaterlooonSeptember5,2014.Credit:JasonThistlethwaite
• Bottomleft:HailinWaterlooRegiononAugust2,2015.Credit:JasonThistlethwaite• Bottomright:FlashfloodingatFairviewParkMallinKitcheneronJune28,2013.Credit:Driveseat
KitchenerviaCTVNewsKitchenerContributors:
• AlexCadel,CandidateforMasterofClimateChange,UniversityofWaterloo• SarahBrown,InterdisciplinaryCentreonClimateChange,UniversityofWaterloo• Dr.ChrisFletcher,DepartmentofGeography,UniversityofWaterloo• Dr.DanielScott,DepartmentofGeography,UniversityofWaterloo• Dr.JasonThistlethwaite,SchoolofEnvironment,Enterprise&Development,UniversityofWaterloo
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ExecutiveSummaryAddressingclimatechangeisoneoftheglobalchallengesofthiscentury.RegionsacrossCanadaareexperiencingchangingclimaticconditionssuchashigheraveragetemperatures,newprecipitationpatterns,andincreasedfrequencyandseverityofextremeweatherevents(e.g.heatwaves,intenserainfall,andstrongwinds)1.ThesechangesarehavingavarietyofimpactsonecosystemsandeverydaylifeinCanadiancommunities,includingpropertydamageandinfrastructurefailureduringextremeevents,shiftinggrowingseasons,arangeofeconomiclosses(e.g.,constructiondelays,cropdamage,tourismpatterns),increasedhealthrisksposedbyextremeweather,andshiftsintherangesofpestsandinfectiousdisease.Climateadaptationreferstomeasurestakentoreducethevulnerabilityofnaturalandhumansystemstoactualorexpectedeffectsofclimatechange.Inadditiontoeffortstomitigateclimatechangebyreducinggreenhousegas(GHG)emissions,theimportanceofclimateadaptationisrisingacrossCanada.Municipalitiesarebeingcalledupontorespondtovulnerabilitiesexposedbycurrentchangingconditionsandrecentextremeweatherevents,aswellastoprepareforbothrisksandopportunitiesthatmayarisegivenlonger-termlocalclimateimpacts.TheCityofWindsorandtheCityofToronto,forexample,havedevelopedclimateadaptationplansthatweredrivenlargelyinresponsetoextremeheatandurbanfloodingpressuresaffectingtheirrespectivecommunities.Toenableeffectiveadaptationplanningandactionthatbuildscommunityresilience,itisimperativeformunicipaldecision-makerstohaveaccesstolocallyrelevantandrobustclimateprojectionsthatexaminehowtemperatureandprecipitationareexpectedtochangeinthefuture.TheefforttodeveloplocalizedclimateprojectionsforWaterlooRegionwasinitiatedinresponsetotheneedexpressedbymunicipalstafftogainagreaterunderstandingofclimate-relatedrisksthatarerelevanttoourregionasameanstoinformmunicipalstrategicandcollaborativeplanning.TheUniversityofWaterloo’sInterdisciplinaryCentreonClimateChange(IC3)preparedthisreporttocontributetoongoingclimatecollaborationsonclimateactionbetweentheCitiesofCambridge,Kitchener,andWaterloo,andtheRegionofWaterloo.Thepurposeofthereportistosummarizeinformationonprojectedclimatechangeforourlocalregionthatcouldaidinadvancingalocaldialogueonclimateadaptationplanningandextremeweatherresilience.Howarelocalizedclimateprojectionsdeveloped?Theprocessofdevelopingclimateprojectionsusesbothhistoricalweatherdataforthisregionaswellasanensembleofclimatemodels,whichprovidethebestavailablescientificassessmentofhowfuturesocialandeconomicconditionswillinfluencetheglobalclimatesystemandclimateinthisregion.
1Warren,F.J.andLemmen,D.S.,editors(2014):CanadainaChangingClimate:SectorPerspectivesonImpactsand
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ForWaterlooRegion,projectionsforanumberofclimateparametersofinteresttomunicipalstakeholders(e.g.monthlymeantemperature,seasonalprecipitation)havebeenprovidedforthreedifferenttimeperiods:the2020s,2050s,and2080s(respectivelycoveringtheyearsof:2011-2040,2041–2070,and2071-2100).Theprojectedchangesinclimatearerelativetohistoricaldatafromthe1990s(1981-2010)asthebaseline(or“observed”)period.Acrosseachofthesetimeperiods,threedifferentfutureGHGemissionsscenariosareexamined:
• Anet-zerocarbonemissionscenariothatwouldbenecessarytolimitglobalwarmingto2oC;• Anaggressiveemissionreductionscenarioconsistentwiththeemissionreductionpledgesof
countries;and,• Abusiness-as-usual(BAU)emissiontrajectory.
OurcurrenttrajectoryasaglobalsocietyisinlinewiththeBAUscenario,andrecentglobaleffortstocurbGHGemissionshavenotyetbeensubstantialenoughtodeviatefromthispath.Theothertwoscenariosconsideredherearestillplausible;however,substantialinternationalcommitmentwillberequiredtoreduceemissionstothelevelsonwhichthosescenariosarebased.WhichclimateconditionsareprojectedtochangeinWaterlooRegion,byhowmuch,andbywhen?
Temperature:
• Annualmeantemperatureisprojectedtoincreasebyabout2-3oCbythe2050sacrossallemissionscenarios.
• Increasesinmonthlytemperaturesareprojectedtobethemostmarkedthroughoutthewinterandintoearlysummer(seeTableES1).Forexample,underallscenariosthemonthlymeantemperatureinFebruaryin2050sinexpectedtobe3-5oCwarmerthanitistoday,pushingtheaveragetemperatureforthemonthcloserto,orslightlyabove,0oC.
• Currently,theregionexperiencesaround10daysperyearwithextremeheat(dailymaximumtemperatureexceeding30oC).UnderaBAUscenario,thenumberofdayswithextremeheatisprojectedtomorethantripleto32daysbythe2050s,andthennearlydoubleagainto60daysbythe2080s(seeFigureES1).
• Currently,around22daysperyearareobservedwithextremelycoldtemperatures(dailyminimumtemperaturelowerthan-15oC).Areductioninthenumberofextremelycolddaysisprojectedunderallscenarios,withlessthanhalfasmanyextremecolddaysoccurringinthe2020saswererecordedduringthe1990s,andfurtherreductionsoccurringovertherestofthecentury(asfewas6daysbythe2080sunderaBAUscenario).
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TableES1:RangeofprojectedchangesforWaterlooRegionbythe2050scomparedtothe1990s
TemperatureChange(
oC) PrecipitationChange(%)
Scenario Summer Winter Summer Winter
Net-zerocarbon +1.1to2.1 +1.1to2.9 6.3to21.5% -5.0to17.1%
Aggressivemitigation +1.6to2.6 +1.8to3.7 -3.8to22.2% 6.1to20.4%
Business-as-usual +2.8to3.5 +2.8to4.4 5.5to25% 4.7to26.9%
FigureES1demonstratestheaverageannualnumberofdaysprojectedtoreachextremeheatconditions,
whichisdefinedasdayswheremaximumairtemperaturesreachorexceed30oC.Thesevaluesareshown
incomparisontotheobservedaveragevalue(10days)forthe1990speriod.Errorbarsrepresentthe
rangeofuncertaintyforthemulti-modelensemble.
Precipitation:• Totalannualprecipitationisprojectedtoincreasebyapproximately4-6%bythe2020s,andby
approximately8-12%inthe2050sand2080s(seeTableES1).• Seasonally,thelargestprecipitationincreasesareexpectedinwinter,spring,andsummer,
althoughthemagnitudeofchangeinsummerisassociatedwiththelargestrangeofuncertaintyduetoalackofconsensusbetweenclimatemodels.
0
10
20
30
40
50
60
70
80
2020s 2050s 2080s
Averageannual#ofdays>30oC
Timeperiod(30-yearaverages)
FigureES1:ExtremeheatinWaterlooRegion
Net-zerocarbon
Aggressivemiigaion
Business-as-usual
Observed (1990s)
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• Increasedamountsofprecipitationarelikelytoinitiallyresultinincreasesintotalannualsnowinthe2020s,yetintothe2050sand2080s,warmerwintertemperaturesarelikelytocauselessprecipitationtofallintheformofsnow,comparedtotoday’sclimate.
• InSouthernOntario,themonthsofDecember,January,andFebruaryareexpectedtoexperience40%morefreezingraineventsbythe2050s,and45%morefreezingraineventsbythe2080s.
• Rainfallintensitiesareprojectedtoincreaseacrossallscenariosandtimeperiods,withlarge-magnituderainfalleventsexpectedtooccurmorefrequentlythaninthehistoricalrecord.Ifthevariabilityofprecipitationeventsdoesnotchange,thenclimatechangeisprojectedtoslightlydecreasethefrequencyof6-daydryspellsfromanaverageof14to13eventsoccurringperyearbythe2050s.
Wind:
• MorewindgusteventsareexpectedinSouthernOntariobytheendofthecentury,asbothlarge-scalefrontalstormsandlocalconvectivewindstormsareprojectedtooccurmorefrequently.
Whatactioncanbetakenatthemunicipalleveltorespondtotheseprojectedchanges?Successfuladaptationtoclimatechangerequiresarobustrangeofprojectionsonhowthelocalclimateisexpectedtoevolveaswellasanunderstandingoftheremainingscientificuncertainties.Projectionscanbeconsideredthestartingpointtowardsdevelopingasharedbaseofinformationuponwhichtodevelopaplanandspecificresponsesorprojects.HavingaccesstofuturescenariosofwhatclimateisexpectedtolooklikeinWaterlooRegionisoneofthefoundationalstepsofanadaptationplanningprocess,alongwithidentifyingstakeholdersandassessingcurrentclimateimpacts(bothrisksandopportunities).Usinglocalizedclimateprojectionsandothercommunityknowledgeasinputs,afullassessmentofpotentialimpactsandvulnerabilityacrosscommunityassetsandservicescanbegin.
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Contents
ExecutiveSummary......................................................................................................................................iListofFigures...............................................................................................................................................viListofTables................................................................................................................................................vi1.0Introduction........................................................................................................................................1
1.1Reportpurposeandcontext..............................................................................................................11.2Studyarea..........................................................................................................................................21.3Whatisthedifferencebetweenweatherandclimate?....................................................................3
2.0Overviewofthemodellingprocess.....................................................................................................42.1Whyuseclimatemodelling?..............................................................................................................42.2Whatisstatisticaldownscaling?........................................................................................................42.3Greenhousegasemission(GHG)scenarios.......................................................................................52.4Summaryofmethodology.................................................................................................................62.5Understandinguncertainty................................................................................................................8
3.0LocalizedclimateprojectionsforWaterlooRegion..........................................................................103.1Meantemperature..........................................................................................................................10
Annualmeantemperature.................................................................................................................10Monthlymeantemperature..............................................................................................................11
3.2Extremetemperaturethresholds....................................................................................................12Extremeheat......................................................................................................................................13Extremecold......................................................................................................................................14Daysbelowfreezing...........................................................................................................................16Freeze-thawcycles.............................................................................................................................16
3.3Degreedays.....................................................................................................................................173.4Precipitation.....................................................................................................................................18
Totalannualandseasonalprecipitation............................................................................................18Dayswithprecipitation......................................................................................................................20Precipitationreturnperiods...............................................................................................................20Snowfall.............................................................................................................................................23Freezingrain......................................................................................................................................24Wetanddryspells..............................................................................................................................24
3.5Wind.................................................................................................................................................254.0Nextsteps:Localclimateimpactsandadaptationplanning.............................................................26
4.1ChangingconditionsandrelatedimpactsforOntariomunicipalities..............................................264.2Climateadaptationplanningatthelocallevel................................................................................274.3Usingclimateprojectionsinongoingadaptationplanning.............................................................27
AppendixA:WaterlooRegionLocalizedClimateProjectionsProcessManual
AppendixB:WaterlooRegionLocalizedClimateProjectionsFullResultsforAllParameters
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ListofFiguresFigure1.1:Locationofstudyarea(WaterlooRegion,Ontario,Canada)………………………………………..........3Figure3.1:Annualmeantemperature(WaterlooRegion)........................................................................11Figure3.2:Monthlymeantemperatures(2050s,WaterlooRegion).........................................................12Figure3.3:ExtremeHeatinWaterlooRegion...........................................................................................14Figure3.4:ExtremeColdinWaterlooRegion............................................................................................15Figure3.5:Averageannualnumberofdays<0(WaterlooRegion)..........................................................16Figure3.6:Freeze-thawcycles(WaterlooRegion)....................................................................................17Figure3.7:HeatingDemand(WaterlooRegion)........................................................................................17Figure3.8:CoolingDemand(WaterlooRegion)........................................................................................18Figure3.9:Seasonalprecipitationamounts(2050s,WaterlooRegion)....................................................20Figure3.10:Totalannualsnowfall(WaterlooRegion)..............................................................................24
ListofTablesTable2.1:Componentsofthemodellingprocess......................................................................................7Table3.1:ChangeinaverageannualmeantemperatureinWaterlooRegion..........................................10Table3.2:PercentagechangeintotalannualprecipitationinWaterlooRegion......................................19Table3.3:Returnlevelsformaximum1-dayprecipitationamounts........................................................21
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1.0Introduction
Addressingclimatechangeisoneoftheglobalchallengesofthiscentury.RegionsacrossCanadaareexperiencingchangingclimaticconditionssuchashigheraveragetemperatures,newprecipitationpatterns,andincreasedfrequencyandseverityofextremeweatherevents(e.g.heatwaves,intenserainfall,andstrongwinds)2.ThesechangesarehavingavarietyofimpactsonecosystemsandeverydaylifeinCanadiancommunities,includingpropertydamageandinfrastructurefailureduringextremeevents,shiftinggrowingseasons,arangeofeconomiclosses(e.g.,constructiondelays,cropdamage,tourismpatterns),increasedhealthrisksposedbyextremeweather,andshiftsintherangesofpests,invasivespeciesandinfectiousdisease.Regardlessofactionsthathavebeentakenorareplannedineffortstoreducegreenhousegas(GHG)emissionsbothlocallyandworldwide,allscenariosmodelledforthefutureindicatethatsomedegreeofclimatechangewillpersistoverthecourseofthiscentury.Thequestionsthatremainare,whichconditionswillchangeincertainregions,byhowmuch;and,howwillourcommunitiesadapt?InadditiontoeffortstomitigateclimatechangebyreducingGHGemissions,theimportanceofclimateadaptationisrisingacrossCanada.Climateadaptationreferstomeasurestakentoreducethevulnerabilityofnaturalandhumansystemstoactualorexpectedeffectsofclimatechange.Municipalitiesarebeingcalledupontorespondtovulnerabilitiesexposedbycurrentchangingconditionsandrecentextremeweatherevents,aswellastoprepareforbothrisksandopportunitiesthatmayarisegivenlonger-termlocalclimateimpacts.TheCityofWindsorandtheCityofToronto,forexample,haveadoptedclimateadaptationplansthatweredrivenlargelyinresponsetoextremeheatandurbanfloodingpressuresfeltintheirrespectivecommunities.Toenableeffectiveadaptationplanningandactionthatbuildscommunityresilience,itisimperativeformunicipaldecision-makerstohaveaccesstolocally-relevantandrobustclimateprojectionsthatexaminehowtemperatureandprecipitationareexpectedtochangeinthefuture.
1.1Reportpurposeandcontext
Climatemodelsallowustoconductsafe,controlled,andreproducibleexperimentswiththeEarth’sclimatesystemthatcanbeusedtomakeprojectionsabouthowtheglobalclimatewillchangeinthefuture.ThepurposeofthisreportistosummarizeinformationonprojectedlocalclimatechangeforWaterlooRegion.Havingaccesstolocalized,ordownscaled,climateprojectionswillprovidesomeofthebaseknowledgeandinformationneededtoguidethoughtfuladaptationplanningandextremeweatherresilienceinWaterlooRegion.
2Warren,F.J.andLemmen,D.S.,editors(2014):CanadainaChangingClimate:SectorPerspectivesonImpactsandAdaptation;GovernmentofCanada,Ottawa,ON,286p.
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In2013,theCitiesofCambridge,KitchenerandWaterloo,andtheRegionofWaterloocollectivelydevelopedandunanimouslyendorsedaClimateActionPlantoprovideWaterlooRegionwitharelevantcourseofactionforaddressingclimatechangemitigationatalocallevel.Tocomplementthiswork,municipalstaffhavecontinuedtocometogethertodiscussissuesandcoordinateactionsrelatedtoclimatechangeadaptation.
InOctoberof2014,theInterdisciplinaryCentreonClimateChange(IC3)attheUniversityofWaterloo(UW),inpartnershipwiththeRegionofWaterloo,convenedaforumtoadvanceadialogueonclimateadaptationplanningandextremeweatherresilienceinWaterlooRegion.TheforumwasintendedtobeapreliminaryworkshopwherestafffromtheRegionandareamunicipalitiescouldgainagreaterunderstandingoftheclimatechangeissuesthatarerelevanttoourregionasameanstoinformmunicipalstrategicandcollaborativeplanning.Theforumilluminatedthefactthatmanylocalstakeholdersarelookingforthesameinformation.Aprioritywasidentified:“Conductacomprehensivestudyonlocalizedclimateprojectionswhichwillhelptocommunicatewhatthepotentialchangingconditionsareandalsotobetterassessriskandplanformediumandlongtermaction.”Toactonthisnextstep,alocalizedclimateprojectionsstudywasconductedbyaresearchteamatUWincollaborationwithfourlocalmunicipalities:theCitiesofCambridge,Kitchener,andWaterloo,andtheRegionofWaterloo.
1.2Studyarea
ThisstudyencompassesthegeographicalareaofWaterlooRegion,whichiscoveredbythepoliticaljurisdictionsofaregionalmunicipality(RegionofWaterloo),threeurbanmunicipalities(CityofCambridge,CityofKitchener,andCityofWaterloo),andfourruraltownships(TownshipofNorthDumfries,TownshipofWellesley,TownshipofWilmot,andTownshipofWoolwich).AsshowninFigure1.1,WaterlooRegionliescentrallyinSouthernOntario,approximately50kilometreswestofLakeOntarioonthewesternedgeofOntario’sgreenbelt.TheentireregionfallswithintheGrandRiverwatershed,whichhasheadwatersnorthofWaterlooRegioninDufferinCounty,andflowssouthboundthroughSouthwesternOntariobeforeemptyingintoLakeErie.Theclimateprojectionswithinthisreporthavebeenlocalizedspecificallytothisstudyarea,toensurethatthebroaderimplicationsofglobalclimatechangecanbeunderstoodintermsoflocalimpacts.
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Figure1.1:Locationofstudyarea(WaterlooRegion,Ontario,Canada)3
1.3Whatisthedifferencebetweenweatherandclimate?
Thisreportpresentsprojectionsforfutureclimate.Onemightwonderhowthelong-termstatisticsofclimaterelatetotheday-to-dayweatherweexperience.Weatherandclimatearedifferentiatedbyameasureoftime.Weatheristheconditionsoftheatmosphereoverashortperiodoftime,andclimateisthelong-termbehaviourortrendsoftheatmosphere.NASAoffersafurtherinterpretationofthisdifferenceasfollows:“Weathercanchangefromminute-to-minute,hour-to-hour,day-to-day,andseason-to-season.Climate,however,istheaverageofweatherovertimeandspace.Aneasywaytorememberthedifferenceisthatclimateiswhatyouexpect,likeaveryhotsummer,andweatheriswhatyouget,likeahotdaywithpop-upthunderstorms.”4Climateisoftenstudiedbyanalyzingaveragemonthlyquantitiesoverminimum30-yearperiods,whichisastandardendorsedbytheWorldMeteorologicalOrganization.Thisfocusonlong-termstatisticsalsoallowsclimatescientiststobereasonablyconfidentinprojectingchangesthatareexpecteddecadesfromnow,evenaspredictingexactweatherconditionsbeyond1-2weeksremainschallenging.
3MapofWaterlooRegion.(n.d.).<http://www.remaxsolidgold.biz/Docs/REM028Docs/Submit/explore_map.jpg>4NASA.(2005).What’sthedifferencebetweenweatherandclimate?<http://www.nasa.gov/mission_pages/noaa-n/climate/climate_weather.html>
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2.0Overviewofthemodellingprocess
2.1Whyuseclimatemodelling?Aclimatemodelisacomplexseriesofmathematicalalgorithmsandequationsthatrepresentthedifferentcomponentsoftheclimatesystem5.Bydividingtheearthintothousandsofthree-dimensionalgridboxes,aclimatemodelcantrackthewaysinwhichenergy,water,GHGs,aerosols,andmanyothercomponentsoftheatmosphere,ocean,andlandinteractacrosstheglobe.Oneofthegreateststrengthsofclimatemodelsisthattheyallowustoconductsafe,controlled,andreproducibleexperimentswiththeEarth’sclimatesystemthatwouldotherwisebeimpossible.Withthesecapabilities,modelscanbeusedtomakeprojectionsabouthowtheglobalclimatewillchangeinthefuture.Thereisnotasingleclimatemodelthatisuniversallyreliedupon,butratheravarietyofindependently-developedmodelsareusedincombinationtoproducethebestresults.Examiningdatafrommultiplemodelsminimizesanyerrorsthatmayexistwithinasinglemodel,helpstoidentifywhichresultsaremostrobust,andimprovesourconfidenceintheprojectionsthatarebeingmadeforthefuture6.Consistentwithbestpractices,thisstudyusesthemostrecentclimatedatafromafullensembleofthelatestgenerationofclimatemodels(CoupledModelIntercomparisonProjectversion5-CMIP5).
2.2Whatisstatisticaldownscaling?Whileclimatemodelsarepowerfultoolsforunderstandingglobalandregionalchange,thevastcomputingresourcesrequiredtorunamodellimititsspatialresolutiontogridboxesofaround100kmby100kminsize.Bridgingthegapbetweenthisclimatemodeloutputandthemorefine-resolutiondataneededtoassesslocalimpactsisoneofthemajorchallengesfacingcurrentclimatechangestudies7,butanumberofdifferentstatisticalanddynamicaldownscalingtechniqueshavebeendevelopedforpreciselythispurpose.Dynamicaldownscalingreliesonrunningregionalclimatemodelswhicharedrivenattheirboundariesbyglobalmodels;anappealingbutcomputationallyexpensiveapproach8.Analternativeapproachisstatisticaldownscaling,whichrequiresanalysisoftherelationshipsbetweenvariablesovertime9.Thisisamoreaccessibleandwidelyusedmethod,andthusithasbeenusedtoproducetheprojectionsinthisreport.Creatingadownscaled,or“localized”,pictureofwhatclimate
5Müller,P.(2010).Constructingclimateknowledgewithcomputermodels.WIREsClimateChange,1,565-580.doi:10.1002/wcc.606ibid.7Fatichi,S.,Ivanov,V.Y.,&Caporali,E.(2011).Simulationoffutureclimatescenarioswithaweathergenerator.AdvancesinWaterResources,34,448-467.doi:10.1016/j.advwatres.2010.12.0138Gaitan,C.F.,Hsieh,W.W.,Cannon,A.J.,&Gachon,P.(2014).EvaluationofLinearandNon-LinearDownscalingMethodsinTermsofDailyVariabilityandClimateIndices:SurfaceTemperatureinSouthernOntarioandQuebec,Canada.Atmosphere-Ocean,52(3),211-221.doi:10.1080/07055900.2013.8576399Fatichi,S.(2011).
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changewilllooklikeatthelocallevelisusefulforbuildingunderstandingandinformingplanningprocesses.TheresultspresentedwithinSection3ofthisreporthavebeenlocalized,whereverpossible,tothelevelwherechangesintemperature,precipitation,andwindarespatiallyrelevantfortheWaterlooRegionarea.Whenusedthroughoutthisreport,theterm“localizedclimateprojections”referstoglobalclimateprojectionsthathavebeenstatisticallydownscaled.
2.3Greenhousegasemission(GHG)scenariosCompoundssuchascarbondioxide,methane,andnitrousoxidearecommonlyreferredtoasGHGs.Intheatmospherethesegasesabsorbandemitinfraredradiation,whichprovidesawarmingeffectfortheglobe.Theexistenceofthis“greenhouseeffect”hasbeenunderstoodsincethe1820s,withtheprocessesandchemistryinvolvedbecomingmoreeffectivelyquantifiedtowardstheendofthe19thcentury.TheIntergovernmentalPanelonClimateChange(IPCC)wasestablishedin1988toprovideaforumforscientistsaroundtheworldtoadvanceourunderstandingofclimatechange.SinceGHGscanpersistintheatmospherefordecadesorevencenturies,predictinghowtheywillaccumulateovertimeiscrucialtogaininganunderstandingoffutureclimatechange.AnemissionsscenariorepresentsoneplausiblerepresentationoffutureGHGemissions,witheachscenariobeingbasedondifferentassumptionsregardinghumanactivitiessuchasratesofeconomicgrowth,technologicaldevelopment,andchangesinlanduse.Sincewecannotpredictthefutureofhumandevelopment,thereisno“best”scenariotouse–allofthemareplausible.Infact,bestpracticeistoconsiderafullrangeofscenariostounderstanddifferentpossibleoutcomes.Therearethreescenariosconsideredinthisreport,eachofwhichhasascientificidentifiercodeandhasalsobeenassignedaplain-languageidentifiertohelpunderstandwhatkindofworldwewouldbelivinginundereachofthescenarios.“RCP”isshort-formfora“representativeconcentrationpathway”.ThistermismeanttoemphasizethatboththeeventualconcentrationofGHGsintheatmosphereandthetrajectorytakenovertimetoreachthesevaluesareimportant.TheassociatednumbersquantifythemagnitudeoftheeffectthatGHGconcentrationsineachscenariowouldhaveontheglobalenergybalance.Thescenariosusedforthisstudyaredescribedbelow.
RCP8.5representsafuturewhereregulareconomicgrowthcontinueswithoutregardforreducingGHGemissions.Ourcurrenttrajectoryasaglobalsocietyisinlinewiththisscenario,andrecentglobaleffortstocurbGHGemissionshavenotyetbeensubstantialenoughtodeviatefromthispath.Theotherscenariosconsideredherearestillplausible,butsubstantialinternationalcommitment,technological,societal,andeconomictransformationwillberequiredtoreduceemissionstotheselevels.Inthisstudy,RCP8.5isreferredtoas“business-as-usual”.
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RCP4.5isanoptimisticscenariothatreliesonglobalemissionsincreasingslowlyandreachingapeakaround2050,beforedrasticallydecliningtowardstheendofthecentury.Itisascenariothatwouldrequiremajorinternationalcooperationtoaggressivelyreduceemissions.Inthisstudy,RCP4.5isreferredtoas“aggressivemitigation”.
TheRCP2.6scenariorepresentsafuturewherethereisstrongandimmediateglobalcommitmenttodrasticallyreduceGHGemissions,withemissionspeakinginapproximately2020anddecliningsubstantiallythereafteruntilnet-zeroemissionlevelsarereachedbytheendofthecentury.Net-zeroreferstoasituationwhereGHGemissionsareeffectivelybalancedbynaturalorartificialsequestrationprocessesthatremovethesegasesfromtheatmosphere.Whileitisconsideredanincrediblyoptimisticscenario,itisimportanttoincludeasitistheonlyscenariothatmeetstheinternationallyagreedupontargetoflimitingtheincreaseinglobalmeantemperatureto2oCabovepre-industriallevels.This2oCtargetisthelevelbeyondwhichanthropogenicinterferencewiththeclimatesystemcouldbecomedangerous10,withsubstantialnegativeeffectsbeingfeltaroundtheglobe.TheIPCChassuggestedthatatthecurrentratesofglobalemissions,atemperatureof2oCislikelytobeexceededby204011.Inthisstudy,RCP2.6isreferredtoas“net-zerocarbon.”
2.4SummaryofmethodologyTheprocessusedtogeneratethelocalizedclimateprojectionsforWaterlooRegionpresentedinthisreportfollowedthreeoverallsteps:
1. Scopinganddatacollection-Alistofclimateparametersofinteresttokeystakeholderswasassembled(e.g.durationandintensityofrainfallevents,temperature,wind,etc.).Modellingscenarios,abaselineperiodandprojectionperiodswerealsochosen,andlocalweatherdatawascollected(seeTable2.1).Thebaselineperiodof1981-2010representsthemostrecent30-yearperiodforwhichcompleteweatherdatawasavailableforWaterlooRegion.However,somerecentclimatewarminghasalreadybeenobservedinWaterlooregion:around0.3oCsincethe1960s.Climatedatafromtheseearliertimeslicesshowsthattheaverageannualtemperaturewas6.7oCinthe1960s,6.6oCinthe1970s,and6.7oCinthe1980s.Thishasincreasedto7.0oCinour1990sbaselineperiod,indicatingthatsomedegreeofclimatechangehasalreadyoccurred.However,alackofquality,consistentdataforWaterlooRegionmadecomparisonswiththeseearliertimeperiodsdifficult.
2. Statisticaldownscaling-DownscaledprojectionsforWaterlooRegionwerecompletedforasmanyparametersaspossiblebasedondataavailabilityandmodellingcapacity.ThemethodsusedforthisstudyinvolveutilizingtheLARS-WGweathergeneratortoderiveavarietyof
10AvoidingDangerousClimateChange:AScientificSymposiumonStabilisationofGreenhouseGases(2005)11Freedman,A.(2013).IPCCReportContains‘Grave’CarbonBudgetMessage.ClimateCentral.
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climatestatisticsusingobservedweatherdataforWaterlooRegion12.Factorsofchangewerethendeterminedbycalculatingthedifferencebetweenclimatemodeldataforthebaselineandfutureperiods,andthesechangefactorswereappliedtothederivedclimatestatisticstogenerateanewseriesofsimulatedobservationsforanalysis13,14).Thisapproachofusingaweathergeneratorhasbeensuccessfulinreproducingthestatisticalcharacteristicsoftheobservedclimateinlocationsaroundtheworld,includingSouthernOntario15,16,17.
3. Literaturereview-TofillanygapswherelocalizedprojectionscouldnotbeproducedforWaterlooRegion,scientificliteraturerelatedtodownscaledclimateprojectionsforSouthernOntariowasreviewed.Thisliteraturewasfromlocationsinproximityand/orcharacteristicallysimilartoWaterlooRegion,andincludedriverbasinsandweatherstationsinandaroundthecitiesofLondon,Hamilton,andToronto.
AfullaccountofthemethodologyusedinthisstudycanbefoundinAppendixA.Table2.1:Componentsofthemodellingprocess
Scenarios
(seeSection2.3)RCP8.5“business-as-usual”RCP4.5“aggressivemitigation”RCP2.6“net-zerocarbon
BaselinePeriod 1990s(1981-2010)
Projection
Periods2020s(2011-2040)2050s(2041-2070)2080s(2071-2100)
InputData EnvironmentCanadaweatherdatafromtheWaterlooRegionAirportweatherstation,supplementedwithdatafromRosevilleweatherstationasneeded.
ModelData Historicalandfuturerunsfroma22-memberensembleofCMIP5climatemodels.AlistofmodelsusedcanbefoundinAppendixA.
12Semenov,M.A.,&Barrow,E.M.(2002).Astochasticweathergeneratorforuseinclimateimpactstudies.13Fatichi,S.(2011).14Wilks,D.S.,&Wilby,R.L.(1999).Theweathergenerationgame:areviewofstochasticweathermodels.ProgressinPhysicalGeography,23(3),329-357.doi:10.1177/03091333990230030215Hashmi,M.Z.,Shamseldin,A.Y.,&Melville,B.W.(2009).Downscalingoffuturerainfallextremeevents:aweathergeneratorbasedapproach.18thWorldIMACS/MODSIMCongress,Cairns,Australia13-17July
2009,3928-3934.16Irwin,S.E.,Sarwar,R.,King,L.M.,&Simonovic,S.P.(2012).AssessmentofClimaticVulnerabilityintheUpper
ThamesRiverBasin:DownscalingwithLARS-WG.(No.081).London,Ontario,Canada:DepartmentofCivilandEnvironmentalEngineering,TheUniversityofWesternOntario.17King,L.M.,Irwin,S.,Sarwar,R.,McLeod,A.I.,&Simonovic,S.P.(2012).TheEffectsofClimateChangeonExtremePrecipitationEventsintheUpperThamesRiverBasin:AComparisonofDownscalingApproaches.CanadianWaterResourcesJournal,37(3),253-274.doi:10.4296/cwrj2011-938
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2.5UnderstandinguncertaintyClimateprojectionsarepowerfultools,butmustbeusedwithinanunderstandingoftheirlimitations.Theclimatesystemisincrediblycomplex,andthetermuncertaintyreferstoallaspectsofclimatescienceandglobalsocio-economicfuturesthatareunknown,suchastheparametersandstructureofclimatemodels,thefutureglobalemissionstrajectory,andevenourinstrumentalweatherobservations.Capturingthisrangeofuncertaintyallowsustodeterminethefractionoffuturechangethatisduetoourimperfectmodels,comparedtothefractionduetoourdecision-making(emissionsscenarios)andnaturalfluctuations.Anotherimportantcontributortotheuncertaintyrangeistheparameteritself.Forexample,thereismoreofaconsensusamongstclimatemodelsabouthowmeanvalueswillchangethanabouthowextremevalueswillchange,asprojectingthefrequencyandintensityofrareeventsisfarmoredifficulttoevaluate.Ingeneral,projectionsfortemperaturearealsomoreconsistentbetweenallclimatemodelsthanprojectionsforprecipitation.Forthisproject,wehaveusedconfidencelimitsthatrepresenttheinterquartile(25thto75thpercentile)rangeofour22-modelensembleforeachparameter.Percentilescanbethoughtofasarankingofallmodels,fromthoseprojectingthemostwarmingtotheleast.Wearethuslookingatthe11totalmodelsthatstraddlethecentralvalueoftheensemble,asthisvaluerepresentsourbestguessforthemostlikelychange.Valuespresentedintablesrepresentthemeanvaluefromtheentireensembleofclimatemodels.Valuesingraphsshowthemedianvalue,withtheerrorbarsrepresentingthe25thand75thpercentilesoftheensemble.Inmostcasesthemedianandmeanareindistinguishable,soonlymeanvaluesarereportedinthetext.Changesaredeterminedtobesignificantwhentherangeofuncertainty(representedbythe25th-75thpercentileerrorbars)separatefromtheobservationalmean.Thisindicatesaparameterhasundergoneanimportantchangefromtheconditionsofthebaselineperiod,andshouldbeparticularlynotedasexamplesofnewlocalclimaticconditionsthatmustbeaddressed.ComparisonswithPreviousAssessmentsandReportsTheprojectionswithinthisreportareareflectionofthemethodologyusedtogeneratethem.Overthecourseofpreparingthisreport,carehasbeentakentocompareourresultswithotheravailableclimateprojectionsforSouthernOntarioandunderstandanydifferencesthatmayexist.Generallyanyinconsistenciesnotedresultfromotherstudiesusingdifferentensemblesofclimatemodels,emissionsscenarios,timeperiods,anddownscalingmethodologies.Throughthesecomparisonswehaveverifiedthatthedownscaledprojectionspresentedherearebroadlyconsistentwithpreviousassessmentsandreports,andarewithinarangeofuncertaintyconsistentwithotherresearch.Studiesthatincorporatetheresultsoffewermodels,orolderversionsofthesemodels,mustbeinterpretedinthislight.
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Asnotedabove,thisprojectusesoutputfroma22-memberensembleofstate-of-the-artclimatemodels,withtherangeofuncertaintyreportedasthemiddle50%ofthesemodelsforeachparameter.TheclimatemodelsthemselveshavebeensubstantiallyupdatedintheinterveningyearsbetweenCMIP3andCMIP5,withmodelinggroupscontinuouslyworkingonimprovingresolutions,addingadditionalphysicalprocesses,andrefiningparameterizations.Developingnewemissionsscenarioshasbeenanotheradvancementinresearchonclimatechange.OlderstudieswilltypicallymakeuseofthescenariosdevelopedintheSpecialReportonEmissionsScenarios(SRES)developedbytheIntergovernmentalPanelonClimateChange18.ItisthususefultonotebriefcomparisonsbetweentheseolderscenariosandtheRCPscenariosusedinthisreport19.
• RCP8.5isroughlycomparabletoSRESA1FI.TemperaturesinRCP8.5risemoreslowlythanA1FIbetween2035and2080,andmorequicklyinotherpartsofthecentury.
• RCP4.5iscomparabletoSRESB1.TemperaturesinRCP4.5risemorequicklythanB1untilmid-century,andmoreslowlyafterwards.
• RCP2.6hasnoanalogueamongsttheSRESscenarios,whichdidnotconsiderafuturescenariowithnet-zerocarbonemissions.
Itmustbenotedthatallprojectionsforchangesinextremequantities(suchasextremetemperatures,precipitation,andreturnperiods)werecalculatedusingmeanquantitiesonly,andassumingnochangesinvariancewouldoccur in the future.Thiswasnecessaryduetodaily-scaleclimatemodeldatabeingunavailable, meaning that no reliable estimate of changes in temperature or precipitation variabilitycould be made. Thus, estimates for temperature and precipitation extremes are necessarily veryconservative,andlikelyrepresentalowerboundfortheexpectedchanges.
18IntergovernmentalPanelonClimateChange.(2000).IPCCSpecialReport:EmissionsScenarios.19Rogelj,J.,Meinshausen,M.,&Knutti,R.(2012).GlobalwarmingunderoldandnewscenariosusingIPCCclimatesensitivityrangeestimates.NatureClimateChange,2,248-253.doi:10.1038/nclimate1385
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3.0LocalizedclimateprojectionsforWaterlooRegionThefollowingsectionpresentsaselectionofkeyresultsfromthelocalizedclimateprojectionsstudycompletedforWaterlooRegion,including:
• Meantemperature(annualmeantemperature,monthlymeantemperature)• Extremetemperaturethresholds(extremeheat,extremecold,daysbelowfreezing,andfreeze-
thawcycles)• Degreedays(heatingdemand,coolingdemand,growingdegreedays)• Totalprecipitation(totalannualandseasonalprecipitation,dayswithprecipitation,snowfall,
freezingrain,wetanddryspells,precipitationreturnperiods)• Windgusts
Resultsarepresentedbyparameter,witheachsectioncontainingbasicinformationonwhateachparameterisandhowitwascalculated,climateprojectiondataintabularorgraphicalformat,andsomeexplanationorcontextfortheseresults.FullresultsfromtheclimatemodeldownscalingexercisecanbefoundinAppendixB.
3.1Meantemperature
AnnualmeantemperatureMeantemperatureistheaverageofthemaximumandminimumtemperaturesrecordedeachday.Averagingallofthedailymeantemperaturesforoneyeargivesanannualmeantemperatureforeachyear,whichhastheninturnbeenaveragedacrosseach30-yearperiod.Thiscalculationsmoothsouttheeffectsofyear-to-yearvariabilityandprovidestheclearestsignalforclimatictrends.Whileanannualmeantemperaturevaluehaslittlepracticalrelevanceinitself,itisacommonmetricforanalyzingtheoveralleffectsofclimatechangeontemperatureandhelpstorelateglobalmeanwarmingtothelocalscale.
Forthisarea,allscenariosresultinasimilaramountofwarminguntilaroundmid-century.ThisisduetowarmingthathasalreadybeencommittedtoastheclimatesystemcatchesupwithalltheadditionalgreenhousegasesthathavebeenemittedsincethetimeoftheIndustrialRevolution.Emissionsscenariosdivergetoagreaterextentafterabout2050,astheclimatechangethatoccursafterthatpointisstronglydependentonthedecisionsmadeoverthenextfewdecades.
Table3.1:Changeinaverageannualmeantemperaturefrom1990sbaselineof7oCinWaterlooRegion
(oC)
2020s 2050s 2080s
Business-as-usual +1.4 +3.2 +5.2Aggressivemitigation +1.2 +2.4 +2.9Net-zerocarbon +1.2 +1.8 +1.7
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Projectionsindicatethat,irrespectiveofemissionsscenario,theannualmeantemperaturefor
WaterlooRegionisverylikelytoincreasebyabout2-3oCbythemiddleofthecentury(Table3.1).This
equatestoincreasesof2-3oCinbothaveragemaximumandminimumdailytemperaturesoverthecourseofayear.Ifextensivemitigationispursuedthenthistemperatureincreaselargelystabilizesby2100,whileinthebusiness-as-usualscenario,averagetemperaturesareprojectedtorisefurthertomorethan5oChigherthanthe1990saverage(Figure3.1).Theprojectedincreasesreportedheremustalsobeunderstoodinthecontextoftheclimatechangethathasalreadyoccurred,asaclimaticwarmingof0.3oChasbeenobservedattheWaterlooAirportweatherstationbetweenthe1961-1990periodandourbaselineperiodof1981-2010.
Figure3.1showsaverageannualmeantemperatureforWaterlooRegionforthreedifferentemissions
scenariosacrossthreetimeperiods.Colouredsquaresrepresentthemedianvalue,whiletheerrorbars
areassociatedwiththe25thand75
thpercentilesoftheCMIP5ensemble.
MonthlymeantemperatureWhileannualmeantemperaturestatisticsareusefultoshowageneralwarmingtrend,lookingatmonthlymeantemperaturecanprovideseasonalinformationthatismorerelevantforplanningatthelocallevel.Monthlymeantemperaturedataforthe2050sshowthatacrossallthreescenariostherearesignificantdeviationsfromtheclimateofthe1990sasmeanmonthlytemperaturesareprojectedtoincreasethroughoutthewinterandintoearlysummer(Figure3.2)20.Thesefindingshaveimportant20Additionalmonthlydataforthe2020sand2080sisprovidedinAppendixB,aswellastabulardataforannual,seasonal,andmonthlymean,maximum,andminimumtemperatures.
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1990s 2020s 2050s 2080s
Averageannualmeantemperature(oC)
Timeperiod(30-yearaverages)
Figure3.1:Annualmeantemperature(WaterlooRegion)
Observed
Net-zerocarbon
Aggressivemiigaion
Business-as-usual
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practicalsignificance.Forexample,meantemperaturesinthe2050speriodinFebruarywillthenbemuchcloserto0oC(comparedto-6oCinthe1990speriod)andmeantemperaturesinMarchwillbeabovefreezing,whichhasimplicationsforroadmaintenanceandoutdoorwinterrecreation.LargetemperatureincreasesinAprilandMayoverthesameperiod(eachover+3oCincrease)willresultingrowingseasonsbeginningearlierintheyear.HighermeantemperaturesinJune,July,andAugustwilllikelyresultinhigherwaterconsumptionandmoreelectricitydemandforairconditioning(seeSection3.3).
InFigure3.2(andseveralotherquantitiestobeexploredlateron),theerrorbarsforourconfidencerangearesoclosetogetherthattheyareindistinguishablefromthemedianvalue(andthusnotseenonthechart).Ingeneral,thesmallertherangebetweentheupperandlowerconfidencelimits,themoreconfidentwecanbeintheprojectionsforthatquantity,sincetheentirerangeofmodelsispredictingverysimilarresults.Asmightbeexpected,therangeofuncertaintytendstoexpandasprojectionsaremadefurtherintothefuture.
3.2ExtremetemperaturethresholdsChangesinmeantemperatureswillalterthedistributionofextremetemperatures.PriorstudiesacrosstheGreatLakesBasinhavefoundthatincreasesinextremehightemperaturesandreductionsinextremelowtemperatureshavealreadybeenoccurring21,22,andthatmorefrequentwarmtemperature
21Alexander,L.V.,Zhang,X.,Peterson,T.C.,Caesar,J.,Gleason,B.,KleinTank,A.M.G.,...Griffiths,G.(2006).Globalobservedchangesindailyclimateextremesoftemperatureandprecipitation.JournalofGeophysicalResearch,111doi:10.1029/2005JD00629022Mohsin,T.,&Gough,W.A.(2013).ImpactofClimateChangeontheExtremesofObservedDailyTemperatureDataintheGreaterTorontoArea.TheInternationalJournalofClimateChange:ImpactsandResponses,5(1),11-33.
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onthlym
eantemperature(oC)
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Figure3.2:Monthlymeantemperatures(2050s,WaterlooRegion)
Observed(1990s)
Net-zerocarbon
Aggressivemiigaion
Business-as-usual
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extremesandfewercoldtemperatureextremesareprojected23.InordertoexaminehowextremetemperaturewillevolveinWaterlooRegionovertheremainderofthecentury,thefollowingfourthresholdsweredecidedtobeofthemostrelevancetomunicipalstakeholders:
• Extremeheat(dayswithmaximumtemperatures>30oC),• Extremecold(dayswithminimumtemperatures<-15oC),• Daysbelowfreezing(dayswithminimumtemperaturesbelow0oC),and• Freeze-thawcycles(dayswithminimumtemperaturebelow0oCandmaximumisabove0oC).
ExtremeheatWhiledefinitionsofextremeheatvary,30oCprovidesasuitablethresholdtoanalyzehowepisodesofextremeheatwillbeaffectedbyclimatechange.Above30oCthereisastrongcorrelationbetweentemperatureandexcessmortality24,ambulancecallsforheat-relatedillness25,andreducedairquality26.ForWaterlooRegion,thefrequencyofextremeheatisnotexpectedtochangesignificantlyduringthe2020sorinanytimeperiodsinthenet-zerocarbonscenario(Figure3.3).Fortheaggressivemitigationandbusiness-as-usualscenarios,however,substantialincreasesinextremeheatareseeninthe2050sand2080s.Underabusiness-as-usualscenario,anaverageoftwofullmonthswithairtemperatures
greaterthan30oCwouldbethenewexpectednormalcondition,comparedtothecurrentannual
averageof10days.TheselocalizedresultsareconsistentwithsimilarstudiescompletedforSouthernOntariowhichindicatethatthenumberofdayseachyearwithtemperaturesover30oCcouldtriplebythe2050sandquadrupletoquintuplebythe2080sacrossmuchofSouthernOntario27.
23Thibeault,J.M.,&Seth,A.(2014).ChangingclimateextremesintheNortheastUnitedStates:observationsandprojectionsfromCMIP5.ClimaticChange,127,273-287.doi:10.1007/s10584-014-1257-224CityofWindsor.(2012).ClimateChangeAdaptationPlan.25Bassil,K.L.,Cole,D.C.,Moineddin,R.,Lou,W.,Craig,A.M.,Schwartz,B.,&Rea,E.(2011).Therelationshipbetweentemperatureandambulanceresponsecallsforheat-relatedillnessinToronto,Ontario,2005.JournalofEpidemiology&CommunityHealth,65,829-831.doi:10.1136/jech.2009.10148526Cheng,C.S.,Campbell,M.,Li,Q.,Li,G.,Auld,H.,Day,N.,...Lin,H.(2008).Differentialandcombinedimpactsofextremetemperaturesandairpollutiononhumanmortalityinsouth–centralCanada.PartII:futureestimates.AirQuality,AtmosphereandHealth,1,223-235.doi:10.1007/s11869-009-0026-227Cheng,C.S.,Li,G.,Li,Q.,&Auld,H.(2008).StatisticaldownscalingofhourlyanddailyclimatescenariosforvariousmeteorologicalvariablesinSouth-centralCanada.TheoreticalandAppliedClimatology,91,129-147.doi:10.1007/s00704-007-0302-8
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Inadditiontoextremeheatbeingmorefrequent,extrememaximumtemperatureswillalsobehigher.ComparabledatafromHamilton,Ontario,foundthatatemperatureof32.4oChistoricallyrepresenteda1-in-50yearmaximumtemperature,whileprojectionsindicateanew1-in-50yearvaluemayincreasebyupto6oCbythe2050s28.WhilewedidnothavesufficientdatatoquantitativelyprojectchangesinheatwavesforWaterlooRegion,comparableresearchshowsthatthehighernumberofdayswithtemperaturesexceedingtheextremeheatthresholdwillleadtoalikelyincreaseinthefrequency,duration,andintensityofheatwaves29,andepisodesofextremelyhotweathermaylastaround20%longer30.Withthesesubstantialchangesinextremeheat,adaptationwillbeessentialtomaintainingahealthysociety.
ExtremecoldToanalyzeextremecold,athresholdof-15oCwaschosenasitalignswithotherstudiesexaminingchangesinextremecoldtemperatureswithinSouthernOntario31.Foranymeasureofextremecold,itshouldalsobenotedthatwindchillfactorsareacrucialdeterminantofhowcoldtemperaturesareexperienced.Thesevaluesrelyonaccurateprojectionsoffuturewindspeedsanddirections,whichwerenotavailableforthisproject,meaningthatonlyairtemperaturesareanalyzedhere.
28Grillakis,M.G.,Koutroulis,A.G.,&Tsanis,I.K.(2011).ClimatechangeimpactonthehydrologyofSpencerCreekwatershedinSouthernOntario,Canada.JournalofHydrology,409,1-19.doi:j.jhydrol.2011.06.01829Smoyer-Tomic,K.E.,Kuhn,R.,&Hudson,A.(2003).HeatWaveHazards:AnOverviewofHeatWaveImpactsinCanada.NaturalHazards,28,463-485.30Eum,H.,&Simonovic,S.P.(2012).AssessmentonvariabilityofextremeclimateeventsfortheUpperThamesRiverbasininCanada.HydrologicalProcesses,26,485-499.doi:10.1002/hyp.814531Alowerthresholdof-30oCwasconsidered,butbecauseairtemperaturesbelowthisvalueareextremelyrare(e.g.therewereonly3daysbelow-30oCinthebaseline30-yearperiod)andthereisanoveralltrendofwarming,itwasdecidedthat-30oCwouldnotbeausefulindexfortrackingchangesinextremecoldintothefuture.
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Averageannual#ofdays>30oC
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Figure3.3:ExtremeHeatinWaterlooRegion
Net-zerocarbon
Aggressivemiigaion
Business-as-usual
Observed(1990s)
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InWaterlooRegion,areductioninthenumberofdayswithextremelycoldtemperatures(<-15oC)isexpected,withlessthanhalfasmanyextremecolddaysoccurringinthe2020saswererecordedduringthe1990s,andfurtherreductionsoccurringovertherestofthecentury(Figure3.4).Itshouldbenotedthatevenoverthecourseofthe30-yearbaselineperiod,decreasesintheoccurrenceofextremecoldtemperatureshavealreadyoccurred32,althoughthesehavenotbeenassubstantialasthechangesprojectedfortherestofthecentury.TheseresultsforWaterlooRegionareconsistentwithdownscaledresultsfortheCityofToronto,whichalsoprojectedreductionsofupto70%intemperaturesbelow-15oC33.Whileextremecoldeventswillbecomelessfrequentduringtherestofthiscentury,theywillnotceaseentirelyassomeyearswillcontinuetoexperiencefewextremecoldevents,andotheryearswillexperienceextremecoldeventscomparabletocurrentfrequencies.ProjectionsforextrememinimumtemperaturesfromHamilton,Ontario,foundthatatemperatureof-21.8oChistoricallyrepresenteda1-in-50yearminimumtemperature,whileanew1-in-50yearvaluemaybeashighas-16.9oCbythe2050s34.Occasionalextremecoldtemperaturesmaystilloccur,asevidencedbyarecordlowtemperatureof-34.1oCbeing
32Allen,S.M.J.,Gough,W.A.,&Mohsin,T.(2015).ChangesinthefrequencyofextremetemperaturerecordsforToronto,Ontario,Canada.TheoreticalandAppliedClimatology,119,481-491.doi:10.1007/s00704-014-1131-133Gough,W.A.,Tam,B.Y.,Mohsin,T.,&Allen,S.M.J.(2014).ExtremecoldweatheralertsinToronto,Ontario,Canadaandtheimpactofachangingclimate.UrbanClimate,8,21-29.doi:10.1016/j.uclim.2014.02.00634Cheng,C.S.,Campbell,M.,Li,Q.,Li,G.,Auld,H.,Day,N.,...Lin,H.(2008).
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Averageannual#ofdays<-15oC
Timeperiod(30-yearaverages)
Figure3.4:ExtremeColdInWaterlooRegion
Net-zerocarbon
Aggressivemiigaion
Business-as-usual
Observed(1990s)
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recordedatWaterlooAirportinFebruaryof2015,buttheseeventswilllikelybecomesignificantlymorerareinthefuture.
DaysbelowfreezingIftemperaturesbelow0oCaretakenasananalogueforwinter-likeconditionsinSouthernOntario,resultsofthisstudyindicatethatthecurrent150-dayperiodofsub-zerotemperatureswillbereducedbyanaverageofapproximatelytwoweeksoverthenext30years,andbytheendofthecentury,itmaybereducedbyatotalof3-9weeks(Figure3.5).Whiletheseresultsareforaveragenumberofdayswheretheminimumtemperaturedropsbelowfreezing,averysimilartrendisprojectedfornumberofdayswithmaximumtemperaturesbelow0oC.
Freeze-thawcyclesCountsoffreeze-thawcyclesaretakenasthenumberofdayswithminimumtemperaturesbelow0oCandmaximumtemperaturesabove0oC,implyingthatsurfacewaterwilltransitionthroughbothfrozenandliquidstatesoverthecourseoftheday.Risingtemperaturesareexpectedtocauseanincreaseinthenumberoffreeze-thawcyclesexperiencedinWaterlooRegion,particularlyoverthenext30years(Figure3.6).Thisincreaseisdrivenbyagreaternumberofwinterdayswheremaximumtemperaturesexceed0oC.Ifsignificantwarmingcontinuesunabated,thenumberofdayswithfreeze-thawcycleswillbegintodeclinebytheendofthecenturyasfewerdayswillreachminimumtemperaturesbelow0oC.
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Figure3.5:Averageannualnumberofdays<0
(WaterlooRegion)
Net-zerocarbon
Aggressivemiigaion
Business-as-usual
Observed(1990s)
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3.3DegreedaysHeatingandcoolingdegreedaysaremeasuresoftheenergyuserequiredtomaintaincomfortableindoortemperaturesinbuildings.Itisaconceptbasedontheideathatatoutdoortemperaturesbelowacertainthresholdheatingisrequiredtoraiseindoortemperatures,andconversely,abovethisthresholdcoolingisneededtolowerindoortemperatures.Temperaturethresholdscanvaryfordegreedaycalculations,andshouldthusalwaysbeexplicitlystated.Forthisreportthethresholdvalueof18oChasbeenused,asitacommonlycitedfigurefordegreedaycalculationsinNorthAmerica(notethatindoortemperaturesaregenerally1-2oCwarmerthanoutdoorairtemperatures,withthisthresholdthusrepresentinga“comfortable”indoortemperatureofapproximately20oC).Degreedaysarecomputedbytakingthedifferencebetweendailymeantemperatureandthethresholdtemperature,andhavebeensummedannually.
0
20
40
60
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Annual#daysofmin<0
oC,m
ax>
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Figure3.6Freeze-thawcycles(WaterlooRegion)
Net-zerocarbon
Aggressivemiigaion
Business-as-usual
Observed(1990s)
050010001500200025003000350040004500
2020s 2050s 2080s
Annualheaengdegreedays
Timeperiod(30-yearaverages)
Figure3.7:HeaengDemand(WaterlooRegion)
Net-zerocarbon
Aggressivemiigaion
Business-as-usual
Observed(1990s)
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Risingairtemperaturesovertherestofthecenturycorrespondtomoderatereductionsindemandforheating(Figure3.7),alongwithmoresubstantialincreasesexpectedindemandforairconditioning(Figure3.8),whichisduetohotdaysincreasingatamorerapidrate.
Growingdegreedays(GDD)areameasurementofheataccumulationinplants,whichcanbeusedtopredictratesofcropdevelopmentandtimingofmaturation.Growingdegreedaysarecalculatedsimilarlytoheatingandcoolingdegreedays,butusingalowerthresholdtemperature.AlthoughbaselinetemperaturesforGDDcalculationsvarydependingonthecropofinterest,thegenericbaselineof5oCusedhereislargelyapplicableacrossSouthernOntario.Modesttomoderateincreasesinthelengthofthegrowingseasonareanticipated.ProjectedresultsforgrowingdegreedayscanbefoundinAppendixB.
3.4PrecipitationTotalannualandseasonalprecipitationWaterlooRegionisexpectedtobecomeslightlywetterovertherestofthecentury,withtotalannual
precipitationincreasingbyapproximately5-12%dependingonthetimeperiodandscenario.Althoughthechangesreportedherearereferencedtoabaselineperiodof1981-2010,earlierweatherstationdataforWaterlooRegionwasalsoexamined.Averageannualprecipitationamountshaveremainedroughlyconstantataround915mmsincethe1960s,indicatingthattheprojectedincreasesinprecipitationduetoclimatechangerepresentsignificantdeparturesfromthenormalconditionsofthepasthalf-century.
0
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800
900
2020s 2050s 2080s
Annualcoolingdegreedays
Timeperiod(30-yearaverages)
Figure3.8:CoolingDemand(WaterlooRegion)
Net-zerocarbon
Aggressivemiigaion
Business-as-usual
Observed(1990s)
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Table3.2:Percentagechangeintotalannualprecipitationfrombaselineof918.5mm
2020s 2050s 2080s
Business-as-usual +3.8% +10.4% +12.0%Aggressivemitigation +6.2% +8.5% +10.0%
Net-zerocarbon +4.3% +11.7% +7.3%Precipitationprojectionsaresubjecttolargeruncertaintythantemperature(asdemonstratedbytheerrorbarsinFigure3.9),mainlybecauseprecipitationexhibitsgreaterspatialandtemporalvariationanditisrepresentedinclimatemodelsusingmorevariedassumptions.Projectionsforseasonalprecipitationinthe2050sshowthatincreasesareexpectedinmanycases,althoughsomeofthesechangesaredifficulttodefinitivelyinterpret.Therangeofuncertaintyforallscenariosandseasonsoverlap,suggestingthatprojectionsoffutureprecipitationaremoresensitivetoyear-to-yearclimatevariabilityandmodeluncertaintythantheyaretotheemissionsscenarioused.Inmanycases,thereisalsonorobustseparationbetweenascenarioandtheobservations.Inparticular,summer(JunetoAugust)istheseasonwithbyfarthelargestuncertaintyrangeacrossallscenarios.Theuncertaintyinsummerprecipitationisdrivenbyalackofconsensusamongstclimatemodelsastohowthecontinental-scaleprocessesdrivingprecipitationwillevolveatdifferentlatitudes.AsprojectedbytheCMIP5multi-modelmean,WaterlooRegionislocatedjustwithinthezoneofnetprecipitationincreases,whichextendsroughlyfromthislatitudenorthwards,whiledecreasesinsummerprecipitationareexpectedfurthertothesouth.However,thereisnoconsensusamongclimatemodelsoverthedirectionofsummerprecipitationchangeacrossSouthernOntario–increasesanddecreasesareprojectedbyalmostequalnumbersofmodels35.DownscaledresultsforWaterlooRegionshowthatmostmodelswithinour25-75%confidenceintervalsprojectprecipitationincreasesinsummer,althoughitshouldbenotedthatseveralmodels(<5)outsidethisconfidencerangedoprojectprecipitationdecreasesinsummer.Duetothislackofconsensusamongstclimatemodels,theresultspresentedhereforprecipitationparametersshouldbeinterpretedcautiously.
35Maloney,E.D.,Camargo,S.J.,Chang,E.,Colle,B.,Fu,R.,Geil,K.L.,...Zhao,M.(2014).NorthAmericanClimateinCMIP5Experiments:PartIII:AssessmentofTwenty-First-CenturyProjections.J.Climate,27,2230-2270.doi:10.1175/JCLI-D-13-00273.1
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DayswithprecipitationTheannualnumberofdayswithprecipitation(>0.1mm)isprojectedtodecreaseby2-5days,irrespectiveofscenario.Viewedinisolation,thisslightdecreasedoesnotrepresentasignificantchangebelowthebaselinevalueof165days.However,areductioninthenumberofprecipitationdays,accompaniedbyanincreaseintotalprecipitation,impliesanincreaseintheintensityofprecipitation.InWaterlooRegion,thenumberofdayswithheavyprecipitation(>10mm)isprojectedtoincreaseby2-3daysfromthecurrentaverageof30daysperyearbythe2050s,andthenumberofdayswithveryheavyprecipitation(>25mm)isexpectedtoincreaseby1.5-2daysfromthecurrentaverageof5daysperyear(seeAppendixB).SimilarresultshavebeenshowninseveralotherstudiesforSouthernOntario,eachfindingsmallincreasesinthenumberofdayswithprecipitation>10mmand>20mmofapproximately3daysbythe2050s.36,37,38
PrecipitationreturnperiodsOneofthemajorconcernsregardingprecipitationandclimatechangeisthatchangesinextremeprecipitationwilloccurmorerapidlythanchangesinmeanprecipitation39.Onemethodforanalyzingprecipitationextremesistocalculateexpectedreturnperiodsforheavyprecipitationevents.Areturnperiodisanestimateofhowfrequentlyastormofagivenmagnitudeisexpectedtooccur.Forexample,36Alexanderet.al.,2006.37Cheng,C.S.,Li,G.,Li,Q.,&Auld,H.(2011).ASynopticWeather-TypingApproachtoProjectFutureDailyRainfallandExtremesatLocalScaleinOntario,Canada.JournalofClimate,24,3667-3685.doi:10.1175/2011JCLI3764.138Gula,J.,&Peltier,W.R.(2011).DynamicalDownscalingovertheGreatLakesBasinofNorthAmericausingthe
WRFRegionalClimateModel.DepartmentofPhysics,UniversityofToronto:ReportfortheOntarioMinistryoftheEnvironment.39Grillakis,M.G.,Koutroulis,A.G.,&Tsanis,I.K.(2011).
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a1-in-20yearstormeventoccursonaverageonceevery20years,oraprobabilityofp=0.05(a5%chanceofoccurringinanygivenyear).Table3.3presentsmaximum1-daytotalprecipitationamounts forreturnperiodsof5,10,20,30,50,and100yearsinWaterlooRegion;whilereturnperiodsfor5-dayaccumulatedtotalsareincludedwithinAppendixB.Thedifferencesinreturnperiodvaluesacrossthethreescenariosareareflectionoftherebeing overlapping uncertainty ranges in the multi-model consensus for precipitation changes (asevidenced by the error bars in Figure 3.9). In the business-as-usual (RCP 8.5) scenario for example,modelsaredividedonthequestionofwhetherprecipitationamountswill increaseordecrease in thelatterhalfofthecentury,leadingtothemulti-modelmeanreflectinglowerquantitiesforprecipitationreturnperiodsunderabusiness-as-usualscenariointhe2080s.Overallhowever,themajorityofmodelsprojectsomedegreeofincreaseinprecipitationby2050,andthestrengthoftheprojectedchangesislargelyindependentofemissionsscenario.ItshouldbeemphasizedthattheestimatesforprecipitationreturnlevelspresentedinTable3.3are
necessarilyconservative,becauseonlyprojectedchangesinthemean,andnotinthevariance,ofdaily
precipitationareincludedinthisfutureestimate.Thus,theresultslikelyrepresentalowerboundforthe
expectedchanges.Regardless,theincreasesinthemagnitudeofrainfallprojectedforeachreturnperiodareconsistentacrossallscenariosandtimeperiods,andmakeacompellingcaseforadjustingexistingintensity-duration-frequency(IDF)curvestoaccountforfutureclimatechange.Inaddition,theseestimatesaregenerallyconsistentinmagnitudewithotherstudiesinSouthernOntario(discussedbelow),whichalsoindicatethatprecipitationintensityisexpectedtoincreaseovertherestofthecentury.
Table3.3:Returnlevelsformaximum1-dayprecipitationamounts(mm)
Return
Period
Observed RCP2.6 RCP4.5 RCP8.5
1990s 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s
2-yr 50.0 59.6 63.1 61.9 58.8 61.2 60.8 58.5 51.6 51.95-yr 66.5 79.8 83.6 83.4 80.7 81.1 82.0 78.0 66.9 67.410-yr 78.5 94.6 98.4 99.4 98.3 94.9 97.5 92.5 78.1 80.325-yr 94.8 114.9 118.7 121.8 124.9 113.3 118.8 112.6 93.4 100.550-yr 107.8 131.2 134.9 140.2 148.4 127.5 136.0 129.2 105.7 118.9
100-yr40 121.6 148.6 152.2 160.0 175.4 142.3 154.4 147.0 118.8 140.6
40Note:Thecalculationfor100-yearreturnperiodhasbeenextrapolatedfromlimiteddataandthusshouldhavealowerdegreeofconfidenceassignedtoit.
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Giventheirimportanceformunicipalwaterinfrastructureandfloodplainmanagement41,theimpactofclimatechangeonprecipitationreturnperiodshasbeenstudiedbynumerousgroupsacrossSouthernOntario.Increasesinthemagnitudeandfrequencyofextremeprecipitationeventshavebeenfoundinallcases.Atsub-dailytimescales,themagnitudeofrainfallassociatedwithintense2-hourand6-houreventsisexpectedtooccurapproximatelytwiceasfrequently,largelyduetomorefrequentandintenseconvectiveweathersystems42.Therainfallintensitiescorrespondingtoextremeprecipitationeventsarealsoexpectedtoincrease,withlargerpercentagechangesbeingfoundforlongerreturnperiods43.Thus,therainfallintensityassociatedwitha1%chanceofoccurringeachyearinthecurrentclimatemaynowhavea3%probabilityofoccurringinagivenyearinthefuture44.Statedinprobabilisticterms,thelikelihoodofexperiencinglargerintensitystormswillincreasesubstantially.Returnperiodscanalsobecalculatedformulti-dayprecipitationevents.Thereturnperiodsfor3-dayaccumulatedrainfallextremesunderabusiness-as-usualscenarioareprojectedtoincreasebyabout20-70%overthepresentcenturyinSouthernOntarioriverbasins,withthepercentageincreaseratesforlongerreturnperiodsagainbeinggreaterthanforshorterreturnperiods45.Intermsofrainfallmagnitude,themostextrememulti-dayprecipitationeventsimulatedfortheUpperThamesRiverBasinwasfoundtobeontheorderof250mmbythe2050s,46
41Peck,A.,Prodanovic,P.,&Simonovic,S.P.(2012).RainfallIntensityDurationFrequencyCurvesUnderClimateChange:CityofLondon,Ontario,Canada.CanadianWaterResourcesJournal,37(3),177-189.doi:10.4296/cwrj2011-93542Mailhot,A.,Duchesne,S.,Caya,D.,&Talbot,G.(2007).Assessmentoffuturechangeinintensity–duration–frequency(IDF)curvesforSouthernQuebecusingtheCanadianRegionalClimateModel(CRCM).JournalofHydrology,347,197-210.doi:10.1016/j.jhydrol.2007.09.01943Wang,X.,Huang,G.,&Liu,J.(2014).Projectedincreasesinintensityandfrequencyofrainfallextremesthrougharegionalclimatemodelingapproach.JournalofGeophysicalResearch:Atmospheres,119,13,271-13,286.doi:10.1002/2014JD02256444Peck,A.,Prodanovic,P.,&Simonovic,S.P.(2012).45Cheng,C.S.,Li,G.,Li,Q.,&Auld,H.(2011).46CummingCockburnLimited.(2000).HurricaneHazelandExtremeRainfallinSouthernOntario.Accessed14August2015from:http://www.iclr.org/images/Hurricane_Hazel_and_extreme_rainfall_in_southern_Ontario.pdf
Whatdoesa“1-in-100”year
stormmean?A1-in-100yearstormdoesnotnecessarilymeanthatthismagnitudeofstormwillonlyoccuronceevery100years,butratherithasa1%statisticalprobabilityofoccurring inanygivenyear.Majorflooding or storm events can cause significantdamages at lower precipitation intensities,dependingonthestormwater infrastructureandfloodplainmanagementinplace.Forexample,inOntarioduringthe2002–2013periodthecitiesofHamilton,London,Peterborough,andTorontoallexperiencedmajorcommunityfloodingevents.The largest storm event used in Ontarioregulations is built upon the observations andweathermeasurements connected to HurricaneHazel thatoccurred inOctober1954,whichwassignificantly more rare than a 1-in-100 yearevent. This storm featured285mmof rain in 48hours, over 200mm in the first 24 hours alone(Cumming Cockburn Limited, 2000). Damagesresultingfromthestormincludedbridges,roads,structures,81peopledead,thousandshomeless,and with an estimated cost of $100million ($1billiontoday).
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comparedtoamaximumof200mmintheobservedrecord47.Alloftheseprojectedincreasesinthunderstormsarerobustacrossasuiteofclimatemodelsandemergeinresponsetorelativelymoderateglobalwarming,whichoccursinallscenariosexamined.Thissuggeststhatcontinuedincreasesingreenhouseforcingarelikelytoincreaseseverethunderstormoccurrence48.TheseexpectedchangesinextremeprecipitationhaveresultedinrecommendationsinsomecasesthatIntensity-Duration-Frequency(IDF)curvesandfloodprotectionplansshouldberevisedtoconsiderclimatechangeimpacts49.Forexample,ithasbeenrecommendedthatIDFcurvesfortheUpperThamesRiverBasinestablishedusinghistoricaldatashouldbeincreasedby30%toaccountforfutureclimatechange50,andthatallcitiesinSouthernOntarioshouldconsiderincreasingthecurrentvaluesofIDFcurvesbyaround20%51.AnalyzingIDFcurvesforareamunicipalitieswasnotwithinthescopeofthisstudy;howeveraseparate2012studyfoundthat“theexistingIDFcurvesfortheCityofWaterlooandtheCityofKitchenerarelikelysomewhatconservativeforrainfalldurationslessthantwohours,althoughtheimpactsofclimatechangecouldresultinmoresevereeventsinthefuture.”52
SnowfallTotalsnowfalliscomputedbyassumingthatallprecipitationfallsassnowwhendailymeantemperaturesarebelow0oC,andthatwhentemperaturesarebetween0oCand2oC,thefractionofsnowtoraincanbecalculatedasalinearfunctionofairtemperature53.Theseresultsnecessarilyassumethat1mmofprecipitationmeasuredattheWaterlooweatherstationisequivalenttothewatercontentof1cmofsnow.Inrealitythewatercontentofsnowdoesvarybetween0.5-1.5mmdependingonitsdensity,thoughonaveragethisassumptionholdstrue.
47Sharif,M.,Burn,D.H.,&Hofbauer,K.M.(2013).GenerationofDailyandHourlyWeatherVariablesforuseinClimateChangeVulnerabilityAssessment.WaterResourcesManagement,27,1533-1550.doi:10.1007/s11269-012-0253-448Diffenbaugh,N.S.,Scherer,M.,&Trapp,R.J.(2013).Robustincreasesinseverethunderstormenvironmentsinresponsetogreenhouseforcing.PNAS,110(41),16361-16366.doi:10.1073/pnas.130775811049Eum,H.,&Simonovic,S.P.(2012).50Das,S.,Millington,N.,&Simonovic,S.P.(2013).DistributionchoicefortheassessmentofdesignrainfallforthecityofLondon(Ontario,Canada)underclimatechange.CanadianJournalofCivilEngineering,40,121-129.doi:10.1139/cjce-2011-054851Peck,A.,Prodanovic,P.,&Simonovic,S.P.(2012).52Burn,D.2012.UpdateofIntensity-Duration-Frequency(IDF)CurvesfortheCityofWaterlooandtheCityofKitchener53Brown,R.D.,Brasnett,B.,&Robinson,D.(2003).GriddedNorthAmericanmonthlysnowdepthandsnowwaterequivalentforGCMevaluation.Atmosphere-Ocean,41,1-14.
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Increasedamountsofprecipitationinitiallyresultinincreasesintotalannualsnowinthe2020s,yetintothe2050sand2080s,higherwintertemperatureseventuallycauseasmallerfractionofprecipitationtofallassnow.Thesetrendsareechoedinthenumberofdayswithsnowfall(>0.1cm)andthenumberofdayswithveryheavysnowfall(>5cm),bothofwhichareprojectedtodecreaseslightlyby205054.Fulltabulardatafortotalsnowanddayswithsnowfall>5cmisinAppendixB.
FreezingrainUnderabusiness-as-usualscenarioinSouthernOntario,themonthsofDecember,January,andFebruaryareexpectedtoexperience40%morefreezingraineventsbythe2050sand45%morefreezingraineventsbythe2080s55.Thisisaneffectofrisingtemperatures,resultinginmoreprecipitationfallingasfreezingrainthatinthepastwouldhavefallenassnow.Incontrast,freezingraininthemonthsofNovember,March,andAprilwilldecreaseby10%bythe2050s,anddecreaseby15%bythe2080s56.Thisdecreaseisalsodrivenbytemperatureincreases,sinceaportionofprecipitationthatwouldhavefallenasfreezingrainduringthesemonthsisnowmorelikelytofallasliquidrain.
WetanddryspellsDryspellsaredefinedasperiodsof6ormoreconsecutivedayswithnoprecipitation,andwetspellsaredefinedasperiodsof6ormoredayswithprecipitationoccurringoneachday.InourprojectionsforWaterlooRegion,noprojectedchangeswerefoundinthemaximumlengthofwetanddryspellsovertime,withthelongestdryspellslasting31-34daysandthelongestwetspellslasting8-10daysacrossallscenariosandtimeperiods.Somesimilarstudieshaveprojectedincreasesof2-3daysinthemaximum54Gula,J.,&Peltier,W.R.(2011).55Cheng,C.S.,Auld,H.,Li,G.,Klaassen,J.,&Li,Q.(2007).Possibleimpactsofclimatechangeonfreezingraininsouth-centralCanadausingdownscaledfutureclimatescenarios.NaturalHazardsandEarthSystemSciences,7,71-87.56Ibid.
0
50
100
150
200
250
2020s 2050s 2080s
Totalannualsnow(cm)
Timeperiod(30-yearaverages)
Figure3.10:Totalannualsnowfall(WaterlooRegion)
Net-zerocarbon
Aggressivemiigaion
Business-as-usual
Observed(1990s)
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lengthofwetanddryspellswithinotherriverbasinsofSouthernOntario57,butthemethodsusedinthisstudy(whichassumeconstantprecipitationvariance)havesuggestedthatthismaynotbethecaseforWaterlooRegion.Projectionsindicatethatthefrequencyofdryspellsisexpectedtodecreaseslightly,butnotsignificantlywithclimatechange.Thefactthatnolargechangesareexpectedfordryspellsisinitselfsignificant,asitcontrastswiththefindingsforotherpartsoftheworldwheredroughtsareexpectedtobecomemorefrequent.Thefrequencyofwetspellswillincreasemoderately,althoughthereisstillanaverageoflessthanone6-daywetspellperyear(seeAppendixB).InSouthernOntario,individualmonthscouldexperienceslightchangesinthefrequencyorlengthofwetanddryspells58,butthiswasnotexaminedfurtherintheWaterlooRegiondataconsideringthesmalloverallannualchanges.
3.5WindWindspeediscloselytiedtotemperatureandairpressure,andthusthefrequencyandspeedofwindgustsareexpectedtovarywithclimatechange.MorewindgusteventsareexpectedinSouthernOntario(inacorridorfromWindsortoLondontoToronto)bytheendofthecentury,asbothlarge-scalefrontalstormsandlocalconvectivewindstormscouldoccurmorefrequently59.Thefrequencyofhourlywindgustsgreaterthan40km/hisexpectedtoincreaseby10-20%,andthefrequencyofwindgustsgreaterthan70km/hwillincreaseby20-40%comparedtothe1994-2007historicalaverage60.Moreseverewindgusteventsaregenerallyexpectedtoincreasebygreatermagnitudes,althoughsincethesearerareeventsthisisamoreuncertainprediction61.Avoidingmoresubstantialincreasesinwindgustswillbetiedtolimitingglobalwarming,asscenarioswithgreaterwarmingindicatealargerincreaseinwindgusts62.
57Sharif,M.,Burn,D.H.,&Hofbauer,K.M.(2013).58Rahman,M.,Bolisetti,T.,&Balachandar,R.(2012).HydrologicmodellingtoassesstheclimatechangeimpactsinaSouthernOntariowatershed.CanadianJournalofCivilEngineering,39,91-103.doi:10.1139/L11-11259Cheng,C.S.,Li,G.,Li,Q.,Auld,H.,&Fu,C.(2012).PossibleImpactsofClimateChangeonWindGustsunderDownscaledFutureClimateConditionsoverOntario,Canada.JournalofClimate,25,3390-3408.doi:10.1175/JCLI-D-11-00198.160Ibid.61Ibid.62Ibid.
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4.0Nextsteps:Localclimateimpactsandadaptationplanning
4.1ChangingconditionsandrelatedimpactsforOntariomunicipalitiesWhileclimatechangeissometimesthoughtofasonlyalong-termphenomenon,Ontariomunicipalitiesarealreadyexperiencingbothnegativeandpositiveimpacts.Canada-wide,theeconomicimpactsofclimatechangeareestimatedtobe$5billionperyearby2020,andbetween$21and$43billionby205063.SomeoftheimpactsthatarerelatedmorespecificallytothetypesofprojectedchangesidentifiedforWaterlooRegioninclude:
• Risingwintertemperatureswithmorewinterdaysabovefreezingwillhavearangeofpositiveandnegativeimplicationsinareassuchasroadmaintenance,outdoorwinterrecreationandreducedheatingdemand,whilehighertemperaturesinthesummerwillincreasecoolingdemand.
• Higherandmorefrequentheatmaximums,heatwaves,smogepisodesandextremeweatherwillincreasetheriskofhumanillness,injuryandprematuredeath64,65.DatafromWindsor,Toronto,Ottawa,andMontrealrevealsthatheat-relatedmortalityisprojectedtodoublebythe2050sandtriplebythe2080s66.
• Higheraveragetemperaturesinthespringwillshiftgrowingseasonstobeginearlierintheyear,whichcouldhaveadvantagesforcropyields,butposeriskforcroplossesduetoearlyfrostand/orchangesinthefreeze-thawcycles.
• Warmingtemperatureswillallowtherangeof‘nuisance’species,suchasinsectpestsandinvasiveplants,toreachEasternCanadacausingnegativeeffectstonativefloraandfaunaaswellasincreasedexposureofhumanstovector-bornediseases.
• Increasedfrequencyandintensityofstormsandextremeprecipitationeventswillexposepropertyandinfrastructure,suchasbuildings,waterandwastewatersystems,transportationandenergytransmissionservices,toriskofdamageandservicedisruptionduetofloodingandwindgusts.67
• Freezingraineventsandicestormsareassociatedwithveryhighdamagecosts68,substantialpoweroutages,anddisruptionstotransportationnetworks69.
63NationalRoundtableontheEnvironmentandtheEconomy(NRTEE).(2012).FramingtheFuture:EmbracingtheLow-CarbonEconomy.Accessed14August2015from:http://collectionscanada.gc.ca/webarchives2/20130322145416/http://nrtee-trnee.ca/wp-content/uploads/2012/10/framing-the-future-summary-eng.pdf64Bassil,K.L.,Cole,D.C.,Moineddin,R.,Lou,W.,Craig,A.M.,Schwartz,B.,&Rea,E.(2011).65Lemmen,D.S.,Warren,F.J.,Lacroix,J.andBush,E.,editors(2008):FromImpactstoAdaptation:CanadainaChangingClimate2007;GovernmentofCanada,Ottawa,ON,448p.66Cheng,C.S.,Li,G.,Li,Q.,&Auld,H.(2008).67ibid68Cheng,C.S.,Auld,H.,Li,G.,Klaassen,J.,&Li,Q.(2007).69Armenakis,C.,&Nirupama,N.(2014).Urbanimpactsoficestorms:TorontoDecember2013.NaturalHazards,74,1291-1298.doi:10.1007/s11069-014-1211-7
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4.2ClimateadaptationplanningatthelocallevelMunicipalitiesaretakingactiontorespondtochangingtrendsandprepareforfutureimpacts.A2012surveyofmunicipalitiesacrossCanadahighlightedthatadaptationplanningatthelocallevelisemergent,andthatlargerandmid-sizecitiesseemmorelikelytohaveadvancedadaptationplanning,butsomesmallcommunitiesarealsoengaged70.TheCityofWindsorandtheCityofToronto,forexample,haveadoptedclimateadaptationplansthatweredrivenlargelyinresponsetoextremeheatandurbanfloodingpressuresfeltintheirrespectivecommunities.DurhamRegionisanothernearbymunicipalitythatisactivelydevelopingaCommunityClimateAdaptationPlan.
AsofSeptember2015,theFederationofCanadianMunicipalitiesencouragesactionstatingonitswebsite,“itisnevertoosoonformunicipalitiestobeginassessingtheirvulnerabilitytoclimatechangeimpactsthatarealreadyoccurring,andtodevelopresponsesthatprotecttheircitizens,thelocalenvironmentandthelocaleconomy.ClimatechangeadaptationcouldsaveCanadiansbillionsofdollars,andpositionoureconomytoprovidesolutionsforachallengethatwillsoonfacecommunitiesaroundtheworld.”Therearesignificantavoidedcoststoberealizedbybolsteringtheresilienceofmunicipalassets(e.g.communityfacilities,parks,roads,waterandwastewatersystems),andincreasingourabilitytoadaptcommunityservicestobestsuitchangingclimaticconditions(e.g.emergencyservices,publichealth,andtransit).A“no-regrets”approachishighlyencouragedsothatadaptiveactionsimprovecommunityresiliencyandhaveotherbenefitsregardlessofwhethertheprojectedclimatechangesfollowthehighorlowscenario.
4.3UsingclimateprojectionsinongoingadaptationplanningNewemissionsscenariosandupdatedclimatemodelswillinevitablybeusedtocreatenewgenerationsofclimateprojections,andadvanceddownscalingtechniquesmayoffernewavenuesofadaptingthese
70Hanna,K.,Dale,A.,Filion,P.,Khan,Z.,Ling,C.,Rahaman,K.andSeasons,M.(2013):Planningforadaptationinanuncertaintysetting:localgovernmentactioninCanada;ConferencepapersubmittedtoAESOP-ACSPJointCongress,15-19July,2013,Dublin
HowareotherCanadiancommunitiesaddressingadaptationplanning?
• 238Canadiancommunitiesareaddressingadaptationplanninginsomeform(55%ofrespondentstoa2012survey).Ofthis,5%haveanadaptationplaninplace(e.g.Durham,Windsor,Peel,Toronto),andafurther8%haveincorporatedadaptationintoanexistingplan.70
• Over30governmententitieshavecompletedprojectsusingthePIEVCEngineeringProtocol.• 19communitiesareparticipatinginICLEI’sBuildingAdaptiveandResilientCommunities(BARC)program.
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28
projectionstolocalareas.Sincetherateofscientificadvancementcannotbepredicted,usersofthisreportshouldfamiliarizethemselveswithanymajorchangesinmodelsorscenariosthathaveoccurredsincethepublishingofthisreport,toensurethatthecontentpresentedherestillmeetsmunicipalneeds.Thistaskmayseemdauntingtopractitionersnotfamiliarwithclimatescience,butmanyresourcesexisttostreamlinetheprocessofdeliveringclimateprojectionstoendusers.UnderstandingthelocalimpactsofclimatechangeisrelevanttoallCanadians,andtothisendnumerousservicesanddataportalsprovidedownscaledclimateprojectiondatatothegeneralpublic,manyofwhichcanbeaccessedfree-of-charge.Academicinstitutionsandconsultantscanalsoprovideguidanceinlocatingandutilizingthesevariousresources.Successfuladaptationtoclimatechangerequireshigh-qualityclimatedata.HavingaccesstoprojectionsaboutwhatthefutureclimatemaylooklikeinWaterlooRegionisoneofthefirststepsofanadaptationplanningprocess,alongwithidentifyingstakeholdersandassessingcurrentclimateimpactsandadaptiveactions.Thisknowledgeprovidesanimportantplatformforasubsequentfullassessmentofpotentialimpactsandvulnerabilityacrosscommunityassetsandservicesalongwithidentificationandprioritizationofriskmanagementmeasures.
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Appendix!A:!Page!1!
Appendix(A:((Waterloo(Region(Localized(Climate(Projections((Process(Manual!
!
The!following!information!details!the!process!that!was!following!to!prepare!a!report!on!localized!climate!
projections!for!Waterloo!Region.!The!material!provided!here!should!be!sufficient!to!resolve!any!
questions!regarding!the!methodology!that!was!used!to!create!downscaled!climate!projections,!as!well!as!
allow!this!procedure!to!be!replicated!in!the!future.!The!approach!taken!for!the!Waterloo!Region!project!
encompassed!the!following!major!steps,!which!will!each!be!described!in!further!detail.!
!
1) Determine!project!scope!
2) Data!analysis!
i. Data!cataloguing!and!preHprocessing!
ii. Statistical!downscaling!with!LARSHWG!
iii. Data!processing!
3) Literature!review!
!
1) DETERMINE(PROJECT(SCOPE!!
Following!an!agreement!with!the!Cities!of!Cambridge,!Kitchener,!Waterloo,!and!the!Region!of!Waterloo!
to!produce!localized!climate!projections!for!Waterloo!Region,!the!first!necessary!task!was!to!confirm!the!
purpose!of!the!proposed!project!and!determine!its!scope.!Two!initial!meetings!between!municipal!
representatives!and!the!University!of!Waterloo!(UW)!team!were!held!to!address!these!questions.!Since!
this!work!was!highly!tailored!to!the!needs!of!municipal!endHusers,!offering!adequate!opportunities!for!
input,!feedback,!and!dialogue!was!crucial!to!the!project’s!success!in!providing!relevant!and!decisionH
useful!information.!
!
Major!decisions!involved!selecting!a!list!of!climate!parameters!to!investigate,!a!set!of!emissions!
scenarios!to!use,!and!the!baseline!and!future!time!periods!to!analyze.!Climate!parameters!were!
determined!through!an!information!gathering!process,!which!involved!consulting!with!municipal!
departments!(e.g.!Facilities,!Transportation,!Water!Services,!Engineering,!and!Fire!Rescue)!by!asking!
questions!about!how!operations!are!impacted!by!climate!and!which!climate!parameters!would!be!useful!
to!guide!future!planning.!Climate!parameters!that!have!been!discussed!in!other!climate!projection!
reports!were!also!reviewed!to!expand!upon!this!list.!The!final!list!of!the!climate!parameters!analyzed!is!
given!in!Table!B1,!which!has!been!sorted!into!the!overall!categories!of!parameters!dependent!on!
temperature,!precipitation,!and!wind.!
!
Table(B1:(Climate(Parameters(Temperature) Mean,!maximum,!and!minimum!temperatures!(monthly,!seasonal,!and!annual)!
Number!of!days!with!temperature:!
>!30oC!(extreme!heat),!
<!0oC!(frost!days),!and!
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!
Appendix!A:!Page!2!
<!H15oC!(extreme!cold)!
FreezeHthaw!frequency!(number!of!days!where!max!temp!>!0oC!and!min!temp!<!0
oC)!
Heat!waves!/!cold!spells!
Heating,!cooling,!and!growing!degree!days!
Precipitation)) Average!total!precipitation:!rain!and!snow!(monthly,!seasonal,!and!annual)!
Average!number!of!precipitation!days!
Number!of!intense!precipitation!events:!
>!10mm,!
>!25mm,!and!
snow!>!5cm!
Dry!spells!/!wet!spells!
Precipitation!return!periods!(e.g.!1HinH10!year!precipitation!event)!
Wind) Wind!gusts!
!
Emissions!scenarios!and!time!periods!were!recommended!by!the!UW!team!to!align!with!best!practices!
and!common!conventions.!Three!emissions!scenarios!were!proposed:!the!businessHasHusual!scenario!of!
RCP!8.5,!the!aggressive!mitigation!scenario!of!RCP!4.5,!and!the!netHzero!carbon!scenario!of!RCP!2.6.!This!
range!of!scenarios!was!important!to!show!the!range!of!potential!future!conditions,!as!well!as!to!
demonstrate!the!beneficial!effects!of!extensive!greenhouse!gas!mitigation.!
!
The!time!periods!chosen!were!all!30!years!in!length,!which!is!the!standard!endorsed!by!the!World!
Meteorological!Organization!for!examining!longHterm!climate!trends.!The!years!1981H2010!were!
selected!as!the!baseline!period,!as!this!represented!the!most!recent!30Hyear!period!for!which!quality!
data!was!available!for!Waterloo!Region.!The!projection!periods!were!chosen!to!align!with!
Intergovernmental!Panel!on!Climate!Change!standards,!comprising!the!2020s,!2050s,!and!2080s.!The!
scoping!decisions!regarding!scenarios!and!time!periods!are!summarized!in!Table!B2.!
!
Table(B2:(Emissions(scenarios(and(time(periods(
Emissions)Scenarios!
RCP!8.5!“businessHasHusual”!
RCP!4.5!“aggressive!mitigation”!
RCP!2.6!“netHzero!carbon!
Baseline)Period! 1990s!!(1981H2010)!
Projection)Periods!
2020s!(2011H2040)!
2050s!(2041H2070)!!
2080s!(2071H2100)!
!
!
!
!
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!
Appendix!A:!Page!3!
2) DATA(ANALYSIS((i. Data&cataloguing&and&pre1processing&The!first!step!in!the!data!analysis!procedure!was!to!obtain!the!required!data!files.!All!necessary!data!
from!the!Coupled!Model!Intercomparison!Project!version!5!(CMIP5)!were!downloaded!through!the!
Earth!System!Grid!Foundation!PeerHtoHPeer!enterprise!system,!an!international!and!interagency!
collaboration!which!acts!as!a!portal!for!the!dissemination!of!climate!model!data.!Monthly!output!from!a!
total!of!22!climate!models!was!used.!!To!be!considered,!each!model!was!required!to!have!output!
available!for!both!variables!of!surface!temperature!and!precipitation,!and!projections!for!each!of!the!
three!scenarios!(RCP!2.6,!RCP!4.5,!and!RCP!8.5)!out!to!the!year!2100.!!The!full!list!of!models!used!is!given!
in!Table!B3.!
!
Table(B3:(Full(list(of(climate(models1(Model( Expanded(Name( Center(
BCCHCSM1H1!Beijing!Climate!Center,!Climate!System!
Model,!version!1.1!
Beijing!Climate!Center,!China!Meteorological!
Administration,!China!
CanESM2!Second!Generation!Canadian!Earth!
System!Model!
Canadian!Centre!for!Climate!Modeling!and!
Analysis,!Canada!
CCSM4!Community!Climate!System!Model,!
version!4!
National!Center!for!Atmospheric!Research,!
United!States!
CESM1H
CAM5!
Community!Earth!System!Model!
version!1,!including!the!Community!
Atmospheric!Model!version!5!
National!Center!for!Atmospheric!Research,!
United!States!
CNRMHCM5!
Centre!National!de!Recherches!
Meteorologiques!Coupled!Global!
Climate!Model,!version!5.1!
National!Centre!for!Meteorological!Research,!
France!
CSIROHMk3H
6H0!
Commonwealth!Scientific!and!Industrial!
Research!Organisation!Mark,!version!
3.6.0!
Commonwealth!Scientific!and!Industrial!
Research!Organization!/!Queensland!Climate!
Change!Centre!of!Excellence,!Australia!
FGOALSHg2!
Flexible!Global!Ocean–Atmosphere–
Land!System!Model!gridpoint,!second!
spectral!version!
State!Key!Laboratory!of!Numerical!Modeling!
for!Atmospheric!Sciences!and!Geophysical!
Fluid!Dynamics!(LASG),!Institute!of!
Atmospheric!Physics,!Chinese!Academy!of!
Sciences!
GFDLHCM3!Geophysical!Fluid!Dynamics!Laboratory!
Climate!Model,!version!3!
NOAA/Geophysical!Fluid!Dynamics!Laboratory,!
United!States!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1!Eric D. Maloney, Suzana J. Camargo, Edmund Chang, Brian Colle, Rong Fu, Kerrie L. Geil, Qi Hu, Xianan Jiang, Nathaniel
Johnson, Kristopher B. Karnauskas, James Kinter, Benjamin Kirtman, Sanjiv Kumar, Baird Langenbrunner, Kelly Lombardo, Lindsey N. Long, Annarita Mariotti, Joyce E. Meyerson, Kingtse C. Mo, J. David Neelin, Zaitao Pan, Richard Seager, Yolande Serra, Anji Seth, Justin Sheffield, Julienne Stroeve, Jeanne Thibeault, Shang-Ping Xie, Chunzai Wang, Bruce Wyman, and Ming Zhao, 2014: North American Climate in CMIP5 Experiments: Part III: Assessment of Twenty-First-Century Projections*. J. Climate, 27, 2230–2270. doi: http://dx.doi.org/10.1175/JCLI-D-13-00273.1
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!
Appendix!A:!Page!4!
GFDLH
ESM2G!
Geophysical!Fluid!Dynamics!Laboratory!
Earth!System!Model!with!Generalized!
Ocean!Layer!Dynamics!(GOLD)!
component!
NOAA/Geophysical!Fluid!Dynamics!Laboratory,!
United!States!
GFDLH
ESM2M!
Geophysical!Fluid!Dynamics!Laboratory!
Earth!System!Model!with!Generalized!
Ocean!Layer!Dynamics!(GOLD)!
component!and!with!Modular!Ocean!
Model!4!(MOM4)!component!
NOAA/Geophysical!Fluid!Dynamics!Laboratory,!
United!States!
GISSHE2HR!
Goddard!Institute!for!Space!Studies!
Model!E,!coupled!with!the!HYCOM!
ocean!model!and!coupled!with!the!
Russell!ocean!model!
National!Aeronautics!and!Space!
Administration!(NASA)!Goddard!Institute!for!
Space!Studies,!United!States!
HadGEM2H
ES!
Hadley!Centre!Global!Environment!
Model,!version!2–Earth!System!Met!Office!Hadley!Centre,!United!Kingdom!
IPSLHCM5AH
LR!
L’Institut!PierreHSimon!Laplace!Coupled!
Model,!version!5,!coupled!with!NEMO,!
low!resolution!
L’Institut!PierreHSimon!Laplace,!France!
IPSLHCM5AH
MR!
L’Institut!PierreHSimon!Laplace!Coupled!
Model,!version!5,!coupled!with!NEMO,!
mid!resolution!
L’Institut!PierreHSimon!Laplace,!France!
MIROC5!Model!for!Interdisciplinary!Research!on!
Climate,!version!5!
Atmosphere!and!Ocean!Research!Institute!
(The!University!of!Tokyo),!National!Institute!
for!Environmental!Studies,!and!Japan!Agency!
for!MarineHEarth!Science!and!Technology,!
Japan!
MIROCHESM!Model!for!Interdisciplinary!Research!on!
Climate,!Earth!System!Model!
Japan!Agency!for!MarineHEarth!Science!and!
Technology,!Atmosphere!and!Ocean!Research!
Institute!(The!University!of!Tokyo),!and!
National!Institute!for!Environmental!Studies,!
Japan!
MIROCHESMH
CHEM!
Model!for!Interdisciplinary!Research!on!
Climate,!Earth!System!Model,!
Chemistry!Coupled!
Japan!Agency!for!MarineHEarth!Science!and!
Technology,!Atmosphere!and!Ocean!Research!
Institute!(The!University!of!Tokyo),!and!
National!Institute!for!Environmental!Studies,!
Japan!
MPIHESMHLR!Max!Planck!Institute!Earth!System!
Model,!low!resolution!
Max!Planck!Institute!for!Meteorology,!
Germany!
MPIHESMH
MR!
Max!Planck!Institute!Earth!System!
Model,!mid!resolution!
Max!Planck!Institute!for!Meteorology,!
Germany!
MRIHCGCM3!
Meteorological!Research!Institute!
Coupled!Atmosphere–Ocean!General!
Circulation!Model,!version!3!
Meteorological!Research!Institute,!Japan!
NorESM1HM!Norwegian!Earth!System!Model,!
version!1!(intermediate!resolution)!Norwegian!Climate!Center,!Norway!
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!
Appendix!A:!Page!5!
NorESM1H
ME!
Norwegian!Earth!System!Model,!
version!1!(intermediate!resolution)!and!
with!carbon!cycle!
Norwegian!Climate!Center,!Norway!
!
Once!all!data!had!been!downloaded!and!catalogued,!a!shell!script!program!was!written!and!run!within!
the!UNIX!commandHline!shell!to!preHprocess!the!data.!The!gridded!data!for!Waterloo!Region!were!
extracted!from!the!global!dataset!by!latitude/longitude!(approximately!43.5oN,!80.5
oW),!and!the!data!
was!segmented!into!proper!time!periods.!Since!historical!GCM!runs!only!extended!to!2005,!the!period!
from!2006H2010!was!filled!using!RCP!4.5!data!from!each!model.!Average!monthly!climatologies!were!
then!calculated!for!each!variable,!scenario,!model,!and!time!period.!By!comparing!the!historical!and!
future!climatologies!for!each!model,!factors!of!change!could!be!calculated!to!express!the!difference!
between!each!pair!of!two!cases.!Change!factors!for!monthly!mean!temperatures!were!calculated!as!
absolute!differences,!while!change!factors!for!monthly!mean!precipitation!were!calculated!as!relative!
changes.!
!
After!all!calculations!were!completed!for!each!model!individually,!the!results!were!aggregated!into!an!
ensemble!and!percentiles!were!calculated.!The!multiHmodel!mean!was!calculated!as!an!average!of!all!
the!change!fields!for!each!model,!while!percentiles!were!calculated!individually!for!each!variable,!
scenario,!and!time!period.!The!advantage!of!this!method!for!determining!percentiles!was!that!a!
different!aggregation!of!models!could!be!used!in!each!case,!as!the!25th!to!75
th!percentile!range!was!
determined!at!each!step!(e.g.!the!25th!and!75
th!percentile!range!for!temperature!under!RCP!2.6!in!the!
2020s!would!not!necessarily!include!the!same!set!of!models!as!for!precipitation!under!RCP!4.5!in!the!
2080s).!Ideally,!this!approach!would!highlight!the!aggregation!of!models!which!best!represented!certain!
parameters!within!each!scenario!at!each!time!period.!For!completeness,!the!ensemble!maximum!and!
minimum!were!also!analyzed!to!understand!the!full!spread!of!all!CMIP5!models.!Since!the!maximum!and!
minimum!represent!the!outliers!of!the!CMIP5!ensemble!and!thus!are!subject!to!a!lower!degree!of!
confidence,!these!results!are!not!presented!within!the!report!and!only!referred!to!where!necessary!to!
establish!greater!context!for!the!results!being!discussed.!
!
In!addition!to!CMIP5!data,!observed!data!from!a!local!weather!station!was!also!required!for!the!
statistical!downscaling!process.!A!full!list!of!all!stations!within!50km!of!Waterloo!Region!was!compiled.!
This!was!narrowed!down!to!the!stations!that!were!situated!closest!to!the!study!area!and!had!complete!
temperature!and!precipitation!data!available!for!the!baseline!period!of!1981H2010.!The!most!
appropriate!location!was!determined!to!be!the!weather!station!at!the!Region!of!Waterloo!International!
Airport,!near!the!town!of!Breslau!in!Woolwich!Township,!Waterloo!Region.!The!station!was!renamed!
(from!Waterloo!Wellington!A,!to!Region!of!Waterloo!Int’l!Airport,!to!Kitchener/Waterloo)!and!moved!
slightly!in!both!2002!and!2010.!Daily!data!for!each!year!of!the!baseline!period!was!downloaded!from!
Environment!Canada’s!National!Climate!Archives.!The!data!record!for!this!station!was!largely!complete,!
with!only!a!very!small!percentage!of!missing!values.!These!missing!values!were!infilled!using!data!from!
Roseville!weather!station!(located!in!the!township!of!North!Dumfries,!Waterloo!Region)!and!where!
necessary,!from!the!weather!station!in!Elora!(located!within!neighbouring!Wellington!County).!A!map!
showing!the!locations!of!these!weather!stations!is!included!below.!
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!
Appendix!A:!Page!6!
!
!
Figure)B1:)Map)of)all)weather)stations)from)which)data)was)used.)!
The!conclusion!of!the!data!cataloguing!and!preHprocessing!portion!of!this!project!thus!resulted!in!two!
sets!of!data:!observed!data!from!a!weather!station!representative!of!Waterloo!Region,!and!monthly!
change!fields!for!temperature!and!precipitation!calculated!using!CMIP5!model!output.!These!two!
datasets!were!then!utilized!together!in!the!following!step!to!generate!downscaled!future!weather!data.!
!
ii. Statistical&downscaling&with&LARS1WG!The!Long!Ashton!Research!Station!Weather!Generator!(LARSHWG)!version!5.0!is!a!statistical!downscaling!
model!that!can!simulate!a!time!series!of!daily!weather!at!a!given!site.!It!is!a!computationallyHinexpensive!
and!robust!model!that!has!been!used!extensively!for!climate!change!impact!assessments!in!locations!
around!the!globe.!To!generate!future!weather!data,!a!time!series!of!historical!data!is!first!input!into!the!
model,!which!analyzes!the!statistical!characteristics!and!probability!distributions!of!observed!weather.!
Scenario!files!were!then!created!for!each!scenario,!time!period,!and!percentile.!These!userHgenerated!
files!specify!changes!in!the!characteristics!of!temperature!and!precipitation,!which!are!applied!to!modify!
the!statistical!properties!of!the!observed!data.!For!each!scenario!file!run!through!LARSHWG,!a!daily!time!
series!is!produced!containing!maximum!temperature,!minimum!temperature,!and!precipitation!data!for!
a!specified!number!of!years.!For!this!project,!50!years!of!data!for!each!scenario!were!produced!for!
analysis.!!
!
iii. Data&processing!All!analysis!was!completed!on!the!output!data!from!LARSHWG!with!the!aid!of!the!Climate!Data!Operators!
(CDO)!toolset!developed!by!the!Max!Planck!Institute!for!Meteorology.!A!shell!script!program!was!
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!
Appendix!A:!Page!7!
created!to!automate!all!processing!tasks,!with!commands!being!written!to!run!calculations!and!extract!
data!for!each!climate!parameter!in!Table!B1.!Necessary!information!on!how!each!parameter!was!
calculated!is!included!within!the!appropriate!sections!of!the!main!report.!Full!data!for!every!parameter!
of!each!scenario,!time!period,!and!percentile!was!compiled!in!a!master!spreadsheet,!from!which!the!
tables!and!figures!throughout!the!main!report!and!Appendix!A!were!created.!
!
Precipitation!return!periods!were!computed!using!a!different!method,!as!yearly!maximum!precipitation!
amounts!were!analyzed!using!R!statistical!software!with!the!aid!of!the!“extRemes”!extreme!value!
analysis!package!developed!by!Gilleland!and!Katz!(2011).!A!generalized!extreme!value!distribution!
function!was!fit!to!the!input!data,!which!then!allowed!return!periods!to!be!calculated!for!specified!
intervals.!Maximum!5Hday!precipitation!totals!were!calculated!using!the!same!method,!but!the!input!
data!were!the!yearly!maximums!of!a!5Hday!running!sum.!
!
3) LITERATURE(REVIEW((A!literature!review!process!was!undertaken!simultaneously!with!ongoing!data!analysis.!The!primary!
focus!of!this!review!was!to!obtain!information!for!climate!parameters!where!localized!climate!
projections!could!not!be!produced.!This!was!the!case!for!parameters!such!as!wind!gusts!and!freezing!
rain,!where!inadequate!historical!data!was!available!for!Waterloo!Region!to!assess!the!occurrences!of!
these!events!in!the!observed!record,!let!alone!attempt!to!project!future!changes!in!these!parameters.!In!
general,!these!parameters!also!represent!more!localized!phenomena!than!what!would!be!captured!at!
the!scale!of!a!global!climate!model.!A!secondary!focus!of!the!literature!review!was!to!corroborate!the!
projected!changes!in!extremes!that!were!calculated!for!Waterloo!Region.!The!relative!rarity!of!extreme!
precipitation!and!temperature!events!makes!them!more!difficult!to!capture!accurately,!and!thus!
comparing!the!results!for!these!quantities!between!studies!using!different!methodologies!helps!to!
improve!overall!confidence!in!the!changes!expected.!Incorporating!scientific!literature!in!this!way!
helped!to!establish!a!more!detailed!story!of!how!climate!change!will!impact!Southern!Ontario.!
!
An!initial!discovery!phase!of!literature!databases!identified!potentially!useful!sources!through!searches!
using!varying!combinations!of!key!words.!Relevant!notes!for!each!paper!were!compiled!and!organized!to!
create!a!comprehensive!literature!review!of!downscaled!projections!for!changes!in!climatic!extremes.!
This!document!was!drawn!from!as!needed!to!flesh!out!the!results!of!the!data!analysis!portion!of!the!
project.!
!
!
!
!
!
!
!
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!
Appendix!A:!Page!8!
!
REFERENCES(AND(ACKNOWLEDGEMENTS((The!Interdisciplinary!Centre!on!Climate!Change!at!the!University!of!Waterloo!would!like!to!acknowledge!
the!following!sources!for!providing!either!data!or!modeling!and!analysis!software.!
!
For!the!use!of!CMIP5!data,!we!acknowledge!the!World!Climate!Research!Programme's!Working!Group!
on!Coupled!Modelling,!which!is!responsible!for!CMIP,!and!we!thank!the!climate!modeling!groups!(listed!
in!Table!B3!of!this!paper)!for!producing!and!making!available!their!model!output.!For!CMIP!the!U.S.!
Department!of!Energy's!Program!for!Climate!Model!Diagnosis!and!Intercomparison!provides!
coordinating!support!and!led!development!of!software!infrastructure!in!partnership!with!the!Global!
Organization!for!Earth!System!Science!Portals.!
!
The!National!Climate!Archives!maintained!by!Environment!Canada.!http://climate.weather.gc.ca/!
!
The!LARS!Weather!Generator!version!5.!http://www.rothamsted.ac.uk/masHmodels/larswg!
Semenov!M.A.!and!Barrow!E.M.!(1997):!Use!of!a!stochastic!weather!generator!in!the!development!of!
climate!change!scenarios.!Climatic!Change!35:!397H414.!
!
The!Climate!Data!Operators!(CDO)!toolset!developed!by!the!Max!Planck!Institute!for!Meteorology.!
CDO!2015:!Climate!Data!Operators.!!Available!at:!http://www.mpimet.mpg.de/cdo!
!
R!statistical!software.!
R!Core!Team!(2015).!R:!A!language!and!environment!for!statistical!computing.!R!Foundation!for!
Statistical!Computing,!Vienna,!Austria.!http://www.RHproject.org/!
!
The!extRemes!package!for!R.!https://cran.rHproject.org/web/packages/extRemes/index.html!
Gilleland!E!and!Katz!RW!(2011).!“New!software!to!analyze!how!extremes!change!over!time.”!Eos,!92(2),!pp.!13–14.!
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!
Appendix!B:!Page!1!!
Appendix(B:((Waterloo(Region(Localized(Climate(Projections((Full(Results(for(All(Parameters(
(Note:(Throughout!this!appendix,!the!following!identifiers!are!used!interchangeably!for!greenhouse!gas!scenarios.!!For!more!details,!refer!to!Section!2.3!in!the!main!report.((Scientific(identifier(code( Plain/language(identifier(RCP!2.6! NetGzero!carbon!RCP!4.5! Aggressive!mitigation!RCP!8.5! BusinessGasGusual!(MEAN(TEMPERATURE(
Table(1a:((Change(in(average(annual(mean(temperature((oC)!
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(7.0(
1.2! 1.8! 1.7!
RCP(4.5( 1.2! 2.4! 2.9!
RCP(8.5( 1.4! 3.2! 5.2!(
Table(1b:((Average(seasonal(mean(temperatures((oC)(
! !Winter( Spring( Summer( Fall(
1990s( Observed( G5.2! 5.8! 18.8! 8.5!
2020s(RCP(2.6( G3.9! 8.0! 19.9! 8.5!
RCP(4.5( G3.8! 8.0! 19.9! 8.6!
RCP(8.5( G3.6! 8.1! 20.1! 8.8!
2050s(RCP(2.6( G3.1! 8.6! 20.4! 9.0!
RCP(4.5( G2.4! 9.1! 21.0! 9.7!
RCP(8.5( G1.6! 9.9! 21.9! 10.3!
2080s(RCP(2.6( G3.2! 8.5! 20.3! 9.1!
RCP(4.5( G1.9! 9.7! 21.5! 10.2!
RCP(8.5( 0.5! 11.7! 24.1! 12.4!(!
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!
Appendix!B:!Page!2!!
!!
!!
!!
(
G10!G5!0!5!
10!15!20!25!
Jan!
Feb!
Mar!
Apr!
May!
Jun! Jul!
Aug!
Sep!
Oct!
Nov!
Dec!Av
erage(mon
thly(m
ean(
tempe
rature((oC)(
Month(
Monthly(mean(temperatures(in(the(2020s(
Observed!
NetGzero!carbon!
Aggressive!miTgaTon!
BusinessGasGusual!
G10!G5!0!5!
10!15!20!25!30!
Jan!
Feb!
Mar!
Apr!
May!
Jun! Jul!
Aug!
Sep!
Oct!
Nov!
Dec!Average(mon
thly(m
ean(
tempe
rature((oC)(
Month(
Monthly(mean(temperatures(in(the(2050s(
Observed!
NetGzero!carbon!
Aggressive!miTgaTon!
BusinessGasGusual!
G10!G5!0!5!
10!15!20!25!30!
Jan!Feb!Mar!Apr!May!Jun! Jul! Aug!Sep!Oct!Nov!Dec!
Average(mon
thly(m
ean(
tempe
rature((oC)(
Month(
Monthly(mean(temperatures(in(the(2080s(
Observed!
NetGzero!carbon!
Aggressive!miTgaTon!
BusinessGasGusual!
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!
Appendix!B:!Page!3!!
MAXIMUM(TEMPERATURE(The!highest!temperature!reached!on!each!day!is!recorded!as!its!maximum!temperature.!!In!the!tables!
below,!maximum!temperatures!are!averaged!annually,!seasonally,!and!monthly.!
!
Table(2a:(Average(annual(maximum(temperatures((oC)(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(12.2!
13.4! 14.0! 13.9!
RCP(4.5( 13.5! 14.6! 15.2!
RCP(8.5( 13.6! 15.5! 17.5!(
Table(2b:(Average(seasonal(maximum(temperatures((oC)(
! !Winter( Spring( Summer( Fall(
1990s( Observed( G1.3! 11.3! 25.0! 13.7!
2020s(RCP(2.6( 0.1! 13.6! 26.1! 13.6!
RCP(4.5( 0.9! 14.2! 26.6! 14.1!
RCP(8.5( 0.8! 14.1! 26.5! 14.1!
2050s(RCP(2.6( 0.2! 13.6! 26.1! 13.7!
RCP(4.5( 1.6! 14.7! 27.2! 14.7!
RCP(8.5( 2.2! 15.2! 27.8! 15.2!
2080s(RCP(2.6( 0.4! 13.7! 26.3! 13.8!
RCP(4.5( 2.5! 15.6! 28.1! 15.3!
RCP(8.5( 4.6! 17.3! 30.2! 17.4!(
(MINIMUM(TEMPERATURE(Minimum!temperatures!represent!the!lowest!temperature!recorded!on!each!day.!!Projected!increases!in!
minimum!temperatures!are!similar!in!magnitude!to!the!expected!increase!in!maximum!temperatures.!
!
Table(3a:(Average(annual(minimum(temperatures((oC)(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(1.9!
3.0! 3.6! 3.5!
RCP(4.5( 3.0! 4.2! 4.7!
RCP(8.5( 3.2! 5.0! 7.0!((((((((
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!
Appendix!B:!Page!4!!
Table(3b:(Average(seasonal(minimum(temperatures((oC)(
! !Winter( Spring( Summer( Fall(
1990s( Observed( G9.2! 0.3! 12.7! 3.4!
2020s(RCP(2.6( G8.0! 2.4! 13.7! 3.5!
RCP(4.5( G7.9! 2.4! 13.6! 3.6!
RCP(8.5( G7.6! 2.5! 13.9! 3.8!
2050s(RCP(2.6( G7.1! 3.1! 14.3! 4.0!
RCP(4.5( G6.4! 3.5! 14.7! 4.7!
RCP(8.5( G5.7! 4.2! 15.7! 5.3!
2080s(RCP(2.6( G7.3! 2.9! 14.1! 4.0!
RCP(4.5( G5.9! 4.2! 15.3! 5.1!
RCP(8.5( G3.5! 6.0! 17.9! 7.4!(!
TEMPERATURE(THRESHOLDS(Table(4a:(Average(annual(number(of(days(with(maximum(temperatures(>(30oC(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(10.1!
12.0! 15.2! 14.5!
RCP(4.5( 11.7! 20.6! 28.2!
RCP(8.5( 12.7! 30.9! 59.3!
!
Table(4b:(Average(annual(number(of(days(with(minimum(temperatures(<(/15oC(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(22.1!
10.8! 8.4! 9.0!
RCP(4.5( 10.3! 7.0! 5.8!
RCP(8.5( 9.9! 5.8! 2.8!
!
Table(4c:(Average(annual(number(of(days(with(minimum(temperatures(<(0oC!
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(153.0!
141.4! 133.1! 133.4!
RCP(4.5( 141.1! 125.1! 117.4!
RCP(8.5( 138.8! 114.5! 90.1!(
Table(4d:(Average(annual(number(of(days(with(freeze/thaw(cycles!
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(74.1!
83.3! 82.3! 82.0!
RCP(4.5( 83.0! 80.1! 78.1!
RCP(8.5( 82.6! 76.7! 66.6!!
!
(
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!
Appendix!B:!Page!5!!
DEGREE(DAYS!Table(5a:(Average(annual(heating(degree(days((%(change)!
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(4199.6!
G9.2%! G13.5%! G13.0%!
RCP(4.5( G9.6%! G17.3%! G20.8%!
RCP(8.5( G10.6%! G22.1%! G33.7%!!
Table(5b:(Average(annual(cooling(degree(days((%(change)!
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(197.7!
19.1%! 42.7%! 36.5%!
RCP(4.5( 18.3%! 67.4%! 96.6%!
RCP(8.5( 26.4%! 116.3%! 242.6%!!
Table(5c:(Average(annual(growing(degree(days((%(change)!
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(2031.6!
9.3%! 15.5%! 14.4%!
RCP(4.5( 9.4%! 21.3%! 27.8%!
RCP(8.5( 11.4%! 30.8%! 55.5%!!
!!
!
PRECIPITATION(AMOUNTS(Table(6a:(Average(total(annual(precipitation((%(change(from(1990s(baseline)(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(918.5mm!
4.3%! 11.7%! 7.3%!
RCP(4.5( 6.2%! 8.5%! 10.0%!
RCP(8.5( 3.8%! 10.4%! 12.0%!
0!
500!
1000!
1500!
2000!
2500!
3000!
3500!
4000!
2020s! 2050s! 2080s!
Annu
al(growing(de
gree(days(
Time(period((30]year(averages)(
Growing(Degree(Days(
NetGzero!carbon!
Aggressive!miTgaTon!
BusinessGasGusual!
Observed!
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!
Appendix!B:!Page!6!!
Table(6b:(Average(total(seasonal(precipitation((%(change(from(1990s(baseline)(
! !Winter( Spring( Summer( Fall(
1990s(Observed((mm)( 196.4( 216.3( 262.7( 243.1(
2020s(RCP(2.6( 6.2%! 8.2%! 3.3%! 0.2%!
RCP(4.5( 5.3%! 9.9%! 8.1%! 1.7%!
RCP(8.5( 7.5%! 9.1%! G0.2%! 0.7%!
2050s(RCP(2.6( 6.5%! 16.7%! 18.0%! 4.7%!
RCP(4.5( 9.8%! 15.0%! 3.3%! 7.5%!
RCP(8.5( 14.4%! 11.5%! 14.1%! 2.1%!
2080s(RCP(2.6( 7.3%! 13.3%! 7.5%! 1.7%!
RCP(4.5( 13.8%! 17.6%! 9.5%! 0.7%!
RCP(8.5( 23.1%! 25.5%! 2.0%! 1.9%!(
((
((
0!20!40!60!80!
100!120!140!160!
Jan! Feb!Mar! Apr!May! Jun! Jul! Aug! Sep! Oct! Nov! Dec!
Total(precipita^o
n((m
m)(
Month(
Monthly(precipita^on(amounts(in(the(2020s(
Observed!
NetGzero!carbon!
Aggressive!miTgaTon!
BusinessGasGusual!
G40!
10!
60!
110!
160!
Jan! Feb!Mar! Apr!May! Jun! Jul! Aug! Sep! Oct! Nov! Dec!
Total(precipita^o
n((m
m)(
Month(
Monthly(precipita^on(amounts(in(the(2050s(
Observed!
NetGzero!carbon!
Aggressive!miTgaTon!
BusinessGasGusual!
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!
Appendix!B:!Page!7!!
((PRECIPITATION(THRESHOLDS(
Table(7a:(Average(annual(number(of(days(with(precipitation:(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(165.3!
162.7! 165.0! 162.8!
RCP(4.5( 162.6! 161.4! 163.6!
RCP(8.5( 162.1! 162.8! 159.7!(
Table(7b:(Average(annual(number(of(days(with(precipitation(>(10mm:(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(30.1!
29.8! 32.7! 30.9!
RCP(4.5( 30.7! 31.6! 32.3!
RCP(8.5( 30.0! 32.9! 33.1!(
Table(7c:(Average(annual(number(of(days(with(precipitation(>(25mm:(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(5.0!
6.2! 6.8! 6.5!
RCP(4.5( 6.3! 6.5! 6.4!
RCP(8.5( 6.2! 6.5! 7.0!
((SNOW(
Table(8a:(Average(annual(total(snowfall((cm)(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(191.4!
222.7! 204.9! 209.9!
RCP(4.5( 224.2! 188.4! 180.8!
RCP(8.5( 222.1! 173.0! 123.2!(
0!
20!
40!
60!
80!
100!
120!
140!
160!
Jan! Feb!Mar! Apr!May! Jun! Jul! Aug! Sep! Oct! Nov! Dec!
Total(precipita^o
n((m
m)(
Month(
Monthly(precipita^on(amounts(in(the(2080s(
Observed!
NetGzero!carbon!
Aggressive!miTgaTon!
BusinessGasGusual!
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!
Appendix!B:!Page!8!!
Table(8b:(Average(annual(number(of(days(with(snowfall(>(5cm(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(11.5!
13.9! 13.0! 12.8!
RCP(4.5( 14.1! 11.5! 11.0!
RCP(8.5( 13.7! 10.3! 7.2!
((WET(&(DRY(SPELLS(
Table(9a:(Average(annual(number(of(dry(spells(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(14.0!
13.1! 13.1! 12.9!
RCP(4.5( 13.2! 13.2! 13.2!
RCP(8.5( 13.2! 13.2! 13.3!
(
((
Table(9b:(Average(annual(number(of(wet(spells(
!1990s( 2020s( 2050s( 2080s(
RCP(2.6(0.47!
0.58! 0.78! 0.64!
RCP(4.5( 0.50! 0.72! 0.72!
RCP(8.5( 0.64! 0.76! 0.74!
(
0!
2!
4!
6!
8!
10!
12!
14!
16!
2020s! 2050s! 2080s!
Average(an
nual(num
ber(o
f(dry(sp
ells(
Time(period((30]year(averages)(
Annual(number(of(dry(spells(
NetGzero!carbon!
Aggressive!miTgaTon!
BusinessGasGusual!
Observed!
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!
Appendix!B:!Page!9!!
((RETURN(PERIODS((
Table(10a:(Return(periods(for(maximum(1/day(total(precipitation(amounts((mm)(
Return(Period(
Observed( RCP(2.6( RCP(4.5( RCP(8.5(1990s( 2020s( 2050s( 2080s( 2020s( 2050s( 2080s( 2020s( 2050s( 2080s(
2Gyr! 50.0! 59.6! 63.1! 61.9! 58.8! 61.2! 60.8! 58.5! 51.6! 51.9!5Gyr! 66.5! 79.8! 83.6! 83.4! 80.7! 81.1! 82.0! 78.0! 66.9! 67.4!10Gyr! 78.5! 94.6! 98.4! 99.4! 98.3! 94.9! 97.5! 92.5! 78.1! 80.3!25Gyr! 94.8! 114.9! 118.7! 121.8! 124.9! 113.3! 118.8! 112.6! 93.4! 100.5!50Gyr! 107.8! 131.2! 134.9! 140.2! 148.4! 127.5! 136.0! 129.2! 105.7! 118.9!
100Gyr! 121.6! 148.6! 152.2! 160.0! 175.4! 142.3! 154.4! 147.0! 118.8! 140.6!(
Table(10b:(Return(periods(for(maximum(5/day(total(precipitation(amounts((mm)(
Return(Period(
Observed( RCP(2.6( RCP(4.5( RCP(8.5(1990s( 2020s( 2050s( 2080s( 2020s( 2050s( 2080s( 2020s( 2050s( 2080s(
2Gyr! 71.9! 85.1! 92.2! 89.5! 87.5! 90.0! 90.2! 85.9! 79.6! 83.8!
5Gyr! 92.0! 113.1! 120.6! 120.1! 117.1! 118.5! 119.4! 112.9! 102.2! 106.7!
10Gyr! 105.7! 132.0! 140.7! 141.5! 137.1! 138.3! 140.8! 131.2! 122.1! 121.9!
25Gyr! 123.5! 156.3! 167.7! 170.2! 162.6! 164.4! 170.3! 154.9! 154.7! 141.0!
50Gyr! 137.1! 174.6! 188.9! 192.6! 181.9! 184.6! 194.2! 172.8! 185.7! 155.1!
100Gyr! 150.8! 193.0! 211.0! 215.9! 201.2! 205.4! 219.7! 191.0! 223.8! 169.0!
0.0!0.1!0.2!0.3!0.4!0.5!0.6!0.7!0.8!0.9!1.0!
2020s! 2050s! 2080s!
Average(an
nual(num
ber(o
f(wet(sp
ells(
Title(
Annual(number(of(wet(spells(
NetGzero!carbon!
Aggressive!miTgaTon!
BusinessGasGusual!
Observed!