Recent trends of minimum and maximum surface temperatures over eastern africa

2876 VOLUME 13J O U R N A L O F C L I M A T E

q 2000 American Meteorological Society

Recent Trends of Minimum and Maximum Surface Temperatures over Eastern Africa

S. M. KING’UYU

Institute for Meteorological Training and Research, Nairobi, Kenya

L. A. OGALLO

Department of Meteorology, University of Nairobi, Nairobi, Kenya

E. K. ANYAMBA

NASA Goddard Space Flight Center, Greenbelt, Maryland

(Manuscript received 20 May 1998, in final form 3 May 1999)

ABSTRACT

This study investigated recent trends in the mean surface minimum and maximum air temperatures over easternAfrica by use of both graphical and statistical techniques. Daily records for 71 stations for the period 1939–92were used.

Attempts were also made to associate the temperature characteristics with the anomalies in the major systemsthat control the climate of the region including the El Nino–Southern Oscillation (ENSO), the quasi-biennialoscillation, and the prevailing convective processes represented by the outgoing longwave radiation.

The northern part of the study region generally indicated nighttime warming and daytime cooling in recentyears. The trend patterns were, however, reversed at coastal and lake areas. The Mozambique channel regionshowed cooling during both nighttime and daytime. There were thus large geographical and temporal variationsin the observed trends, with some neighboring locations at times indicating opposite trends.

A significant feature in the temperature variability patterns was the recurrence of extreme values. Suchrecurrences were significantly correlated with the patterns of convective activities, especially ENSO, cloudiness,and above/below normal rainfall. Although some of the variations in the trend patterns could be attributed tourbanization and land use patterns, such effects were not delineated in the current study.

1. Introduction

Climate change has been the subject of many inves-tigations in recent years, especially in issues related tothe detection and attribution of human-induced signals(e.g., IPCC 1990, 1992, 1995; Barnett and Schlesinger1987; Santer et al. 1995). One of the major problemsin most of these studies has been the nonexistence ofaccurate homogeneous and long period instrumental re-cords, due to changes in observational practices, ur-banization effects, changes in instrument types, expo-sure, and location, among other causes.

Some of these changes have been blamed on tech-nological advancements. It is hardly expected that ob-servations taken before and after such changes will bestrictly comparable. Quality control of the climatolog-ical records and the removal of urbanization and otherbiases that may be associated with the above data prob-

Corresponding author’s address: Dr. Stephen Mutua King’uyu,Meteorological Services, P.O. Box 101000, Gaborone, Botswana.E-mail: [email protected]

lems are the key steps in any climate change studies.This ensures that the information derived from suchclimatological records are true reflections of the actualstates of the environment at the particular locations andtime (Wang et al. 1990; Jones et al. 1990; Barrows andCamillons 1994; Grossman et al. 1991; Erscherd et al.1995; Karl et al. 1995a,b; Christy and Goodridge 1995).

Most of the studies of the past and present patternsof climate at the global and regional scales have beenderived from temperature and precipitation (Vinnikovet al. 1990; Nicholson 1994; Nicholls and Lavey 1992;Jones 1994, 1995; Parker et al. 1993, 1994; Gregory etal. 1991; Deser and Blackman 1993; Grossman et al.1991; Briffa et al. 1995; Trenbeth 1990; Diaz et al. 1989;Deming 1995; Folland and Salinger 1996; Karl et al.1995a,b; Karl et al. 1988; Antonov 1993; Bloomfield1992; Christy and McNider 1994; Zheng et al. 1997).Studies using temperature records have shown that themean global surface temperature has increased by about0.38–0.68C over the last 100 yr. There are however largegeographical variations in the observed warming trendswith some locations indicating some general coolingsignals (IPCC 1990, 1992, 1995).

15 AUGUST 2000 2877K I N G ’ U Y U E T A L .

This study investigated the trends in the mean month-ly minimum and maximum temperature records overeastern Africa. The interannual patterns in the meanmonthly minimum and maximum temperature valueswere also examined. The term ‘‘eastern Africa’’ is herebroadly used to imply 19 countries located on the easternpart of the African continent and extending from thesub-Saharan Sudan and Ethiopia to the Horn of Africa,East Africa, and central and southern Africa. The regionis enclosed by latitudes 208–608E and longitudes 258–308S. Figure 1 is a map of the area of study and thedata network.

The major systems that control the spatial and tem-poral characteristics of the climate of the region includethe intertropical convergence zone, subtropical anticy-clones, monsoon wind systems, the African jet streams,easterly/westerly waves, tropical cyclones, and telecon-nections with regional and large-scale quasi-periodicclimate systems like the quasi-biennial oscillation(QBO), intraseasonal waves, and El Nino–Southern Os-cillation (ENSO), among others. Thermally induced me-soscale systems associated with orography and largewater bodies, which include inland lakes, also introducesignificant modifications to the large-scale flow over theregion. An example is the Lake Victoria, with an arealexpanse of over 69 000 km2 and a unique circulationof its own. Details of the regional climatology may beobtained from Ogallo (1987, 1993), King’uyu (1994),and Anyamba (1992), among others.

The major objectives of the present study were toexamine the existence of any significant trends in bothminimum and maximum temperature over the study re-gion. Attempts were also made to explore the potentialcauses of any observed temperature anomalies. The dataused in the study are highlighted in the following sec-tion.

2. Data and quality control

a. Data

The data used in the study consisted of the dailyminimum and maximum temperature records from 71stations within eastern Africa, obtained from theDrought Monitoring Centre, Nairobi, for eastern andsouthern Africa. The 71 stations were the only ones, outof hundreds within the region, that satisfied our accep-tance criteria, based on the record length, percentage ofmissing data, quality control, and homogeneity tests.Data entry and archiving was done in the climate com-puting (CLICOM) format. The distribution of the sta-tions used was shown in Fig. 1, while Table 1 is a listof the stations used. The daily records were used togenerate monthly mean maximum and minimum tem-perature series for each station. The period of studyextended from 1939 to 1992.

The northern and southern sectors of the study regionobserve maximum precipitation and temperature values

during the respective summer months, centered aroundJuly and January, respectively. The equatorial sector ofthe region has two distinct rainfall seasons centeredaround the northern autumn and spring months of Sep-tember–November and March–May, respectively. Themonths of January, April, July, and October were there-fore used in the study to investigate any seasonal shiftsin the interannual temperature characteristics.

Other data were also used in the current study toinvestigate the potential association between the ob-served interannual characteristics of minimum and max-imum temperature anomalies and anomalies that are of-ten observed in the regional climate. These included themonthly Southern Oscillation index (SOI) as derivedfrom the normalized sea level pressure difference be-tween Tahiti and Darwin and obtained from the ClimateAnalysis Center, Washington, D.C.

Phases of the upper-level zonal winds over Nairobiwere also used to represent the interannual patterns ofthe QBO over the area of study. Ogallo et al. (1994)observed that the QBO signal is well discernible usingthe easterly (zonal) wind over Nairobi for levels 30–70hPa. Actual cloudiness data and out-going longwaveradiation (OLR) were used to investigate the uniquespace–time anomalies in the convective patterns overthe study region, which may be associated with anom-alous maximum and minimum temperature patterns.Cloudiness records were available for Kenyan stationsonly for both 0800 and 1200 UTC. The OLR data wasfrom the National Oceanic and Atmospheric Adminis-tration (NOAA) satellite observations in grids of 2.58lat 3 2.58 long for the period 1977–88. Since most ofthe stations lay away from grid points, interpolation wasused to estimate station values.

Urbanization was not explicitly delineated in the cur-rent study due to nonavailability of data for non-Kenyanstations. For Kenyan stations, however, a simple non-quantitative approach was used. This involved a cate-gorization of trends for urban stations and those for ruralstations in order to examine if there was any difference.Any station with a population of below 2000 peoplewas treated as rural, while stations with populations of2000 or more were treated as urban.

A common problem with the maximum and minimumtemperature records from the selected locations was thatof missing values. Such records were estimated usingcorrelation and regression methods. The correlation andregression methods used were derived from the bestinstantaneous/time-lagged interstation correlation/auto-correlation values. The estimated data were, however,less than 10% of the record at any given location. Sta-tions with more than 10% of the record missing werenot included in the study.

Interstation correlation was evaluated by calculatingthe simple correlation coefficient between each two sta-tions. This resulted in a ‘‘71 3 71’’ correlation matrix.The matrix was used to determine those stations withthe highest correlation with the station with missing


FIG. 1. Map of study area and data network.


TABLE 1. List of stations used in study.

Code Name Code Name Code Name

123456789

10

Port SudanAtbaraKassalaKhartoumEl-FasherAsmaraDjiboutiKadugliCombolchaDebre-Marcos

25262728293031323334

DagorettiMakinduLamuMuyingaBujumburaMombasaKigomaTaboraDodomaMorogoro

49505152535455565758

LusakaZumboMakokaNampulaVacoasPlaisanceKaribaMutokoQuelimaneShakawe

11121314151617181920

Dire-DawaAdiss-AbabaNegheleJubaLodwarMoyaleAruaWajirKaseseMbarara

35363738394041424344

Dar-Es-SalaamMbeyaKasamaSongeaTangaMoroniAgalegaPembaMzuzuZambezi

59606162636465666768

MaunBulawayoBeiraFrancistownInhambaneMahalapyeXai-XaiMaputoBigbendTshane

21222324

EntebbeKampalaKisumuGarissa

45464748

NdolaChipitaLichingaLivingstone

697071

GaboroneTsabongSt. Brandon

data. The least-squares method was then used to developa linear regression equation expressing the observationsat the station of interest in terms of observations at thestation with which it was most strongly correlated. It issuch an equation that was used to estimate any missingdata. Only those stations with an interstation correlationcoefficient of at least 0.5 were used to estimate missingdata.

b. Quality control

All the records were subjected to quality control testsbefore any analysis to ensure both internal consistencyand consistency with neighboring observations. Someof the techniques used included the nonparametricWald–Walfowitz (1943) runs tests, Maronna–Yohai(1978), and Spearman rank statistics to discriminate ho-mogeneity against trend (WMO 1966; Kendall et al.1961). Mass curves and range validation techniqueswere also used. Details of such methods are availablein many standard climatological references includingWMO (1966, 1986). The above methods were in ad-dition to quality control procedures resident in CLICOMas recommended in WMO (1992).

The CLICOM package is designed for data stored ona long-term basis. It uses a database that organizes andstores input data consisting of numerical input valuesfor climatic study, and descriptive information like thestation location, period for which data are available forthe station, the climate elements measured, types of in-struments used, times of observation, etc. Managementof the data is done by a commercial software calledDataEase. DataEase has seven security levels to ensure

that only authorized personnel have access to their re-spective levels (WMO 1988a).

The data are automatically validated for inaccuraciesbefore being registered in the database. This way, valuesexceeding specified quality limits are flagged (WMO1988b). Validation is normally done by a meteorologist,who has hands-on experience in the relevant data col-lection, and training in statistical quality control meth-ods. The validator can override the quality control rulesif he is convinced any flagged values are accurate ob-servations, or replace them if his investigations revealthat they may have been erroneously input. It is onlyafter such a process that the values are registered in thedatabase (WMO 1988b). This process ensures the qual-ity of climatic records archived in CLICOM (WMO1988b). It is, however, noteworthy that minute errorsthat may not affect the totals significantly may passwithout been detected.

3. Methods

The above climatological records were subjected toseveral analyses, which included trend, spectral, andcorrelation analyses. Trend analysis examined the ex-istence of any significant trends in the interannual pat-terns of maximum and minimum temperature within theregion. Spectral analysis was used to delineate the in-terannual cycles that are dominant in the various tem-perature series. Correlation analysis was also used toinvestigate the potential association between any ob-served interannual anomalies in the maximum and min-imum surface air temperature patterns and anomalies inthe climate systems that control the seasonal climatevariability over the region.

Several methods were used in the study to determinethe existence of any significant trends in the year-to-year patterns of maximum and minimum temperatureover the region. The techniques used included graphicaland statistical techniques. The graphical methods dis-played the visual patterns of the mean interannual trendsof the respective temperature records. A five-term mov-ing average filter was used to smooth the interannualtemperature trends. The most objective trend analysesin this study were however based on the analysis ofvariance approach and the nonparametric Spearmanrank correlation statistic (WMO 1966; Kendall et al.1961).

Spectral analysis delineated the major cycles in theinterannual patterns of the maximum and minimum sur-face temperature values over the region of study. Detailsof the maximum entropy method of spectral analysisthat was used in this study may be found in Kendall etal. (1961) and Kay and Marple, (1981) among others.

Interannual anomalies in meteorological parametersare often linked to interannual variations in the systemsthat control the global and regional climate. Three ofthe systems with quasi-periodic fluctuations that are as-sociated with interannual climate anomalies over the


FIG. 2. Cumulative temperature series at Makindu in Kenya[28179S, 378509E, 100 m above mean sea level (MSL)].

region are ENSO, QBO, and intraseasonal waves (Ogal-lo 1987, 1993; Ogallo et al. 1994; Anyamba 1992).Attempts were therefore made in the current study toinvestigate the existence of ENSO and QBO signals inthe interannual temperature anomaly patterns throughspectral and correlation analyses.

Correlation analysis was used to examine the rela-tionship between temperature anomalies and anomaliesin the cloudiness together with the associated regionalclimate systems. Under this method, the simple corre-lation coefficient, r, was calculated. Two variables (Xt

and Yt) are perfectly correlated if |r | 5 1, while negative/positive r values indicate inverse/positive associationbetween the two variables. The statistical significanceof the computed r was tested by use of the Student’st-test. The computed r were used to determine linkagesbetween maximum and minimum temperature anoma-lies and the interannual variations in the large-scale cli-mate systems.

A number of authors have noted that Simple corre-lation analysis may not detect complex linkages betweenpairs of variables including time-lagged linkages. Thisis especially true for variables that may be correlatedwithin positive or negative phases only. While severalcomplex statistical methods are available to study suchcomplex relationships, some authors have used verysimple statistical techniques, which include x2 testsbased on simple contingency tables, which compareunique anomaly categories derived from classes ofpaired variables. Others have examined the interannualpatterns of the sum/difference between the correspond-ing normalized values for the pair of variables. Suchmethods can help to clearly amplify the anomalies andprovide better composites for the linkages between thepair of variables. Both simple correlation and contin-gency tables were used in the current study.

In this study, a 3 3 3 contingency table was used tocategorize below normal, normal, and above normal oc-currences for all the variables used in the analysis,namely, minimum and maximum surface temperature,SOI, cloudiness/OLR, and QBO. The correspondingstandard deviations were used to determine the thresholdlimits for each of the anomaly classes. An observationwas considered to be significantly different from themean if the corresponding anomaly was less than a halfof the standard deviation.

4. Observed temperature trends

Quality control tests of the few estimated daily max-imum and minimum temperature records indicated thatsuch records were generally homogeneous with thoseobserved at the respective locations. A typical exampleof the mass curves obtained from the quality controlanalysis is shown in Fig. 2. The homogeneous temper-ature records formed the fundamental base for most ofthe investigations carried out in the study. Significantshifts in the mass curves were however noted at some

locations that later indicated significant change in theminimum and maximum temperature trends. Historicalrecords were used to examine any changes in the lo-cation or type of instruments within the study regionthat could be associated with any observed shifts in themass curves. If any such shifts were attributed to chang-es in instrument types or station sites, the records werenot included in the analysis.

Typical patterns of the time series of the maximumand minimum temperature records are presented in Figs.3–7, while the spatial distribution of temperature changefor January is presented in Fig. 8. A general minimum(nighttime) temperature warming in recent years is quiteevident, especially at land locations in the northern sec-tor of the study area and extending up to about 58S(Figs. 3, 4, 5, 8). Similar patterns were observed for theother seasons. The diurnal temperature range within thisarea therefore showed a decreasing trend (Fig. 5). Thegeographical patterns of the observed warming trendswere, however, very complex with some locations show-ing no change or decreasing trends of minimum tem-perature, especially over the coastal zones and near largeinland water lakes (Fig. 8).

Such locations often have strong thermally inducedmesoscale circulation, which together with the localmoisture sources often modify patterns of the large-scalecirculation significantly. Seesaw relationships betweenlocations over land and those near the large water bodiesof East Africa have been noted with ENSO by Ogallo(1987) among many others.

Some land locations to the south of 58 lat showeddecreasing nighttime and daytime temperature trends(Fig. 6), while others showed increasing trends. Otherstations within this subregion showed decreasing night-time and increasing daytime temperature trends (Fig. 7).An interesting feature of the observed trends was alsoobserved over the Mozambique channel region, wheresignificant nighttime and daytime cooling was observed


FIG. 3. Temperature series during Nov at Debremarcos in Ethiopia (108219N, 378439E; 2440 m MSL).

FIG. 4. Temperature series during Jul at Dagoretti-Corner in Kenya (018189S, 368459E; 1798 m MSL).

during all seasons of the year (Figs. 6, 8). Similar pat-terns have in the past been associated with a weakeningof the Mozambique warm current (Hastenrath 1985).These patterns of decreasing/increasing trends havehowever been observed at many other locations world-wide (Karl et al. 1984, 1991; Razuveav et al. 1995; Jones1995). It is important to note that some of the trends inFig. 8 are quite significant, being in excess of 0.68C atsome locations.

The geographical patterns of the diurnal temperaturerange also varied significantly. Nighttime warming anda decreasing diurnal temperature range have been re-ported by a number of authors. The observed decreasein the diurnal temperature range has also been associatedwith an increase in cloud cover and not always due toincreased nighttime temperature since such trends may

at times also be linked to decreasing maximum tem-perature trends (Jones 1995; Razuveav et al. 1995; Park-er et al. 1993, 1994; Jones et al. 1990; IPCC 1995;Plummer et al. 1995; Salinger et al. 1993; Karl et al.1984, 1991, 1993, 1995a,b; Kukla and Karl 1993; Parkeret al. 1995; Briffa et al. 1995).

No significant trends could be delineated from theinterannual patterns of the OLR and the few cloud coverrecords that were used in this study. Attempts were madeto compare the differences in the maximum and mini-mum temperature patterns for the rural and urban lo-cations. No unique differences could be detected in theinterannual temperature patterns between the domi-nantly rural and the dominantly urban locations.

The most dominant feature in the interannual patternsat all the locations was, however, the recurrence of very


FIG. 5. Temperature range series for Jan at Dagoretti-Corner in Kenya (018189S, 368459E; 1798 m MSL).

FIG. 6. Temperature series during Jul at Pemba in Mozambique (128589S, 408309E; 49 m MSL).

high/low maximum and minimum temperature values.Spectral analysis indicated that the periods of recurrenceincluded 2–3.3 yr, 3.5–4.5 yr, 5–6 yr, and 10–13 yr(Table 2). Some stations also showed cycles of greaterthan 13 yr. The magnitudes of the spectral peaks variedsignificantly from location to location as reflected inFig. 9.

5. Linkages between temperature anomalies andthe large-scale circulation

Results of correlation analysis indicated that zero-lagcorrelation between daytime–nighttime temperature val-

ues and the SOI together with OLR were very low atmany locations. Relatively large values were howevercommon within the southern sector of the study region.Time-lagged correlation values were however signifi-cant at greater than 95% confidence level at many lo-cations (Tables 3 and 4). The time lags ranged between2 and 9 months although peak correlation values wereconcentrated around 3–6 months. The high degree ofpersistence that was observed in the correlation patternsis consistent with the persistent nature of ENSO (Panand Oort 1983). The relationship between temperatureand SOI were clearer when contingency tables wereused. Significant correlation between ENSO and oc-


FIG. 7. Temperature series during Apr at Lusaka in Zambia (158199S, 288279E; 1152 m MSL).

FIG. 8. Spatial distribution of temperature change for Jan: (a) observed trends of minimum temperature for Jan and (b) contour map ofthe same data showing areas of cooling and warming.

currences of above/below normal rainfall over the studyregion has been reported by Ogallo (1987) and Ogalloet al. (1994), among others. Above/below normal cloudcover is often associated with the occurrences of above/below normal rainfall. Such effects must therefore bereflected in the diurnal temperature characteristics.

Zero-lag and time-lagged correlation between maxi-mum/minimum temperature values and the QBO weregenerally complex and no unique geographical influencecould be delineated, even with the use of contingencytables in the detailed analysis of the temperature anom-alies during westerly and easterly QBO phases.


TABLE 2. Summary of some of the observed spectral cycles.

StationMin tempcycles (yr)

Max tempcycles (yr)

AtbaraAsmaraDagorettiLamuMbararaMuyingaPlaisanceAgelegaKaribaTshaneKasamaMaputo

16, 10.7, 2.9, ,222, 11, 2.8, ,218, 3, 327, 5.4, 3, 212.5, 2.512.5, 5, 2.56, 3, 230, 15, 5, 2.712.5, 3.6, 215, 3.3, ,210, 3.33.6, 2.1

16, 3, ,222, 11, 2.8, ,212, 6, 4, 25.4, 3, 28.3, 6.3, 2.5, 212.5, 5, 2.540, 5.7, 3, 230, 5, 2.525, 12.5, 2.310, 3.3, 2.519, 3.8, 25.7, 3.3, 2.4

TABLE 3. Correlation between prevailing cloudiness and temper-ature. Here r is the simple correlation coefficient and C.L. is theconfidence level.

0800 UTC cloudiness

r C.L./%

1200 UTC cloudiness

r C.L./%

Min tempMax tempTemp range

20.3520.6220.34

99.999.999.9

20.3420.07

0.20

99.9,90

95

TABLE 4. Some of the time-lagged correlation between temperatureand SOI. Here C.L. is the confidence level.

Station Variable MonthSOI

month

Time-Lag

(months) r C.L./%

Asmara Min T

Max T

JulOctApr

AprJulJan

333

20.3520.45

0.42

999997.5

Khartoum Min T

Max T

Jul

Jan

AprJulJan

300

20.4320.43

0.33

999999

Lodwar Min T AprJul

JanApr

33

20.3420.50

97.597.5

Kisumu Min T JanOct

JanAprJul

063

20.3920.4120.44

999999

Francistown Min T Jan

Jul

AprJulOctAprOct

86339

20.3620.5320.5220.3520.32

97.599999595

Agalega Min T

Max T

JanJulApr

OctNovNov

365

20.5120.4320.43

999999

FIG. 9. Spectral cycles of temperature at (a) Lamu in Kenya(028169S, 408509E; 6 m MSL) and (b) Kariba in Zimbabwe (168319S,288539E; 718 m MSL).

6. Conclusions

The results from this study indicated a significant risein the nighttime temperature at several locations overeastern Africa. The distribution of the warming trendswere, however, not geographically uniform with manycoastal locations and those near large water bodies in-

dicating significant opposite trends, especially to thenorth of 58S. An interesting feature was also observedover the Mozambique channel where both significantnighttime and daytime cooling was dominant. Locationsnorth of 58S indicated more organized decreasing orincreasing diurnal trend in the daytime/nighttime tem-perature patterns.

The complex nature of the observed geographical pat-terns of the observed trends made it extremely difficultfor attribution of the observed daytime/nighttime tem-perature trends to be given in the current study. Closeassociation between recurrences of the extremely largenighttime/daytime temperature and anomalies in thelarge-scale systems, which control rainfall over the re-gion, especially ENSO, were very evident. The influ-ence of the large-scale water bodies was also evident.At some locations near these large water bodies, op-posite phase relationship signals were dominant.

Further investigations are required in order to attri-bute the causes of some of the observed daytime/night-time temperature trends over eastern Africa. Such stud-ies should include the examination of urbanization andany other biases in the climatological data that wereused in the study. No clear differences could, however,


be discerned from the interannual patterns of daytime/nighttime temperature from the rural and urban locationsused in the study.

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Recent trends of minimum and maximum surface temperatures over eastern africa

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