Download Projection of Temperature and Precipitation in Southern Iran Using

Iran. J. Sci. Technol. Trans. Sci. (2016) 40:39–49
DOI 10.1007/s40995-016-0009-8
RESEARCH PAPER
Projection of Temperature and Precipitation in Southern Iran
Using ECHAM5 Simulations
M. J. Nazemosadat1 • V. Ravan1 • E. Kahya2 • H. Ghaedamini1
Received: 10 June 2015 / Accepted: 4 September 2015 / Published online: 13 April 2016
Ó Shiraz University 2016
Abstract This study applied a statistical approach to
project temperature and precipitation condition in southern
parts of Iran for the period 2016–2045. The applied
methodology transformed the probability distribution of
the observed historical series into the corresponding simulated time series of the ECHAM5 GCM model. Results
showed that the mean annual temperature of the study area
for the period 2016–2045 is greater than corresponding
climatological mean by about 0.5–1.0 °C, if the emissions
are within the range of the 20C3M scenario. These values,
however, increased to about 2.0–2.4 °C when the high
emission scenario of 1PTO2X was applied. We examined
conformity between the trend lines of the historical temperature data and corresponding projected time series. The
conformity was substantially improved when the outcomes
of the 1PTO2X rather than 20C3M scenario were used.
Under the 20C3M scenario, the mean values of the projected precipitation data were less than corresponding
amounts of the historical time series by about 20%.
Keywords
Climate change GCM ECHAM5 Iran
& M. J. Nazemosadat
[email protected]
1
Atmospheric and Oceanic Research Center, Water
Engineering Department, College of Agriculture, Shiraz
University, Shiraz, Iran
2
Faculty of Civil Engineering, Istanbul Technical University,
Istanbul, Turkey
1 Introduction
Global warming and its associated causes and aftermaths
are among the recent challenges in various scientific disciplines including meteorology, climatology, oceanography
and environmental and socio-economic sciences (Ding and
Houghton 2001; National Research Council 2001; Parry
et al. 2007; Meehl et al. 2007). The primary greenhouse
gases in the earth’s atmosphere are water vapor, carbon
dioxide, methane, nitrous oxide, and ozone that absorb and
emit radiation within the thermal infrared range. The net
function of these gases on absorbing and emitting the terrestrial outgoing long-wave radiation has increased the
biosphere’s theoretical temperature by about 33 °C and
decreased the difference between day and night temperatures globally. It is worthwhile to note that, from theoretical point of view, average temperature of the earth’s
surface is about -18 °C, although due to this radiative
function of these gases, it is about ?15 °C in reality. In
spite of the fact that water vapor is one of the main
greenhouse gases which modulates global air temperature,
abnormalities have not yet reported in its concentration in
terrestrial scale.
According to the recent observations, the current upward
trend in the global temperature is mostly harmonized with
the similar trend in the atmospheric concentrations of CO2,
CH4, N2O and O3 that are mostly due to the human
activities (Mestre-Sanchis and Feijoo-Bello 2009). It
means that, in addition to possible natural sources, global
warming is critically affected by those anthropogenic
causes that are responsible for further release of these
gases. In other words, the potential impact of the global
warming on human activities such as agriculture, industry,
urbanization and water supply depends on the future concentration and mixture of the greenhouse gases.
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Iran. J. Sci. Technol. Trans. Sci. (2016) 40:39–49
A number of climate forecast models with different
temporal and spatial resolutions have been developed to
project the future climate condition. Various scenarios of
industrialization and gas emission are considered to make
these models more compatible with the desires of decision
makers and scientific communities. Projection of the future
climate change and estimation of the potential consequences of this change on both human societies and natural
system are the concern of the most well-known climate
models (Meehl et al. 2007). Prediction accuracy of these
models mostly depends on the adapted initial condition and
the number of independent biospheric parameters considered for their development (U.S. National Assessment
Synthesis Team 2000).
Harmonized with advancement in computational technology, the emphasis of today’s exploration has mostly shifted
toward the use of more complicated forecast schemes known
as the general climate models (GCMs). The current dynamical
GCMs are the outcome of mathematical analysis of the
planetary ocean–atmosphere circulations using the Navier–
Stokes equations on a rotating sphere (Roeckner et al. 2003).
These equations are the basis of the complex computer programs commonly used to simulate the air-sea circulations and
their interactions in various spatial and temporal scales. The
atmospheric and oceanic GCMs (namely AGCM and OGCM)
are the key parts of global climate models along with sea ice
and land surface components. The GCM models are widely
used for the weather forecasting, understanding current climate and projecting future climate. Some of the well-known
GCM models include HADCM3, ECHAM5, CCSM3, PCM1,
CNRM-CM3, CSIRO-MK3 MIROC-3.2, IPSL-CM4,
CGCM-3.1, and GISS-ER.
The GCM models were firstly being used for the climate
forecasting at the global or continental coarse scale with an
effective resolution of 200–300 km (Palmer and Anderson
1994; Hunt 1997; Mason et al. 1999; Gates et al. 1999;
Goddard et al. 2001; Landman and Goddard 2002; Washington and Preston 2006). Although such spatial resolutions
are appropriate for planetary scale analysis, they are hardly
able to characterize local sub-grid features such as coastlines, steep topography, and vegetation gradients with an
acceptable accuracy. Due to these deficiencies, biases were,
frequently reported between GCMs typical outputs and
observed meteorological parameters (Joubert and Hewitson
1997; Mason and Joubert 1997; Salathe et al. 2007).
To obtain climate change information at local and
regional levels (critical for impact assessments) and to
account for land surface heterogeneity at the same time,
GCM outputs need to be downscaled to finer resolutions
(Christensen et al. 2007). Landman and Tennant (2000)
and Landman et al. (2001), applied statistical approach to
downscale the GCM outputs over southern Africa. Washington and Preston (2006) studied the role of the Indian
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Ocean sea surface temperatures on the occurrence of wet
years over southern parts of Africa by downscaling outputs
of the HadAM3 GCM model. For the assessment of the
effects of climate change on daily precipitation in India,
Raje and Mujumdar (2009) used a statistical approach to
downscale GCM simulations. Almazroui (2012) used
regional climate model (RegCM4) nested in the European
Community–Hamburg atmospheric model (ECHAM5) and
the European Centre for Medium-Range Weather Forecasts’ (ECMWF) 40-year reanalysis (ERA40) data to
analyze the effects of climate change on the Arabian
Peninsula. While ECHAM5 and ERA40 exhibited drier
and warmer biases over the Peninsula, RegCM4 predicted
higher rainfall intensity and lower temperatures.
ECHAM5 is one of the most recent versions in a series
of ECHAM models, developed at the Max Planck Institute
for Meteorology, Germany uses 1.8° 9 1.8° grid cells of a
31-layer atmosphere and a 40-layer ocean (Roeckner et al.
2003). Two scenario of this model, namely 20C3M and
1PTO2X, are used here for simulating historical data and
projection of climate change in southern parts of Iran. The
20C3M scenario is an experiment with the assumption that
the concentration of greenhouse gases will increase with
the rate as was observed through the 20th century. This
increasing rate is, however, steeper for the 1PTO2X scenario, since this experiment assumes that the increase rate
of the greenhouse gases is 1 % per year from pre-industrial
level until the concentration has doubled and held constant
thereafter (1 % to double). Guo et al. (2012) have reproduced four sets of twenty year simulation that were
respectively carried out by the ECHAM5 with three spatial
resolutions and the RegCM3 nested in ECHAM5 in oneway mode (ECHAM5-RegCM3).
Altitudinal and latitudinal dependence of future warming
in Taiwan simulated by WRF nested with ECHAM5/
MPIOM has been evaluated by Lin et al. (2014). Simulation
results showed close correlation between fine resolution
downscaling by WRF nested with ECHAM5/MPIOM and
the actual observation data for the period 1979–2003. Projection of future climate changes revealed both altitudinal
and latitudinal variations in warming trend, with more significant temperature increase in mountain areas than in plain
areas towards the end of the 21st century and more obvious
warming in the north than in the south of Taiwan.
Fei (2014) evaluated the possibility of simulating variability in the ENSO indicators using the ECHAM5/MPIOM
model. They found that conformity between the observed
and simulated data is quite realistic in terms of structure,
strength and period. The annual cycle of SST and the phase
of ENSO events were successfully reproduced by the
ECHAM5/MPIOM. Reboita et al. (2014) studied climate
projections of air temperature and precipitation over South
America from the Regional Climate Model version 3
Iran. J. Sci. Technol. Trans. Sci. (2016) 40:39–49
41
(RegCM3) nested in ECHAM5 and HadCM3 global models.
According to their report the RegCM3 projections indicated
general warming throughout all regions and seasons. The
RegCM3 projections suggested a negative trend of precipitation over northern parts of South America. Mohammadi
et al. (2015) assessed the impacts of future climatic change
on the irrigated rice yield over southern coast of the Caspian
Sea under three scenarios of climate change. In addition
to local climate data, they utilized the outputs of some
GCMs including HADCM3, ECHAM5, IPCM4, GFCM2,
NCCCSM and INCM3. Their validation results suggested
that for projecting future conditions of temperature and
precipitation in this part of Iran, the ECHAM5 climate
model has the highest correlation with the lowest error. Due
to high temporal and spatial resolutions and also easier
accessibility of the model output (from authors point of
view), the ECHAM5 model and its two mentioned scenarios
were used to project future status of temperature and precipitation in southern Iran.
The coupled ocean–atmosphere GCMs do not provide
predictions of the distant future in the sense that we try to
predict the weather one day or a few days ahead. The good
GCMs will generate realistic sequences of main global
climate phenomenon such as El Niño, hurricanes, or
monsoons, but not the same sequence as has been observed
historically (Dettinger et al. 2004). In other words, only the
statistics (mean rates, frequencies of occurrence, etc.)
would ever be expected to be reproduced in the GCMs.
This study has, therefore, been prompted to project changes in the magnitudes of future temperature and
precipitation in southern parts of Iran for the period
2016–2045 by downscaling ECHAM5 model outputs. The
outcome of the study is, hopefully, beneficial for long-term
planning of socio-economical activities in Iran, where the
shortage of water resources and frequent of climatic hazards cost lives and billions of dollars annually.
Fig. 1 The geographical location of the considered synoptic stations
on the global 1.8° 9 1.8° network of the ECHAM5 model. While the
black circles represent the position of the ground stations, rectangles
signify the selected nodes whose data were used for downscaling
procedure as indicated in Table 2
2 Materials and Methods
2.1 Data
Monthly mean temperature and precipitation data for 12
synoptic stations spread over various parts of southern
Iran were gratefully obtained from the website of the I.R
Iran Meteorological Organization (http://www.weather.ir).
Geographical locations and record length of these stations
are shown in Fig. 1 and Table 1. Since about 85–90 % of
the annual precipitation of the considered stations occurs
during the October–March period, precipitation data were
merely analyzed for these 6 months. Temperature data
were, however, studied for all months of the year. The
record lengths of the considered time series vary from 55 to
16 years as indicated in Table 1.
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Iran. J. Sci. Technol. Trans. Sci. (2016) 40:39–49
Table 1 The geographical position and record length of the selected
stations
Station
Latitude (°N)
Longitude (°E)
Period
Abadan
30.22
48.15
1951–2005
Ahwaz
31.20
48.40
1957–2005
Bandar Abass
27. 13
56.22
1957–2005
Bandar Daier
27.50
51.56
1994–2005
Bandar Mahshahr
30.33
49.90
1988–2005
Boushehr
28.59
50.50
1951–2005
Fasa
Jask
28.58
25.38
53.41
57.46
1967–2005
1985–2005
Abomoosa Island
25.50
54.50
1984–2005
Kangan Jam
27.49
52.22
1990–2005
Lar
27.41
54.17
1990–2005
Shiraz
29.36
52.32
1951–2005
Monthly simulations of temperature and precipitation
data for both 20C3M and 1PTO2X scenarios were
appreciatively extracted from the http://cera-www.dkrz.
de/wdcc/ui/index.jsp webpage for the period 1951–2045.
These data were collected for 20 nodes that surround the
ground-based stations so that each of these stations is
encircled by four nodes (Fig. 1). As it will be shown later,
these four sets of simulated data were then examined to
select the most proper nodes for final downscaling
procedure.
Due to an intrinsic structure of the GCM models, no yearto-year correspondence between the historical observations and simulations is expected. However, ideally, one
would hope that the probability distributions of the
observed and projected series are comparable. Statistical
downscaling has an important advantage over the regional
models since it is computationally efficient and allows the
consideration of a large set of climate scenarios (Dettinger et al. 2004).
The applied procedure consisted of the following steps:
A:
B:
C:
2.2 Statistical Downscaling
Correlation analysis was performed to investigate the
linkage between the station data and four sets of the model
output around each station (Fig. 1). The linkage was found
to be poor for all stations which implicates that the GCM
model could not directly reproduce the historical data in a
fine resolution. The simple deterministic downscaling
function as described by Dettinger et al. (2004) was,
therefore, applied to capture the probability distribution of
the historical series and transfer it into the GCM projected
dataset. According to this methodology, the historical
GCM’s output data are firstly modified so that their probability distributions approximate the corresponding distribution of the weather station data. The same deterministic
transformations were then applied to produce climate
projection for the period of 2016–2045 (O’Brien et al.
2001). Pryor et al. (2005) has successfully used a similar
approach for downscaling wind data. Their results were
generally found to be comparable with the outcomes of the
sophisticated regional dynamical models.
We adapt the inherent probability distribution of the
observed data as the main basis for downscaling and
subsequent estimation of the projected climate change.
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Monthly time series of temperature and precipitation
data were sorted for each individual weather station.
Likewise, the sorted historical data were also
obtained for the GCM simulated data. Since each
weather station was surrounded by four 1.8° 9 1.8°
nodes, corresponding to one set of the observed data,
four sets of the simulated values with similar record
length were arranged for each of the GCM’s
scenarios.
Exceedance probability was computed for each of the
sorted data using the Weibull probability function
m
(i.e., P ¼ Nþ1
, where m and N denote rank of the
considered data and sample size, respectively). Since
observed and simulated data have identical sample
size, the assigned probability was also equal for the
observed and simulated data laying in an identical
row.
A simple regression analysis was applied to derive
the regression equation between the sorted values of
observed and simulated data around each station.
Among four surrounding nodes, the one with the
highest value of the correlation determination (R2)
and least of mean square error (RMSE) was selected
for further analysis, and the other three were
discarded (Table 2; Fig. 1). The applied equations
for R2 and RMSE are:
PK
2
m¼1 Xm Ym
R ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ð1Þ
PK
P 2ffi
2
Y
X
m
m¼1 m
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u K
uP
2
u
tm¼1 ðXm Ym Þ
ð2Þ
RMSE ¼
K
where Xm and Ym denote the observed and simulated
values, respectively. K is the sample size of the
series. The application of this form of bias correction
(i.e., rank regression) is recommended when the
GCM outputs are used to reproduce the specific
patterns of historical events rather than an attempt to
‘‘correct’’ day-by-day GCM outputs (Dettinger et al.
2004).
Iran. J. Sci. Technol. Trans. Sci. (2016) 40:39–49
D:
E:
By plugging observed values into the developed
regression equation (i.e., between observed and
selected node), a new set of data, namely, historical
downscaled values were generated. With this generation, corresponding to each of the proposed
exceedance probabilities, three sets of data consisting
of observed, model simulated and downscaled values
were obtained.
A proper regression analysis was performed between
the observed and the downscaled data. The obtained
regression equation was then used to project the
temperature and precipitation values for the period
2016–2045. The linear or polynomial regression
approach generally leads to more appropriate good fit
for temperature or precipitation data, respectively.
The better the regression fit, the more preservation of
statistical moments through the future projection.
3 Results and Discussion
3.1 The Probability Distributions (Temperature vs.
Precipitation)
The probability distribution of precipitation was found
somewhat more complex than the essentially Gaussian
temperature distribution. This is because precipitation data
includes many months without precipitation and because
precipitation seldom exhibits a Gaussian distribution.
Moreover, due to the different mechanisms of rainfall production, the probability distributions might be dissimilar
even for the stations not far from each other. For instance,
according to Nazemosadat and Ghaedamini (2010), the
measure of the effects of the Madden Julian Oscillation
(MJO), on Iran’s precipitation is different between southwestern and southeastern districts. Such discrepancies were
also found when the influences of the El Niño Southern
Oscillation phenomenon on Iran’s precipitation were analyzed (Nazemosadat and Ghasemi 2004). Overall, for those
stations located in the arid zones of subtropical high, the
precipitation data generally have a complicated probability
distribution, and downscaling procedure of these data is
more tedious than temperature time series.
Figure 2 illustrates probability distributions for the
observed, simulated and downscaled temperature and precipitation data (in January) at Shiraz station, the capital city
of Fars province in Iran as an example. A simple comparison between the Fig. 2a, b implicates that the probability distribution of precipitation is more complicated than
that for temperature data. For instance, if probability distribution of the simulated temperature data shifted to the
left by about 5 °C, it would be roughly overlaid with the
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corresponding distribution of the observed records. In other
words, for this particular month and station and if the
extreme values are ignored, by subtracting model outputs
by 5 °C, they will be transformed into the observed data.
Such a simple relationship is, however, not evident
between the observed and simulated precipitation data.
As indicated in Fig. 2b, for the period 1951–2005,
January precipitation was above 130 mm for 8 times and
even it reached to 324.5 mm in 1965. The simulated data
are, however, essentially below 130 mm and the applied
model failed to reproduce data greater than this value.
Incompatibility between the observed and simulated data
is not restricted to wet years, but also evident during the
years with low or even moderate precipitation. This
inconsistency between the observed and simulated time
series implies that, without performing a proper downscaling procedure, the GCM outputs cannot directly project future climate of an arid zone such as southern Iran.
Using the GCM simulations without careful examination
of historical climate behavior might also lead to a wrong
conclusion.
Table 3 shows the mean monthly values (i.e., average
values of 12 months) of R2 and RMSE for the correlations
between observed and simulated temperature data before
and after downscaling. As indicated, the applied downscaling procedure has dramatically improved the earlier
poor linkage between these two datasets so that all correlations become strongly significant after downscaling.
Similar enhancement in the correlations was also found for
Fig. 2 The probability distribution of observed, simulated and
downscaled data for Shiraz station during January 1951–2005:
a temperature and b precipitation data
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Iran. J. Sci. Technol. Trans. Sci. (2016) 40:39–49
Table 2 The nodes whose data were used for downscaling the observed data at the mentioned stations (see Fig. 1)
Station
Boushehr
Mahshahr
Bandar Daier
Bandar Abass
Ahwaz
Abadan
Node
N9
N4
N14
N16
N2
N3
Station
Shiraz
Lar
Kangan Jam
Abomoosa Island
Jask
Fasa
Node
N6
N15
N14
N18
N20
N10
Table 3 The mean monthly values of R2 and RMSE between observed and simulated temperature data before and after downscaling for the
scenarios of 20C3M and 1PTO2X
20C3M scenario
Obs-Sim
R
2
a
1PTO2X scenario
Obs-Ds
RMSE
R
2
Obs-Sim
RMSE
R
2
Obs-Ds
RMSE
R2
RMSE
Abadan
0.02
2.28
0.92
0.69
0.01
2.49
0.88
0.67
Ahwaz
0.03
2.39
0.99
0.14
0.02
2.90
0.98
0.47
Bandar Abass
0.01
3.11
0.95
0.35
0.01
2.91
0.94
0.83
Bandar Daier
Mahshahr
0.01
0.06
3.23
3.20
0.92
0.95
0.41
0.19
0.01
0.03
3.32
3.41
0.83
0.94
0.58
0.25
Boushehr
0.01
3.17
0.95
0.26
0.01
3.19
0.92
0.35
Fasa
0.03
2.73
0.96
0.43
0.02
2.85
0.96
0.62
Jask
0.08
3.09
0.98
0.17
0.06
3.11
0.96
0.19
Abomoosa Island
0.02
2.65
0.98
0.25
0.01
3.01
0.96
0.60
Kangan Jam
0.04
3.13
0.97
0.20
0.04
3.16
0.93
0.23
Lar
0.01
2.49
0.98
0.11
0.01
2.67
0.97
0.13
Shiraz
0.06
2.07
0.94
0.37
0.05
2.21
0.92
0.39
The presented values are the arithmetic mean of the given statistics for monthly data during the January–December period. All the correlation
coefficients are statistically significant after downscaling
a
The abbreviations Obs, Sim and Ds signify the observed, simulated and downscaled data, respectively
the precipitation data as indicated in Table 4. Greater
values of the error index in this Table (compare to Table 3)
are mostly due to the bigger absolute values as well as the
complexity and non-linearity in the precipitation data as
they compared with temperature series.
indicated in this Table, measure of the increase or
decrease in precipitation data is site specific and varies
from about (-21.28 %) to (?42.80 %) of the base period.
The next two sections are devoted for further discussion
about the projected series.
3.2 Future Projection
3.3 Temperature Analysis
By plugging the 2016–2045 simulated data into the
already developed regression equations (i.e., between
observed and downscaled values), the projected values of
temperature and precipitation were obtained for this period. Table 5 and 6 summarizes the average values of
temperature and precipitation data after downscaling. In
these Tables, monthly values were averaged into annual
or 6 monthly data for temperature or precipitation,
respectively. According to the given statistics, regardless
of the applied scenarios, a widespread warmer condition is
anticipated for southern Iran during the next three decades. Precipitation, however, showed suppression or
enhancement according to either 20C3M or 1PTO2X
scenario is used, respectively (Table 6). Furthermore, as
According to the low emission scenario of 20C3M, the
mean annual temperature of the considered station for the
period 2016–2045 will be warmer than the corresponding
values of observed records by about 0.5–1.0 °C in different
stations. This temperature rise, however, jumps to about
2–2.4 °C if simulation is conducted under the high emission scenario of 1PTO2X. Since these statistics are the
mean values for three decades, some years will, therefore,
be warmer than the presented mean values.
As an example, Fig. 3a, b illustrate the historical as well
as projected fluctuations of January temperature data for
Shiraz station according to the 20C3M and 1PTO2X scenarios, respectively. While the historical data are identical
in both figures, the projected parts (i.e., data after 2005) are
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Iran. J. Sci. Technol. Trans. Sci. (2016) 40:39–49
45
Table 4 Like Table 3, but for precipitation data during the October–March period
Station name
20C3M
1PTO2X
Raw data
R2
DS data
RMSE
R2
Raw data
R2
RMSE
DS data
RMSE
R2
RMSE
Abadan
0.07
3.19
0.96
3.02
0.01
4.60
0.92
4.57
Ahwaz
0.07
7.69
0.95
1.72
0.01
13.70
0.92
3.26
Bandar Abass
Bandar Daier
0.05
0.14
4.19
4.32
0.93
0.90
3.95
3.82
0.26
0.32
6.52
11.26
0.96
0.91
4.35
4.49
Mahshahr
0.01
11.43
0.94
6.87
0.001
15.44
0.91
8.05
Boushehr
0.04
7.81
0.91
5.78
0.22
10.05
0.81
6.32
Fasa
0.05
9.95
0.98
0.66
0.01
7.99
0.92
1.49
Jask
0.03
25.60
0.91
7.99
0.09
22.02
0.71
13.56
Abomoosa Island
0.10
11.82
0.95
11.01
0.02
19.64
0.89
12.73
Kangan Jam
0.09
22.41
0.92
10.88
0.08
31.02
0.94
9.45
Lar
0.09
34.96
0.92
14.88
0.22
51.69
0.86
19.37
Shiraz
0.12
11.66
0.97
9.02
0.11
22.92
0.92
4.54
Table 5 Mean annual air temperature data for the observed records (Obs) and projected (20C3M or 1PTO2X) scenarios
Station
Annual mean temperature
Obs
20C3M
(°C) Diff.
1PTO2X
(°C) Diff.
Abadan
25.32
25.90
0.58
27.37
2.05
Ahwaz
26.12
26.76
0.64
28.10
1.98
Bandar Abass
26.99
27.68
0.69
28.99
2.00
Bandar Daier
27.09
27.60
0.51
29.04
1.95
Mahshahr
25.89
26.59
0.70
28.31
2.42
Boushehr
24.53
25.39
0.86
26.64
2.11
Fasa
Jask
20.28
27.51
20.84
28.47
0.56
0.96
22.51
29.83
2.23
2.32
Abomoosa Island
27.98
28.56
0.58
30.14
2.05
Kangan Jam
20.46
21.07
0.61
22.55
2.16
Lar
25.77
26.34
0.57
27.88
2.11
Shiraz
17.90
18.55
0.65
20.25
2.35
The positive value of differences (Diff) indicate that the projected air temperatures are warmer than historical data in all stations
the downscaled records. The slope of trend lines for the
projected part (2016–2045) was found to be around zero
and 0.016 °C per year for these two scenarios, respectively
(not shown). These statistics are less than the slope if the
55 years of historical data are used that is around 0.45 °C.
Therefore, in spite of the fact that the mean global
projected values are consistently greater than the mean
historical temperature, their trends are less for the projected
data particularly for 20C3M scenario. This suggests the
weakness of the GCMs for precise prediction of meteorological variables in chronological sequence as was discussed earlier.
Our examination indicated that the warming trend in
historical data was consistently increased for recent periods.
A sequential regression analysis for the period 1951–2005
with 25 years of window width was performed to detect
successive slopes of the relevant trend lines (Fig. 4). This
figure exhibits continued trend for the periods of 1951–1975,
1952–1976 and eventually 1981–2005 (-0.026, -0.025,
and eventually ?0.06 °C per year, respectively). As indicated, the warming trend is positive and much steeper for the
recent periods supporting the hypothesis that the current
increasing trend could be continued during the projected
period. In other words, it is reasonable if someone assumes
that the slope of trend line during the 25 years period of
2021–2045 is greater than 0.06 °C per year. A shift from a
negative to a steep positive trend was found for all stations
with more than 40 years of records. It is worthwhile to note
123
46
Iran. J. Sci. Technol. Trans. Sci. (2016) 40:39–49
Table 6 Mean monthly values of the observed precipitation records (Obs) and projected series for the period 2016–2045 (mm/month)
Station
Mean precipitation
Obs
20C3M
% Diff.
1PTO2X
% Diff.
Abadan
22.99
22.98
-0.04
23.07
Ahwaz
34.55
33.21
-3.87
35.12
1.66
Bandar Abass
27.95
20.35
-27.19
31.86
13.96
Bandar Daier
40.27
32.40
-19.53
44.07
9.44
Mahshahr
33.28
23.11
-30.56
43.90
31.92
Boushehr
44.48
39.50
-11.19
46.55
4.65
Fasa
Jask
46.87
20.42
44.01
16.07
-6.10
-21.28
48.12
29.15
2.67
42.80
Abomoosa Island
19.16
15.10
-21.20
23.63
23.29
Kangan Jam
63.37
52.76
-16.74
67.66
6.76
Lar
31.89
25.91
-18.75
36.18
13.45
Shiraz
51.61
44.47
-13.83
62.08
20.30
0.35
0.0800
21
19
y = 0.0448x + 16.728
R² = 0.7917
1951
1956
1961
1966
1971
1976
1981
1986
1991
1996
2001
2006
2011
2016
2021
2026
2031
2036
Mean annual
temperature (c)
23
15
(b)
Fig. 3 Fluctuations of mean annual temperature in past and future
period from: a 20C3M scenario and b 1PTO2X scenario for Shiraz.
For the 1951–2005 time period, the slope of trend line is 0.035. When
the data of 2016–2045 added to the applied time series, the slope
changes into 0.016 or 0.0448 under the 20C3M or IPTO2X scenario,
respectively. The difference between 0.035 and these two slopes are
smaller for the latter rather than former scenario
that the increasing trend during the recent 20 years of data in
Shiraz is also about 0.06 °C/year. This suggests much steeper
trend for this station during the 2012–2045 period. In this
case, the mean temperature for the predicted data (by trend
analysis) is about 2.5–3 °C greater than the corresponding
value for the historical series that was projected by the
123
0.0400
0.0200
1975
1973
1971
1969
1965
1967
1963
1961
1959
1957
1955
-0.0400
1953
0.0000
-0.0200
Start year of window width
Fig. 4 Sequential values of the slopes of the trend lines for the
regression analysis between time and Shiraz temperature for the
period 1951–2005 using 25 years of window width. The given values
in horizontal axis delineate the start year of the 25 year period
considered in the regression analysis
(a)
17
0.0600
1951
y = 0.0164x + 17.365
R² = 0.33
Trend-line's slope
21
20
19
18
17
16
15
1951
1956
1961
1966
1971
1976
1981
1986
1991
1996
2001
2006
2011
2016
2021
2026
2031
2036
Mean annual
temperature (c)
Precipitation analysis was conducted for October–March period. The differential (Diff) values are computed by this formula [100 9 (a-b)/a],
where a and b are the mean values of the observed and projected precipitation series, respectively. 20C3M and 1PTO2X are the names of climate
scenarios
1PTO2X scenario. In spite of some differences, most of the
slope values in the considered stations were greater than
0.035 °C per year for all of 25 years window widths after
1964–1989 periods.
As indicated in Fig. 3, if the projected values of the
1PTO2X scenario are the continuation of the historical
data, the correlation determination is much stronger than if
the values are from the 20C3M scenario. The slope of the
trend line is also more realistic if this high emission scenario is used. Based on the provided evidence and for the
purpose of future planning and decision making, accepting
the outcomes of 1PTO2X scenario or steep warming trend
is a reasonable choice.
Figure 5 depicts the concurrent variations of the
observed and projected monthly temperature data for all
station according to 20C3M and 1PTO2X scenarios. As
indicated, greater or lower temperature rise are mostly
associated to the warmer or colder months of the year,
Iran. J. Sci. Technol. Trans. Sci. (2016) 40:39–49
47
Ahwaz
Month
20.00
Month
40.00
DEC
NOV
SEP
JULY
Month
Bushehr
Bandar Mahshahr
Bandar Daier
JUNE
APR
MAY
FEB
JAN
NOV
DEC
SEP
0.00
OCT
Observed
20C3M scenario
1PTO2X scenario
10.00
OCT
JUNE
APR
MAY
FEB
MAR
DEC
NOV
SEP
0.00
30.00
AUG
Observed
20C3M scenario
1PTO2X scenario
10.00
OCT
JUNE
APR
MAY
FEB
MAR
JAN
0.00
AUG
10.00
20.00
AUG
Observed
20C3M scenario
1PTO2X scenario
JAN
20.00
30.00
JULY
30.00
MAR
40.00
Bandar Abass
40.00
Temperature (C)
40.00
Temperature (C)
50.00
JULY
Temperature (C)
Abadan
50.00
40.00
50.00
DEC
NOV
OCT
DEC
NOV
OCT
SEP
Month
Shiraz
Lar
40.00
JUNE
APR
MAY
FEB
JAN
DEC
NOV
OCT
SEP
0.00
AUG
Observed
20C3M scenario
1PTO2X scenario
10.00
Month
Kangan Jam
JUNE
APR
20.00
MAR
Month
AUG
APR
MAY
MAR
FEB
JAN
DEC
NOV
0.00
JULY
Observed
20C3M scenario
1PTO2X scenario
JULY
20.00
10.00
OCT
SEP
JUNE
APR
MAY
MAR
AUG
Observed
20C3M scenario
1PTO2X scenario
JUNE
20.00
Temperature (C)
30.00
Temperature (C)
30.00
FEB
MAY
Jazireh Abomoosa
30.00
JAN
MAR
Jask
40.00
0.00
FEB
JAN
DEC
NOV
OCT
SEP
AUG
Month
40.00
JULY
40.00
50.00
Month
20.00
DEC
NOV
OCT
SEP
AUG
JUNE
APR
MAY
MAR
FEB
DEC
NOV
SEP
0.00
JULY
Observed
20C3M scenario
1PTO2X scenario
10.00
OCT
APR
MAY
FEB
0.00
JAN
DEC
NOV
OCT
10.00
AUG
Observed
20C3M scenario
1PTO2X scenario
30.00
JAN
20.00
MAR
Month
SEP
JUNE
APR
MAY
MAR
FEB
JAN
0.00
AUG
10.00
JULY
Observed
20C3M scenario
1PTO2X scenario
30.00
JULY
Temperature (C)
20.00
Temperature (C)
40.00
30.00
JUNE
Temperature (C)
JULY
JUNE
APR
MAY
MAR
FEB
JAN
DEC
NOV
Month
Fassa
Temperature (C)
0.00
40.00
10.00
Observed
20C3M scenario
1PTO2X scenario
10.00
0.00
Month
20.00
SEP
Observed
20C3M scenario
1PTO2X scenario
AUG
20.00
30.00
JULY
30.00
10.00
OCT
JUNE
APR
MAY
MAR
FEB
JAN
0.00
SEP
10.00
AUG
Observed
20C3M scenario
1PTO2X scenario
Temperature (C)
Temperature (C)
20.00
JULY
Temperature (C)
40.00
30.00
Month
Fig. 5 Comparison between the observed and projected monthly temperature data for all stations. The GCM scenario’s names are depicted
inside the figures
respectively. In other words, the rate of increase in
summer (July–September) temperature will be steeper
than that for cold seasons. An acute increase in the
regional potential evapotranspiration and water consumption is, therefore, predicted during dry and hot
months of the years.
In addition to monthly analysis for each station (i.e.,
Fig. 5), the mean monthly values of the projected temperature of all twelve stations were also computed. These
monthly values were then transformed to seasonal series by
averaging three monthly values. The difference between
the projected and historical values was then calculated to
estimate the measure of regional climate change in seasonal scale. According to the given results, based on the
low emission scenario of 20C3M, the study area will be
warmer than the historical mean by about 0.9, 0.29, 0.57
and 0.44 °C during winter, spring, summer and autumn,
respectively. These values, correspondingly, will increase
to 1.7, 2.64, 3.51 and 1.54 °C, if the high emission rate
scenario of 1PTO2X is under consideration. As mentioned
earlier, these values are more sensible than the outcome of
20C3M scenario.
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Iran. J. Sci. Technol. Trans. Sci. (2016) 40:39–49
3.4 Precipitation Analysis
Table 6 summarizes the mean values of historical and
projected precipitation data for all stations. As indicated,
the mean values of the projected precipitation are consistently less than their corresponding historical statistics
from about zero to 35 % if the 20C3M scenario is used.
The projected deficiencies are, however, mostly less than
20 %. On other hand, mean precipitation increases from
about 0–43 % if climate change is within the range of
1PTO2X scenario. However, out of twelve considered
stations, only four sites precipitation enhancement is above
15 %. This implicates no substantial development in water
resources even precipitation according to the high emission
scenario. Overall, the projected increase in precipitation
can hardly compensate the ever-increasing of water
demand over the dry zones of southern Iran which also
faces a substantial warming trend.
Since the study area is vulnerable to prolong drought and
precipitation abnormality, the projected temperature and
precipitation data suggest a considerable amount of temperature rise and serious water shortage over the study area.
While both scenarios predict warmer condition for the next
few decades, a significant increase in regional precipitation
is not anticipated. Moreover, the probable precipitation
deficiency (particularly under the low emission scenario)
will severely affect the regional climate condition.
Nazemosadat et al. (2006) have analyzed concurrent
variations of the southern oscillation index (SOI) and Iran’s
precipitation for the period 1951–1999 seeking changepoint years in the considered series. They reported that the
mid 1970s are the most probable changepoint years in the
SOI data. The frequency and intensity of El Niño/La Niña
events have increased/decreased during 1975–1999 as it
compared with the former 25 years of 1951–1974 period.
Consistent with SOI data, precipitation records in Iran have
also shown significant change around the year 1975.
Compared to the 1951–1975 period, a significant precipitation enhancement was evident for the period of
1975–1999; the era with more negative SOI data. This
means that, in addition to local geographical attributes,
increase or decrease in the considered precipitation data is
significantly associated to the climate condition over the
ocean tropics. Due to such dependencies and because of
inconsistency between the results of the applied scenarios,
significant enhancement or suppression in mean annual
precipitation for the period 2016–2045 is in question.
Although climate condition during the next few decades
depends critically on temperature and precipitation status at
that time, current policies on soil, water and air management are also important on the upcoming climate conditions. For instance, agricultural activities over the study
area are essentially dependent on the groundwater
123
condition. The recent rapid depletion in the water table restricts these activities over thousands of hectares of farmlands. Reduction in cultivated and pasture areas, in turn,
will increase the local temperature due to the lack of
evapotranspiration. In other words, in addition to radiative
climate forcing, the abuse of local water and soil resources
exacerbate the impact of climate change in regional scale
that might not be fully considered in dynamical climate
modeling.
4 Conclusions
This study has been prompted to project future climate
change in southwestern parts Iran for the period of
2016–2045. Monthly, seasonal and annual time series of
temperature and precipitation were considered as the climate indicators. Two scenarios of the ECHAM5 GCM
model, namely 20C3M and 1PTO2X were used for projecting temperature and precipitation condition during the
next three decades. The adapted statistical downscaling
procedure transformed the probability distribution of the
historical time series into the projected data sets. The poor
correlations between the observed and simulated data were
dramatically improved as the model outputs were downscaled. The correlations were, however, stronger for the
Gaussian distributed temperature data than for the precipitation series.
The given results suggest that the annual temperature in
southern parts of Iran will increase by about 2.0–2.4 °C
during the period 2016–2045, if climate changes according
to the high emission scenario of 1PTO2X condition. This
range of temperature rise was found to be more realistic
than the 0.5–1.0 °C warmer condition that was projected
by the low emission scenario of 20C3M. Higher or lower
increase rate is anticipated for warm or cold months of the
year, respectively.
While the low emission scenario of 20C3M predicted
less than climatological mean precipitation for southern
Iran, precipitation enhancement is anticipated according to
the 1PTO2X scenario. This enhancement is, however,
mostly less than 15 % of the mean precipitation, suggesting
no substantial improvement in accessible water resources
even on the basis of this scenario. Due to a sharp increase
in temperature trend, a higher value of water demand is
expected for domestic use and agricultural activities.
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