kruger andries final.qxd
An analysis of Skukuza climate data
A.C. KRUGER, L.B. MAKAMO and S. SHONGWE
Kruger, A.C., L.B. Makamo and S. Shongwe. 2002. An analysis of Skukuza climate
data. Koedoe 45(1): 1–7. Pretoria. ISSN 0075-6458.
Data from the climate station at Skukuza, which has been open from 1912 to the pre-
sent, are analysed. This exercise was done to provide the South African Regional Sci-
ence Initiative (SAFARI-2000) experimental program with long-term climate statistics
and trends. Climate parameters analysed are rainfall, temperature, humidity and sun-
shine. In the case of rainfall and temperature, the data was first tested for homogeneity
and in only three out of 36 monthly cases, the data proved not to be homogeneous. No
monthly rainfall trends proved to be significant (at the 5 % level), with five months indi-
cating slightly negative trends and seven slightly positive. Only the monthly maximum
temperature series for June proved to be non-homogeneous. The June maximum tem-
perature trend and the February, March, May, July and December minimum temperature
trends were significantly positive. The annual time series for minimum temperature
were also significantly positive. The monthly results were reiterated by the seasonal
results, with the winter maximum temperature trend and the autumn and summer mini-
mum temperature trends significantly positive. Ten months showed negative tempera-
ture diurnal range trends with only March being significant. All long-term statistics of
rainfall, temperature, humidity and sunshine were found to be typical of a savanna type
climate in the southern hemisphere, although average monthly sunshine hours were
somewhat less than the norm, due to frequent influx of moist air from the Mozambique
Channel.
Key words: Skukuza, SAFARI-2000, climate, trends.
A.C. Kruger, Directorate: Climate Systems, South African Weather Service, Private Bag
X097, Pretoria, 0001 South Africa (andries@weathersa.co.za); L.B. Makamo and S.
Shongwe, Directorate: Climate Systems, South African Weather Service, Private Bag
X097, Pretoria, 0001 South Africa.
ISSN 0075-6458 1 Koedoe 45/1 (2002)
Introduction
The South African Regional Science Initia-
tive (SAFARI-2000) experimental program
is developed for southern Africa to explore,
study and address linkages between land-
atmosphere processes and the relationship of
biogenic, pyrogenic or anthropogenic emis-
sions and the consequences of their deposi-
tion to the functioning of biogeophysical and
biogeochemical systems (SAFARI-2000
Home Page). One of the SAFARI-2000 sites
is about 13 km WSW of Skukuza in the
Mpumalanga province. Many scientists are
involved in the project or will use data forth-
coming from it. However, climate data from
the site itself only has a measuring period of
about two years, from 2000 to 2001. The pur-
pose of this paper is to provide an analysis of
the data of the South African Weather Ser-
vice climate station at Skukuza, entailing the
calculations of long-term averages and
trends, for the purpose of background data to
interpret measurements at the flux measure-
ment site.
General climate characteristics of the
area
The climate station of Skukuza is situated at
24º59'S, 31º36'E at a height of 263 m above
sea level. The area the station is situated in is
generally known as the Lowveld and is
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directly east of the eastern escarpment.
According to the new climate regions devel-
oped by the South African Weather Service,
loosely based on the vegetation regions of
Low & Rebelo (1996) and the Köppen cli-
mate classification, Skukuza is situated in
the Lowveld Bushveld region, which
receives moderate summer precipitation
(500–700 mm p.a.) with maximum rainfall
in January. Warm to hot temperatures are
usually experienced and no frost occurs. The
humidity is usually fairly high and this
makes summer days uncomfortable. Sun-
shine duration during summer is below aver-
age for this typical savanna climate, due to
the influx of moist air from the coast and
exacerbated by the proximity of the southern
African escarpment close by towards the
west. This type of climate lends itself to
game, cattle and goat farming, subtropical
fruit, vegetables and sugarcane through irri-
gation, and ecotourism.
Analysis of rainfall
To obtain a general idea of the rainfall cli-
mate of Skukuza, trends and long-term sta-
tistics are calculated. But before any analy-
ses can be done, the data first have to be
checked for overall quality, as inhomo-
geneities can influence the results signifi-
cantly, especially in the case of trend calcu-
lations.
Homogeneity of rainfall data
To test for the homogeneity of Skukuza rain-
fall data, the non-parametric run test
described by Thom (1966) was applied to
monthly time series for January to Decem-
ber. This test is based on the number of runs
(or groups of consecutive data points) above
or below the median value of the time series.
The results are shown in Table 1. In the sec-
ond column the number of runs are shown
which should ideally be about half of the
total years used in the calculation (in this
case about 80 years of data), to accept the
time series as homogeneous. The third col-
umn shows the results when compared to the
Koedoe 45/1 (2002) 2 ISSN 0075-6458
Table 1
Results of run test to determine homogeneity of
monthly rainfall time series
Month Number of runs Result
Jan 49 Homogeneous
Feb 38 Homogeneous
Mar 34 Non-homogeneous
Apr 37 Homogeneous
May 34 Non-homogeneous
Jun 44 Homogeneous
Jul 39 Homogeneous
Aug 37 Homogeneous
Sep 36 Homogeneous
Oct 43 Homogeneous
Nov 38 Homogeneous
Dec 38 Homogeneous
lower and upper 10 % significance limits.
Two of the months, March and May, showed
possible inhomogeneities in their series, with
much fewer than the ideal number of runs.
This might indicate a possible trend or slip-
page in the mean. This is however not evi-
dent from the trend calculations, of which
the results are shown in the following sec-
tion. Thus, another possible explanation for
the inhomogeneity results is that there might
be relatively large clusters of consecutive
months with rainfall above or below the
median value, but not distributed in a way to
cause a trend in the data. For March the
largest clusters or runs with four or more
consecutive values above or below the medi-
an are from 1912 to 1919 (below the medi-
an), 1929 to 1934 (above the median), 1943
to 1946 and 1962 to 1966 (below the medi-
an), 1967 to 1972 (above the median), and
1973 to 1976 and 1982 to 1986 (below the
median). For May the runs are 1934 to 1937
(above the median), 1943 to 1946 (below the
median), 1948 to 1951 (above the median),
1962 to 1965 and 1967 to 1970 (below the
median), and 1979 to 1983 and 1990 to 1993
(above the median). Except for 1943 to 1946
and 1962 to 1965, none of the above periods
are the same for both March and May, thus
mostly excluding the probability of artificial
biases introduced to the climate series, caus-
ing recorded values to be too high or low.
Also, no mention is made in the metadata of
kruger andries final.qxd 2005/12/09 11:32 Page 2
the station of changes in exposure during,
just before or just after the mentioned peri-
ods, which lets one come to the conclusion
that the above clusters are most possibly nat-
ural phenomena, a true reflection of the rain-
fall received at the station. No attempt was
thus made to adjust data in the series before
further analysis.
Trends in rainfall
Table 2 shows the analysis of linear trends in
monthly rainfall time series for Skukuza for
the period 1912 to 2001. The month with the
highest trend is December with a very small
positive value of 0.41 mm p.a. None of the
trends calculated were significant at the 5%
significance level. Trends were also calcu-
lated for seasonal rainfall, where summer is
defined as the months from December to
February, autumn from March to May, win-
ter from June to August and spring from
September to November. The results are
shown in Table 3. Again, very small trends
were detected, the highest being 1.4 mm p.a.
for summer while none of the trends were
significant. These results indicate that the
long-term rainfall average for Skukuza has
remained fairly constant over the past 90
years.
ISSN 0075-6458 3 Koedoe 45/1 (2002)
Long-term rainfall statistics
Figure 1 and Table 4 show long-term rainfall
statistics for the period 1912 to 2001. Simi-
lar statistics is supplied by Scholes et al.
(2001), but only for a shorter period, ending
in 1999. The analyses reveal a typical sum-
mer rainfall climate in the southern hemi-
sphere. From the absolute maximum and
minimum monthly rainfall it is clear that the
rainfall is highly variable, making the area
prone to frequent droughts and floods.
Temperature
The same approach was followed with tem-
perature analyses as in the case of rainfall.
Firstly the data was checked for homogene-
ity, and after that long-term trends and statis-
tics were calculated.
Homogeneity of temperature data
As with rainfall, the run test described by
Thom (1966) was used to test monthly time
series for homogeneity. The total number of
years of data is 42 years (1960 to 2001).
Table 2
Monthly rainfall trends (mm p.a.) for the period 1912 to 2001
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Trend –0.25 0.25 –0.13 0.13 –0.02 –0.03 –0.03 0.08 0.07 0.18 0.08 0.41
Table 3
Seasonal rainfall trends (mm p.a.) for the period
1912 to 2001
Season Autumn Winter Spring Summer
Trend –0.07 0.07 1.1 1.4 Fig. 1. Average monthly rainfall (mm) for the period
1912 to 2001.
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Koedoe 45/1 (2002) 4 ISSN 0075-6458
Table 4
Rainfall statistics (mm) for the period 1912 to 2001
Statistic Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Absolute
Monthly
Maximum 835 531 260 222 83 110 132 182 193 231 434 331
Absolute
Monthly
Minimum 8 0 4 0 0 0 0 0 0 0 0 1
Maximum
daily
rainfall 103.5 119.5 86.5 71.5 56.5 40.1 75.2 40.0 85.6 44.4 67.3 99.5
Average
amount of
rainy days
(>= 0.1 mm) 9.8 9.6 8.8 5.7 3.0 2.1 1.8 2.3 3.3 7.0 10.6 9.8
Table 5
Results of run test to determine homogeneity of maximum and
minimum temperature time series
Month Maximum Maximum Minimum Minimum
temperature temperature temperature temperature
runs result runs result
Jan 23 Homogeneous 17 Homogeneous
Feb 19 Homogeneous 16 Homogeneous
Mar 21 Homogeneous 24 Homogeneous
Apr 24 Homogeneous 20 Homogeneous
May 23 Homogeneous 24 Homogeneous
Jun 26 Non-homogeneous 19 Homogeneous
Jul 19 Homogeneous 18 Homogeneous
Aug 23 Homogeneous 17 Homogeneous
Sep 22 Homogeneous 22 Homogeneous
Oct 25 Homogeneous 19 Homogeneous
Nov 23 Homogeneous 17 Homogeneous
Dec 23 Homogeneous 25 Homogeneous
According to the test, the ideal number of
runs should be approximately 21 for a homo-
geneous data set. The results for the maxi-
mum and minimum temperatures are shown
in Table 5. The third and last columns show
the results of the test when compared to the
lower and upper 10 % significance limits.
Only the maximum temperature series for
July showed possible inhomogeneities in its
data series, indicating a high number of short
runs in the series. This may in turn indicate a
climatic vaccilation of the mean, or relative-
ly short cycles in the series, although this
was not verified by graphical analysis.
Trends in maximum and minimum tempera-
ture
Table 6 shows the trends of monthly average
maximum and minimum temperatures. For
the maximum temperature, only July showed
kruger andries final.qxd 2005/12/09 11:32 Page 4
a significant trend at the 5 % level. For the
minimum temperature, five of the months
showed significantly positive trends, as well
as the annual average. These results are con-
sistent with global trends where the mini-
mum temperatures show higher positive
trends than the maximum temperature
(Easterling et al. 2000), causing the diurnal
range in temperature to become progres-
sively smaller. Table 7 shows the seasonal
trends for maximum and minimum temper-
atures. The maximum temperature trend in
winter, as well as the minimum temperature
trend in autumn and summer, showed sig-
nificantly positive trends.
ISSN 0075-6458 5 Koedoe 45/1 (2002)
Table 6
Monthly and average trend of maximum and mini-
mum temperatures (°C p.a.) for the period 1960 to
2001 (* indicates significance at the 5% level)
Month Maximum Minimum
Temperature Temperature
Jan –0.020 0.007
Feb –0.005 0.029*
Mar –0.006 0.035*
Apr 0.026 0.021
May 0.010 0.034*
Jun 0.037* 0.011
Jul 0.008 0.041*
Aug 0.009 0.015
Sep –0.002 0.026
Oct –0.009 0.018
Nov 0.015 0.018
Dec –0.001 0.019*
Average 0.005 0.024*
Table 7
Seasonal trends of maximum, minimum and aver-
age temperatures (°C p.a.) for the period 1960 to
2001 (* indicates significance at the 5 % level)
Season Maximum Minimum Average
Temperature Temperature Temperature
Autumn 0.010 0.030* 0.020
Winter 0.018* 0.029 0.024*
Spring 0.000 0.020 0.010
Summer –0.009 0.018* 0.013
Table 8
Trends of monthly diurnal temperature range (°C p.a.) for the period 1960 to 2001
(* indicates significance at the 5 % level)
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Trend -0.027 -0.034 -0.041* 0.005 -0.025 0.027 -0.033 -0.007 -0.028 -0.027 -0.004 -0.020
Trends in diurnal range of temperature
Table 8 shows the monthly trends in diurnal
range of temperature. As can be expected,
corresponding with the relative trends in
maximum and minimum temperature, the
trend is negative for most months, although
only the trend for March is significant. For
the seasonal trends of diurnal range, as
shown in Table 9, all the results were nega-
tive. Autumn shows the highest negative
trend of –0.023 ºC per year.
Long-term temperature statistics
Figure 2 and Table 10 show temperature sta-
tistics for the total period of record (1960 to
2001). Similar statistics is supplied by
Scholes et al. (2001), but for the period until
1999. Summer temperatures are hot, while
during the winter it is mostly warm and
pleasant. Minimum temperatures rarely drop
below freezing point during winter, but even
at this time of the year maximum tempera-
tures can reach levels in the mid-thirties.
kruger andries final.qxd 2005/12/09 11:32 Page 5
Humidity
Because of frequent influxes of moist air
from the east, the humidity should be rela-
tively high. This is reflected in Table 11. The
average monthly humidity at 14:00 SAST,
Koedoe 45/1 (2002) 6 ISSN 0075-6458
Table 9
Trends of seasonal diurnal temperature range (°C
p.a.) for the period 1960 to 2001
Season Autumn Winter Spring Summer
Trend –0.023 –0.010 –0.021 –0.017
Table 10
Long-term temperature statistics (°C) for the period 1960 to 2001
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Absolute
minimum 11.3 10.0 7.6 5.6 0.8 –4.4 –3.8 –4.2 1.3 6.4 9.9 2.0
Absolute
maximum 43.0 45.6 42.0 41.3 39.2 35.3 36.4 38.0 42.6 43.6 42.8 42.4
Table 11
Relative humidity (%) at 14:00 SAST for the period 1978 to 2001
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Average 53 54 53 48 43 38 39 38 41 45 52 51
Lowest
monthly 39 37 40 35 30 29 28 30 33 36 39 36
Highest
monthly 66 77 67 57 64 57 58 60 57 56 84 62
Table 12
Monthly sunshine data (hours) for the period 1960 to 2001
Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Total
average 7.3 7.3 7.2 7.5 8.0 8.1 8.1 8.1 7.5 6.7 6.2 7.0
Lowest
average 4.8 5.2 4.4 5.4 6.4 6.7 6.4 5.8 5.5 5.0 4.2 4.8
Highest
average 9.1 9.6 9.3 9.4 9.4 9.4 9.6 9.5 9.3 8.5 7.8 10.8
which is usually the driest time of the day,
ranges from a maximum of 54 % for Febru-
ary to a minimum of 38 % for June and
August.
Sunshine
Average sunshine data for 1960 to 2001 is
shown in Table 12. One can see that,
although daylight hours in summer is much
longer than in winter, the sunshine hours
show the opposite, indicating cloudier
weather during summer. This region actual-
ly receives markedly less sunshine than the
kruger andries final.qxd 2005/12/09 11:32 Page 6
norm for savanna regions, the reason being
the frequent influx of moist air from the east,
with its accompanying fog and cloud.
Discussion and conclusions
The motivation for this paper is to supply
researchers, and other interested parties
involved in the SAFARI-2000 project, suffi-
cient long-term background data to serve as
a baseline for comparison to future observa-
tions at the flux measurement site close by.
Meta data and homogeneity tests suggest
that the data is generally of a high quality,
suitable for trend analyses. The average rain-
fall (monthly and seasonal) stayed fairly
constant during the previous century. How-
ISSN 0075-6458 7 Koedoe 45/1 (2002)
Fig. 2. Average monthly maximum and minimum
temperature (°C) for the period 1960 to 2001.
ever, there has been a marked increase in
minimum temperature since the 1960s, espe-
cially during the summer and autumn
months. This result corresponds to the aver-
age global trend (Easterling et al. 2000), and
can also be considered a true reflection of
temperature trends in the area, since no
meaningful urbanisation or increased pollu-
tion has taken place since the starting point
of the time series.
Long-term rainfall and temperature statistics
indicate a highly variable climate, typical of
the African savanna.
References
EASTERLING D.R., T.R. KARL, K.P. GALLO, D.A.
ROBINSON, K.E. TRENBERTH & A. DAI. 2000.
Observed climate variability and change of rel-
evance to the biosphere. Journal of Geophysical
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LOW, A.B. & A.G. REBELO (eds.). 1996. Vegetation
of South Africa, Lesotho and Swaziland. Preto-
ria: Department of Environmental Affairs and
Tourism.
SCHOLES, R.J., N. GUREJA, M. GIANNECCHINNI, D.
DOVIE, B. WILSON, N. DAVIDSON, K. PIGGOTT, C.
MCLOUGHLIN, K. VAN DER VELDE, A. FREEMAN,
S. BRADLEY, R. SMART & S. NDALA. 2001. The
environment and vegetation of the flux mea-
surement site near Skukuza, Kruger National
Park. Koedoe 44(1): 73–83.
SAFARI-2000 WWW HOME PAGE. 2002. Virginia
State University. Virginia. U.S.A.
(http:// safari.gecp.virginia.edu/)
THOM, H.C.S. 1966. Some methods of climatologi-
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/SVE <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>
>>
>> setdistillerparams
<<
/HWResolution [2400 2400]
/PageSize [612.000 792.000]
>> setpagedevice