Atmospheric and Climate Sciences, 2012, 2, 307-321
http://dx.doi.org/10.4236/acs.2012.23028 Published Online July 2012 (http://www.SciRP.org/journal/acs)
Variability of Wintertime Su rface Air Temperature over
the Kingdom of Saudi Arabia
Hosny Mohamed Hasanean, Abdel Rahman Khalaf Al-Khalaf
Department of Meteorology, Faculty of Meteorology, Environment, and Arid Land Agriculture,
King Abdulaziz University, Jeddah, KSA
Email: h_eg2001@yahoo.com, hasanean@operamail.com
Received January 22, 2012; revised March 17, 2012; accepted April 17, 2012
ABSTRACT
Variability of wintertime surface air temperature (SAT) in the Kingdom of Saudi Arabia (KSA) is studied. The study is
based on time series over thirty one years in length (1978-2008). For the analysis, we use the coefficient of variability
(COV) Mann-Kendal statistical test, running mean and cumulative annual mean (CAM). The coefficient of variability
(COV) for wintertime SAT decreases gradually from the north to the south of KSA. The higher values for COV occur
in northern and northeastern KSA; there are du e to the effec t of the trave ling Mediterranean depressions and their inter-
action with the inverted-V shape trough of the Sudan low. The relationship between COV and latitude is highly signifi-
cant, while with longitude it is not significant. The Mann-Kendal statistical test illustrates that positive trends (warming)
in wintertime SAT series occurs over the all stations, and that the trends are significant at middle and southern regions
of KSA. Recent warming has only occurred during the last two decades at most stations. While cooling in the winter-
time SAT appears for the short period of about 5 years, 1978-1983 and 1988-1992. These trends are consistence with
trends in the global mean SAT. The results obtained from CAW lead to the conclusion that the thermic regime is modi-
fying in the KSA. This dramatic enhancement, occurred at the beginning of the year 1993, is reflected in net modifica-
tion in the SAT time series. The analysis of the SAT also shows a significant warming trend after the year 1997 with a
rate of 0.03˚C/year.
Keywords: Winter Temperature; Saudi Arabia; Coefficient of Variability; Trends; Cumulative Annual Mean
1. Introduction
Changes in climatic variability continue to be major
global issues, not only for the present generation, but also
for future generations. One aspect of climate change is
change in variability of weather elements, such as SAT.
The SAT database has been extensively reviewed on
several earlier occasions, most notably by [1-3]. Recently
many researchers [4-8] have investigated the trends of
climate variables and the characteristics of the climate
change. Adaptation to climate change and efforts to
mitigate the impacts of climate change need to empha-
size not only changes in long-term mean weather attrib-
utes, but also trends in the variability of climatic pa-
rameters [9]. Given that climatic conditions evidently
vary from one period to another, variability is an integral
part of climate change. Consequently, response strategies
and adaptations to climatic change, both at the regional
and global levels, must address climatic variability. [6]
has recently concluded that global warming is unequivo-
cal. Observed to occur concurrently are changes in the
regional climate in different parts of the world. The
trends of regional temperature variations are important
aspects of the baseline against which the po tential effects
of climate change should be assessed [6]. On a global
scale, climatologically studies indicate an increase of
0.3˚C - 0.6˚C of the surface air temperature (0.5˚C -
0.7˚C for the Northern Hemisphere) since 1865 [10].
Climate scientists have concluded that: 1) The earth’s
surface air temperature increased by about 0.6˚C dur-
ing the 20th century; and 2) The temperature augmenta-
tion was highest during the 1990s (Jones, et al., 1999).
The study of [11] indicates that there a gradual warming
until about 1940, cooling (1940-1970) and a second
warming trend begins about 1970 in land surface air
temperature. They pointed out that the recent 1976-2000
warming was larg ely globally synchronous but was more
pronounced in the Northern Hemisphere continents dur-
ing winter and spring. Wintertime surface air temperature
is an issue of great concern, as its variability and spe-
cially extreme events have important economical and
social implications. The successive periods of global
warming, cooling and warming in the 20th century show
distinctive patterns of temperature change suggestive of
C
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H. M. HASANEAN, A. R. K. AL-KHALAF
308
roles for both climate forcing and dynamical variability
[12]. In the Arabian Peninsula, investigations of long-
term variations and trends in temperature data are not
receiving enough attention even though, these countries
suffer serious environmental, agricultural and water re-
sources problems.
In this work, the behavior of wintertime SAT over
KSA since 1978 is examined with regard to persistence,
non-linear trends and inter-annual and inter-decadal
variations. Observation dataset and its homogeneity are
described in Sections 2 and 3 respectively. Section 4 de-
scribes the methods that used, while Section 5 contains
the results together with a discussion and studying the
wintertime SAT changes and variability over KSA. Fi-
nally conclusions are drawn in Section 6.
2. Homogeneity
Lack of homogeneity in data series creates a big problem
for studying time series. The time series of a clima-
tological variable can only be said to be homogenous
where the variability is caused by variations in weather
and climate [13]. However, long time series without arti-
ficial changes in their statistical characteristics are rare
[14]. Non-homogeneities may be caused by relocations
of instruments or changes of instruments, observers and
observation practices, etc. Slow changes of the sur-
roundings of the observation site may gradually cause
non-homogeneities, e.g. the case of urbanization. The
timing and size of significant non-homogeneities can be
estimated with statistical tests. The au thors here used the
short-cut Bartlett test [15] to examine the homogeneity of
the surface air temperature series at designated stations.
The short-cut Bartlett test of homogeneity of variance for
winter air temperature is applied by dividing the series
into equal sub-periods, where . In each of
these sub-periods the sample variance is calculated thus;
k2k

2
11
kii
Sxx
nn



nk
2
S2
mi
S2
k
S
. Where the summations range
over the values of the series in the sub-period . Let
max and n denote the maximum and the minimum
values of , respectively. The 95% significance points
ratio 22
SS
maxmin can be obtained by comparing this ratio
with the values given in Biometrika Table 31 [16]. All
time series used are fo und to be homog enous as show n in
Table 1.
Table 1. Bartlet test (short-cut) result for the KSA stations (n is the number of terms in each sub-period k, and k is the num-
ber of the sub-period).
Station Period n k 95% Significance point Homogeneity
Turaif 31 15 3 5.34 1.53
Guriat 24 12 2 3.50 1.06
Arar 31 15 3 5.34 1.50
Al-Jouf 31 15 3 5.34 1.46
Rafha 31 15 3 5.34 1.48
Tabouk 31 15 3 5.34 1.55
Al-Qaysumah 31 15 3 5.34 1.45
Hail 31 15 3 5.34 1.54
Gassim 31 15 3 5.34 1.52
Dhahran 31 15 3 5.34 1.56
Wejh 31 15 3 5.34 1.60
Al-Ahsa 24 12 2 3.50 1.26
Riyadh 31 15 3 5.34 1.56
Madina 31 15 3 5.34 1.68
Yanbo 31 15 3 5.34 1.66
Jeddah 31 15 3 5.34 1.61
Taif 24 12 2 3.50 1.08
Makkah 24 12 2 3.50 1.21
AL-Baha 24 12 2 3.50 1.27
Bisha 31 15 3 5.34 1.39
Khamis Mushait 31 15 3 5.34 1.57
Abha 31 15 3 5.34 1.58
Najran 31 15 3 5.34 1.38
Sharurah 24 12
2 3.50 1.32
Gizan 31 15 3 5.34 1.48
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H. M. HASANEAN, A. R. K. AL-KHALAF 309
3. Data
Monthly mean SAT data for the twenty five stations
were obtained from the Presidency of Meteorology and
Environment in KSA (Table 2 and Figure 1). Twenty
five (25) stations cover all regions over the KSA. The
selection of these stations is based on the quality and
length of their recor ds. The beginning and en d of all ti me
series are the years 1978 and 2008, respectively except
Al-Ahsa, Al-Baha, Guriat, Makkah, and Shrurah station
started in 1985 (Table 2). From the monthly SAT values,
the wintertime series were calculated for each year by
averaging the values SAT of the months December,
January, and February. The stations under study are dis-
tributed all over KSA, although their spatial density is
low and uneven over some parts of the country. Tem-
perature varies over space and time and this highlights
the existence of large diversities of temperature over
KSA. Besides spatial differences, inter-annual variations
of temperature are also occurring. The complex structure
of the temperature over KSA derives from the vast area
of th e countr y (about, 2,250,000 km2), its wide latitudinal
extent (15.5˚N - 32.5˚N) and its pronounced relief.
4. Methodology
A coefficient of variation for each individual
station has been determined as follows:
(COV)
Table 2. KSA stations location and record.
No Name Latitude (˚N) Longitude (˚E) Elevation (m) Data period Years
1 Turaif 31.68 38.73 852 1978-2008 31
2 Gurait 31.40 37.28 504 1985-2008 24
3 Arar 30.90 41.14 550 1978-2008 31
4 Al-Jouf 29.78 40.98 670 1978-2008 31
5 Rafha 29.62 43.49 445 1978-2008 31
6 Tabouk 28.37 36.60 770 1978-2008 31
7 Al-Qaysumah 28.31 46.13 360 1978-2008 31
8 Hail 27.43 41.69 1000 1978-2008 31
9 Gassim 26.30 43.76 648 1978-2008 31
10 Dhahran 26.25 50.16 22 1978-2008 31
11 Wejh 26.20 38.47 20 1978-2008 31
12 Al-Ahsa 25.29 49.48 180 1985-2008 24
13 Riyadh 24.92 46.72 610 1978-2008 31
14 Madina 24.54 39.69 630 1978-2008 31
15 Yenbo 24.14 38.06 8 1978-2008 31
16 Jeddah 21.71 39.18 18 1978-2008 31
17 Taif 21.48 40.55 1455 1978-2008 24
18 Makkah 21.43 39.79 273 1985-2008 24
19 Al-Baha 20.29 41.64 1655 1985-2008 24
20 Bisha 19.99 42.61 1167 1985-2008 31
21 Khamis Mushait 18.29 42.80 2047 1978-2008 31
22 Abha 18.23 42.66 2100 1978-2008 31
23 Najran 17.61 44.41 1213 1978-2008 31
24 Sharurah 17.46 47.10 727 1985-2008 24
25 Gizan 16.90 42.58 4 1978-2008 31
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H. M. HASANEAN, A. R. K. AL-KHALAF
310
Figure 1. The name and position of KSA stations.
COV 100*SD
.
where, SD is the standard deviation and
is the tem-
poral mean. The evaluation of the trend analysis is based
on the [17] method. The 11-year running mean is a fil-
tering method, it removes variations with periods shorter
than 10-year in a time series and retains variations of
inter-decadal timescales, which are the focus of this
study. The symmetry of the weight distribution guaran-
tees no phase shift in the variations within the time series
after the filter is applied. The response function of the
running mean is similar to that of an ordinary filter, see
for example [18]. Also, it has little effect on variations
whose frequencies are lower than the cutoff frequency of
the filter but has great effect on variations of frequency
near its cutoff frequency, for example, the 12-year varia-
tion.
The non-parametric Mann-Kendall (M-K) statistical
test [19-21] is used to detect any possible trend in the
temperature series, and to test whether or not any such
trends are statistically significant. A detailed assessment
for the testing of climatic data that are unevenly distrib-
uted in time and a comparison of methods for estimating
the significance level of any trend can be found in a
study performed by [22]. The M-K statistical test delivers
provides a value that indicates direction (or sign) and the
statistical magnitud e of the trend in a series.
To visualize the decadal and inter-decadal fluctuations
or “persistence” in the behavior of the KSA temperature,
cumulative annual means method is used [23]. The ad-
vantage of this is to reveal time varying structures in time
series. The cumulative annual means time series can be
defined as;
1
1
j
j
i
i
y
x
j
, Where,
1, 2,,.jN i
x
is
the total annua l temp er a ture and is the number of ye ars
of data used. Of course, N

jN
xN
.
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H. M. HASANEAN, A. R. K. AL-KHALAF 311
5. Results and Discussion
5.1. Coefficient of Variation (COV)
In this section, the variability of the wintertime SAT over
KSA is explored by examining the coefficient of varia-
tion during the study period. The results are dis-
played in Table 3, and Figure 2(a). The for
wintertime SAT decrease gradually from the north to the
south of KSA. The higher values occur in the north and
northeast of KSA with the highest one at Turaif (13.4%),
the northernmost station KSA, while the lowest value of
of wintertime SAT appears at Gizan (1.9%), the
southernmost station in KSA. The higher winter-
time values over north and northeast are due to the effect
of the traveling Mediterranean depressions and their in-
teraction with the inverted V-shaped trough of th e Sudan
low. Table 3 illustrates also that the values
ranged from 1.9% at Gizan to 13.4% at Turaif and the
COV COV
COV COV
COV
COV
(SD) COV
COV
COV
COVCOV
COV
average of the of wintertime SAT is usually
about 6%. The higher and lower values of the standard
deviations are associated with the higher and
lower values of the (Table 3). Generally, the
of wintertime SAT is high. Therefore the winter
SAT is less stable over KSA. The relationship between
and latitude is positive and highly sign ificant (r =
0.8, 99% significant level, Figure 2(b)) while it is nega-
tive and not significant with longitude (r = –0.24, Figure
2(c)). So, the values increase with increasin g lati-
tude (the values of at the north stations are more
than those at the south) and increase with decreasing
longitudes (the values of at the west stations are
more than those at the east). This result is reasonable in
winter season where the north of KSA has considerable
difference of temperature than in the south while the dif-
ference in temperature from west to east is small.
Table 3. The standard deviation, mean coe ffic ient of variation and non-line ar trend (de ri ve d by M ann-Ke ndall statistic a l test)
of wintertime SAT for KSA stations.
Station Latitude (˚N) Longitude (˚E) SD Mean COV Trend
Turaif 31.68 38.73 1.11 8.34 13.4 0.10
Guriat 31.40 37.28 1.00 9.56 10.4 0.10
Arar 30.90 41.14 1.15 9.96 11.5 0.10
Al-Jouf 29.78 40.98 1.33 10.70 12.4 0.17
Rafha 29.62 43.49 1.18 11.48 10.3 0.10
Tabouk 28.37 36.60 1.15 11.94 9.6 0.10
Al-Qqaysumah 28.31 46.13 1.21 13.02 9.3 0.18
Hail 27.43 41.69 1.21 11.69 10.3 0.32*
Gassim 26.30 43.76 1.23 13.99 8.8 0.20
Dhahran 26.25 50.16 0.96 16.59 5.8 0.40**
Wejh 26.20 38.47 0.93 19.74 4.7 0.18
Al-Ahsa 25.29 49.48 0.88 16.23 5.5 0.10
Riyadh 24.92 46.72 1.07 15.87 6.8 0.20
Madina 24.54 39.69 1.19 19.15 6.2 0.21
Yanbo 24.14 38.06 1.13 21.31 5.3 0.21
Jeddah 21.71 39.18 0.89 23.66 3.8 0.10
Taif 21.48 40.55 0.83 16.29 5.1 0.32*
Makkah 21.43 39.79 0.99 24.62 4.0 0.32*
Al-Baha 20.29 41.64 0.71 16.55 4.3 0.52**
Bisha 19.99 42.61 0.99 18.64 5.3 0.41**
Khamis Mushait 18.29 42.80 0.86 14.65 5.9 0.75**
Abha 18.23 42.66 0.57 13.81 4.1 0.52**
Najran 17.61 44.41 0.95 18.45 5.1 0.40**
Sharurah 17.46 47.10 0.89 20.77 4.3 0.32*
Gizan 16.90 42.58 0.51 26.38 1.9 0.48**
*Significant at level 95%; **Significant at level 99%.
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H. M. HASANEAN, A. R. K. AL-KHALAF
312
30 3
5
40 45 50 55 60
10
15
20
25
30
35
(a)
(b)
(c)
Figure 2. Coefficient of variation pattern (COV) of wintertime SAT for 25 KSA stations (a); relationship between COV and
Latitude (b); and relationship betw een COV and Longitude (c) (r means correlation coefficient).
5.2. Trend Analysis
The wintertime SAT series for the KSA stations under
study here are investigated to d etermine their trends. The
trend analysis is performed by means of both simple and
robust tools. The evaluation of the trend is based on the
Mann-Kendall (M-K) statistical test, which makes no
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H. M. HASANEAN, A. R. K. AL-KHALAF 313
assumption regarding probability distribution for the
original data, the data are tested for significance using a
standard normal distribution. The spatial distribution
pattern is not complex, even though the resultant M-K
statistical test give both negative and positive trends.
Table 3 and Figure 3 show the M-K statistical test for
the 25 sites in KSA. The values of M-K statistical test
were computed according to [19]. Positiv e trends (warm-
ing) are observed over all stations. Table 3 and Figure 3
indicate that the trends are high and significant for the
southern and middle regions stations.
Further insight into the results are gained through the
[17] method. Persistent phases of alternating increase or
decreases in temperature, which vary in length, are rec-
ognizable within the time series for wintertime SAT.
Figure 4 illustrates the behavior of the temperature dur-
ing the available data period of each station. It is evident
from Figure 4 that, from the first period under study up
to about 1983, a noticeable decrease in SAT occurs at all
stations. The decrease in mean wintertime SAT reaches
about 1˚C but it is not uniform across the areas under
investigation. Another noticeable decrease of more than
1˚C is evident for all stations round 1987 and 1988. The
results reveal that there has been an increasing in winter-
time SAT (warming) at most stations in the last two
decades beginning around 1993 and 1994 and continuing
up to the end of the period under study. Also, an impor-
tant increase of SAT in southern region (Bisha, Khamis-
Mushait, Abha, Najran, Sharurah and Gizan stations,
Figure 4(c)) from 1984 up to the end of the period under
study (2008). These trends are in general consistence
with trends in the global mean SAT since the late 19th
century. The most probable cause of the observed warm-
ing in recent climate change is a combination of inter-
nally and externally forced natural variability and an-
thropogenic sources.
5.3. Cumulative Annual Mean (CAM)
In this section, we analyzed the long-term behavior of the
wintertime SAT through CAM. The CAM can be de-
tected the climatic shift in wintertime SAT [23]. Persis-
tent phases of alternating increase or decrease of the
temperature, which vary in length, are recognizable in
the time series of the wintertime SAT. Moreover, to
visualize the decadal and inter-decadal fluctuations pre-
sent in the wintertime SAT, CAM is used, because they
can reveal time varying structures in time-series that
cannot be obtained using the original time series. The
results of CAM are shown in Figures 5. The CAM pat-
terns for all stations approximately have the same be-
havior throughout the observational period with an ex-
ception three stations (Wejh, Khamis Mushait and Sha-
rurah, Figures 5(b) and 5(c) respectively). In general,
they show a negative temperature trend (cooling) during
the first period (1978-1982) followed by a positive trend
(warming) until the year of 1988 followed by another
decrease (cooling) during the period from 1988 up to
1992. A gradual warming is found from 1993 up to the
end of the period 2008. The average warming trend
evaluates approximately by about 0.5˚C. On other hand,
the average cooling trend evaluates by 1.0˚C in the pe-
riod (1978-1982) and 0.3˚C in the period (1988-1992).
30 3
10
15
20
25
30
3
5
5
40 4
5
50 5
5
60
Figure 3. Trend pattern of wintertime SAT for 25 KSA stations by using M-K statistic test (trend values above 0.30 are statis-
tically significant at 95% confidence level).
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H. M. HASANEAN, A. R. K. AL-KHALAF
314
(a)
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H. M. HASANEAN, A. R. K. AL-KHALAF 315
(b)
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H. M. HASANEAN, A. R. K. AL-KHALAF
316
(c)
Figure 4. (a) Trend analysis of the winter surface temperature of the stations Turaif, Guriat, Arar, Al-Jouf, Rafha, Tabouk,
Al-Qaysumah, and Hail (dotted line is the mean, dotted smoothed curve is a trend, solid curve is the observation data; (b) As
in Figure 2(a) but for the stations Gassim, Dhahran, Wejh, Ahsa, Riyadh, M adina, Yanbo, Jeddah, Taif, and M akkah; (c) As
in Figure 2(a) but for the stations Al-Baha, Bisha, Khamis-Mushait, Abha, Najran, Sharurah and Gizan.
Copyright © 2012 SciRes. ACS
H. M. HASANEAN, A. R. K. AL-KHALAF
ACS
317
Copyright © 2012 SciRes.
The change point or climatic shift in wintertime SAT
from cooling to warming is the more pronounced feature
in the first half of 1990’s in all stations with an exception
southern region (Khamis Mushait, Abha, Najran, Sha-
rurah, and Gizan stations), Figure 5. The climatic shift in
wintertime SAT in southern region, Figure 5(c) begin in
the mid of the 1980’s. Also, in Figure 5 we compare
j
y,
1, 2,jN with
y
. The most important feature here is
the change, from below
y
to above
y
during the
second half of the 1990’s (1997) in the most stations with
an exception of Guriat, Tabouk, Taif and Sharurah sta-
tions. In the first of 21’s century the change from below
y
to above
y
in these stations is found. The changes
of the SAT tend to warming after the year 1997 with a
rate of 0.03˚C/year approximately. These results seem to
coincide with the [6] scientific report. Globally, it is
(a)
H. M. HASANEAN, A. R. K. AL-KHALAF
318
(b)
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H. M. HASANEAN, A. R. K. AL-KHALAF
ht © 2012 SciRes. ACS
319
(c)
Figure 5. (a) The cumulative annual mean (CAM, solid curve) time series and the averaged CAM (dashed line) in Turaif,
Guriat, Arar, Al-jouf, Rafha, Tabouk, Al-Q aysumah, and Hail; (b) As Figure 5(a) but for Gassim, Dharhan, Wejh, Al-Ahsa,
Riyadh, Madina, Yenbo, Jeddah, Taif and Makkah; (c) As in Figure 5(a) but for the stations Al-Baha, Bisha, Khamis-
Mushait, Abha, Najran, Sharurah and Gizan.
very likely that the 1990’s was the warmest decade and
1998 the warmest year in the instrumental record, since
1861 [6].
Copyrig
6. Conclusions
Variability in the wintertime SAT over KSA has been
investigated throughout the available data period from
twenty five stations. In order to obtain a clear and repre-
sentative picture wintertime SAT in KSA, the coefficient
of variation
COV
COV
V
is adopted to assess the durability
and stability of the SAT in different regions of KSA. We
found that the of wintertime SAT over KSA
ranged from 1.9% to 13.4%, and it is usually about 6%.
Also we concluded that the reason of the spatial varia
tions of CO is due to the effect of the traveling
Mediterranean depressions and its interaction with the
H. M. HASANEAN, A. R. K. AL-KHALAF
320
inverted V-shape trough of the Sudan low. Relationship
between and latitudes is highly significant, while
with longitudes is not significant. Mann-Kendall (M-K)
statistical test illustrates that po sitive trends (warming) in
wintertime SAT series occurs over the all stations and the
trends of wintertime SAT are significant at mid and
southern regi o n of the KS A.
COV
The use of the [17] method for surface temperature is
provide to be fruitful approach to studying inter-annual
climate fluctuations, because they reveal time varying
structure in the raw data or in the more traditional statis-
tical analyses. Examination of the [17] method winter-
time SAT over KSA has revealed support for the notion
of extended “persistence” over several years, even
though simple year-to-year persistence may be evident.
The wintertime SAT of the area is characterized by warm
periods 1993-2008 at all regions of KSA stations, and
1984-2008 in southern region stations. While cooling in
the wintertime SAT appears for the short period of about
5 years, 1978-1982 and 1988-1992. A warm period was
not uniform, continuous or of the same order. Recent
warming has only occurred during the last two decades at
most stations. These trends are in general consistence
with the global trends in the mean surface temperature.
The most probable cause of the observed warming in the
recent climate change is a combination of internally and
externally forced natural variability and anthropogenic
sources.
Regarding to the analysis of CAM, one can see that,
the CAM patterns for all stations approximately h ave the
same behavior throughout the observational period.
Fluctuation every 5 years from cooling to warming and
in reverse is found in the most stations from the begin-
ning period up to under study up to 1992. A gradual
warming is found from 1993 up to the end of the period
2008. The average warming trend evaluates approxi-
mately by about 0.5˚C. On other hand, the average cool-
ing trend evaluates by 1.0˚C in the period (1978-1982)
and 0.3˚C in the period (1988-1992). The climatic shift in
wintertime SAT from cooling to warming is found in the
first half of 1990’s in all stations with an exception
southern region. In southern region the climatic shift in
wintertime SAT, begin in the mid of the 1980’s. More-
over, the most important feature is the change, from be-
low average of CAM to above average of CAM, during
the second half of the 1990’s in the most stations. These
results seem to coincide with the [6] scientific report.
Globally, it is very likely that the 1990’s was the warm-
est decade and 1998 the warmest year in the instrumental
record, since 1861 [6].
7. Acknowledgements
The authors are grateful for being enabled to use monthly
mean KSA station temperature series from Presidency of
Meteorology and Environment in Kingdom of Saudi
Arabia (K SA). We are indebted to the faculty of meteor-
ology and environmental, for making available the com-
puter and other facilities in this work.
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