Heat waves in Africa 1981–2015, observations and …...Heat waves in Africa 1981–2015,...

Nat Hazards Earth Syst Sci 17 115ndash125 2017wwwnat-hazards-earth-syst-scinet171152017doi105194nhess-17-115-2017copy Author(s) 2017 CC Attribution 30 License

Heat waves in Africa 1981ndash2015 observations and reanalysisGuido Ceccherini1 Simone Russo2 Iban Ameztoy1 Andrea Francesco Marchese3 and Cesar Carmona-Moreno1

1European Commission Joint Research Centre (JRC) Directorate D ndash Sustainable Resources Water and Marine ResourcesVia E Fermi 2749 21027 Ispra (VA) Italy2European Commission Joint Research Centre (JRC) Directorate I ndash Modelling Indicators and Impact Evaluation Unit ViaE Fermi 2749 21027 Ispra (VA) Italy3Universitagrave degli Studi di Catania Dipartimento di Fisica e Astronomia Via Santa Sofia 64 95123 Catania Italy

Correspondence to Guido Ceccherini (guidoceccheriniextjrceceuropaeu)

Received 18 March 2016 ndash Published in Nat Hazards Earth Syst Sci Discuss 18 April 2016Revised 5 January 2017 ndash Accepted 11 January 2017 ndash Published 30 January 2017

Abstract The purpose of this article is to show the extremetemperature regime of heat waves across Africa over re-cent years (1981ndash2015) Heat waves have been quantified us-ing the Heat Wave Magnitude Index daily (HWMId) whichmerges the duration and the intensity of extreme temperatureevents into a single numerical index The HWMId enablesa comparison between heat waves with different timing andlocation and it has been applied to maximum and minimumtemperature records The time series used in this study havebeen derived from (1) observations from the Global Sum-mary of the Day (GSOD) and (2) reanalysis data from ERA-Interim The analysis shows an increasing number of heatwaves of both maxima and minima temperatures in the lastdecades Results from heat wave analysis of maximum tem-perature (HWMIdtx) indicate an increase in intensity and fre-quency of extreme events Specifically from 1996 onwards itis possible to observe HWMIdtx spread with the maximumpresence during 2006ndash2015 Between 2006 and 2015 thefrequency (spatial coverage) of extreme heat waves had in-creased to 245 observations per year (601 of land cover)as compared to 123 per year (373 of land area) in theperiod from 1981 to 2005 for GSOD stations (reanalysis)

1 Introduction

Africa is considered one of the most vulnerable regions toweather and climate variability (Solomon 2007) extremeevents such as heat waves have an impact to public healthwater supplies food security According to Albrecht (2014)climate change will increase its pressure in northern Africa

All African capital cities are anticipated to face more excep-tionally hot days in the future with respect to the rest of theworld

In fact recent findings of the World Meteorological Orga-nization (WMO) indicate that the years 2011ndash2015 have con-stituted the warmest 5-year period on record (WMO 2015)and heat waves of maximum temperature have increased bothin severity and number accordingly

Despite its vulnerability the distribution of African heatwaves is still poorly understood due to the lack of accuratebaseline data on current climate (UNECA 2011) Specifi-cally there are still uncertainties in the state of the art of ouractual understanding of temperature extreme regime only afew studies have considered the whole of Africa (Collins2011) Such information is paramount since it is necessaryto assess the impacts of climate change on human and natu-ral systems and to develop suitable adaptation and mitigationstrategies at country level

Daily records are needed in order to analyse extreme tem-perature regimes To this end the Global Surface Summaryof the Day (GSOD) meteorological dataset has been em-ployed GSOD is a compilation of daily meteorological dataproduced by the National Climatic Data Center availablefrom 1929 to present which displays a reasonably dense spa-tial coverage across Africa However a general caveat withthe GSOD dataset is the limit imposed by its sparse gaugenetwork There are many regions especially across CentralAfrica where the absence of temperature records precludes acomprehensive and robust analysis To circumvent this limi-tation daily reanalysis data have also been used Reanalysisis a combination of observations and climatological models

Published by Copernicus Publications on behalf of the European Geosciences Union

116 G Ceccherini et al Heat waves in Africa 1981ndash2015 observations and reanalysis

through data assimilation systems to produce a single uni-form global dataset (Kalnay et al 1996) thus enabling a ho-mogeneous coverage of Africa

The magnitude of heat waves for both observations andreanalysis is quantified on annual basis by means of the HeatWave Magnitude Index daily (HWMId Russo et al 2015)for the period 1981ndash2015 across Africa The HWMId hasbeen applied to maximum and minimum temperature

The objective of this paper is to analyse African heat waveregime and identify the most important of heat waves dur-ing 1981ndash2015 These analyses draw attention to the spatialdistribution of temperature extremes and their temporal evo-lution in the past decades still largely unknown Consider-ing both its wide geographic scope and spatial resolution thestudy represents an important step towards the assessment ofheat wave frequency in the last 3 decades using records ofdaily maximum and minimum temperatures acquired at re-gional level

The availability of such information is paramount Themore reliable the assessment of heat waves is the betterAfrican countries will be equipped to strengthen their copingcapacities This study also provides insight in human expo-sure to heat waves in Africa

Some early exploratory research using similar methodol-ogy for South America showed some promising preliminaryresults (Ceccherini et al 2016) In this paper the heat waveclassification scheme has been consolidated and improved(Russo et al 2015) and both observations and reanalysisdatasets have been employed

2 Data and methodology

21 Data

The time series of temperature used in this study have beenderived from (1) observations and (2) reanalysis GSOD isthe dataset of observations GSOD records produced bythe National Climatic Data Center are mainly recorded atinternational airports and include maximum and minimumvalues of temperature GSOD records underwent extensiveautomated quality controls to eliminate many of the ran-dom errors found in the original data (further details on theGSOD data can be obtained from the website httpwwwclimategovglobal-summary-day-gsod) GSOD has been al-ready employed to assess heat waves magnitude at global(Mishra et al 2015) and local (Ceccherini et al 2016) scaleHistorical data are generally available for the last 80 yearswith data from 1973 onwards being the most complete Thetotal number of GSOD stations available across Africa isequal to 958 However only 260 of them satisfy the con-ditions needed to calculate heat wave magnitude indices asfurther described in the Methodology section

ERA-Interim (Berrisford et al 2011 Dee et al 2011) isthe dataset of reanalysis providing hydrometeorological vari-ables such as maximum and minimum temperature evapora-tion snowfall runoff and precipitation across land at varioustemporal scales Reanalysis has been increasingly used to ad-dress a variety of climate-change issues and has by now be-come an important method in climate-change research (Fanand van den Dool 2004 Marshall and Harangozo 2000Uppala et al 2005) ERA-Interim is a reanalysis productof the European Centre for Medium-Range Weather Fore-casts (ECMWF) available from 1979 and continuously up-dated in real time The data assimilation system used to pro-duce ERA-Interim is based on a 4-D variational scheme (4D-Var) with a 12 h analysis window (for further information on4D-VAR see Courtier et al 1994)

The reanalysis dataset used in this study has a spatial reso-lution of 075 (ie approximately 80 km at the equator) anda temporal resolution of 1 day (ie a time step of 24 h) Thevariables of interest are daily maximum and minimum tem-perature ranging from 1981 to 2015 ERA-Interim allows aconsistent spatial and temporal resolution over 3 decades in-corporating millions of observations into a stable data assimi-lation system However observational constraints and there-fore reanalysis reliability can considerably vary dependingon the location time period and variable considered thus in-troducing spurious variability and trends

For both observations and reanalysis dataset the timespanconsidered in this study refers to the period 1 January 1981ndash30 June 2015

22 Methodology

In this paper the HWMId recently defined by Russo etal (2015) has been employed to detect African heat wavesfor the period 1981ndash2015 The HWMId is a simple numer-ical indicator that takes both the duration and the intensityof the heat wave into account Basically the magnitude in-dex sums excess temperatures beyond a certain normalizedthreshold and merges durations and temperature anomaliesof intense heat wave events into a single indicator accordingto the methodology described in Russo et al (2014 2015)The HWMId is an improvement on the previous Heat WaveMagnitude Index (ie HWMI by Russo et al 2014) and ableto overcome its limitations More precisely HWMI has someproblems in assigning magnitude to very high temperaturesin a changing climate thus resulting in an underestimation ofextreme events

The HWMId is defined as the maximum magnitude of theheat waves in a year Specifically a heat wave is defined asa periodge 3 consecutive days with maximum temperatureabove a daily threshold calculated for a 30-year-long refer-ence period The threshold is defined as the 90th percentileof daily maxima temperature centred on a 31-day window

The interquartile range (IQR ie the difference betweenthe 25th and 75th percentiles of the daily maxima tempera-

Nat Hazards Earth Syst Sci 17 115ndash125 2017 wwwnat-hazards-earth-syst-scinet171152017

G Ceccherini et al Heat waves in Africa 1981ndash2015 observations and reanalysis 117

Figure 1 Heat Wave Magnitude Index daily of maximum temperature (HWMIdtx ) for 5-year periods of GSOD records from 1981 to 2015The bottom-right panes show the spatial distribution of the GSOD station employed in this study

ture) is used as the heat wave magnitude unit since it repre-sents a non-parametric measure of the variability If a day ofa heat wave has a temperature value equal to IQR its corre-sponding magnitude value will be equal to 1 According tothis definition if the magnitude on the day d is 3 it meansthat the temperature anomaly on the day d is 3 times the IQRThe HWMId has been already used to evaluate future im-pacts of heat waves in Africa until 2100 under different rep-resentative concentration pathways scenarios (Russo et al2016)

The HWMId computations requires at least a 30-year timeseries of daily temperature records GSOD stations with lessthan 30-year records and with more than 30 of gaps havebeen excluded from our analysis (for further details see Cec-cherini et al 2016) As a result 260 GSOD stations out of958 satisfy these conditions

Heat waves are computed using (1) maximum (here-after HWMIdtx) and (2) minimum (hereafter HWMIdtn)daily temperature giving thus complementary informationrespectively on warm day and night conditions HWMId hasbeen computed on annual basis

1 for each GSOD station and

2 for the entire spatial domain of the daily ERA-Interimmaxima and minima temperatures dataset across AfricaSpecifically the index has been computed separately foreach cell at sim 80 km spatial resolution

Heat waves generally occur between December and Januaryin the Southern Hemisphere and between June and July inthe Northern In order to avoid splitting event occurrencesthat happen within a regular calendar year starting and end-ing dates have been redefined accordingly Therefore theHWMId computation starts on 1 January 1981 and endson 31 December 2014 in the Northern Hemisphere Sim-ilarly the HWMId computation starts on 1 July 1981 andends on 30 June 2015 in the Southern Hemisphere (for fur-ther information see HWMId function in Gilleland and Katz2011) Note that this scheme leaves the tropics out of consid-eration Also the southern hemispheric 6-month time shiftcauses temporal inconsistency in the dataset the time span is34 years in the Southern Hemisphere and 35 in the NorthernHowever starting our analysis from 1980 would have furtherreduced the number of available GSOD stations from 260to sim 220 thus exaggerating the already patchy spatial distri-bution of the observational network

3 Results

Figures 1 and 2 display the maximum value in 5-year periodsof the HWMId of GSOD observations from 1981 to 2015for maximum and minimum temperature respectively Thebottom-right panel of Fig 1 shows the spatial distribution ofthe 260 GSOD temperature stations employed in this study

There is a clear indication that both intensity and spa-tial distribution of heat waves of maximum temperature are

wwwnat-hazards-earth-syst-scinet171152017 Nat Hazards Earth Syst Sci 17 115ndash125 2017


Figure 2 As Fig 1 but applied to minimum temperature (HWMIdtn)

increasing Specifically from 1996 onwards it is possibleto observe a positive increase in heat wavesrsquo magnitudeand spread across Africa with the maximum presence dur-ing 2011ndash2015 HWMIdtxrsquos frequency is below 40 eventsper 5-year period until 1995 and it then increases (his-tograms of heat waves for three classes of magnitude levelare shown in Fig S1 of the Supplement) Such increaseof heat wave frequency corresponds with the findings ofFontaine et al (2013) the occurrence of heat waves hasclearly increased after 1996

Despite the generally high correlation between maximumand minimum temperature HWMId results with minimumtemperature differ significantly from those with maximumtemperature Specifically the number of stations affectedby heat wave events of minimum temperature is low iesim 10 events in the period 1981ndash1985 and sim 40 events in theperiod 2011ndash2015 all over Africa Figure 1 also shows thatthe temporal increase of heat wave events is not monotonicfor all regions eg in Sahel which displays an intermittentbehaviour rather than a monotonic growth

The interannual evolution of heat waves is fully detailedin Fig 3 which shows the occurrence of HWMId greaterthan a given magnitude level (ie HWMIdge 3 6 9 15)for maximum and minimum temperature respectively Fre-quently reoccurring heat events are captured on these plots

Results confirm previous findings of Figs 1 and 2 For max-imum temperature the linear regression of occurrence ofheat waves is statistically significant (α= 001) for HWMIdgreater than 3 6 and 9 Interestingly the slope is reducedby half every step Conversely for minimum temperature itis possible to observe a statistical significant upwards trendonly for heat waves with magnitude level greater than 3 Be-sides minimum temperature exhibits a lower value of theslope and number of occurrences thereof

As for GSOD Fig 4 shows the HWMIdtx of the reanaly-sis dataset for 5-year periods from 1981 to 2015 Each pixelof the map represents the highest HWMId over the 5-yearperiod On the one hand the 5-year temporal aggregationsimplifies the visualization on the other hand it hinders thedetection of frequently reoccurring events However theseevent occurring at annual scale are shown in Fig S3 In thecase of ERA-Interim instead of counting the number of oc-currences we have estimated the spatial extent of heat wavesas the land area fraction exceeding a fixed HWMId valueThe area fraction is expressed in percentage Reanalysis spa-tial coverage throughout Africa is continuous circumventingthus the limitations of GSOD where the locations of the ob-serving stations might lead to a difference between the in-creases in occurrences in all of African versus observationsmade at these specific stations The major limitation of the



Figure 3 Annual distribution of events exceeding four different thresholds (ie HWMIdge 3 6 9 15) for maximum (top panel) and minimum(bottom panel) temperature The blue line represents the statistically significant (α= 001) linear regression

reanalysis is that uncertainties are difficult to understand andquantify (Simmons et al 2010)

Spatial patterns indicate an increase of HWMIdtx duringthe last 20 years The major hot spot of increases in HWMIdfrequency and magnitude are located in northern Africaranging from Morocco to Egypt and in Angola CongoSouth Sudan Kenya and Madagascar

Unlike our finds for GSOD the temporal evolution ofHWMIdtn ndash shown in Fig 5 ndash do not differ significantly fromHWMIdtx It is possible to distinguish an increase of heatwave intensity This is noticeable from the analysis of thehistograms in Fig S2 where the maximum value of the per-centage of land where heat waves occurred rises fromsim 25 tosim 60 per 5-year period

Also HWMIdtn exhibits different spatial patterns gener-ally it occurs ndash with less intensity ndash in the same zone hit bymaximum temperatures but it also affects other zones Thisincrease in HWMIdtn is noticeable across Angola Congo

Zambia Namibia and Botswana while it is not present innorthern Africa

Just as for GSOD Fig 6 shows the occurrence of HWMIdgreater than a given magnitude level for maximum and min-imum temperature respectively Maximum temperature dis-plays a positive and statistically significant trend for the firstthree classes The slopes of the linear regression are compa-rable with those of GSOD observations

Unlike GSOD the percentage of area affected by heatwaves increase also for minimum temperature which ex-hibits slopes comparable with those of maximum tempera-ture even if they are slightly lower

A visual comparison of heat wave detection from observa-tions (GSOD) and reanalysis (ERA-Interim) is given in theFig S3 The maps show the HWMIdtx as detected by GSODnetwork (blue circles) and ERA-Interim (gridded maps)For 1988 there is a good match between GSOD and reanal-ysis results across northern Africa In addition 1998 heatwaves across western Africa and Morocco are well captured



Figure 4 Heat Wave Magnitude Index daily of maximum temperature (HWMIdtx ) for 5-year periods of ERA-Interim dataset from 1981to 2015




Figure 6 Annual distribution of events exceeding four different thresholds (ie HWMIdge 3 6 9 15) for maximum (top panel) and minimum(bottom panel) temperature Since the reanalysis-based HWMId is a gridded product plots show the percentage of land area affected by heatwaves rather than the number of events

by both datasets In regard to 2005 there is a good matchthroughout northern Africa The 2012 heat wave events inMorocco and Algeria from GSOD are consistent with thereanalysis Interestingly this heat wave is well documentedin both newspapers and literature (eg Blunden and Arndt2013) It is possible to observe consistency also during 2014across Madagascar Zimbabwe and northern Africa

Although the qualitative character of the comparison dueto the low GSOD station number ERA-Interim shows goodagreement with observations

Although quantitative comparison between observationand reanalysis is crucial it is as fraught with difficulties asit is necessary Even when GSOD observations are availablethey are limited to a few points in space and time that maynot represent a sim 80 km reanalysis pixel Besides the spatialscales and resolutions of space-based and field instrumentsare so different that comparing the values generated by bothapproaches is difficult at best

A quantitative comparison has been carried out by com-puting the confusion matrix of heat wave detection fromobservation and reanalysis Tables 1 and 2 show the con-fusion matrices for the entire period 1981ndash2015 for maxi-mum and minimum temperature respectively Heat waveshave been classified into four classes ie HWMIdle 11ltHWMIdle 3 3ltHWMIdle 6 and HWMIdgt 6 For thesake of simplicity we omitted those events not classified asheat waves (ie HWMIdle 1) by both observation and reanal-ysis

The vast majority of the elements of the matrices are noton the top-left to bottom-right diagonal ie the correct clas-sification Besides we can observe a ldquodecayrdquo of the numberof events correctly classified when the magnitude level in-creases This is also due to the lower number of intense heatwaves compared to the moderate ones

The off-diagonal elements represent classification errorsie the number of heat waves that ended up in another class



Table 1 Confusion matrix of heat wave detection from observation (GSOD) and reanalysis (ERA-Interim) for maximum temperature for theperiod 1981ndash2015

ERA-Interim

HWMIdle 1 1ltHWMIdle 3 3ltHWMIdle 6 HWMIdgt 6

GSOD

HWMIdle 1 ndash 501 143 401ltHWMIdle 3 894 172 53 173ltHWMIdle 6 321 101 30 15HWMIdgt 6 149 42 19 10

Sensitivity 069 021 012 012

Table 2 Confusion matrix of heat wave detection from observation (GSOD) and reanalysis (ERA-Interim) for minimum temperature for theperiod 1981ndash2015

ERA-Interim


GSOD



during GSOD and ERA-Interim classification For both max-imum and minimum temperature we can observe that ERA-Interim often underestimates GSOD-based heat waves

Overall the values of accuracy of classification for max-imum and minimum temperature are 058 and 064 respec-tively Note that these values are highly influenced by the cor-rect detection of HWMIdle 1 which represent the vast ma-jority of the events The sensitivity ie an indicator of theperformance of the classifier indicates that HWMIdle 3 areeasier to be detected than higher classes by both databasesThe scatterplots of observations versus reanalysis are shownin Fig S4

Figure 7 shows the density plot of population affected byheat waves of maximum temperature (HWMIdtx) detectedby reanalysis in 2011 Population count refers to the Land-Scan database (Bright et al 2012) and it has been resampledto the ERA-Interim cell size ie 075times 075 Note that thecell area of the gridded ERA-Interim product varies depend-ing on the longitude As expected low-intensity heat waves(ie HWMIdle 3) are more frequent across highly populatedareas but interestingly the highest events (ie HWMIdge 9on the right of the black vertical line) might affect the vastmajority of African population

2011 results indicate that heat waves generally occur onpopulated areas Similar results shown in Fig S5 using an-other population count dataset namely the Joint ResearchCentre Urban Settlement Layer (Freire and Pesaresi 2016)are obtained for 2014 The Joint Research Centre UrbanSettlement Layer has been used to test another population

Figure 7 Density plot of population affected by heat waves of max-imum temperature detected by reanalysis in 2011 using the Land-Scan population count database The y axis refers to the popula-tion count per cell where the cell is the 075times 075 ERA-Interimpixel The black vertical line is at HWMId= 9

dataset and further highlight this specific risk The fact thattwo different years present similar patterns highlights thevulnerability of the African region and the importance of heatwave assessment and prediction



4 Conclusions

In this work we present the results of the application of theHWMId in the assessment of climate change across AfricaObservation from GSOD and reanalysis from ERA-Interimdatasets are used to identify heat waves their temporal andspatial variability and their impacts on African populationGSOD observations are able to capture heat wave events atfine spatial scales but they show a sparse coverage acrossAfrica Conversely the reanalysis dataset despite the coarsespatial resolution and the uncertainties displays a homoge-neous coverage

Results from maximum temperature (HWMIdtx) indicatean increase in intensity and frequency of extreme events us-ing both observation and reanalysis

Specifically from 1996 onwards it is possible to observeHWMIdtx spread with the maximum presence during 2006ndash2015

Between 2006 and 2015 the frequency (spatial coverage)of extreme heat waves had increased to 245 observations peryear (601 of land cover) as compared to 123 per year(373 of land area) in the period from 1981 to 2005 forGSOD stations (reanalysis) Both observation and reanaly-sis show a positive trend of stationsland area experiencingHWMId values greater than magnitude level equal to 3 6and 9 The slopes of linear regressions decrease with increas-ing of magnitude levels

Minimum temperature exhibits incoherence between theresults of GSOD and ERA-Interim GSOD-based HWMIdtnshows a positive trend only for stations experiencing HWMIdvalues greater than 3 Conversely the reanalysis-basedHWMIdtn shows a dramatic increase in extreme events Thisrise albeit minor is comparable with that pertaining to max-imum temperature Besides there is spatial coherence be-tween maximum and minimum temperature from reanaly-sis even when heat waves of minimum temperature also oc-curred in other regions

The pertinence of ERA-Interim HWMId is evaluatedthrough comparisons with GSOD records recognizing thatpoint measurements on the ground may not adequately rep-resent the hydrometeorological variability generally presentin the reanalysis pixel Our results also show coherence be-tween observation-based and reanalysis-based heat waves us-ing a visual comparison Instead the quantitative analysis in-dicates that heat waves with a high (ie HWMIdgt 3) magni-tude level are difficult to be detected at the same time by bothdatabases However a correct comparison is fraught with dif-ficulties and could be possible only with very dense (andwell-established) meteorological networks such as those inEurope or the US

Many events are well documented in the news indicatingthat HWMId is able to capture events that are perceived asheat waves by a broader public Finally the analysis of pop-ulation hit by heat waves shows that the highest events affectthe most populated regions rather than the uninhabited ones

Our work has direct relevance for both scientists and pol-icy makers Increasing numbers of heat waves may pose chal-lenges on health care and on electric supply for residentialcooling demands among others These implications arguefor the importance of enhancing the density of hydromete-orological stations to provide the baseline data that will beessential (1) for climate-change adaptation and (2) to reducethe uncertainties of reanalysis products Further applicationsinclude (1) the employment of HWMId scheme to climato-logical models to quantify the increase in heat wave for thenext decades (2) the wider and quantitative implications ofAfrican heat waves on health crops and finance and (3) theanalysis of teleconnections between the 20152016 El Nintildeoevent (Cesare 2015) and heat waves in eastern Africa

5 Data availability

Dataset can be accessible from (1) Global Summary ofthe Day (GSOD) version 8 National Climatic Data Cen-ter (ftpftpncdcnoaagovpubdatagsod) and (2) ERA-Interim produced daily by the European Centre for Medium-Range Weather Forecasts (ECMWF)(httpappsecmwfintdatasetsdatainterim-full-daily)

The Supplement related to this article is available onlineat doi105194nhess-17-115-2017-supplement

Competing interests The authors declare that they have no conflictof interest

Acknowledgements Authors would like to thank the valuable sup-port from JRC This work has received funding from EuropeanCommission EuropeAid Co-operation Office under the grant agree-ment RALCEA The data used in this paper can be obtainedfrom (1) Global Summary of the Day (GSOD) version 8 NationalClimatic Data Center (ftpftpncdcnoaagovpubdatagsod) and(2) ERA-Interim produced daily by the European Centre forMedium-Range Weather Forecasts (ECMWF) (httpappsecmwfintdatasetsdatainterim-full-daily) The authors also thank thoseresponsible for the efforts on providing free tools such as R usedin this work The R packages were obtained from the Compre-hensive R Archive Network (httpcranr-projectorg) or R-forge(httpsr-forger-projectorg) Heat Wave Magnitude Index dailyhas been calculated using the R library ldquoextRemesrdquo (Gilleland2015) Google data are registered trademarks of Google Inc usedwith permission Population analysis was made by utilizing theLandScan (2011 2012)TM High Resolution Global Population DataSet copyrighted by UT-Battelle LLC operator of Oak Ridge Na-tional Laboratory under contract no DE-AC05-00OR22725 withthe US Department of Energy Maps are available upon request Theauthors acknowledge Hugh Eva for his help editing the paper



Edited by V KotroniReviewed by D Lee and two anonymous referees

References

Albrecht T httpdocumentsworldbankorgcurateden20141120404287turn-down-heat-confronting-new-climate-normal-vol-2-2-main-reportor httpwww-wdsworldbankorgexternaldefaultWDSContentServerWDSPIB20141120000406484_20141120090713RenderedPDF927040v20WP00O0ull0Report000Englishpdf (last accessJanuary 2017) 2014

Berrisford P Dee D P Poli P Brugge R Fielding K FuentesM Karingllberg P W Kobayashi S Uppala S and Simmons AThe ERA-Interim archive Version 20 ECMWF Shinfield ParkReading 2011

Blunden J and Arndt D S State of the Climatein 2012 B Am Meteorol Soc 94 S1ndashS258doi1011752013BAMSStateoftheClimate1 2013

Bright E A Coleman P R Rose A N and Urban M L Land-Scan 2011 available at httpwwwornlgovlandscan (last ac-cess 16 May 2016) 2012

Ceccherini G Russo S Ameztoy I Romero C P andCarmona-Moreno C Magnitude and frequency of heat and coldwaves in recent decades the case of South America Nat Haz-ards Earth Syst Sci 16 821ndash831 doi105194nhess-16-821-2016 2016

Cesare C Developing El Nintildeo could be strongest on record Na-ture doi101038nature201518184 in press 2015

Collins J M Temperature Variability over Africa J Climate 243649ndash3666 doi1011752011JCLI37531 2011

Courtier P Theacutepaut J-N and Hollingsworth A A strategyfor operational implementation of 4D-Var using an incremen-tal approach Q J Roy Meteorol Soc 120 1367ndash1387doi101002qj49712051912 1994

Dee D P Uppala S M Simmons A J Berrisford P PoliP Kobayashi S Andrae U Balmaseda M A Balsamo GBauer P Bechtold P Beljaars A C M van de Berg L Bid-lot J Bormann N Delsol C Dragani R Fuentes M GeerA J Haimberger L Healy S B Hersbach H Hoacutelm E VIsaksen L Karingllberg P Koumlhler M Matricardi M McNallyA P Monge-Sanz B M Morcrette J-J Park B-K PeubeyC de Rosnay P Tavolato C Theacutepaut J-N and Vitart F TheERA-Interim reanalysis configuration and performance of thedata assimilation system Q J Roy Meteorol Soc 137 553ndash597 doi101002qj828 2011

Fan Y and van den Dool H Climate Prediction Centerglobal monthly soil moisture data set at 05 resolutionfor 1948 to present J Geophys Res-Atmos 109 D10102doi1010292003JD004345 2004

Fontaine B Janicot S and Monerie P-A Recent changes in airtemperature heat waves occurrences and atmospheric circula-tion in Northern Africa J Geophys Res-Atmos 118 8536ndash8552 doi101002jgrd50667 2013

Freire S and Pesaresi M GHS population grid derived fromGPW4 multitemporal (1975 1990 2000 2015) ndash CKAN avail-

able at httpdatajrceceuropaeudatasetjrc-ghsl-ghs_pop_gpw4_globe_r2015a last access 2 December 2016

Gilleland E extRemes Extreme Value Analysis availableat httpscranr-projectorgwebpackagesextRemesindexhtmllast access 9 September 2015

Gilleland E and Katz R W New Software to Analyze How Ex-tremes Change Over Time Eos T Am Geophys Un 92 13ndash14doi1010292011EO020001 2011

Kalnay E Kanamitsu M Kistler R Collins W Deaven DGandin L Iredell M Saha S White G Woollen J ZhuY Leetmaa A Reynolds R Chelliah M Ebisuzaki W Hig-gins W Janowiak J Mo K C Ropelewski C Wang JJenne R and Joseph D The NCEPNCAR 40-Year ReanalysisProject B Am Meteorol Soc 77 437ndash471 doi1011751520-0477(1996)077lt0437TNYRPgt20CO2 1996

Marshall G J and Harangozo S A An appraisal ofNCEPNCAR reanalysis MSLP data viability for climate stud-ies in the South Pacific Geophys Res Lett 27 3057ndash3060doi1010292000GL011363 2000

Mishra V Ganguly A R Nijssen B and Lettenmaier DP Changes in observed climate extremes in global urbanareas Environ Res Lett 10 024005 doi1010881748-9326102024005 2015

Russo S Dosio A Graversen R G Sillmann J Carrao HDunbar M B Singleton A Montagna P Barbola P andVogt J V Magnitude of extreme heat waves in present cli-mate and their projection in a warming world J Geophys Res-Atmos 119 12500ndash12512 doi1010022014JD022098 2014

Russo S Sillmann J and Fischer E M Top ten Europeanheatwaves since 1950 and their occurrence in the comingdecades Environ Res Lett 10 124003 doi1010881748-93261012124003 2015

Russo S Marchese A F Sillmann J and Immeacute GWhen will unusual heat waves become normal in a warm-ing Africa Environ Res Lett 11 054016 doi1010881748-9326115054016 2016

Simmons A J Willett K M Jones P D Thorne P W andDee D P Low-frequency variations in surface atmospheric hu-midity temperature and precipitation Inferences from reanal-yses and monthly gridded observational data sets J GeophysRes-Atmos 115 D01110 doi1010292009JD012442 2010

Solomon S Intergovernmental Panel on Climate Change Inter-governmental Panel on Climate Change and Working Group IClimate change 2007 the physical science basis contribution ofWorking Group I to the Fourth Assessment Report of the Inter-governmental Panel on Climate Change Cambridge UniversityPress Cambridge UK 2007

UNECA Assessment of Africarsquos Climatic Records and Record-ing Networks Including Strategic for Rescuing of Cli-matic Data UNECA available at httpwww1unecaorgacpcacpcworkingpapersaspx (last access 5 January 2016) 2011

Uppala S M KAringllberg P W Simmons A J Andrae U Bech-told V D C Fiorino M Gibson J K Haseler J HernandezA Kelly G A Li X Onogi K Saarinen S Sokka N AllanR P Andersson E Arpe K Balmaseda M A Beljaars A CM Berg L V D Bidlot J Bormann N Caires S ChevallierF Dethof A Dragosavac M Fisher M Fuentes M Hage-mann S Hoacutelm E Hoskins B J Isaksen L Janssen P aE M Jenne R Mcnally A P Mahfouf J-F Morcrette J-J



Rayner N A Saunders R W Simon P Sterl A TrenberthK E Untch A Vasiljevic D Viterbo P and Woollen J TheERA-40 re-analysis Q J Roy Meteorol Soc 131 2961ndash3012doi101256qj04176 2005

WMO WMO five-year analysis available athttpswwwwmointmediacontentwmo-2015 last access30 November 2015


Abstract

Introduction

Data and methodology

Data

Methodology

Results

Conclusions

Data availability

Competing interests

Acknowledgements

References


through data assimilation systems to produce a single uni-form global dataset (Kalnay et al 1996) thus enabling a ho-mogeneous coverage of Africa

The magnitude of heat waves for both observations andreanalysis is quantified on annual basis by means of the HeatWave Magnitude Index daily (HWMId Russo et al 2015)for the period 1981ndash2015 across Africa The HWMId hasbeen applied to maximum and minimum temperature

The objective of this paper is to analyse African heat waveregime and identify the most important of heat waves dur-ing 1981ndash2015 These analyses draw attention to the spatialdistribution of temperature extremes and their temporal evo-lution in the past decades still largely unknown Consider-ing both its wide geographic scope and spatial resolution thestudy represents an important step towards the assessment ofheat wave frequency in the last 3 decades using records ofdaily maximum and minimum temperatures acquired at re-gional level

The availability of such information is paramount Themore reliable the assessment of heat waves is the betterAfrican countries will be equipped to strengthen their copingcapacities This study also provides insight in human expo-sure to heat waves in Africa

Some early exploratory research using similar methodol-ogy for South America showed some promising preliminaryresults (Ceccherini et al 2016) In this paper the heat waveclassification scheme has been consolidated and improved(Russo et al 2015) and both observations and reanalysisdatasets have been employed

2 Data and methodology

21 Data

The time series of temperature used in this study have beenderived from (1) observations and (2) reanalysis GSOD isthe dataset of observations GSOD records produced bythe National Climatic Data Center are mainly recorded atinternational airports and include maximum and minimumvalues of temperature GSOD records underwent extensiveautomated quality controls to eliminate many of the ran-dom errors found in the original data (further details on theGSOD data can be obtained from the website httpwwwclimategovglobal-summary-day-gsod) GSOD has been al-ready employed to assess heat waves magnitude at global(Mishra et al 2015) and local (Ceccherini et al 2016) scaleHistorical data are generally available for the last 80 yearswith data from 1973 onwards being the most complete Thetotal number of GSOD stations available across Africa isequal to 958 However only 260 of them satisfy the con-ditions needed to calculate heat wave magnitude indices asfurther described in the Methodology section

ERA-Interim (Berrisford et al 2011 Dee et al 2011) isthe dataset of reanalysis providing hydrometeorological vari-ables such as maximum and minimum temperature evapora-tion snowfall runoff and precipitation across land at varioustemporal scales Reanalysis has been increasingly used to ad-dress a variety of climate-change issues and has by now be-come an important method in climate-change research (Fanand van den Dool 2004 Marshall and Harangozo 2000Uppala et al 2005) ERA-Interim is a reanalysis productof the European Centre for Medium-Range Weather Fore-casts (ECMWF) available from 1979 and continuously up-dated in real time The data assimilation system used to pro-duce ERA-Interim is based on a 4-D variational scheme (4D-Var) with a 12 h analysis window (for further information on4D-VAR see Courtier et al 1994)

The reanalysis dataset used in this study has a spatial reso-lution of 075 (ie approximately 80 km at the equator) anda temporal resolution of 1 day (ie a time step of 24 h) Thevariables of interest are daily maximum and minimum tem-perature ranging from 1981 to 2015 ERA-Interim allows aconsistent spatial and temporal resolution over 3 decades in-corporating millions of observations into a stable data assimi-lation system However observational constraints and there-fore reanalysis reliability can considerably vary dependingon the location time period and variable considered thus in-troducing spurious variability and trends

For both observations and reanalysis dataset the timespanconsidered in this study refers to the period 1 January 1981ndash30 June 2015

22 Methodology

In this paper the HWMId recently defined by Russo etal (2015) has been employed to detect African heat wavesfor the period 1981ndash2015 The HWMId is a simple numer-ical indicator that takes both the duration and the intensityof the heat wave into account Basically the magnitude in-dex sums excess temperatures beyond a certain normalizedthreshold and merges durations and temperature anomaliesof intense heat wave events into a single indicator accordingto the methodology described in Russo et al (2014 2015)The HWMId is an improvement on the previous Heat WaveMagnitude Index (ie HWMI by Russo et al 2014) and ableto overcome its limitations More precisely HWMI has someproblems in assigning magnitude to very high temperaturesin a changing climate thus resulting in an underestimation ofextreme events

The HWMId is defined as the maximum magnitude of theheat waves in a year Specifically a heat wave is defined asa periodge 3 consecutive days with maximum temperatureabove a daily threshold calculated for a 30-year-long refer-ence period The threshold is defined as the 90th percentileof daily maxima temperature centred on a 31-day window

The interquartile range (IQR ie the difference betweenthe 25th and 75th percentiles of the daily maxima tempera-










3 Results






































ERA-Interim


GSOD




ERA-Interim


GSOD











4 Conclusions









5 Data availability








References































Abstract

Introduction


Data

Methodology

Results

Conclusions

Data availability

Competing interests

Acknowledgements

References









3 Results






































ERA-Interim


GSOD




ERA-Interim


GSOD











4 Conclusions









5 Data availability








References































Abstract

Introduction


Data

Methodology

Results

Conclusions

Data availability

Competing interests

Acknowledgements

References



































ERA-Interim


GSOD




ERA-Interim


GSOD











4 Conclusions









5 Data availability








References































Abstract

Introduction


Data

Methodology

Results

Conclusions

Data availability

Competing interests

Acknowledgements

References


4 Conclusions









5 Data availability








References































Abstract

Introduction


Data

Methodology

Results

Conclusions

Data availability

Competing interests

Acknowledgements

References



References































Abstract

Introduction


Data

Methodology

Results

Conclusions

Data availability

Competing interests

Acknowledgements

References





Abstract

Introduction


Data

Methodology

Results

Conclusions

Data availability

Competing interests

Acknowledgements

References

Heat waves in Africa 1981–2015, observations and …...Heat waves in Africa 1981–2015,...

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