2876 JOURNAL OF CLIMATE VOLUME 13 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 eastern Africa by use of both graphical and statistical techniques. Daily records for 71 stations for the period 1939–92 were used. Attempts were also made to associate the temperature characteristics with the anomalies in the major systems that control the climate of the region including the El Nin ˜o–Southern Oscillation (ENSO), the quasi-biennial oscillation, 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 recent years. The trend patterns were, however, reversed at coastal and lake areas. The Mozambique channel region showed cooling during both nighttime and daytime. There were thus large geographical and temporal variations in 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. Such recurrences 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 to urbanization and land use patterns, such effects were not delineated in the current study.1. Introduction lems are the key steps in any climate change studies. This ensures that the information derived from such Climate change has been the subject of many inves- climatological records are true reﬂections of the actualtigations in recent years, especially in issues related to states of the environment at the particular locations andthe detection and attribution of human-induced signals time (Wang et al. 1990; Jones et al. 1990; Barrows and(e.g., IPCC 1990, 1992, 1995; Barnett and Schlesinger Camillons 1994; Grossman et al. 1991; Erscherd et al.1987; Santer et al. 1995). One of the major problems 1995; Karl et al. 1995a,b; Christy and Goodridge 1995).in most of these studies has been the nonexistence of Most of the studies of the past and present patternsaccurate homogeneous and long period instrumental re- of climate at the global and regional scales have beencords, due to changes in observational practices, ur- derived from temperature and precipitation (Vinnikovbanization effects, changes in instrument types, expo- et al. 1990; Nicholson 1994; Nicholls and Lavey 1992;sure, and location, among other causes. Jones 1994, 1995; Parker et al. 1993, 1994; Gregory et Some of these changes have been blamed on tech- al. 1991; Deser and Blackman 1993; Grossman et al.nological advancements. It is hardly expected that ob- 1991; Briffa et al. 1995; Trenbeth 1990; Diaz et al. 1989;servations taken before and after such changes will be Deming 1995; Folland and Salinger 1996; Karl et al.strictly comparable. Quality control of the climatolog- 1995a,b; Karl et al. 1988; Antonov 1993; Bloomﬁeldical records and the removal of urbanization and other 1992; Christy and McNider 1994; Zheng et al. 1997).biases that may be associated with the above data prob- Studies using temperature records have shown that the mean global surface temperature has increased by about 0.3 –0.6 C over the last 100 yr. There are however large Corresponding author’s address: Dr. Stephen Mutua King’uyu, geographical variations in the observed warming trendsMeteorological Services, P.O. Box 101000, Gaborone, Botswana. with some locations indicating some general coolingE-mail: firstname.lastname@example.org signals (IPCC 1990, 1992, 1995). 2000 American Meteorological Society
15 AUGUST 2000 KING’UYU ET AL. 2877 This study investigated the trends in the mean month- during the respective summer months, centered aroundly minimum and maximum temperature records over July and January, respectively. The equatorial sector ofeastern Africa. The interannual patterns in the mean the region has two distinct rainfall seasons centeredmonthly minimum and maximum temperature values around the northern autumn and spring months of Sep-were also examined. The term ‘‘eastern Africa’’ is here tember–November and March–May, respectively. Thebroadly used to imply 19 countries located on the eastern months of January, April, July, and October were there-part of the African continent and extending from the fore used in the study to investigate any seasonal shiftssub-Saharan Sudan and Ethiopia to the Horn of Africa, in the interannual temperature characteristics.East Africa, and central and southern Africa. The region Other data were also used in the current study tois enclosed by latitudes 20 –60 E and longitudes 25 – investigate the potential association between the ob-30 S. Figure 1 is a map of the area of study and the served interannual characteristics of minimum and max-data network. imum temperature anomalies and anomalies that are of- The major systems that control the spatial and tem- ten observed in the regional climate. These included theporal characteristics of the climate of the region include monthly Southern Oscillation index (SOI) as derivedthe intertropical convergence zone, subtropical anticy- from the normalized sea level pressure difference be-clones, monsoon wind systems, the African jet streams, tween Tahiti and Darwin and obtained from the Climateeasterly/westerly waves, tropical cyclones, and telecon- Analysis Center, Washington, D.C.nections with regional and large-scale quasi-periodic Phases of the upper-level zonal winds over Nairobiclimate systems like the quasi-biennial oscillation were also used to represent the interannual patterns of(QBO), intraseasonal waves, and El Nino–Southern Os- ˜ the QBO over the area of study. Ogallo et al. (1994)cillation (ENSO), among others. Thermally induced me- observed that the QBO signal is well discernible usingsoscale systems associated with orography and large the easterly (zonal) wind over Nairobi for levels 30–70water bodies, which include inland lakes, also introduce hPa. Actual cloudiness data and out-going longwavesigniﬁcant modiﬁcations to the large-scale ﬂow over the radiation (OLR) were used to investigate the uniqueregion. An example is the Lake Victoria, with an areal space–time anomalies in the convective patterns overexpanse of over 69 000 km 2 and a unique circulation the study region, which may be associated with anom-of its own. Details of the regional climatology may be alous maximum and minimum temperature patterns.obtained from Ogallo (1987, 1993), King’uyu (1994), Cloudiness records were available for Kenyan stationsand Anyamba (1992), among others. only for both 0800 and 1200 UTC. The OLR data was The major objectives of the present study were to from the National Oceanic and Atmospheric Adminis-examine the existence of any signiﬁcant trends in both tration (NOAA) satellite observations in grids of 2.5minimum and maximum temperature over the study re- lat 2.5 long for the period 1977–88. Since most ofgion. Attempts were also made to explore the potential the stations lay away from grid points, interpolation wascauses of any observed temperature anomalies. The data used to estimate station values.used in the study are highlighted in the following sec- Urbanization was not explicitly delineated in the cur-tion. rent study due to nonavailability of data for non-Kenyan stations. For Kenyan stations, however, a simple non- quantitative approach was used. This involved a cate-2. Data and quality control gorization of trends for urban stations and those for rurala. Data stations in order to examine if there was any difference. Any station with a population of below 2000 people The data used in the study consisted of the daily was treated as rural, while stations with populations ofminimum and maximum temperature records from 71 2000 or more were treated as urban.stations within eastern Africa, obtained from the A common problem with the maximum and minimumDrought Monitoring Centre, Nairobi, for eastern and temperature records from the selected locations was thatsouthern Africa. The 71 stations were the only ones, out of missing values. Such records were estimated usingof hundreds within the region, that satisﬁed our accep- correlation and regression methods. The correlation andtance criteria, based on the record length, percentage of regression methods used were derived from the bestmissing data, quality control, and homogeneity tests. instantaneous/time-lagged interstation correlation/auto-Data entry and archiving was done in the climate com- correlation values. The estimated data were, however,puting (CLICOM) format. The distribution of the sta- less than 10% of the record at any given location. Sta-tions used was shown in Fig. 1, while Table 1 is a list tions with more than 10% of the record missing wereof the stations used. The daily records were used to not included in the study.generate monthly mean maximum and minimum tem- Interstation correlation was evaluated by calculatingperature series for each station. The period of study the simple correlation coefﬁcient between each two sta-extended from 1939 to 1992. tions. This resulted in a ‘‘71 71’’ correlation matrix. The northern and southern sectors of the study region The matrix was used to determine those stations withobserve maximum precipitation and temperature values the highest correlation with the station with missing
2878 JOURNAL OF CLIMATE VOLUME 13 FIG. 1. Map of study area and data network.
15 AUGUST 2000 KING’UYU ET AL. 2879 TABLE 1. List of stations used in study. that only authorized personnel have access to their re-Code Name Code Name Code Name spective levels (WMO 1988a). The data are automatically validated for inaccuracies 1 Port Sudan 25 Dagoretti 49 Lusaka 2 Atbara 26 Makindu 50 Zumbo before being registered in the database. This way, values 3 Kassala 27 Lamu 51 Makoka exceeding speciﬁed quality limits are ﬂagged (WMO 4 Khartoum 28 Muyinga 52 Nampula 1988b). Validation is normally done by a meteorologist, 5 El-Fasher 29 Bujumbura 53 Vacoas who has hands-on experience in the relevant data col- 6 Asmara 30 Mombasa 54 Plaisance 7 Djibouti 31 Kigoma 55 Kariba lection, and training in statistical quality control meth- 8 Kadugli 32 Tabora 56 Mutoko ods. The validator can override the quality control rules 9 Combolcha 33 Dodoma 57 Quelimane if he is convinced any ﬂagged values are accurate ob-10 Debre-Marcos 34 Morogoro 58 Shakawe servations, or replace them if his investigations reveal11 Dire-Dawa 35 Dar-Es-Salaam 59 Maun that they may have been erroneously input. It is only12 Adiss-Ababa 36 Mbeya 60 Bulawayo13 Neghele 37 Kasama 61 Beira after such a process that the values are registered in the14 Juba 38 Songea 62 Francistown database (WMO 1988b). This process ensures the qual-15 Lodwar 39 Tanga 63 Inhambane ity of climatic records archived in CLICOM (WMO16 Moyale 40 Moroni 64 Mahalapye 1988b). It is, however, noteworthy that minute errors17 Arua 41 Agalega 65 Xai-Xai18 Wajir 42 Pemba 66 Maputo that may not affect the totals signiﬁcantly may pass19 Kasese 43 Mzuzu 67 Bigbend without been detected.20 Mbarara 44 Zambezi 68 Tshane21 Entebbe 45 Ndola 69 Gaborone22 Kampala 46 Chipita 70 Tsabong 3. Methods23 Kisumu 47 Lichinga 71 St. Brandon24 Garissa 48 Livingstone The above climatological records were subjected to several analyses, which included trend, spectral, and correlation analyses. Trend analysis examined the ex- istence of any signiﬁcant trends in the interannual pat-data. The least-squares method was then used to develop terns of maximum and minimum temperature within thea linear regression equation expressing the observations region. Spectral analysis was used to delineate the in-at the station of interest in terms of observations at the terannual cycles that are dominant in the various tem-station with which it was most strongly correlated. It is perature series. Correlation analysis was also used tosuch an equation that was used to estimate any missing investigate the potential association between any ob-data. Only those stations with an interstation correlation served interannual anomalies in the maximum and min-coefﬁcient of at least 0.5 were used to estimate missing imum surface air temperature patterns and anomalies indata. the climate systems that control the seasonal climate variability over the region.b. Quality control Several methods were used in the study to determine the existence of any signiﬁcant trends in the year-to- All the records were subjected to quality control tests year patterns of maximum and minimum temperaturebefore any analysis to ensure both internal consistency over the region. The techniques used included graphicaland consistency with neighboring observations. Some and statistical techniques. The graphical methods dis-of the techniques used included the nonparametric played the visual patterns of the mean interannual trendsWald–Walfowitz (1943) runs tests, Maronna–Yohai of the respective temperature records. A ﬁve-term mov-(1978), and Spearman rank statistics to discriminate ho- ing average ﬁlter was used to smooth the interannualmogeneity against trend (WMO 1966; Kendall et al. temperature trends. The most objective trend analyses1961). Mass curves and range validation techniques in this study were however based on the analysis ofwere also used. Details of such methods are available variance approach and the nonparametric Spearmanin many standard climatological references including rank correlation statistic (WMO 1966; Kendall et al.WMO (1966, 1986). The above methods were in ad- 1961).dition to quality control procedures resident in CLICOM Spectral analysis delineated the major cycles in theas recommended in WMO (1992). interannual patterns of the maximum and minimum sur- The CLICOM package is designed for data stored on face temperature values over the region of study. Detailsa long-term basis. It uses a database that organizes and of the maximum entropy method of spectral analysisstores input data consisting of numerical input values that was used in this study may be found in Kendall etfor climatic study, and descriptive information like the al. (1961) and Kay and Marple, (1981) among others.station location, period for which data are available for Interannual anomalies in meteorological parametersthe station, the climate elements measured, types of in- are often linked to interannual variations in the systemsstruments used, times of observation, etc. Management that control the global and regional climate. Three ofof the data is done by a commercial software called the systems with quasi-periodic ﬂuctuations that are as-DataEase. DataEase has seven security levels to ensure sociated with interannual climate anomalies over the
2880 JOURNAL OF CLIMATE VOLUME 13region 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 (X tand Y t ) are perfectly correlated if |r | 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 linkages FIG. 2. Cumulative temperature series at Makindu in Kenyabetween maximum and minimum temperature anoma- [2 17 S, 37 50 E, 100 m above mean sea level (MSL)].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 between locations that later indicated signiﬁcant change in thepairs of variables including time-lagged linkages. This minimum and maximum temperature trends. Historicalis especially true for variables that may be correlated records were used to examine any changes in the lo-within positive or negative phases only. While several cation or type of instruments within the study regioncomplex statistical methods are available to study such that could be associated with any observed shifts in thecomplex relationships, some authors have used very mass curves. If any such shifts were attributed to chang-simple statistical techniques, which include 2 tests es in instrument types or station sites, the records werebased on simple contingency tables, which compare not included in the analysis.unique anomaly categories derived from classes of Typical patterns of the time series of the maximumpaired variables. Others have examined the interannual and minimum temperature records are presented in Figs.patterns of the sum/difference between the correspond- 3–7, while the spatial distribution of temperature changeing normalized values for the pair of variables. Such for January is presented in Fig. 8. A general minimummethods can help to clearly amplify the anomalies and (nighttime) temperature warming in recent years is quiteprovide better composites for the linkages between the evident, especially at land locations in the northern sec-pair of variables. Both simple correlation and contin- tor of the study area and extending up to about 5 Sgency tables were used in the current study. (Figs. 3, 4, 5, 8). Similar patterns were observed for the In this study, a 3 3 contingency table was used to other seasons. The diurnal temperature range within thiscategorize below normal, normal, and above normal oc- area therefore showed a decreasing trend (Fig. 5). Thecurrences for all the variables used in the analysis, geographical patterns of the observed warming trendsnamely, minimum and maximum surface temperature, were, however, very complex with some locations show-SOI, cloudiness/OLR, and QBO. The corresponding ing no change or decreasing trends of minimum tem-standard deviations were used to determine the threshold perature, especially over the coastal zones and near largelimits for each of the anomaly classes. An observation inland water lakes (Fig. 8).was considered to be signiﬁcantly different from the Such locations often have strong thermally inducedmean if the corresponding anomaly was less than a half mesoscale circulation, which together with the localof the standard deviation. moisture sources often modify patterns of the large-scale circulation signiﬁcantly. Seesaw relationships between locations over land and those near the large water bodies4. Observed temperature trends of East Africa have been noted with ENSO by Ogallo Quality control tests of the few estimated daily max- (1987) among many others.imum and minimum temperature records indicated that Some land locations to the south of 5 lat showedsuch records were generally homogeneous with those decreasing nighttime and daytime temperature trendsobserved at the respective locations. A typical example (Fig. 6), while others showed increasing trends. Otherof the mass curves obtained from the quality control stations within this subregion showed decreasing night-analysis is shown in Fig. 2. The homogeneous temper- time and increasing daytime temperature trends (Fig. 7).ature records formed the fundamental base for most of An interesting feature of the observed trends was alsothe investigations carried out in the study. Signiﬁcant observed over the Mozambique channel region, whereshifts in the mass curves were however noted at some signiﬁcant nighttime and daytime cooling was observed
15 AUGUST 2000 KING’UYU ET AL. 2881 FIG. 3. Temperature series during Nov at Debremarcos in Ethiopia (10 21 N, 37 43 E; 2440 m MSL).during all seasons of the year (Figs. 6, 8). Similar pat- at times also be linked to decreasing maximum tem-terns have in the past been associated with a weakening perature trends (Jones 1995; Razuveav et al. 1995; Park-of the Mozambique warm current (Hastenrath 1985). er et al. 1993, 1994; Jones et al. 1990; IPCC 1995;These patterns of decreasing/increasing trends have Plummer et al. 1995; Salinger et al. 1993; Karl et al.however been observed at many other locations world- 1984, 1991, 1993, 1995a,b; Kukla and Karl 1993; Parkerwide (Karl et al. 1984, 1991; Razuveav et al. 1995; Jones et al. 1995; Briffa et al. 1995).1995). It is important to note that some of the trends in No signiﬁcant trends could be delineated from theFig. 8 are quite signiﬁcant, being in excess of 0.6 C at interannual patterns of the OLR and the few cloud coversome locations. records that were used in this study. Attempts were made The geographical patterns of the diurnal temperature to compare the differences in the maximum and mini-range also varied signiﬁcantly. Nighttime warming and mum temperature patterns for the rural and urban lo-a decreasing diurnal temperature range have been re- cations. No unique differences could be detected in theported by a number of authors. The observed decrease interannual temperature patterns between the domi-in the diurnal temperature range has also been associated nantly rural and the dominantly urban locations.with an increase in cloud cover and not always due to The most dominant feature in the interannual patternsincreased nighttime temperature since such trends may at all the locations was, however, the recurrence of very FIG. 4. Temperature series during Jul at Dagoretti-Corner in Kenya (01 18 S, 36 45 E; 1798 m MSL).
2882 JOURNAL OF CLIMATE VOLUME 13 FIG. 5. Temperature range series for Jan at Dagoretti-Corner in Kenya (01 18 S, 36 45 E; 1798 m MSL).high/low maximum and minimum temperature values. ues and the SOI together with OLR were very low atSpectral analysis indicated that the periods of recurrence many locations. Relatively large values were howeverincluded 2–3.3 yr, 3.5–4.5 yr, 5–6 yr, and 10–13 yr common within the southern sector of the study region.(Table 2). Some stations also showed cycles of greater Time-lagged correlation values were however signiﬁ-than 13 yr. The magnitudes of the spectral peaks varied cant at greater than 95% conﬁdence level at many lo-signiﬁcantly from location to location as reﬂected in cations (Tables 3 and 4). The time lags ranged betweenFig. 9. 2 and 9 months although peak correlation values were concentrated around 3–6 months. The high degree of persistence that was observed in the correlation patterns5. Linkages between temperature anomalies and is consistent with the persistent nature of ENSO (Pan the large-scale circulation and Oort 1983). The relationship between temperature Results of correlation analysis indicated that zero-lag and SOI were clearer when contingency tables werecorrelation between daytime–nighttime temperature val- used. Signiﬁcant correlation between ENSO and oc- FIG. 6. Temperature series during Jul at Pemba in Mozambique (12 58 S, 40 30 E; 49 m MSL).
15 AUGUST 2000 KING’UYU ET AL. 2883 FIG. 7. Temperature series during Apr at Lusaka in Zambia (15 19 S, 28 27 E; 1152 m MSL).currences of above/below normal rainfall over the study Zero-lag and time-lagged correlation between maxi-region has been reported by Ogallo (1987) and Ogallo mum/minimum temperature values and the QBO wereet al. (1994), among others. Above/below normal cloud generally complex and no unique geographical inﬂuencecover is often associated with the occurrences of above/ could be delineated, even with the use of contingencybelow normal rainfall. Such effects must therefore be tables in the detailed analysis of the temperature anom-reﬂected in the diurnal temperature characteristics. alies during westerly and easterly QBO phases. FIG. 8. Spatial distribution of temperature change for Jan: (a) observed trends of minimum temperature for Jan and (b) contour map of the same data showing areas of cooling and warming.
2884 JOURNAL OF CLIMATE VOLUME 13 TABLE 2. Summary of some of the observed spectral cycles. TABLE 3. Correlation between prevailing cloudiness and temper- ature. Here r is the simple correlation coefﬁcient and C.L. is the Min temp Max temp conﬁdence level. Station cycles (yr) cycles (yr) 0800 UTC cloudiness 1200 UTC cloudiness Atbara 16, 10.7, 2.9, 2 16, 3, 2 Asmara 22, 11, 2.8, 2 22, 11, 2.8, 2 r C.L./% r C.L./% Dagoretti 18, 3, 3 12, 6, 4, 2 Min temp 0.35 99.9 0.34 99.9 Lamu 27, 5.4, 3, 2 5.4, 3, 2 Max temp 0.62 99.9 0.07 90 Mbarara 12.5, 2.5 8.3, 6.3, 2.5, 2 Temp range 0.34 99.9 0.20 95 Muyinga 12.5, 5, 2.5 12.5, 5, 2.5 Plaisance 6, 3, 2 40, 5.7, 3, 2 Agelega 30, 15, 5, 2.7 30, 5, 2.5 Kariba 12.5, 3.6, 2 25, 12.5, 2.3 Tshane 15, 3.3, 2 10, 3.3, 2.5 Kasama 10, 3.3 19, 3.8, 2 dicating signiﬁcant opposite trends, especially to the Maputo 3.6, 2.1 5.7, 3.3, 2.4 north of 5 S. An interesting feature was also observed over the Mozambique channel where both signiﬁcant nighttime and daytime cooling was dominant. Locations north of 5 S indicated more organized decreasing or increasing diurnal trend in the daytime/nighttime tem-6. Conclusions perature patterns. The results from this study indicated a signiﬁcant rise The complex nature of the observed geographical pat-in the nighttime temperature at several locations over terns of the observed trends made it extremely difﬁculteastern Africa. The distribution of the warming trends for attribution of the observed daytime/nighttime tem-were, however, not geographically uniform with many perature trends to be given in the current study. Closecoastal locations and those near large water bodies in- association between recurrences of the extremely large nighttime/daytime temperature and anomalies in the large-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 and any other biases in the climatological data that were used in the study. No clear differences could, however, TABLE 4. Some of the time-lagged correlation between temperature and SOI. Here C.L. is the conﬁdence level. Time- SOI Lag Station Variable Month month (months) r C.L./% Asmara Min T Jul Apr 3 0.35 99 Oct Jul 3 0.45 99 Max T Apr Jan 3 0.42 97.5 Khartoum Min T Jul Apr 3 0.43 99 Jul 0 0.43 99 Max T Jan Jan 0 0.33 99 Lodwar Min T Apr Jan 3 0.34 97.5 Jul Apr 3 0.50 97.5 Kisumu Min T Jan Jan 0 0.39 99 Oct Apr 6 0.41 99 Jul 3 0.44 99 Francistown Min T Jan Apr 8 0.36 97.5 Jul 6 0.53 99 Oct 3 0.52 99 Jul Apr 3 0.35 95 Oct 9 0.32 95 Agalega Min T Jan Oct 3 0.51 99 FIG. 9. Spectral cycles of temperature at (a) Lamu in Kenya Jul Nov 6 0.43 99(02 16 S, 40 50 E; 6 m MSL) and (b) Kariba in Zimbabwe (16 31 S, Max T Apr Nov 5 0.43 9928 53 E; 718 m MSL).
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