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Storylines:Analternativeapproachtorepresentinguncertaintyinphysical1aspectsofclimatechange2

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).Usefulcommentswerereceivedfrom61MichaelaHegglin,LisaLloyd,andthreeanonymousreviewers.62

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Theauthorsdeclarenoconflictofinterest.64

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Abstract66

Asclimate-changeresearchbecomesincreasinglyapplied,theneedforactionable67informationisgrowingrapidly.Akeyaspectofthisrequirementisthe68representationofuncertainties.Theconventionalapproachtorepresenting69uncertaintyinphysicalaspectsofclimatechangeisprobabilistic,basedon70ensemblesofclimatemodelsimulations.Inthefaceofdeepuncertainties,the71knownlimitationsofthisapproacharebecomingincreasinglyapparent.An72alternativeisthusemergingwhichmaybecalleda‘storyline’approach.Wedefinea73storylineasaphysicallyself-consistentunfoldingofpastevents,orofplausible74futureeventsorpathways.Noaprioriprobabilityofthestorylineisassessed;75emphasisisplacedinsteadonunderstandingthedrivingfactorsinvolved,andthe76plausibilityofthosefactors.Weintroduceatypologyoffourreasonsforusing77storylinestorepresentuncertaintyinphysicalaspectsofclimatechange:(i)78improvingriskawarenessbyframingriskinanevent-orientedratherthana79probabilisticmanner,whichcorrespondsmoredirectlytohowpeopleperceiveand80respondtorisk;(ii)strengtheningdecision-makingbyallowingonetowork81backwardfromaparticularvulnerabilityordecisionpoint,combiningclimate-82changeinformationwithotherrelevantfactorstoaddresscompoundriskand83developappropriatestresstests;(iii)providingaphysicalbasisforpartitioning84uncertainty,therebyallowingtheuseofmorecredibleregionalmodelsina85conditionedmanner;and(iv)exploringtheboundariesofplausibility,thereby86guardingagainstfalseprecisionandsurprise.Storylinesalsoofferapowerfulwayof87linkingphysicalwithhumanaspectsofclimatechange.88

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Keywords:Climatechange,uncertainty,risk,vulnerability90

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RevisedforClimaticChange,2May201892

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1.Introduction94

Whatwillthefutureclimatelooklike?Conventionalresponsestothisquestion95offeredbytheclimatesciencecommunity(asintheIntergovernmentalPanelon96ClimateChangeassessmentreports)involvecreatinglargeensemblesofsimulations97offutureclimateusingavarietyofglobalclimatemodels(GCMs).Theseareusedin98turntoderivepredictionsofmeteorologicalfields(e.g.,rainfall),expressedinterms99ofstatisticalquantitiesorprobabilitiesofchangewithinthemodelledworld.The100predictionsaremadeunderanassumedscenariooffutureclimateforcings(e.g.,101greenhousegasemissions),andarecalledprojections.Theimplicationsofthese102projectionsforparticularimpacts(suchasagriculture)aretheninvestigatedby103propagatingthedatathroughachainofimpactmodels.Althoughtheforcing104scenariosareunderstoodtobenon-probabilistic,beingsubjecttohumanactions,it105isstandardpracticetorepresentthephysicalaspectsofclimatechange106probabilistically(Swartetal.2009).107

Asthescientificandpolicydiscussionshiftsfromwhetheranthropogenicclimate108changeisrealtotheevaluationofpotentialregionalimpactsandresponseoptions,109thelimitationsoftheseprobabilisticapproachestothephysicalaspectsofclimate110changearebecomingincreasinglyapparent(Kenneletal.2016).Climatemodels111havestructuralerrors,manyofwhichareshared,whichchallengesaprobabilistic112interpretationofmulti-modelensembles(Knuttietal.2013),andthereisnowayto113directlyassessthereliabilityoffutureclimateprojections,asthereiswithweather114forecasting(Parker2010).Asregionalclimatephenomenasuchasstormtracks115responddifferentlytoclimatechangeindifferentmodels(Shepherd2014),amulti-116modelmeancanleadtoawashed-outresponsethatdoesnotcorrespondtoany117modelsimulation.Effectivebias-correctionofsystematicerrors—especiallyof118multivariaterelationships,suchasthoseinvolvedincompoundevents—requires119vastamountsofdatathatgenerallydonotexist.Inanycase,itisnotknownhowto120correctmodelbiasesinsimulatingclimatechanges(asopposedtosimulationsofthe121presentclimatestate)(Maraunetal.2017).Estimatesofuncertaintiesatthe122regionalscalecanquicklyaccumulatetoapointwherethisknowledgehinders123ratherthansupportsscenario-ledclimateadaptationdecision-making(Wilbyand124Dessai2010).125126Alternativeapproachesarethusemergingthatdonotseektoquantifyprobabilities,127butinsteadtodevelopdescriptive‘storylines’,‘narratives’,or‘tales’ofplausible128futureclimates.Webroadlyrefertotheseas‘storyline’approaches.Whilsttherehas129beenvariationintheuseoftheseterms,therearesomecommoncharacteristics:in130particular,theemphasisonqualitativeunderstandingratherthanquantitative131precision,andtheacceptancethatstorylinesarenotprobabilistic.Herewedefinea132storylineasaphysicallyself-consistentunfoldingofpastevents,orofplausible133futureeventsorpathways.(Atrendcanbeconsideredalong-lastingevent.)Asnoa134prioriprobabilityofthestorylineisassessed,itisnotaprediction.Emphasisis135placedinsteadonunderstandingthedrivingfactorsinvolved,andtheplausibilityof136thosefactors(orofchangesinthosefactors).Typicallymorethanonestorylineis137

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considered,toexploremultipleplausiblefutures.Howeverwealsoincludepast138events,becausehistoricaleventsarenotsimplysingledatapointsbutinvolve139detailedstorieswhichcanbeunpacked(Marchetal.1991;Wooetal.2017).140

Anexampleofsuchastorylineisprovidedbytherain-on-snoweventintheSwiss141Alpsthatoccurredon10October2011,whichledtoseverefloodingandmudflows142(Figure1).On8October2011acoldfrontreachedSwitzerland,andinthefollowing143twodayssustainednortherlywindsresultedinsubstantialsnowaccumulationsin144theAlps.Thiswasfollowedbythepassageofawarmfronton10October2011,145combinedwithanatmosphericriver—anelongatedandnarrowareaofveryhigh146atmosphericmoisturetransport—flowingdirectlyintoSwitzerland.Thecombined147effectofthewarmingandthelargemoisturetransportagainsttheAlpsresultedin148snowmelttogetherwithlargeamountsofrainfallinthewesternSwissAlps(Rössler149etal.2014,Piagetetal.2015).Thecombinedwaterfromthesnowmeltandthe150rainfallledtofloodingandtheformationofmudflowsintheKanderandLötschen151valleys.Theeffectswereextremelylocal,asthesoutheastwardfacingslopes152receivedmorethanthreetimestheprecipitationamountsthatfellonthe153northwestwardfacingslopes,becauseoforographiceffects(Rössleretal.2014).154Themudflowsonthesoutheastwardfacingslopesandthefloodshadsignificant155impactsonthelocaltransportationinfrastructure,blockingroadsandatunnel.A156mudflowdykewassubsequentlybuilttoprotectthetunneldamagedintheevent157fromfuturesimilarevents.158

Therearemanyreferencestostorylines,narratives,andscenariosinclimatechange159literature,andthetermsaresometimesusedinterchangeably.Theterm‘narrative’160isoftenusedbysocialscientiststocharacterisepeoples’views,understandings,or161perspectives.Narrativeanalysisisusedtoinvestigateclimatechangediscoursesand162theframingofclimatechangebythemedia,policy-makers,orotherstakeholders.163Whilsttheseareallimportantaspectsofclimate-changeresearch(Fløttumand164Gjerstad2017),ourfocushereisontheratherdifferenttaskofconstructing165storylinestorepresentuncertainty.Theterm‘scenario’isoftenusedindecision-166makingtorepresentanimaginedfuture,withreferencetothescenariotechniqueof167HermanKahnandcolleagues(KahnandWiener1967).Theemphasisonself-168consistencyandplausibility,andthefactitisnotaprediction,hasmuchincommon169withstorylines,asdoesthebenefitforriskawareness(Lempert2013).Indeed,the170termstorylineisoftenusedinthiscontext.Withinclimate-changescience,theterm171‘scenario’isgenerallyassociatedwithsharedsocio-economicpathwaysandtheir172associatedclimateforcings,wheretheuncertaintyliesinthedomainofhuman173choices,andconcernsthefuture.Ourfocushereisinsteadonuncertaintyinthe174physicalaspectsofclimatechange,includingconsiderationofpastevents.175

Storylinescanbeperceivedasanecdotalandthusunscientific.Itis,therefore,176importanttounderstandtheirbasisandhowtheycontributetorepresentingand177communicatinguncertaintyinthephysicalaspectsofclimatechange.Weidentify178someofthemainwaysinwhichthestorylineconceptisbeingused,structuringour179evaluationaroundatypologyoffourreasonsfortakingastorylineapproach.180

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Figure1showshowallfourreasonsaremanifestintheconsiderationoftheSwiss181rain-on-snoweventdiscussedabove.Thefloodingandmudflows(panelc)improve182riskawarenessbyprovidingasalientevent,moreoveronethatwasnotforecasted.183Strengtheneddecision-makingisevidentinthedevelopmentofdefense184infrastructure(paneld).Thesynoptic-scaleweathersetting(panela)providesa185physicalbasisforpartitioninguncertaintytoassessfuturerisk,includingthe186distinctionbetweenthermodynamic(warmthandmoisture)anddynamic(wind)187factors.Finally,thestrongenhancementoftheprecipitationonthesoutheastward188facingslopesofthevalleywasduetoaverylocalcavitycirculationandassociated189‘seeder-feeder’effect(paneld),ameteorologicalphenomenonnotcapturedbythe190weatherforecastmodels—whichiswhytheeventwasnotforecasted—andwhich191therebyexplorestheboundariesofplausibilityrelativetowhatiscurrently192representedinthemodels.193

1942.Improvingriskawareness195

Twohumantendenciesareintuitivelyevidentinhowwethinkaboutrisk.Itmaybe196usefultoclassifythemusingadistinctionmadefirstbyTulving(1972,2002)about197memory:knowingfacts(semantic)versusrelivingevents(episodic).Sincethen,it198hasbecomeclearthatepisodicmemoryhasaroleinanticipatingthefuture199(Schacteretal.2007),andnewneuroscientificdiscoveriesaregivingapictureof200constructivememoryandepisodicfuturesimulation,whichactaswarningbells,201andhelpustoconceiveofpossibleextremephenomena.Consideredwithinthis202broadercontext,theconventionalapproachtoclimate-changeriskissemantic(e.g.,203whatisa1in1000yearevent?),whereasstorylineapproachesareepisodic(e.g.,204haveweseenthisbefore;andifso,whatmightthenexteventbelike?).205

Behaviouralpsychologyshowsthathumanshavedifficultyrespondingrationallyto206risksfromeventsthatareoutsidetheirexperience,evenwhenaccuratequantitative207informationontheserisks,andthebenefitsofrationalmitigationactions,is208available.Evenwhengivensuchinformation,weactasthoughtheprobabilityofa209badoutcomeislessthanitreallyisifaneventofthattypehasnothappenedtous210recently(orever),andmoreprobablethanitreallyisifithas.Thisasymmetrical211responseisknownasthe‘availabilitybias’(Kahneman2011).Essentially,we—212eventhosewithquantitativescientifictraining—aremorelikelytorespondto213episodicthantosemanticinformation.214

Theavailabilitybiasisapparentinthehistoryofphysicalinfrastructuremeasuresto215mitigatenaturaldisasterrisk,whichshowsthatsuchmeasuresareusuallytaken216onlyindirectresponsetodisastersthathavejustoccurred.Thiswasthecasewith217theNorthSeafloodof1953,whichledtheNetherlandstodeveloptheDeltaWorks218andtheUKtheThamesbarrier,andmorerecentlyHurricaneSandyinNewYork219City(Sobel2014).Threatsfromnaturaleventssufficientlyrareastobeoutsidethe220livingmemoryoflocalpopulationsanddecision-makersdonotmotivate221investmentsinphysicalinfrastructurethatwouldreducerisk,despitegood222semanticunderstandingofthecaseforsuchinvestments,becausethenecessary223

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episodicunderstandingisnotpresent.Inthebestcases,scientificinformationwell224inadvanceofaneventhasledto(lesscostly)effortstoimproveemergency225managementprocedures.Thisappearsmostlikelytohappenwhenthescientific226informationismadeavailableinsufficientlyepisodicform,i.e.,asastoryline.This227arguablyoccurredinthecaseofapossibleearthquakeandtsunamiinthePacific228NorthwestoftheUnitedStates,whereapopularmagazinearticle(Schulz2015)—229augmented,perhaps,bywideawarenessofthe2011FukushimaeventinJapan—230ledtoorganizedpreparationeffortsbythefederalgovernment(FEMA2016).231

Climatechangeisessentiallysimilar.Thebestscientificinformationpredictsa232futurefundamentallydifferentfromthepast,buteventhosewhoareawareofthe233sciencehavedifficultyprioritizingactionspreciselybecauseoftheunfamiliarityof234thatfuture(Weber2006).Here,scientificallyconstructedstorylineshelpasa235complementaryapproachtoraiseriskawareness,byincorporatingepisodic236informationandmakingthepredictedfuturemoretangible(e.g.Matthewsetal.2372016,2017).238

ThisistheapproachadvocatedbyHazelegeretal.(2015),whoconstructstorylines239(called‘tales’)offutureweatherthatillustratetheimplicationsofclimatechangefor240real-lifehigh-impactweatherevents.Thisisdonebymappinghistoricalor241hypotheticaleventsontofutureclimateconditions.Examplesoftheapproach242includererunningweatherepisodesinalimited-areaweather-predictionmodel243withelevatedtemperaturesasboundaryconditions(Attemaetal.2014;Preinetal.2442016),diagnosingunprecedentedstormsinfutureclimateintegrations(Haarsmaet245al.2013),andexploringmultivariatedriversoflocalextremewaterlevels(Vanden246Hurketal.2015).Thatsuch‘simulatedexperience’ismoreeffectiveatconveying247riskthanstatisticalcharacterizationshasbeenrecognizedmorewidelyinthe248decision-makingliterature(HogarthandSoyer2015),buildingoninsightsfrom249experimentalpsychology(GigerenzerandHoffrage1995).250

Thereisalsotheissueofhowtocommunicatethestoryline.Well-designedboard251andcardgamescanofferanimmersiveexperienceandtellstories,andhavebeen252usedtoaiddevelopmentdecisions(Tintetal.2015),kick-startdiscussionsonthe253effectsofclimatechange(Chuang2017),andinformparticipantsaboutclimate254change(ReckienandEisenack,2013),althoughmostgamesfocusonnationallevel255policymakers(e.g.MatznerandHerrenbrück,2017).TheRedCrossClimateCentre256(http://climatecentre.org/resources-games/)hasdevelopedmanysimplegamesto257aidstakeholders’understandingofdifferenthumanitarianissues.Onewayto258engageinfrastructureownersandmanagerswithpotentialfutureclimatechange259couldbetoproduceadeckofcardswitheachcardcontainingasimpletextual260descriptionofaweatherorclimateevent.Thedeckofeventsisastorylineofa261potentialfutureclimate.Theparticipantsthendiscussthepotentialimpactofeach262eventontheirinfrastructure,howtheimpactofthateventonotherinfrastructure263mightaffectthem,andhowtheymightmitigatethedamagefromtheevent.This264allowsdecision-makerstounderstandhowpotentiallycascadingeventsmightaffect265theirinfrastructure,includingthecumulativeimpactofmultipleevents.The266

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participantswouldworkthroughthecarddeckdiscussingeachevent,observedand267supportedbyafacilitator.Importantly,theparticipants,likeinreality,wouldnot268knowwhateventsarestilltocome.Attheendofthedeckthefacilitatorwouldthen269discuswiththeparticipantswhichevents,orcombinationofevents,matteredto270themandforwhichonesdamagemitigationwouldrequiresubstantialinvestment.271Itisforthoseeventsthatsubsequentriskassessmentcouldbemade.272

Thedialecticbetweensemanticvsepisodicknowledgeseemsrelatedtothat273betweentheinvisibilityvsvisibilityofclimatechange(Rudiak-Gould2013).The274orthodoxclimate-scienceviewwouldbethatclimatechangeisinvisibletothenaked275eye,asitisinherentlyastatisticalconcept(semanticknowledge).However,many276wouldarguethatsomeaspectsofclimatechangearealsovisible,asinthecaseof277manyoftheobservedcryosphericchanges(episodicknowledge).Ifanobserved278changeissufficientlylargethatevenasingleoccurrenceisattributable(Shepherd2792016),andifitisvisibletothenakedeye(asopposedtosomethingthatcanonlybe280measured),thenvisibilityofclimatechangecanbereconciledwiththestatistical281perspective.Inasimilarway,storylinesallowepisodicknowledgeofclimatechange282tobesetwithinthecontextofsemanticknowledge.283

3.Strengtheningdecision-making284

Fewsocietaldecisionsaredrivensolelyby,orareframedonlyby,concernsabout285climatechange,butratherbysustainabledevelopmentmoregenerally.Storyline286approachesacknowledgethiscontextbycommunicatingthepotentialconsequences287ofclimatechangeinwaysthatarerelevanttothespecificdecisionandthespecific288decision-maker(Hazelegeretal.2015).Theyarealsonaturallysuitedtonon-289probabilisticdecision-makingframeworks,whichseekadaptationoptionsthatare290robustwithinacontextofdeepuncertainty(Kalraetal.2014;Simpsonetal.2016).291Storylinesallowawaytoexploresuchuncertaintiesinatraceableandphysically292plausiblemanner(seealsothediscussioninSection5concerningsea-levelriskin293theNetherlands).Itisimportanttoundertakesuchanalysisusinganiterative294processinwhichdecision-makerconcernsandinterestsshapethescientific295researchandmodellinginaprocessofco-production(Kalraetal.2014;Bhaveetal.2962018).297

Anexampleofsuchaniterativeprocessisprovidedbyananalysisofthephysical298andlegaldimensionsofwaterdiversionsfromtheUpperColoradoRiverBasin299understorylinesofclimatechange(Yatesetal.2015).Inthiscase,regionalclimate300simulationswereusedtostresstestamodelofthewatersupplysystemwithand301withoutanadaptationoption.Stakeholderswereengagedfromtheoutsetinthe302developmentofplausiblestorylinesthatcaptureboththedirectandindirect303consequencesofclimatechangeontheheadwaterareas.Forinstance,ifhotterand304drierconditionswereimagined,thenwatermodelparameterswereadjustedto305reflectpossiblehydrologicalchangesassociatedwithdustonsnowpackorwildfires306destroyingforestedareas.Stakeholdersalsospecifiedmetricsofwatersystem307behaviourandstaterelevanttotheirriskmanagementcontext.Inthisway,both308watermanagersandresearchersarrivedatasharedunderstandingofthesystem309

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vulnerabilitiesandtheextenttowhichclimateriskscanbemanagedusingalegal310adaptationinstrument.311

Storylinesareespeciallyeffectiveforconsideringrisksfromcompoundextreme312events,namelythosewheresevereimpactsaretriggeredbytheinteractionofmore313thanonevariable.Examplesincludedroughtwithheatwaves(Ciaisetal.2005),314windstormswithheavyprecipitation(Martiusetal.2016),andfluvialfloodingwith315stormsurges(Kewetal.2013).Figure1providesanotherexample.Neithervariable316itselfneedstobeveryextremebutthecombinationresultsinsevereimpacts317(Leonardetal.2014).Tostudycompoundextremesstatistically,longtimeseries318andrefinedstatisticalmethodssuchascopulasareneededtocaptureandquantify319theinter-dependenceofthevariables.Astorylineapproachisaveryattractive320complementarymethodtoaddresscompoundextremesasitallowsonetodefine321verycomplexeventsstartingfromtheimpactsinaveryflexibleway,including322spatialandtemporaldependence(clustering)ofextremes,whichcangeneratethe323impacts(e.g.,Muchanetal.2015).Storylinesbasedonimpactscanthenbeassigned324acategoricalplausibilityusingstatementslike“worldwideatleastonesimilarevent325occurredbefore”.TheSwissFederalOfficeforCivilProtectionusessuchan326approachtoprepareforabroadrangeofperils,includingatmospherichazards,but327alsotechnicalandsocialthreats(BABS2013).Foreachperil,theydefinethree328storylineswithincreasingseverityandimpacts.329

Anotheremergingapplicationofthestorylineapproach,althoughverymuchinits330infancy,isintheinsurancesector.Theinsuranceandreinsuranceindustriesare331slowlybeginningtoacknowledgetheimpactsofclimatechangeonworldwide332insuredlosses(Lloyd’sofLondon2014).Havingarobustunderstandingofpossible333catastrophelosses,particularlyoftheextremecatastrophes,isessentialforany334insurance/reinsurancecompany’sinternalcapitalmanagement,definitionofown335riskappetite,andsolvency(EIOPA2016).Themaintoolsforassessingsuchrisk336fromnaturalperilsarecatastrophemodels.Bynature,catastrophemodelsarebuilt337toestimateprobabilisticrisk,throughtheextrapolationofthelimitedobserved338record,inordertoestimatearangeofpotentialcatastrophes,theirassociated339probabilitiesandcorrespondinglosses(GrossiandKunreuther2005).However,the340samemodelscanbeandfrequentlyareusedtoestimatedeterministicriskfrom341variousstormscenarios.Assuch,modelsareruntocomputepossiblelossesfroma342relevanthistoricaleventoramodifiedversionofthehistoricalevent,foragiven343portfolio(Wooetal.2017).Thisstorylineapproachofassessingriskforinsured344propertiesisanextremelyusefulmethodofstress-testingtheclient’sexposureto345variousweatherandclimateconditions.3464.Partitioninguncertainty347

Manystudieshaveshownthattheresponsetoclimatechangecanbeusefully348understoodasacombinationofwhatmightbecalledathermodynamiccomponent349(surfacewarming,moistening,meltingofice)andadynamiccomponent(changesin350circulation)(e.g.,Deseretal.2014).Thedistinctionisnotprecisebecauseboth351componentsoftheclimatesystemarecoupled;inpractice,eitherthedynamic352

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componentisdefinedasthecomponentcongruentwithinternalvariability(Deser353etal.2004)withthethermodynamiccomponentobtainedasaresidual,or354(especiallyforprecipitation)thethermodynamiccomponentisdefinedasthe355componentobtainedintheabsenceofchangesincirculation,withthedynamic356componentobtainedasaresidual(Pfahletal.2017).357

Thereasonthedistinctionisusefulis,first,thatthereisastrikingcontrastbetween358thedegreeofconfidencewehaveinthetwocomponents(Shepherd2014;359Trenberthetal.2015).Thermodynamicaspectsofclimatechangearegenerally360robustintheory,observations,andmodels(althoughthereissubstantial361quantitativeuncertaintyassociatedwithclimatesensitivity).Incontrast,dynamic362aspectsarenotanchoredinacceptedtheories,havenotyetemergedinobservations,363anddivergebetweenmodels(Shepherd2014).Second,modeluncertaintiesin364climatesensitivityandindynamicaspectsofclimatechange(afterscalingbyglobal-365meanwarming)appeartobeuncorrelated(e.g.,ZappaandShepherd2017),which366makessenseastheyareassociatedwithdifferentaspectsofmodelerror.The367distinctionbetweenthermodynamicanddynamicaspectsofclimatechange368providesanalternativetotheusualapproachofconsideringensemblesofmodel369simulations,whichmixtogetheruncertaintiesinthetwoaspects,andmayhelpto370provideinformationonwherethelargestuncertaintieslie(e.g.,Pfahletal.2017).In371particular,storylinescanbeconsideredforeachaspect.372

Understandingtheroleofclimatechangeinweatherandclimateextremesisof373considerablesocietalrelevance.Thisisnotstraightforwardtoresolve,sinceextreme374eventsalwaysresultfromanintersectionofnaturalvariabilityofsomesort,riding375onandaugmentingglobalwarmingeffects,andeveryextremeeventisunique(NAS3762016).Theconventionalapproachtothisquestionfocusesonchangesin377probability,butdoesnotdealsatisfactorilywiththeuniquenatureofeachevent378(Shepherd2016).Analternativestorylineapproachmightaskinsteadhowmuch379worsetheeventoutcomeswerebecauseoftheknownthermodynamicaspectsof380climatechange,suchasoceanwarming(Trenberthetal.2015).381

Thedistinctionbetweenthermodynamicanddynamicaspectsofclimatechangeis382alsousefulwhenitcomestorepresentingtheuncertaintyinprojectionsoffuture383change.ApracticalexampleofthisistheKNMIClimateChangeScenarios(see384http://www.climatescenarios.nl/),whichprovidefourdiscretesetsofweatherand385sea-levelvariables,assumingagivenglobaltemperatureincreaseandaregional386amplificationduetocirculationresponses(VandenHurketal.2014).Inessence,387theseprovidestorylinesofregionalclimategivenlarge-scalechangesinphysical388climate.389

Onacontinentalscale,itispossibletomakestatementsaboutfutureclimate(such390asextremeprecipitationinthemid-latitudeswillincrease)withahighdegreeof391confidencesincethespatiallyaggregatedsignalisdrivenprimarilybythe392thermodynamicresponsetoradiativeforcingandglobaltemperatureincrease393(Fischeretal.2013).However,ataregionalscale,mid-latitudeprecipitation394extremesarestronglyinfluencedbythedynamicresponse,andtheycouldeither395

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increaseordecreasedependingonthechangesincirculation(Fischeretal.2013;396Pfahletal.2017).Manzinietal.(2014)showedthatmuchofthemodelspreadin397wintertimecirculationchangesoverthenorthernextratropicsisrelatedtothe398modelspreadinremotedriverssuchastropicalwarming,Arcticwarming,and399stratosphericvortexchange.ZappaandShepherd(2017)usedthisframeworkto400developstorylinesofEuropeancirculationchange,conditionedonagivenlevelof401globalwarming.Thattheinfluenceoftheremotedriversiscausalissupportedby402evidencefromseasonalpredictionandfromsingle-forcingmodelexperiments.It403wasfoundthatforcentralEuropeanwintertimestorminessandwintertime404Mediterraneandrying,twoimportantclimate-changeimpactsforEurope,the405differencebetweenthemostextremestorylineswasequivalenttothatfromseveral406degreesofglobal-meantemperatureincrease.407

Theuncertaintyinlong-termglobaltemperatureincrease(foragivengreenhouse408gasforcing)ismainlyassociatedwiththatinclimatesensitivity.Theobserved409warmingprovidesonlyaveryweakconstraintonlong-termequilibriumwarming410orclimatesensitivity,anditsdirectapplicabilityisamatterofconsiderablecurrent411debate.Astorylineapproachattemptstoarticulatethemuch-richerphysical412understandingofhowclimateprocesseschangeasclimatewarms(orcoolsunder413pastclimatechanges)toprovideamoremechanisticwayofconstrainingthe414problem.Usingphysicallydevelopedstorylines,Stevensetal.(2016)showthat415greaterconfidenceinanoverallnetpositivecloudfeedbackorbetter416reconstructionsoftropicaltemperaturesattheLastGlacialMaximumcouldbe417enoughtoraisethelowerboundonclimatesensitivity.Criticaltestsfortheupper418boundcouldincludetestingofrecentargumentsforamoremodestaerosolforcing.419

5.Exploringtheboundariesofplausibility420

Ifclimateprojectionscouldbereliablyarticulatedasprobabilitypredictions421(conditionedonscenariosoffutureGHGemissions)thenthesocietalconsequences422couldbepresentedasquantifiedrisksoraschangingriskprofiles.Theconventional423approachtoclimateprojectionisfocusedonmakingsuch(conditional)probability424predictionsbyapplyingcomplexpost-processingmethodologies(includingbias425correctionanddownscaling)totheoutputofGCMs.Iftheclimatesystemwaswell426understoodandcouldbesimulatedaccuratelyatallrelevantscales,thenthis427engineeringapproachtoclimateprojectionswouldbeappropriate.However,sucha428propositionhasbeenchallenged(Smith2002;McWilliams2007;VanOldenborghet429al.2013).Furthermore,21stcenturyprojectionswithGCMshaveonlypartial430explorationofmodeluncertainty(Knuttietal.2013)andofinitialcondition431uncertainty(Hawkinsetal.2016).Confidenceintheprocessessimulatedbyclimate432modelsrestsinsteadontherobustnessofthemodelbehaviourwhencompared433withavarietyoftypesofobservations,withinacausalframework(Lloyd2015).As434aconsequence,thereisastrongneedtoincorporatephysicalunderstandingofthe435processesofclimatechangedirectlyintoclimate-relatedstatementsaboutthe436future(Maraunetal.2017).437

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Storylineapproachescandothisbyplacingtheemphasisonphysicalplausibility438whenmakingstatementsaboutfutureclimate.Theydosobyusingmodelsastools439tosupportandtesttheoriesregardingtheinteractionsofclimateprocesses,rather440thanasasourceofquantitativeclimatepredictions.Scientificunderstandingisused441topushouttheboundariesofplausiblefuturesbyproposingmechanismsthrough442whichoutcomesnotcurrentlyseeninGCMs,eitherbecauseofmissingphysicsor443becauseofinsufficientsampling,couldarise(Hazelegeretal.2015;Zscheischleret444al.2018).Thisprovidesamorethoroughexplorationoftherangeofuncertainty445reflectedbytoday’slevelofscientificunderstanding,comparedtothelimitedrange446representedbytheextantensemblesofGCMprojections.447

Thefactthatclimatemodelsdonotfullysimulatetherangeofresponseuncertainty448isparticularlyevidentforlocal-scaleextremeevents.Forexample,ithasbeenfound449thatshort-duration(hourly)summertimeprecipitationextremesarewell-simulated450onlyinvery-high-resolution(<4km)regionalclimatemodels(RCMs)thatexplicitly451representdeepconvection,andnotinconventionalRCMsthatparameterisedeep452convection,andthatintensityincreaseswithwarmingaremuchlargerinthehigh-453resolutionsimulations(Kendonetal.2017).Thissuggeststhatconventional454nationalclimatechangescenariosmayunderestimatethepotentialchangesto455short-durationprecipitationextremes.However,very-high-resolutionregional456simulationscanbeusedtogetherwithobservationsandtheoreticalunderstanding457toprovideriskestimateswithinastorylineframework.Meredithetal.(2015)458arguedthataconvectiveeventleadingtointensefloodingcouldbeattributedto459BlackSeawarmingusinganRCMwithexplicitconvectionconstrainedbylarge-scale460reanalysis,butthattheresponsesimulatedwithparameterisedconvectionwas461physicallyimplausible.Preinetal.(2016)usedasimilarapproachtomake462statementsaboutfutureriskofextremeprecipitationandfloodingfortheUnited463States.Suchtargetedvery-high-resolutionsimulationsofrelevantphenomena464combineprocessunderstandingwiththesamplingofotherwiseunexplored465uncertaintiesandtherebygreatlycontributetotheestablishmentofrobust466projectionsoflocalisedextremeevents.467

Asanexampleofanapplicationtoclimateimpacts,UKWaterIndustryResearch468addressedthechallengeoflong-termchangeinintensestormsbycommissioninga469studytobuildaplausible‘story’usingthebestscientificevidenceofhowthese470stormsmightevolveunderglobalwarming.Thestudyusedtheoretical471understandingofpotentialchangeinprecipitationextremesfromtheClausius-472Clapeyronrelation(7%/K),andexaminedclimateanalogues(usingsummer473temperatureandprecipitationclimatologyasanalogues)andoutputsfrommodels474withexplicitconvectiontoproduceplausiblefuturescenarios,whichwerethen475translatedintodesignguidanceforstormdrainagenetworks(Daleetal.2017).In476thiscasetheclimateanalogues,high-resolutionmodelpredictionsandtheory477agreedonacommontrajectory,whichgaveadditionalconfidenceintheresults.478

IntheNetherlands,wheresealevelriseconstitutesanexistentialrisk,therehas479beenaparadigmshiftfrompredictingtoexploringthefuture,whichhasincreased480

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theopportunityforrobustdecision-making(HaasnootandMiddelkoop2012).The481DeltaCommissionoftheNetherlandsaddressedthechallengeofhigh-end,long-482rangesealevelchangebydeconstructingallthecomponentsofsealevelriseand483buildingplausibleaccountsofhoweachmightevolveunderextremeglobalmean484warming(Katsmanetal.2011).Inthisstudyeachsourceofinformation,whether485climatemodeloutput,palaeoclimaticdata,observationsorexpertelicitation,is486presentedinatransparentandauditableformatthatisopentochallengeand487revisionasscientificunderstandinggrows.Thisfieldofexpertiseisdeveloping488rapidly,andfrequentlynewinsightsonmajorcontributorstosealevelriseare489explored(e.g.DeContoandPollard2016),whichtriggerscientificandpublic490debatesontheseverityoftherisksforextremesealevelrise.Usingasimilar491process-baseddecompositionmethod,LeBarsetal.(2017)presentedprojectionsof492sealevelriseexceedingthoseofKatsmanetal.(2011),whichtriggeredtheDutch493DeltaProgrammetocommissionanewstudyonpotentialconsequencesofthese494projectionsforthesafetyoftheNetherlands.Suchaconceptof“adaptivedelta495management”asappliedintheNetherlandsisbasedontheprinciplethatnew496insightsoreventsmaytriggertheneedtochangethedefensestrategy,andrelies497heavilyonitsabilitytomonitoranddigestnewevidenceandinformationofferedby498consecutivestorylinesofmajorclimaticphenomena.499

6.Conclusion500

Wehavepresentedatypologyoffourreasonsfortakingastorylineapproachin501climate-changeresearchandcommunication.Storylinescanraiseriskawarenessby502framingriskinanevent-orientedratherthanaprobabilisticmanner—where503eventscanbelong-lasting,suchasatrend—whichcorrespondsmoredirectlyto504howpeopleperceiveandrespondtorisk.Storylinescanprovideaneffective505mechanismforstrengtheningdecision-makingbyallowingonetoworkbackward506fromaparticularvulnerabilityordecisionpoint,combiningclimate-change507informationwithotherrelevantfactorstoaddresscompoundriskanddevelop508appropriatestresstests.Storylinescanprovideaphysicalbasisforpartitioning509uncertainty,therebyallowingtheuseofmorecredibleregionalmodelsina510conditionedmanner.Finally,storylinescanexploretheboundariesofplausibility,511beyondwhatmightbeprovidedbyastandardsetofmodellingtools,thereby512guardingagainstfalseprecisionandsurprise(ParkerandRisbey2015).513

Theterm‘post-normalscience’wasintroducedbyFuntowiczandRavetz(1993)to514describeasituationwhereeitherthedecisionstakesorthesystemsuncertainties515arehigh,andhencetheuseofevidenceiscontested.Theynotedthatmany516environmentalissuesfallintothiscategory,includingclimatechange.Insucha517situation,traditionalmethodsofscientificproblem-solving(reductionist,expert-518based)areineffective.Thefailuretorecognizetheselimitationshelpsexplainwhy519therehasbeenapersistentgapbetweentheproductionanduseofclimate520information(Kirchhoffetal.2013).521

AccordingtoFuntowiczandRavetz(1993),post-normalscienceacknowledges522unpredictability,incompletecontrol,andapluralityoflegitimateperspectives.The523

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goalisnottobanishuncertainty,buttomanageit.Themodelforscientificargument524isnotaformalizeddeduction,butaninteractivedialogue.Storylinesoffermany525possibilitiesintheserespects,notleastas‘conversationstarters’.Morebroadly,526theyprovideameansofnavigatingthe‘cascadeofuncertainty’(e.g.Wilbyand527Dessai2010)byrepresentinguncertaintyinphysicalaspectsofclimatechange528withoutlosingsightoftherobustaspects,whilstusingconceptsthatrelateto529people’sexperienceandtapintotheirepisodicwayofthinking.Giventheir530prevalenceinconsiderationofthehumandimension,storylinesalsooffera531powerfulwayoflinkingphysicalwithhumanaspectsofclimatechange.532

533

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Figure1.a)Schematicdepictionofthesynoptic-scaleweathersituationover707Europeon10October2011at00UTC.Awarmfront(WF)islocatedovertheAlps;708behindtheWF,anatmosphericriver(AR)reachesthewesternAlpsfromthe709northwest,providingasupplyofmoisturetotheAlps.Thecoldfront(CF1)over710GreecehadcrossedtheAlpstwodaysearlier,providingheavysnowfall.TheL711symbolsindicatethelocationsofthelow-pressurecentresassociatedwiththetwo712fronts.b)Schematiccross-section(northwesttosoutheast)acrosstheLötschen713Valley,Switzerland,illustratingthestrongcontrastinprecipitationbetweenthetwo714sidesofthevalley;theprecipitationvalues[mm/12hours]indicatethe715accumulationatseverallocationsbetween00UTCand12UTCon10October2011.716Thesolidbluearrowindicatestheatmosphericriver(AR)andthedashedlinethe717cavitycirculation,resultinginafeedercloud.c)Amudflowblockinganddamaging718theonlyroadintotheLötschenValley,locatedattheredstarinpanelb).Figure719fromBerncantonalpolice,usedwithpermission.d)Mobilefloodprotection720measure.FigurefromBeaverCompany,usedwithpermission.721

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Figureownership:c)cantonalpoliced)Beavercompany

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Adaptationmeasures

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95110

170AR

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a) b)

c) d)

Caption:a)Schematicdepictionofthesynoptic-scaleflowsituationoverEuropeon10October201100UTC.Awarmfront(WF)islocatedovertheAlpsandbehindtheWFanatmosphericriver(AR)reachesthewesternAlpsfromthenorthwest.Thecoldfront(CF1)overGreecehadcrossedtheAlpstwodaysearlier.b)Schematiccross-section(north-westtosouth-east)acrosstheLötschenValley,Switzerlandillustratingthestrongprecipitationgradientsacrossthevalley,precipitationvalues[mm/12hours]indicateprecipitationaccumulationbetween00UTCand12UTC10October2011.Thesolidbluearrowindicatestheatmosphericriver(AR)andthedashedlinethecavitycirculationresultinginafeedercloud.Theredstarindicatesthelocationofpanelc.c)AmudflowblockinganddamagingthemainroadintheLötschenValley.D)Mobilefloodprotectionmeasure.

seederfeeder