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Temperature Variability over Africa
JENNIFER M. COLLINS
University of South Florida, Department of Geography, Environment, and Planning, Tampa, Florida
(Manuscript received 26 March 2010, in final form 2 March 2011)
ABSTRACT
The variation of near-surface air temperature anomalies in Africa between 1979 and 2010 is investigated
primarily using Microwave Sounding Unit (MSU) total lower-tropospheric temperature data from the Remote Sensing Systems (RSS) and the University of Alabama in Huntsville (UAH) datasets. Significant increasing temperature trends were found in each of the following regions examined: all of Africa, Northern
Hemisphere Africa, Southern Hemisphere Africa, tropical Africa, and subtropical Africa. Considering
the months June–August, regions in both North and South Africa saw significantly warmer temperatures in the
most recent period 1995–2010 than in the period 1979–94. However, for the months December–February, the
significant warming was concentrated in the north of Africa. When the two most recent decades are compared
with the period 1979–90, warming is observed over these same regions and is concentrated in the most recent
decade, from 2001 to 2010. The results presented here indicate that the climate change over Africa is likely not
predominantly a result of variations in the El Niño–Southern Oscillation (a teleconnection that has been previously shown to affect climate in some parts of Africa). Instead the climate changes likely occur owing to other
natural variability of the climate and/or may be a result of human activity. However, even without ascertaining
the specific causes, the most important finding in this work is to demonstrate that a significant rise in African
temperatures occurred between 1979 and 2010.
1. Introduction
The Intergovernmental Panel on Climate Change
(IPCC) Fourth Assessment Report (AR4) (Pachauri and
Reisinger 2007) considered that progress in understanding of how climate is changing in space and in time has
been gained through improvements and extensions of
numerous datasets and data analyses, broader geographical coverage, better understanding of uncertainties,
and a wider variety of measurements. However, data
coverage remains limited in some regions. The AR4 report shows, through the use of station data, that the
warming of the climate system is unequivocal, with 11 of
the last 12 years (1995–2006) ranking among the 11
warmest years in the instrumental record of global surface
temperatures since 1850 (Trenberth et al. 2007). The report also notes that the total temperature increase from
the period 1850–99 to the period 2001–05 is 0.768C
(Trenberth et al. 2007). The main conclusion of AR4 is
Corresponding author address: Dr. Jennifer M. Collins, Dept. of
Geography, University of South Florida, 4202 East Fowler Ave.,
Tampa, FL 33612.
E-mail: [email protected]
DOI: 10.1175/2011JCLI3753.1
Ó 2011 American Meteorological Society
that most of the observed increase in globally averaged
temperatures since the midtwentieth century is very
likely due to the observed increase in anthropogenic
greenhouse gas emissions (Hegerl et al. 2007). Brown
et al. (2008) also indicate that extreme daily maximum
and minimum temperatures have warmed for most regions of the world since 1950 and that the total area exhibiting positive trends is significantly greater than that
which can be attributed to natural variability.
Difficulties remain in reliably simulating and attributing observed temperature changes at smaller scales.
On these scales natural climate variability is relatively
larger, making it harder to distinguish changes expected
due to external forcings. Uncertainties in local forcings
and feedbacks also make it difficult to estimate the contribution of greenhouse gas increases to observed smallscale temperature changes (Hegerl et al. 2007).
Africa is unique in that it is the only continent that,
almost symmetrically, straddles the equator, and hence
experiences such a varied climate with both Northern
and Southern Hemispheric climatic influences (Odada
and Olago 2005). Africa is about 8000 km in length from
northern Tunisia to the southern tip of the country of
South Africa. In addition, Africa encompasses more
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than 20% of the world’s total land area and has a population of greater than 1 billion. Due in part to its location, vast size, and varying topography (Fig. 1), Africa
has eight climate zones according to the Köppen climate
classification system. The northern half of Africa is primarily desert or arid, while the central and southern
areas contain savanna and rain forest regions. Some of
the extreme temperature variations that have been experienced in Africa include a high of 57.88C (1368F) at El
Aziza, Libya, on 13 September 1922 and a low of
223.98C (2118F) at Ifrane, Morocco, on 11 February
1935.
There have been few studies about climate change
focusing on temperature that consider the whole of
Africa. Part of the reason for this may be that, as Frich
et al. (2002) note, there are large regions in the world
where no digital data are readily available for analysis.
Caesar and Alexander (2006) also emphasize this point
regarding the sparse data coverage of freely available
station data for Africa. Desanker and Magadza (2001)
discuss some of the problems in trying to establish a
weather station network that includes the technological
and scientific underdevelopment of many African countries exacerbated by civil war, extensive poverty, and
political instability. In addition, Lamptey (2009) notes
that Africa has been underrepresented in international
efforts to improve research capabilities, observing facilities, operational forecasting, and meteorological education. This is in spite of the fact that sub-Saharan
countries are especially vulnerable to weather and climate variation, and that Sahelian weather directly affects the United States in the form of hurricanes and
dust clouds.
Nicholson (2001) notes that weather stations first
emerged in the south of Africa and then along the coasts.
These locations would therefore have the longest records.
Figure 2 taken from Eischeid et al. (1995) illustrates the
lack of temperature measurements in Africa (and other
locations such as South America) in comparison to North
America and Europe. This is reinforced by Washington
et al. (2004) who reported that the network of 1152 World
Meteorological Organization (WMO) World Weather
Watch (WWW) stations in Africa has an average density
of weather stations of just one per 26 000 km2 (this is 8
times lower than the minimum recommended level given
by the WMO). The location of these weather stations is
also unevenly distributed.
Of the operational weather stations, some do not have
long climate records and some are not publically available. Easterling et al. (2003) discuss a workshop that was
held in 2001 bringing together scientists from 23 African
countries to address some of the issues of data availability and data analysis in Africa. They note that,
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FIG. 1. Map of Africa. Elevation in meters.
‘‘further effort is required to encourage continued collaboration, data exchange, and analysis . . .’’ (Easterling
et al. 2003, p. 1407). Despite this in 2009, the author of
this paper had great difficulty obtaining publically
available weather station data at no cost.
Of the few studies that are available on climate change
in Africa, many focus on rainfall. Hulme et al. (2001)
note that good databases exist with regard to rainfall
(although they do note some areas such as the Congo
basin have extremely patchy and limited coverage).
Nicholson (2000, 2001) has well documented the longterm reduction in rainfall in the semiarid regions of West
Africa. New et al. (2006) identified significant trends in
some southern Africa precipitation indices. In particular, a spatially coherent increase in consecutive dry days
was found over much of southern Africa in the last decades of the twentieth century. Kruger (2006) analyzes
rainfall data in South Africa because of the longer dataset there. Landman and Mason (1999) also examined
rainfall in South Africa and Namibia as they looked at
the association of sea surface temperatures (SSTs) of the
Indian Ocean and their effect on the rainfall. Reason
and Mulenga (1999) analyzed rainfall and SST anomalies in the southwestern Indian Ocean. Rowell et al.
(1995) also examined rainfall trends but focused on
northern Africa.
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FIG. 2. Global distribution of mean monthly temperature measurements (taken from Eischeid
et al. 1995).
Those studies that focus on temperature changes in
Africa often concentrate on individual countries or a
small area, however, climate varies considerably across
the continent. Muhlenbruch-Tegen (1992) selected 18
weather stations in South Africa to analyze during the
time period of 1940–89. His analysis showed that the
coastal stations had significant increasing temperature
trends, which he attributed to increases in SSTs, but the
inland stations had no significant temperature changes.
Hulme et al. (2001) noted that throughout the twentieth century, Africa has warmed at a rate of 0.58C
century21 and from 1987 to 1998, the six warmest years
in Africa’s temperature record occurred with increasing
intensity making 1998 the warmest. From 1901 to 1995,
they report that the Mediterranean coastal countries
in northwestern Africa and inland southern Africa
warmed at 28C century21. However, over the same time
period, a few regions such as Nigeria–Cameroon and
Senegal–Mauritania actually cooled. Hulme et al. (2001)
note that there is slightly larger warming in the June–
August and September–November seasons than in
December–February and March–May. The Sahel, the
Mediterranean coast, and significant portions of Botswana,
Zimbabwe, and the Transvaal have dried by about 25%
century21. Meanwhile, eastern and equatorial Africa have
seen wetting at around 10% century21.
Brown et al. (2008) indicate that extreme daily maximum and minimum temperatures have warmed for
most regions of the world since 1950. However, for South
Africa, Muhlenbruch-Tegen (1992) found that several
stations had an increasing trend in their minimum temperature, while in other cases trends in minimum and
maximum temperatures counteracted each other. His
conclusion indicates that the data are inconclusive as to
whether South Africa was warming or cooling for the
duration of his study. In support of this, Kruger and
Shongwe (2004) found that for annual mean minimum
temperature, 21 stations showed positive trends, with
86% of them being statistically significant. Four stations
had negative trends, but none of them were statistically
significant. They also found that, ‘‘days and nights with
relatively high temperatures have increased, while days
and nights with relatively low temperatures have decreased’’ (Kruger and Shongwe 2004, p. 1929). Christy
et al. (2009) analyzed temperature trends in the eastern
part of Africa. They used data that included 60 weather
stations in Kenya and 58 in Tanzania and concluded that
there appears to be little change in East Africa for the
maximum temperatures. Defining eastern Africa as 19
countries located on the eastern part of the African
continent and extending from the sub-Saharan Sudan and
Ethiopia to the Horn of Africa, East Africa, and central
and southern Africa, King’uyu et al. (2000) studied data
from 71 weather stations from 1939 to 1992. More specifically, the 19 countries are located inside the latitudes
of 258–308S and longitudes of 208–608E. King’uyu et al.
(2000) concluded that nighttime temperatures are increasing, but that this trend is not spatially uniform over
the given study area. This again has been confirmed recently by Aguilar et al. (2009) who conducted a study on
the regions of west central Africa, Guinea Conakry, and
Zimbabwe. They deduced that the areas are warming,
with hot extremes increasing, while there are fewer cold
extremes. This is consistent with literature focusing on
other areas of the world. For instance, Vincent et al.
(2005) examined trends over the period 1960–2000 in the
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indices of daily temperature extremes for South America
and found that there were no consistent changes in the
indices based on daily maximum temperature. However,
significant trends were observed when considering minimum temperature. They found significant increasing
trends in the percentage of warm nights and decreasing
trends in the percentage of cold nights at many stations.
However, other parts of the world have shown more record daily high maximum temperatures than record low
minimum temperatures and this ratio has been increasing in recent decades (United States, Meehl et al. 2009;
Australia, Trewin and Vermont 2010).
Another paper by Hughes and Balling (1996) referencing Jones’ (1994) dataset noted that South Africa saw
a cooling in mean temperature from 1885 to 1915, a
warming from 1915 to 1945, a slight cooling from 1945 to
1970, and rapid warming from 1970 onward. Much of
this warming has occurred in the last 30 years, but they
conclude that urban growth may have compromised the
data records since their examination of 19 nonurban
weather stations in South Africa indicated no significant
warming during this recent period. Adding to this body
of research, Kruger and Shongwe (2004) completed a
study of temperature trends in Africa using 26 weather
stations between 1960 and 2003. They did not go back
further in time because the data before 1960 is not sufficiently quality controlled. These stations had not
changed location nor had any changes in their surrounding area. Of those 26 stations, only 3 of them did
not show a positive trend in annual mean maximum
temperatures and the trend in 57% of these stations
were statistically significant. They noted, contrary to
Muhlenbruch-Tegen (1992), that stations which were
located inland had higher trends than the ones located
near the coast.
Hulme et al. (2001) note that ENSO is a potentially
important driver on future African climate. Kruger and
Shongwe (2004) examined whether ENSO had any effects on the trends in temperature, specifically for the
late austral summer period of January–March. They
concluded that increases in late summer temperatures
are not forced by the occurrence of El Niño and La Niña
events. However, Klopper et al. (1998) examined data
from 77 weather stations located in South Africa. They
used a method called singular value decomposition to
see if they could find any relevant correlation between
ocean temperatures and South African seasonal temperature and conclude that ENSO-related features
resemble an important influence on the seasonal temperature of South Africa with the warm ocean surface
temperatures in the equatorial Pacific corresponding to
warm temperatures in South Africa (with the opposite
occurring for cool ocean surface temperatures).
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The IPCC regional climate projections for Africa suggest that for West Africa, East Africa, South Africa, and
the Sahara, regardless of the season, the predicted average
increase in temperature for 1900–2100 ranges from 38 to
48C (Christensen et al. 2007). This amount is about 1.5
times the projected global mean temperature increase for
1900–2100 when compared with one of the more conservative cases of the Special Report on Emissions Scenarios (SRES). The 38C rise pertains to coastal and
equatorial areas of Africa, while the larger 48C change is
projected to occur in the western Sahara region. In addition, Christensen et al. (2007) note that it is possible,
further along in this century, that there will be a significant increase in the effect that land use changes will have
on climate. Paeth et al. (2009) set out to produce climate
simulations that included land use change. By 2050, the
strongest warming in their model occurs in sub-Saharan
Africa. Paeth et al. (2009) conjecture that a possible
reason for this is that the changes in land cover are also
heavily responsible for climate trends and hence they
note that protecting land cover is just as important as
reducing greenhouse gas emissions in order to mitigate
future climate change. Carto (2009) points out that due to
marine pollen records, we can deduce whether the rain
forests were in fact more extensive than they are today
and that from the point of rain forest decrease the temperature began to rise as well. This mechanism being that
reduced vegetation cover leads to decreasing evapotranspiration, such that more energy is available for nearsurface heating. Christy et al. (2009) note that changes in
the observation sites in East Africa, replacing vegetation
with concrete, likely accounts for some warming. Hulme
et al. (2001) note that, much like other climate change
studies/projections have shown, the Sahara (interior) and
central-southern Africa will experience the greatest
warming, while equatorial and coastal regions of Africa
will experience the least warming.
Although Hulme et al. (2001) note that no simple
correlation exists between temperature and rainfall in the
Sahel, East Africa, and southeastern Africa, Hulme
(1996) does assert that drying in the Sahel was associated
with a moderate warming trend. Glantz (1992) discusses
the importance of temperature changes as he notes the
indirect role that temperature plays on evaporation rates
and hence water availability, particularly in areas where
there is a delicate balance between evaporation and
precipitation. As increasing evidence grows on temperature controls of important physiological processes
in insects, plants, and crops, the topic of climate change
in Africa, specifically temperature, becomes imperative.
In this work mean near-surface air temperatures in
Africa are investigated for the most part utilizing the
mean total lower-tropospheric temperature profiles
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from Remote Sensing Systems (RSS; Mears et al. 2003)
and the University of Alabama in Huntsville (UAH;
Christy et al. 2000) datasets. Supplementary data for comparative purposes are drawn from the National Centers for
Environmental Prediction (NCEP)–National Center for
Atmospheric Research (NCAR) reanalysis (NNR; Kalnay
et al. 1996) and from the 40-yr European Centre for
Medium-Range Weather Forecasts (ECMWF) reanalysis
(ERA-40; Uppala et al. 2005). All datasets are analyzed
from 1979 to 2010. As Hulme et al. (2001) point out, such
a historical perspective is essential if the simulated climates
of the next century are to be put into their proper context.
2. Methodology
Mean total lower-tropospheric temperature variability above the earth’s surface over Africa between 1979
and 2010 is investigated. Data (to the nearest grid point)
are considered for the following regions: the whole of
Africa, Northern Hemisphere (NH) Africa, Southern
Hemisphere (SH) Africa, tropical Africa (23.58N–23.58S),
subtropical Africa (north of 23.58N and south of 23.58S),
Northern Hemisphere tropical Africa (08–23.58N),
Southern Hemisphere tropical Africa (08–23.58S), Northern Hemisphere subtropical Africa, and Southern Hemisphere subtropical Africa. Because of the difficulties in
obtaining accurate meteorological and climatological records in continental Africa highlighted above, which include a lack of funding in the countries, topography, and
remoteness of areas, this, in addition to the fact that station data is not freely available, leads to the use primarily
of satellite instead of station data. Two satellite datasets
are available, from RSS (Mears et al. 2003) and from
UAH (Christy et al. 2000), each at 2.58 3 2.58 resolution.
Statistical analysis of both datasets is included, while
spatial plots draw solely from the RSS dataset.
Other studies such as Collins et al. (2009) for South
America have utilized the Climate Research Unit’s
(CRU) Global Temperature version 3 (CRUTEM3)
dataset, which consists of land air temperature anomalies available on a 58 3 58 gridbox basis (Brohan et al.
2006). However, while the use of this dataset may be
appropriate for other areas of the world, it did not have
good spatial coverage over Africa, particularly the Sahara. In lieu of data-sparse surface observational datasets,
the satellite data are also compared statistically to two
reanalysis datasets, the NNR reanalysis (Kalnay et al.
1996) and the ERA-40 reanalysis (Uppala et al. 2005).
Temperatures used in the NNR are at the 2-m level,
with T62 Gaussian grid resolution. Temperature is
classified as a B category variable in the NNR. This
designation indicates that, although there are observational data that directly affect the value of the variable,
FIG. 3. Difference of the mean RSS total lower-tropospheric
temperatures (K) between the periods 1979–94 and 1995–2010 in
(a) DJF and (b) JJA. The shaded areas represent statistical significance at the 5% level.
the model also has a very strong influence on the analysis
value (Kalnay et al. 1996). The NNR is available from
1948; however, only data from 1979 is considered in this
study since there has been substantial evolution of the
observing system since 1948. Kistler et al. (2001) note
three major phases: the first from the 1940s to 1957 when
the first upper-air observations were established, the
second from 1958 to 1978 with the modern rawinsonde
network, and the third from 1979 to the present.
Trenberth et al. (2001) also give caution to potential
artificial jumps and trends in the NNR as a result of
changes in the observing systems when using the data
back to 1948. This study therefore considers the more
reliable post-1979 period, which coincides with the
availability of satellite data.
Temperatures used in the ERA-40 are also at the 2-m
level, with 2.58 3 2.58 resolution in the ERA-40 until
2002 and 1.58 3 1.58 resolution after 2002 in the ERAinterim dataset. Bengtsson et al. (2004) examined the
viability of assessing climate trends through reanalysis
data and concluded that while the temperature trends in
the ERA-40 were slightly higher than that of the satellite
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FIG. 4. Annual mean near-surface air temperature anomalies (K) between 1979 and 2010 for selected regions.
Black lines indicate satellite data: solid (RSS) and dashed (UAH). Gray lines indicate reanalysis data: dashed-dotted
(NNR) and dotted (ERA-40).
data they did fall within estimated error limitations. All
dataset analyses are considered over the same period,
from 1979 to 2010, and temperature anomalies are used
to facilitate intercomparison. Washington et al. (2006)
comment on the problem of the poor level of peerreviewed journal publications in Africa, which is among
the lowest anywhere in the world and the limited extent
of international engagement, which they note severely
isolates African climate science. However, the use of
freely available global datasets such as the ones used in
this study does facilitate an increased understanding of
Africa’s climate.
Trends are examined by examining the slope of the
best-fit linear trend from an ordinary least squares (OLS)
regression, and the trend is determined to be significant at
the 5% level (a , 0.05). It should be noted that the
Durbin–Watson statistic was calculated to analyze autocorrelation in the yearly averaged time series. No
correction was deemed necessary based on the results of
this analysis such that each yearly averaged time series
has a Durbin–Watson test statistic around 2, suggesting
that the null hypothesis that there is no autocorrelation
would not be rejected. The nonparametric Mann–
Kendall test (described by Onoz and Bayazit 2003) has
been used for temperature trend detection in other studies, for example, Collins et al. (2009) who used it particularly for considering station data in South America.
However, for the Africa data used here, OLS regression
is the appropriate and more robust methodology because
all mean temperatures in Africa are considered with
residuals that are normally distributed. In addition, the
coefficients of skewness and kurtosis are all less than
the absolute value of 1 following the Z distribution for
normally distributed data.
TABLE 1. Temperature trends (slope and significance obtained
from a linear regression). Data obtained from the RSS and UAH
satellite datasets.
Region of
Africa
Slope
RSS
Significance
RSS
Slope
UAH
Significance
UAH
All
Tropical
Subtropical
Subtropical NH
Subtropical SH
Tropical NH
Tropical SH
NH
SH
0.016
0.015
0.020
0.023
0.013
0.019
0.011
0.018
0.011
0.001
0.003
0.001
0.001
0.036
,0.001
0.049
0.001
0.080
0.010
0.008
0.019
0.019
0.018
0.012
0.006
0.010
0.003
0.035
0.111
0.003
0.015
0.001
0.023
0.193
0.051
0.533
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FIG. 5. Mean RSS total lower-tropospheric temperatures (K) in DJF and JJA during (a),(d) 1979–94, (b),(e) 1995–2010, and (c),(f) the
difference of the mean between these two periods. The shaded areas in (c) and (f) represent statistical significance at the 5% level.
The means and standard deviations of the RSS temperature are determined for all months of the year for
the periods 1979–94 and 1995–2010 and the results
presented in this paper are the average of December–
February (DJF), June–August (JJA), September–
November (SON), and March–May (MAM). To evaluate
the temperature variability in recent decades, the data
is further subdivided into 1991–2000 and 2001–10 and
a similar analysis is conducted. The temperatures of the
last 10 years (2001–10) are also considered since in the
recent period there is a marked temperature increase
starting at the beginning of this century in Africa and
globally. The statistical significance determined to the 5%
level (a , 0.05) of the difference between the composites,
for the different periods considered in this work, is calculated from the OLS regression applying the Student’s
two-tailed t test, where a significant t value is noted at
more than 2.04.
The results shown in this paper could be associated
with natural and anthropogenic variability; therefore, the
occurrence of all ENSO events is verified based on the
current National Oceanic and Atmospheric Administration (NOAA) operational index, the Oceanic Niño Index
(ONI), by considering the three-month seasonal values.
To examine the ENSO effect, in addition to examining
the periods 1979–94 and 1995–2010, the differences in
temperature between these periods after the El Niño and
La Niña seasons identified above have been removed
(i.e., just considering the neutral years) is then examined.
In addition, the periods were then also investigated solely
analyzing El Niño and La Niña years separately in each
period.
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FIG. 6. Difference of the mean RSS total lower-tropospheric temperatures (K) in (a) DJF and (b) JJA between
2001–10 and 1991–2000. The shaded areas in (a) and (b) represent statistical significance at the 5% level.
An analysis involving the five strongest El Niño and
the five strongest La Niña events observed between 1979
and 2010 is also performed. For the DJF season, the
following El Niño events are considered: 1983, 1987,
1992, 1998, and 2010, and the following La Niña events
are considered: 1985, 1989, 1999, 2000, and 2008. For the
JJA season, the El Niño events are considered: 1982,
1987, 1997, 2002, and 2009, and the La Niña events are
considered: 1985, 1988, 1998, 1999, and 2010.
3. Results
a. Africa compared with other continents
When considering the temperature change in the DJF
season between the earlier period 1979–94 with the most
recent period 1995–2010 (Fig. 3a), only Africa shows
significant temperature increases across much of the
continent [(data are unavailable for both polar regions
and elevations above 3000 ft (;900 m)]. When considering the JJA season (Fig. 3b), considering continents
within the low and middle latitudes, both Africa and
South America show significant temperature increases
over the majority of their areas. This highlights the need
to study temperature change in Africa in more detail.
b. Annual mean temperature: NNR
Figure 4 compares annual mean near surface air temperature anomalies across all four datasets between 1979
and 2010 for the whole of Africa, Northern Hemisphere
Africa, Northern Hemisphere subtropical Africa, and
Northern Hemisphere tropical Africa as these are the
regions with the most notable trends. It should be noted
that while some differences exist in the datasets, they do
compare reasonably well regarding the interannual variability and general trends. Table 1 provides a summary of
the slope (and significance) from a linear regression for
this period for all these regions utilizing the satellite data.
It can be seen that increasing trends were observed in
both satellite datasets with most regions experiencing
significant trends to the 1% level with the RSS data.
While the Southern Hemisphere regions had slightly
lower significance values, they were still high (subtropical
and tropical Southern Hemisphere having a significance
value greater than 5% and Southern Hemisphere Africa
having a significance greater than 10%). The IPCC AR4
reported that the period between 2000 and 2006 is
considered the globally warmest of the last 100 years
(Trenberth et al. 2007). In the African regions investigated (as noted above), it was also noted that the
temperatures have increased since 2000.
Considering the RSS data for Africa over the period
1979–2010, there is a significant (to the 1% level) upward linear trend noted with an estimate of 0.168C
decade21 based on the results of the regression. This
increase is larger than that noted for South America
(0.098C decade21) by Collins et al. (2009), although it
should be noted that the Collins et al. (2009) analysis did
not utilize the RSS dataset.
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FIG. 7. Difference of the mean RSS total lower-tropospheric temperatures (K) in DJF and JJA between (a),(c)
2001–10 and 1979–1990, and between (b),(d) 1991–2000 and 1979–90. The shaded areas represent statistical significance at the 5% level.
The temporal series of annual mean temperature
anomalies over Northern Hemisphere Africa is similar to
the series for the whole of Africa. Over the period 1979–
2010, there is a significant (to the 1% level) upward linear
trend noted with an estimate of 0.188C decade21 based on
the results of the regression. The temporal series of the
annual mean temperature anomalies over Southern
Hemisphere Africa shared some of the similarities of the
other two African series described above. The results
from the OLS regression show a trend also with an estimate of 0.118C decade21 significant to the 10% level.
Now just considering tropical Africa, again the increasing trend looks similar to the other regions examined with a significant (to the 1% level) upward linear
trend noted with an estimate of 0.158C decade21 based on
the results of the regression. Separating the tropics into
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FIG. 8. Mean RSS total lower-tropospheric temperatures (K) in (a) DJF and (b) JJA during 2001–10.
Northern Hemisphere tropical Africa and Southern
Hemisphere tropical Africa, both of these regions show an
increasing trend (0.198C decade21 and 0.118C decade21,
respectively) indicative of the overall tropical situation
described here.
The temporal series of the annual mean temperature
over subtropical Africa shows a significant upward linear
trend with an estimate of 0.28C decade21 based on the results of the regression. As expected, due to the larger African subtropical landmass in the Northern Hemisphere,
when dividing the subtropics into Northern Hemisphere
and Southern Hemisphere subtropics, the Northern Hemisphere subtropics looks more akin to the overall subtropical situation. The results from the OLS regression
show a trend with an estimate of 0.238C decade21 for
the Northern Hemisphere subtropics but only 0.138C
decade21 for the Southern Hemisphere subtropics.
It should be noted that for every region examined, the
trend in the Southern Hemisphere (whole of Southern Hemisphere, tropical and subtropical Southern
Hemisphere) was always smaller than the Northern
Hemisphere counterpart. This is consistent with previous work by Jones et al. (1994) among others who
note that globally the Northern Hemisphere is warming
faster than the Southern Hemisphere.
c. December–February
The spatial patterns of the temperature during
December–February in Africa are strikingly similar in
both periods, 1979–94 and 1995–2010 (Figs. 5a,b).
However, there are some notable differences, for example, in the period 1979–2010 a change in the temperature pattern in Africa is observed, with enlargement
of the area enclosed by the 280-K isotherm around the
coast of Kenya and Tanzania (Fig. 5b). The temperature
differences between the two periods are shown in Fig. 5c
(with significant results shown to the 5% level in gray). It
can be seen that significant warming occurs over much of
the tropical region of the Northern Hemisphere between
the period 1979–94 and the period 1995–2010. The observed warming in South Africa occurs largely on parts of
the southern and eastern coast. This is in agreement with
Muhlenbruch-Tegen (1992) studying the period 1940–
1989 using station data who noted that only the coastal
stations of South Africa had significant increasing temperature trends.
To evaluate temperature variability in the recent decades only, the data are further subdivided: 1991–2000
and 2001–10 (Fig. 6a). The spatial patterns of the temperature are similar in both periods (figure not shown).
The area over the northern Sahara, which previously
showed warming in the recent period (1995–2010 compared with 1979–94), now shows an area of significant
warming in Libya when examining 2001–10 compared
with 1991–2000.
When the differences between 1991–2000 and 1979–
1990, and between 2001–10 and 1979–1990 are considered (Figs. 7a,b), a significant warming is observed in more
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FIG. 9. As in Fig. 5, but in each period ENSO ONI $ j0.58Cj have been removed.
of the continent when the most recent period is compared
with 1979–90. However, this significant warming is not
observed in the 1991–2000 period. The mean temperature
patterns for DJF for the period 2001–10 (Fig. 8) look
similar to earlier periods shown in Figs. 5a and 5b.
Figures 9a–c show the analysis of the period 1979–94
compared with 1995–2010 like in Figs. 5a–c but now with
El Niño and La Niña events removed so that only neutral ENSO years are considered. It can be seen when
comparing Fig. 5 and Fig. 9 that similar but reduced
results emerge, with a significant increase in temperatures for the region directly north of the equator in the
more recent period. While there is a localized temperature reduction centered in Egypt, it is not found to be
significant. Examining La Niña seasons only (Fig. 10)
shows a significant warming pattern very similar to that
found in Fig. 5. However, El Niño seasons only (Fig. 11)
show widespread significant warming across Northern
Hemisphere Africa. That this warming is not represented in the long-term plots suggests that ENSO does
not account for the noted warming in Africa.
Figure 12c shows the mean temperature difference
between the five strongest El Niño and La Niña events in
the period 1979–2010. Warming is observed over the
western Sahara and most of southern Africa in the strongest El Niño years compared with the strongest La Niña
years, but the strongest areas of warming are not collocated with the significant long-term trend.
The interannual temperature variability in DJF
(Figs. 13a–c), determined by the standard deviation, is
largest over northern Africa with a secondary area of increased variability to the south. There is little temperature
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FIG. 10. As in Fig. 5, but in each period only La Niñas # 20.58C are included.
variability in the equatorial region, and for all areas the
magnitude of temperature variability is consistent across all
time periods.
d. June–August
The June–August temperature patterns in Africa are
largely similar for the periods 1979–94 and 1995–2010
(Figs. 5d,e), with moderate expansion of the 284-K zone
over the Sahara. However, an examination of the temperature differences between the two periods (Fig. 5f)
shows that, unlike what was observed in DJF, significant
warming occurs in both the Sahara and a band south of
the equator. Equatorial regions see significant warming
only in the coastal regions.
Again for the JJA season, recent decades are also analyzed in two parts—1991–2000 and 2001–2010—and the
difference in temperatures between the two periods is
noted in Fig. 6b. Little significant difference in warming is
observed between the two decades. The area with significant warming increases over much more of the continent with greater temperature differences over the
Sahara in the period 2001–10 more than in 1991–2000
when compared with the temperatures in the period
1979–90 (Figs. 7c,d).
Similar to the analysis of the DJF season, Figs. 9d–f
show the analysis of the period 1979–94 compared with
1995–2010 as in Figs. 5d–f but now with significant
ENSO events removed leaving just the neutral years.
It can be seen when comparing Figs. 5 and 9 that again
similar results emerge, that is, there is a significant
increase in temperatures for the majority of Africa in
the more recent period. Thus again supporting the
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3661
FIG. 11. As in Fig. 5, but in each period only El Niños $ 0.58C are included.
suggestion that ENSO does not account for the noted
warming considering most of Africa. Similar results
are found when considering the La Niña years (Figs.
10d–f) and El Niño years (Figs. 11d–f) separately.
Figure 12f shows the mean temperature difference between the five strongest El Niño and La Niña events
during the period 1979–2010. Equatorial warming is observed over most of Africa in the strongest El Niño years
compared with the strongest La Niña years with areas of
cooling at the northern and southern extremes of the
continent. However, areas with significance to the 5%
level are minimal, not widespread, and not in regions
previously identified as having significant long-term temperature trends. While this identifies the regions where
El Niño and La Niña impacts most differ, it also further
acts to confirm that ENSO is not a primary driver of longterm temperature variability in the region.
In the JJA season, little interannual variability is observed over much of the Sahara (Figs. 13d–f). Peak
interannual variability is observed in South Africa in all
periods and is strongest in the most recent decade.
e. March–May and September–November
The spatial patterns of March–May and September–
November are shown in Figs. 14 and 15, respectively, for
the periods 1979–94 and 1995–2010. Similar to what we
observed for DJF and JJA, the temperatures in the MAM
and SON seasons also changed when the most recent
period was compared with the earlier period. For 1995–
2010 compared with 1979–94, over much of the eastern
and western coasts of Africa the temperatures significantly increased. Figure 15 also showed significant increases in temperature in the central Sahara for the SON
period.
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FIG. 12. Mean RSS total lower-tropospheric temperatures (K) in DJF and JJA during (a),(b) the top five El Niño’s between 1979 and
2010, (d),(e) the top five La Niña’s between 1979 and 2010, and (c),(f) the difference of the mean between these two ENSO phases. The
shaded areas in (c) and (f) represent statistical significance at the 5% level.
4. Summary and conclusions
This work shows how the near-surface mean air temperature in Africa varies in the multiple datasets between
1979 and 2010, with a focus on mean total lowertropospheric temperature profiles. There are significant
increasing temperature trends observed in both the RSS
and UAH satellite datasets as well as in reanalysis
comparison datasets (the NNR and ERA-40). Finding
these trends in the satellite datasets provides us greater
confidence in the results from the NNR and ERA-40.
Considering mean annual temperatures from the RSS,
warming was observed in the Northern Hemisphere and
Southern Hemisphere, and tropical and subtropical regions, with greater warming in Northern Hemisphere
regions. Considering the DJF season, significant warming
was observed over northern tropical Africa comparing the period 1995–2010 with 1979–94. An examination
of the more recent period shows more warming occurring
in the most recent 10 years than the other periods examined. The difference between the time periods (1994–
2010 and 1979–94) in the JJA season is different in that
significant warming extends across both northern and
southern regions of the continent, with larger temperature
increases observed in the Sahara than anywhere else on
the continent. During MAM and SON significant warming is also observed across central Africa but not to the
levels seen in JJA. SON warming was significantly larger
than the warming observed in MAM. This result is also
consistent with Hulme et al. (2001) who examined the
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FIG. 13. Standard deviation of the mean RSS total lower-tropospheric temperatures (K) in DJF and JJA during (a),(d) 1979–94, (b),(e)
1995–2010, and (c),(f) 2001–10.
period 1901–95 and notes that a slightly larger warming in
Africa occurs in the months of JJA (and SON) than in the
months of DJF (and MAM).
The analysis from the RSS also suggested while
slightly greater warming is observed across Africa in
El Niño events than La Niña events, the warming during
the most recent period (1995–2010) compared with the
earlier period (1979–94) is not a result of ENSO. When
considering each of these phases, and the temperature
difference between the two periods, again one can see in
DJF a significant increase in temperatures for the majority of northern tropical Africa in the more recent
period. Likewise in each of these three phases, in JJA
one can observe warming throughout the whole continent. Because of these similarities between phases, this
suggests that the temperature increases in Africa may
not be due to ENSO but some other component of
natural variability of the climate and/or may be a result
of human activity.
The author would like to provide caution in the results. In particular, other natural forcing may be responsible for some of these results. In particular, when
considering volcanic forcing, clearly the eruptions of
volcanoes such as the April 1982 eruption of the Mexican volcano, El Chichón, and the subsequent June 1991
eruption of the Philippine volcano, Mount Pinatubo,
both caused significant cooling in the tropical troposphere for a couple of years after the event (Kelly and
Jones 1996) owing to a reduction in the total radiation
received at the surface as a result of the volcanic aerosol
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FIG. 14. Mean RSS total lower-tropospheric temperatures (K) in MAM during (a) 1979–94 and (b) 1995–2010, and (c) the difference of the
mean temperature between these two periods. The shaded areas in (c) represent statistical significance at the 5% level.
clouds. This cooling would have been reflected in the
dataset of the earlier period, which certainly could affect
the period differences and trends. However, Pachauri
and Reisinger (2007) note that only models that include
anthropogenic forcing can simulate the observed patterns of warming (Hegerl et al. 2007). Hegerl et al. (2007,
p. 727) note that, ‘‘no model that used global natural
forcing only has reproduced the observed global mean
warming trend or the continental mean warming trends
in individual continents (except Antarctica) over the
second half of the twentieth century.’’ In fact when
considering a more modest increase for the global scale,
some authors (Wigley and Barnett 1992; Hulme and
Jones 1994) state that natural processes alone cannot
explain this increase, thus making the argument for anthropogenic warming for Africa stronger. Indeed, there
has been more land use change and greater carbon dioxide release in the most recent period (Kalnay and Cai
2003). Only global and regional climate models with
anthropogenic forcing can perhaps explain temperature
warming due to human activities. It is possible that
changes in the observational networks could influence
the results; however, to verify this hypothesis it would be
necessary to examine complete datasets from meteorological stations over a long period of time (which are
hard to obtain for Africa).
FIG. 15. As in Fig. 14, but for SON.
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China and India are considered to be the major developing-country contributors to greenhouse gas–
induced warming, while the contribution from countries
in Africa has been relatively low. Each American produces nearly 16 metric tons of carbon dioxide annually,
while each African produces just 1 metric ton year21
(Fields 2005). Despite this difference, the effects and
potential effects of climate change in Africa have already been well noticed and documented. The Pew
Center on Global Climate notes that Ethiopia is the
country considered to be most vulnerable to the effects
of global climate change. Ogodo (2006) warns that
a third of Africa’s coastal settlements could be wiped out
this century by rising sea levels and floods threaten as
many as 70 million people. Increasing temperatures have
caused fish populations in African lakes to decline, with
Lake Tanganyika being a prime example (Verburg et al.
2003; O’Reilly et al. 2003). Thompson et al. (2009), who
have mapped deglaciation of Mount Kilimanjaro, assert
that if current climatological conditions are sustained, the
ice fields atop Mount Kilimanjaro will likely disappear
within several decades.
Rutherford et al. (1999) note that a number of species
of flora are likely to become extinct on several different
nature reserves if climate change continues on its estimated path of warming/drying in the majority of regions.
The analysis maintains that, due to both changes in the
number of optimum growth days and temperature, from
10.4% to 42.4% of species of flora are expected to become extinct in a range of natural parks in Africa.
Downing et al. (1997) notes that whether the warming
and drying trends in Africa persist and grow worse depend on how the nation handles mitigation and further
development practices. The work shown here can help
African countries see, on a continental scale, what
temperature changes are being observed and where,
which will help them better plan for a changing climate.
It should be noted, however, that the trends observed
must be considered as a preliminary estimate because of
the short analysis period involved.
Acknowledgments. The author would like to acknowledge the use of the satellite data from Remote Sensing
Systems (RSS) and the University of Alabama at
Huntsville (UAH) and reanalysis data from the National Centers for Environmental Prediction–National
Center for Atmospheric Research (NCEP–NCAR) and
the European Centre for Medium-Range Weather
Forecasts (ECMWF). In addition the author would like
to thank Ph.D. student David R. Roache for assistance
with data analysis and scripting. The author would also
like to thank the anonymous reviewers and editor for
comments and suggestions on the manuscript.
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