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ClimateChangeand ExtremeEvents ClimateChangeand ExtremeEvents Editedby AliFares
CollegeofAgricultureandHumanSciences,PrairieViewA&M University,PrairieView,TX,UnitedStates
Elsevier
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Contributors
VidyaAnderson
ClimateLab,DepartmentofPhysical&EnvironmentalSciences,UniversityofToronto Scarborough,Toronto,ON,Canada
RipendraAwal
CollegeofAgricultureandHumanSciences,PrairieViewA&MUniversity,PrairieView,TX, UnitedStates
MarkAnthonyAyure-IngaAgana
UniversityofArkansas,Fayetteville,AR,UnitedStates
BilalM.Ayyub
DepartmentofCivilandEnvironmentalEngineering,DirectoroftheCenterforTechnologyand SystemsManagement,UniversityofMaryland,CollegePark,MD,UnitedStates
PhilipB.Bedient
DepartmentofCivilandEnvironmentalEngineering,RiceUniversity,Houston,TX,UnitedStates
NormandE.Bergeron
InstitutNationaldelaRechercheScientifique,CentreEauTerreEnvironnement;Centre InteruniversitairedeRecherchesurleSaumonAtlantique,QuebecCity,QC,Canada
UditBhatia
IndianInstituteofTechnologyGandhinagar,Gandhinagar,India
ClaudineBoyer
InstitutNationaldelaRechercheScientifique,CentreEauTerreEnvironnement,QuebecCity, QC,Canada
DanielCaissie FisheriesandOceansCanada,Moncton,NB,Canada
RomneyB.Duffey
Private,IdahoFalls,ID,UnitedStates
ZhengN.Fang
CivilEngineering,TheUniversityofTexasatArlington,Arlington,TX,UnitedStates
AliFares
CollegeofAgricultureandHumanSciences,PrairieViewA&MUniversity,PrairieView,TX, UnitedStates
AndrewJ.Felton
DepartmentofWildlandResourcesandTheEcologyCenter,UtahStateUniversity,Logan,UT, UnitedStates
Vittorio(Victor)A.Gensini
NorthernIllinoisUniversity,DeKalb,IL,UnitedStates
WilliamA.Gough
ClimateLab;DepartmentofPhysical&EnvironmentalSciences,UniversityofToronto Scarborough,Toronto,ON,Canada
HamidehHabibi
CollegeofAgricultureandHumanSciences,PrairieViewA&MUniversity,PrairieView,TX, UnitedStates
AsifIshtiaque
SchoolforEnvironmentandSustainability,UniversityofMichigan,AnnArbor,MI,UnitedStates
NishantKamboj
IndianInstituteofTechnologyGandhinagar,Gandhinagar,India
ShahzaibKhan
IndianInstituteofTechnologyGandhinagar,Gandhinagar,India
TuDamNgocLe
FacultyofArchitecture,MienTrungUniversityofCivilEngineering,TuyHoa,PhuYen,Vietnam
DongfengLi
CivilEngineering,TheUniversityofTexasatArlington,Arlington,TX,UnitedStates
TahaB.M.J.Ouarda
InstitutNationaldelaRechercheScientifique,CentreEauTerreEnvironnement,QuebecCity,QC, Canada
AndreSt-Hilaire
InstitutNationaldelaRechercheScientifique,CentreEauTerreEnvironnement;Centre InteruniversitairedeRecherchesurleSaumonAtlantique,QuebecCity,QC,Canada
YatingZhang
CenterforTechnologyandSystemsManagement,UniversityofMaryland,CollegePark,MD, UnitesStates
EnricoZio
EnergyDepartment,PolitecnicodiMilano,Milano,Italy;MINEParisTech,PSLResearch University,CRC,SophiaAntipolis,France;EminentScholar,DepartmentofNuclearEngineering, CollegeofEngineering,KyungHeeUniversity,Seoul,RepublicofKorea
Extremeeventsandclimatechange: Amultidisciplinaryapproach 1 AliFares,HamidehHabibi,andRipendraAwal CollegeofAgricultureandHumanSciences,PrairieViewA&MUniversity,PrairieView,TX,UnitedStates
Introduction Aneventcanbeidentifiedasanextreme(weatherorclimate)whentheweatherorclimatevariable exceedsathreshold,closetotheupperorlowerendsoftheusualrangeoverapredefinedduration (Seneviratneetal.,2012).Manystudiesshowedthatfrequency,intensity,spatialextent,duration, andtimingofheavy-to-extremeeventshaveincreasedacrosstheworld,whichcouldbebecauseof globalwarming(Norouzietal.,2019; Stott,2016; Booetal.,2006).Globalwarming/climatechange effectshaveacceleratedinrecentdecades(SheffieldandWood,2011).AccordingtotheUS National ClimateAssessment,overthepast50years,thenumberandstrengthofweather-relatednatural catastrophes,suchasmajorhurricanes, heatwaves,floods,droughts,andtorrentialdownpours, have increasedintheUnitedStates(http://www.c2es.org/content/national-climate-assessment/).
Asaresultofincreasinggreenhousegasesfromanthropogenicsources(Solomonetal.,2007), significanttrendshavebeenobservedinmaximumandminimumtemperatureextremesinmanyareas acrosstheworld,whichcausefewercolddaysandnightsandmorewarmdaysandnights(HeimJr, 2015). Safeeqetal.(2013) analyzedtrendsinobservedtemperatureduring1969–2007ontheisland ofOahu,Hawaii,toevaluatethespatialandtemporalvariabilityaswellasquantifytherelationship betweenlocaltemperatureandregionalclimateindices.Theirresultsrevealedthatdespitethesubstantialspatialandtemporalvariabilityinthetemperaturetrendsontheisland,therewasa0.17 °C/decade islandwideminimumtemperatureincreaseduringthefourdecadesoftheirstudyperiod.Thecomplexityoftheissueofclimatechangeandextremeevents(Faresetal.,2014; HabibiandSeo,2018) requiresaninterdisciplinaryapproachtohelpunderstandthedifficultyoftheproblemandalsoto theinterconnectivitybetweenclimatechangeandextremeevents.Thisbookusesamultidisciplinary approachinaddressingextremeeventsunderachangingclimatebasedonanin-depthanalysisofpast andcurrentconditionsaswellasfutureoutlooks.Inaddition,itdiscussestherelationshipbetweenclimatechangeandextremeeventsandtheirimpactonseveralaspectsofhumandailyactivitiesand manifestations.
Overviewofthechapters Thebookcomprises12chapters;afterthefirstchapter,therestaregroupedintothreecomplementing sections.Thefirstsectionaddressestemperaturesandsevereconvectivestorms;thesecondsectionof thebookcontainschaptersrelatedtohydrologicalresponsesthatgiveanoverviewofthecurrent knowledgeandfutureoutlookoftherespectivetopics.Thethirdsectionofthebookcoversmitigation, adaptationmeasures,andgovernance.Thissectiondiscussesthepotentialimpactsofclimatechange andextremeeventsonecosystemresponses,lifelineinfrastructures,greeninfrastructure,andsea-level rise.Italsoreportsonhowgovernanceandpoliciesaredealingwithclimatechangeandextreme events.
Weatherparameters Thissectioncoversthreechapters. Chapter2,TemperatureExtremesinaChangingClimate,coversthe pastandprojectedhottemperatureextremesandtheirexpectedsocialandenvironmentalimpacts.Surfacetemperatureandtheoccurrencesofhotextremeshavebeenincreasingsincethepreindustrialtimes (almost140yearsago),whichcouldbetheconsequenceofglobalwarming(Seneviratneetal.,2012; IPCC(IntergovernmentalPanelonClimateChange),2013).Anin-depthoverviewofobserved changesintemperatureanditsimpactarediscussedinthefirstpartofthechapter.Theindicesand metricscommonlyusedforevaluatingandpredictingthechangesintemperatureextremesfollowed bytheobservationaldatacollectionandanalysisarepresented.Differentregionalandglobalclimate models,aswellasintegratedanddownscalingtechniques,areoneofthemaincomponentsofunderstandingthefuture/projectedtemperatureconditionoftheearth.Thesemodelsandtechniquesarediscussedindetailinthesecondpartofthechapter.Thelastpartof Chapter2 givesusanoverviewof mitigationandadaptationstrategies(e.g.,reducinggreenhousegasemissionsandincreasingtheresilienceofinfrastructureandcriticalbuildings)appliedacrosstheUnitedStates.Themethodsforevaluationandoptimizationofthesestrategiesareintroduced,whichhelpgovernmentsandstakeholders developandimplementthebestclimateadaptationsandresiliencymeasures.
Changesinglobalairtemperatureaffectdailytemperaturesofsurfacewaterbodiessuchaslakes, streams,andriversthroughouttheyear.Oneofthevitalclimateelementsofaquaticecosystems(e.g., rivers,oceans,andseas)isthewatertemperaturebecauseitinfluencesmostprocessesaffectingwater quality,biologicalactivities,andmarinelives,forexample,fish,insects,zooplankton,andphytoplankton(Dallas,2009).Thetemperatureofsurfacewaterbodiesdeterminestheorganismsthatcantolerate suchtemperatureandthriveinsuchfreshwatersystems. Chapter3 givesanin-depthoverviewofthe mainphysicalprocessesthatdefineextremetemperaturesinthecontextofpotentialchangesresulting fromclimatechangebecauseoftheirpotentialimpactondecreasingaquaticspecies’growthanddistributions.Detailedinformationabouttemperatureextremes,theirtrends,physicalprocesses,andhow climatechangemayimpacttheseprocessesarediscussedinthischapter.
Human-causedclimatechangeisincreasingthefrequencyandstrengthoftorrentialdownpours; thesetrendsarepredictedtocontinueastheearthgetswarmer(Brooks,2013);consequently,itisnecessarytounderstandtheimpactsofclimatechangeonseverethunderstormsandtornadoes,especially thosethatproducelargehailanddamagingstraightwinds. Chapter4,SevereConvectiveStormsina ChangingClimate,summarizesthecurrentknowledgeonthistopic,someofthecriticalquestionsthat stillneedanswers,andresearchopportunitiesinthisarea.Thefirstpartofthechaptercontainsa
concisesummaryofthepresentknowledgeonthetopicfollowedbyasectionexplainingtheconducive settingsthatinducesuchoccurrencesandtheirdocumentedchangeability.Thefollowingsection coversthesignificantlarge-scaledriversoftheseeventsandtheirrelationstoclimatevariability. Thechapteralsoincludesadiscussiononclimateteleconnections(e.g.,ElNinoSouthernOscillation), whicharerecognizedfortheirinfluenceontheglobalweatherpatternsandextremeweather.Inthelast partofthechapter,theauthordiscussesthelatestresearchexaminingsevereconvectivestormsand theirrelationstoclimatechange,withexplicitattentiontotheconvection-permittingdynamicaldownscalingmethodology.Furthermore,theauthorhighlightssomeofthecutting-edgeresearchnecessary toincreasetheabilitytopredictsuchevents,lessentheirenvironmentalimpacts,andminimizetheir harmfulandadverseeffectsonhumanlivesandresources.
Hydrologicalresponses Chapter5 discussesthepatterns,mechanisms,anduncertaintiesintheresponseofterrestrialnetprimaryproductivitytoprecipitationextremes.Anin-depthoverviewofprojectedandobservedincreases inprecipitationextremesandtheirlinkstoanthropogenic-drivenatmosphericwarmingaregiveninthe introductionsectionofthechapter.Thegoalsofthechapterandtheoutlineofitsremainingcontent markthelastpartofthechapter’sintroductionsection.Thefollowingsectionenumeratessomekey examplesoftheimpactsthatprecipitationextremeshaveonecological,social,andeconomicsystems. Thehistoryofresearchandconceptualthoughtsonprecipitationextremeswithinecologyarethendiscussed.Thewovenwithinhistoryisthestandingdebateaboutwhatisan“extreme”event.Patternsand mechanismsofecosystemresponsestoprecipitationextremes,withemphasisontheirtemporalvariability,arepresentedinthemiddlesectionsofthechapter.Currentandfutureresearchneedsofforests, savannas,grasslands,deserts,andotherecosystems,includinghuman-managedecosystems,werediscussedinthelastsectionbeforethesummaryofthechapter.Thechaptersummaryhasanoverviewand keytakeaways,knowledgegaps,andpathwaysonhowtomoveforward.
FloodEarlyWarningSystems(FWS)underChangingClimateandExtremeEventsisthetitleofthe secondchapterofthehydrologicalresponsessectionofthebook.Itfocusesonsomeofthecurrentflood warningsystemsoperatingintheUnitedStatesandintroducesthebasicconceptsofaneffectiveflood warningsystem,includingrainfallmonitoringsystems.Thechapterdiscussesthemaincomponentsof atypicalFWS,including(1)raingaugenetworksandtheiroperationplatformand(2)aremotely sensedrainfallthatencompassesradarsystemssuchasNext-GenerationWeatherRadar(NEXRAD) andsatellitesystemssuchasTropicalRainfallMeasuringMission,theGlobalPrecipitationMeasurementmission,andtheGeostationaryOperationalEnvironmentalSatellitesmission.Hydrological modelingisthenextimportantcomponentoftheFWS;itusesdataofrainfallandotherparameters toinitiatefloodwarningsandestimationoftheirpotentialdamages.Itinvolveshydrologicandhydraulicsoftwarepackages.Oneofthemaintakeawaysisthatthereisaneedtoinvestinfloodwarning systemsimprovementasapartofsustainableurbandevelopmentpracticesandfloodadaptationprogramstominimizeflooddamages.
Theimpactofglobalwarming,suchasrisingsealevelsinresponsetochangingweatherpatterns,is alreadyaffectingecosystems,freshwatersupplies,andhumanhealth.Climatechangeishardtoavoid; however,reducingandpossiblystabilizingthesubstantialamountofheat-trappinggases(greenhouse gasconcentrations)releasedintotheatmospherecouldlessenitsmostdestructiveimpact (VijayaVenkataRamanetal.,2012; Kabischetal.,2016).Thiseffortisonlydealingwithclimate
changemitigation.Inrecentyears,theadaptationprocesseshavebeenconsideredasaviableoptionto reducethevulnerabilitytothepredictednegativeimpactsofclimatechange(Nyongetal.,2007).Itis morespecifictoadoptanintegratedapproachthatcombinesmitigationandadaptationstrategiesand practicestoaddressclimatechangeandassuremorereliableoutcomes(Kabischetal.,2016; Nyong etal.,2007).
Mitigation,adaptation,andgovernance Thelastsectionofthebook,mitigation,adaptation,andgovernance,containssixchapters. Chapter7, LifelineInfrastructuresandHydroclimateExtremesClimate:AFutureOutlook,isthefirstchapterof thesection.Itintroducesthecriticalgapsinknowledgeandmethodologiesofclimateandweatherextremestressorsandstressedinfrastructurelifelinesaswellasascience-basedapproachforresilient transportationnetworksunderexacerbatedstressesfromprecipitationextremesinchangingclimate scenarios.Naturalorhuman-inducedhazardsarepredictable,andbestrecoverystrategiesareadopted toensureresiliencyandrobustlifelineofagiventransportnetworkduetoadvancesinnetworkscience. Thischapterhastwomainparts.PartIdiscussesthedifferentaspectsofcharacterizingandquantifying riskstotransportationinfrastructuresfromextremeeventsandtheirassociatedmethodologies;supportingexamplesarealsoillustrated.Inthesecondpartofthechapter,anetworkedperspectiveoncritical infrastructureispresentedinthecontextofposthazardrecoveryusingtheIndianRailwaysNetwork spanning,servingthedailycommuteofthelargermetropolitanareaofMumbai,asacasestudy.
Thesecondchapterofthemitigation,adaptation,andgovernancesectionofthisbookfocuseson greeninfrastructuresandtheirroleinmitigatingextremeeventsunderpast,current,andfutureclimate changeconditions.Thedesignandimplementationofgreeninfrastructures(e.g.,greenways,parks, gardens,greenroofs,woodlands,waterways,communityfarms,forests,andwildernessareas)could reducegreenhousegasemissionsandprovidenatural-basedsolutionstofillthegapbetweenclimate changemitigationandadaptationactions.Suchinfrastructuresarepartofamultifacetedecosystemsbasedapproachandaneffectivestrategythatsupports ecosystemresilience andhumanbenefits throughecosystemservices. Chapter8 containsfourparts.Asystematicreviewofdifferenttypes andapplicationsofgreeninfrastructuresispresentedinthefirstpartofthechapter.Airpollutionremoval,temperatureregulation,carbonsequestration,andimportantecosystemservicesareincludedin thissecondpartofthechapter,whichalsodealswiththecharacteristicsandcategoriesofgreeninfrastructures.Greeninfrastructurehasmultipleenvironmentalandhealthbenefitsforcommunities,and itsapplicationcanincreasethehealthandenvironmentalequityacrosscommunitiesinthefaceofclimatechange.Healthandenvironmentalequityisthetopicofthethirdpartofthischapter.Thelastpart concentratesonthekeycharacteristicsandclassificationsofgreeninfrastructurebyfunction,inadditiontoitscategorizingnomenclature.Ifgreeninfrastructurepracticesareadoptedglobally,theycould haveapparentmitigatingeffectsonclimatechangeimpacts.However,futurestudiesarerequiredto developguidanceforcommunitiesanddecision-makerstodetermineandapplythemostsite-specific suitedgreeninfrastructures.
Adaptationtoclimateextremesandsea-levelriseincoastalcitiesofdevelopingcountriesisthe topicofthenextchapterofthissection.Itgivesanin-depthoverviewoftheresponsesofthosecities tothechallengesofsea-levelriseunderachangingclimatefacingtheirvulnerarypopulationsandtheir combinedmitigationandadaptationmeasures.Althoughadaptationtoclimatechangeandsea-level riseoptionsarecontext-based,itisessentialtoshareindividualexperiencestohelpothercitiesfind
themostsuitableadaptationpathwaysspecifictotheirconditions. Chapter9 hastwomainparts.The firstpartdiscussesthestate-of-the-artofclimatechangeadaptationinsmallandmediumcoastalcities indevelopingcountriesthroughcharacterizingcoastalexposureandimpactsofclimatechangeand examiningadaptationstrategiestothoseexposuresandimpacts.Thesecondpartdiscusseshowtoconductasynthesisofadaptationstrategiesforcoastalcities,especiallysmallandmediumtownsthathave feweradaptationcapacities.
Thecontentoftheseabove-discussedchapterssupportstheconceptthattheimpactofclimate changeismultidimensionalandhasacascadingeffect.Thesuccessofmitigationandadaptationefforts needsthesupportofmultiplepolicyactors(e.g.,individual,community,andorganization)atdifferent levelsofgovernance(i.e.,global,national,regional,andlocallevels)toparticipateinclimateadaptationandmitigationprocesses(MoserandBoykoff,2013).
Chapter10,titledMultilevelGovernanceinClimateChangeAdaptation:ConceptualClarification andFutureOutlook,concentratesonthestrengthsandlimitationsofthemultilevelgovernanceconcept anditsroleinfutureclimateadaptationandmitigationmeasuresandpractices.Thischapterprovides insightsoninteractionsamongalltheinvolvedactorsandtheirrolesindecipheringthecomplexitiesof adaptationgovernance.Despitetheneed,itisessentialtoexamineandvalidatetheusabilityofthe conceptinrelevantcontextsbeforeapplyingittoadaptationstudiesthatarediscussedafterward.Fundamentalquestionsandissuesthathavebeenaddressedthroughoutthechapterareasfollows:How manylevelsofgovernancearerequiredinclimatechangeadaptation?Shouldwefocusonthearrangementofmultilevelnetworksortheprocesses?Woulditbeallrighttoconsideronlyhorizontalorverticallevels?
Despitethelargeneedforamultiactornetworkofadaptationgovernance,onlysomeclimate changeactions(discursivelyandmaterially)havebeenobservedatlocalscales,particularly,across theUnitedStates.AlthoughtheUnitedStateshasbeenapioneerinclimatechangeresearches,it hasnotbeenactiveinwritingandimplementingpoliciesinresponsetoscientificunderstanding. Chapter11 givesanoverviewofclimatechangegovernanceintheUnitedStates,evaluatesitsactual positioninrelationtolocalClimateActionPlans(CAPs),andidentifiestheextenttowhichcitiesare engaginginGHGmitigationactivitiesusingdescriptivestatisticscomingfromacontentanalysisof cities’CAPsaswellasinterviewsoflocalclimatemanagers.Thischapterhasfourmainparts.Part1 discussesthepoliticsandpoliciesofclimatechangeintheUnitedStatesatdifferentlevelstoidentify intergovernmentalrelationstomultilevelnetworksinthecontextofclimatechangeactions.Thissectionalsodiscussesspecificfederallevelactionsthathaveenabledorhinderedclimategovernancein general.Thesecondpartgivesanoverviewofthestudiesonclimatechangegovernanceatthelocal levelandthespatialdistributionsoflocalCAPswiththelocalpoliticallandscape.Thethirdpartadvocatesfortheimportanceofunderstandingtheprevailingconditionsandfactorsthatinfluencepioneeringlocalities’innovationinclimategovernancetoimprovetheadoptionofclimatechange governanceatthelocallevel.ThelastpartofthischapterassessesthepositionoflocalCAPsin GHGemissionsreductionactionsbyanalyzingpreplanandpostplanGHGemissionsdata.Oneof thekeyfindingsisthatthenumberoflocalitiesacrosstheUnitedStatesthathavesymbolicallyjoined climateactionnetworksorsignedtheclimatemitigationagreementsismuchmorethantheonesthat implementedit.
Naturalandhuman-madedisasterscausefailuresofelectricalinfrastructuresthatwouldeventually affectmillionsofpeopleandresultinintersystemscascadingfailures(PescaroliandAlexander,2016; Zio,2016).Thusoneofthesignificantchallengesinthedesignofresilientelectricalinfrastructuresis
tounderstandthefragilityinducedbysysteminterdependenciesbecauseitcould“paralyzeentireregions,withgraveimplicationsforthenation’seconomicandsocialwell-being”(NIAC,2018).Forsuch matters,thereisasubstantialneedtoadoptanationalapproachtopreparefor,respondto,andrecover fromcatastrophicpoweroutagesaswellastoimproveourunderstandingofhowcascadingfailures acrosscriticalinfrastructureaffectrestorationandsurvival.Thelastchapterexplorespastandcurrent riskstotheelectricalpowergridduetonaturalhazardsandrecoverychallenges.Thischapterconcentratesonthedesignofanationalapproachthatincludesclearplansofmitigationandadaptationfrom catastrophicpoweroutagesandanadvancedunderstandingoftheimpactofcascadingfailuresfrom criticalinfrastructuretopowerrestorationandhumansurvival.Experiencesfrompastdisasters,along withsystemsengineeringimplications,arealsodiscussedinthischapter.
Allthetopicscoveredinthisbookhelptounderstandthecomplexityofclimatechangeandextreme eventsandtheirconnectivityandimpactonhumanlives.Thismultidisciplinaryapproach,aswellas thein-depthanalysisofpastandcurrentconditionsandfutureoutlooks,isrequiredinaddressingthe extremeeventsunderachangingclimate.
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Temperatureextremesinachanging climate 2 YatingZhanga andBilalM.Ayyubb
CenterforTechnologyandSystemsManagement,UniversityofMaryland,CollegePark,MD,UnitesStatesa DepartmentofCivilandEnvironmentalEngineering,DirectoroftheCenterforTechnologyandSystems Management,UniversityofMaryland,CollegePark,MD,UnitedStatesb
Background Temperaturechange Onaverage,theEarth’ssurfacetemperaturegrewby0.85°Coverthepast140years,whereastheland generallywarmedfastercomparedwiththeocean(IPCC(IntergovernmentalPanelonClimate Change),2013).Thecarbonemissionsfromthepreindustrialagetothepresentwillcontinuetocause changesintheclimatesystem,buttheseemissionsalonewillnotresultinglobalwarmingof1.5°Cin the21stcentury(IPCC(IntergovernmentalPanelonClimateChange),2019).However,iftheemission patternisnotaltered,cumulativegreenhousegasescanleadtoa2.6–4.8°Cincreaseofglobaltemperaturebytheendofthe21stcentury(IPCC(IntergovernmentalPanelonClimateChange),2013).
TheannualaveragetemperaturesacrossthecontiguousUnitedStateshaverisenby0.7–1.0°Csince thebeginningofthe20thcentury,andanadditionalincrementof2.8–4.8°Cisexpectedbytheendof the21stcenturyifnomitigationactionistaken.Extremelyhotandcoldtemperaturesarelikelyto increasemorethanaveragetemperatures,meaningwarmdayscanbehotterandcolddayscanbeless freezing.Moreover,warmdaysareprojectedtoincrease,andcolddaysareexpectedtodecreaseinthe restofthe21stcentury(Voseetal.,2017).
HeatwaveshavebecomemorefrequentinthecontiguousUnitedStatessincethemid-1960s(Vose etal.,2017).Astudyfoundthattheannualnumberofheatwavesincreasedby3,intensityroseby0.5°C, anddurationgrewby1dayfortheaverageUScityfromtheyear1961to2010.Meanwhile,heatwave seasonstarted17.5daysearlierandlasted11.5dayslonger(Habeebetal.,2015).Moreintenseheat wavesandlessintensecoldwavesareexpectedintherestofthe21stcentury(Voseetal.,2017).
Theincreaseofextremelyhottemperaturesandheatwavesisattributabletobothnaturalandanthropogenicfactors;however,thelatterhasledtoa1–3°Cincreaseofmaximumtemperaturesduring heatwavesovermostlandareas(Wehneretal.,2018).Naturalfactorreferstothechangeinsolarand volcanicactivities.Anthropogenicfactorsincludegreenhousegasemissionsresultingfromhuman activities,land-usechangeduetourbanization,andwasteheatdischarges.Carbondioxideconcentrationhasincreasedby40%sincethepreindustrialtimes,whichisprimarilyduetofossilfuelcombustion
andsecondarilyduetoland-usechange(IPCC(IntergovernmentalPanelonClimateChange),2013; EPA(EnvironmentalProtectionAgency),2016).Methaneconcentrationhasincreasedbymorethan 100%asaresultoffarmingandproduction,transmission,anddistributionofnaturalgasandpetroleum (IPCC(IntergovernmentalPanelonClimateChange),2013; EPA(EnvironmentalProtectionAgency), 2016).Nitrousoxideconcentrationhasincreasedby18%mainlyduetofoodproduction(IPCC (IntergovernmentalPanelonClimateChange),2013; EPA(EnvironmentalProtectionAgency), 2016).Theseincreasedgreenhousegasesabsorbandre-radiateextraSun’senergythatisoriginally releasedbacktospace,heatingtheatmosphereandtheEarth’ssurfaceandchangingtheEarth’soverall climate.Also,urbanizationreplaceslarge-scalevegetationwithasphaltandconcreteroads,buildings, andotherstructures,increasingheatabsorptionandstorageinurbansurfacesduringthedaytime.This heatwarmsthesurroundingenvironment,especiallyatnight,exacerbatingheatstressonpopulations duringheatwaves.Intensehumanactivitiesreleaseconsiderableheatfromfactories,vehicles,andair conditioners,furtherelevatingurbantemperaturesinsummers.
Impactsofextremelyhottemperatures Overthelasttwodecades,extremelyhotweathercausedmorethan8000deathsintheUnitedStates (CDC(U.S.CentersforDiseaseControlandPrevention),2019).Astatisticfor43UScitiesfromthe year1987to2005suggestedthatmortalityriskmayincreaseby2.49%per1F(0.56°C)escalationof heatwaveintensityandriseby0.38%per1-dayincrementinheatwaveduration(AndersonandBell, 2011).Theelderly,children,andinfantsareparticularlysusceptibletoextremeheat,andpatientswho takemedicationsthataffectthebody’sheatregulatorysystemarealsovulnerable.
Extremelyhotweatherintensifiescoolingenergydemandsandincreaseselectricalloadson powersystems.Itwasfoundthat1°Coftemperatureincrementthatstartsfrom18°Ccanresultin 0.45%–4.6%riseofpeakelectricityloadand0.5%–8.5%growthoftotalelectricitydemand,dependent onbuildingcharacteristics,climatezones,urbanmorphologies,andthetypeofenergyservicesprovided,suchasfansorairconditioners(Santamourisetal.,2015).Moreover,thecapacityandefficiency ofpowersystemsingenerating,transmitting,anddistributingelectricitydecreaseunderhotweather conditions,andthustheriskofpoweroutagesmayincreaseindenseurbanareas.
Extremehotweathercancauseasphaltmelting,concretehogging,andmalfunctionofsignaling equipment.Modernrailinfrastructureisparticularlyvulnerablebecauserailroadtracksarewelded toformacontinuousrailthatistypicallyseveralkilometerslong.Whensuchtracksexpanddueto intenseheatstress,bucklingmayoccuratspotsofweakness.Moreover,inpassengertrains,airconditioningunitsaretypicallydesignedtooperateunder38°C,andtemperaturesabove38°Ccanresultin electronicfaultandseriousdamagestoelectronicunits.Thisisthemainreasonfortraincancellation duringextremelyhotdays.
Extremehotweathercandeteriorateoutdoorairqualitybecausehightemperaturesandabundant sunshinecanspeeduptherateofchemicalreactions,whichpropelstheformationofozoneandfine particulatematters.Ozoneintheupperatmosphereplaysakeyroleinblockingharmfulsolarradiation, whereasozoneinthegroundlevelimpairshumanhealth.Duringheatwaves,atmosphericpressureis relativelyhigh,whichcanconstrainairconvectionandhinderthedissipationofpollutants,resultingin adangerouslyhigh-levelconcentrationofozoneandfineparticulatematters.
Temperatureextremeanalysisandprojection Metricsandindices TheExpertTeamonClimateChangeDetectionandIndiceshasdefinedasetofextremeindicesfor evaluatingandpredictingthechangeoftemperatureextremes,asshownin Table1.Theseindiceshave beenwidelyusedinclimatestudiessuchasthefifthIntergovernmentalPanelonClimateChange (IPCC)report.
Observationalanalysis Collectingobservationdatafortheatmosphere,ocean,andland-basedsystemsisessentialforscientists tounderstandanddescribetheconditionsofourclimatesystem.Thereareover11,000weatherstations aroundtheworld,whichmeasureandmonitorlandandatmosphericconditions.Dailyweatherrecords fromobservingstationsintheUnitedStatesareaccessiblefromtheNationalCentersforEnvironmental Information(https://www.ncdc.noaa.gov/cdo-web/datatools/findstation).Satellites,ships,andaircraft areadditionalapproachesfordatacollection.TheUSNationalOceanandAtmosphericAdministration operatesaconstellationofgeostationaryandpolar-orbitingsatellitestocollectcontinuousglobalenvironmentalobservations,includingprecipitation,seasurfacetemperature,atmospherictemperatureand humidity,seaiceextent,andglobalvegetation.ThesedataarearchivedintheComprehensiveLarge Array-DataStewardshipSystem(https://www.avl.class.noaa.gov/saa/products/catSearch).
TheUKMetOfficeHadleyCenterhasproducedarangeofobservationdatasetsbasedondaily weatherdatacollectedfromthousandsofstationsoversixcontinents.TheHadEXdatasethasbeen updatedtwicetoincludeamoresignificantnumberofstationsandindicesandtocoveramoreextended
Table1Extremetemperatureindices.
IndicesDescription
Usage
TNThecoldestdayofayear,season,ormonth Extremeprojection
TXThehottestdayofayear,season,ormonth Extremeprojection
FDThenumberofdayswhenTNisbelow0°C Impactstudy
FRThenumberofdayswhenTNisabove20°C Impactstudy
TN10pTheexceedancerateofdayswhereTNisbelowthe10thpercentileColdnightprobability estimation
TX10pTheexceedancerateofdayswhereTXisbelowthe10thpercentileColddayprobability estimation
TN90pTheexceedancerateofdayswhereTNisabovethe90thpercentileWarmnightprobability estimation
TX90pTheexceedancerateofdayswhereTNisbelowthe10thpercentileWarmdayprobability estimation
WSDIThenumberofdaysinayearwhenTXisabovethe90thpercentilefor 6consecutivedaysorlonger Heatwaveprojection
CSDIThenumberofdaysinayearwhenTXisbelowthe10thpercentilefor 6consecutivedaysorlonger Coldwaveprojection
period.Thelatestversion,HadEX3,comprises17extremetemperatureand12extremeprecipitation indicesatagridresolutionof1.25° 1.875°,spanningfromtheyear1901to2018(Dunnetal., 2020).Theseextremeindicesarecalculatedataround7000locationsfortemperatureand17,000sites forprecipitation.Anotherrenowneddatasetisthegriddedtemperatureandprecipitationclimateextremesindices(GHCNDEX)developedbytheUSNationalCenterforAtmosphericResearch(NCAR). Thisdatasetcomprises26extremeindicesfromtheyear1951tothepresentwithagridresolutionof 2.5° 2.5°.Theabilitytocontinuouslyupdatethedata,eithermonthlyorannually,makesGHCNDEX anexcellenttoolforclimatemonitoring.Thesedatasetsarewidelyusedforclimatechangedetectionand attributionstudies,climatemodelevaluation,andextremeclimateeventsinvestigation.Thequalityof observationdatasetsisaffectedbyinstrumentsanddataprocessingtechniques,whicharetheprimary sourcesofuncertainty.Dataprocessingaddressesthedilemmathatweatherstationsaredistributedunevenlyontheglobeandsparselyinsomeregions.Thusstatisticalapproximation(e.g.,angulardistance weightingscheme)isemployedtoadjustthoseobservationstoaregulargrid.Moreover,dataprocessing isrequiredforvariablesthatarenotmeasureddirectlybutderivedfromotherobservations.
Dataassimilationisatechniquethatcombinesobservationsfromawidevarietyofsourcesand modelsimulationstoprovidethebestestimateofthestateofasystem.Variousassimilationmethods havebeendevelopedtosupportnumericalweatherprediction,numericaloceanprediction,landsurface processes,hydrologicalcycleprediction,carboncycleprediction,andsoforth(Carrassietal.,2018).In particular,climatereanalysisprojectsusetheassimilationtechniquetodeterminethemostlikelyclimateconditionintherecentpast.TheEuropeanCenterforMedium-RangeWeatherForecast (ECMWF)hasgeneratedalargenumberofreanalysisdatasetsforpublicuse.Thelatestproductis thefifthgenerationofECMWFatmosphericreanalysis(ERA5),whichincludeshundredsofatmospheric,land,andoceanicclimatevariablesfromtheyear1979towithin5daysofrealtime.Other renownedreanalysisdatasetsincludetheNationalCenterforEnvironmentalProtection(NCEP)reanalysis,Japanese55-yearreanalysis,NationalAeronauticsandSpaceAdministration’sModernEra ReanalysisforResearchandApplicationsversion2,andNCEP’sClimateForecastSystemReanalysis. Thequalityofreanalysisdatasetsisaffectedbytheperformanceoftheassimilationmodel,thequality anddistributionoftheunderlyingobservations,andthetechniqueusedincombiningheterogeneous observationsintoaregulargrid.Theuncertaintyofreanalysesisgenerallylargerthanobservations butsmallerthanmodelsimulations.Still,itisnotalwaysthecasebecausetheaccuracyofmodelsimulationshasbeenimprovedmarkedlyinrecentdecades(ReichlerandKim,2008).
Temperatureprojection Globalclimatemodels Energybalancemodels(the1950s)
Energybalancemodelsaretheearliestandmostbasicnumericalclimatemodels,whichsimulatethe balancebetweentheenergyenteringtheEarth’satmospherefromthesunandtheenergyreleasedback tospace.ThesurfacetemperatureoftheEarthistheonlyvariableconsideredinthesemodels.
Radiativeconvectivemodels(the1960sto1970s) Laterresearchersdevelopedradiative-convectivemodelsbyincorporatingtheverticaldimension(air convection)intoenergybalancemodels.Thesemodelscansimulateenergytransferthroughtheheight oftheatmosphereandcalculatethetemperatureandhumidityofdifferentlayersoftheatmosphere.
Generalcirculationmodels/Globalclimatemodels(the1970sto1980s)
Thedevelopmentprocessofthegeneralcirculationmodelcanbedividedintotwostages.Theearly stagefocusedoncharacterizingtheevolutionofthedynamicandthermodynamicstateoftheatmosphereandocean,respectively.Thedevelopedmodelscancaptureairandwaterflowsandheattransfer intheatmosphereandoceans.Thelaterstagecoupledatmosphereandoceanmodelsandnamedthem thecoupledatmosphere-oceangeneralcirculationmodels(AOGCMs).TheAOGCMscansimulatethe exchangeofheatandwaterbetweentheland,atmosphere,andocean.
Earthsystemmodels(startingfromthe1990s)
Toimprovetheunderstandingofhowclimaterespondstoincreasedgreenhousegasemissions,more complicatedtreatmentsofseaiceandlandsurfacewereincludedinAOGCMs,alongwithsubmodels ofvegetation,ecosystems,andbiogeochemicalcycles.TheseformedtheEarthsystemmodel,which includesthesimulationofthecarboncycle,nitrogencycle,atmosphericchemistry,oceanecology,and changesinvegetationandlanduse.
Regionalclimatemodels Thefirstregionalclimatemodel(RCM)wasdevelopedinthelate1890s,andsincethen,regional modelinghasexperiencedtremendousgrowth.RegionalmodelingiscommonlyreferredtoasdynamicaldownscalingbecauseRCMsusethedataofglobalclimatemodels(GCMs)asinputtogenerate fine,subgridscaleresults.Thefine-gridresultscanhelpdecision-makersevaluatepotential impactsofclimatechangeoncropproduction,hydrology,speciesdistribution,andsuchatscalesof 10–50km.TheGCMsconsiderglobalcirculationandlarge-scaleforcings,suchasgreenhousegases andsolarradiation,whereasRCMssimulatealimitedareaoftheEarthandaccountforsgridscale forcingsandprocesses,includingcomplextopography,inlandwaterbodies,andmesoscaledynamical processes.
SeveralRCMshavebeendevelopedandmaintainedascommunitytoolsforregionalclimateresearch,includingtheUSRegionalClimateModel,theUSNCAR’sWeatherResearchandForecasting (WRF)model,theUSNationalCenterforEnvironmentalPrediction’sEtamodel,CanadianRegional ClimateModel,UKMetOfficeHadleyCentre’sRegionalClimateModel,GermanConsortiumfor Small-scaleModelingandClimateLimited-areaForecasting,GermanREgionalMOdel(REMO),EuropeanApplicationsofResearchtoOperationsatMesoscale(AROME/ALADIN),SwedishRossby Centre’satmosphericmodel(RCA),andScrippsExperimentalClimatePredictionCenter’sregional spectralmodel(ECPC-RSM).
TheCoordinatedRegionalClimateDownscalingExperiment(CORDEX)programwaslaunched bytheWorldClimateResearchProgramin2009tocoordinateandadvancethescienceandpracticeof regionalclimatemodelinganddownscaling.Theprogramprovidedframeworksformodelevaluation andclimateprojectionandproducedimprovedregionalprojectionsinsupportofthefifthandsixth IPCCreports.TheprojectionsofCORDEXcoveralllandareas,includingtheArctic,considervarious emissionscenarios,andattainahighgridresolutionof50kmandeven25kmforsomeregions.The climateprojectionsforNorthAmericaareaccessiblefromtheClimateGatewayattheNCAR(https:// www.earthsystemgrid.org/search/cordexsearch.html?nsc¼true).
TheuncertaintyofRCMsismostlyinheritedfromGCMs.GCMsmayproducedifferentresponses tothesameclimaticforcingbecausetheyarebuiltonvariousphysicalschemes,parameterization methods,andland-oceancouplingtechniques.ItisworthnotingthatRCMsaredrivenbyasmallsubset
ofGCMs,andthustheydonotadequatelysamplemodeluncertainty.Thisleadstotheuncertainty associatedwithdomainselection.Othersourcesofuncertaintyarerelatedtofutureemissionscenarios, internalclimatevariability,andthechoiceofRGM.
Integratedassessmentmodels Integratedassessmentmodels(IAMs)weredevelopedoverthepastyearsbycombiningclimate,economic,landuse,andenergymodelsthroughcomputercodesordatafilesthatexchangeinformation amongeachmodule.TheinputsofIAMsaretypicallycountry-levelpopulationandgrossdomestic productionbasedonexistingorassumedfutureeducation,urbanization,andeconomicconditions. Theoutputstypicallyincluderegional-scalegreenhousegasemissions,airpollution,andaerosolemissions,energysupplyanddemand,andland-useandland-coverchange.Themodelscanalsogenerate thecostsofmitigationpoliciesandthepricesoffossilfuelsandrenewableenergy,whichhelpstoevaluatepolicyoptionsthatcouldbedeployedtotackletheemissionproblem.
Anewscenarioframework,namedtheSharedSocioeconomicPathway(SSP),wascreatedtofacilitatetheintegratedanalysisoffutureclimateimpacts,vulnerabilities,andadaptationandmitigation potential,byusingIAMsdevelopedbysixresearchgroups.ThescenarioframeworkcontainsfiveSSPs (SSP1,SSP2,SSP3,SSP4,andSSP5),representingfiveassumedglobaldevelopmentpaths:sustainabledevelopment,middle-of-the-roaddevelopment,regionalrivalry,inequality,andfossil-fueleddevelopment.Sustainabledevelopmentmeansthattheworldwillshifttowardamoresustainablepath, emphasizingmoreinclusivedevelopmentthatrespectsperceivedenvironmentalboundaries.Middleof-the-roaddevelopmentimpliesthattheworldwillfollowapathinwhichsocial,economic,andtechnologicaltrendsdonotshiftobviouslyfromhistoricalpatterns.Regionalrivalryscenarioassumesthat countrieswillconcernmoreaboutcompetitivenessandsecurityandincreasinglyfocusondomesticor regionalissues.Theinequalityscenarioassumesincreasingdisparitiesineconomicopportunityand politicalpowerinthefuture,whichwillleadtoincreasinginequalitiesandstratificationbothacross andwithincountries.Fossil-fueleddevelopmentmeansthattheworldwillspeedtechnology,economy, andsocietydevelopmentandexperiencerapidpopulationgrowth,alongwiththeexploitationofabundantfossilfuelresourcesandtheadoptionofresource-andenergy-intensivelifestylesaroundtheworld (Rahiletal.,2017).
Ensemblemodeling Coupledmodelintercomparisonprojectphasefive
Thereareroughly30researchgroupsthathavedevelopedtheirclimatemodels.Thesemodelsaresimilarinthestructurebutdifferentindetails,suchasphysicalschemesandthenumberofverticallayers. Toenableacomparisonbetweentheresultsofdifferentmodels,theCoupledModelIntercomparison Projectsetupthesemodelsinthesamewayandusedthesameinputs.TheCoupledModelIntercomparisonProjectPhaseFive(CMIP5)waspublishedinthefifthIPCCclimatechangeassessmentreport, incorporatingthelatestandmostsophisticatedclimatemodelexperimentsworldwide.Comparedtoa singleAOGCM,amultiplemodelensemblehasshownsuperiorperformanceforhistoricalclimateassessment,becausecouplingthesemodelscantakeadvantageoftheirstrengthsandcompensate limitations.
Toaddressfutureuncertaintiesregardingemissionsandconcentrationsofgreenhousegasses,aerosols,landchange,andsolarradiation,CMIP5adoptsfourclimatescenariosthatarerepresentativeconcentrationpathway(RCP)2.6,RCP4.5,RCP6.0,andRCP8.5,correspondingtoaradiativeforcingof
2.6,4.5,6.0,and8.5W/m2 by2100,respectively. Radiativeforcing isdefinedasthenetchangeinthe energybalanceoftheEarthsystembecauseofnaturalandanthropogenicsubstancesandprocesses, relativetothereferenceyearof1750.ThefourscenariosassumeCO2 concentrationsof421,538, 670,and936ppmby2100,andgreenhousegasconcentrations(CO2,CH4,N2O)of475,630,880, and1313ppmby2100,respectively.
Coupledmodelintercomparisonprojectphasesix TheCoupledModelIntercomparisonProjectPhaseSix(CMIP6)wasinitiatedin2014tofillscientific gapsremaininginCMIP5andaddressnewchallengesemerginginclimatemodeling(Eyringetal., 2016).CMIP6hasendorsed23modelintercomparisonprojectsinvolving33modelinggroupsin16 countriesandformedthebasisofthesixthIPCCassessmentreporttobepublishedin2021.Forclimate projection,CMIP6considerstheimpactofsocioeconomicconditions(e.g.,population,economy)on greenhousegasemissionsbylinkingRCPstoSSPs,whichenhancestherobustnessofclimateprojectionsandprovidesbettersupporttoclimatepolicies.Also,CMIP6keepsfourscenariosusedinCMIP5 (SSP1-2.6,SSP2-4.5,SSP4-6.0,andSSP5-8.5)andaddsfournewscenarios(SSP1-1.9,SSP4-3.4, SSP5-3.4-OS,andSSP3-7.0)togiveawiderselectionoffuturesforscientiststosimulate.CMIP6data areopentopublicuseandaccessiblefromtheLawrenceLivermoreNationalLaboratory(https://esgfnode.llnl.gov/search/cmip6/).
Weightedandunweightedensemblesimulation TheCMIP5archivecontainssimulationsof29institutionsand62models.Someofthemodelsare similartoothersbecausetheysharethesamephysicalschemesandnumericalmethods.ThefifthclimateassessmentofIPCCandthethirdnationalclimateassessmentconsideredeachmodeltobe equallylikelyindepictingfutureclimatechange,whereasthefourthnationalclimateassessmentof theUSGlobalChangeResearchProgramadoptedaweightingstrategytocouplingmodelsbased ontheirskillsandindependence. Sandersonetal.(2017) indicatedthattheoverallperformanceof theweightedmodelensemblecanbebetterthanthatofmodeldemocracy,especiallywhenselected modelsaresignificantlyindependentofeachother.However,thereisatradeoffbetweenmodelskills andmodeluniqueness,whichmayweakentheperformanceofweightedmodels.Modelskillsdeterminewhetherthesimulationisofsufficientaccuracy,whereasmodeluniquenessensuresthatuncertaintiesandbiasaresmallenough.Itshouldbenotedthattheweightingvariesforvariedregions becausethecapacityofmodelsisdifferentinsimulatingtheclimateofdifferentgeographicalregions.
Downscalingtechniques Climatechangeimpactsregionsandpopulationsunevenlyduetodifferentgeographical,socioeconomic,andtechnologicalconditions.Toassesslocal-scaleimpacts,high-resolutionclimatedataare needed.Thedatacanbeobtainedusingdownscalingtechniquesthatgeneratelocaldetailedinformationbasedonglobalcoarsesimulation.Therearetwomaindownscalingtechniques—statisticaldownscalinganddynamicaldownscaling—whichcanbeusedaloneortogether.
Statisticaldownscaling Statisticaldownscalinghastheadvantageofinexpensivecomputingandiseasytousecomparedwith dynamicalmethods.Moreover,statisticaldownscalingiscapableofproducingsite-specificclimate projections(e.g.,thelocationofaweatherstation),whereasdynamicaldownscalingcannotprovide
site-specificclimateprojections.Statisticaldownscalingisbuiltontheassumptionthattherelationship betweenmodelsimulationandobservationisstationary,andhencetherelationshipderivedfromthe historicalperiodcanbeappliedtoprojectingfutureconditions.Thethreemajorcategoriesofstatistical downscalingmethodsarelinearmethod(e.g.,deltamethod,simpleandmultiplelinearregression,canonicalcorrectionanalysis,singularvaluedecomposition),weatherclassification(e.g.,analogmethod, clusteranalysis,artificialneuralnetwork,self-organizingmap),andweathergenerator(e.g.,LongAshtonResearchStationWeatherGenerator,MarkSimWeatherGenerator,nonhomogeneoushiddenMarkovmodel).See TrzaskaandSchnarr(2014) fordetails.
AsynchronousRegionalRegressionModeling(ARRM)isoneoftheempiricalstatisticaldownscalingtechniques,usingaquantilemappingmethodtoadjustglobalprojectionstostation-basedorfine grid-basedobservations.TheARRMassumesthattwoindependenttimeseriesshouldhavesimilar probabilitydensityfunctionsiftheydescribethesamevariableandareatapproximatelythesamelocation,suchastemperaturessimulatedbyaclimatemodelandobservedbyaweatherstationforthe samelocation.ThreestepsarerequiredforapplyingARRMtodownscalingtheinformationofGCMs. Thefirststepistoranktheobservedhistoricaldataandmodel-simulatedhistoricaldatainanascending sequencebasedonrespectivevaluesregardlessoftimestamps,asshownin Fig.1A.Astrongcorrelation shouldbeobservedinascatterplotforobservationsversusmodelsimulations.Thesecondstepistousea mathematicalfunctiontosimulatetherelationshipbetweenrankedobservationsandsimulations. Fig.1Ashowsapiecewiselinearregressionfunctionusedtofitrankedresultsofstationobservations andCMIP5simulations.Thethirdstepistovalidatethedevelopedregressionfunctionusinghistorical
FIG.1
Observedandsimulatedtemperaturesatthelocationofaweatherstationduring1965–2000:(A)observedversus CMIP5-simulatedmaximumdailytemperaturesandquantilemappingbetweenobservationsandsimulations (breakpointsrepresentingthe1st,10th,25th,50th,75th,90th,and99thquantiles);(B)probabilitydensity distributionsofobserved,CMIP5-simulatedanddownscaledCMIP5-simulatedtemperatures.
AdaptedfromZhang,Y.,Ayyub,B.,2018.Urbanheatprojectionsinachangingclimate:Washington,DC,casestudy.ASCE-ASME J.RiskUncertaintyEng.Syst.Civ.Eng.4(4),04018032.
data,andthelaststepistodownscalemodelprojectionsusingthedevelopedregressionfunction. Fig.1B comparesCMIP5-simulatedtemperaturesanddownscaledresultsanddemonstratesthatdownscaling helpscapturelocalclimatecharacteristicsandimprovetheaccuracyofthesimulation.ThemajorweaknessoftheARRMisthattherealrelationshipbetweenobservationsandsimulationscanchangeover time,especiallyunderdifferentfutureclimateconditions,whichcanweakentheaccuracyofprojections.Also,theerrorcausedbythevariedrelationshipcannotbemeasuredusingstatisticalmeans.
TheLocalizedConstructedAnalogs(LOCA)isastate-of-the-artdownscalingtechniquethatuses multiscalematchingtoimprovetherepresentationoftemperatureandprecipitationextremesandspatialpatternsindownscaledresults.Multiscalematchingcomprisestwosteps.Steponeselectsobserved days(typically30days),whichbestmatchthemodeldayinawiderregionaroundthelocationtobe downscaled.Steptwoselectsthesingleoneofthoseobserveddaysthatbestmatchesthemodeldayin animmediateneighborhoodofthelocationtobedownscaled.Theobserveddayschoseninsteponeare calledanalogdays,andthelocationtobedownscalediscalledanalogpoolpointoranalogpoollocation,whichisselectedfromthecoarsegrid.Steponerequiresdefiningtherangeofregionthatsharesa similartemperatureorprecipitationpatternwiththeanalogpoolpoint,whichisresolvedbymeasuring thePearsoncorrelationbetweentheanalogpoolpointandnearbycoarse-gridpoints.Themagnitudeof correlationtypicallydecreasesfromonetozeroasthedistancefromtheanalogpoolpointincreases, andzeromeansnocorrelation,whichformstheedgeoftheregion.Analogdaysaredeterminedinsuch aregionbyminimizingtheroot-mean-squaredifferencesfromthemodelday.Oncesteponeiscompleted,fine-gridanalogresultscanbedrawnfromanalogdaysatthenearestanalogpoolpoints.Step twostartswithadjustingcoarse-gridsimulationsandcoarse-gridanalogresultstothefinegridthrough bicubicinterpolation,andcalculatingtheroot-mean-squaredifferencesbetweentheirinterpolatedresults,overasmallsquareregioncenteredontheanalogpoolpoint.Thenthesingledaywiththeminimumdifferenceisidentified,andthefine-gridanalogresultsofthesingledayarescaledtothemodel dayfortheregiondefinedinstepone.Indownscalingtemperature,thescalingissimplyaddingthe meandifferencebetweeninterpolatedsimulationandfine-gridanalogresult,whereasindownscaling precipitation,thescalingismultiplyingthemeanratioofinterpolatedsimulationtofine-gridanalog result.Thisensuresthattheextremesandspatialpatternsoftemperatureandprecipitationsimulatedby climatemodelsarekeptinthedownscaledresults.Consideringthatfutureanomaliesoftemperatureor precipitationmayexceedtherangeofhistoricalvariability,theLOCAmethodcorrectsdownscaled projectionsbyaddingthemeandifferencebetweenfine-resolutionandcoarse-resolutionanalogresults.Thiscorrectionisnotrequiredfordownscaledhistoricalsimulations.TheLOCAmethodcan alsobeextendedtodownscalemultipleclimatevariablesatthesametimebyassigningaweightto eachvariable,whichcansignificantlyimprovecomputingefficiency.
Dynamicaldownscaling Dynamicaldownscalingadjustscoarse-gridsimulationsofGCMstofine-gridresultsbyusingRCMs. AprevioussectionintroducedRCMsandtheircharacteristics,andthissectionusesoneoftheRCMsto illustratedownscalingmechanisms.TheWRFmodelisdevelopedbytheNCARincollaborationwith manyresearchinstitutesanduniversities.Themodelhasbeenimprovedmanytimessincethefirstversioncameoutin2000,anditslatestversionWRF4.1wasdevelopedasasoftwareprogramreleasedlast yearforclimateresearchcommunityuse.TheinputsoftheWRFmodelincludethree-dimensional temperature,windspeed,geopotentialheight,andrelativeorspecifichumidity,andtwo-dimensional surfacepressure,meansealevelpressure,skintemperature,2-mtemperature,2-mrelativeorspecific
humidity,and10-mwindspeed.Optionalinputsincludesoiltemperature,soilmoistureandsoforth, dependingonresearchneeds.TheseinputsareobtainedfromGCMsforadomaindesignatedbyusers. Inadditiontodirectdownscaling,theWRFmodelusesnestdomainstoimprovetheresolutionofclimatesimulation.Thegridresolutionofnestdomainsisseveraltimesgreaterthanthatoftheirpresent domains,andthusincreasingthenumberofnestingcanexponentiallyimprovetheresolutionofthe innermostdomain,butmorecomputationloadsaregenerated.TheWRFmodelproducestheresults ofnesteddomainsbasedonthesolutionsoftheirparentdomains,andhence,theoutermostdomain shouldbelargeenoughtoenablethedevelopmentofaregionalclimateeventthatisusuallyrelevant tothetopography,atmosphere,andotherfactorsinalargespace.
TheWRFsoftwareprogramprovidesmultiplephysicalschemesandnumericalordynamicsoptions foruserstochooseanappropriatecombinationandallowsuserstomodifyparametervalues(e.g.,urban morphology,anthropogenicheatemission)basedontheirresearchneeds.TheoutputsoftheWRF modelcontainallinputvariablesatahigherspatialandtemporalresolutionandotherstatuevariables suchasheatfluxes. Fig.2 showsanexampleofusingtheWRFmodeltodownscaletemperaturessimulatedbytheCommunityEarthSystemModelversionone(CESM1).Thegridresolutionsbeforeand afterdownscalingare0.9 1.25degrees(approximately100 106km)and4 4km,respectively.
AlthoughdynamicaldownscalingimprovesGCMinformationatafinescale,systematicbiasin GCMisprorogatedtodownscaledresults.ThiscanberesolvedbyremovingthebiasofGCMdata beforedownscaling.Inaddition,RCMitselfmayproducebias,whichcanbecorrectedusingstatistical methods.
FIG.2
ProjectedheatwavesintheWashingtonDCmetropolitanregionforthreefutureperiods2036–2040,2066–2070, and2096–2100,andthereferenceperiod2011–2005,plottedat4-kmgridintervals.(A)–(D)Maximum temperatureduringheatwaves;(E)–(H)Annualnumberofheatwavedays. Blacklines arecountyboundaries, andthewhiteareaiswaters.
ReprintedfromZhang,Y.,Ayyub,B.,2020a.Projectingheatwavestemporallyandspatiallyforlocaladaptationsinachangingclimate: WashingtonD.C.asacasestudy.Nat.Hazards,1–20, https://doi.org/10.1007/s11069-020-04008-6
Mitigationandadaptationstrategies Overviewofmitigationandadaptationstrategies TheParisAgreementenactedin2016broughtallnationsintoacommoncausetopreventtheriseof globaltemperaturesthiscenturyabove2°Crelativetopreindustriallevels,andtomakefurtherefforts tolimitthetemperatureincreaseto1.5°C.ManyUScitieshaveevaluatedtheimpactsofclimatechange ontheircommunitiesanddevelopedactionplanstoreducegreenhousegasemissions.Forinstance, WashingtonDCpublishedasustainableplanin2013,aimingtoreduce50%and80%ofgreenhouse gasemissionsinthedistrictbytheyear2032and2050,respectively,relativetotheemissionlevelof 2006.Thisplanwasadjustedin2018tonet-zerocarbonfootprintby2050.LosAngelesreleasedanaction planin2007,intendingtoreducegreenhousegasemissionsto35%below1990levelsbytheyear2030.
Climatechangemitigationintendstoreducegreenhousegasemissionsandconcentrationsinthe atmosphere.ThestrategiesemployedbymajorUScitiesareasfollows:
•Retrofittingexistingcommercialandmultifamilybuildingstoimprovetheirenergyefficiencyand reducetheirrelianceonfossilfuelsforheatingandcooling
•Implementingnet-zeroenergybuildingcodefornewconstruction
•Improvingenergyefficiencyandreducingoverallconsumption
•Increasingelectricitygenerationandoptimizeenergydistributionsystem
•Increasingtheshareofrenewableenergyinenergysupply
•Developingrenewableportfoliostandardstosteadilyincreasetheuseofrenewableenergy
•Reducingdependenceonprivatevehiclesandincreasingtheuseofpublictransit
•Increasingbikingandwalking
•Deployingzero-emissionelectricvehicles
Climatechangeadaptationmeanstotakeappropriateactiontopreventandminimizedamagesandto takeadvantageofopportunitiescreatedbysuchchange.ThestrategiesadoptedbymajorUScitiesare asfollows:
•Deployingcoolroofs,greenroofs,reflectivepavementsandothernewtechnologiestomitigatethe urbanheatislandeffect
•Improvingtransportationandutilityinfrastructuretomaintainvariabilityduringextremeweather events(e.g.,heatwaves,severestorm,flooding)
•Upgradingexistingbuildingsanddesigningnewbuildingsanddevelopmentprojectstowithstand climatechangeimpacts
•Strengtheningcommunity,social,andeconomicresiliencetomakeneighborhoodsand communitiessaferandmoreprepared
Strategyevaluation TheIPCCproposedalinearprocessforclimateadaptationinits1994technicalguidelines.Thisprocessstartswithscientificanalysisofclimatechange,includingvariability,thenmeasuresresidualor netclimateimpactsafterautonomousadaptations,andfinally,determinesadaptationneeds.Subsequentlyintroducedframeworksforclimateadaptationaccountedforpolicycriteria,populationgrowth, economicdevelopment,andothernonclimatefactorstogenerateaccessibleandaffordableoptions.
FIG.3
Theframeworkforclimateadaptationplanningincorporatingtheprobabilisticriskmethod.
ReprintedfromZhang,Y.,Ayyub,B.,2020b.Electricitysystemassessmentandadaptationtorisingtemperaturesinachangingclimate withWashingtonMetroAreaasacasestudy.ASCEJ.Infrastruct.Syst.26(2),04020017.
Arecentstudyincorporatesaprobabilisticriskmethodintotheframeworktoenablequantitativeassessmentsofadaptationoptionsanduncertaintieswithinoutcomes,asshownin Fig.3.Theprobabilisticriskmethodisdefinedbythefollowingequation.
where R isthenetpresentvalueofclimaterisks. P(C)istheprobabilitythataclimate-changescenario occurs. P(S j C)istheprobabilitythatastressorintensifieswhenclimatechanges. P(F j S)istheprobabilityofsystemfailurewhenthestressorintensifies. P(L j F)istheprobabilityofalosswhenthesystemfails. L isthemonetizedloss. r istheannualdiscountrate. t isthetimeinyearsstartingfromthe year t0. n isthenumberofyearsaccountedforriskestimation. Ifweconsideronlyoneclimatescenarioandclimatestressorandonetypeoffailureandloss, Eq. (1) canberewrittenasfollows.
Aftertakingadaptionactions,theriskisreducedasfollows.
where Δ R isthecoefficientofriskreductionduetoclimateadaptation,rangingbetween0and1.
Thefirststepoftheassessmentframework(Fig.3)isselectingclimatestressorsandprojectfuture exposures.Theclimatestressorcanbetemperatureshift,precipitationchange,orsealevelrise.The probabilitythatthestressorincreasesunderaclimatescenario, P(S j C),canbeestimatedbasedon theprojectionresultsofclimatemodelssupplementedwithprofessionaljudgmentinsomecases. Thesecondstepisidentifyingvulnerablesystemsandassetsandmeasuringthesensitivityofeachsystemtotheclimatestressor.Thefailureprobabilityofasystemwhenexposedtotheclimatestressor, P(F j S),canbeestimatedusinghistoricaldata,modelsimulationresults,orexpertopinionelicitation. Thethirdstepistoselectadaptationoptionsandassessriskreductionsassociatedwithrespectiveadaptationactions.Adaptationoptionsmayincludedeployingnewtechniquesandequipment,developingnewregulationsonsomeindustrysectors,andestablishingincentiveprogramsforsomepractice. Theadaptationcapacityofasystem, △ P(F j S),ismeasuredasthereductioninfailureprobabilitywhen adaptationactionsaretaken.Itisworthnotingthatadaptationcapacitymayvarywithdifferentgeographicregionsandsystemcharacteristics.
Thelaststepistoanalyzethebenefitandcostofeachadaptationschemeandcompareandoptimize theseschemes.Benefit-costanalysisiswidelyusedintheengineeringdecision-makingprocess,where thebenefit(B)ispotentialriskreductionandcost(C)istheinvestmentmadetomitigaterisks.The probabilitythatbenefitisgreaterthanthecost, P(B > C),isametricfordeterminingwhetheranadaptationoptioniscost-effectiveornot.Theothermetricsusedfordecisionmakingarenetbenefit (B C)andbenefit-to-costratio(B/C).Strategycomparisonaimstofindtheoptionthatreduces theriskincost-effectivetermswithinanybudgetaryorregulatoryconstraints,andoptimizationintends toseektheappropriatetimingfortheadaptationtoenabletheinvestmenttobemostcost-effective.
Summaryandconclusions Carbonemissionfromhumanactivitieshaselevatedglobaltemperaturesandintensifiedextremeheat events.Theurbanheatislandeffectfurtherincreaseslocaltemperaturesandstrengthenstheimpactsof heatwaves.Heatwavescanimpairhumanhealth,deteriorateairquality,intensifycoolingenergydemands,andcausedamagestotheelectricalpowerandtransportationsystems.Considerableefforts havebeenmadeinrecentdecadestoimprovethetechnologyinmonitoringandsimulatingclimate change.Researcherscreatedobservationdatasets(e.g.,HadEX3,GHCNDEX)usingdailyweatherdata collectedfromthousandsofstationstorevealtherealclimateconditionsandproducedreanalysisdatasets(e.g.,ERA5)usingdataassimilationmethodsandmillionsofobservationstoestimatethemost likelyclimateconditions.Thesedatasetsareessentialforclimatechangedetectionandattributionstudies,climatemodelevaluation,andextremeclimateeventsinvestigation.
Researchersalsocoupledatmosphere,ocean,vegetation,ecosystem,andbiogeochemicalcycle modelstosimulateamorecomplicatedclimatesystem.Thesemodelingeffortshaveimprovedour understandingofhowclimaterespondstoincreasedgreenhousegasemissions.GCMsdeveloped bydifferentinstitutesandgroupshavebeencoupledtogeneratecredibleprojectionsfortemperature, precipitation,andothervariablesthroughoutthe21stcentury(e.g.,CMIP6,CMIP5).Climate,economic,landuse,andenergymodelshavebeenintegratedtoprovideimportantinsightsintotherelationshipbetweensocialdevelopmentandclimatechange,whichfacilitatescomprehensivepolicy analysis.Meanwhile,RCMsarecoordinatedtoencourageglobaleffortsonimprovingmodelingtechniquesandevaluatingandcomparingmodels’performances(i.e.,CORDEX).Theimprovementin
globalandregionalclimatemodelingwillhelpgovernmentsandstakeholdersbetterforeseeandassess potentialrisksandopportunitiesunderlyingachangingclimate.
Becauseclimateimpactsaredistributedunevenlyoverdifferentregions,agrowinginterestinassessingregionalclimateimpactsandsimulatingregionalclimatechangecanbeobservedinrecentstudies. ThisdrivesthedevelopmentofstatisticaldownscalingmethodsthatadjustinformationofGCMsto regionalorlocal-scaleresults(e.g.,LOCA).Moreover,governmentsatdifferentlevelshavestarted tomitigateclimateimpactsbyreducinggreenhousegasemissionsandincreasingtheresilienceofinfrastructureandcriticalbuildings.Thischapterreviewedmitigationandadaptationstrategies employedbymajorUScitiesandpresentedthemethodsforstrategyevaluationandoptimization, whichrepresentsasmallportionofthestate-of-the-artpractice.Readersmayrefertosomerecentreviewarticlestogainamorecomprehensiveunderstanding.
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