139

Precipitation Climatology over the Middle East Simulated by
the High-Resolution MRI-AGCM3
Akio KITOH* and Osamu ARAKAWA
Meteorological Research Institute
1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan*e-mail: kitoh@mri-jma.go.jp

Abstract
We investigated the simulation performance of two versions of the atmospheric general circulation
model (MRI-AGCM3) in reproducing precipitation climatology over the Middle East in comparison withhigh-resolution gridded precipitation datasets. Both models overestimated precipitation amounts comparedwith the observations over this region. The new version, MRI-AGCM3.2, overestimated precipitation much
more than the old version, MRI-AGCM3.1, particularly in the winter and spring rainy seasons, but the newversion was better than the old version in the summer dry season. We also investigated resolution dependency.
The higher the resolution the more the precipitation indicated, but the higher resolution version showed better
performance in reproducing the spatial distribution of annual and seasonal mean precipitation, probably due
to better representation of orography. These results indicate difficulty in climate model development in termsof regional climate reproducibility.
Key words: high-resolution, Middle East, MRI-AGCM3, precipitation

1. Introduction
The Middle East is located in the Mediterranean climate area with precipitation in winter exceeding that insummer. There is a large north-to-south gradient inprecipitation, with desert over the Arabian Peninsula.
Figure 1 shows the topography of this region, which issurrounded by the Mediterranean Sea, Black Sea,
Caspian Sea, Red Sea and Persian Gulf. Notable mountain ranges are present, such as the Caucasus, Alborz andZagros Mountains. The Fertile Crescent is a region where
ancient civilizations developed. It is located between the
Euphrates and Tigris Rivers and is fed by water frommountains to the north. The Taurus Mountains in Turkey
form the border of Anatolia with the Mediterranean Sea.
There is a north-south oriented narrow range of hills in
Lebanon. This topography creates the climatologicalfeatures of the precipitation distribution in this region.
According to climate-change projections of multiple atmosphere-ocean general circulation models(AOGCMs), the annual precipitation in this region is
projected to decrease at the end of the 21st century (IPCC,
2007). However, the horizontal resolution of the climate
models used in these projections (between 400 km and
120 km) is not sufficient to resolve the topography well.
The increasing ability of computing resources enables us
to use climate models with much higher horizontal
resolutions. It is anticipated that high-resolution models
Global Environmental Research
15/2011: 139-146
printed in Japan

will simulate orographic precipitation much better thanlower-resolution models. Kitoh et al. (2008) used a
20-km mesh atmospheric general circulation model(AGCM) to project future streamflows in the “Fertile
Crescent” region. Their model has been updated justrecently. The new version shows general improvements
in simulating heavy monthly-mean precipitation aroundthe tropical Western Pacific, the global distribution oftropical cyclones, the seasonal march of the East Asiansummer monsoon, and blockings in the Pacific (Mizuta
et al., 2012), but an overall assessment of its ability toreproduce regional climates is yet to be addressed.
Therefore, validation of the simulated climate is neededin many aspects. In this paper, we compare the precipitation reproducibility of two (old and new) versionsof MRI-AGCM. The impact of the horizontal resolutionof the model is also investigated, where three versions,
i.e., 20 km, 60 km and 180 km mesh models, are compared.

2. Data
For model validation, spatially high-resolution
observed precipitation data are needed. The main
observed dataset used is APHRO_ME1101 (APHRO
hereafter), which is daily gridded precipitation for the
Middle East, developed by the project of Asian
Precipitation - Highly-Resolved Observational Data
©2011 AIRIES140

A. KITOH and O. ARAKAWA

Fig. 1 Topography of the Middle East and surrounding areas. White denotes sea areas.

Integration Towards Evaluation of water resources(APHRODITE) (Yatagai et al., 2009). This daily precipitation dataset covers 20°E-65°E, 15°N-45°N with
0.25° resolution during 1951-2007. The APHRO data
used in Kitoh et al. (2008) (hereafter EM0605) differ
from those used in this study. The difference between
them comes from differences in (1) the number of station
observations, (2) the quality control (QC) system applied
to station observations, and (3) the spatial interpolation
method. APHRO_ME1101 uses more daily rain gauge
observations than EM0605 over Iran, Israel and Saudi
Arabia. They are individually collected by the
APHRODITE project. In EM0605, QC is manually applied to rain gauge observations (e.g., location check). In
APHRO_ME1101, an automated objective QC system isutilized (Hamada et al., 2011). For spatial interpolation,
APHRO_ME1101 uses the Mountain Mapper method(Schaake et al., 2004), that spatially interpolates the ratio
of precipitation to its climatology. EM0605 uses the
Shepard-type method. Other observed data used are
CRU_TS2.1 with a 0.5°x0.5° grid from Mitchell andJones (2005), developed and distributed by the Climatic
Research Unit (CRU), University of East Anglia;
GPCP_1DD_v1.1 of the Global Precipitation Climatology Project (GPCP) (Huffman & Bolvin, 2009) with a
1.0°x1.0° grid; and the 2.5°x2.5° grid Climate Prediciton
Center (CPC) Merged Analysis Precipitation (CMAP)(Xie & Arkin, 1997). APHRO_ME1101 and CRU arebased on rain gauges, GPCP is based on satellites, andCMAP uses a combination of rain gauges and satellites.
The GCMs used in this study were MRI-AGCM3.1
and MRI-AGCM3.2, which were jointly developed by

the Japan Meteorological Agency (JMA) and theMeteorological Research Institute (MRI). Mizuta et al.
(2006) described MRI-AGCM3.1, which is based on theoperational numerical weather prediction model used at
JMA with some modifications added in radiation andland surface processes as a climate model at MRI. The
model has a horizontal spectral triangular truncation of
spherical function at wave number 959 with a linear gridfor wave-to-grid transformation corresponding to about
20 km horizontal grid spacing and has 60 vertical layerswith the top at 0.1 hPa. The model has 1,920 longitudinal
grids and 960 latitudinal grids over the global domain(there are 242 and 135 grids in the target domain in thispaper). The new version, MRI-AGCM3.2, has various
new parameterization schemes (Mizuta et al., 2012).
Improvement in the calculation stability of the semiLagrangian scheme enables the time step to be increased
from 6 minutes to 10 minutes with this 20-km meshversion. The number of vertical levels has been increased
from 60 layers (top at 0.1 hPa) to 64 layers (top at
0.01 hPa) with refinements above the tropopause. A new
cumulus parameterization scheme, called the Yoshimura
cumulus scheme, has been introduced, where theconcepts of both the Arakawa-Schubert-type schemes
and Tiedtke-type schemes are incorporated (Yukimoto
et al., 2011). Both models are simulated for the period
from 1979 to 2003 as an Atmospheric Model Intercomparison Project (AMIP)-type experiment where thesame observed monthly mean sea surface temperatureand sea-ice concentration are prescribed as boundary
conditions in both the old and new model experiments.
The Hadley Centre Sea Ice and Sea Surface TemperaturePrecipitation over the Middle East Simulated by MRI-AGCM3

data set (HadISST1, Rayner et al., 2003) is used. For
other specifications, see Mizuta et al. (2012). To examine
the effect of horizontal resolution, we also performed
simulations with lower spatial resolutions, using thesame model framework. The resolutions were 60 km(640 longitudinal grids, 320 latitudinal grids), and 180
km (192 and 96 grids).

3. Observations
Figure 2 shows the long-term average annual precipitation for the Middle East region from four observations. The region analysed is 20°E-65°E, 20°N-45°N. In
order to make the observation period as even as possible
with the model data which are available for 1979-2003,
we used APHRO and CMAP covering the 25-year periodof 1979-2003, while CRU covers 1979-1998 and GPCP
covers 1997-2008, due to data restriction. As our focus inmodel validation is long-term average precipitation,different data periods do not affect the result.
The APHRO data clearly show a crescent-shaped
precipitation belt of more than 500 mm stretching from
Israel and southeastern Turkey through the region along
the Zagros Mountains. They also show precipitation
maxima along the Mediterranean and Black Sea coasts,
as well as over the Caucasus Mountains. Precipitation incentral Turkey (Anatolia) is around 1 mm/day and is less141

than that in the coastal areas. There are also precipitation
minima over the northwestern part of Iran and overAzerbaijan. A dry area with precipitation less than 100
mm occupies most of the Arabian Peninsula, northern
Africa and eastern part of Iran.
The CRU annual precipitation is similar overall tothat of APHRO, and shows precipitation maxima along
the Mediterranean and Black Sea coasts and over the
Caucasus Mountains, along with a minimum overAnatolia. CRU also shows a crescent-shaped precipitation belt, which is not as sharp compared to that of the
APHRO data. When compared more precisely with
APHRO, the CRU precipitation is less over the crescentarea, the Black Sea coast and the southern coast of
Caspian Sea. On the other hand, the CRU precipitation islarger than that of APHRO over the Arabian Peninsula.
Area averaged annual precipitation for the land region
20°E-65°E, 20°N-45°N is 224 mm in CRU, which isabout 20% larger than that of APHRO (179 mm). In short,
CRU shows more precipitation than APHRO, while
APHRO shows more precipitation over the narrow
region of precipitation local maxima.
The horizontal resolution affects the spatial characteristics of precipitation. Therefore, the GPCP and
CMAP data do not show detailed structures comparedwith the previous two datasets. GPCP has a hint of a local
maximum along the Zagros Mountains, but fails to show

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Fig. 2 Observed annual precipitation (mm/year) from four different data sources. (a) APHRO (1979-2003 average), (b) CRU(1979-1998 average), (c) GPCP (1997-2008 average), (d) CMAP (1979-2003 average). (a) and (b) cover land area only.

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the local minimum over Anatolia. Both GPCP and
CMAP seem to overestimate precipitation over the
Arabian Peninsula.
The amounts of precipitation of these four datasets
differ sharply. In order to avoid a land-sea mask ambiguity, we calculated the area-average annual precipitation
for a 10° by 10° box of 37°E-47°E, 30°N-40°N. It cameto 254 mm, 289 mm, 339 mm and 285 mm for APHRO,
CRU, GPCP and CMAP, respectively. GPCP showedmuch larger precipitation amounts in this region. It isnoted that the CMAP and CRU estimates were close.
APHRO showed the least. GPCP may overestimate
precipitation over the analysis region. The smallerarea-averaged precipitation in APHRO than in CRU may
be associated with APHRO’s use of a larger number ofrain gauge observations compared with CRU, mainly
over Iran.
Figure 3 shows a seasonal breakdown of climatological mean precipitation by APHRO. The area-averaged(20°E-65°E, 20°N-45°N) precipitation is largest in
December-February (DJF; 67 mm), followed by MarchMay (MAM; 56 mm) and September-November (SON;
37 mm), and least in June-August (JJA; 20 mm). Peculiar
characteristics found in annual precipitation such as acrescent-shaped precipitation band and maxima along thecoasts of the Mediterranean and Black Sea are also seen
in DJF, MAM and SON with different magnitudes. When

DJF is compared with MAM, there is more precipitationin DJF than in MAM over the coastal region along theMediterranean and along the northern Persian Gulf. On
the other hand, there is more precipitation over the
Caucasus Mountains in MAM than in DJF. In JJA, there
is a relatively wet region over the Caucasus Mountains.
The SON precipitation has similar spatial characteristics
to annual precipitation but with slightly less amplitude indaily precipitation.

4. Model Results
Figure 4 shows the annual precipitation simulated by
the model in the present-day conditions. Results by
MRI-AGCM3.1 and MRI-AGCM3.2 with three different
horizontal resolutions are shown. The simulated annual
precipitation distribution, particularly by the higher
resolution models, reproduces well the one observed by
APHRO. Precipitation maxima such as a crescent-typefeature along the Zagros Mountains and coastal precipitation along the Mediterranean and Black Sea coasts
together with a minimum over Anatolia are all simulatedby the 20-km and 60-km mesh models. The 180-km meshmodel, however, fails to show the above-mentioned characteristics, except for a broad maximum over the
Caucasus Mountains. The new version (MRI-AGCM3.2)
clearly shows more precipitation than the old version

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(a) December-February, (b) March-May, (c) June-August, (d) September-November.

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Precipitation over the Middle East Simulated by MRI-AGCM3

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Fig. 4 Simulated annual precipitation (mm/year) for MRI-AGCM3.1 and MRI-AGCM3.2 with three different horizontal
resolutions. (a) MRI-AGCM3.1S (20km), (b) MRI-AGCM3.2S (20km), (c) MRI-AGCM3.1H (60km), (d)MRI-AGCM3.2H (60km), (e) MRI-AGCM3.1L (180km), (f) MRI-AGCM3.2L (180km).

(MRI-AGCM3.1) in each resolution category.
Table 1 summarizes the annual and seasonal total
precipitation averaged for the land area of the region
analysed (20°E-65°E, 20°N-45°N) for the six modelversions and two observations (APHRO and CRU). First,
it is clear that this model as a whole overestimated theannual and seasonal mean precipitation in this region.
Second, there is a resolution dependency with the 20-km
mesh model simulating the largest precipitation, the60-km mesh model the second, and the 180-km meshmodel the least within the same model version. Third,
MRI-AGCM3.2 generally simulates more precipitation

than MRI-AGCM3.1. Therefore, the newer version
model actually performs more poorly than the old modelcompared to APHRO data over the Middle East, and thus
the resolution produces a bigger discrepancy in thearea-mean precipitation as far as the precipitation amountis concerned. Mizuta et al. (2006) showed that the annual
global precipitation of MRI-AGCM3.1 overestimated theobservations (GPCP and CMAP) by about 15%. The
global annual average precipitation rate in MRIAGCM3.2 is 3.00 mm/day and is about 3% lower thanthat of MRI-GCM3.1, but still larger than the observed
estimate (Mizuta et al., 2012). Regionally, they differ by

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A. KITOH and O. ARAKAWA

more than 400 mm in the tropics.
Next we investigated the spatial distribution ofclimatological mean precipitation. Table 2 shows thepattern correlation coefficients of annual and seasonalmean precipitation distribution between the six modelversions and APHRO. Before the calculation, all the data
were interpolated into the 60-km mesh grids, and only theland points were used. The higher resolution model better
represented the spatial distribution of precipitation thanthe lower resolution model within the same version of themodel. The low-resolution models (180-km mesh models) had a negative bias along the coastal region and overthe southern side of the Zagros Mountains, and a positive
bias over Anatolia and the northern side of the Zagros
Mountains (Fig. 4). These biases decreased as resolution
increased. It is noted that the old model (MRIAGCM3.1) better represents the spatial distribution of
precipitation than the new model (MRI-AGCM3.2). In
short, the high-resolution model shows better representation of spatial features of annual and seasonal mean
precipitation, but with excessive amounts in the region
analysed. This caveat becomes larger in the new versionof the model. The reason needs to be investigated.
Figure 5 shows the seasonal march of monthly mean
precipitation averaged for the land area of the region
analysed (20°E-65°E, 20°N-45°N). The two observations(APHRO and CRU) show a similar seasonal march, withdouble maxima in December and March and minima
during July-September. However, they differ quantitatively, with CRU being about 1.2~1.3 times larger than

APHRO in all months. The annual mean value from
APHRO is 0.49 mm/day, while that from CRU is
0.61 mm/day. The wettest month (December in APHRO,
March in CRU) has almost four times the precipitation of
the driest month (September in APHRO, August in
CRU).
We found that the simulated precipitation has systematic biases compared with the observations. The twomodel versions show quite different seasonal marches ofMiddle Eastern precipitation. MRI-AGCM3.1 (Fig. 5a)shows a modest seasonal variation, while MRIAGCM3.2 (Fig. 5b) shows a distinct seasonal variation.
In MRI-AGCM3.1, the amount of wet season precipitation is comparable to the observations, while the dry
season precipitation is overestimated, resulting in asmaller contrast of monthly precipitation within a year. It
is noted that the higher the resolution, the larger themonthly precipitation in all months. The good performance of area-averaged precipitation in the low resolution model, however, comes from cancellation of model
errors: in MAM, the 180-km mesh MRI-AGCM3.1L has
a negative bias in the coastal area and over the Zagros
Mountains, and a positive bias in Anatolia and theCaucasus Mountains, and they cancel out in the areaaveraged precipitation with a false resemblance to theobservations. In MRI-AGCM3.2, the dry season precipitation is comparable to the observed estimates, while thatof the wet season is highly overestimated by the model.
In particular, this model shows excessive precipitation in
the MAM season. This overestimation comes mainly

Table 1 Annual and seasonal mean precipitation in mm, averaged,
land only of 20°E-65°E, 20°N-45°N. ANN: Annual, DJF:
Decmber-February, MAM: March-May, JJA: JuneAugust, SON: September-November.
Data
APHRO
CRU
MRI-AGCM3.1S (20km)
MRI-AGCM3.2S (20km)
MRI-AGCM3.1H (60km)
MRI-AGCM3.2H (60km)
MRI-AGCM3.1L (180km)
MRI-AGCM3.2L (180km)

ANN
179
224
286
328
244
317
196
287

DJF
67
75
91
106
79
105
67
95

MAM JJA
56
20
62
54
90
47
118
38
68
41
113
35
56
26
104
29

SON
37
52
58
67
56
63
47
58

Table 2 Spatial correlation coefficients of annual and seasonalmean precipitation against APHRO over the land area of
20°E-65°E, 20°N-45°N. ANN: Annual, DJF: DecmberFebruary, MAM: March-May, JJA: June-August, SON:
September-November.
Data
MRI-AGCM3.1S (20km)
MRI-AGCM3.2S (20km)
MRI-AGCM3.1H (60km)
MRI-AGCM3.2H (60km)
MRI-AGCM3.1L (180km)
MRI-AGCM3.2L (180km)

ANN
0.872
0.863
0.860
0.842
0.800
0.797

DJF
0.847
0.844
0.818
0.833
0.740
0.758

MAM JJA SON
0.847 0.848 0.845
0.846 0.835 0.809
0.811 0.817 0.854
0.824 0.811 0.810
0.774 0.812 0.765
0.785 0.843 0.746

Fig. 5 Seasonal march of land-area averaged monthly mean
precipitation (mm/day) for the area analysed(20°E-65°E, 20°N-45°N).
(a) Solid black line: APHRO, dashed black line: CRU,
red line: MRI-AGCM3.1S (20km), blue line: MRIAGCM3.1H (60km), orange line: MRI-AGCM3.1L(180km).
(b) As in (a) except for MRI-AGCM3.2.

Precipitation over the Middle East Simulated by MRI-AGCM3

from excessive precipitation over the mountainous areas.
Again, the higher resolution model tends to show larger
precipitation than the lower resolution model throughout
the year.
Figure 6 shows the elevation dependency of annual
mean terrestrial precipitation over the region of
20°E-65°E, 20°N-45°N. The grids of APHRO include
rain gauge observations at all altitudes. It is noted that the
spatial distribution of land areas at one altitude in one
horizontal resolution is not always identical to that inother horizontal resolutions. The precipitation in two
observations (APHRO and CRU) increases with altitude.
The precipitation by CRU is greater than that of APHRO
at all altitudes, with the difference between them being
0.1-0.2 mm/day. The two model versions show aqualitatively similar elevation dependency but MRIAGCM3.2 shows more precipitation than MRIAGCM3.1 at all altitudes. MRI-AGCM3.2 overestimates
precipitation at all altitudes compared with the observations. In MRI-AGCM3.1, precipitation increases at all
altitudes as horizontal resolution becomes higher.
However, in MRI-AGCM3.2, that dependency is not soclear. What the two model versions have in common isthat the difference in annual precipitation between highand low altitudes becomes larger when the horizontal
resolution is finer. The 180 km and 60 km mesh models
tend to show a peak in precipitation rate at some altitude,
but the 20 km mesh models show the largest precipitation
rate at the highest elevation bin. The APHRO data support the 20 km mesh model results, though the region
with elevation higher than 3000 m is very limited, foundonly within the Caucasus Mountains and the Alborz
Mountains (Fig. 1).

145

5. Concluding Remarks
In this paper, we investigated the precipitation
climatology of MRI-AGCM3 over the Middle Eastagainst observations. Generally, the models reproduced
the regional pattern of precipitation distributions well,
but overestimated precipitation amounts compared withthe observations. Investigation of resolution dependency
among 20, 60 and 180 km mesh models shows that thehigher the resolution, the better the performance in
capturing the spatial distribution of seasonal and annual
mean precipitation in this region, indicating the impact of
topography in simulating regional characteristics of climate. At the same time, the higher the resolution, themore the precipitation amount was overestimated,particularly in high mountain regions, probably due tobetter representation of orography. We also investigatedthe precipitation climatology of two model versions. The
new version, MRI-AGCM3.2, overestimated precipitation much more than the old version, MRI-AGCM3.1,
particularly in the winter and spring rainy seasons over
the Middle East, while the new version was better thanthe old version in the summer dry season. The reason forthis discrepancy should be investigated for further model
development.

Acknowledgements
The APHRODITE's Water Resources project was
supported by the Environment Research & Technology
Fund of the Ministry of the Environment, Japan. The
model development and calculation was conducted under
the framework of the “Projection of the change in future
weather extremes using super-high-resolution atmospheric models” of the KAKUSHIN Program funded by
the Ministry of Education, Culture, Sports, Science andTechnology, Japan. The calculations were performed on
the Earth Simulator of the Japan Agency for MarineEarth Science and Technology.
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Fig. 6 Elevation dependency of annual mean precipitation
intensity (mm/day) for the area analysed (20°E-65°E,
20°N-45°N). Solid black line: APHRO, dashed black
line: CRU, solid red line: MRI-AGCM3.1S (20km),
solid blue line: MRI-AGCM3.1H (60km), solid orange
line: MRI-AGCM3.1L (180km), dashed red line: MRIAGCM3.2S (20km), dashed blue line: MRI-AGCM
3.2H (60km), dashed orange line: MRI- AGCM3.2L(180km).

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<http://www.mri-jma.go.jp/Publish/Technical/index_en.html>

Akio KITOH
Akio KITOH is Director of the Climate Research
Department of the Meteorological ResearchInstitute (MRI), Japan Meteorological Agency(JMA). He has a Doctor of Science degree from
Kyoto University. He is leading the projects“Research on Prediction of Climate and Environmental Change to Contribute to Mitigation Plan
Decision Making Against Climate Change” funded by the Japan
Meteorological Agency (JMA) (FY2010-2014) and “Projection of Change
in Future Weather Extremes Using Super-High Resolution Atmospheric
Models” funded by the Ministry of Education, Culture, Sports, Science andTechnology (MEXT) (FY2007-2011). He was lead author of the IPCC
Working Group I Second, Third and Fourth Assessment Reports, and islead author of the IPCC Working Group I Fifth Assessment Report.

Osamu ARAKAWA
Osamu ARAKAWA is a Research Associate at theMeteorological Research Institute (MRI) of theJapan Meteorological Agency (JMA). He isinterested in atmospheric science and climatology. He has used climate models to study
climate variations from intra-seasonal to interdecadal timescales mainly over the tropics.

(Received 26 May 2011, Accepted 7 November 2011)