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Storylines:Analternativeapproachtorepresentinguncertaintyinclimate1change2
TheodoreG.Shepherda,EmilyBoydb,RaphaelA.Calelc,d,SandraC.Chapmane,f,Suraje3Dessaig,IoanaM.Dima-Westh,HayleyJ.Fowleri,RachelJamesj,k,DouglasMaraunl,4OliviaMartiusm,CatherineA.Seniorn,AdamH.Sobelo,DavidA.Stainforthd,SimonF.5B.Tettp,KevinE.Trenberthq,BartJ.J.M.vandenHurkr,s,NicholasW.Watkinsd,e,f,6RobertL.Wilbyt,DimitriA.Zenghelisd7
aDepartmentofMeteorology,UniversityofReading,ReadingRG66BB,UK8bLundUniversityCentreforSustainabilityStudies,22100Lund,Sweden9
cMcCourtSchoolofPublicPolicy,GeorgetownUniversity,Washington,DC2005710
dLondonSchoolofEconomics,LondonWC2A2AE,UK11eCentreforFusion,SpaceandAstrophysics,DepartmentofPhysics,Universityof12Warwick,CoventryCV47AL,UK13
fCenterforSpacePhysics,DepartmentofAstronomy,BostonUniversity,Boston,MA140221515
gSustainabilityResearchInstitute,SchoolofEarth&Environment,Universityof16Leeds,LeedsLS29JT,UK17
hWillisRe,LondonEC3M7DQ,UK18iSchoolofEngineering,NewcastleUniversity,NewcastleuponTyneNE17RU,UK19
jEnvironmentalChangeInstitute,UniversityofOxford,OxfordOX13QY,UK20
kDepartmentofOceanography,UniversityofCapeTown,Rondebosch7701,South21Africa22
lWegenerCenterforClimateandGlobalChange,UniversityofGraz,8010Graz,23Austria24
mInstituteofGeography,OeschgerCentreforClimateChangeResearch,University25ofBern,3012Bern,Switzerland26
nMetOffice,ExeterEX13PB,UK27
oDepartmentofAppliedPhysicsandAppliedMathematicsandDepartmentofEarth28andEnvironmentalSciences,ColumbiaUniversity,NewYork,NY10027pSchool29ofGeosciences,UniversityofEdinburgh,EdinburghEH93FF,UK30
qNationalCenterforAtmosphericResearch,Boulder,CO8030731rRoyalNetherlandsMeteorologicalInstitute(KNMI),3730AEDeBilt,Netherlands32
sInstituteforEnvironmentalStudies,VUUniversityAmsterdam,1081HV33Amsterdam,Netherlands34
tDepartmentofGeography,LoughboroughUniversity,LoughboroughLE113TU,UK35
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Authorfullnamesandemailaddresses37
TheodoreG.Shepherd,theodore.shepherd@reading.ac.uk(correspondingauthor;38Phone:+441183788957;ORCID0000-0002-6631-9968)39
EmilyBoyd,emily.boyd@lucsus.lu.se40
RaphaelA.Calel,Raphael.Calel@georgetown.edu41
SandraC.Chapman,S.C.Chapman@warwick.ac.uk42
SurajeDessai,S.Dessai@leeds.ac.uk43
IoanaM.Dima-West,Ioana.DimaWest@WillisTowersWatson.com44
HayleyJ.Fowler,hayley.fowler@newcastle.ac.uk45
RachelJames,rachel.james@eci.ox.ac.uk46
DouglasMaraun,douglas.maraun@uni-graz.at47
OliviaMartius,olivia.romppainen@giub.unibe.ch48
CatherineA.Senior,cath.senior@metoffice.gov.uk49
AdamH.Sobel,ahs129@columbia.edu50
DavidA.Stainforth,D.A.Stainforth@lse.ac.uk51
SimonF.B.Tett,Simon.Tett@ed.ac.uk52
KevinE.Trenberth,trenbert@ucar.edu53
BartJ.J.M.vandenHurk,bart.van.den.hurk@knmi.nl54
NicholasW.Watkins,nickwatkins62@fastmail.com55
RobertL.Wilby,R.L.Wilby@lboro.ac.uk56
DimitriA.Zenghelis,D.A.Zenghelis@lse.ac.uk57
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Acknowledgements:ThisworkdevelopedfromaworkshopsponsoredbytheRoyal59Society,withadditionalfundingprovidedbytheEuropeanResearchCouncil60AdvancedGrantACRCC(grant339390).61
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Theauthorsdeclarenoconflictofinterest.63
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Abstract65
Asclimate-changeresearchbecomesincreasinglyapplied,theneedforactionable66informationisgrowingrapidly.Akeyaspectofthisrequirementisthe67representationofuncertainties.Theconventionalapproachtorepresenting68uncertaintyisprobabilistic,basedonensemblesofclimatemodelsimulations.The69limitationsofthisapproachhavebeenknownforsometime,butarebecoming70increasinglyapparent.Analternativewayisthusemergingwhichmaybecalleda71‘storyline’approach.Wedefineastorylineasaphysicallyself-consistentunfolding72ofpastevents,orofplausiblefutureeventsorpathways.Noaprioriprobabilityof73thestorylineisassessed;emphasisisplacedinsteadonunderstandingthedriving74factorsinvolved,andtheplausibilityofthosefactors.Weintroduceatypologyof75fourreasonsforusingstorylinestorepresentuncertaintyinclimatechange:(i)76improvingriskawarenessbyframingriskinanevent-orientedratherthana77probabilisticmanner,whichcorrespondsmoredirectlytohowpeopleperceiveand78respondtorisk;(ii)strengtheningdecision-makingbyallowingonetowork79backwardfromaparticularvulnerabilityordecisionpoint,andcombineclimate-80changeinformationwithotherrelevantfactorstoaddresscompoundrisk;(iii)81extractinginformationfromdatasets,byoptimizingthestatisticalanalysisand82allowingtheuseofmorecrediblemodelsinaconditionedmanner;(iv)exploring83theboundariesofplausibility,therebyguardingagainstfalseprecisionandsurprise.84Wealsoshowhowstorylinescanbecastwithinthecontextofaprobabilistic85framework,andthusbereconciledwiththemoreconventionalapproachto86representinguncertaintyinclimatechange.87
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Keywords:Climatechange,uncertainty,risk,vulnerability89
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1.Introduction92
Whatwillthefutureclimatelooklike?Conventionalresponsestothisquestion93offeredbytheclimatesciencecommunity(asintheIntergovernmentalPanelon94ClimateChangeassessmentreports)involvecreatinglargeensemblesofsimulations95offutureclimateusingavarietyofglobalclimatemodels(GCMs).Theseareusedin96turntoderivepredictionsofmeteorologicalfields(e.g.,rainfall),expressedinterms97ofstatisticalquantitiesorprobabilitiesofchangewithinthemodelledworld.The98predictionsaremadeunderanassumedscenariooffutureclimateforcings(e.g.,99greenhousegasemissions),andarecalledprojections.Theimplicationsofthese100projectionsforparticularimpacts(suchasagriculture)aretheninvestigatedby101propagatingthedatathroughachainofimpactmodels.102
Asthescientificandpolicydiscussionshiftsfromwhetheranthropogenicclimate103changeisrealtotheevaluationofpotentialregionalimpactsandresponseoptions,104thelimitationsoftheseprobabilisticapproachesarebecomingincreasingly105apparent(Kenneletal.2016).Climatemodelshavesharedstructuralerrors(Knutti106etal.2013),sothereisnotheoreticalbasisfortreatingensemblesofmodels107probabilistically,andthereisnowaytodirectlyassessthereliabilityoffuture108climateprojections,asthereiswithweatherforecasting(Parker2010).Asregional109climatephenomenasuchasstormtracksresponddifferentlytoclimatechangein110differentmodels(Shepherd2014),amulti-modelmeancanleadtoawashed-out111responsethatdoesnotcorrespondtoanymodelsimulation.Effectivebias-112correctionofsystematicerrors—especiallyofmultivariaterelationships,suchas113thoseinvolvedincompoundevents—requiresvastamountsofdatathatgenerally114donotexist.Inanycase,itisnotknownhowtocorrectmodelbiasesinsimulating115climatechanges(asopposedtosimulationsofthepresentclimatestate)(Maraunet116al.2017).Estimatesofuncertaintiesattheregionalscalecanquicklyaccumulatetoa117pointwheretheinformationobscuresratherthanclarifiestherobustphysical118understandingthatexistsandisrelevantforclimateadaptationdecision-making119(WilbyandDessai2010).120121Alternativeapproachesarethusemergingthatdonotseektoquantifyprobabilities,122butinsteadtodevelopdescriptive‘storylines’,‘narratives’,or‘tales’ofplausible123futureclimates.Webroadlyrefertotheseas‘storyline’approaches.Whilsttherehas124beenvariationintheuseoftheseterms,therearesomecommoncharacteristics:in125particular,theemphasisonqualitativeunderstandingratherthanquantitative126precision,andtheacceptancethatstorylinesarenotprobabilistic.Herewedefinea127storylineasaphysicallyself-consistentunfoldingofpastevents,orofplausible128futureeventsorpathways.Asnoaprioriprobabilityofthestorylineisassessed,itis129notaprediction.Emphasisisplacedinsteadonunderstandingthedrivingfactors130involved,andtheplausibilityofthosefactors(orofchangesinthosefactors).131Typicallymorethanonestorylineisconsidered,toexploremultipleplausible132futures.133
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Therearemanyreferencestostorylines,narratives,andscenariosinclimatechange134literature,andthetermsaresometimesusedinterchangeably.Theterm‘narrative’135isoftenusedbysocialscientiststocharacterisepeoples’views,understandings,or136perspectives.Narrativeanalysisisusedtoinvestigateclimatechangediscoursesand137theframingofclimatechangebythemedia,policy-makers,orotherstakeholders.138Whilsttheseareallimportantaspectsofclimate-changeresearch(Fløttumand139Gjerstad2017),ourfocushereisontheratherdifferenttaskofconstructing140storylinestorepresentuncertaintyinclimatechange.Wealsoavoidtheuseofthe141term‘scenario’becauseinclimate-changescienceitmeansaspecificsetoffuture142climateforcings.143
Storylinescanbeperceivedasanecdotalandthusunscientific.Itis,therefore,144importanttounderstandtheirbasisandhowtheycontributetorepresentingand145communicatinguncertaintyinclimatechange.Weidentifysomeofthemainwaysin146whichthestorylineconceptisbeingused,aswellasthemeansbywhichitcanbe147reconciledwiththemoretraditionalapproachtorepresentinguncertainty.We148structureourevaluationaroundatypologyoffourreasonsfortakingastoryline149approach:(i)improvingriskawareness,(ii)strengtheningdecision-making,(iii)150extractinginformationfromdatasets,and(iv)exploringtheboundariesof151plausibility.Thesearenotmutuallyexclusive,andanyparticularapplicationof152storylinesmayaddressmorethanonecategory.Nevertheless,itisusefulto153understandthedistinctaspectsofeach.Wealsoshowhowstorylinescanbecast154withinthecontextofaprobabilisticframework.1551562.Improvingriskawareness157
Twohumantendenciesareintuitivelyevidentinhowwethinkaboutrisk.Itmaybe158usefultoclassifythemusingadistinctionmadefirstbyTulving(1972,2002)about159memory:knowingfacts(semantic)versusrelivingevents(episodic).Sincethen,it160hasbecomeclearthatepisodicmemoryhasaroleinanticipatingthefuture161(Schacteretal.2007),andnewneuroscientificdiscoveriesaregivingapictureof162constructivememoryandepisodicfuturesimulation,whichactaswarningbells,163andhelpustoconceiveofpossibleextremephenomena.Consideredwithinthis164broadercontext,theconventionalapproachtoclimate-changeriskissemantic(e.g.,165whatisa1in1000yearevent?),whereasstorylineapproachesareepisodic(e.g.,166haveweseenthisbefore;andifso,whatmightthenexteventbelike?).167
Behaviouralpsychologyshowsthathumanshavedifficultyrespondingrationallyto168risksfromeventsthatareoutsidetheirexperience,evenwhenaccuratequantitative169informationontheserisks,andthebenefitsofrationalmitigationactions,is170available(Kahneman2011).Evenwhengivensuchinformation,weactasthough171theprobabilityofabadoutcomeislessthanitreallyisifaneventofthattypehas172nothappenedtousrecently(orever),andmoreprobablethanitreallyisifithas.173Thisasymmetricalresponseisknownasthe‘availabilitybias’.Essentially,we—174eventhosewithquantitativescientifictraining—aremorelikelytorespondto175episodicthantosemanticinformation.176
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Theavailabilitybiasisapparentinthehistoryofmeasurestomitigatenatural177disasterrisk,whichshowsthatsuchmeasuresareusuallytakenonlyindirect178responsetodisastersthathavejustoccurred.ThiswasthecasewiththeNorthSea179floodof1953,whichledtheNetherlandstodeveloptheDeltaWorksandtheUKthe180Thamesbarrier,andmorerecentlyHurricaneSandyinNewYorkCity(Sobel2014).181Threatsfromeventssufficientlyrareastobeoutsidethelivingmemoryoflocal182populationsanddecision-makersdonotmotivateactionsthatwouldreducerisk,183despitegoodsemanticunderstandingofthecaseforsuchactions,becausethe184necessaryepisodicunderstandingisnotpresent.185
Climatechangeisessentiallysimilar.Thebestscientificinformationpredictsa186futurefundamentallydifferentfromthepast,buteventhosewhoareawareofthe187sciencehavedifficultyprioritizingactionspreciselybecauseoftheunfamiliarityof188thatfuture(Weber2006).Here,scientificallyconstructedstorylineshelpasa189complementaryapproachtoraiseriskawareness,byincorporatingepisodic190informationandmakingthepredictedfuturemoretangible(e.g.Matthewsetal.1912016,2017).192
ThisistheapproachadvocatedbyHazelegeretal.(2015),whoconstructstorylines193(called‘tales’)offutureweatherthatillustratetheimplicationsofclimatechangefor194real-lifehigh-impactweatherevents.Thisisdonebymappinghistoricalor195hypotheticaleventsontofutureclimateconditions.Examplesoftheapproach196includererunningweatherepisodesinalimited-areaweather-predictionmodel197withelevatedtemperaturesasboundaryconditions(Attemaetal.2014;Preinetal.1982016),diagnosingunprecedentedstormsinfutureclimateintegrations(Haarsmaet199al.2013),andexploringmultivariatedriversoflocalextremewaterlevels(Vanden200Hurketal.2015).Thatsuch‘simulatedexperience’ismoreeffectiveatconveying201riskthanstatisticalcharacterizationshasbeenrecognizedmorewidelyinthe202decision-makingliterature(HogarthandSoyer2015),buildingoninsightsfrom203experimentalpsychology(GigerenzerandHoffrage1995).204
Thereisalsotheissueofhowtocommunicatethestoryline.Well-designedboard205andcardgamescanofferanimmersiveexperience,tellstories,andhavebeenused206toaiddevelopmentdecisions(Tintetal.2015)andkick-startdiscussionsonthe207effectsofclimatechange(Chuang2017).TheRedCrossClimateCentre208(http://climatecentre.org/resources-games/)havedevelopedmanysimplegames209toaidstakeholders’understandingofdifferenthumanitarianissues.Onewayto210engagedecisionmakerswouldbetoproduceadeckofcardswitheachcard211containingasimpletextualdescriptionofaweatherorclimateevent.The212participantswoulddiscusswhatthepotentialimpactofthiseventwouldbeontheir213infrastructure,howtheimpactofthateventonotherinfrastructuremightaffect214them,andhowtheymightmitigatethedamagefromtheevent.Thiscouldallow215decision-makerstounderstandhowpotentiallycascadingeventsmightaffecttheir216infrastructure,includingthecumulativeimpactofmultipleevents.Theparticipants217wouldworkthroughthecarddeckdiscussingeachevent,observedandsupported218byafacilitator.Importantly,theparticipants,likeinreality,wouldnotknowwhat219
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eventsarestilltocome.Attheendofthedeckthefacilitatorwouldthendiscuswith220theparticipantswhichevents,orcombinationofevents,matteredtothemandfor221whichonesdamagemitigationwouldrequiresubstantialinvestment.Itisforthose222eventsthatsubsequentriskassessmentcouldbemade.223
Thedialecticbetweensemanticvsepisodicknowledgeseemsrelatedtothat224betweentheinvisibilityvsvisibilityofclimatechange(Rudiak-Gould2013).The225orthodoxclimate-scienceviewwouldbethatclimatechangeisinvisibletothenaked226eye,asitisinherentlyastatisticalconcept(semanticknowledge).However,many227wouldarguethatsomeaspectsofclimatechangearealsovisible,asinthecaseof228manyoftheobservedcryosphericchanges(episodicknowledge).Ifanobserved229changeissufficientlylargethatevenasingleoccurrenceisattributable(Shepherd2302016),andifitisvisibletothenakedeye(asopposedtosomethingthatcanonlybe231measured),thenvisibilityofclimatechangecanbereconciledwiththestatistical232perspective.Inasimilarway,storylinesallowepisodicknowledgeofclimatechange233tobesetwithinthecontextofsemanticknowledge(seealsoSection6).234
3.Strengtheningdecision-making235
Fewsocietaldecisionsaredrivensolelyby,orareframedonlyby,concernsabout236climatechange,butratherbysustainabledevelopmentmoregenerally.Storyline237approachesacknowledgethiscontextbycommunicatingthepotentialconsequences238ofclimatechangeinwaysthatarerelevanttothespecificdecisionandthespecific239decision-maker(Hazelegeretal.2015).240
Stakeholders(suchaseldercommunitymembers,watermanagersorinsurance241companies)whohaveamemoryofhistoricextremescanbeinvitedtoinitiatethe242discussionwithscientistsoftheirpast,current,andpotentialfutureneeds.Ifco-243designedwithstakeholders,theprocessofcreatingthestorylineshasthepotential244tocreateempoweringsociallearningsystems(Wenger2000),enabling245communitiesofpracticetoengageinthereframingofquestionsofaprobabilistic246natureto‘whatif’questions.Suchquestionscouldallowadialoguebetween247stakeholders(e.g.,toidentifyandagreeonwhatwouldbetheimpactifaparticular248weathereventoccurredinawarmerworld).Thisapproachnotonlyprovides249relevantplausiblefuturesbutdoessoinacontextthatisreadilyrelevantand250comprehensibletothedecision-maker.Storylinesletthemwalkthroughphysically251self-consistentrenditionsoffutureevents,andtoidentifydecisions(includingthe252decisiontopostponeaction)thatmightinadvertentlycreateacrisisorturnacrisis253intoacatastrophe.Conversely,storylinescanhelptoidentifyinvestmentsthatcould254bemadetodaythatwouldbringsubstantialinsurancevalue,orevenexploit255beneficialchanges(ECA2009).Itisimportanttoundertakesuchanalysisusingan256iterativeprocessinwhichdecision-makerconcernsandinterestsshapethe257scientificresearchandmodellinginaprocessofco-production.258
Anexampleofthisiterativedecisionsupportprocessisprovidedbyananalysisof259thephysicalandlegaldimensionsofwaterdiversionsfromtheUpperColorado260RiverBasinunderstorylinesofclimatechange(Yatesetal.2015).Inthiscase,261
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regionalclimatesimulationswereusedtostresstestamodelofthewatersupply262systemwithandwithoutanadaptationoption.Stakeholderswereengagedfromthe263outsetinthedevelopmentofplausiblestorylinesthatcaptureboththedirectand264indirectconsequencesofclimatechangeontheheadwaterareas.Forinstance,if265hotteranddrierconditionswereimagined,thenwatermodelparameterswere266adjustedtoreflectpossiblehydrologicalchangesassociatedwithdustonsnowpack267orwildfiresdestroyingforestedareas.Stakeholdersalsospecifiedmetricsofwater268systembehaviorandstaterelevanttotheirriskmanagementcontext.Inthisway,269bothwatermanagersandresearchersarrivedatasharedunderstandingofthe270systemvulnerabilitiesandtheextenttowhichclimateriskscanbemanagedusinga271legaladaptationinstrument.272
Storylinesareespeciallyeffectiveforconsideringrisksfromcompoundextreme273events,namelythosewheresevereimpactsaretriggeredbytheinteractionofmore274thanonevariable.Examplesincludedroughtwithheatwaves(Ciaisetal.2005),275windstormswithheavyprecipitation(Martiusetal.2016),andfluvialfloodingwith276stormsurges(Kewetal.2013).Neithervariableitselfneedstobeveryextremebut277thecombinationresultsinsevereimpacts(Leonardetal.2014).Tostudycompound278extremesstatistically,longtimeseriesandrefinedstatisticalmethodssuchas279copulasareneededtocaptureandquantifytheinter-dependenceofthevariables.A280storylineapproachisaveryattractivecomplementarymethodtoaddress281compoundextremesasitallowsonetodefineverycomplexeventsstartingfromthe282impactsinaveryflexibleway,includingspatialandtemporaldependence283(clustering)ofextremes,whichcangeneratetheimpacts(e.g.,Muchanetal.2015).284Storylinesbasedonimpactscanthenbeassignedacategoricalplausibilityusing285statementslike“worldwideatleastonesimilareventoccurredbefore”.TheSwiss286FederalOfficeforCivilProtectionusessuchanapproachtoprepareforabroad287rangeofperils,includingatmospherichazards,butalsotechnicalandsocialthreats288(BABS2013).Foreachperil,theydefinethreestorylineswithincreasingseverity289andimpacts.290
Anotheremergingapplicationofthestorylineapproach,althoughverymuchinits291infancy,isintheinsurancesector.Theinsuranceandreinsuranceindustriesare292slowlybeginningtoacknowledgetheimpactsofclimatechangeonworldwide293insuredlosses.Havingarobustupperboundonpossiblecatastrophelossesis294essentialforthesurvivalofaninsuranceorreinsurancecompany.Themaintools295forassessingsuchriskfromnaturalcatastrophesarecatastrophemodels.Bynature,296catastrophemodelsarebuilttoestimateprobabilisticrisk.However,thesame297modelscanbeandfrequentlyareusedtoestimatedeterministicriskfromvarious298stormscenarios:modelsareruntocomputepossiblelossesfromahistoricalevent299oramodifiedversionofthehistoricalevent,foragivenportfolio(Wooetal.2017).300Thisstorylineapproachisausefulmethodofstress-testingtheclient’sexposureto301variousweatherandclimateconditions.302
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4.Extractinginformationfromdatasets304
Manystudieshaveshownthattheresponsetoclimatechangecanbeusefully305understoodasacombinationofwhatmightbecalledathermodynamiccomponent306(surfacewarming,moistening,meltingofice)andadynamiccomponent(changesin307circulation)(e.g.,Deseretal.2014).Thedistinctionisnotprecisebecauseboth308componentsoftheclimatesystemarecoupled;inpractice,eitherthedynamic309componentisdefinedasthecomponentcongruentwithinternalvariability(Deser310etal.2004)withthethermodynamiccomponentobtainedasaresidual,or311(especiallyforprecipitation)thethermodynamiccomponentisdefinedasthe312componentobtainedintheabsenceofchangesincirculation,withthedynamic313componentobtainedasaresidual(Pfahletal.2017).314
Thereasonthedistinctionisusefulis,first,thatthereisastrikingcontrastbetween315thedegreeofconfidencewehaveinthetwocomponents(Shepherd2014;316Trenberthetal.2015).Thermodynamicaspectsofclimatechangearegenerally317robustintheory,observations,andmodels(althoughthereissignificant318quantitativeuncertaintyassociatedwithclimatesensitivity).Incontrast,dynamic319aspectsarerobustneitherintheory,observations,normodels.Second,model320uncertaintiesinclimatesensitivityandindynamicaspectsofclimatechange(after321scalingbyglobal-meanwarming)appeartobeuncorrelated(e.g.,Zappaand322Shepherd2017),whichmakessenseastheyareassociatedwithdifferentaspectsof323modelerror.Theuncertaintyinclimatesensitivityismainlyassociatedwiththe324responseoflowclouds,whilstthatindynamicaspectsisassociatedwith325teleconnectionsfromregionalclimatefeedbackssuchasArcticamplificationand326tropicalupper-tropospherewarming.Thedistinctionbetweenthermodynamicand327dynamicaspectsofclimatechangeprovidesanalternativetotheusualapproachof328consideringensemblesofmodelsimulations,whichmixtogetheruncertaintiesin329thetwoaspects,andmayhelptoprovideinformationonwherethelargest330uncertaintieslie(e.g.,Pfahletal.2017).Inparticular,storylinescanbeconsidered331foreachaspect.332
Understandingtheroleofclimatechangeinweatherandclimateextremesisof333considerablesocietalrelevance.Thisisnotstraightforwardtoresolve,sinceextreme334eventsalwaysresultfromanintersectionofnaturalvariabilityofsomesort,riding335onandaugmentingglobalwarmingeffects(NAS2016),andeveryextremeeventis336unique.Theconventionalapproachtothisquestionfocusesonchangesin337probability,butdoesnotdealsatisfactorilywiththeuniquenatureofeveryevent338(Shepherd2016).Analternativestorylineapproachmightaskinsteadhowmuch339worsetheeventoutcomeswerebecauseoftheknownthermodynamicaspectsof340climatechange,suchasoceanwarming(Trenberthetal.2015).341
Thedistinctionbetweenthermodynamicanddynamicaspectsofclimatechangeis342alsousefulwhenitcomestorepresentingtheuncertaintyinprojectionsoffuture343change.ApracticalexampleofthisistheKNMIClimateChangeScenarios(see344http://www.climatescenarios.nl/),whichprovidefourdiscretesetsofweatherand345sea-levelvariables,assumingagivenglobaltemperatureincreaseandaregional346
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amplificationduetocirculationresponses(VandenHurketal.2014).Inessence,347theseprovidestorylinesofregionalclimategivenlarge-scalechangesinphysical348climate.349
Onacontinentalscaletheuncertaintyofthethermodynamicresponsetoagiven350globaltemperatureincreaseismuchsmallerthantheuncertaintyinthedynamic351response.Hence,statements(suchasextremeprecipitationinthemid-latitudeswill352increase)arepossibleandquitecertainsincethespatiallyaggregatedsignalis353drivenprimarilybythethermodynamicresponse(Fischeretal.2013).However,at354aregionalscale,mid-latitudeprecipitationextremesarestronglyinfluencedbythe355dynamicresponse,andtheycouldeitherincreaseordecreasedependingonthe356changesincirculation(Fischeretal.2013;Pfahletal.2017).Manzinietal.(2014)357showedthatmuchofthemodelspreadinwintertimecirculationchangesoverthe358northernextratropicsisrelatedtothemodelspreadinremotedriverssuchas359tropicalwarming,Arcticwarming,andstratosphericvortexchange.Thatthe360influenceiscausalissupportedbyevidencefromseasonalpredictionandfrom361single-forcingmodelexperiments.ZappaandShepherd(2017)usedthisframework362todevelopstorylinesofEuropeancirculationchange,conditionedonagivenlevelof363globalwarming.ItwasfoundthatforcentralEuropeanwintertimestorminessand364wintertimeMediterraneandrying,twoimportantclimate-changeimpactsfor365Europe,thedifferencebetweenthemostextremestorylineswasequivalenttothat366fromseveraldegreesofglobal-meantemperatureincrease.367
Theuncertaintyinlong-termglobaltemperatureincrease(foragivengreenhouse368gasforcing)ismainlyassociatedwiththatinclimatesensitivity.Theobserved369warmingprovidesonlyaveryweakconstraintonlong-termequilibriumwarming370orclimatesensitivity,anditsdirectapplicabilityisamatterofconsiderablecurrent371debate.Astorylineapproachattemptstoarticulatethemuch-richerphysical372understandingofhowclimateprocesseschangeasclimatewarms(orcoolsunder373pastclimatechanges)toprovideamoremechanisticwayofconstrainingthe374problem.Usingphysicallydevelopedstorylines,Stevensetal.(2016)showthat375greaterconfidenceinanoverallnetpositivecloudfeedbackorbetter376reconstructionsoftropicaltemperaturesattheLastGlacialMaximumcouldbe377enoughtoraisethelowerboundonclimatesensitivity.Criticaltestsfortheupper378boundcouldincludetestingofrecentargumentsforamoremodestaerosolforcing.379
5.Exploringtheboundariesofplausibility380
Ifclimatepredictionscouldbearticulatedasprobabilitypredictions(conditionedon381scenariosoffutureGHGemissions)thenthesocietalconsequencescouldbe382presentedasquantifiedrisksorofchangingriskprofiles.Theconventional383approachtoclimatepredictionisfocusedonmakingsuchprobabilitypredictionsby384applyingcomplexpost-processingmethodologiestotheoutputofGCMs.Ifthe385climatesystemwaswellunderstoodandcouldbesimulatedaccuratelyatall386relevantscales,thenthisengineeringapproachtopredictionwouldbeappropriate.387However,suchapropositionhasbeenchallenged(Smith2002;McWilliams2007;388VanOldenborghetal.2013).Furthermore,21stcenturyprojectionswithGCMshave389
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onlypartialexplorationofmodeluncertainty(Knuttietal.2013)andofinitial390conditionuncertainty(Hawkinsetal.2016).Asaconsequence,thereisastrong391needtoincorporatephysicalunderstandingoftheprocessesofclimatechange392directlyintoclimate-relatedstatementsaboutthefuture(Maraunetal.2017).393
Storylineapproachescandothisbyplacingtheemphasisonphysicalplausibility394whenmakingstatementsaboutfutureclimate.TheoutputsofGCMsimulationsare395notseenasasourceofquantitativeclimatepredictions;ratherthemodelsareseen396astoolstosupportandtesttheoriesregardingtheinteractionsofclimateprocesses.397Scientificunderstandingisusedtopushouttheboundariesofplausiblefuturesby398proposingmechanismsthroughwhichoutcomesnotcurrentlyseeninGCMscould399arise(Hazelegeretal.2015).Thisprovidesamorethoroughexplorationofthe400rangeofuncertaintyreflectedbytoday’slevelofscientificunderstanding,compared401tothelimitedrangerepresentedbytheensembleofGCMprojections.402
Thefactthatclimatemodelsdonotfullysimulatetherangeofresponseuncertainty403isparticularlyevidentforlocal-scaleextremeevents.Forexample,ithasbeenfound404thatshort-duration(hourly)summertimeprecipitationextremesarewell-simulated405onlyinvery-high-resolution(<4km)regionalclimatemodels(RCMs)thatexplicitly406representdeepconvection,andnotinconventionalRCMsthatparameterisedeep407convection,andthatintensityincreaseswithwarmingaremuchlargerinthehigh-408resolutionsimulations(Kendonetal.2017).Thissuggeststhatconventional409nationalclimatechangescenariosmayunderestimatethepotentialchangesto410short-durationprecipitationextremes.However,very-high-resolutionregional411simulationscanbeusedtogetherwithobservationsandtheoreticalunderstanding412toprovideriskestimateswithinastorylineframework.Meredithetal.(2015)413arguedthataconvectiveeventleadingtointensefloodingcouldbeattributedto414BlackSeawarmingusinganRCMwithexplicitconvectionconstrainedbylarge-scale415reanalysis,butthattheresponsesimulatedwithparameterisedconvectionwas416physicallyimplausible.Preinetal.(2016)usedasimilarapproachtomake417statementsaboutfutureriskofextremeprecipitationandfloodingfortheUnited418States.Suchtargetedvery-high-resolutionsimulationsofrelevantphenomena419combineprocessunderstandingwiththesamplingofotherwiseunexplored420uncertaintiesandtherebygreatlycontributetotheestablishmentofrobust421projectionsoflocalisedextremeevents.422
Asanexampleofanapplicationtoclimateimpacts,UKWaterIndustryResearch423addressedthechallengeoflong-termchangeinintensestormsbycommissioninga424studytobuildaplausible‘story’usingthebestscientificevidenceofhowthese425stormsmightevolveunderglobalwarming.Thestudyusedtheoretical426understandingofpotentialchangeinprecipitationextremesfromtheClausius-427Clapeyronrelation(7%/K),andexaminedclimateanalogues(usingsummer428temperatureandprecipitationclimatologyasanalogues)andoutputsfrommodels429withexplicitconvectiontoproduceplausiblefuturescenarios,whichwerethen430translatedintodesignguidanceforstormdrainagenetworks(Daleetal.2017).In431
12
thiscasetheclimateanalogues,high-resolutionmodelpredictionsandtheory432agreedonacommontrajectory,whichgaveadditionalconfidenceintheresults.433
Inlightoftheissueswithprobabilisticprojections,theirvalueforadaptation434decision-makinghasbeenquestioned(DessaiandHulme2004;Hall2007).Thishas435ledtothedevelopmentofnon-probabilisticapproaches,whichseekadaptation436optionsthatarerobusttoawiderangeofuncertainty(DessaiandHulme2007;437Brownetal.2012;Poffetal.,2016).Suchapproachesexplicitlyallowfordeepor438severeuncertainties,includingeventsthathaveneverbeenobserved.Storylines439allowawaytoexploresuchuncertaintiesinaphysicallyplausiblemanner.440
IntheNetherlands,wheresealevelriseconstitutesanexistentialrisk,therehas441beenaparadigmshiftfrompredictingtoexploringthefuture,whichhasincreased442theopportunityforrobustdecision-making(HaasnootandMiddelkoop2012).The443DeltaCommissionoftheNetherlandsaddressedthechallengeofhigh-end,long-444rangesealevelchangebydeconstructingallthecomponentsofsealevelriseand445buildingplausibleaccountsofhoweachmightevolveunderextremeglobalmean446warming(Katsmanetal.2011).Plausiblehigh-endvaluesforthermalexpansionof447theoceanwerefoundedonclimatemodelsensitivityanalysisupto8°Cglobal448warmingby2200,combinedwithpalaeoclimaticevidenceofformersealevelsfrom449RedSeacorals.High-endvaluesweresimilarlyassignedtoothercomponentssuch450asice-meltcontributionstosealevel,changesinthegravitationalfield,verticalland451movements,stormsurge,oceancurrentandsalinitychanges.Importantly,each452sourceofinformation,whetherclimatemodel,palaeoclimatedata,observationsor453expertelicitation,ispresentedinatransparentandauditableformatthatisopento454challengeandrevisionasscientificunderstandinggrows.455
6.Combiningapproaches456
Althoughstorylineshavebeendiscussedasanalternativetoprobabilistic457descriptions,itisimportanttobeabletounderstandhowthetwoapproachesrelate458toeachother,especiallysinceusersmaybepresentedwithbothformsofclimate459information.Usersmayalsoquestionaparticularstorylineandaskforatleasta460roughestimateofitslikelihood.Thus,inmanycasesonecanexpectthatstorylines461willbethebeginningofaniterativedialogue.Moreover,manyoftheexamplesof462storylineapplicationsgivenabovehaveaprobabilisticaspectembeddedwithin463them(e.g.simulationsofanextremeweathereventconstrainedbyobserved464circulationpatternsunderacounter-factualstorylineofsea-surfacetemperatures).465Evidently,bothapproacheshavetheirstrengthsandweaknesses,andonemay466imaginecombiningthem.Atleastinprinciple,thisshouldbepossible.467
WithintheframeworkofaBayesiancausalnetwork(Pearl2009),ajointprobability468ofnvariablesP(x1,…,xn)canbeexpressedastheproductofconditionalprobabilities469P(xj|paj),wherepajarethe‘parent’factorsinfluencingxj,accordingto470
𝑃 𝑥!,… , 𝑥! = 𝑃 𝑥! 𝑝𝑎! .! (1)471
13
Astorylineamountstospecifyingoneormoreofthevariablesinaphysically472coherentway;forexample,onemightspecifyglobal-meanwarmingtobe2°Cand473wintertimewarmingoverNorthernEuropetobe4°C.Astorylinexi=xi’fora474particularicanbedefinedbyimposingthatparticularconditionwithin(1),475representedsymbolicallyby𝑥!!,whichleadsto476
𝑃 𝑥!,… , 𝑥!|𝑥!! = 𝑃 𝑥! 𝑝𝑎! = ! !!,…,!!
! !!! !"!
𝑖𝑓 𝑥! = 𝑥!!!!!
0 𝑖𝑓 𝑥! ≠ 𝑥!! . (2)477
Theexpression(2)isthusa‘truncatedfactorization’oftheexpression(1)forthe478unconditionalprobability,representingablendofprobabilisticanddeterministic479factors.Inthisway,storylinescanbecastwithinthecontextofaprobabilistic480framework.481
Aspecialcaseofthisisthethermodynamic/dynamicfactorizationforextremeevent482attribution,representedsymbolicallyasariskratioaccordingto483
!!(!,!)!!(!,!)
= !!(!|!)!!(!|!)
× !!(!)!!(!)
(3)484
(NAS2016).Herep1isthefactualprobability(withclimatechange),p0isthe485counter-factualprobability(withoutclimatechange),EistheextremeeventandCis486thecirculationregimeconducivetothatevent.IfCisidentifiedwithxi’,thenthelast487termin(3)correspondstothechangeinthefactor𝑃 𝑥!! 𝑝𝑎! in(2)arisingfrom488climatechange.AsdiscussedinSection4,settingthistermtounityisaparticular489storylinewhereoneignoresthepossibilitythatclimatechangemaychangethe490probabilityorstructureofthesecirculationregimes,whichmaybejustifiableinthe491absenceofreliableevidencetothecontrary,whileotherchoicesofpotential492circulationregimechangewouldbealternativestorylines.Theratioofconditional493probabilities[thefirsttermontheright-handsideof(3)]couldbedeterminedusing494anensembleof(credible)modelsimulations,suchasfromaweathermodelthathas495beenevaluatedattheprocesslevel(NAS2016).496
Thedevelopmentofcombinedapproacheswouldappeartobeaninteresting497avenueforfutureresearch.Bethkeetal.(2017)providessuchanexampleforthe498potentialeffectoffuturevolcaniceruptions,wherethevolcaniceruptionscanbe499consideredasstorylines.The‘RepresentativeClimateFutures’approachofWhetton500etal.(2012)isafirststepinthisdirectionforregionalclimatechangeimpactand501adaptationassessments.Vautardetal.(2016)appliedthethermodynamic/dynamic502factorization(3)toeventattributionwithinaprobabilisticframework,albeit503withoutanyassessmentoftheplausibilityofthedynamicchanges.504
7.Conclusion505
Wehavepresentedatypologyoffourreasonsfortakingastorylineapproachin506climate-changeresearchandcommunication.Storylinesraiseriskawarenessby507framingriskinanevent-orientedratherthanaprobabilisticmanner,which508
14
correspondsmoredirectlytohowpeopleperceiveandrespondtorisk.Storylines509provideaneffectivemechanismforstrengtheningdecision-makingbyallowingone510toworkbackwardfromaparticularvulnerabilityordecisionpoint,andcombine511climate-changeinformationwithotherrelevantfactorstoaddresscompoundrisk.512Storylinesprovideameansforextractinginformationfromdatasets,byoptimizing513thestatisticalanalysisandallowingtheuseofmorecrediblemodelsina514conditionedmanner.Finally,storylinescanexploretheboundariesofplausibility,515beyondwhatmightbeprovidedbyastandardsetofmodellingtools,thereby516guardingagainstfalseprecisionandsurprise(ParkerandRisbey2015).These517reasonsarenotmutuallyexclusive.Forexample,pushingtheboundariestoimagine518worldsattheinterfaceofclimatescienceandclimatefictioncanhelpinidentifying519‘cliffedge’effectswherebyabruptchangesinriskcanarisewithrelativelymodest520changesinclimateatthefringesoftheclimatemodelrange.521
Theterm‘post-normalscience’wasintroducedbyFuntowiczandRavetz(1993)to522describeasituationwhereeitherthedecisionstakesorthesystemsuncertainties523arehigh,andhencetheuseofevidenceiscontested.Theynotedthatmany524environmentalissuesfallintothiscategory,includingclimatechange.Insucha525situation,traditionalmethodsofscientificproblem-solving(reductionist,expert-526based)areineffective.Thefailuretorecognizetheselimitationshelpsexplainwhy527therehasbeenapersistentgapbetweentheproductionanduseofclimate528information(Kirchhoffetal.2013).529
AccordingtoFuntowiczandRavetz(1993),post-normalscienceacknowledges530unpredictability,incompletecontrol,andapluralityoflegitimateperspectives.The531goalisnottobanishuncertainty,buttomanageit.Themodelforscientificargument532isnotaformalizeddeduction,butaninteractivedialogue.Storylinesoffermany533possibilitiesintheserespects;inparticular,theyprovide‘conversationstarters’and534a‘thirdplace’fordialogue.Morebroadly,theyprovideameansofnavigatingthe535‘cascadeofuncertainty’(e.g.WilbyandDessai2010)byrepresentinguncertaintyin536climatechangewithoutlosingsightoftherobustaspects,whilstusingconceptsthat537relatetopeople’sexperienceandtapintotheirepisodicwayofthinking.538
539
540
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