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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 actual
tigations in recent years, especially in issues related to
                                                                      states of the environment at the particular locations and
the 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 patterns
accurate homogeneous and long period instrumental re-                 of climate at the global and regional scales have been
cords, due to changes in observational practices, ur-                 derived from temperature and precipitation (Vinnikov
banization 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; Bloomfield
ical 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 trends
Meteorological Services, P.O. Box 101000, Gaborone, Botswana.         with some locations indicating some general cooling
E-mail: bots.met@info.bw                                              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 around
ly minimum and maximum temperature records over              July and January, respectively. The equatorial sector of
eastern Africa. The interannual patterns in the mean         the region has two distinct rainfall seasons centered
monthly 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. The
broadly 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 shifts
sub-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 to
is 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 the
poral characteristics of the climate of the region include   monthly Southern Oscillation index (SOI) as derived
the 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 Climate
easterly/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 Nairobi
climate 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 using
soscale systems associated with orography and large          the easterly (zonal) wind over Nairobi for levels 30–70
water bodies, which include inland lakes, also introduce     hPa. Actual cloudiness data and out-going longwave
significant modifications to the large-scale flow over the      radiation (OLR) were used to investigate the unique
region. An example is the Lake Victoria, with an areal       space–time anomalies in the convective patterns over
expanse 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 stations
and 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 2.5
minimum and maximum temperature over the study re-           lat    2.5 long for the period 1977–88. Since most of
gion. Attempts were also made to explore the potential       the stations lay away from grid points, interpolation was
causes 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 rural
a. 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 of
minimum 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 minimum
Drought Monitoring Centre, Nairobi, for eastern and          temperature records from the selected locations was that
southern Africa. The 71 stations were the only ones, out     of missing values. Such records were estimated using
of hundreds within the region, that satisfied our accep-      correlation and regression methods. The correlation and
tance criteria, based on the record length, percentage of    regression methods used were derived from the best
missing 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 were
of 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 calculating
perature 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 with
observe 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 reveal
11     Dire-Dawa        35    Dar-Es-Salaam     59    Maun          that they may have been erroneously input. It is only
12     Adiss-Ababa      36    Mbeya             60    Bulawayo
13     Neghele          37    Kasama            61    Beira         after such a process that the values are registered in the
14     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 (WMO
16     Moyale           40    Moroni            64    Mahalapye     1988b). It is, however, noteworthy that minute errors
17     Arua             41    Agalega           65    Xai-Xai
18     Wajir            42    Pemba             66    Maputo
                                                                    that may not affect the totals significantly may pass
19     Kasese           43    Mzuzu             67    Bigbend       without been detected.
20     Mbarara          44    Zambezi           68    Tshane
21     Entebbe          45    Ndola             69    Gaborone
22     Kampala          46    Chipita           70    Tsabong       3. Methods
23     Kisumu           47    Lichinga          71    St. Brandon
24     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 the
a 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 to
such 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 in
data.                                                               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 temperature
before any analysis to ensure both internal consistency             over the region. The techniques used included graphical
and 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 trends
Wald–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 interannual
mogeneity against trend (WMO 1966; Kendall et al.                   temperature trends. The most objective trend analyses
1961). Mass curves and range validation techniques                  in this study were however based on the analysis of
were also used. Details of such methods are available               variance approach and the nonparametric Spearman
in 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 the
as 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. Details
a long-term basis. It uses a database that organizes and            of the maximum entropy method of spectral analysis
stores input data consisting of numerical input values              that was used in this study may be found in Kendall et
for 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 parameters
the station, the climate elements measured, types of in-            are often linked to interannual variations in the systems
struments used, times of observation, etc. Management               that control the global and regional climate. Three of
of 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 13


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 to
investigate the existence of ENSO and QBO signals in
the interannual temperature anomaly patterns through
spectral and correlation analyses.
   Correlation analysis was used to examine the rela-
tionship between temperature anomalies and anomalies
in the cloudiness together with the associated regional
climate systems. Under this method, the simple corre-
lation coefficient, r, was calculated. Two variables (X t
and Y t ) are perfectly correlated if |r | 1, while negative/
positive r values indicate inverse/positive association
between the two variables. The statistical significance
of the computed r was tested by use of the Student’s
t-test. The computed r were used to determine linkages
                                                                   FIG. 2. Cumulative temperature series at Makindu in Kenya
between 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 the
pairs of variables including time-lagged linkages. This         minimum and maximum temperature trends. Historical
is 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 region
complex statistical methods are available to study such         that could be associated with any observed shifts in the
complex 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 were
based 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 maximum
paired 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 change
ing normalized values for the pair of variables. Such           for January is presented in Fig. 8. A general minimum
methods can help to clearly amplify the anomalies and           (nighttime) temperature warming in recent years is quite
provide 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 S
gency 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 this
categorize below normal, normal, and above normal oc-           area therefore showed a decreasing trend (Fig. 5). The
currences for all the variables used in the analysis,           geographical patterns of the observed warming trends
namely, 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 large
limits 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 induced
mean if the corresponding anomaly was less than a half          mesoscale circulation, which together with the local
of 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 bodies
4. 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 showed
such records were generally homogeneous with those              decreasing nighttime and daytime temperature trends
observed at the respective locations. A typical example         (Fig. 6), while others showed increasing trends. Other
of 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 also
the investigations carried out in the study. Significant         observed over the Mozambique channel region, where
shifts 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; Parker
wide (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 the
Fig. 8 are quite significant, being in excess of 0.6 C at           interannual patterns of the OLR and the few cloud cover
some 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 the
ported 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 patterns
increased 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 at
Spectral analysis indicated that the periods of recurrence        many locations. Relatively large values were however
included 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 between
Fig. 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 patterns
5. 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 were
correlation 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 were
et al. (1994), among others. Above/below normal cloud                generally complex and no unique geographical influence
cover is often associated with the occurrences of above/             could be delineated, even with the use of contingency
below 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 difficult
eastern 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. Close
coastal 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    99
28 53 E; 718 m MSL).
15 AUGUST 2000                                            KING’UYU ET AL.                                                                  2885

be discerned from the interannual patterns of daytime/                         , H. F. Diaz, and G. Kukla, 1988: Urbanization, its detection and
nighttime temperature from the rural and urban locations                       effects in the United States climate record. J. Climate, 1, 1099–
                                                                               1123.
used in the study.                                                             , G. Kukla, V. N. Razuvayer, M. J. Changery, R. G. Quayle, R.
                                                                               R. Heim, D. R. Easterling, and C. F. Fu, 1991: Global warming:
                                                                               Evidence of asymmetric diurnal temperature change. Geophys.
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Recent trends of minimum and maximum surface temperatures over eastern africa

  • 1. 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 actual tigations in recent years, especially in issues related to states of the environment at the particular locations and the 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 patterns accurate homogeneous and long period instrumental re- of climate at the global and regional scales have been cords, due to changes in observational practices, ur- derived from temperature and precipitation (Vinnikov banization 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; Bloomfield ical 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 trends Meteorological Services, P.O. Box 101000, Gaborone, Botswana. with some locations indicating some general cooling E-mail: bots.met@info.bw signals (IPCC 1990, 1992, 1995). 2000 American Meteorological Society
  • 2. 15 AUGUST 2000 KING’UYU ET AL. 2877 This study investigated the trends in the mean month- during the respective summer months, centered around ly minimum and maximum temperature records over July and January, respectively. The equatorial sector of eastern Africa. The interannual patterns in the mean the region has two distinct rainfall seasons centered monthly 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. The broadly 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 shifts sub-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 to is 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 the poral characteristics of the climate of the region include monthly Southern Oscillation index (SOI) as derived the 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 Climate easterly/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 Nairobi climate 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 using soscale systems associated with orography and large the easterly (zonal) wind over Nairobi for levels 30–70 water bodies, which include inland lakes, also introduce hPa. Actual cloudiness data and out-going longwave significant modifications to the large-scale flow over the radiation (OLR) were used to investigate the unique region. An example is the Lake Victoria, with an areal space–time anomalies in the convective patterns over expanse 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 stations and 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 2.5 minimum and maximum temperature over the study re- lat 2.5 long for the period 1977–88. Since most of gion. Attempts were also made to explore the potential the stations lay away from grid points, interpolation was causes 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 rural a. 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 of minimum 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 minimum Drought Monitoring Centre, Nairobi, for eastern and temperature records from the selected locations was that southern Africa. The 71 stations were the only ones, out of missing values. Such records were estimated using of hundreds within the region, that satisfied our accep- correlation and regression methods. The correlation and tance criteria, based on the record length, percentage of regression methods used were derived from the best missing 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 were of 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 calculating perature 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 with observe maximum precipitation and temperature values the highest correlation with the station with missing
  • 3. 2878 JOURNAL OF CLIMATE VOLUME 13 FIG. 1. Map of study area and data network.
  • 4. 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 reveal 11 Dire-Dawa 35 Dar-Es-Salaam 59 Maun that they may have been erroneously input. It is only 12 Adiss-Ababa 36 Mbeya 60 Bulawayo 13 Neghele 37 Kasama 61 Beira after such a process that the values are registered in the 14 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 (WMO 16 Moyale 40 Moroni 64 Mahalapye 1988b). It is, however, noteworthy that minute errors 17 Arua 41 Agalega 65 Xai-Xai 18 Wajir 42 Pemba 66 Maputo that may not affect the totals significantly may pass 19 Kasese 43 Mzuzu 67 Bigbend without been detected. 20 Mbarara 44 Zambezi 68 Tshane 21 Entebbe 45 Ndola 69 Gaborone 22 Kampala 46 Chipita 70 Tsabong 3. Methods 23 Kisumu 47 Lichinga 71 St. Brandon 24 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 the a 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 to such 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 in data. 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 temperature before any analysis to ensure both internal consistency over the region. The techniques used included graphical and 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 trends Wald–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 interannual mogeneity against trend (WMO 1966; Kendall et al. temperature trends. The most objective trend analyses 1961). Mass curves and range validation techniques in this study were however based on the analysis of were also used. Details of such methods are available variance approach and the nonparametric Spearman in 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 the as 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. Details a long-term basis. It uses a database that organizes and of the maximum entropy method of spectral analysis stores input data consisting of numerical input values that was used in this study may be found in Kendall et for 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 parameters the station, the climate elements measured, types of in- are often linked to interannual variations in the systems struments used, times of observation, etc. Management that control the global and regional climate. Three of of 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
  • 5. 2880 JOURNAL OF CLIMATE VOLUME 13 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 to investigate the existence of ENSO and QBO signals in the interannual temperature anomaly patterns through spectral and correlation analyses. Correlation analysis was used to examine the rela- tionship between temperature anomalies and anomalies in the cloudiness together with the associated regional climate systems. Under this method, the simple corre- lation coefficient, r, was calculated. Two variables (X t and Y t ) are perfectly correlated if |r | 1, while negative/ positive r values indicate inverse/positive association between the two variables. The statistical significance of the computed r was tested by use of the Student’s t-test. The computed r were used to determine linkages FIG. 2. Cumulative temperature series at Makindu in Kenya between 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 the pairs of variables including time-lagged linkages. This minimum and maximum temperature trends. Historical is 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 region complex statistical methods are available to study such that could be associated with any observed shifts in the complex 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 were based 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 maximum paired 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 change ing normalized values for the pair of variables. Such for January is presented in Fig. 8. A general minimum methods can help to clearly amplify the anomalies and (nighttime) temperature warming in recent years is quite provide 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 S gency 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 this categorize below normal, normal, and above normal oc- area therefore showed a decreasing trend (Fig. 5). The currences for all the variables used in the analysis, geographical patterns of the observed warming trends namely, 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 large limits 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 induced mean if the corresponding anomaly was less than a half mesoscale circulation, which together with the local of 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 bodies 4. 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 showed such records were generally homogeneous with those decreasing nighttime and daytime temperature trends observed at the respective locations. A typical example (Fig. 6), while others showed increasing trends. Other of 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 also the investigations carried out in the study. Significant observed over the Mozambique channel region, where shifts in the mass curves were however noted at some significant nighttime and daytime cooling was observed
  • 6. 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; Parker wide (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 the Fig. 8 are quite significant, being in excess of 0.6 C at interannual patterns of the OLR and the few cloud cover some 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 the ported 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 patterns increased 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).
  • 7. 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 at Spectral analysis indicated that the periods of recurrence many locations. Relatively large values were however included 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 between Fig. 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 patterns 5. 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 were correlation 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).
  • 8. 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 were et al. (1994), among others. Above/below normal cloud generally complex and no unique geographical influence cover is often associated with the occurrences of above/ could be delineated, even with the use of contingency below 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.
  • 9. 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 difficult eastern 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. Close coastal 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 99 28 53 E; 718 m MSL).
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