NairobiClimateProfile:FullTechnicalVersion
Preparedby:
UniversityofCapeTown
November2017
ForenquiriesregardingthisClimateProfile,pleasecontactLisavanAardenne([email protected])
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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.