NairobiClimateProfile:FullTechnicalVersion

Preparedby:

UniversityofCapeTown

November2017

ForenquiriesregardingthisClimateProfile,pleasecontactLisavanAardenne(lisa@csag.uct.ac.za)

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NairobiClimateProfile

Summary

Nairobihasasubtropicalhighlandsclimate.ItislocatedclosetotheeasternedgeoftheEastAfricanRiftValleyatanaltitudeofroughly1800metresabovesealevelwhichstronglyinfluencesitsclimate.Nairobireceivesjustover610mmofrainfallayearoccurringprimarilyintworainfallseason.ThelongrainsfromMarchandMay,whichgenerallyrecordsaround310mm,andtheshortrainsduringNovember–December,wherearound200mmisrecorded.RainfalldoesalsooccurduringJanuaryandFebruarybutismuchlessthanthetwocoreseasons(80mm).ArelativelydryperiodlastsfromJune–October(Figure1below).

Rainfallvariesquitestronglyfromyeartoyear.Theannual(July-June)totalrainfallvariesfromaround300-900mm/year,thoughduringtheextremeyearsitcanbemuchhigher,suchasthe1997/8yearwhichrecorded1400mmofrainfall(Figure2).RainfalloverNairobiexhibitsvariabilityonthemulti-yeartimescale.SomeofwhichisrelatedtolargescaleremoteforcingssuchastheElNinoSouthernOscillation(ENSO),withElNinoconditionsgenerallybeingassociatedwithaboveaveragerainfallandLaNinaconditionstobelowaveragerainfallduringtheshortrains(Figure3).Thereislittleevidenceofclearorstatisticallysignificanttrendsintherainfalloverthelast30years(Figure10–12).

TemperatureatNairobidisplaysvariabilityatanumberoftime-scales.Themostobviousbeingthedaily,ordiurnal,cyclewheretemperaturevariesbyalmost12°C.Temperaturealsochangesthoughtheyear,butbecauseofNairobi’slocationjustsouthoftheequator,theseasonalcycleisrelativelysmallwithdailymaximumtemperaturevaryingbyabout6°Canddailyminimumtemperaturevaryingbyaround5°C.Thelongterm(1981-2010)averagedailymaximumtemperatureiswarmestduringJanuary–March(27.5°C)withasecondarypeakduringSeptember–November(26°C),whichcorrespondtothestartoftherainyseasons.DailymaximumtemperatureiscoolestduringJune–August(22.5°C).Theseasonalityoflong-term(1981-2010)averagedailyminimumtemperaturegenerallyfollowsthatofrainfall,withwarmestnight-timetemperaturesoccurringduringMarch–May(15°C)andNovember–December(14.5°C)andcoldestnight-timetemperaturesoccurringduringJuly(11.4°C)(Figure1).

Yeartoyeardifferencesintheaverageannualtemperaturearesmall,varyingbyonlyaround1°C.SomeofthisisrelatedtoElNinoSouthernOscillationwheretemperaturesaregenerallywarmerduringtheElNinophaseandcoolerduringtheLaNinaphase(Figure3).

Dailymaximumandminimumtemperatureshowclearandstatisticallysignificantwarmingtrendsoverthelast30orsoyears(Figure4and5).Thewarmingtrendisseenduringallseasonsandinthemeantemperatureaswellasinthefrequencyanddurationofextremetemperatureevents(Figure6–9).

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TheclimateofNairobiisprojectedtogetwarmerintothefuture.Usinganensembleof15GlobalClimateModels(GCMs)thedailymaximumtemperatureisprojectedtoincreaseby0.5°Cto+2°Cby2040s,andby+2°Cto+5°Cbytheendofthecentury(Figure13).Similarlydailyminimumtemperatureisprojectedtoincreaseby+1°Cto+1.5°Cbythe2040s,andbetween+3°Cand+5°Cbytheendofthecentury(Figure14).Thiswarmingisexpectedtoincreasethefrequencyandseverityofheatwavesintothefuture(Figure15–16).ThemessageforrainfallislesscertainwiththeensembleofGCMsgenerallyprojectingnochange,oraslightincreaseinrainfallbytheendofthecentury(Figure17–20).Similarresultsareobtainedforbothtemperatureandrainfallfromanensembleof11statisticallydownscaledGCMprojections(Figure21–28).

HistoricClimate

Rainfall

Rainfallvariesonanumberoftimescalesfromsub-dailytodecadalorevenlonger:

TheseasonalcycleofrainfallinNairobiisstrongwithtwocorerainyseasonsandasingledryseason(Figure1).Thisseasonalityofrainfallisdrivenbythenorth-southmigrationoftheInter-TropicalConvergenceZone(ITCZ)overtheregion:TheshortrainsoccurduringNovember–DecemberastheITCZmigratessouthwardsovertheregion.Thisseasongenerallyreceivesaround200mmofrainfall.DuringJanuaryandFebruarytheITCZislocatedtothesouthoftheregion,butsomerainfallstilldoesoccurinNairobi(80mm).Duringthelongrains(March–May)theITCZmigratesbacknorthovertheregionandthelongrainsrecordaround310mmofrainfall.DuringJune–OctobertheITCZislocatedtothenorthandlittlerainfalloccursinNairobi.

Year-to-yearorinterannualvariabilityinrainfallislargeforNairobi,withsomeyearsrecordingover750mmaboveor370mmbelowthelong-termaverage(615mm)(Figure2).Rainfalltotalswithinthetworainfallseasonsfluctuateindependentlyofeachother(Figure10–12).Thelongrains(March–May)varybyasmuchas260mmaboveorbelowthelong-termaverage(310mm).Theshortrains(November–December)varybyasmuchas250mmaboveorbelowthelong-termaverage(200mm)andwithatleast1yearrecordingnorainfallduringthisseasonatall.Thefrequencyofrainfallevents,ornumberofraindays,alsoexhibitsstronginterannualvariabilitywhichiscloselylinkedtorainfalltotals.Theintensityofrainfall,orthedailymeanrainfallamount,exhibitsstronginterannualvariabilityinbothrainyseasons,butdoesnotappeartobestronglylinkedtotheseasonaltotalsorfrequencyofrainevents.

RainfalloverNairobiexhibitvariabilityonthemulti-yeartimescale.SomeofthisisrelatedtolargescaleremoteforcingssuchastheElNinoSouthernOscillation(ENSO),withElNinoconditionsgenerallybeingassociatedwithaboveaveragerainfallandLaNinaconditionstobelowaveragerainfallduringtheshortrains(figure3).RainfalloverNairobimayalsodisplaydecadalvariability,howeverthe35-yearlengthoftherecorddoesnotprovidesufficienttimetoclearlyidentifyvariabilityatthisscaleusingthisdataset.

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Thelengthoftheclimaterecordisjustlongenoughtodetermineiftherehasbeenalong-termtrendinrainfall.Eachoftherainfallseasonsisexploredseparatelysincetheyvaryindependentlyofeachother.Lookingattheperiod1981-2016therehasnotbeenaclearorsignificantlineartrendintheseasonaltotalrainfallforthelongrains(March–Mach).Therehasalsonotbeenacleartrendintheaveragedailyrainfallintensity,thefrequencyofheavyrainfalleventsortheaveragewetspellordryspellduration.Theonlystatisticthatdoesshowastatisticallysignificanttrendisthefrequencyofrainfallevents(-1.8daysperdecade),butthismaybeinfluencedbytheveryhighvalueatthebeginningoftherecord.Asimilarmessageisseenfortheshortrainswheretheonlystatisticallysignificanttrendisfoundintherainfallfrequency(-1.7daysperdecade)andtheaveragewetspellduration(-0.2daysperdecade)(Figure10–12).

Figure1:1981-2010historicalaverageclimateseasonalityforthegridcelloverNairobi.Meanmonthlytotalrainfall(mm/month)fromCHIRPSdatasetdepictedasbluebars,whiskersshow+-2standarddeviations.MonthlymeandailymaximumandminimumtemperaturefromtheWFDEIdatasetpresentedbytheredandgreenlinesrespectively.Dashedlinesrepresentthe+-2standarddeviationsaroundthesemeans.

Temperature

Temperaturealsoshowsclearvariabilityonarangeoftimescales.

Thediurnaltemperaturerange–ordifferencebetweenthemaximumtemperatureandtheminimumtemperaturewithina24-hourperiod–isanimportantmodeofvariability.Nairobihasanaveragediurnaltemperaturerangeofalmost12°Cwithinaday.TherangeislargestduringJanuaryandFebruary(14°C)andsmallestduringtheendofthelongrainsintothecoreofthedryseasonandalsoduringNovember(between9.5–10.6°C)(Figure1).

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Theseasonalcycleoftemperatures-orthedifferencebetweenthetemperaturesduringthehottestandcoldesttimeoftheyear-isrelativelyweak,averagingonlyaround6°and5°Cfordailymaximumandminimumtemperaturerespectively(1981-2010).Thelong-termaverage(1981-2010)dailymaximumtemperaturesarewarmestduringJanuary–March(27.5°C)withasecondarypeakduringSeptember–November(25.9°C),whichcorrespondstothestartoftherainyseason,andiscoolestduringJune–August(22.5°C).Thelong-term(1981-2010)averagedailyminimumtemperaturegenerallyfollowstheseasonalityofrainfall,withwarmestnight-timetemperaturesoccurringduringMarch–May(15°C)andNovember–December(14.5°C)andcoldestnight-timetemperaturesoccurringduringJuly(11.4°C).Thismeansthatthedifferencebetweentheaverageday-timeandnight-timetemperatureswithinaseasonaregenerallylargerthanthedifferenceintemperaturebetweenthehottestandthecoldesttimeoftheyearforeitherdailymaximumorminimumtemperature(Figure1).

Temperaturesshowlowvariabilityfromyeartoyearintermsoftheaveragemonthlyorseasonaltemperature,varyingbylessthan1°Cfromthelong-termmeaninalmostallmonths(Figure2).WhatinterannualvariabilitydoesexistappearstoberelatedtotheElNinoSouthernOscillation,wheretemperaturesaregenerallywarmerduringtheElNinophaseandcoolerduringtheLaNinaphase(Figure3).

ThedailymaximumandminimumtemperaturesinNairobidisplayaclearandstatisticallysignificantlinearwarmingtrendininallseasonoftheyearofbetween0.3and0.4°Cperdecadeovertheperiod1979-2015.Lookingmorecloselyathowthetemperaturechangedovertime,dailymaximumtemperaturesgenerallydecreasedslightlyduringthe1980safterwhichthetrendispositiveandmoststronglypositiveduringthe1990sespeciallyduringthewarmesttimeoftheyear(JanuaryandFebruary)(Figure4).Fordailyminimumtemperaturetherewasaslightcoolingtrendinthe1980sduringJanuaryandFebruarybeforeincreasingfromthe1990stothepresent.Thetrendwaspositiveforminimumtemperaturethroughouttheperiodforallotherseasons(Figure5).

Extremedaytimehoteventswhichareclassifiedasbeingthosedayswherethetemperaturefallsabovethe90thpercentile(tmax>28.9°C),mostlyoccurduringFebruaryandMarch,butdooccurfromDecembertoAprilandalsoinOctober.Extremenight-timetemperatures(tmin>15.8°C)occurprimarilyfromFebruarytoMayandalsoduringOctobertoDecember.Theseeventshavebecomefarmorecommonovertheperiodoftherecord(1979–2014)(Figure6and7).Thenumberofdayswherethemaximumtemperatureexceededthe90thpercentile(28.9°C)increasedby12daysperdecadeandtheaveragelengthoftheseheatspellsincreasedby0.3daysperdecade(orby1dayoverthefullperiod).Withinthisperiod,thefirst10yearsactuallyshowedadecreaseinboththefrequencyanddurationofthesehotdays,butthesubsequentperiodfromtheearly1990sto2014showedastrongpositivetrend.Astrongtrendisalsoevidentinthenumberofnightsexceedingthe90thpercentile(tmin>15.8°C)whichincreasedbyalmost22daysperdecadeandtheaveragedurationincreasedbyhalfadayperdecade.Nocleartrendisevidentduringthefirst15yearsofrecordafterwhichastrongpositivetrendoccurredinboththefrequencyanddurationoftheextremewarmnights.

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Extremecoldevents(tmax<21.4°Candtmin<11°C)occurprimarilyfromJunetoAugust,butdoalsosometimesoccurduringJanuaryandFebruaryandothermonths.Thefrequencyanddurationofcoldeventshasdecreasedovertime(Figure8and9).Thenumberofdayswithextremecolddailymaximumtemperatures(tmax<21.4°C)decreasedbyalmost8daysperdecadeandthedurationby-0.1daysperdecade.Similarlythefrequencyofextremecoldnights(tmin<11°C)hasdecreasedby7.6daysperdecadeandtheaveragedurationby-0.2daysperdecade.Thestrongestrateofchangeoccurredduringthe1990swithmoregradualdecreaseduringtheearlierandlaterdecades.

Table1:Summaryoftrendsintemperatureandrainfallattributes.TherainfalltrendsarecalculatefromthegridcellvalueoverNairobifromtheCHIRPSdataset(1981-2016)andthetemperaturetrendswerecalculatedfromtheWFDEIdataset.Thelong-termmeanvaluesareincludedinbrackets.Statisticallysignificanttemperaturetrendarecolouredrediftheyareupwardtrends.Statisticallysignificantrainfalltrendsarecolouredbrowniftheyaredryingtrendsandgreeniftheyarewettingtrends.

Temperature Jan–Feb Mar–May Jun–Oct Nov–Dec

Tmax[°C/decade] +0.4(27.5) +0.4(25.9) +0.3(23.8) +0.4(25.3)

Tmin[°C/decade] +0.4(13.5) +0.4(14.9) +0.3(12.4) +0.4(14.5)

Annual(July–June)

Tmaxextremehotevents[days] Frequency:+12.1Duration:+0.3Threshold:28.9°C

Tminextremehotevents[days] Frequency:+21.7Duration:+0.5Threshold:15.8°C

Tmaxextremecoldevents[days] Frequency:-7.9Duration:-0.1Threshold:21.4°C

Tminextremecoldevents[days] Frequency:-7.6Duration:-0.2Threshold:11.0°C

Rainfall Jan-Feb Mar-May Nov-Dec

Totalrainfall[mm/decade] +3.6(79) -11.0(308) -3.1(199)

Rainintensity[mm/day] -0.3(26.5) +2.7(76.7) -1.7(75.3)

Raindayfrequency[days/decade]

+0.4(5.4) -1.8(15.3) +5.2(6.6)

Heavyraindayfrequency[days/decade]

notrend(3.0) notrend(10.9) notrend(4.5)

Wetspell[consecutivedays] +0.2(2.7) -0.1(6.1) -0.2(2.8)

Dryspell[consecutivedays] -3.3(76.2) +2.1(91.6) +4.8(88.2)

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Climatechangeprojections

GlobalClimateModels

Projectionsoffutureclimate,basedon15CMIP5GCMsimulations1undertheRCP8.5pathway2showaclearandstatisticallysignificantincreaseinbothminimumandmaximumtemperatureintothefuture(Figure13and14).By2040themeandailymaximumtemperaturesmaybebetween0.5-2°Cwarmerthanthecurrentclimate,whiledailyminimumtemperaturesareprojectedtowarmbybetween+0.7°Cto+2°Cby2040.Themodelssuggestthatthewarmingduetoanthropogenicclimatechangemaybegintobedistinguishedfromthatofnaturalvariabilitywithinthiscurrentdecade.Thisissupportedbytheobservationswhichalreadyshowevidenceofrecordbreakingwarming.

Thefrequencyofheatspellsisprojectedtoincreaseintothefuture.Howevermodelsdisagreeontheexactchangewiththefrequencyofdayswherethemaximumtemperatureisoverthe90thpercentilerangingfroma100%increasetoover400%increasebytheendofthecentury(Figure15),andtheincreaseinthefrequencyofnightsoverthe90thpercentilerangingfromalmost200%to600%(Figure16).

Annualrainfalltotalsareprojectedtoremainwithinthehistoricrangeofvariability,ortoincreaseslightlyinthesecondhalfofthecentury(Figure17.Howevertwooutliermodelsdisagreewiththerest,theoneprojectingadecreasewhiletheotherprojectingaverysignificantincreaseinrainfallintothefuture.Anumberofmodelsprojectthatthechangeinrainfallduetoglobalwarmingmaybecomediscernablefromthatofnaturalvariabilityinthe2040sbuttherestdonotprojectanycleartrendduetoglobalwarming.Asimilaroverallmessageisshownintheprojectedchangeinrainfalldailyintensity,thefrequencyofraindaysandheavyraindays(Figure18–20).Table3belowprovidesasummary.

1 ThefifthiterationoftheCoupleModelInter-comparisonProject(CMIP)isacoordinateactivityamongstinternationalmodelingcenterstoproduceasuiteofclimatesimulationsusingcommonexperimentalparameters.CMIP5iscurrentlytheprimarysourceofglobaltoregionalscaleclimateprojectionsandextensivelyinformedtheIPCCFifthAssessmentReport(AR5)2 Althoughthisemissions/developmentpathwayrepresentsthe“worst-casescenario”amongstthepathwayssimulatedbytheIPCCCMIP5models,atthisstageitisthemostrealisticreflectionoftherecentprogressionofanthropogenicemissions.Itispresentedhere,inspiteoftheParisagreement,aseffectsofitscommitmentsremaintobeshown.

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Table2:Summaryofprojectedclimatechangesmessagesforkeyclimatevariablesfromanensembleof15GlobalClimateModelsforNairobi.

Statistic Annual(July–June)

AverageTmax[°C]Increasing:+0.5°Cto+2°Cby2040s,andby+2°to+5°Cbytheendofthecentury.Warminggenerallybecomesdistinctfromnaturalvariabilityinthisorthenextdecade.

AverageTmin[°C]Increasing:+0.7°Cto+2°Cby2040s,andby+2.5°to+6.5°Cbytheendofthecentury.Warminggenerallybecomesdistinctfromnaturalvariabilityinthisorthenextdecade.

Frequencyofdaytimeheatspells(days)

Increasing:+10to+130daysby2040,andby+60to270daysbytheendofthecentury.Strongdisagreementintherateofchangebetweenmodels.Increaseinfrequencydiscernablefromthatofnaturalvariabilitywithinthisdecadeformostmodels,butonlyinthesecondhalfofthecenturyforallmodels.

Frequencyofnighttimeheatspells(days)

Increasing:+20to+160daysby2040,andby+100to300daysbytheendofthecentury.Increaseinfrequencydiscernablefromthatofnaturalvariablilityfromthebeginningofthisdecadeformostmodels.

Note:nighttimeheatspellsincreasingmuchmorethandaytimeheatspells

RainfallTotals[mm/year]

Normaltoincreasingrainfall,rangingfromnochangetomoderateincreasefrom2040(with2outlyingmodels,oneshowingverystrongincreasewhiletheothermoderatedecrease).

Rainfalldailyintensity[mm/day]

Nochangetostrongpositivechange:halfthemodelsprojectnoclearchangeintothefuture,withonemodelshowingadecreaseandtherestprojectinganincreaseinintensityespeciallytowardstheendofthecentury.

Rainfallfrequency[days]

Nochangetodecreasingorincreasingfrequency:roughlyhalfofthemodelsshownochangeinfrequencyintothefuture,whileacoupleshowadecreaseandtherestanincreaseinfrequencyfromthe2020sonwards.

Heavyrainfallfrequency(over10mm)[days]

Nochangetoincreasingordecreasingfrequency:roughlyhalfofthemodelsshownochangeinfrequencyuntilafter2040.Oneshowsadecreaseandtherestanincreaseinfrequencyfromthe2020sandespeciallyafter2050.

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Statisticallydownscaledprojections

Projectionsoffutureclimate,basedon11statisticallydownscaledCMIP5GCMsimulationsundertheRCP8.5pathwayshowaclearandstatisticallysignificantincreaseinbothminimumandmaximumtemperatureintothefuture(Figure21and22).By2040thedailymaximumtemperaturesmaybebetween1°Cto1.5°Cwarmerthanthecurrentclimateandbytheendofthecenturyitmayincreasebybetween2.5°Cand4.5°C,dependingonthemodelselected.Similarlydailyminimumtemperateisalsoprojecttoincreaseintothefuturebybetween1°Cand1.5°Cby2040andby3°Cto5°Cbytheendofthecentury.Themodelssuggestthatthewarmingduetoanthropogenicclimatechangemaybegintobedistinguishedfromthatofnaturalvariabilitywithinthiscurrentdecade.Thisissupportedbytheobservationswhichalreadyshowevidenceofrecordbreakingwarming.

Thefrequencyofheatspellsisprojectedtoincreaseintothefuture.Howevermodelsdisagreeontheexactchange.Daytimeextremeheateventsareprojectedtoincreasebybetween20to50daysby2050andby75to180extradaysbytheendofthecentury(thisequatestoa100%toalmost300%increasefromthehistoricalnorm)(Figure23).Nighttimeextremeheateventsareprojectedtoincreasebybetween70to110daysby2040andby210to310extradaysbytheendofthecentury(thisequatestoanincreaseofbetween600%and850%fromthehistoricalnorm)(Figure24).

Annualtotalrainfallisprojectedtoremainwithinthehistoricrangeofvariabilityduringmostofthe21stCentury,butcouldincreasetowardstheendofthecentury(2070sonwards)(Figure25).Asimilarmessageisprojectedforraindayandheavyrain(>10mm/day)frequency,butnochangeisprojectedinthedailyintensityofrainfall(Figure26–28).

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Table3:Summaryofprojectedclimatechangesmessagesforkeyclimatevariablesfromanensembleof11statisticallydownscaledGlobalClimateModelsforNairobi.

Statistic Annual

AverageTmax[°C]

Increasing+1to+1.5°Cby2040,andbetween+2.5to+4.5°Cbytheendofthecentury.Warmingtrendmayalreadybediscernablefromthatofnaturalvariability.

AverageTmin[°C]

Increasing+1°Cto+1.5°Cby2040s,andbetween+3and+5°Cbytheendofthecentury.Warmingtrendmayalreadybediscernablefromthatofnaturalvariability.

Daytimeextremeheatevents[days]

Increasing,+20to50extradaysby2040,andbetween+75to+180extradaysbytheendofthecentury.Warmingtrendmayalreadybediscernablefromthatofnaturalvariabilityinthisdecade.

Nighttimeextremeheatevents[days]

Increasing,+70to110extranightsby2040,andbetween+210to+310extranightsbytheendofthecentury.Warmingtrendmayalreadybediscernablefromthatofnaturalvariability.

Totalrainfall[mm/year]

Normaltoincreasingrainfall,rangingfromslightdryingtosignificantwettingfrom2070onwards.

Rainintensity[mm/day]

Nochangeindailyintensity.

Raindayfrequency[days]

Nochangetoincreasingraindayfrequency,rangingfromnochangetosignificantincreasefrom2070onwards

Heavyraindayfrequency[days]

Nochangetoincreasingraindayfrequency,rangingfromnochangetosignificantincreasefrom2070onwards.

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Supportingevidence

Theabovesummaryinformationissupportedbyrigorousanalysisofobservedandmodelprojectionsdata.Moredetailsofthisanalysisandsupportingfigurescanbefoundbelow.

DataThisstudyfocusesonhowtheclimateforNairobihaschangedinthepastandhowitmaychange in the futureduetoanthropogenicclimatechange. Ideallyonewould like tobasethe historical analysis on data from a number of weather stations to obtain a detailedunderstandingofthelocalclimatesinthedifferentpartsofthecity.Unfortunatelytheonlypublicly-available weather station data for Dar es Salaam are of insufficient length andqualitytouseinthisanalysis.InsteadthisanalysisreliesontemperaturedatafromagriddedproductcalltheWATCHForcingDataERA-Interim(WFDEI)3wheretheWATCHForcingDatamethodologyisappliedtoERA-Interimdata(Weedonetal.2014)4.Itprovidesdataforthegloballandsurfaceat0.5°x0.5°coveringtheperiod1979-2014.ThedailyrainfalldatausedinthehistoricalanalysisisobtainedfromtheClimateHazardsGroupInfraRedPrecipitationwith Station data (CHIRPS)5(Funk et al. 2015)6. CHIRPS incorporates 0.05° resolutionsatellite imagerywith stationdata to createagridded rainfall time series formostof theglobe.Theversion2.0isusedinthisanalysiswhichprovidesdataona0.05°grid.Twodifferentsetsofclimatechangedataareusedtoexplorethepossiblefuturechangesintheclimateduetoanthropogenicclimatechange.Thefirstsetisanensembleof15GlobalClimate Models (GCMs) from the Climate Model Intercomparison Projection version 5(CMIP5)(alistofthemodelsandmodellinggroupsisprovidedintable4below).

3EUWATCH–DataforResearchers:http://www.eu-watch.org/data_availability4Weedon,G.P.,Balsamo,G.,Bellouin,N.,Gomes,S.,Best,M.J.&Viterbo,P.(2014)TheWFDEImeteorologicalforcingdataset:WATCHForcingDatamethodologyappliedtoERA-Interimreanalysisdata.WaterResourcesResearch,50:7505–7514.5CHG–Data–CHIRPS:http://chg.geog.ucsb.edu/data/chirps/6Funk,C.,Peterson,P.,Landsfeld,M.,Pedreros,D.,Verdin,J.,Shukla,S.,Husak,G.,Rowland,J.,Harrison,L.,Hoell,A.&Michaelsen,J.(2015)Theclimatehazardsinfraredprecipitationwithstations–anewenvironmentalrecordformonitoringextremes.ScientificData2,Articlenumber:150066.

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Table4:CMIP5modellingcentresandmodelsusedintheanalysis(thosemodelsinitalicsarealsousedinthestatisticaldownscaling)

MODELINGCENTRE(ORGROUP) INSTITUTEID MODELNAME

BeijingClimateCenter,ChinaMeteorologicalAdministration BCC BCC-CSM1.1

CollegeofGlobalChangeandEarthSystemScience,BeijingNormalUniversity

GCESS BNU-ESM

CanadianCentreforClimateModellingandAnalysis CCCMA CanESM2

CentreNationaldeRecherchesMeteorologiques/CentreEuropeendeRechercheetFormationAvanceesenCalculScientifique

CNRM-CERFACS CNRM-CM5

LASG,InstituteofAtmosphericPhysics,ChineseAcademyofSciences

LASG-IAP FGOALS-s2

NOAAGeophysicalFluidDynamicsLaboratory

NOAAGFDL

GFDL-ESM2G

GFDL-ESM2M

InstitutPierre-SimonLaplace IPSLIPSL-CM5A-MRIPSL-CM5B-LR

InstituteforNumericalMathematics INM INM-CM4

AtmosphereandOceanResearchInstitute(TheUniversityofTokyo),NationalInstituteforEnvironmentalStudies,andJapanAgencyforMarine-EarthScienceandTechnology

MIROC

MIROC5

JapanAgencyforMarine-EarthScienceandTechnology,AtmosphereandOceanResearchInstitute(TheUniversityofTokyo),andNationalInstituteforEnvironmentalStudies

MIROC

MIROC-ESM

MIROC-ESM-CHEM

MaxPlanckInstituteforMeteorology(MPI-M MPI_M MPI-ESM-LR

MeteorologicalResearchInstitute MRI MRI-CGCM3

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Daily rainfall,maximumandminimum temperature from thehistorical experiment (1960-2005)and theRCP8.5 futureemissionexperiment (2006-2100)wereused toexplorehowthesevariablesareprojectedtochange intothe future.Thesecondsetofclimatechangedataisanensembleof11statisticallydownscaledCMIP5GCMs.CirculationfieldsfromtheGCMs were used as predictor variables, while the WFDEI daily rainfall, maximum andminimum temperature datawere used as predictant datasets in a statistical downscalingmethodology called Self-Organising Map based Downscaling (SOMD) developed by theClimateSystemAnalysisGroup(CSAG)(Hewitson&Crane20067).Thedownscalingprovidesdaily rainfall,maximumandminimumtemperature foreachGCMfor thehistorical (1960-2005)andRCP8.5future(2006-2100)experimentata0.5°resolution.

A time series for the gridcell covering Nairobi was extracted from each of the observeddatasetsandalso fromallof theGCMandstatisticallydownscaleddata.Thesedatawereusedinalltheanalyses.

Historicaltrendsandvariabilityanalysis

Theanalysisofhistoricaltrendsandvariabilityofkeyclimatevariablesispresentedbelow.ThisanalysisusesdailymaximumandminimumtemperaturedataobtainedfromtheWATCHwhichcoverstheperiod1979-2014.TherainfalldatasetusedistheCHIRPSdatasetcoverstheperiodJanuary1981–December2016.ThesegriddeddatasetswereusedsincethequalityandlengthoftheweatherstationrecordforNairobiwastoopoortobeusedinthisanalysis.Derivedstatisticswerecalculatedattheseasonalandannualtimescale.ThesewereusedtoexplorethelongtermtrendsandvariabilityoftheclimateatNairobi.

7Hewitson,B.C.&Crane,R.G.(2006)ConsensusbetweenGCMclimatechangeprojectionswithempiricaldownscaling:precipitationdownscalingoverSouthAfrica.InternationalJournalofClimatology26:1315-1337.

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Figure2:TimeseriesofmonthlymeanmaximumandminimumtemperatureandtotalrainfallforNairobi,redandgreencolouredlinesrepresenta12monthrunningaverageformaximumandminimumtemperaturerespectively.Lightbluebarspresenttheannual(July–June)totalrainfall.

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Figure3:AssociationbetweenENSOandtheclimateatNairobithroughtime.TimeseriesoftheNINO3.4SSTmonthlyanomaliesispresentedasthegreyline;positive(ElNino)phasesarecolouredred,whilenegative(LaNina)phasesareshadedinblue.Blacklineintoppanelshowsthemonthlymeanmaximumtemperatureanomaliessmoothedwitha12-valuerunningmean.Thesecondpanelshowsthesameasabove,butforminimumtemperature.Theblackbarsinthebottompanelshowtheannual(July-June)totalrainfallanomalies(mm/year).

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Figure4:TimeseriesandtrendinseasonalaveragemaximumtemperatureforthegridcelloverNairobifromtheWFDEIdataset.Timeseriesofseasonalmeanmaximumtemperature(bluedots).Theil-Sentrend(redline)andtheLowesssmooth(blackline)and95thconfidenceinterval(dashedlines)

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Figure5:TimeseriesandtrendinseasonalaverageminimumtemperatureforthegridcelloverNairobifromtheWFDEIdataset.Timeseriesofseasonalmeanmaximumtemperature(bluedots).Theil-Sentrend(redline)andtheLowesssmooth(blackline)and95thconfidenceinterval(dashedlines)

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Figure6:Timeseriesofthetimingofextremelyhotdays(tmax>90thpercentile(28.9°C))forthegridcelloverNairobifromtheWFDEIdataset.Toppaneldisplaysthetimingandlengthofhotspells.BottompaneldisplaysthetotalnumberofdayswhichexceedthisthresholdforeachJuly–Junecalendaryear(pinkbars).TheTheil-Senlineartrend(redline)andtheLowesssmoothinterpolation(blackline)and95thconfidenceinterval(dashedlines).

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Figure7:Timeseriesofthetimingofextremelyhotnights(tmin>90thpercentile(21.4°C))forthegridcelloverNairobifromtheWFDEIdataset.Toppaneldisplaysthetimingandlengthofhotspells.BottompaneldisplaysthetotalnumberofdayswhichexceedthisthresholdforeachJuly–Junecalendaryear(pinkbars).TheTheil-Senlineartrend(redline)andtheLowesssmoothinterpolation(blackline)and95thconfidenceinterval(dashedlines).

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Figure8:Timeseriesofthetimingofextremelycolddays(tmax<10thpercentile(15.8°C))forthegridcelloverNairobifromtheWFDEIdataset.Toppaneldisplaysthetimingandlengthofhotspells.BottompaneldisplaysthetotalnumberofdayswhichexceedthisthresholdforeachJuly–Junecalendaryear(pinkbars).TheTheil-Senlineartrend(redline)andtheLowesssmoothinterpolation(blackline)and95thconfidenceinterval(dashedlines).

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Figure9:Timeseriesofthetimingofextremelycoldnights(tmin<10thpercentile(11°C))forthegridcelloverNairobifromtheWFDEIdataset.Toppaneldisplaysthetimingandlengthofhotspells.BottompaneldisplaysthetotalnumberofdayswhichexceedthisthresholdforeachJuly–Junecalendaryear(pinkbars).TheTheil-Senlineartrend(redline)andtheLowesssmoothinterpolation(blackline)and95thconfidenceinterval(dashedlines).

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Figure10:Timeseriesandtrendintheshortdryseason(January-February)rainfallstatisticsforNairobi.Bluebarsdepictthetimeseriesoftheannualstatistic.TheTheil-Senlineartrendlineisshowinredalongwiththetrend(perdecade)andpvalue.Themedian(solidblackline)and95thconfidenceinterval(dashedline)froma1000memberensembleoftheLowessregression.

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Figure11:Timeseriesandtrendinthelongrainsseason(March–May)rainfallstatisticsforNairobi.Bluebarsdepictthetimeseriesoftheannualstatistic.TheTheil-Senlineartrendlineisshowinredalongwiththetrend(perdecade)andpvalue.Themedian(solidblackline)and95thconfidenceinterval(dashedline)froma1000memberensembleoftheLowessregression.

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Figure12:Timeseriesandtrendintheshortrainsseason(November–December)rainfallstatisticsforNairobi.Bluebarsdepictthetimeseriesoftheannualstatistic.TheTheil-Senlineartrendlineisshowinredalongwiththetrend(perdecade)andpvalue.Themedian(solidblackline)and95thconfidenceinterval(dashedline)froma1000memberensembleoftheLowessregression.

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GlobalClimateModels

Theplotsbelow(Figures13to20)arecalledplumeplotsandtheyareusedtorepresentthedifferentlongtermprojectionsacrossthemultipleclimatemodelsintheCMIP5modelarchiveusedtoinformtheIPCCAR5report.TheplotsshowprojectedvariationsindifferentvariablesforthegridcelloverNairobiproducesbyanensembleof15models.Thebluecoloursindicatevariationsthatwouldbeconsideredwithintherangeofnaturalvariability,soinotherwords,notnecessarilytheresultofclimatechange.Theorangecoloursindicateprojectiontimeserieswherethechangeswouldbeconsideredoutsideoftherangeofnaturalvariabilityandsolikelyaresponsetoclimatechange.

ItisimportanttonotethattheseareGlobalClimateModel(GCM)projectionsandsolikelydonotcapturelocalscalefeaturessuchastopographyandlandoceanboundarydynamics.Theyalsomaynotcapturesmallscalefeaturessuchasseverethunderstormsthatcanhaveimportantsocietalimpacts.Finally,theseprojectionsareaveragesoverrelativelylargespatialareawhichdiffersbetweenGCMsanditispossiblethatdifferentmessageswouldbeobtainedatsmallerspatialscalesandifvariousformsofdownscalingareperformed.

Figure13:CMIP5projectedchangesinseasonalmeandailymaximumtemperatureundertheRCP8.5concentrationpathwayforNairobi.Theblacklineshowsthemulti-modelmeanvalueacrossallmodelsinthereferenceperiod1986-2005.Thecolouredlinesshowthe20-yearmovingaverageofresultsfromeachmodelandtheshadingaroundeachlineshowsthe95%confidencerangearoundthosemodelresults.Wherethelineandassociatedshadingchangesfrombluetored/orangeindicateswhen20-yearmovingaveragemovesoutsideofthe95%confidencerangeofthereferenceperiod.

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Figure14:CMIP5projectedchangesinannualmeandailyminimumtemperatureundertheRCP8.5concentrationpathwayforNairobi(refertofigure13forfurtherdetails).

Figure15:CMIP5projectedchangesinannualfrequencyofdaysexceedingthe90thpercentileformaximumtemperatureundertheRCP8.5concentrationpathwayforNairobi(refertofigure13forfurtherdetails).

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Figure16:CMIP5projectedchangesinannualfrequencyofnightsexceedingthe90thpercentileforminimumtemperatureundertheRCP8.5concentrationpathwayforNairobi(refertofigure13forfurtherdetails).

Figure17:CMIP5projectedchangesinannualtotalrainfallundertheRCP8.5concentrationpathwayforNairobi(refertofigure13forfurtherdetails).

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Figure18:CMIP5projectedchangesinannualaveragedailyrainfallintensityundertheRCP8.5concentrationpathwayforNairobi(refertofigure13forfurtherdetails).

Figure19:CMIP5projectedchangesinannualrainfallfrequencyundertheRCP8.5concentrationpathwayforNairobi(refertofigure13forfurtherdetails).

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Figure20:CMIP5projectedchangesinannualheavyrainfallfrequencyundertheRCP8.5concentrationpathwayforNairobi(refertofigure13forfurtherdetails).

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Statisticaldownscaling

Theplotsbelow(Figures21to28)arecalledplumeplotsandtheyareusedtorepresentthedifferentlongtermprojectionsacrossthemultiplestatisticallydownscaledclimatemodelsintheCMIP5modelarchiveusedtoinformtheIPCCAR5report.TheplotsshowprojectedvariationsindifferentvariablesforthegridcelloverNairobiproducesbyanensembleof11models.Thebluecoloursindicatevariationsthatwouldbeconsideredwithintherangeofnaturalvariability,soinotherwords,notnecessarilytheresultofclimatechange.Theorangecoloursindicateprojectiontimeserieswherethechangeswouldbeconsideredoutsideoftherangeofnaturalvariabilityandsolikelyaresponsetoclimatechange.

ItisimportanttonotethatthesearedownscaledGCMprojections,whichhaveaspatialresolutionofroughly50km.TheyprovidehigherresolutionoutputthantherawGCManddepictthefirstorderresponsetoanthropogenicresponse.Howevertheyareunlikelytoaccuratelycapturelocalscalefeaturessuchastopographyandlandoceanboundarydynamics.Theyalsomaynotcapturesmallscalefeaturessuchasseverethunderstormsthatcanhaveimportantsocietalimpacts.

Figure21:StatisticallydownscaledprojectedchangesinannualmeandailymaximumtemperatureundertheRCP8.5concentrationpathwayforNairobi.Theblacklineshowsthemulti-modelmeanvalueacrossallmodelsinthereferenceperiod1986-2005.Thecolouredlinesshowthe20-yearmovingaverageofresultsfromeachmodelandtheshadingaroundeachlineshowsthe95%confidencerangearoundthosemodelresults.Wherethelineandassociatedshadingchangesfrombluetored/orangeindicateswhen20-yearmovingaveragemovesoutsideofthe95%confidencerangeofthereferenceperiod.

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Figure22:StatisticallydownscaledprojectedchangesinannualmeandailyminimumtemperatureundertheRCP8.5concentrationpathwayforNairobi(refertoFig21forfurtherdetails)

Figure23:Statisticallydownscaledprojectedchangesinannualfrequencyofdayswithmaximumtemperatureabovethe90thpercentileofthehistoricalperiod(1986-2005)undertheRCP8.5concentrationpathwayforNairobi(refertoFig21forfurtherdetails)

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Figure24:Statisticallydownscaledprojectedchangesinannualfrequencyofnightswithminimumtemperatureabovethe90thpercentileofthehistoricalperiod(1986-2005)undertheRCP8.5concentrationpathwayforNairobi(refertoFig21forfurtherdetails)

Figure25:StatisticallydownscaledprojectedchangesinannualtotalrainfallundertheRCP8.5concentrationpathwayforNairobi(refertoFig21forfurtherdetails)

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Figure26:StatisticallydownscaledprojectedchangeinthedailyintensityofrainfallundertheRCP8.5concentrationpathwayforNairobi(refertoFig21forfurtherdetails)

Figure27:Statisticallydownscaledprojectedchangeinthefrequencyofraindays(rainfall>0.02mm)undertheRCP8.5concentrationpathwayforNairobi(refertoFig21forfurtherdetails)

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Figure28:Statisticallydownscaledprojectedchangeinthefrequencyofheavyraindays(rainfall>10mm)undertheRCP8.5concentrationpathwayforNairobi(refertoFig21forfurtherdetails)

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Acknowledgements

We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling,

which is responsible for CMIP, and we thank the climate modelling groups (listed in Table 4 of this

report) for producing and making available their model output. For CMIP the U.S. Department of

Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support

and led development of software infrastructure in partnership with the Global Organization for Earth

System Science Portals

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ThecontentsofthisWorkingPaperreflecttheviewsoftheauthoronlyandnotthoseoftheUKDepartmentforInternationalDevelopmentortheEconomicandSocialResearchCouncil.