Temperature Variability over Saudi Arabia and its ... · The coefficient of variation (CV) is used...

JKAU: Met., Env. & Arid Land Agric. Sci., Vol. 23, No. 1, pp: 85-108 (2012 A.D./1433 A.H.) DOI: 10.4197/Met. 23-1.6

____________________

Author’s Address: Dr. Mansour Almazroui, Center of Excellence for Climate Change Research

(CECCR) / Dept of Meteorology, Faculty of Meteorology, Environment and Arid Land

Agriculture, King Abdulaziz University, P. O. Box 80234, Jeddah 21589, Saudi Arabia.

85

Temperature Variability over Saudi Arabia and its

Association with Global Climate Indices

Mansour Almazroui

Center of Excellence for Climate Change Research / Department of

Meteorology, Faculty of Meteorology, Environment and Arid Land

Agriculture, King Abdulaziz University, Jeddah, Saudi Arabia

[email protected]

Abstract. This paper investigates the effect of large-scale forcings on

the variability of the mean air temperature of Saudi Arabia, using the

observed data and the National Centers for Environmental Prediction

(NCEP) reanalysis gridded datasets for the period 1978-2010. The

analysis shows that the seasonal mean temperature variability is high

in the northern and central regions compared to the southern.

Moreover, the temperature variability is highest (~30%) in the winter

season and lowest (~2 %) in the summer. Following global warming

indications, the interannual variability of the mean air temperature of

Saudi Arabia indicates a warming phase that started in the late 1990s.

The analysis reveals that the strong variability in temperature

over Saudi Arabia is closely associated with the North Atlantic

Oscillation (NAO) index for all seasons except the autumn; however,

the relationship is most prominent during the winter season. The study

indicates that the winter season temperature is also influenced by the

Arctic Oscillation (AO) index, whereas the spring season temperature

is influenced by the El Niño Southern Oscillation (ENSO). This

research concludes that the negative phases of ENSO, AO and NAO

all play a major role in the temperature increase over most parts of

Saudi Arabia.

Keywords: Temperature, Saudi Arabia, ENSO, NAO, AO.

86 Mansour Almazroui

Introduction

Temperature is one of the most important climatic parameters, and it can

seriously impact the socio-economic condition of a region. Temperature

is intimately related to agriculture, drought, water resources, power

generation, human health, urbanization, and cold- and heat-wave

extremes. For example, rising temperature has a direct effect on crop

yields as well as indirect effects on the availability of irrigation water

(Nelson et al., 2009). Cueto et al. (2009) found that the minimum

temperatures within the urban area of Mexicali city are increasing at a

faster rate than in the surrounding rural area, they found an intimate

relationship between urbanization and local temperature. Ultimately, the

increase in temperatures in the urban climate has negative implications

for energy, water consumption and human health.

In the present era of global warming and climate change,

understanding the exact climatic situation, especially information relating

to temperature extremes, is critical. The average global surface air

temperature has increased by about 0.6 ± 0.2 °C since the late nineteenth

century (Folland et al., 2001), where the rate of warming in the global

surface air temperature during the last half century (1956-2005) was

0.128°C/decade (IPCC, 2007). However, the temperature of a region

varies with topographic characteristics such as high and low elevations,

land coverage and land types. In addition, temperature falls with increase

in altitude, whilst the warmness of a region also depends on surface

terrestrial albedo, which is high over sandy areas like the Rub Al-Khali

desert in Saudi Arabia.

Saudi Arabia is situated in the southwest of Asia at the junction of

Africa and Asia and is the largest country (occupying 80% of area) in the

Arabian Peninsula (CSD, 2010). The country has mountain ranges in the

western region, situated parallel to the coast of the Red Sea. The Rub Al-

Khali is the world’s largest sand desert, which covers almost the entire

south-eastern region of Saudi Arabia (Atlas, 1984; Edgell, 2006; and

Bishop, 2010). The climate of Saudi Arabia is extremely hot and dry

(Almazroui, 1998; and Ragab and Prudhomme, 2000). The rainfall

climatology, as studied by Almazroui (2011), is such that the country

receives large amounts of rainfall in the northern, central-northern and

south-western regions, whereas the south-eastern region is almost

entirely dry. The south-western region of Saudi Arabia receives rainfall

http://en.wikipedia.org/wiki/Southwest_Asia

http://en.wikipedia.org/wiki/Africa

http://en.wikipedia.org/wiki/Asia

http://en.wikipedia.org/wiki/Red_Sea

Temperature Variability Over Saudi Arabia and its Association with … 87

in almost all months of the year, whereas the northern region receives

most of its rainfall during the winter and spring seasons (Almazroui,

2011; Abdullah and Almazroui, 1998; and Al-Jerash, 1985). In the

northern region of the country, the climate parameters are associated with

westerly disturbances, the Sudan trough and the storms of the

Mediterranean region, whereas the southern climate is influenced by the

Indian Ocean monsoon circulation (Sen and Al-Suba’i, 2002).

A few studies have been conducted in the Arabian region to

understand the behaviour of its temperature climatology: For the whole

of the Middle East, (Bou-Zeid and El-Fadel, 2002); for Saudi Arabia,

(El-Nesr et al., 2010); for Kuwait, (Nasrallah and Balling, 1995); and for

Jordan (Smadi, 2006). However, most of these studies mainly addressed

the temperature trends and their relationship with water resource

management. Furthermore, these studies used the available surface

observation data, which are very sparse in this region.

Of further significance is the fact that ENSO, NAO and AO are

also major drivers of the climate of the northern hemisphere, as discussed

in several studies, and it is therefore of vital importance to examine the

effects of these external forces (NAO and AO) on the region’s climate.

However, in the past, very little research has been conducted to address

the relationship between the temperature and the large-scale forcings in

the study region. For example, Almazroui (2006) studied the mean

temperature of only four stations over Saudi Arabia and their relationship

with ENSO phenomena. Unfortunately, no attempt has been made so far

to detect the impact of NAO and AO on the seasonal temperatures of

Saudi Arabia. This work is thus the first attempt at investigating the

effects of these large-scale forcings on the temperature over Saudi

Arabia. Specifically, the current study investigates the relationship

between the mean temperatures in Saudi Arabia with the large-scale

forcings, using the available observed data (from 27 stations) and the

gridded reanalysis datasets. It is envisaged that the research will be useful

in understanding how ENSO, AO and NAO affect the temperature

climatology of the study area.

Data and Methodology

The daily temperature data of 27 stations from the years 1978-2010

were acquired from the Presidency of Meteorology and Environment


(PME), Saudi Arabia. The National Centers for Environmental Prediction

(NCEP) surface mean air temperature (Kalnay et al., 1996) gridded data

are also utilized. The station details, together with mean temperatures

(averaged over 1978-2010) on the annual and seasonal timescales in

Saudi Arabia, are given in Table 1. The station acronyms from this table

are used for the rest of the study. The large-scale features, such as

Niño3.4, AO and NAO data, are utilized to identify their effects on the

temperature of Saudi Arabia. These large-scale monthly time series data

were obtained from the Climate Prediction Center (CPC) at the National

Oceanic and Atmospheric Administration (NOAA) website (http: //www.

cpc.ncep.noaa.gov/).

Table 1. The observation station details, with mean temperature (°C) on annual and

seasonal scales in Saudi Arabia, averaged over 1978-2010.

Station Information Temperature (°C)

Name Acronym Lat

(oN)

Lon

(oE)

Elevation

(m) Annual DJF MAM JJA SON

Turaif S1 31.68 38.73 852 18.96 8.57 18.28 28.32 20.62

Gurayat S2 31.4 37.28 504 20.08 10.12 19.70 28.90 21.59

Arar S3 30.9 41.14 550 22.17 10.54 21.72 32.72 23.67

Al Jouf S4 29.78 40.1 670 21.86 10.96 21.45 31.50 23.50

Rafha S5 29.62 43.49 445 23.28 11.81 23.13 33.24 24.90

Tabuk S6 28.37 36.6 770 22.15 12.31 22.02 30.67 23.58

Al

Qaysumah S7 28.33 46.12 360 25.34 13.42 25.41 35.67 26.82

Hafer

Albaten S8 27.9 45.53 412 25.00 13.18 25.05 35.33 26.48

Hail S9 27.44 41.69 1000 21.90 11.73 21.71 30.78 23.38

Gassim S10 26.3 43.77 648 24.81 14.19 24.90 34.02 26.12

Dhahran S11 26.26 50.16 22 26.91 16.99 26.45 35.74 28.45

Wejh S12 26.2 36.47 20 24.96 19.77 24.36 29.11 26.60

Ahsa S13 25.3 49.49 180 27.28 16.37 27.40 36.91 28.42

Riyadh New S14 24.92 46.72 612 25.47 15.05 25.90 34.64 26.31

Riyadh Old S15 24.71 46.73 610 26.59 16.07 26.92 35.82 27.53

Madinah S16 24.54 39.7 630 28.17 19.13 28.07 35.90 29.53

Yenbo S17 24.14 38.06 8 27.84 21.60 27.33 32.83 29.57

Jeddah S18 21.71 39.18 18 28.70 24.22 28.16 32.44 29.98

Taif S19 21.48 40.55 1455 22.98 16.42 22.94 29.18 23.38

Makkah S20 21.43 39.79 273 31.42 25.39 31.48 36.11 32.67

Al Baha S21 20.29 41.64 1655 23.12 17.05 23.20 28.99 23.25

Bisha S22 19.99 42.61 1167 25.21 18.40 26.10 31.73 24.64

Khamis

Mushait S23 18.29 42.8 2047 19.89 15.24 20.15 24.48 19.72

Abha S24 18.23 42.66 2100 18.90 14.15 19.08 23.56 18.79

Najran S25 17.61 44.41 1213 24.99 18.32 26.23 31.48 23.95

Sharurah S26 17.47 47.12 727 27.74 20.18 29.12 34.33 27.37

Jazan

(Gizan) S27 16.9 42.58 4 30.64 26.68 30.62 33.97 31.30


The spatial and temporal analyses of temperature on the annual and

seasonal scales are performed using standard statistical methods. The

annual cycle, mean and standard deviation of temperature are obtained

from the observed seasonal dataset. The temperature climatology is

obtained from the NCEP data on the monthly, seasonal and annual

timescales. The interannual variability of temperature obtained from the

NCEP dataset for an area confined between 35°-60°E and 12°-22°N is

compared with the observed country average data across Saudi Arabia.

The coefficient of variation (CV) is used to obtain the mean temperature

variability over the region. The effects of the large-scale natural forcings,

such as ENSO, NAO, and AO, on the mean seasonal temperature over

Saudi Arabia are studied using both the observed station and the gridded

datasets. The Student’s t-test is used to assess the significance of the

relationship of these forcings with the seasonal mean temperatures over

the region under study.

Results and Discussion

Figure 1 shows the topography of the study region and its

surroundings because the temperature varies with the elevation and other

topographic factors such as land characteristics and vegetation. It is

apparent in Fig. 1 that there is a flat, low-elevation (<500m) north-south

inclined strip located in eastern Saudi Arabia. This relatively low-

elevation belt is narrower in the north and wider in the south. To the west

of this low-elevation belt, another north-south inclined belt is located

with relatively high elevation. In this belt, most of the land is under

1000m, with the exception of a very few locations where elevation

reaches up to 1500m. The elevation of this mountain range, however,

reaches over 2000m in southwestern Yemen. The temperature over Saudi

Arabia varies with this topographic variation.

The surface observation stations are mostly located in the western

and northern parts of the country. The low-dense network of observation

stations in the north is not able to reflect the exact characteristics of the

spatial temperature distribution. In addition, no observational facilities

are available in the southeast of the country, where the Rub Al-Khali is

situated.


Fig. 1. The topography in and around Saudi Arabia. The acronyms of the surface

observation stations (see Table 1) and their locations (red dots) are also shown.

Temperature Variability

Seasonal Variability

At first, the temperature variability at seasonal scale obtained from

the observed data is discussed followed by the NCEP data explanation.

The mean and one sigma deviation of the observed temperature

from 27 stations over Saudi Arabia are shown in Fig. 2. The horizontal

axis shows the station acronym from north to south of Saudi Arabia

according to Table 1. In each graph the center point (dot mark) is the

mean of the temperature at a particular station for the study period (1978-

2010) and the dispersion is the one sigma standard deviation. In general,

relatively low temperature is observed for the northern stations during

winter season, whilst the temperature of the southern stations are low in

other (spring, summer and autumn) seasons. The seasonal temperature


variability for the stations located in the north and the central areas of

Saudi Arabia is high compared to the stations situated in the south (also

see Table 1). There is a gradient of high variability in the north to the low

variability in the south.

For example, the mean temperature at the extreme north station

(S1) ranges from 8.57°–28.32°C in different seasons, however at the

southern station (S27) it ranges from 26.68°–33.97°C (see Table 1). This

infers a large seasonal cycle in the northern region compared to the

southern. Overall, during the winter season this gradient of temperature

variability seems to be high and it is almost stagnant during the summer.

This happened because the northern and central regions of Saudi Arabia

are under the influence of westerly disturbances from the Mediterranean

Sea during the winter season.

The low-density network of surface observation stations across

Saudi Arabia means that we are unable to infer the temperature

distribution in the country with a sufficiently high degree of accuracy.

Thus, the NCEP gridded data were considered for assessing the level of

accuracy of the temperatures obtained from the observed data.

The Coefficient of Variability (CV) obtained from the NCEP data

during winter, spring, summer and autumn seasons are shown in Fig. 3.

In general, the temperature variability is high in the northern region of

Saudi Arabia, compared to the southern region of the country, for all

seasons; the gradient of this temperature variability is again from the

northern region (high) to the southern region (low). The variability

gradient is more prominent during the winter season, compared to the

other seasons. These results are fully consistent with the results of the

observed data discussed earlier (see Fig. 2). In the northern region, the

CV reached about 30% during the winter season, whilst during the

summer it was much less pronounced, reaching only about 2%.

Interannual Variability

The interannual variability of the seasonal temperature anomaly

obtained from the observed and the NCEP data is shown in Fig. 4. The

anomalies are obtained for all seasons, considering the reference period

from 1978 to 2010. The observed temperature is averaged from all 27

stations used, whereas the NCEP temperature is the areal averaged over

the region between 35°-60°E and 12°-22°N.


(a) Winter (DJF) (c) Summer (JJA)

(b) Spring (MAM) (d) Autumn (SON)

Fig. 2. The temperature variability in Saudi Arabia obtained from the observed station data

for the (a) Winter (b) Spring (C) Summer, and (d) Autumn seasons averaged over

1978-2010.

Figure 4 shows that the interannual variability of the observed

seasonal temperature is apparent in both the warm (positive) and cold

(negative) phases. Since the late 1990s, all seasons have shown a

warming phase over Saudi Arabia, although four cold phases were

observed during the winter season. This could be linked with the increase

in temperature over the study region due to global warming. The NCEP

data captured well the interannual variability in almost all seasons.

Importantly, the NCEP data clearly identifies the warm phase from the

late 1990s. During the winter season, the correlation between the

observed and the NCEP data is 0.94, which is statistically significant at

the 95% level according to the Student’s t-test.

6 9

12 15 18 21 24 27 30 33 36 39

S1

S3

S5

S7

S9

S11

S13

S15

S17

S19

S21

S23

S25

S27

Tem

pe

ratu

re (

°C)

Station

6 9

12 15 18 21 24 27 30 33 36 39

S1

S4

S7

S10

S13

S16

S19

S22

S25

Tem

pe

ratu

re (

°C)

Station

6 9

12 15 18 21 24 27 30 33 36 39

S1

S3

S5

S7

S9

S11

S13

S1

5

S17

S19

S21

S2

3

S25

S27

Tem

pe

ratu

re (

°C)

Station

6 9

12 15 18 21 24 27 30 33 36 39

S1

S3

S5

S7

S9

S11

S1

3

S15

S1

7

S19

S2

1

S23

S2

5

S27

Tem

pe

ratu

re (

°C)

Station




Fig. 3. The Coefficient of Variation (CV in %) obtained from the NCEP mean temperature

in and around Saudi Arabia for the (a) Winter, (b) Spring, (c) Summer and (d)

Autumn seasons, averaged over 1978-2010.

According to the One Sigma standard deviation (0.97°C) during the

winter season, the observed dataset identified ten extreme temperature

years (1983, 1989, 1992, 1993, 1994, 1999, 2002, 2004, 2006 and 2010)

over the country (Fig. 5a). It is interesting to note that the years 1983,

1989, 1992 and 1993 are the cold years, according to the One Sigma

standard deviation criterion; however, since 1994 all the extreme years

are found to be extremely hot, relative to the ‘normal’ years. The year

2010 is the warmest (2.22°C w.r.t. 1978-2010 average reference period)

among all the extreme years, whereas 1983 is the coldest (-2.25°C w.r.t.


1978-2010 average reference period) among all the cold years. It was

also noted that after 1995, the temperature shows an increasing trend

during the winter season. The NCEP data also capture the extreme years

quite well but with some underestimation of temperature compared to the

observed data.

During the spring season, the correlation between the two datasets

is found to be 0.83, which is statistically significant at the 95% level (Fig.

4b). According to the One Sigma standard deviation criterion (0.86°C),

the observed data identified nine extreme temperature years (1982, 1983,

1992, 1997, 2001, 2003, 2007, 2008 and 2010) in Saudi Arabia. Except

for the year 1982, all the extreme years are captured well by the NCEP

data. In the spring season, 1983 is the extreme coldest year (-1.84°C

w.r.t. 1978-2010 average reference period), whereas 2010 is the extreme

warmest year (1.95°C w.r.t. 1978-2010 average reference period).

The correlation between the observed and the NCEP data is 0.88

during the summer season, which is statistically significant at the 95%

level (Fig. 4c). According to the One Sigma standard deviation criterion

(0.95°C), the observed data identified nine extreme temperature years

(1978, 1982, 1984, 1998, 2000, 2006, 2008, 2009 and 2010) in Saudi

Arabia. Except for the years 1978, 1982, 1984, 1998 and 2009, all the

extreme years were captured well by the NCEP data. In the summer

season, 1984 is the extreme coldest year (-1.81°C) whereas 2010 is the

extreme warmest year (2.1°C).

During the autumn season, the correlation between the two datasets

is 0.89, which is 95% statistically significant (Fig. 4d). According to the

One Sigma standard deviation criterion (0.75°C), the observed data

identified eleven extreme temperature years (1978, 1981, 1982, 1983,

1984, 1998, 1999, 2002, 2007, 2009 and 2010) in Saudi Arabia. All of

the extreme years are captured well by the NCEP data, with some

underestimated values compared to the observed data. In the autumn

season, 1978 is the extreme coldest year (-1.54°C), whereas 2010 is the

extreme warmest year (1.6°C). Overall, the results show that 2010 was

the warmest year in Saudi Arabia during the study period, observed

during all four seasons.




Fig. 4. Area averaged mean temperature anomalies obtained from the observed and the

NCEP data for the (a) Winter, (b) Spring, (c) Summer and (d) Autumn seasons. The

anomaly is taken with respect to the reference period 1978-2010.

Temperature Climatology

The ability of the NCEP data to capture the overall temperature profiles over Saudi Arabia encouraged us to further utilize them to obtain the spatial distribution of the temperature climatology over the country, which is not possible from the direct use of the observed station values.

Seasonal and Annual Temperature Climatology

The seasonal mean temperature climatology averaged over 1978-2010 is shown in Fig. 5. The results show that the temperature in the south-eastern region of Saudi Arabia is high, compared to the other regions of the country. The reason for the relatively high temperatures (reaching above 50°C and not shown in the color bar) in this south-eastern region is due to the presence of the world’s largest sand desert, the Rub Al-Khali (Empty Quarter).

During the winter season, the temperature distribution over the country is in three distinct areas: the northwest is below 10°C, the center is 10°-15°C, and the east and the western coastal areas are 15°-20°C (Fig. 5a). As the northern region of the study area is under the influence of

1980 1985 1990 1995 2000 2005 2010

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western disturbances originating from the Mediterranean, it is colder than the southern region, which is under the influence of the tropics and hence is considerably warmer in this season. During the spring season, the temperatures increase all over the country, ranging between 20° and 35°C (Fig. 5b), climbing more quickly in the north, whilst in the summer season, the whole country is warm, with temperatures reaching above 30°C (Fig. 5c). In the summer season, the highest temperatures are observed in the south-eastern region, where the mean is above 35°C. During autumn, moderate temperatures are observed over the country, which range between 25° and 30°C, except in the northwest where they are below 25°C (Fig. 5d). However, in the south-eastern tip of the country, high temperatures are observed during this season.



Fig. 5. The spatial distribution of seasonal mean temperature (°C) climatology obtained

from the NCEP data for the (a) Winter, (b) Spring, (c) Summer, and (d) Autumn

seasons averaged over 1978-2010.


Fig. 6. The spatial distribution of annual mean temperature climatology (°C) obtained from

the NCEP data, averaged over 1978-2010.

The spatial distribution of annual mean temperature obtained from

the NCEP data averaged over 1978-2010 is displayed in Fig. 6.

Generally, there are two temperature zones over the country: one covers

the central-to-northwestern region, with temperature ranging between 20°

and 25°C, whilst the other is central-to-eastern and southern and western

areas, having temperatures ranging between 25° and 30°C. It is

interesting to note that the lowest temperature (<20°C) is observed in a

small part of the north-western region, whilst the highest temperature

(>35°C) is observed in two small pockets of the Rub Al-Khali area. On

the annual scale, the temperature gradient is from the southeast to the

northwest of the country.

Effects of large-scale forcings on temperatures over Saudi Arabia

This section discusses the effects of large-scale features such as

ENSO, AO and NAO on the temperature of the Saudi Arabia, obtained

for both the observed and the NCEP datasets.

Effect of the ENSO Index

The correlations between the observed mean temperatures from the

station dataset and the Niño3.4 index during different seasons are shown

in Fig. 7. Overall, a negative correlation is found between the Niño3.4

index and the observed mean seasonal temperature of the region for most


of the stations. In addition, the correlations for stations S17 and S18 are

negative and are statically significant at the 95% level, however for the

stations S25 and S26, the correlations are positive and statistically

significant at the 95% level during the winter season. During the spring

season, all the stations show a negative correlation except for S25, which

shows a weak positive correlation. The stations in northern, central

(except S13 and S14) and S18 and S20 in south-western Saudi Arabia

show strong negative correlation (95% statistically significant). In

summer, only S2, S6 and S8 show a negative statistically significant

correlation, whereas in autumn, none of the stations has a statistically

significant relationship with the Niño3.4 index.

Fig. 7. The correlation of Niño 3.4 SSTs and the mean temperatures obtained from the

observed data for the winter, spring, summer, and autumn seasons averaged over

1978-2010.

The spatial distribution of the correlation of the Niño3.4 index with

the NCEP mean temperature for different seasons is shown in Fig. 8. The

shaded areas indicate the correlations with a 95% statistical significance

according to Student’s t-test. During the winter season, weak correlation

is found with the NCEP data over Saudi Arabia except for a small area in

the south-western region. During the spring season, the mean

temperatures over most parts of Saudi Arabia are under the influence of

the Niño3.4 region. In the summer and autumn seasons, no statistically

significant correlation is found over the country. These results are

consistent with the results obtained from the observed data as discussed

earlier (see Fig. 7).

-0.80

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S1

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S6

S7

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S9

S10

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S13

S14

S15

S16

S17

S18

S19

S20

S21

S22

S23

S24

S25

S26

S27

Co

rre

lati

on

NINO3.4 Winter Spring Summer Autumn




Fig. 8. The correlation of Niño 3.4 index and the mean temperature obtained from the

NCEP data for the (a) Winter, (b) Spring, (c) Summer, and (d) Autumn seasons

averaged over 1978-2010.

The negative correlation indicates that when there is a positive

phase (El Niño) of ENSO temperatures over Saudi Arabia tend to fall;

however, for the negative phase (La Niña) of ENSO, temperatures tend to

rise across the country. Accordingly, the spring temperatures of Saudi

Arabia are found to be affected by ENSO, which reduces the temperature

mostly from the central to the northern parts of the country.

Effects of the AO Index

The correlation between the AO index and the observed mean

temperature in different seasons for the period 1978-2010 is shown in

Fig. 9. During the winter season, all stations show a negative correlation,

except S23. In this season, all stations show strong negative correlation

with a 95% statistical significance, except for the southern stations (S22,

S23, S24, S25, S26 and S27). The highest correlation is obtained for the


northern station S2 (-0.79). In other seasons, there is no statistically

significant correlation between the AO index and the mean temperature,

except for S18 during the spring (-0.37) and the autumn (-0.35).

The correlation of the AO index with the NCEP temperature for the

period 1978–2010 is shown in Fig. 10. The shaded areas indicate the

statistically significant correlation at the 95% level according to

Student’s t-test. The AO affects the winter season mean temperature of

Saudi Arabia, except for the southern areas of the country. The negative

correlation is evident from Fig. 10 for other seasons, however no

statistically significant relationship is found except over the Red Sea

during the autumn. These results are consistent with the results obtained

from the observed data as discussed earlier.

As discussed earlier the variability in winter season temperature is

high in the northern and the central areas of the country (see Fig. 3a).

This high variability is associated with the AO, and so it could be used as

an indicator for predicting the winter mean temperature in the study

region.

Fig. 9. The correlation of AO and the mean temperature obtained from the observed data

for the winter, spring, summer, and autumn seasons averaged over 1978-2010. (see

Fig. 12). The strong negative correlation indicates that the mean temperature during

the winter season over this region is strongly associated with AO phenomena. This

indicates that during the negative phase of AO, the temperature in the region tends

to increase, and vice versa.

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

S1

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12

S13

S14

S15

S16

S17

S18

S19

S20

S21

S22

S23

S24

S25

S26

S27

Co

rre

lati

on

AO Winter Spring Summer Autumn




Fig. 10. The correlation of the AO index and the mean temperature obtained from the

NCEP data for (a) Winter, (b) Spring, (c) Summer, and (d) Autumn seasons


Effect of the NAO Index

The correlation between the NAO index and the observed mean temperature for the period 1978-2010 in the four seasons is shown in Fig. 11. During the winter season, all stations show negative correlation, except for S23 (0.11). Out of the 27 stations, 18 stations show strong correlation (95% statistically significant). Similarly, during the other seasons, negative correlation is found between the observed mean temperature and the NAO index for all stations, except for S27 (0.07), where a positive correlation is found in the autumn season. During the spring and summer seasons, most of the stations show a statistically significant relationship among the temperatures and NAO index, however significant relationships are few during the autumn season. This


indicates that the mean temperature over Saudi Arabia is heavily affected by the NAO phenomena during the winter, spring and summer seasons.

Fig. 11. The correlation of NAO and the mean temperature obtained from the observed data

for the winter, spring, summer, and autumn seasons averaged over 1978-2010.

The spatial distribution of the correlation between the NAO index

and the NCEP temperature over the period 1978–2010 is shown in Fig.

12. The negative correlation with 95% statistically significance is

prominent during the winter, spring and summer seasons. There is no

significant correlation over Saudi Arabia during the autumn except at the

central coast line of the Red Sea. These results are consistent with the

results obtained from the observed data (Fig. 11). The negative

correlation indicates that during the negative phase of the NAO, the

temperature over Saudi Arabia tends to increase, whilst for the positive

phase of the NAO, the mean temperature tends to decrease over the

region.

Consequently, the seasonal mean temperature of Saudi Arabia is

closely related to the variability of ENSO, the AO and the NAO. These

large-scale features show clear indication about the control of seasonal-

mean temperature over Saudi Arabia, which is quite significant in

diagnosis of the climate over the region.

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6 S1

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12

S13

S14

S15

S16

S17

S18

S19

S20

S21

S22

S23

S24

S25

S26

S27

Co

rre

lati

on

NAO Winter Spring Summer Autumn




Fig. 12. The correlation of the NAO index and the mean temperature obtained from the

NCEP data for (a) Winter, (b) Spring, (c) Summer, and (d) Autumn seasons


Conclusions

The monthly, seasonal and annual temperature obtained from the

surface observations at 27 stations over Saudi Arabia and the NCEP data

are analyzed for the period 1978-2010. The effects of large-scale

forcings, specifically ENSO, AO and NAO, on the mean temperatures

over the country are also investigated. The results show that the

variability in seasonal mean temperature in northern and central Saudi

Arabia is high compared to the southern region of the country. The

temperature gradient is found to be from the south to the north of Saudi


Arabia; however the temperature variability gradient is from the northern

and central region to the southern region. During the winter and autumn

seasons, the gradient in mean temperature variability is high, compared

to the spring and summer seasons. During the summer season, the

temperature variability is low over Saudi Arabia. It is found that the high

variability in the temperature over Saudi Arabia is closely associated

with the large-scale forcings, for instance, the NAO index is strongly

related to the temperature with a 95% statistically significant negative

correlation in all seasons except autumn; however this relationship is

more prominent during the winter season. The AO phenomenon also

influences the winter season temperature over Saudi Arabia, whereas

ENSO events affect the spring temperature. The strong and positive

phase of El Niño has a strong negative correlation with the temperature

of Saudi Arabia; it reduces temperatures across this region. With the

positive (negative) phase of AO, the temperature of Saudi Arabia tends to

decrease (increase). Similarly, during the negative phase of NAO, the

temperature over Saudi Arabia tends to increase, and vice versa. As a

subsequent study, this analysis may be extended to the utilization of

global and regional climate model outputs, in the diagnosis as well as the

projection of temperature effects in this region, which could result in

significant socio-economic benefits.

Acknowledgements

Deanship of Scientific Research, King Abdulaziz University is

acknowledged for funding this project under the grant no. (305/428).

The author also acknowledges the Presidency of Meteorology and

Environment (PME) of Saudi Arabia for providing surface observation

data, and NOAA for providing the ENSO, AO, and NAO data through its

website.

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تغير درجات الحرارة على المملكة العربية السعودية وعالقتها بمؤشرات المناخ العالمية

منصور المزروعي ،قسم األرصاد ،مركز التميز ألبحاث التغير المناخي

كلية األرصاد والبيئة وزراعة المناطق الجافةالمملكة العربية السعودية –جدة ،جامعة الملك عبدالعزيز

[email protected]

يهتم هذا البحث بدراسة تأثير القوى الواسعة النطاق على . المستخلصالمتوسررطة فرري المملكررة العربيررة السررعودية تغيررر وتبررايج درجررات الحرررارة

الررردوليلمركرررز الشررربكية لبيانرررات المقاسرررة وكرررذلك البيانرررات الباسرررتخدام بينرررتوقرررد . م0282-8791خرررالل الةتررررة مرررج (NCEP) ئررريللتنبرررؤ البي

فري ارتةرا درجة الحرارة المتوسرطة وجرود ل البيج سنويةدراسة التغيرات ذلررررك فرررريمتبعررررة الماضرررريدرجررررة الحرررررارة منررررذ نهايررررة تسررررعينات القرررررج

التغيررات الةصرلية أجوباإلضافة إلى ذلك، فقد وجرد . العالميالتسخيج للمملكررررة المنرررراطق الشررررمالية والوسررررطى فرررريدرجررررة الحرررررارة عاليررررة فرررري

قريم لهررذا أعلرىبالمقارنرة مرا المنطقرة الجنوبيرة، وعرالوة علرى ذلرك فراج بينمررا اقررل قررريم ترهررر فررري ( ٪02~ )التبررايج ترهررر فررري فصررل الشرررتا

التبرررايج الشرررديد فررري أوضرررحت الدراسرررة أج هرررذا وقرررد (. ٪0~ )الصررري رراهرة بشكل كبير بدرجة الحرارة فوق المملكة العربية السعودية مرتبط

ذات داللررررة عكسرررريبنسرررربة ترررررابط ( NAO)األطلسرررري شررررمالالتذبررررذ ٪ لجميرررا الةصرررول باسرررتثنا فصرررل الخريررر 79 إلرررىإحصرررائية تصرررل

ج درجرة إفر إضرافة إلرى ذلرك. الشرتا فصرلخرالل أكثر وضوحا وتكوج ، (AO)بمالمر ذبذبرة القطر الشرمالي أيضا حرارة فصل الشتا تتأثر


ربيرا بررراهرة النوسرراج فصررل الالمملكررة فري فري حرريج تترأثر درجررة حررارة لكررل السررلبي الطررور أج إلررىويخلررص هررذا البحررث (. ENSO) الجنرروبي ،(AO)وذبذبرررررة القطررررر الشرررررمالي ،(ENSO) الجنررررروبيالنوسررررراج مرررررج

ارتةرررررا تلعررررر دورا رئيسرررررا فررررري ( NAO)شرررررمال األطلسررررري الوتذبرررررذ .درجات الحرارة على المملكة العربية السعودية

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