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1 Anthropogenic influence on the 2018 summer warm spell in Europe: the impact of different spatio- temporal scales Nicholas Leach 1 *, Sihan Li 2,3 , Sarah Sparrow 2 , Geert Jan van Oldenborgh 4 , Fraser C. Lott 5 , Antje Weisheimer 1,6,7 , Myles R. Allen 1,3 , 1 Department of Physics, Atmospheric Oceanic and Planetary Physics, University of Oxford, United Kingdom. 2 Oxford e-Research Centre, Department of Engineering Science, University of Oxford, United Kingdom. 3 Environmental Change Institute, School of Geography and the Environment, University of Oxford, United Kingdom. 4 Koninklijk Nederlands Meteorologisch Instituut, De Bilt, The Netherlands. 5 UK Met Office Hadley Centre, Exeter, United Kingdom. 6 Department of Physics, National Centre for Atmospheric Science (NCAS), University of Oxford, United Kingdom. 7 European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom. *Corresponding author email: [email protected]

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Page 1: Anthropogenic influence on the 2018 summer warm spell in ...ametsoc.net/eee/2018/8_Leach0201_w.pdf · 1 Anthropogenic influence on the 2018 summer warm spell in Europe: the impact

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Anthropogenic influence on the 2018

summer warm spell in Europe: the impact

of different spatio-temporal scales

NicholasLeach1*,SihanLi2,3,SarahSparrow2,GeertJanvanOldenborgh4,FraserC.Lott5,

AntjeWeisheimer1,6,7,MylesR.Allen1,3,

1DepartmentofPhysics,AtmosphericOceanicandPlanetaryPhysics,UniversityofOxford,United

Kingdom.

2Oxforde-ResearchCentre,DepartmentofEngineeringScience,UniversityofOxford,United

Kingdom.

3EnvironmentalChangeInstitute,SchoolofGeographyandtheEnvironment,UniversityofOxford,

UnitedKingdom.

4KoninklijkNederlandsMeteorologischInstituut,DeBilt,TheNetherlands.

5UKMetOfficeHadleyCentre,Exeter,UnitedKingdom.

6DepartmentofPhysics,NationalCentreforAtmosphericScience(NCAS),UniversityofOxford,

UnitedKingdom.

7EuropeanCentreforMedium-RangeWeatherForecasts(ECMWF),Reading,UnitedKingdom.

*Correspondingauthoremail:[email protected]

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Abstract

Wedemonstratethatinattributionstudies,eventsdefinedoverlongertimescalesgenerally

producehigherprobabilityratiosduetolowerinterannualvariability,reconcilingseemingly

inconsistentattributionresultsofEurope's2018summerheatwavesinreportedstudies.

Introduction

Thesummerof2018wasextremelywarminpartsofEurope,particularlyScandinavia,the

IberianPeninsula,andCentralEurope,witharangeofall-timetemperaturerecordsset

acrossthecontinent(Johnston,2018;NESDIS,2018).ImpactswerefeltacrossEurope,with

wildfiresburninginSweden(Krikken,Lehner,Haustein,Drobyshev,&vanOldenborgh,

2019;Watts,2018),heatstrokedeathsinSpain(Publico,2018),andwidespreaddrought

(Harris,2018).Duringthesummer,theWorldWeatherAttribution(WWA)initiative

releasedananalysisoftheheat-spell(WorldWeatherAttribution,2018)basedon

observations/forecastsandmodelsinspecificlocations(Dublin,Ireland;DeBilt,

Netherlands;Copenhagen,Denmark;Oslo,Norway;Linkoping,Sweden;Sodankyla,Finland;

Jokionen,Finland),whichconcludedthattheincreaseinlikelihoodduetohuman-induced

climatechangewasatleasttwotofivetimes.InDecember,theUKMetOffice(UKMO)

statedthattheyfoundthe2018UKsummertemperaturesweremadethirtytimesmore

likely(PressOffice,2018;McCarthy,etal.,2019).Thesetwoestimatesappearto

quantitativelydisagree,howeverweshowtheycanbereconciledbyinvestigatingthe

effectsofusingdifferentspatialdomainsandtemporalscalesintheeventdefinition.We

alsodemonstratethatprescribedSSTmodelsimulationscanunderrepresentthevariability

oftemperatureextremes,especiallynearthecoast,withimplicationsforanyderived

attributionresults.

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Event Definition

Weconsidervarioustemperature-basedeventdefinitionstodemonstratetheimpactofthis

choiceinattributionassessments,andassesstowhatextenthumaninfluenceaffectedthe

seasonalandpeakmagnitudesofthe2018summerheat-eventonarangeofspatialscales.

Thestatisticweuseistheannualmaximumofthe1,10and90dayrunningmeanofdaily

mean2mtemperature,(hereafterTM1x,TM10xandTM90xrespectively).Weanalysethree

spatialscales:modelgridbox,regionalandEuropean.ForregionalandEuropeanevent

definitions,thespatialmeaniscalculatedbeforetherunningmean.Regionalextentsare

takenfromChristensenandChristensen(2007),andEuropeanextentistheE-OBS(Cornes,

vanderSchrier,vandenBesselaar,&Jones,2018)domain[landpointsin25N-71.5N,25W-

45E].TheWWAusedtheannualmaximaof3-daymeandailymaximumtemperaturesat

specificgridpointsforitsconnectiontolocalhealtheffects(D’Ippoliti,etal.,2010),whilethe

UKMOusedtheJJAmeantemperatureovertheentireUKinordertoanswerthequestion

ofhowanthropogenicforcingshaveaffectedthelikelihoodofUKsummersaswarmas

2018.Thesamejustificationscanbeusedhere,thoughweaddthatdifferentheat-event

timescalesareimportanttodifferentgroupsofpeople,andassuchusingseveraltemporal

definitionsmayincreaseinterestinheat-eventattributionstudies.However,werecognise

thatotherdefinitionsthanthoseusedheremaybemorerelevanttosomeimpacts

observed(suchasdefiningtheeventinthecontextoftheatmosphericflowpatternand

droughtwhichaccompaniedtheheat),andotherlinesofreasoningforselectingone

particulareventdefinitionexist(Cattiaux&Ribes,2018).

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Model Simulations and Validation

ThreesetsofsimulationsfromtheUKMOHadleyCentreHadGEM3-Aglobalatmospheric

model(Christidis,etal.,2013;Ciavarella,etal.,2018)areused.Theseareahistorical

ensemble(1960-2013,Historical),andfactual(ACT)andcounterfactual(a``natural"world

withoutanthropogenicforcings,NAT)ensemblesof2018.Wecompareresultsfromthis

factual-counterfactualanalysiswiththosefromatrend-basedanalysisofHistorical,

ensemblesfromEURO-CORDEX(Vautard,etal.,2013;Jacob,etal.,2014;Vrac,Vaittinada

Ayar,&Ayar,2017)(1971-2018)andRACMO(Aalbers,Lenderink,vanMeijgaard,&vanden

Hurk,2018;Lenderink,etal.,2014)(1950-2018),andobservationsfromE-OBS(1950-2018).

AfullmodeldescriptionisprovidedintheSupplementaryInformation.Initially,we

performedouranalysiswiththeweather@homeHadRM3PEuropean-25kmsetup(Massey,

etal.,2015),butfoundthismodeloverestimatesthevariabilityoverallEuropefordaily

throughseasonalscaleeventstatisticsandsowasomitted.

Methodology

Wecalculatethereturnperiod,RP,forthe2018eventinadistributionfittoE-OBSusing

thegeneralisedextremevalue(GEV)distributiontomodelTM1xandTM10x,andthe

generalisedlogisticdistributiontoempiricallymodelTM90xthroughout.Sincethe

distributionoftemperatureextremeschangesastheclimatedoes,toaccountforthenon-

stationarityofthetimeserieswefirstremovethetrendattributabletolow-pass-filtered

globally-averagedmeansurfacetemperature[GMST,fromBerkeleyEarth(Rohde,etal.,

2013)]inanordinary-least-squaresregression[theregressioncoefficientortrendisshown

inSIFig.1(Diffenbaugh,etal.,2017)].Wethenfindthetemperaturethreshold

correspondingtoRPinadistributionfittothemodel’sclimatology.Inthefactual-

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counterfactualanalysis,wedothisbyfittingparameterstoadetrended(againstGMST,

trendsshowninSIFig.2c7-c9)climatologicalensembleofHistoricalplus15randomly

sampledmembersofACT.Wefinallycalculatetheprobability(𝑃)ofexceedingthis

climatologicaltemperaturethresholdindistributionsfittotheACTandNATensemblesand

calculatetheprobabilityratio,PR = 𝑃!"#/𝑃!"#,representingtheincreasedlikelihoodof

the2018eventinthefactualcomparedtothecounterfactualworld.Usingestimatedevent

probabilitiesratherthanobservedmagnitudesconstitutesaquantilebiascorrection(Jeon,

Paciorek,&Wehner,2016),minimizingmodelbiasesinthemeanandvariabilityofthe

temperaturesanalysed.Adescriptionofuncertaintycalculationandthetrend-based

analysisdiscussedbelowisincludedintheSI.

Results

Extremedailyheat-events,measuredbyTM1x,aredistributedheterogeneouslythroughout

Europe(SIFig.1i).Thisisparalleledinthefactual-counterfactualPRsseeninFig.1a,with

largeproportionsofIberia,theNetherlands,andScandinaviaexperiencingeventsthatwere

highlyunlikelyinaclimatewithoutanthropogenicinfluence.Asimilarresultisfoundonthe

regionalscale(Fig.1d)withScandinaviaandtheIberianPeninsularespectivelyexperiencing

1-in-150[26,26000]1and1-in-30[9,550]yeareventsinthecurrentclimatethatwere

highlyunlikelyinthenaturalclimatesimulatedinNAT.Theremainingregionsrecord

maximumdailytemperatureslikelytoberepeatedwithin4years.Consideringthewholeof

Europe,thelikelihoodofthe2018maximumofdailyEuropeanmeantemperatureoccurring

withoutclimatechangeiszero.ThisresultisconsistentwithUhe,etal.,(2016)andAngélil,

1Numbersin[]representa90\%confidenceinterval

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etal.,(2018),whoshowedthatincreasingspatialscaletendstoincreasetheprobability

ratio.

Extreme10-dayheat-events,TM10x,werealsowidespreadinEurope,withthemost

extremeoccurringinScandinavia(SIFig.1j).Regionally,thePRsbecomemoreuniform(Fig.

1d),thoughScandinaviaandtheIberianPeninsulastillhaveveryhighbest-estimatePRsof

185[17,infinite]and110[18,56000]respectively.Thebest-estimatePRfortheaverageof

Europeisstillformallyinfinite.

ThePRmapforseason-longheat-eventsmeasuredbyTM90xismoreuniformthroughout

Europe(Fig.1c).Scandinavia,BritishIsles,France,andCentralandEasternEuroperegions

allexperiencedontheorderof1-in-10yearevents(SIFig.1l),andthecorrespondingbest-

estimatePRsarebetween10and100forallregions(Fig.1d),includingthosewithlower

returnperiods.ThePRfortheEuropeanaverageis1000[500-2000].

Trend-basedanalysis,SIFigs.1m-p(observations)and2b(models),yieldssimilarresults,

thoughwenotethatforHadGEM-3AthisresultsingenerallyhigherPRs,duetothelinear

trendwithGMSTintheclimatologybeinggreaterthanthedifferencebetweenthetwo

ensemblesusedinthefactual-counterfactualanalysis.Observationalandmodelanalysis

contradictinsomegridboxesinNorthernScandinaviaforTM1xandTM10x,sincethe

observedbest-estimatetrendagainstGMSTisnegative,reducingtheeventprobabilityfor

thepresent-daycomparedtothepre-industrialclimate,thereforeyieldingPRsoflessthan

1.Comparingregionalfactual-counterfactualmodelwithobservationalanalysis(Fig.1dvsSI

Fig.1p)showsthatthelargeobservationaluncertaintiesoverlapwiththemodelresults:the

differencecouldbeduetonaturalvariabilityaffectingthesmallobservationalsamplesize.

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However,wearecautiousofdrawinganyconclusionsregardingthechangeinlikelihoodof

extremeheat-eventsasdefinedherefortheselocations.

ThePRincreaseswiththeeventstatistictimescaleforthemajorityofgridpointsandregions

(showninFig.1).Fig.2illustratesthecauseusingtheBritishIslesregion:asthetimescale

increases,theeventstatisticdistributionvariancedecreases,whilethemeanshiftbetween

thefactualandcounterfactualdistributionsremainsconstant.SIFig1tshowsthatthe

similarityintrendswithGMSTbetweenthethreetimescalesisalsotrueforthe

observations.ThedecreaseinvarianceusuallyresultsinhigherPRs,givenaparticularevent

returntime,forthelongertimescales.Thereareexceptionsduetotheboundeduppertail

ofaGEVdistributionwithanegativeshapeparameter,resultingintheveryhighPRsfor

TM1xinScandinavia,IberiaandtheNetherlands.Thesolidanddottedblacklinescompare

thetemperaturethresholdswhenusingeventreturnperiodstoanomalymagnitudesinE-

OBS.ThisexplainswhytheTM90xPRismuchhigherthantheothertimescalesforthe

BritishIsles:inadditiontothedecreasedvariance,theseasonal-scaleheat-eventwasmore

unusualthantheothertimescales,withalongerreturnperiod(10.6[5.7,21]years)than

TM10x(2.6[1.8,3.9]years)andTM1x(3.6[2.5,6.2]years).Thesefactorstogetherresultin

PRsof3.6[2.9-4.8]forTM1xand43[27-84]forTM90x.Wesuggestthatthechangein

variancebetweenthetimescalesusedlargelyreconcilesthedifferencesbetweenthe``two

tofive"and``thirty"timesincreasesinlikelihoodfoundbytheWWAandUKMOreports,

withothermethodologicalfactorsplayingaminorrole.Althoughthehigherreturnperiod

forTM90xhassomeimpact,itislesssignificantthatthechangeinvariability.

Fig.2alsodemonstratesarelevantdeficiencyinthemodel:themodeldistributionsare

narrowerthantheobserveddistributions,meaningthemodelhaslowervariabilitythanthe

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realworld.Thisreducedvariancehasasignificantimpactonattributionresults(Bellprat,

Guemas,Doblas-Reyes,&Donat,2019),andmeansthatthePRsfortheBritishIsles

presentedhere,especiallyforTM90x,arelikelytobeoverestimated.Underrepresented

variabilityoftenoccursinprescribedSSTmodels(Fischer,Beyerle,Schleussner,King,&

Knutti,2018),andisvisibleinHadGEM-3AformanycoastallocationsoverEurope(SIFig.

2a7-a9).Fig.2dshowsthepowerspectrumofJJAsummertemperaturesovertheBritish

Isles,indicatingthatHadGEM3-AhassimilarspectralcharacteristicstoE-OBS,but

underrepresentstheintraseasonal2mtemperaturevariabilityatalmostallfrequencies,

whichwilllikelyresultinoverestimatedPRs.Powerspectraforothermodelensemblesare

shownforcomparison,demonstratingthatthefullybias-correctedEURO-CORDEXensemble

hasthesamevariabilitycharacteristicsandmagnitudeastheobservations.

Discussion

Ouranalysishighlightsakeypropertyofextremeweatherattribution:thevarianceofthe

eventdefinitionused,bothintermsofthestatisticitselfanditsrepresentationwithinany

modelsused.Theuseoflongertemporaleventscalesingeneralincreasesboththespatial

uniformityandmagnitudeoftheprobabilityratiosfound,consistentwithKirchmeier-Young,

Wan,Zhang,&Seneviratne(2019),duetoadecreaseinvariancecomparedtoshorter

scales.Thedifferenceintemporalscalebetweentworeportsconcerningthe2018summer

heatissufficienttoexplainthelargediscrepancyinattributionresultbetweenthem.We

findthatseveralEuropeanregionsexperiencedseason-longheat-eventswithapresent-day

returnperiodgreaterthan10years.Thepresent-daylikelihoodofsucheventsoccurringis

approximately10to100timesgreaterthana``natural"climate.Theattributionresultsalso

showthattheextremedailytemperaturesexperiencedinpartsofScandinavia,the

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NetherlandsandIberiawouldhavebeenhighlyunlikelywithoutanthropogenicwarming.

TheprescribedSSTmodelexperimentsusedheretendtounderestimatethevariabilityof

temperatureextremesnearthecoast,whichmayleadtotheattributionresultsoverstating

theincreaseinlikelihoodofsuchextremesduetoanthropogenicclimatechange(Bellprat,

Guemas,Doblas-Reyes,&Donat,2019).Weaimtoproperlyquantifytheimpactofthe

underrepresentedvariabilityinfurtherwork.Althoughherewehaveusedanunconditional

temperaturedefinitionforconsistencywiththestudieswetrytoreconcile,weplanto

furtherinvestigatetheeffectofincludingboththeatmosphericflowcontextandother

impactrelatedvariablessuchasprecipitationintheeventdefinition,andaddressissues

modelsmighthavewithrealisticallysimulatingthephysicaldriversofheatwaves.

Acknowledgements

WeacknowledgetheE-OBSdatasetfromtheEU-FP6projectUERRA(http://www.uerra.eu)

andtheCopernicusClimateChangeService,andthedataprovidersintheECA\&Dproject

(https://www.ecad.eu).Wewouldliketothankallofthevolunteerswhohavedonatedtheir

computingtimetoClimateprediction.netandWeather@Home.

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https://www.worldweatherattribution.org/attribution-of-the-2018-heat-in-

northern-europe/

Figure Captions

Figure1:Probabilityratiosforthe2018summerheat-eventderivedfromHadGEM3-A

factual-counterfactualsimulations.Panelsa-cshowmapofincreasedlikelihoodinthereal

worldatgridboxscaleforthethreeeventtimescalesanalysedrespectively.Notethatthe

upperlimitonthecolorscaleis1000andgridboxeswithaninfiniteprobabilityratioare

shownindarkred.Paneldshowstheregionalprobabilityratiosforthethreetimescales;

thecrossesdenotethebest-estimate,andthebarsdenotethe5th-95thpercentiles.Note

thatthebest-estimateprobabilityratiosforTM1xinScandinaviaandtheIberianPeninsula

wereinfiniteand2000respectively.

Figure2:Panelsa-c,probabilitydensityfunctionsofthethreetemporalscalesofevent

statisticfortheBritishIsles,showingHadGEM3-AACT(factual)andNAT(counterfactual)

simulations,andobservationsfromE-OBS;allasanomaliesabovethemodelorobserved

1961:1990meanclimatology.Thickblacklinesshowthe2018eventdefinedprobabilistically

astheHadGEM3-AHistoricaltemperaturethresholdcorrespondingtotheE-OBSreturn

period,dottedblacklinesshowtheeventdefinedintermsofthemagnitudeobserved

directlyfromE-OBS.Paneld,periodogramsofJJAdailymeantemperatureintheBritishIsles

(seasonalityandmeanremoved)calculatedasthemeanofintraseasonalperiodogramsfor

allavailableyears.TheHadGEM3-ApowerspectrumiscalculatedfromtheHistorical

ensemble.

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Figures

Figure1:Probabilityratiosforthe2018summerheat-eventderivedfromHadGEM3-A

factual-counterfactualsimulations.Panelsa-cshowmapofincreasedlikelihoodinthereal

worldatgridboxscaleforthethreeeventtimescalesanalysedrespectively.Notethatthe

upperlimitonthecolorscaleis1000andgridboxeswithaninfiniteprobabilityratioare

shownindarkred.Paneldshowstheregionalprobabilityratiosforthethreetimescales;

thecrossesdenotethebest-estimate,andthebarsdenotethe5th-95thpercentiles.Note

thatthebest-estimateprobabilityratiosforTM1xinScandinaviaandtheIberianPeninsula

wereinfiniteand2000respectively.

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Figure2:Panelsa-c,probabilitydensityfunctionsofthethreetemporalscalesofevent

statisticfortheBritishIsles,showingHadGEM3-AACT(factual)andNAT(counterfactual)

simulations,andobservationsfromE-OBS;allasanomaliesabovethemodelorobserved

1961:1990meanclimatology.Thickblacklinesshowthe2018eventdefinedprobabilistically

astheHadGEM3-AHistoricaltemperaturethresholdcorrespondingtotheE-OBSreturn

period,dottedblacklinesshowtheeventdefinedintermsofthemagnitudeobserved

directlyfromE-OBS.Paneld,periodogramsofJJAdailymeantemperatureintheBritishIsles

(seasonalityandmeanremoved)calculatedasthemeanofintraseasonalperiodogramsfor

allavailableyears.TheHadGEM3-ApowerspectrumiscalculatedfromtheHistorical

ensemble.