Journal of Environmental Protection, 2010, 1, 401-409
doi:10.4236/jep.2010.14046 Published Online December 2010 (http://www.SciRP.org/journal/jep)
401
Analysis of Monthly, Seasonal and Annual Air
Temperature Variability and Trends in Taiz City -
Republic of Yemen
Mahyoub H. Al Buhairi
Physics Department, Faculty of applied Science, Taiz University, Taiz, Yemen.
Received March 23rd, 2010; revised August 9th, 2010; accepted August 15th, 2010.
ABSTRACT
Climate change is one of the most important issues of today’s World. Climate scientists have concluded that the earth’s
surface air temperature warmed by 0.6 ± 0.2℃ during the 20th century, accompanied by changes in the hydrologic cy-
cle. Of all the climate elements, temperature plays a major role in detecting climate change brought about by urbaniza-
tion and industrialization. This study focuses on the variability and trends of the mean annual, seasonal and monthly
surface air temperature in Taiz city, Republic of Yemen, during the period 1979-2006. The results of the analysis of the
whole period reveal a statistically significant increasing trend in practically all the months and seasons. A tendency has
also been observed towards warmer years, with significantly warmer summer and spring periods and slightly warmer
autumn and winter, an increase of 1.79℃ and 1.18℃ has been observed in the mean summer and mean winter tem-
perature, respectively. Positive trends of about 1.5℃ in the annual mean temperature were found for the whole period.
The air temperature time series are analyzed, so that the variability and trends can be described.
Keywords: Air Temperature, Climate Change, Republic of Yemen, Taiz City, Mann–Kendall Test, Trends
1. Introduction
Climatic change is one of the most important issues of
present times, therefore World-wide interest in global
warming and climate change has led to numerous trend
detection studies. Anthropogenic interference in the en-
vironment is one of the greatest causes of the process of
climatic change in several regions of the world. This
complex phenomenon, which includes natural and human
processes, depends on a multiplicity of factors and is an
almost irreversible scenario [1]. The Earth’s atmosphere
is warming and that human activities that release green-
house gases are an important cause. Warming of the at-
mosphere affects the temperature of air, land, and water,
which in turn affects patterns of precipitation, evapora-
tion, and wind, as well as ocean temperature and currents.
Greenhouse gases are atmospheric gases such as carbon
dioxide, methane, and nitrous oxide. The concentration
of CO2, one of the major greenhouse gases, in the at-
mosphere has increased significantly. They trap solar
energy, warming the atmosphere and the surface of the
Earth, and play a critical role in maintaining the tem-
perature of the Earth within a range suitable for life.
However, as the levels of these gases build up in the at-
mosphere, they act like the transparent roof of a green-
house, which allows in sunlight while trapping the heat
energy.
Climate change over the last century is a subject of
great topical interest. This problem worries the scientific
community, as it could have a major impact on natural
and social systems at local, regional and national scales.
Numerous climatologists [2,3]; Intergovernmental Panel
on Climate Change (IPCC), [4-6] agree that there has
been a large-scale warming of the Earth’s surface over
the last hundred years or so. This warming up of the
Earth during the 20th century brought with it a decrease
in the area of the world affected by exceptionally cool
temperatures, and, to a lesser extent, an increase in the
area affected by exceptionnally warm temperatures [2].
Some analyses of long time-series of temperatures on a
hemispheric and global scale [4] have indicated a warm-
ing rate of 0.3-0.6℃ since the mid-19th century, due to
either anthropogenic causes [4] or astronomic causes
[7,8]. The Third Assessment Report projections for the

Analysis of Monthly, Seasonal and Annual Air Temperature Variability and Trends in Taiz City - Republic of Yemen 402
present century are that average temperature rises by
2100 would be in the range of 1.4-5.8℃ [4,9]. Records
show that global temperatures, averaged world-wide over
the land and sea, rose 0.6 ± 0.2℃ during the 20th century.
A number of recent studies have been devoted to global,
hemispherical, or regional long-term temperature varia-
tions. On a global scale, climatological studies indicate
an increase of 0.3-0.6℃ of the surface air temperature
0.5-0.7℃ for the Northern Hemisphere) since 1860 [2,
10,11], while the eighth warmest years ever recorded
were observed after [12] .
A broad consensus of scientists has concluded that, the
earth’s surface air temperature increased by about 0.6℃
during the 20th century, that most of the warming during
the latter half of the century is attributable to human
emissions of greenhouse gases, and that temperature in-
creases were greatest during the 1990s [4]. Numerous
other factors such as variations in solar radiation and
pollutant aerosols also contribute to climate change
[13,14]. The IPCC panel further concluded that global
temperature increases are likely to persist in the 21st
century and will probably be accompanied by changes in
precipitation and runoff amounts. Future climate change
is more difficult to predict with great certainty at the re-
gional scale due to spatial resolution limitations of cur-
rent climate models and to the likely influence of unac-
counted for factors such as regional land use change [15].
The Republic of Yemen lies in the South of The Ara-
bian Peninsula, south-west of Asia. South-West Asia (the
Middle East), is a relatively data sparse region of the
world. Rapid population growth and water scarcity are
common throughout the area, rendering it sensitive to
changes in climate. This emphasizes the importance of
meteorological data and climatic knowledge to the region.
In the Middle East, investigations of long-term variations
and trends in temperature data are not receiving enough
attention even though, these countries suffer serious en-
vironmental, agricultural and water resources problems.
The dominating climatic feature in the region is the
summer Southwest Asian Monsoon, which influences the
climatology of the nations within the subregion to vary-
ing degrees and in diverse ways [4]. Climate trends and
variability in Asia are generally characterized by in-
creasing surface air temperature which is more pro-
nounced during winter than in summer. The observed
increases in some parts of Asia during recent decades
ranged between less than 1℃ to 3℃ per century. In-
creases in surface temperature are most pronounced in
North Asia [16]; Climate Change in Russia [17,18].
The Third Assessment Report predicted that the
area-averaged annual mean warming would be about
3℃in the decade of the 2050s and about 5℃ in the dec-
ade of the 2080s over the land regions of Asia as a result
of future increases in atmospheric concentration of
greenhouse gases [19]. The rise in surface air tempera-
ture was projected to be most pronounced over boreal
Asia in all seasons. Many investigators have studied cli-
matic changes in various regions of the world including:
United States [20-24]; Philippines [25]; Europe [26,27];
Kenya [28]; Arab Region [29-33]; Taiwan [34,35]; Israel
[36]; and Italy [37]. Thus, given the relevance of the cli-
mate change in the world, the present paper aimed to
ascertain the occurrence of climatic variability in Taiz
City, which is considered one of the largest cities in Ye-
men, which lies to the north west of the Yemeni Republic.
The climate in Yemen is various and depends on the dif-
ferent altitudes of the regions. There are no distinctive
limits between the seasons. Generally there are two main
seasons; summer and winter. During summer the climate
is hot with high humidity dominating in the coastal area.
In winter the climate in the coastal area is relatively
moderate. Occasional rains in the summer are caused by
the monsoon coming from the Indian Ocean. These rains
decrease the high temperatures in the coastal area during
the summer. The weather in the mountain area is moder-
ate in summer and relatively cold in winter. During win-
ter it becomes especially cold in the night and in the early
morning, with pleasant sunny days.
In this study, the variability and trends of the monthly
mean, mean annual and seasonal surface air temperature
in Taiz are examined. The climatic data used concern
mean monthly values of air temperature Meteorological
Service, for the period 1979-2006. The air temperature
time series are analyzed, so that the variability and trends
be described. The monthly, seasonal and annual tem-
perature trends of mean, mean maximum, and mean
minimum air temperature are discussed. The work then
focuses on the statistics of the annual, seasonal and
monthly maximum, minimum and mean temperatures.
2. Methods
2.1. Study Area
Taiz city is one of the largest cities in Yemen located in
the southwest of Yemen (located at 13°35΄ N and 44°01΄
E). Its average height from sea level is 1311 m.
Daily air temperature data during the period 1979-
2006, were obtained from agro meteorological station of
the Agricultural Research and Extension Authority
(AREA) in Taiz. The station elevation is 1200 m above
sea level with latitude N 13º42΄ and longitude E 44º55΄.
The mean annual rainfall and temperature is 588 mm and
24℃, respectively, while the mean monthly evaporation
is 140 mm. The geographical location of Yemen and the

Analysis of Monthly, Seasonal and Annual Air Temperature Variability and Trends in Taiz City - Republic of Yemen 403
Osiefera station in Taiz City provide a sign and indica-
tion about temperature changes and trends in the region.
For the purpose of this study, the uninterrupted tem-
perature series from 1979 to 2006 has been used, con-
sisting of maximum and minimum air temperatures
measured with thermometers. The readings, maximum
and minimum, are averaged for the calculation of month-
ly, seasonal and annual temperatures.
Daily air temperature data were first calculated as
monthly maximum, minimum, and mean temperature.
Monthly temperature values were averaged to obtain
seasonal and annual values.
Trends were determined using the computer’s program
template. This template uses a nonparametric Mann-
Kendall test to assess the probability that there is a trend
statistically different from zero, and evaluate increasing
or decreasing slope of trends in the climate variables.
2.2. Trend Detection
Trends were detected in the time series of all the indices
analysed by means of the Mann–Kendall test [38,39].
This is a rank correlation statistic test based on the com-
parison of the observed number of discordances and the
value of the same quantity expected from a random series.
The Mann–Kendall method has been suggested by the
World Meteorological Organization to assess the trend in
environmental data time-series [35]. This test consists of
comparing each value of the time-series with the others
remaining, always in sequential order. The number of
times that the remaining terms are greater than that under
analysis is counted [40,41]. The Mann–Kendall statistic
is given by:

1
21
ni
ij
ij
Ssignxx



 (1)
where n is the length of the data set, i
x
and
j
x
are
two generic sequential data values, and the function


ij
s
ign xx assumes the following values:




1, 0,
0, 0,
1, 0.
ij
ij ij
ij
if xx
signxx ifxx
if xx



 




(2)
The S statistic therefore represents the number of posi-
tive differences minus the number of negative differences
found in analyzed time series. Under the null of that there
is no trend in the data no correlation between considered
variable and time, each ordering of the data set is equally
likely. Under this hypothesis the statistic S is approxi-
mately normally distributed with the mean E(S) and the
variance Var (S) as follows:

0ES

(3)
 

 
1
112 5125
18
q
pp p
p
VarSn nnttt








 (4)
where n is the length of the times-series, tp is the number
of ties for the pth value and q is the number of tied values
i.e., equals values. The second term represents an ad-
justment for tied or censored data. The standardized test
statistic Z is given by:


10,
00,
10.
Sif S
Var S
ZifS
Sif S
Var S












(5)
The presence of a statistically significant trend is
evaluated using the Z value. This statistic is used to test
the null hypothesis such that no trend exists. A positive Z
indicates an increasing trend in the time-series, while a
negative Z indicates a decreasing trend. To test for either
increasing or decreasing monotonic trend at p signifi-
cance level, the null hypothesis is rejected if the absolute
value of Z is greater than Z(1-p/2); where Z(1-p/2) is obtained
from the standard normal cumulative distribution tables.
In this work, the significance levels of 0.01, 0.05 and 0.1
were applied, and the significant level p-value was ob-
tained for each analyzed time-series. It is also possible to
obtain a non-parametric estimate for the magnitude of the
slope of trend [42].



, for all
ji
XX
bMediani j
ji








(6)
where is the slope between data points
j
X
and i
X
;
measured at times j and i; respectively.
3. Results and Discussion
The standard deviation (σ) and mean (μ) of the maximum
(Tmax), minimum (Tmin) and mean (Tmean) tempera-
tures are specified in Table 1. From the basic tempera-
ture data, (Tmax), (Tmin) and (Tmean) temperature,
along with their standard deviation (σ) have been statis-
tically computed for each month, year and the four sea-
sons; spring, summer, autumn, and winter. These means
are depicted in Table 1. Seasons were defined using the
standard meteorological definition: winter = December,
January and February, spring = March, April and May;
summer = June, July and August and autumn = Septem-
ber, October and November.
Table 1 reports the temperature characterrristics in
Taiz city. It is clear from the Table that, the mean
monthly temperature is highest in June 26.6℃ and lowest

Analysis of Monthly, Seasonal and Annual Air Temperature Variability and Trends in Taiz City - Republic of Yemen 404
Table 1. Statistics of the monthly, seasonal and annual temperature means (μ) and standard deviation (σ).
Tmax. (℃) Tmin. (℃) Tmean (℃)
Month
μ σ μ σ μ σ
January 27.3 0.88 14.1 2.02 20.7 1.15
February 28.4 1.28 15.3 2.02 21.8 1.19
March 29.9 1.10 17.3 1.61 23.6 1.08
April 31.3 1.42 18.8 1.67 25.1 1.28
May 32.9 1.23 19.5 2.03 26.2 1.36
June 33.3 0.80 19.9 1.45 26.6 0.98
July 32.5 0.82 20.2 1.91 26.3 1.16
August 32.0 0.56 19.1 2.13 25.6 1.14
September 31.8 1.15 17.8 2.06 24.8 1.36
October 31.1 0.92 16.9 1.81 23.9 1.13
November 29.6 0.90 15.7 1.50 22.7 0.89
December 28.1 0.97 14.9 1.90 21.5 0.83
Annual 30.7 0.58 17.5 1.44 24.1 0.84
Spring 31.4 1.07 18.6 1.61 25 1.12
Summer 32.6 0.75 19.8 1.74 26.2 1.00
Autumn 30.8 0.8 16.8 1.50 23.8 0.95
Winter 27.9 0.81 14.8 1.67 21.3 0.81
in January 20.7℃. However, the mean maximum tem-
perature for June is 33.3℃. On the average, January is
the coldest month of the year and June is the warmest
(only slightly warmer than May and July). The lowest
mean monthly temperature occurred in January 1987
(8.7℃) and the warmest month ever recorded was May
1999 (35.2℃). The temperature variability between the
different years and the average annual temperature is
24.1℃, while the annual mean maximum temperature
reached 30.7℃ and the annual mean minimum tempera-
ture was 17.5℃.
The Mann–Kendall test statistics of the Tmax, Tmin,
and Tmean are given in Table 2. The statistically sig-
nificant levels, high 0.01, medium 0.05 and low 0.1 were
used in this paper [1]. The nonparametric estimate for the
magnitude of the slope, b, was computed for the signifi-
cant trends, which certify all the trends in ℃/year. Table
1 reports the temperature characteristics in Taiz city. It is
clear from the Table that, the mean monthly temperature
is highest in June 26.6℃ and lowest in January 20.7℃.
However, the mean maximum temperature for June is
33.3℃. On the average, January is the coldest month of
the year and June is the warmest (only slightly warmer
than May and July). The lowest mean monthly tempera-
ture occurred in January 1987 (8.7℃) and the warmest
month ever recorded was May 1999 (35.2℃). The tem-
perature variability between the different years and the
average annual temperature is 24.1℃, while the annual
mean maximum temperature reached 30.7℃ and the an-
nual mean minimum temperature was 17.5℃.
The Mann–Kendall test statistics of the Tmax, Tmin,
and Tmean are given in Table 2. The statistically sig-
nificant levels, high 0.01, medium 0.05 and low 0.1 were
used in this paper. The nonparametric estimate for the
magnitude of the slope, b, was computed for the signifi-
cant trends, which certify all the trends in ℃/year.
Behavior of the Tmax, Tmin, and Tmean was studied
for individual months, seasons and annually by subject-
ing them to the Mann–Kendall test. The results of the
standardized test statistics Z, significance level p-value
and the slope b; corresponding to the temperature vari-
ables trend analysed in this study are presented in Table
2. It is to be noted have that the Tmax and Tmean
shows a significant trend in the majority of the months,
while Tmin shows a significant trend in nearly half the
number of the months.

Analysis of Monthly, Seasonal and Annual Air Temperature Variability and Trends in Taiz City - Republic of Yemen 405
Table 2. Mann-Kendall trend test results (Z and p-value) and the slope b.
Tmax Tmin Tmean
Month
Z P-value b (℃/Year) Z P-value b (℃/Year) Z P-value b (℃/Year)
January 1.73 0.084 0.037 0.97 0.33 0.026 2.14 0.033 0.038
February 2.43 0.014 0.074 0.00 1 0.00 2.26 0.024 0.073
March 2.99 0.003 0.071 0.98 0.84 0.009 2.20 0.03 0.041
April 2.14 0.03 0.075 1.48 0.14 0.043 2.04 0.042 0.066
May 3.43 0.001 0.105 2.08 0.04 0.067 2.87 0.004 0.088
June 2.98 0.003 0.056 2.24 0.025 0.071 2.53 0.011 0.064
July 2.99 0.003 0.06 3.19 0.0014 0.097 4.01 0.0001 0.092
August 1.89 0.059 0.029 0.83 0.41 0.032 2.23 0.026 0.031
September 1.68 0.092 0.033 1.99 0.046 0.091 2.10 0.038 0.057
October 1.19 0.23 0.026 −0.099 0.92 0.00 0.97 0.33 0.021
November 2.73 0.006 0.06 1.54 0.12 0.05 2.57 0.01 0.058
December 2.42 0.015 0.05 −0.06 0.95 0.00 1.84 0.066 0.031
Annual 8.32 0.000 0.05 4.19 0.11 0.042 8.07 0.000 0.053
Spring 4.96 0.0002 0.084 2.19 0.03 0.04 4.12 0.000 0.064
Summer 4.56 0.0002 0.044 3.63 0.0003 0.067 5.10 0.000 0.064
Autumn 3.26 0.014 0.04 1.99 0.05 0.047 3.27 0.001 0.045
Winter 3.82 0.003 0.05 0.53 0.60 0.0098 3.62 0.0003 0.042
3.1. Annual Temperature Trends
The annual mean, annual maximum and annual mini-
mum temperatures and trend line are presented in Figure
1. The Mann–Kendall test confirmed that the positive
trend observed is statistically significant see Table 2.
The mean annual temperature and the mean annual maxi-
mum temperature show an increasing trend, which is
statistically significant at P < 0.01 level, while the mean
annual minimum temperature is significant at 0.1 level.
A linear fit to the ensemble averaged annual means of
mean, minimum and maximum temperature anomalies
confirms significant and quite large trends of 0.053℃
/year for mean temperatures. This trend corresponds
to an increase of 1.5℃ in the total period analysed
(1979-2006) of mean temperature. The annual mean
maximum air temperatures trend is that of 0.05℃/year.
This trend corresponds to an increase of 1.4℃ in the total
period analyzed of mean maximum temperature. Annual
mean minimum temperature trend was a little less
0.042℃/year. The temperature increase corresponding to
the total period was 1.2℃ of annual mean minimum tem-
perature.
3.2. Seasonal Temperature Trends
In the climatic seasons we observe a different tendency
of changes in air temperature. The mean temperature,
Tmax and Tmin for spring, summer, autumn and winter
seasons during the period 1979-2006 are presented in
Figure 2. The parameters analysed show a positive trend
practically for all seasons and is most important for
maximum temperatures Table 2. The air temperature
time-series in Taiz for the whole period analysed showed
an increasing trend statistically significant at P < 0.01, P
< 0.05 and P < 0.1 in practically all the months and sea-
sons. However, some months showed a declining trend
February, October and December.
The winter mean temperature shows an increasing
trend, which is statistically significant at P < 0.01 level
Table 2. Tmin also shows warming. However, this
warming trend of Tmin is not statistically significant. The
Tmax during winter shows an increasing trend, which is
statistically significant at P < 0.01. In spring the Mann–
Kendall test indicates that the mean temperature shows
an increasing trend, significant at P < 0.01 level. Tmax
also shows an increasing trend, significant at P < 0.01

Analysis of Monthly, Seasonal and Annual Air Temperature Variability and Trends in Taiz City - Republic of Yemen 406
Figure 1. Annual temperature trends in Taiz.
level and Tmin shows an increasing trend, which is sta-
tistically significant at P < 0.05 level.
The summer mean temperature, Tmax and Tmin also
shows an increasing trend, significant at P < 0.01 level.
Autumn is characterized by a significant increase in
temperature. The mean and mean maximum temperature
show an increasing trend, which is statistically signifi-
cant at P < 0.01 level. The mean minimum also shows an
increasing trend, significant at P < 0.05 level.
3.3. Monthly Temperature Trends
Behavior of mean, mean minimum and mean maximum
temperature was also studied for individual months by
subjecting them to the Mann–Kendall test. The results
are presented also in Table 2. The mean temperature
shows a significant trend practically for all months ex-
cept October. Statistically significant at the P < 0.05
level increases in mean air temperature were noted for
January, February, March, April, August and September.
However, this increase is significant for May, June, July
and November at P < 0.01 level and December at 0.1
level.
Tmax shows a significant trend practically for all
months except October. Statistically significant at the
level 0.01 increases in maximum air temperature were
noted for February, March, May, June, July, November
and December. However, this increase is significant for
January, April, August and September at 0.05 level.
April, May and July showed the greatest increasing trend
in the mean maximum air temperature in the whole pe-
riod analysed. Tmin also shows a significant trend in the
majority of the months. The trend analysis of winter
months shows an increasing trend in minimum tempera-
ture, which is not statistically significant. The Tmin at the
beginning of spring, though it shows an increasing trend
in minimum temperature, is not statistically significant.
The later part of spring, particularly April and May,
shows an increasing trend significant for April at 0.1
level and May at 0.05 level. The trend of Tmin during the
summer months shows an increasing trend significant for
June at 0.05 level, July at 0.01 level and August are not
statistically significant. The mean minimum temperature
during the autumn months shows an increasing trend
significant for November at 0.1 level, while October and
December are not statistically significant.
This increase in temperature is attributed to the green
house gases emissions, especially CO2, from the different
motor vehicles used in the city. The rough terrain of the
city, as the city is an area that is shaped ridges, depres-
sions and dissecting wadis, leads to only partial combus-
tion of the fuel which, in its turn, leads to an increase in
the effort of the engine. The factories situated around the
city as well the increase in human activities and the
dearth of green areas and parks in the city also contribute
to the warming of the city. Moreover, the mountains
which surround the city, especially Saber Mountain
which is bout 3100 meters above sea level, act like natu-
ral wind shields preventing smooth circulation of air and
leading to an increase in the warming of the city.
4. Conclusions
This study investigated monthly, seasonal and annual
climatic variability in Taiz City based on mean maxi-
mum, mean minimum and mean air temperatures. One of
the main results of this study is the confirmation of a
significant warming trend in average temperatures in

Analysis of Monthly, Seasonal and Annual Air Temperature Variability and Trends in Taiz City - Republic of Yemen 407

Analysis of Monthly, Seasonal and Annual Air Temperature Variability and Trends in Taiz City - Republic of Yemen 408
Figure 2. Temperature trends for spring, summer, autumn and winter in Taiz.
Taiz city, of about 1.5℃ in the past 30 years, concen-
trated in spring and summer months. Analysis of maxi-
mum and minimum temperatures reveals a warming
trend for the annual and all seasonal series. The warming
trend for the summer and winter seasons is statistically
significant at P < 0.01 level with a rate of increase of
0.064℃/year, 0.042℃/year, respectively.
REFERENCES
[1] V. P. R. Silva, “On Climate Variability in North-East of
Brazil,” Journal of Arid Environments, Vol. 58, No. 4,
2004, pp. 575-596.
[2] P. D. Jones, E. B. Horton, C. K. Folland, M. Hulme, D. E.
Parker and T. A. Basnett, “The Use of Indices to Identify
Changes in Climatic Extremes,” Climatic Change, Vol.
42, No. 1, 1999, pp. 131-149.
[3] D. E. Parker and E. B. Horton, “Global and Regional
Climate in 1998,” Weather, Vol. 54, 1999, pp. 173-184.
[4] Inter Governmental Panel on Climte Change (IPCC),
“Climate Change 2001: Synthesis Report, Contribution of
Working Groups I and III to the Third Assessement of the
(IPCC),” Cambridge University Press, Cambridge, 2001.
[5] P. D. Jones and A. Moberg, “Hemispheric and Large-
Scale Surface Air Temperature Variations: An Extensive
Revision and Update to 2001,” Journal Climate, Vol. 16,
2003, pp. 206-223.
[6] K. Y. Vinnikov and N. C. Grody, “Global Warming
Trend of Mean Tropospheric Temperature Observed by
Satellites,” Science, Vol. 302, 2003, pp. 269-272.
[7] W. Soon, S. Baliunas, E. S. Posmentier and P. Okeke,
“Variations of Solar Coronal Hole Area and Terrestrial
Lower Tropospheric Air Temperature from 1979 to
Mid-1998: Astronomical Forcings of Change in Earth’S
Climate,” New Astron, Vol. 4, No. 8, 2000, pp. 563-579.
[8] T. Landscheidt, “Solar Wind near Earth: Indicator of
Variations in Global Temperature,” Proceedings of the
1st Solar and Space Weather Euroconference on the So-
lar Cycle and Terrestrial Climate, Tenerife, 2000, pp.
497-500.
[9] IPCC, “Climate Change 2001: Impacts, Adaptation and
Vulnerability,” In: J. McCarthy, O. Canziani, N. Leary, D.
Dokken and K. White, Eds., Contribution of Working
Group II to the Third Assessment Report of the (IPCC),
World Meteorological Organisation and United Nations
Environment Programme, Cambridge University Press,
Cambridge, 2001.
[10] P. D. Jones, T. M. L. Wigley and P. B. Wright, “Global
Temperature Variations between 1861 and 1984,” Nature,
Vol. 322, 1986, pp. 430-434.
[11] P. D. Jones, “Hemispheric Surface Air Temperature
Variations: Recent Trends and an Update to 1987,” Jour-
nal of Climate Vol. 1, 1988, pp. 654-660.

Analysis of Monthly, Seasonal and Annual Air Temperature Variability and Trends in Taiz City - Republic of Yemen 409
[12] WMO, “WMO Statement on the Status of the Global
Climate in 1996,” WMO, No. 858, World Meteorological
Organization, Geneva, 1997.
[13] G. P. Brasseur and E. Roeckner, “Impact of Improved Air
quality on the Future Evolution of Climate,” Geophysical
Research Letters Vol. 32, No. L23704, 2005, p. 4
[14] N. Scafetta and B. J. West, “Estimated Solar Contribution
to the Global Surface Warming Using the ACRIM TSI
Satellite Composite,” Geophysical Research Letters, Vol.
32, 2005, p. L18713.
[15] R. A. Pielke, “Land Use and Climate Change,” Science,
Vol. 310, No. 5754, 2005, pp. 1625-1626.
[16] I. P. Savelieva, L. N. Semiletov, Vasilevskaya and S. P.
Pugach, “A Climate Shift in Seasonal Values of Mete-
orological and Hydro Logical Parameters for Northeast-
ern Asia,” Progress in Oceanography, Vol. 47, No. 2,
2000, pp. 279-297.
[17] Climate Change in Russia, IGCE Rusian Agency on
Hydrometeorology and Environmental Monitoring Pub-
lishing, Moscow, 2003.
[18] G. Gruza and E. Rankova, “Detection of Changes in Cli-
mate State, Climate Variability and Climate Extremity,”
Institute of Global Climate and Ecology, Moscow, No. 4,
2004, pp. 90-93.
[19] M. Lal, H. Harasawa, D. Murdiyarso, W. N. Adger, S.
Adhikary, M. Ando, Y. Anokhin, R. V. Cruz, et al., “Asia
Climate Change (2001): Impacts, Adaptation, and Vul-
nerability Contribution of Working Group II to the Third
Assessment Report of the IPCC, J. J. Mc- and K. S.
White, Eds., Cambridge University Press, Cambridge,
2001(a).
[20] R. C. Balling Jr. and S. W. Brazel, “The Impact of Rapid
Urbanization on Pan Evaporation in Phoenix, Arizona,”
Journal of Climatology, Vol. 7, No. 6, 1987, pp. 593-597.
[21] A. C. Comrie, and B. Broyles, “Variability and Spatial
Modeling of Fine-Scale Precipitation Data for the Sono-
ran Desert of South-West Arizona,” Journal of Arid En-
vironments, Vol. 50, No. 4, 2002, pp. 573-592.
[22] C. K. Folland, C. Miller, D. Bader, M. Crowe, P. Jones, N
Plummer, M. Richman, D. E. Parker, J. Rogers and P.
Scholefield, “Work Shop in Indices and Indicators for
Climate Extremes,” Geophysical Research Letters, Vol.
32, 1997, p. L18713.
[23] D. R. Easterling, H. F. Diaz, A. V. Douglas, W. D. Hogg,
K. E. Kunkel, J. C. Rogers and J. F. Wilkinson, “Long-
Term Observations for Monitoring Extremes in the
Americas,” Climatic Change, Vol. 42, 1999, pp. 285-308.
[24] T. R. Karl and D. R. Easterling, “Climate Extremes: Se-
lected review and Future Research Directions,” Climatic
Change, Vol. 42, No. 1, 1999, pp. 309-325.
[25] A. M. Jose, R. V. Francisco and N. A. Cruz, “A Study on
Impact of Climate Variability Change on Water Re-
sources in the Philippines,” Chemosphere, Vol. 33, No. 9,
1996, pp. 1687-1704.
[26] N. W. Arnell, “The Effect of Climate Change on Hydro-
logical Regimes in Europe: A Continental Perspective,”
Global Environmental Change, Vol. 9, No. 1, 1999, pp.
5-23.
[27] A. A. Velichkov, N. Catto, A. N. Drenova, V. A. Kli-
manov and K. V. Kremenetski, “Climate Change in East
Europe and Siberia at the Late Glacial-Holocene Transi-
tion,” Quaternary International, Vol. 91, No. 1, 2002, pp.
75-99.
[28] E. C. Kipkorir, “Analysis of Rainfall Climate on the
Njemps Flats, Baringo District, Kenya,” Journal of Arid
Environments, Vol. 50, No. 3, 2002, pp. 445-458.
[29] S. Al-Fahed, O. Al-Hawaj and W. Chakroun, “The Re-
cent Air Temperature Rise in Kuwait,” Renewable Energy,
Vol. 12, No. 1, 1997, pp. 83-90.
[30] N. A. Elagib and A. S. Abdu, “Climate Variability and
Aridity in Bahrain,” Journal of Arid Environmental, Vol.
36, No. 3, 1997, pp. 405-419.
[31] A. A. Abahussain, A. S. Abdu, W. K. Al-Zubari, N. A.
El-Deen and M. Abdul-Raheem, “Desertification in the
Arab Region: Analysis of Current Status and Trends,”
Journal of Arid Environments, Vol. 51, No. 4, 2002, pp.
521-545.
[32] M. S. Mahmoud and Z. Ahmed, “A Sudden Change in
Rainfall Characteristics in Amman, Jordan during the
Mid 1950s,” American Journal of Environmental Sci-
ences, Vol. 2, No. 3, 2006, pp. 84-91.
[33] M. S. Mahmoud, “Observed Abrupt Changes in Mini-
mum and Maximum Temperatures in Jordan in the 20th
Century,” American Journal of Environmental Sciences,
Vol. 2, No. 3, 2006, pp. 114-120.
[34] C. C. Chang, “The Potential Impact of Climate Change
on Taiwan’s Agriculture,” Agricultural Economics Vol.
27, No. 3, 2002, pp. 51-64.
[35] P. S. Yu, T. C. Yang and C. K. Wu, “Impact of Climate
Change on Water Resources in Southern Taiwan,” Jour-
nal of Hydrology, Vol. 260, 2002, pp. 161-175.
[36] S. Cohen, A. Ianetz, and G. Stanhill, “Evaporative Cli-
mate Change at Bet Dagan, Israel, 1964-1998,” Agricul-
tural and Forest Meteorology, Vol. 111, No. 2, 2002, pp.
83-91.
[37] A. C. Moonen, L. Ercoli, M. Mariotti and A. Masoni,
“Climate Change in Italy Indicated by Agrometeorologi-
cal Indices over 122 Years,” Agricultural and Forest Me-
teorology, Vol. 111, No. 1, 2002, pp. 13-27.
[38] H. B. Mann, “Nonparametric Tests against Trend,”
Econometrica, Vol. 13, No. 3, 1945, pp. 245-259.
[39] M. G. Kendall, “Rank Correlation Measures,” Charles
Griffin, London, 1975.
[40] D. P. Lettenmaier, E. F. Wood and J. R. Wallis, “Hydro-
climatological Trends in the Continental United States
1948-1988,” Journal Climate, Vol. 7, 1994, pp. 586-607.
[41] H. B. Burn and M. A. H. Elnur, “Detection of Hydrologic
Trends and Variability,” Journal of Hydrology, Vol. 255,
2002, pp. 107-122.
[42] R. M. Hirsch, J. R. Slack and R. A. Smith, “Techniques
of Trend Analysis for Monthly Water Quality Data,” Wa-
ter Resources Research, Vol. 18, No. 1, 1982, pp. 107-
121.