Journal of the Meteorological Society of Japan, Vol. 90A, pp. 233--258, 2012.

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DOI:10.2151/jmsj.2012-A12

Climate Simulations Using MRI-AGCM3.2 with 20-km Grid

Ryo MIZUTA, Hiromasa YOSHIMURA
Meteorological Research Institute, Tsukuba, Japan

Hiroyuki MURAKAMI, Mio MATSUEDA
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Hirokazu ENDO, Tomoaki OSE, Kenji KAMIGUCHI, Masahiro HOSAKA
Meteorological Research Institute, Tsukuba, Japan

Masato SUGI
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Seiji YUKIMOTO, Shoji KUSUNOKI, and Akio KITOH
Meteorological Research Institute, Tsukuba, Japan(Manuscript received 21 February 2011, in ﬁnal form 12 October 2011)

Abstract
A new version of the atmospheric general circulation model of the Meteorological Research Institute (MRI),
with a horizontal grid size of about 20 km, has been developed. The previous version of the 20-km model, MRIAGCM3.1, which was developed from an operational numerical weather-prediction model, provided information on possible climate change induced by global warming, including future changes in tropical cyclones, theEast Asian monsoon, extreme events, and blockings. For the new version, MRI-AGCM3.2, we have introduced
various new parameterization schemes that improve the model climate. Using the new model, we performed apresent-day climate experiment using observed sea surface temperature. The model shows improvements in simulating heavy monthly-mean precipitation around the tropical Western Paciﬁc, the global distribution of tropical
cyclones, the seasonal march of East Asian summer monsoon, and blockings in the Paciﬁc. Improvements in themodel climatologies were conﬁrmed numerically using skill scores (e.g., Taylor’s skill score).

1. Introduction
As progressively more detailed and localized information is required regarding future change in extreme weather and climate events resulting fromglobal warming, there is increasing demand for
Corresponding author: Ryo Mizuta, MeteorologicalResearch Institute, 1-1 Nagamine, Tsukuba 305-0052,
Japan.
E-mail: rmizuta@mri-jma.go.jp
6 2012, Meteorological Society of Japansimulations by high-resolution climate models.
Changes are anticipated in the frequency and distribution of disaster events associated with localized
heavy rainfall or heavy storms in mountainous
regions, such as ﬂooding, landslides, and strong
winds. In terms of planning adaptation to such
localized changes, even the most recent atmosphere–
ocean coupled models do not have su‰cient resolution to yield useful information.
By employing atmospheric climate models rather
than atmosphere–ocean coupled models, it is234

Journal of the Meteorological Society of Japanpossible to perform long-term climate simulations
at a much higher resolution. The use of higherresolution models (horizontal resolutions up to
20 km) enables the simulation of large-scale phenomena that possess small-scale structures, such as
tropical cyclones, and regionally localized phenomena associated with small-scale orography. The statistical climate of such phenomena, such as theirgeographical frequency distribution, can be obtained by climate simulations performed for periods
longer than several decades.
In this context, we developed the MeteorologicalResearch Institute (MRI)/Japan Meteorological
Agency (JMA) atmospheric climate model with a
20-km grid (Mizuta et al. 2006). This model isbased on the JMA’s operational weather prediction model, in which we implemented quasiconservative semi-Lagrangian dynamics, a radiation scheme, and a land surface scheme developed
for a climate model. Simulations of the present-day
and future climates were performed by using theobserved sea surface temperature (SST) and SSTchange projected by atmosphere–ocean coupledmodels as the lower boundary condition. Smallscale phenomena are realistically simulated in thehigh-resolution model, with keeping the same quality of global-scale climate representation as thelower-resolution models. Accordingly, the futureclimate simulation results provide a large amountof information including possible changes in tropical cyclones (Oouchi et al. 2006; Murakami et al.
2011), the East Asian monsoon (Kusunoki et al.
2006), extreme events (Kamiguchi et al. 2006), andblocking (Matsueda et al. 2009).
Moreover, various issues related to local and regional climate change were examined using thismodel. The horizontal resolution employed in themodel (20 km) is as ﬁne as that employed by regional climate models (RCMs) in recent studies.
Therefore, our model provides information on regions that are covered by few RCMs, including
river discharge in a river basin within Colombia(Nakaegawa and Vergara 2010), the Latin America
and Caribbean regions (Vergara et al. 2010), andrainfall and temperature in Bangladesh (Rahman
et al. 2012). In other regions, our model provides
information on regional climate change as well asother RCMs, as shown by Xue et al. (2010) on themodel intercomparison of the West African Monsoon, and Jin et al. (2010) on Mediterranean water
cycle. In the Japan region, outputs of our model areused as lateral boundary conditions for a non-Vol. 90A

hydrostatic cloud-resolving RCM with resolutionsof 5, 2, and 1 km (Wakazuki et al. 2007). An increase in the 90th percentile values of daily precipitation is projected in the 5-km model, associatedwith intensiﬁed convective instability (Kanada et al.
2010).
However, there were still some biases which werenecessary to be improved in the 20-km model, including the geographical distribution of tropical cyclones (Murakami and Sugi 2010) and insu‰cientprecipitation amounts over the Western Paciﬁc.
Kang et al. (2002) reported that recent atmosphericgeneral circulation models (AGCMs) still have difﬁculties in simulating precipitation amounts over
the Western Paciﬁc, based on an intercomparisonof 10 AGCMs using AMIP-type experiments. A
possible solution to this problem is to use a muchhigher resolution to simulate smaller-scale processes, as shown by the use of a non-hydrostatic
AGCM with 7-km grid (Satoh et al. 2008) andby an operational forecast model with 10-km grid
(ECMWF 2009). However, not only individual
small-scale phenomena but their climatologicalstates and variances are also required to be simulated well at a ﬁne scale to obtain reliable projections related to global warming. To improve theperformance of the 20-km model in simulating
regional-scale climate, our priority has been placedon reﬁning the physical schemes embedded in the
20-km model.
In the present paper, we introduce new parameterization schemes that yield improvements in themodel. Using the new model, we have performed
an AMIP-type experiments using observed SST forthe present-day climate. The results are comparedwith those of the same simulation using the previous version of the model (Kitoh et al. 2009).
The remainder of the paper is organized as follows. The improvements made to the model are
outlined in Section 2, and the experimental designis presented in Section 3. Section 4 provides anassessment of the model performance in terms ofrepresenting the present-day climate of the global
seasonal-mean state, variations in the Asian region,and tropical/extratropial storm activity. Finally,summary and discussion are provided in Section 5.
2. Model development
2.1 Model outline
The model used for the experiment is the atmospheric general circulation model of MRI (MRIAGCM3.2). This is developed as the atmosphericFebruary 2012

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Table 1.

Horizontal resolution
Vertical levels
Time step
Cumulus convection
Cloud
Radiation
Gravity wave dragLand surface
Boundary layer
Aerosol (direct)

235

Speciﬁcations of the new and previous versions of the model.
MRI-AGCM3.1S

MRI-AGCM3.2S

TL959 (20 km, 1920  960)
60 (top at 0.1 hPa)
6 minutes
Prognostic Arakawa–Schubert
Smith (1990)
Shibata and Aoki (1989); Shibata and Uchiyama (1992)
Iwasaki et al. (1989)
Hirai et al. (2007)
Mellor Yamada (level 2)
Sulfate aerosol

TL959 (20 km, 1920  960)
64 (top at 0.01 hPa)
10 minutes
Yoshimura
Tiedtke (1993)
JMA (2007)
Iwasaki et al. (1989)
Hirai et al. (2007)
Mellor Yamada (level 2)
Five species

component of MRI-ESM1 (Yukimoto et al. 2011),
which is the earth system model developed by MRI.
MRI-AGCM3.2 is based on a model developed
jointly by JMA and MRI (Mizuta et al. 2006) andits very slightly revised version (MRI-AGCM3.1)
used for the previous 20-km experiments (Kitoh
et al. 2009). Many parameterization schemes forvarious physical processes are newly developed andintroduced into the model by both of JMA and
MRI. The schemes are implemented to be switchedeasily from the conventional schemes. The models
with 20-km resolution are referred to as MRIAGCM3.1S and MRI-AGCM3.2S (where ‘S’ refers
to super-high resolution) in the case that we needto distinguish them from lower-resolution models.
Table 1 lists the di¤erences between the schemes
used for MRI-AGCM3.1S (hereafter v3.1) and forthe new MRI-AGCM3.2S (hereafter v3.2). Thissection provides an overview of the di¤erences between the models.
2.2 Dynamical framework
The dynamical framework remains unchanged
from the previous model, which is a hydrostatic
primitive equation system using a spectral transform method of spherical harmonics, as originallydesigned by Kanamitsu et al. (1983). A two-timelevel semi-implicit semi-Lagrangian scheme is used
for time integration (Yoshimura and Matsumura
2005). Improvements in the computational stabilityof the semi-Lagrangian scheme are implemented in
the new model, enabling an increase in the time
step from 6 to 10 minutes.
The highest-resolution experiments are performed at a triangular truncation at wave number
959 (TL959) in the horizontal, for which the transform grid uses 1920  960 grid cells, corresponding
to a grid interval of roughly 20 km. The number ofvertical levels has been changed from 60 layers (top
at 0.1 hPa) in the previous model to 64 layers (top
at 0.01 hPa) in the new model. Only those levels
above the tropopause have been changed.
2.3 Cumulus convection
A new cumulus parameterization scheme, basedon a scheme by Tiedtke (1989), has been developedand introduced to the model, replacing the prognostic Arakawa–Schubert scheme (Arakawa andSchubert 1974; Randall and Pan 1993). The description of the new scheme, called the Yoshimura
cumulus scheme, is summarized by Yukimoto
et al. (2011). Arakawa–Schubert-type schemes andTiedtke-type schemes are both categorized as
mass-ﬂux type cumulus schemes. In the Arakawa–
Schubert-type scheme, multiple convective updrafts
with di¤erent heights (depending on the entrainment rate) are explicitly calculated within a single
grid cell (Fig. 1a), although each updraft is a
simpliﬁed entraining plume. In the Tiedtke-type
scheme, on the other hand, only a single convective
updraft is calculated within a single grid cell, but
is represented as a more detailed entraining anddetraining plume (Fig. 1b). In the new scheme, detailed entraining and detraining plumes (as with the
Tiedtke-type scheme) are calculated for two convective updrafts within a single grid cell. The two
updrafts represent the tallest updraft with a minimum turbulent entrainment rate, and the shortest
updraft with a maximum turbulent entrainment
rate. Multiple convective updrafts with di¤erent
heights (as with the Arakawa–Schubert-type
scheme) are assumed to exist, where temperature,
the water vapor mixing ratio, the entrainment rate,
and other variables are obtained by linear interpolation between the two extreme updrafts (Fig. 1c).
During the development phase of the cumulus

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Fig. 1. Schematic diagrams of (a) Arakawa–Schubert-type, (b) Tiedtke-type, and (c) the introduced cumulus
schemes.

parameterization scheme, modiﬁcations are madein terms of how the model can capture the characteristics of the real atmosphere. In the new scheme,
there are arbitrary assumptions in determining theorganized entrainment, similarly to the Tiedtke
scheme. Precipitation in the areas upstream ofmountains (e.g., the northeastern part of the Bayof Bengal) has been enhanced by assuming that theorganized entrainment is approximately proportional to the horizontal convergence at each gridpoint. Under this assumption, the organized entrainment becomes smaller in the areas upstreamof mountains, which is thought to promote the development of cumulus convection. In addition, theorganized entrainment above the level of minimum
moist static energy has also been taken into ac-

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count. That has a¤ected mean precipitation andtropical cyclone genesis over the tropics.
Moreover, two further modiﬁcations has been
implemented in the conversion of cloud water toprecipitation within the updraft. One is reducing theconversion rate. By reducing the conversion rate,
the detrained cloud amount has been increased,a¤ecting the reﬂection of shortwave radiation andthe mean precipitation over the tropics. The otheris suppressing the conversion near the cloud bottom. The conversion from cloud water to precipitation is suppressed between the cloud bottom and a
speciﬁed distance from the cloud bottom. An increase in this distance results in the enhanced detrainment of cloud water, especially just above theboundary layer, which facilitates the organizationof convection around the lower troposphere.
2.4 CloudThe previous version of the model used a largescale cloud scheme similar to that proposed bySmith (1990), in which cloud water and cloudamount are estimated by a simple statistical approach. In the new version of the model, theTiedtke cloud scheme (Tiedtke 1993; ECMWF
2004; Jakob 2001) is incorporated and used (Kawai2006). Cloud water and cloud amount are treatedas prognostic variables in the scheme. Clouds areformed via adiabatic and diabatic cooling, and via
detrainment from the cumulus convection scheme.
The model considers the dissipation of clouds
through evaporation by heating, evaporation by
turbulent mixing with a surrounding air mass, andconversion to precipitation. The new model does
not use a parameterization speciﬁc to stratocumulus, while a simple parameterization of stratocumulus was used in the previous model.
2.5 Radiation
The radiation scheme has been changed to thesame scheme used in the JMA operational model(JMA 2007), except for the interaction withaerosols. Infrared (longwave) radiation (up to3000 cm1 ) and solar (shortwave) radiation aretreated separately. Because of the relatively large
computational cost, full radiation computations
are made for every two grid-spacings in the zonal
direction, and for every 1 and 3 hours in the shortwave and longwave regions, respectively. The calculated values are adjusted for each grid of eachtime step depending on its own solar zenith angle
and surface temperature. The spectrum is divided
in 9 bands in the longwave region and 22 bands in237

the shortwave region, and the radiative ﬂux is calculated in each band. The model considers major
absorptions due to water vapor (line and continuum absorption), carbon dioxide (in the 15 mmband, near-infrared region, etc.), and ozone (in the
9.6 mm band, visible and ultraviolet region). Also
taken into account is absorption due to methane(CH4 ), dinitrogen monoxide (N2 O), and chloroﬂuorocarbons (CFCs) in the longwave scheme, forconsideration of their greenhouse e¤ect. Absorption by oxygen and Rayleigh scattering by molecules of atmospheric gas is also calculated in the
shortwave radiation.
2.6 Aerosol
In the representation of the direct e¤ect of aerosol, optical parameters are conﬁgured to ﬁve types
of aerosol species: sulfate, black carbon, organiccarbon, mineral dust, and sea-salt. The extinction/
absorption coe‰cients and asymmetry factors forthese species are computed based on an assumptionof Mie scattering by spherically shaped particles,
based in turn on complex refraction index datafrom OPAC (Optical Properties of Aerosols andClouds) by Hess et al. (1998). For species with hygroscopic properties, the model considers their dependency on ambient relative humidity (Chin et al.
2002). The indirect e¤ects of aerosol are not considered in this experiment. The e¤ective radius of ice
cloud particles is parameterized depending only on
cloud water content, based on McFarquhar et al.
(2003).
2.7 Other schemes
The level 2 turbulence closure scheme by Mellor
and Yamada (1974) is used as the planetary boundary layer scheme in v3.2, as with v3.1. The landsurface scheme by Hirai et al. (2007), improved
from the Simple Biosphere model, is also used in
v3.2, as with v3.1.
To represent diurnal temperature variations at
the air–sea interface due to short-term variations
in wind and solar radiation, a simple skin seasurface scheme is introduced. As described by Yukimoto et al. (2011), the scheme has one sub-skinlayer with a thickness of 1 m. The coe‰cient for
the heat ﬂux from the bottom of the layer depends
on the wind speed. Temperature at the bottom of
the layer (1 m below the air–sea interface) is given
from the boundary condition ﬁle, and the temperature at the air–sea interface is calculated and usedas the lower boundary of the atmospheric model.
Using this scheme, the interface temperature in

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Journal of the Meteorological Society of Japanthe model shows diurnal variations of up to 2 K on
moderately clear days in the tropics, which is comparable to observed values (Yasunaga et al. 2008).
For most regions, the di¤erence between themonthly mean of the calculated skin temperatureand the given SST is less than 0.1 K.
The model uses the orographic gravity wave dragscheme by Iwasaki et al. (1989), in which gravity
waves are partitioned into long waves (wavelength
b 100 km) and short waves (wavelength @ 10 km).
The long waves propagate upward and deposit momentum in the middle atmosphere, while the shortwaves are trapped in the troposphere and exert dragin this region. The drag coe‰cients in v3.2 are thesame as those in the operational model (JMA2007), which are smaller than the values in v3.1. A
Rayleigh friction term is introduced above 50 hPa.
3. Methods
3.1 Experimental settings
The ability of the new model (v3.2) to simulate
the climate is examined by performing an AMIPtype experiment for the period from 1979 to 2003.
Monthly-mean data from HadISST1 (Rayner et al.
2003) with 1  1 resolution for 1979–2003 areused for the observed SST and sea-ice concentration data, along with the monthly climatology ofsea ice thickness from Bourke and Garrett (1987).
Concentrations of greenhouse gases (CO2 , CH4 ,N2 O, and CFCs) are set to the observed globalmean, annual-mean values, changing from year toyear. Three-dimensional, monthly-mean distributions of ozone and aerosols are also given as
boundary conditions. The ozone is from the resultsof the Reference Simulation 2 for the Chemistry
Climate Models Validation (Eyring et al. 2005)
using the MRI Chemical Transport Model (Shibata
et al. 2005). The aerosols are from the results ofa present-day experiment using a prototype versionof MRI-ESM1, in which the historical emission
ﬂux of anthropogenic SO2 , invariant SO2 ﬂux from
non-eruptive volcanoes, and the surface emission
inventories for carbonaceous aerosols are prescribed (Yukimoto et al. 2011). The 5-year runningmeans of both results are incorporated into themodel. We do not use the observed ozone and aerosol distributions because this experiment is carriedout as the ‘control’ climate simulation to be compared against the global warming simulation forced
by warmed surface conditions, increased concentrations of greenhouse gas, and changed ozone/aerosol
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The results are compared with the same AMIPtype experiment performed using v3.1 (Kitoh et al.
2009). The same SST, sea-ice concentration, andsea-ice thickness were used, but the zonal-meanozone is used instead of three-dimensional ozone,
and aerosols from a previous version of the MRI
aerosol chemical transport model (Tanaka et al.
2003) were used in the v3.1 experiment.
3.2 Observational and reanalysis climatologies
The results of the experiments are evaluatedby comparison with observational and reanalysis
climatologies. The Japanese 25-year Re-Analysis(JRA-25; Onogi et al. 2007), with a spatial resolution of 1.25 longitude by 1.25 latitude, is used as
the reanalysis climatology. ERA40 (Uppala et al.
2005) is used as an additional reanalysis climatology. The climatology of radiation at the top of theatmosphere is compared with satellite measurements from ERBE (Harrison et al. 1990). We use
precipitation datasets from CMAP (Xie and Arkin1997), GPCP (Hu¤man et al. 2001; Adler et al.
2003), and TRMM-3B43 (Hu¤man et al. 2007),
which are based on sensors onboard satellites andrain gauge observations. In addition, we also use
precipitation data with relatively high resolution;that is, APHRODITE daily grid precipitation (Yatagai et al. 2009) based only on rain gauge observation data, and TRMM-3A25 (Iguchi et al. 2000)
based only on radar observations from onboard
satellite measurements.
4. Results
4.1 Global climate
a. Precipitation
Figure 2 shows the horizontal distributions oflong-term mean precipitation for v3.2, the di¤erence between v3.2 and CMAP, and the di¤erence
between v3.1 and CMAP, from December to February (DJF) and from June to August (JJA).
Although v3.1 performs well in capturing theseasonal-mean spatial patterns (e.g., ITCZ, SPCZ,
Asian summer monsoon, and wintertime storm
tracks; Mizuta et al. 2006), the degree of bias fromobservations is reduced in v3.2. In DJF, overestimations are reduced around the eastern Paciﬁc,western Atlantic, and western Indian Ocean near
the equator. In JJA, v3.2 shows an improvement inregions of heavy rainfall from the South China Sea
to the tropical Western Paciﬁc, for which an underestimate was obtained from v3.1. This improvement is due mainly to reﬁnements in the perfor-

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Fig. 2. Horizontal distribution of long-term mean precipitation (mm day1 ) in (left) December to February(DJF) and (right) June to August (JJA) for (top) v3.2, (middle) di¤erence between v3.2 and CMAP, and(bottom) di¤erence between v3.1 and CMAP.

mance of the new cumulus scheme, as described in
Section 2. In the Arakawa–Schubert scheme usedin v3.1, it is di‰cult to simulate the precipitation
pattern around the Western Paciﬁc without unrealistic parameter settings. Some improvements arealso seen around India, as described in Section 4.2,
although overestimates remain. Overestimates also
remain in the eastern part of the ITCZ and in thetropical eastern Atlantic. In the extratropics, there
is little di¤erence between v3.1 and v3.2. Biases inthe extratropics of the Southern Hemisphere areslightly improved in both seasons. The global annual average precipitation rate is reduced from3.09 mm day1 (v3.1) to 3.01 mm day1 (v3.2),
which remains higher than those in CMAP
(2.67 mm day1 ) and GPCP (2.61 mm day1 ).
Zonal-mean precipitation in DJF and JJA isshown in Fig. 3. In v3.2, seasonal ﬂuctuations in

the tropical precipitation peak are clearer than in
v3.1; that is, the overestimations around 0–10 N
in DJF and 0–10 S in JJA, found in v3.1, are reduced in v3.2. Precipitation around 10 N–20 N in
JJA remains more than that observed, corresponding to the overestimation around India, the easternpart of the ITCZ, and the tropical eastern Atlantic(Fig. 2). Zonal means in the extratropics agree wellwith the estimation of GPCP in both seasons, whilean overall overestimation is seen when comparedwith CMAP.
b. Surface temperature
Figure 4 shows the horizontal distributions of climatological monthly-mean surface temperature in
January and July for v3.2 and v3.1, with the di¤erences from the climatology of JRA25 reanalysis.
The surface air temperature in the model is deﬁned

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Fig. 3. Zonal-mean precipitation (mm day1 ) in (a) DJF and (b) JJA for v3.2 (thick solid line), v3.1 (thickdashed line), and the climatological estimates of CMAP (thin solid line) and GPCP (thin dashed line).

Fig. 4. Horizontal distribution of climatological monthly-mean surface (2 m) temperature ( C) in (left) January and (right) July for (top) v3.2, (bottom) v3.1. Shading indicates di¤erences from the climatology ofJRA25 reanalysis.

as air temperature 2 m above the surface, which isdiagnosed from the vertical temperature proﬁle ofthe lowest model layers. Underestimation of thetemperature is reduced in v3.2 around North America and the central Africa in both season. A reduction of bias is also seen around the Amazon in January and the northern part of Russia in July, while
an enhancement of bias is seen around the northernpart of China.

c. Zonal wind and temperature
Figure 5 shows seasonal averages of zonal-meantemperatures and zonal wind velocities for v3.2 andv3.1, along with the di¤erence from the climatology of JRA25 reanalysis. In v3.1, the di¤erence
in zonal-mean temperature (Fig. 5b, d) is alreadywithin 2 K in most regions of the troposphere, except for the tropical upper troposphere. The bias in
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241

Fig. 5. Seasonal averages of (a–d) zonal-mean temperatures and (e–h) zonal wind velocities in (a, b, e, f )DJF and (c, d, g, h) JJA, for (a, c, e, g) v3.2 and (b, d, f, h) v3.1. Shading indicates di¤erences from theclimatology of JRA25 reanalysis. Units are K in (a–d) and m s1 in (e–h).

v3.2 (Fig. 5a, c), and the di¤erence from the reanalysis is within 1 K in the troposphere, except
for the wintertime mid-latitudes of the Northern
Hemisphere, which has large interannual variability. However, the temperature is higher than thereanalysis and v3.1 in the tropical stratosphere,
mainly because the given ozone distribution, which
is derived from present-day experiments using achemical transport model, yield a bias from observed data.

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Zonal-mean zonal wind also becomes closer tothe reanalysis in v3.2 (Fig. 5e, g) compared with
v3.1 (Fig. 5f, h), especially in the extratropics of
the Southern Hemisphere in DJF. In addition,around the subtropical jet at 100–200 hPa, overestimations of about 3 m s1 are seen in both hemispheres of both seasons in v3.1 and are reduced inv3.2. They are consistent with an improvementin the meridional temperature gradient between thetropical upper troposphere and extratropics.

tive (near 45 S) and negative (near the South Pole)biases in v3.1. In the Northern Hemisphere in JJA(Fig. 6c, g), overestimation around North Paciﬁcand underestimation around North America are reduced in v3.2. The improvements in this region in
the both season could be related to the improvements in the tropical convective activity throughteleconnection patterns. However, relatively large
bias remains in the Southern Hemisphere in JJA(Fig. 6d, h).

d. Z500
Seasonal averages of 500 hPa height during DJF
and JJA for each hemisphere are shown in Fig. 6.
Shading indicates di¤erences from the climatology
of JRA25 reanalysis. In the Northern Hemispherein DJF (Fig. 6a, e), a common pattern of the bias
is seen in v3.1 and v3.2, that is, positive bias aroundthe northern part of Russia and eastern North Paciﬁc, and negative bias around central Asia andthe east of Japan. The amplitude of the bias pattern is much smaller in v3.2, while negative bias
is seen around North America. The seasonal average becomes closer to the reanalysis climatology
also in the Southern Hemisphere in DJF (Fig.
6b, f ), where there was a zonal structure of posi-e. Skill scores
The degree to which the monthly climate of themodel averaged over 25 years is di¤erent from theobservations and the reanalysis is evaluated usinga skill score deﬁned by Taylor (2001). We also assess the change in skill score from v3.1 to v3.2.
The monthly-mean spatial patterns of various variables are compared with those of an observationally based climatology, using the ratio of the model’s standard deviation to that from observations(^sf ) and the correlation coe‰cients (R).
Figure 7 shows Taylor’s diagrams for the global
and tropical regions for the monthly climate in January and July averaged over 25 years, for precipitation, wind/height ﬁelds, and radiation at the top of

Fig. 6. Seasonal averages of 500 hPa height (m) in (a, b, e, f ) DJF and (c, d, g, h) JJA, for (a–d) v3.2 and(e–h) v3.1. (a, c, e, g) are for the Northern Hemisphere, and (b, d, f, h) are for the Southern Hemisphere.
Shading indicates di¤erences from the climatology of JRA25 reanalysis. Di¤erent shading intervals areused between winter hemisphere and summer hemisphere, according to the di¤erent amplitudes of interannual variation.

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243

the atmosphere. The distance from the origin is s^f ,and R is the cosine of the polar angle. Arrows indicate the evolution of the ﬁelds from v3.1 to v3.2.
The isolines denote the skill score, which is deﬁned
asS¼

Fig. 7. Taylor diagrams for (top) the globaland (bottom) the tropics (60 E–150 E, 0 –30 N). The statistics for v3.1 are plotted at
the tail of the arrows, and the arrowheads
point to the statistics for v3.2. Arrows
with open arrowheads indicate the statistics
for January, and those with closed arrowheads indicate the statistics for July. The
isolines denote the Taylor skill score. The
terms ‘‘Precip’’, ‘‘Z’’, ‘‘SLP’’, ‘‘T’’, ‘‘U’’,and ‘‘V’’ indicate precipitation, geopotential height, sea level pressure, temperature,zonal velocity, and meridional velocity, respectively. The terms ‘‘Netrad’’, ‘‘OLR’’,and ‘‘OSR’’ denote the net, longwave, andshortwave radiation at the top of the atmosphere, respectively.

4ð1 þ RÞ 4ð^
sf þ 1=^
sf Þ 2 ð1 þ R0 Þ 4:

ð1ÞHere, R0 is the maximum correlation attainable
and set to 1. The distribution of precipitation iscompared with two or three observational datasets(CMAP, GPCP, and TRMM in the tropics). Somevariables are also evaluated by eddy (deviationfrom the zonal-mean). The obtained score values
are listed in Table 2.
For the global patterns, most of the variables
have better scores in v3.2 than in v3.1. Precipitation
patterns show an improvement when using GPCP
as the reference, as well as when using CMAP as
the reference. While the standard deviations become larger and depart from the observations forsome variables, the correlation coe‰cients become
higher, resulting in higher skill scores. For January,
all the variables for the global domain listed here
show improvements from v3.1 to v3.2. The patterns
over the tropics are also improved, except for somevariables (e.g., geopotential height at 500 hPa andtemperature at 850 hPa).
We also performed 25-year simulations withlower spatial resolutions of TL319 (60 km) andTL95 (180 km), using the same model withoutchanging the parameter settings in the physicalschemes. The vertical levels are the same (64 levels),
and the time steps are 20 minutes for TL319 and 30
minutes for TL95. At each resolution, an improvement in skill score is seen compared with v3.1 inmany of the variables (data not shown). Amongthe three resolutions of v3.2, the score is better inthe higher-resolution model than in the lower-resolution model, showing the advantage of enhanced
resolution due to better representation of topographical e¤ects and physical processes. However,
the resolution dependence of the results in the
global-scale climate is small in v3.2, whereas theglobal-mean precipitation amount was resolutiondependent in the previous version of the model(Mizuta et al. 2006).
4.2 Asian monsoon
a. Asian summer monsoon
Figure 8 shows the simulated JJA-mean precipitation and vertically integrated moisture ﬂux clima-

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Table 2. Skill scores (see Taylor 2001) for the global and tropical (25 S–25 N) distribution of monthly means over the
25-year simulation for di¤erent variables, and di¤erent observational and reanalysis datasets. Bold entries indicatethat the score is better than that obtained for the other model.
January

July

VariableObs. Dataset

Region

v3.1

v3.2

v3.1

v3.2

PrecipPrecip
Z500
SLP
T850
U850
U200
V200

CMAP
GPCP
JRA25
JRA25
JRA25
JRA25
JRA25
JRA25

GlobalGlobalGlobalGlobalGlobalGlobalGlobalGlobal

0.77160.74600.99280.9322
0.99490.93630.95800.81980.80300.7814
0.99700.97350.99500.96510.97020.8584

0.78620.74290.9951
0.95290.99080.94350.96480.77580.81890.7566
0.99430.95330.99430.94010.97780.8085

Netrad
OLR
OSR

ERBE
ERBE
ERBE

GlobalGlobalGlobal

0.95770.93870.87780.97140.95030.9076

0.94990.94250.85500.9644
0.95390.8873

GZ5eddy
SLPeddy
T850eddy
U850eddy
U200eddy

JRA25
JRA25
JRA25
JRA25
JRA25

GlobalGlobalGlobalGlobalGlobal

0.89180.90620.94010.84330.89590.91450.91370.94430.86290.91540.81080.87100.92910.87220.84630.85030.89090.93420.90280.9137

PrecipPrecipPrecip
Z500
SLP
T850
U850
U200
V200

TRMM-3B43
CMAP
GPCP
JRA25
JRA25
JRA25
JRA25
JRA25
JRA25

Tropics
Tropics
Tropics
Tropics
Tropics
Tropics
Tropics
Tropics
Tropics

0.77600.77110.72600.96790.92230.96080.89130.96440.83360.80890.81060.77530.96210.93910.95620.91450.97070.84970.77190.79260.74050.97640.95250.97180.9012
0.97230.80180.79990.83370.75870.9791
0.96790.97980.93150.98850.8192

GZ5eddy
SLPeddy
T850eddy
U850eddy
U200eddy

JRA25
JRA25
JRA25
JRA25
JRA25

Tropics
Tropics
Tropics
Tropics
Tropics

0.82580.82250.89490.84060.90280.82430.86150.89080.85650.92180.8651
0.91270.92740.88270.87680.9191
0.94380.94540.91800.9390

tology for the two versions of the model and observations. The vertically integrated moisture ﬂux
calculated from the JRA-25 reanalysis dataset isshown in Fig. 8a. Major convective centers overthe eastern Arabian Sea, the Bay of Bengal, theSouth China Sea, the Philippine Sea, and southwest
Japan are simulated well in v3.2. Compared with
v3.1, precipitation is enhanced over South Asiaand the western Paciﬁc, while it decreases over the
Arabian Sea, the western Bay of Bengal, the equatorial Indian Ocean, and the Maritime Continent(Fig. 8e). In addition to an improvement in simulating the rainfall amount over the western Paciﬁc (seeSection 4.1), v3.2 yields an improvement in theunderestimated rainfall over northeastern India, asreported for the previous version of the model by
Rajendran and Kitoh (2008). However, v3.2 still
performs insu‰ciently in simulating a precipitation
maximum on the southern coast of China (near20 N, 110 E), which was reported for the previous
version by Yatagai et al. (2005), and abundant
rainfall around Bangladesh. The overall moistureﬂux circulation ﬁeld in v3.2 performs well in reproducing the observed features, with westerly ﬂuxes
over south Asia, easterly ﬂuxes over the tropicalPaciﬁc Ocean, and southerly ﬂuxes from the Philippines. Compared with v3.1, v3.2 shows a larger
moisture ﬂow over south Asia, penetrating into theFebruary 2012

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Fig. 8. JJA mean precipitation (shading;
mm day1 ) and vertically integrated moisture ﬂux (vectors; kg m1 s1 ) for (a)JRA25 and CMAP, (b) TRMM-3B43, (c)v3.2, and (d) v3.1. (e) Di¤erence between
v3.2 and v3.1. The averaged period is
1979–2003, except for TRMM-3B43(1998–2008). Moisture ﬂuxes less than 100(15) kg m1 s1 are not shown in (c, d)(in (e)).

Philippine Sea, which is in agreement with observations.
Figure 9 shows a Taylor’s diagram over the
Asian monsoon region (10 S–30 N, 40 E–160 E),
for precipitation, zonal wind at 200 and 850 hPa,
and meridional wind at 850 hPa, in order to makea quantitative comparison of performance between
the two models. Improvements from v3.1 to v3.2
are seen for all the variables in terms of the skill
score (Eq. 1). For precipitation, the standard deviation of its spatial distribution is larger than thatfor v3.1 and for observations. Averaged precipitation over the Asian monsoon region is 6.42 mm
day1 for v3.2, which is slightly less than thatin v3.1 (6.53 mm day1 ), comparable with CMAP
(6.27 mm day1 ), and larger than GPCP (5.34 mm
day1 ).
To analyze the topography-regulated precipitation more clearly, Fig. 10 shows the zonal distribution of JJA mean precipitation averaged over14 N–15 N. Observations indicate four precipitation peaks: west of the Western Ghats (74 E), westof Myanmar (98 E), west of the Annam Cordillera(107 E), and west of the Philippines (120 E). The
locations of peak in v3.2 are in good agreementwith observations, whereas v3.1 fails to simulate
the peak located west of the Annam Cordillera andincorrectly reproduces a peak over the eastern Bay

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Journal of the Meteorological Society of Japan

Fig. 9. Taylor diagram over the Asian monsoon region (10 S–30 N, 40 E–160 E) in
JJA for the statistics for precipitation,
zonal wind at 200 and 850 hPa, and meridional wind at 850 hPa. The statistics forv3.1 are plotted at the tail of arrows, andarrowheads point to the statistics for v3.2.

Fig. 10. JJA mean precipitation (mm day1 )averaged over 14 N–15 N. Colors are
as follows: GPCP (blue; Hu¤man et al.
2001), TRMM-3A25 (green), APHRODITE (red), v3.2 (solid black), and v3.1
(dashed black). Also shown is a topographic cross-section at the latitude. Alldata are displayed after interpolated onto
a 1.0 grid. GPCP and TRMM-3A25 areaveraged in 1997–2008.

Vol. 90A

of Bengal. Excessive precipitation in v3.1 over thearea west of the Western Ghats and over the Arabian Sea is improved in v3.2. A gradual increasein precipitation toward the east over the Bay of
Bengal is also simulated well in v3.2. In terms ofprecipitation amount over land, v3.2 yields similarvalues to APHRODITE; however, v3.2 shows excessive rainfall west of Myanmar and from theSouth China Sea to the Philippine Sea.
Figure 11 shows the seasonal evolution of the
Asian monsoon, evaluated in terms of precipitation
and wind ﬁelds. Figure 11a–c shows the areaaveraged precipitation over three major monsoon
regions: India and Bengal (Fig. 11a), the western
North Paciﬁc (Fig. 11b), and East Asia (Fig. 11c).
In the India and Bengal region, v3.2 is able to reproduce the observed seasonal evolution (e.g., thesudden onset and gradual withdrawal of the monsoon), but it shows excessive rainfall in the warm
season and a somewhat earlier onset. The same
biases were reported in an intercomparison of 11
AGCMs (Wang et al. 2004). In the western North
Paciﬁc region, v3.2 performs well in simulating not
only the seasonal evolution of rainfall, but also theamount, which is comparable to CMAP. However,neither model reproduces the observed sub-seasonalrainfall peak from late July to August.
Figure 11d shows the well-known wind shear index by Webster and Yang (1992), which measures
broad-scale circulation associated with the South
Asian monsoon. It is deﬁned as zonal wind difference between at 850 hPa and 200 hPa over 0 N–
20 N, 40 E–110 E. Also shown in Fig. 11e is eachcomponent of the index. The seasonal evolution of
the wind shear index and each component are wellsimulated by v3.2, although with a slightly weakeasterly in the upper troposphere during autumn.
Figure 11f shows the western North Paciﬁc monsoon index, deﬁned by Wang et al. (2001) as the difference of 850 hPa zonal winds between 5 N–15 N,
100 E–130 E and 20 N–30 N, 110 E–140 E. It is
an index of the circulation, which depicts largescale vorticity of the monsoon trough over thewestern North Paciﬁc in the lower troposphere.
The monsoon trough in the warm season is slightly
deeper in v3.2 compared with observations.
b. East Asian summer monsoon
Figure 11c shows the climatological seasonal
cycle of precipitation over Japan (30 N–37.5 N,
125 E–145 E). There is little di¤erence between
the two observational data sets. The observations

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247

Fig. 11. Climatological seasonal cycle of three-pentad running mean precipitation (mm day1 ) averaged
over (a) the Indian monsoon, (b) the western North Paciﬁc monsoon, and (c) the East Asian monsoon
region. Also shown is the climatological seasonal cycle of (d) the Webster-Yang index (Webster and Yang
1992), (e) each component of the index, and (f ) the western North Paciﬁc monsoon index (Wang et al.
2001) based on monthly mean data. The v3.2 (v3.1) model is shown by a thick solid (thick dashed) line.
CMAP (GPCP) is shown by a thin solid (thin dashed) line in (a–c). JRA-25 is shown by a thin solid linein (d–f ).

show two rainfall peaks: a distinct peak in earlysummer, called the Baiu (Ninomiya and Akiyama
1992), and a peak in early autumn, called the
Shurin (Matsumoto 1988). The period between thetwo peaks (late July to August) is relatively dry.
Thus, the seasonal cycle in the warm season overJapan shows complexities at the subseasonal scale.
Nevertheless, v3.2 is successful in simulating thecharacteristic features of the seasonal cycle; i.e., itcorrectly reproduces the peak and break timing,and the precipitation amount.
Figure 12 shows the mean precipitation, sea levelpressure, and zonal wind speed at 200 hPa from
June through September. Observations (Fig. 12a)show a narrow rain band at around 30 N in June.
The North Paciﬁc anticyclone expands westward

on the southern side of the rain band, and westerly
jet at upper troposphere is located slightly north of
the rain band. The rain band, the anticyclone, andthe westerly jet migrate northward with the seasonal progress in early summer, becoming gradually weaker. In August, the anticyclone expands
northwestward, prevailing over Japan and bringing
rainfall to north China with a southerly wind. In
September, the rain band and the westerly jet migrate southward and become strong again, and theanticyclone is located over the Paciﬁc east of Japan.
These large-scale features of the complex seasonal
march in East Asia are well captured by v3.2 (Fig.
12b), whereas v3.1 cannot reproduce the relatively
dry spell around Japan in August (Fig. 12c), whichis also shown in Fig. 11c. In v3.2, however, the248

Journal of the Meteorological Society of Japan

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Fig. 12. Climatological monthly mean ﬁeld in (a) JRA and CMAP, (b) v3.2, and (c) v3.1 from June to
September. Black contours: sea level pressure (hPa, contour interval is 2 hPa). Thick orange contours:
zonal wind speed at 200 hPa (m s1 , contour interval is 10 m s1 , and contours for values less than
20 m s1 are omitted). Shading: precipitation (mm day1 ).

westward extension of the anticyclone is stillslightly weak in early summer.
c.

Intraseasonal variability
Intraseasonal variability (ISV) simulated by v3.2
is investigated and compared to observation as wellas to v3.1. Previous studies reported that tropicalintraseasonal variability such as the Madden-Julian
oscillation (MJO) in the previous version of themodel is not so realistic, which simulated low amplitudes in convection and low-level winds in the
30–60-day band and showed standing oscillation(Rajendran et al. 2008; Liu et al. 2009).
Figure 13 shows wavenumber-frequency spectraof precipitation and 850 hPa zonal wind (U850)
along the equator (10 N–10 S mean) during boreal
winter (November to April). In observation, thepower is concentrated at 30–80 days and at zonal
wavenumbers 1–3 for precipitation and at zonal
wavenumber 1 for U850. V3.2 improves the power
spectra in low frequency band compared to v3.1.
However, the spectral power in the MJO band is

still weaker than the observation, and the simulated
power is distributed more in the time periods longer
than 90 days.
In boreal summer, observations show the ISV
propagates northward as well as eastward in south
Asia (Wang and Rui 1990). In order to show thespace-time variability associated with boreal summer intraseasonal variability (BSISV), distinct
BSISV events are composited based on the extended empirical orthogonal function (EEOF)
analysis following Waliser et al. (2003). The EEOF
analysis includes 60 E–180 E and 30 S–30 N domain and 4 to þ5 pentad lags in 20–90-day ﬁltered precipitation anomaly (from climatology)
from May to September. The events are selectedbased on the ﬁrst EEOF time series having peakvalues exceeding 1.0 standard deviation. The number of selected events is 38 in the observation, 30 in
v3.2, and 29 in v3.1.
Figure 14 shows the composited time evolutionof the BSISV events in the models and the observation. The observation shows northward propaga-

February 2012

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249

Fig. 13. Wavenumber-frequency spectra of 10 N–10 S averaged precipitation (shade; mm 2 day2 ) and
850 hPa zonal wind (contour; m 2 s2 ) along the equator (10 N–10 S mean) during boreal winter (November to April), for (left) JRA25 and GPCP, (center) v3.2, and (right) v3.1. Horizontal axis shows frequency(day1 ) and vertical axis shows zonal wave number. Counter interval is 0.01 m 2 s2 .

tion of intraseasonal precipitation anomalies fromthe equator with the northwest-southeast tilted
band as documented by many previous studies(e.g., Wang and Rui 1990; Waliser et al. 2003).
The feature is reproduced realistically to some extent, although the amplitude over the equatorial
eastern Indian Ocean remains smaller comparedwith the observation. On the contrary, v3.1 does
not reproduce the feature at all.
4.3 Inter-annual variability
Interannual variations in tropical precipitation
are compared with observations using an empiricalorthogonal function (EOF) analysis. Figure 15
shows the leading eigenvectors of JJA and DJF
mean precipitation over the tropics (30 S–30 N).
The precipitation for GPCP and the models isinterpolated on a common 2.5 grid, and an EOF
analysis is applied. The ﬁrst leading modes in bothJJA and DJF are associated with ENSO: the correlation coe‰cient between the principal componentof the ﬁrst mode and SST over the Nino3.4 regionis 0.92 in JJA and 0.94 in DJF. In JJA, the ﬁrst
mode in the observations and the models explains
about 20% of the total variance. V3.2 improves thespatial pattern of the ﬁrst mode (e.g., over the equatorial Indian Ocean, Southeast Asia, and the Philippine Sea). Kitoh and Kusunoki (2008) reported thatv3.1 has less skill in simulating the anomaly of theﬁrst mode over the Philippine Sea. In DJF, the ﬁrst
mode in the observations and the models explains
more than 30% of the total variance. Contrasting
zonal anomalies over the Maritime Continent andthe equatorial Paciﬁc are successfully produced in

v3.2, whereas v3.1 fails to simulate these anomalies
and shows the dominance of meridionally contrasting anomalies over the equatorial Paciﬁc. Kang
et al. (2004) reported that most AGCMs have di‰culty in simulating the negative anomalies over the
Maritime Continent during the winter of 1997/98,
based on an intercomparison of AMIP-type experiments with 11 AGCMs. A band-shaped anomaly
over southern China is also well simulated in v3.2.
The spatial correlation coe‰cient in the ﬁrst mode
between the observations and the models is 0.78 in
JJA and 0.89 in DJF for v3.2, while 0.63 in JJA
and 0.75 in DJF for v3.1. It is noted that the ﬁrstmode pattern over the Asian–Australian monsoon
region is greatly improved in v3.2, despite the useof an AMIP-type simulation without air–sea interaction.
4.4 Precipitation intensity
To simulate geographical distributions of extreme precipitation events such as heavy rainfall
or severe drought and their future change, the frequency of heavy precipitation as well as timeaveraged amount must be simulated in the model.
Kamiguchi et al. (2006) reported that the previous
version of the model overestimates the frequency
of weak rain, and underestimates the frequency ofheavy rain, especially in low latitudes. Here, theprobability distribution functions (PDFs) of daily
precipitation are compared with observations. Figure 16 shows the PDFs of daily precipitation in
JJA for regions of low latitudes (30 S–30 N), middle latitudes (30 N–40 N), India (80 E–90 E,
5 N–30 N), Indochina (95 E–110 E, 0–20 N),

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Fig. 14. Composited time evolution for distinct events of boreal summer intraseasonal variability in termsof 20–90-day ﬁltered precipitation (anomaly from climatology) from May to September in (left) GPCP,(middle) v3.2 and (right) v3.1. Contour interval is 1 mm day1 . The events are identiﬁed using an extended
EOF (EEOF) analysis (see the text for details).

Borneo (109 E–110 E, 4 S–6 N), and South Japan(129 E–146 E, 30 N–40 N). All the data is regridded to a 1 grid. The frequency of heavy rain
more than 40 mm day1 becomes higher in v3.2
than that in v3.1 for all regions. For low latitudes(Fig. 16a, c, d, e), the frequency of heavy rain inv3.2 comes between that in GPCP and that in
TRMM-3B42 except for Borneo (Fig. 16e). The

frequency of weak rain less than 15 mm day1 isstill overestimated even if the observational uncertainty is taken into account, although the overestimation is improved in v3.2. For mid latitudes (Fig.
16b) and Japan (Fig. 16f ), the PDFs are between
the two observations in v3.2 as well as v3.1. The
PDF in v3.2 is closer to TRMM-3B42 comparedwith that in v3.1.

February 2012

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251

Fig. 15. The leading EOF mode in the tropics (30 S–30 N) based on (a–d) the JJA mean precipitation(mm day1 ) and (e–h) the DJF mean precipitation for the period 1979–2003 for (a, e) GPCP, (b, f ) v3.2,and (c, g) v3.1. (d,h) EOF coe‰cients for GPCP (black), v3.2 (red), and v3.1 (blue). The EOF modes arenormalized in order that its time coe‰cients have one standard deviation.

4.5 Tropical cyclones
The climatological features of tropical cyclones(TCs) are signiﬁcantly improved in v3.2. This section describes some of these improvements. Details
will be discussed in separate publications.
Self-organized TCs in the models are detected using the same method and threshold values as thoseused in Oouchi et al. (2006), which use six criteriaon (1) the minimum surface pressure relative to thesurrounding 7 grid box, (2) the maximum relative
vorticity at 850 hPa, (3) the maximum wind speed
at 850 hPa, (4) the sum of the temperature deviations at 300, 500 and 700 hPa, (5) the maximum
wind speed at 850 hPa relative to that at 300 hPa,and (6) the duration time. The threshold values forthe criteria are intended to make the global TC frequency in v3.1 comparable to the observed annual
frequency (about 80 TCs per year). Table 3 shows
the ratio of the annual mean TC genesis number
for each basin relative to the global mean number,by v3.2, v3.1, and the observational ‘‘best track’’data provided by Unisys (2011). A signiﬁcant improvement is seen in the western North Paciﬁc(WNP), where a lower ratio of TCs was detectedin v3.1 (Murakami and Sugi 2010; Murakami et al.
2011). This improvement is consistent with theincrease in monthly precipitation in this area. Improvements are also seen in most other regions, except for the North Indian Ocean. However, considering that the TC ratio in the North Indian Ocean
is relatively small compared with other ocean basins, the global distribution of TCs is signiﬁcantly
improved.
Figure 17 shows the probability density of thelife-cycle maximum surface wind speed over the
global domain. In v3.1, TC intensity is largely
underestimated compared with observations. Although the question of how realistically the 20-kmmesh model should reproduce the observed TC intensity is a topic for debate, v3.1 is too weak inJournal of the Meteorological Society of Japan

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Vol. 90A

Fig. 16. Probability distribution functions of daily precipitation in JJA in regions of (a) low latitudes (30 S–
30 N), (b) middle latitudes (30 N–40 N), (c) India (80 E–90 E, 5 N–30 N), (d) Indochina (95 E–110 E,
0 N–20 N), (e) Borneo (109 E–110 E, 4 S–6 N), and (f ) South Japan (129 E–146 E, 30 N–40 N). Thev3.2 (v3.1) model is shown by a blue (green) line. GPCP (TRMM-3B42) is shown by a red (orange) line.

Table 3. Ratio of the annual mean tropical-cyclone genesis number for each basin (percent) relative to the global
mean number during the period 1979–2003, by v3.2, v3.1, and observational best track data. Bold entries denotean overestimation by more than 5%, and italics denote an underestimation by more than 5%.

v3.2
v3.1
Observed

N. Indian

N.W. Paciﬁc

E. PaciﬁcN. Atlantic

S. Indian

S. Paciﬁc

RMSE

11.2
5.5
4.6

26.6
21.0
26.9

20.1
25.4
16.7

8.3
7.5
10.5

18.4
25.2
16.0

14.7
15.1
10.0

3.8
6.2

terms of intensity when the results are comparedwith other modeling studies with coarser resolutions (e.g., Fig. 6 of Zhao et al. (2009), who used a
60-km-mesh model). TC intensity is also signiﬁcantly improved in v3.2: stronger TCs with a higher
maximum wind speed (more than 50 m s1 ) are
better simulated in v3.2 compared with v3.1.
4.6 Extratropics
As an index of synoptic activity over the extratropics, Fig. 18 shows the zonal-mean 2–8-day
eddy kinetic energy on 300 hPa for 25 years of

boreal winter, for JRA25, v3.2, and v3.1. Shading
denotes the model bias from JRA25. We used thevelocity data interpolated onto a 1.25 grid for themodels and the reanalysis. Positive biases aroundthe eastern Paciﬁc and from the eastern Atlantic
to Europe, as seen in v3.1 (Fig. 18c), are reducedin v3.2 (Fig. 18b). However, a positive bias is seenon the southern side of the Atlantic storm track inv3.2. In both v3.1 and v3.2, activity is higher
around the eastern Mediterranean sea and smalleraround southern Siberia, compared with the reanalysis.

February 2012

R. MIZUTA et al.

253di‰culty in simulating the Ural blocking frequency(50 E–70 E) in JJA. The Paciﬁc blocking frequency
in winter is overestimated in v3.2 as well as in v3.1
(Matsueda et al. 2009). The Paciﬁc blocking insummer, especially the Western Paciﬁc blocking(120 E–180 E), remains underestimated in v3.2.
5. Summary and discussion

Fig. 17. Probability density of the life-cycle
maximum surface wind speed (m s1 ) for
the global domain during the period 1979–
2003 for observational best track data (thinsolid line), v3.2 (thick solid line), and v3.1
(thick dashed line). Bin widths are 5 m s1 .

Figure 19 shows a Hovmöller diagram of climatological annual cycle of blocking frequency in the
Northern Hemisphere for the period 1979–2003.
Occurrence of blocking is deﬁned by using the objective blocking index based on D’Andrea et al.
(1998). There are well-known blocking maxima
over the Euro-Atlantic region (30 W–70 E) fromwinter to summer, and over the Paciﬁc region(120 E–120 W) in winter and summer. The frequency and seasonal variations of Euro-Atlantic
blocking are simulated well in v3.2, but encounters

We have developed MRI-AGCM3.2, a new version of the atmospheric general circulation modelof the Meteorological Research Institute, with ahorizontal grid size of 20 km. Various new parameterization schemes have been introduced to improve
the simulation of the present climate. An AMIPtype climate experiment was performed for 25 years
using the new model (v3.2), and the results arecompared with those of the same experiment conducted using the previous version of the model(v3.1).
Improvements are seen in v3.2 in simulating
the monthly-mean precipitation (Fig. 2), especiallyheavy precipitation around the tropical Western
Paciﬁc during boreal summer (Fig. 8). Zonal-meantemperature and zonal wind (Fig. 5), and otherseasonal-mean climatologies are also improved,
which are conﬁrmed numerically using Taylor’s
skill score (Table 2 and Fig. 7).
By virtue of the high resolution, topographyregulated precipitation has better agreement withobservations, as shown in Asia from India to thePhilippines (Fig. 10). Seasonal evolution of the
Asian monsoon is well simulated in the model(Fig. 11). In particular, v3.2 is successful in simulating the characteristic features of the seasonal cycleof the East Asian summer monsoon (Fig. 12).

Fig. 18. Zonal-mean 2–8-day eddy kinetic energy (m 2 s2 ) on 300 hPa for 25 years of DJF for (a) JRA25,(b) v3.2, and (c) v3.1. Shading in (b) and (c) indicates di¤erence from JRA25.

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Vol. 90A

Fig. 19. Hovmöller diagrams of climatological annual cycle of blocking frequency for (top) JRA25 and (bottom) v3.2 during the period 1979–2003.

Improvements are also found in the inter-annual
variability of tropical precipitation (Fig. 15), andthe global distribution and intensity of tropical cyclones (Table 3 and Fig. 17). Although tropical intraseasonal variability (Figs. 13, 14), precipitation
intensity (Fig. 16), and extratropical storm tracks(Fig. 18) also show better performance to someextent, more detailed investigation from many different perspectives would be required to produce amore realistic simulation in the model.
The introduction of a new cumulus parameter-

ization scheme contributed to these improvements,
especially in terms of precipitation around the tropical Western Paciﬁc and tropical cyclones. Weakprecipitation is more frequent in v3.1 than that observed (Fig. 16), associated with a tendency of the
Arakawa–Schubert scheme used in v3.1 to resolve
instability faster and to readily reduce the convective available potential energy (CAPE), even under
dry atmospheric conditions. In v3.2, CAPE is
maintained at a higher level (E. Shindo 2010, personal communication), associated with the changeFebruary 2012

R. MIZUTA et al.

that the new cumulus scheme includes turbulent
entrainment and turbulent detrainment, unlike
the Arakawa–Schubert scheme. Cloud top in thetropics generally becomes lower, and more heatand moisture are supplied to the middle troposphere (E. Shindo 2010, personal communication).
Corresponding to these changes, weak precipitation
becomes less frequent, and convection is more
frequently organized into the scale of tropical
cyclones. Moreover, moisture transport from theIndian Ocean to the Western Paciﬁc has been enhanced, which might lead in turn to the enhanced
precipitation in the tropical Western Paciﬁc.
Tropical intraseasonal variability in the previous
version of the model (e.g., Madden–Julian oscillation; MJO) has a much weaker amplitude thanthat observed (Rajendran et al. 2008; Liu et al.
2009). Although the variance in v3.2 is larger thanthat in v3.1 (Fig. 13), it appears to be insu‰cientcompared with observations. Moistening of the
middle troposphere by the change in the cumulus
parameterization could contribute to the larger amplitude. A higher-resolution cloud-resolving model
is able to simulate intraseasonal variability. Miuraet al. (2007) showed that a global cloud-resolving
model with a horizontal resolution of 7 km is able
to simulate the slow eastward migration of an
MJO event. Even when using hydrostatic models
with cumulus parameterization, a realistic amplitude of MJO can be simulated by improving the parameterization scheme (Bechtold et al. 2008). The
task of obtaining a more realistic simulation of intraseasonal variability remains a topic for future
study under a similar framework to that of the current model.
We have already performed future climate
experiments using v3.2. Two time-slice 25-year simulations, corresponding to the near future (2015–2039) and at the end of the 21st century (2075–
2099), were performed using boundary SST data
derived by superposing the future change in theensemble of SST projected by the CMIP3 multimodel dataset on the observed SST. The future climate change in this high-resolution model, using
the di¤erence between these results and the resultsof the present study, will be reported in subsequent
publications.
Acknowledgements
The development of the model is based on thework of many people, especially those in the Numerical Prediction Division of JMA and in the255

development team of MRI-ESM. This work was
conducted under the framework of the ‘‘Projectionof the Change in Future Weather Extremes using
Super-high-resolution Atmospheric Models’’ supported by the KAKUSHIN Program of the Ministry of Education, Culture, Sports, Science, andTechnology. The calculations were performed on
the Earth Simulator. We thank the U.S. CLIVAR
MJO Working Group for providing the software
package, which is used to depict Fig. 13. We also
thank two anonymous reviewers for constructive
comments.
References
Adler, R. F., G. J. Hu¤man, A. Chang, R. Ferrano, P.-P.
Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin,and E. Nelkin, 2003: The version–2 global precipitation climatology preject (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4,1147–1167.
Arakawa, A., and W. H. Schubert, 1974: Interactionof cumulus cloud ensemble with the large-scale
environment. Part I. J. Atmos. Sci., 31, 674–701,
doi:10.1175/1520-0469(1974)031h0674:IOACCEi2.0.CO;2.
Bourke, R. H., and R. P. Garrett, 1987: Sea ice thickness
distribution in the Arctic Ocean. Cold Regions
Sci. and Tech., 13, 259–280, doi:10.1016/0165232X(87)90007-3.
Bechtold, P., M. Köhler, T. Jung, F. Doblas-Reyes, M.
Leutbecher, M. J. Rodwell, F. Vitart, and G. Balsamo, 2008: Advances in simulating atmosphericvariability with the ECMWF model: From synoptic to decadal time-scales. Quart. J. Roy. Meteor.
Soc., 134, 1337–1351, doi: 10.1002/qj.289.
Chin, M., P. Ginoux, S. Kinne, O. Torres, B. N. Holben,
B. N. Duncan, R. V. Martin, J. A. Logan, A.
Higurashi, and T. Nakajima, 2002: Troposphericaerosol optical thickness from the GOCART
model and comparisons with satellite and sun photometer measurements. J. Atmos. Sci., 59, 461–
483.
D’Andrea, F., S. Tibaldi, M. Blackburn, G. Boer, M.
Déqué, M. R. Dix, B. Dugas, L. Ferranti, T. Iwasaki, A. Kitoh, V. Pope, D. Randall, E. Roeckner,
D. Straus, W. Stern, H. Van den Dool, and D.
Williamson, 1998: Northern Hemisphere atmospheric blocking as simulated by 15 atmosphericgeneral circulation models in the period 1979–
1988. Clim. Dyn., 14, 385–407.
Eyring,V., N. R. P. Harris, M. Rex, T. G. Shepherd,
D. W. Fahey, G. T. Amanatidis, J. Austin, M. P.
Chipperﬁeld, M. Dameris, P. M. De F. Forster,A. Gettelman, H. F. Graf, T. Nagashima, P. A.

256

Journal of the Meteorological Society of Japan

Newman, S. Pawson, M. J. Prather, J. A. Pyle,
R. J. Salawitch, B. D. Santer, and D. W. Waugh,
2005: A strategy for process-oriented validationof coupled chemistry-climate models. Bull. Amer.
Meteor. Soc., 86, 1117–1133.
European Centre for Medium-Range Weather Forecasts,
2004: IFS Documentation CY28r1. (Available
online at: http://www.ecmwf.int/research/ifsdocs/
CY28r1/index.html).
European Centre for Medium-Range Weather Forecasts,
2009: IFS Documentation CY33r1. (Available
online at: http://www.ecmwf.int/research/ifsdocs/
CY33r1/index.html).
Harrison, E. F., P. Minnis, B. R. Barkstrom, V. Ramanathan, R. D. Cess, and G. G. Gibson, 1990: Seasonal variation of cloud radiative forcing derived
from the Earth Radiation Budget Experiment. J.
Geophys. Res., 95, 18,687–18,703.
Hess, M., P. Koepke, and I. Schult, 1998: Optical properties of aerosols and clouds: The software package OPAC. Bull. Amer. Meteor. Soc., 79, 831–
844.
Hirai, M., T. Sakashita, H. Kitagawa, T. Tsuyuki, M.
Hosaka, and M. Oh’izumi, 2007: Development andvalidation of a new land surface model for JMA’s
operational global model using the CEOP observation dataset. J. Meteor. Soc. Japan, 85A, 1–24.
Hu¤man, G. J., R. F. Adler, M. M. Morrissey, D. T.
Bolvin, S. Curtis, R. Joyce, B. McGavock, and J.
Susskind, 2001: Global precipitation at one-degree
daily resolution from multi-satellite observations.
J. Hydrometeor., 2, 36–50.
Hu¤man, G. J., R. F. Adler, D. T. Bolvin, G. Gu, E. J.
Nelkin, K. P. Bowman, E. F. Stocker, and D. B.
Wol¤, 2007: The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear,
combined-sensor precipitation estimates at ﬁne
scale. J. Hydrometeor., 8, 38–55.
Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and K.
Okamoto, 2000: Rainproﬁling algorithm for the
TRMM Precipitation Radar. J Appl. Meteor., 39,
2038–2052.
Iwasaki, T., S. Yamada, and K. Tada, 1989: A parameterization scheme of orographic gravity wave dragwith the di¤erent vertical partitioning, part 1: Impact on medium range forcast. J. Meteor. Soc. Japan, 67, 11–41.
Jakob, C., 2001: The representation of cloud cover inatmospheric general circulation models. PhD thesis, Ludwig-Maximilians-Universitaet Muenchen,
193 pp.
Japan Meteorological Agency, 2007: Outline of the operational numerical weather prediction at the Japan
Meteorological Agency (Appendix to WMO numerical weather prediction progress report). Japan
Meteorological Agency, 194 pp. (Available online

Vol. 90A

at http://www.jma.go.jp/jma/jma-eng/jma-center/
nwp/outline-nwp/index.htm).
Jin, F. J., A. Kitoh, and P. Alpert, 2010: Water cyclechanges over the Mediterranean: A comparison
study of a super-high-resolution global model with
CMIP3. Phil. Trans. R. Soc. A, 368, 1–13.
Kamiguchi, K., A. Kitoh, T. Uchiyama, R. Mizuta, and
A. Noda, 2006: Changes in precipitation-based extremes indices due to global warming projected bya global 20-km-mesh atmospheric model. SOLA,
2, 64–67, doi:10.2151/sola.2006-017.
Kanada, S., M. Nakano, and T. Kato, 2010: Changes
in mean atmospheric structures around Japan
during July due to global warming in regional climate experiments using a cloud-system resolving
model. Hydrological Research Letters, 4, 11–14,
doi:10.3178/HRL.4.11.
Kanamitsu, T., K. Tada, T. Kudo, N. Sato, and S. Isa,
1983: Description of the JMA operational spectral
model. J. Meteor. Soc. Japan, 61, 812–828.
Kang, I.-S., K. Jin, B. Wang, K.-M. Lau, J. Shukla,
V. Krishnamurthy, S. D. Schubert, D. E. Wailser,
W. F. Stern, A. Kitoh, G. A. Meehl, M. Kanamitsu, V. Y. Galin, V. Satyan, C.-K. Park, and Y.
Liu, 2002: Intercomparison of the climatologicalvariations of Asian summer monsoon precipitation
simulated by 10 GCMs. Clim. Dyn., 19, 383–395,
doi:10.1007/s00382-002-0245-9.
Kawai, H., 2006: PDF cloud scheme and prognostic
cloud scheme in JMA global model. CAS/JSC
WGNE Research Activities in Atmospheric andOcean Modeling, 36, 4.15–4.16.
Kitoh, A., and S. Kusunoki, 2008: East Asian summer
monsoon simulation by a 20-km mesh AGCM.
Clim. Dyn., 31, 389–401, doi:10.1007/s00382-0070285-2.
Kitoh, A., T. Ose, K. Kurihara, S. Kusunoki, M. Sugi,
and KAKUSHIN Team-3 Modeling Group, 2009:
Projection of changes in future weather extremes
using super-high-resolution global and regional atmospheric models in the KAKUSHIN Program:
Results of preliminary experiments. HydrologicalResearch Letters, 3, 49–53, doi:10.3178/hrl.3.49.
Kusunoki, S., J. Yoshimura, H. Yoshimura, A. Noda, K.
Oouchi, and R. Mizuta, 2006: Change of Baiu rainband in global warming projection by an atmospheric general circulation model with a 20-km
grid size. J. Meteor. Soc. Japan, 84, 581–611.
Liu, P., Y. Kajikawa, B. Wang, A. Kitoh, T. Yasunari,
T. Li, H. Annamalai, X. Fu, K. Kikuchi, R. Mizuta, K. Rajendran, D. E. Waliser, and D. Kim,
2009: Tropical intraseasonal variability in the
MRI-20km60L AGCM. J. Climate, 22, 2006–
2022, doi:10.1175/2008JCLI2406.1.
Matsueda, M., R. Mizuta, and S. Kusunoki, 2009:
Future change in wintertime atmospheric blocking

February 2012

R. MIZUTA et al.

simulated using a 20-km-mesh atmospheric global
circulation model. J. Geophys. Res., 114, D12114,
doi:10.1029/2009JD011919.
Matsumoto, J., 1988: Large-scale features associated withthe frontal zone over East Asia from late summer
to autumn. J. Meteor. Soc. Japan, 66, 565–579.
McFarquhar, G. M., S. Iacobellis, and R. C. J. Somerville, 2003: SCM simulations of tropical ice clouds
using observationally based parameterizations of
microphysics. J. Climate, 16, 1643–1664.
Mellor, G. L., and T. Yamada, 1974: A hierarchy of turbulence closure models for planetary boundary
layers. J. Atmos. Sci., 31, 1791–1806.
Miura, H., M. Satoh, T. Nasuno, A. T. Noda, andK. Oouchi, 2007: A Madden-Julian Oscillation
event simulated using a global cloud-resolving
model. Science, 318, 1763–1765, doi:10.1126/
science.1148443.
Mizuta, R., K. Oouchi, H. Yoshimura, A. Noda, K. Katayama, S. Yukimoto, M. Hosaka, S. Kusunoki,
H. Kawai, and M. Nakagawa, 2006: 20-km-meshglobal climate simulations using JMA-GSM
model—mean climate states—. J. Meteor. Soc. Japan, 84, 165–185, doi:10.2151/jmsj.84.165.
Murakami, H., B. Wang, and A. Kitoh, 2011: Futurechange of western North Paciﬁc typhoons: Projections by a 20-km-mesh global atmospheric model.
J. Climate, in press.
Murakami, H., and M. Sugi, 2010: E¤ect of model resolution on tropical cyclone climate projections.
SOLA, 6, 73–76, doi:10.2151/sola.2010-019.
Nakaegawa, T., and W. Vergara, 2010: First projectionof climatological mean river discharges in themagdalena river basin, Colombia, in a changing
climate during the 21st century, Hydrological Research Letters, 4, 50–54.
Ninomiya, K., and T. Akiyama, 1992: Multi-scale features of Baiu, the summer monsoon over Japanand the East Asia. J. Meteor. Soc. Japan, 70, 467–
495.
Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S. Kobayashi, H. Hatsushika, T. Matsumoto, N. Yamazaki, H. Kamahori, K. Takahashi, S. Kadokura,
K. Wada, K. Kato, R. Oyama, T. Ose, N. Mannoji, and R. Taira, 2007: The JRA-25 reanalysis.
J. Meteor. Soc. Japan, 85, 369–432, doi:10.2151/
jmsj.85.369.
Oouchi, K., J. Yoshimura, H. Yoshimura, R. Mizuta, S.
Kusunoki, and A. Noda, 2006: Tropical cyclone
climatology in a global-warming climate as simulated in a 20 km-mesh global atmospheric model:
Frequency and wind intensity analyses. J. Meteor.
Soc. Japan, 84, 259–276, doi:10.2151/jmsj.84.259.
Rahman, Md. M., N. Ferdousi, Y. Sato, S. Kusunoki,
and A. Kitoh, 2012: Rainfall and temperature scenario for Bangladesh using 20 km mesh AGCM.

257

Int. J. Climate Change Strategies and Management,
in press.
Rajendran, K., and A. Kitoh, 2008: Indian summer
monsoon in future climate projection by a superhigh-resolution global model. Current Science, 95,
1560–1569.
Rajendran, K., A. Kitoh, R. Mizuta, S. Sajani, and T.
Nakazawa, 2008: High resolution simulation ofmean convection and its intraseasonal variability
over the tropics in MRI/JMA 20-km Mesh
AGCM. J. Climate, 21, 3722–3739, doi: 10.1175/
2008JCLI1950.1.
Randall, D., and D.-M. Pan, 1993: Implementation of the
Arakawa-Schubert cumulus parameterization witha prognostic closure. Meteorological Monograph/
The representation of cumulus convection in numerical models, 46, 145–150.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent,
and A. Kaplan, 2003: Global analyses of sea
surface temperature, sea ice, and night marine airtemperature since the late nineteenth century.
J. Geophys. Res., 108(D14), 4407, doi:10.1029/
2002JD002670.
Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga, 2008: Nonhydrostatic Icosahedral
Atmospheric Model (NICAM) for global cloud resolving simulations. J. Computational Phys., 227,
3486–3514, doi:10.1016/j.jcp.2007.02.006.
Shibata, K., and T. Aoki, 1989: An infrared radiative
scheme for the numerical models of weather andclimate. J. Geophys. Res., 94, 14923–14943.
Shibata, K., M. Deushi, T. T. Sekiyama, and H. Yoshimura, 2005: Development of an MRI chemicaltransport model for the study of stratosphericchemistry. Pap. Meteor. Geophys., 55, 75–118.
Shibata, K., and A. Uchiyama, 1992: Accuracy of thedelta-four-stream approximation in inhomogeneous scattering atmospheres. J. Meteor. Soc. Japan,
70, 1097–1109.
Smith, R. N. B., 1990: A scheme for predicting layer
clouds and their water content in a general circulation model. Quart. J. Roy. Meteor. Soc., 116, 435–
460, doi:10.1002/qj.49711649210.
Tanaka, T. Y., K. Orito, T. Sekiyama, K. Shibata, M.
Chiba, and H. Tanaka, 2003: MASINGAR, aglobal tropospheric aerosol chemical transportmodel coupled with MRI/JMA98 GCM: Modeldescription. Pap. Meteor. Geophys., 53, 119–138.
Taylor, K. E., 2001: Summarizing multiple aspectsof model performance in a single diagram. J.
Geophys. Res., 106, 7183–7192, doi:10.1029/
2000JD900719.
Tiedtke, M., 1989: A comprehensive mass ﬂux scheme
for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800.

258

Journal of the Meteorological Society of Japan

Tiedtke, M., 1993: Representation of clouds in largescale models. Mon. Wea. Rev., 121, 3040–3061.
Unisys, 2011: Unisys weather hurricane tropical data.
[Available online at http://weather.unisys.com/
hurricane/].
Uppala, S. M., P. W. Kallberg, A. J. Simmons, U. Andrae, V. D. C. Bechtold, M. Fiorino, J. K. Gibson,
J. Haseler, A. Hernandez, G. A. Kelly, X. Li, K.
Onogi, S. Saarinen, N. Sokka, R. P. Allan, E.
Andersson, K. Arpe, M. A. Balmaseda, A. C. M.
Beljaars, L. Van De Berg, J. Bidlot, N. Bormann,
S. Caires, F. Chevallier, A. Dethof, M. Dragosavac, M. Fisher, M. Fuentes, S. Hagemann, E.
Holm, B. J. Hoskins, L. Isaksen, P. A. E. M. Janssen, R. Jenne, A. P. Mcnally, J.-F. Mahfouf, J.-J.
Morcrette, N. A. Rayner, R. W. Saunders, P.
Simon, A. Sterl, K. E. Trenberth, A. Untch, D.
Vasiljevic, P. Viterbo, and J. Woollen, 2005: The
ERA-40 reanalysis. Quart. J. Roy. Meteor. Soc.,
131, 2961–3012.
Vergara, W., and S. M. Scholz, 2010: Assessment of therisk of Amazon dieback: main report. Environmentally and Socially Sustainable Development Department, The World Bank, 86 pp.
Wakazuki, Y., S. Kanada, C. Muroi, A. Hashimoto,T. Kato, M. Nakamura, A. Noda, M. Yoshizaki,
and K. Yasunaga, 2007: Regional climate projection experiments on the Baiu frontal activity
around the Japan islands using a non-hydrostatic
cloud-system-resolving model. J. Earth Simulator,
8, 13–25.
Waliser, D. E., K. Jin, I.-S. Kang, W. F. Stern, S. D.
Schubert, M. L. C. Wu, K.-M. Lau, M.-I. Lee,
V. Krishnamurthy, A. Kitoh, G. A. Meehl, V. Y.
Galin, V. Satyan, S. K. Mandke, G. Wu, Y. Liu,and C.-K. Park, 2003: AGCM simulations of
intraseasonal variability associated with the Asiansummer monsoon. Clim. Dyn., 21, 423–446.
Wang, B., and H. Rui, 1990: Synoptic climatology oftransient tropical intraseasonal convection anomalies 1975–1985. Meteor. Atmos. Phys., 44, 43–61.
Wang, B., R. Wu, and K. M. Lau, 2001: Interannual
variability of the Asian summer monsoon: Contrasts between the Indian and the western North
Paciﬁc-East Asian monsoons. J. Climate, 14,4073–4090.
Wang, B., I.-S. Kang, and J.-Y. Lee, 2004: Ensemble
simulations of Asian-Australian monsoon variability by 11 AGCMs. J. Climate, 17, 803–818.
Webster, P. J., and S. Yang, 1992: Monsoon andVol. 90A

ENSO: Selectively interactive systems. Quart. J.
Roy. Meteor. Soc., 118, 877–926, doi:10.1002/
qj.49711850705.
Xie, P., and P. A. Arkin, 1997: Global precipitation: a17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539–2558.
Xue, Y., F. Sales, W. K. M. Lau, A. Boone, J. Feng,
P. Dirmeyer, Z. Guo, K.-M. Kim, A. Kitoh, V.
Kumar, I. Poccard-Leclercq, N. Mahowald, W.
Mufouma-Okia, P. Pegion, D. P. Rowell, J.
Schemm, S. D. Schubert, A. Sealy, W. M. Thiaw,
A. Vintzileos, S. F. Williams, and M.-L. C. Wu,
2010: Intercomparison and analyses of the climatology of the West African Monsoon in the West
African Monsoon Modeling and Evaluation project (WAMME) ﬁrst model intercomparison experiment. Clim. Dyn., 35, 3–27.
Yasunaga, K., M. Fujita, T. Ushiyama, K. Yoneyama,
Y. N. Takayabu, and M. Yoshizaki, 2008: Diurnal
variations in precipitable water observed by shipborne GPS over the tropical Indian Ocean. SOLA,
4, 97–100, doi:10.2151/sola.2008-025.
Yatagai, A., P. Xie, and A. Kitoh, 2005: Utilization ofa new gauge-based daily precipitation dataset overmonsoon Asia for validation of the daily precipitation climatology simulated by the MRI/JMA 20km-mesh AGCM. SOLA, 1, 193–196, doi:10.2151/
sola.2005-050.
Yatagai, A., O. Arakawa, K. Kamiguchi, H. Kawamoto,
M. Nodzu, and A. Hamada, 2009: A 44-year daily
gridded precipitation dataset for Asia based on adense network of rain gauges. SOLA, 5, 137–140,
doi:10.2151/sola.2009-035.
Yoshimura, H., and T. Matsumura, 2005: A two-timelevel vertically-conservative semi-Lagrangian semiimplicit double Fourier series AGCM. CAS/JSC
WGNE Research Activities in Atmospheric andOcean Modeling, 35, 3.27-3.28.
Yukimoto, S, H. Yoshimura, M. Hosaka, T. Sakami, H.
Tsujino, M. Hirabara, T. Y. Tanaka, M. Deushi,
A. Obata, H. Nakano, Y. Adachi, E. Shindo, S.
Yabu, T. Ose and A. Kitoh, 2011: MeteorologicalResearch Institute-Earth System Model v1 (MRIESM1)—Model description—. Tech. Rep. Meteor.
Res. Inst., 64, 88 pp.
Zhao, M., I. M. Held, S.-J. Lin, and G. A. Vecchi, 2009:
Simulations of global hurricane climatology, interannual variability, and response to global warming
using a 50 km resolution GCM. J. Climate, 22,6653–6678, doi:10.1175/2009JCLI3049.1.