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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114, D24110, doi:10.1029/2009JD011803, 2009

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ArticleIntermodel variability of future changes in the Baiu rainband
estimated by the pseudo global warming downscaling method
Hiroaki Kawase,1 Takao Yoshikane,2 Masayuki Hara,2 Fujio Kimura,2,3 Tetsuzo Yasunari,2,4
Borjiginte Ailikun,5 Hiroaki Ueda,6 and Tomoshige Inoue6
Received 26 January 2009; revised 10 August 2009; accepted 27 August 2009; published 31 December 2009.

[1] Changes in the Baiu rainband owing to global warming are assessed by the pseudo

global warming downscaling method (PGW-DS). The PGW-DS is similar to theconventional dynamical downscaling method using a regional climate model (RCM), but
the boundary conditions of the RCM are obtained by adding the difference between the
future and present climates simulated by coupled general circulation models (CGCMs)
into the 6-hourly reanalysis data in a control period. We conducted the multiple PGW-DS
runs using the selected Coupled Model Intercomparison Project Phase 3 (CMIP3)
multimodel data set, giving better performance around East Asia in June, and the PGW-DS
run using the multiselected CGCM model ensemble mean (PGW-MME run). The
PGW-MME and PGW-DS runs show an increase in precipitation over the Baiu rainband
and the southward shift of the Baiu rainband. The PGW-MME run has good similarity to
the average of all PGW-DS runs. This fact indicates that an average of the multiple
PGW-DS runs can be replaced by a single PGW-DS run using the multiselected CGCM
ensemble mean, reducing the significant computational expense. In comparison with the
GCM projections, the PGW-DS runs reduce the intermodel variability in the Baiu
rainband caused by the CGCMs themselves.
Citation: Kawase, H., T. Yoshikane, M. Hara, F. Kimura, T. Yasunari, B. Ailikun, H. Ueda, and T. Inoue (2009), Intermodel
variability of future changes in the Baiu rainband estimated by the pseudo global warming downscaling method, J. Geophys. Res., 114,
D24110, doi:10.1029/2009JD011803.

1. Introduction[2] The Baiu/Meiyu/Changma front, as it called in Japan/
China/Korea, is strongly related to the Asian Summer
Monsoon (ASM) and brings a large amount of rainfall overEast Asia in summer (hereafter, referred to as the Baiu
rainband) [Kurashima and Hiranuma, 1971]. The climatic
variation in the Baiu rainband causes not only frequent
serious flooding but also occasional droughts in East Asia.
The Intergovernmental Panel on Climate Change (IPCC)[2001, 2007] reported that the increase in greenhouse gases
could change the ASM intensity and duration, resulting inchanges in the Baiu rainband in the future.
[3] The Ocean-Atmosphere Coupled General Circulation
Models (CGCMs) have been used to investigate climate
1
Atmospheric Physics Section, Atmospheric Environment Division,
National Institute for Environmental Studies, Tsukuba, Japan.
2
Research Institute for Global Change, Japan Agency for Marine-Earth
Science and Technology, Yokohama, Japan.
3
Also at Graduate School of Life and Environmental Sciences,
University of Tsukuba, Tsukuba, Japan.
4
Also at Hydrospheric Atmospheric Research Center, Nagoya
University, Nagoya, Japan.
5Institute of Atmospheric Physics, Chinese Academy of Science,
Beijing, China.
6
Graduate School of Life and Environmental Sciences, University ofTsukuba, Tsukuba, Japan.

Copyright 2009 by the American Geophysical Union.
0148-0227/09/2009JD011803$09.00

changes owing to an increase of greenhouse gases. The
analysis method using the multiple CGCM model ensemble
mean (hereafter, the MME mean) is effective for the
improvement of projections owing to the reduction of biases
and uncertainties of individual CGCMs [e.g., Giorgi and
Mearns, 2002]. Climate changes were investigated using the
MME mean obtained from the WCRP Coupled Model
Intercomparison Project Phase 3 (CMIP3) multimodel data
set [Meehl et al., 2007] during the ASM season [Min et al.,
2004; Ueda et al., 2006; Kitoh and Uchiyama, 2006]. The
activity of the ASM would be enhanced in association withthe strengthening of anticyclonic circulations to its south
and north [Kimoto, 2005], resulting in the change in theBaiu rainband.
[4] The simulation of the Baiu rainband is extremely
difficult for GCMs owing to the lack of resolution as wellas to shortcomings in the physical process parameterizations[Kang et al., 2002; Ninomiya et al., 2002]. Kawatani and
Takahashi [2003] demonstrated that the structures of theBaiu front, from large scale to mesoalpha scale, were wellsimulated in a high-resolution AGCM with a grid size of
about 110 km. A comparison of high- and medium-resolution
CGCMs showed that the higher-resolution version better
reproduced not only the mean precipitation but also thefrequency of precipitation over Japan in the warm season in
the present climate [Kimoto et al., 2005]. The time-slice
experiment was conducted using a very high-resolution
AGCM with 20 km grid size developed by the Japan

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D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

Meteorological Agency (JMA) and the MeteorologicalResearch Institute (MRI) (hereafter, MRI 20 km AGCM)[Mizuta et al., 2006]. In comparison with lower-resolution
model experiments, the MRI 20 km AGCM showed superiority in simulating orographic rainfall regarding not only
its location but also its amount [Kusunoki et al., 2006].
[5] Downscaling methods are the techniques to complement the GCM simulations with the detailed temporal andspatial structures. Two major downscalings have been
proposed: one is a statistical downscaling, and the other isa dynamical downscaling (DDS). A conventional DDS
consists of a numerical simulation using a regional climate
model (RCM). The RCM applies the GCM outputs to theinitial and boundary conditions. Wang et al. [2004] statedthe history of the development of DDSs. Original RCMs
were conducted using the hydrostatic primitive equations.
With the development of computational performances,some RCMs have been converted into models on the basisof nonhydrostatic equations. Kato et al. [2001] wereconcerned that the regional climate changes were mainly
influenced by the reproducibility of the present climate
simulated by the GCM and indicated that the GCM should
be of a good quality when the regional climate wasestimated in detail by the DDSs.
[6] Using the RCM with a 20 km grid size, Kurihara etal. [2005] indicated that the precipitation would increaseduring the warm season in Western Japan because theintensification of the anticyclonic circulation over the east
Pacific Ocean resulted in strong moisture flux convergenceover Western Japan in the late 21st Century under theSpecial Report on Emissions Scenario A2 (SRES-A2)
scenario by IPCC [2000]. More detailed changes in theBaiu rainband owing to global warming were investigatedusing a nonhydrostatic cloud-resolving regional climate
model (NHM). The initial and lateral boundary conditions
were obtained from the outputs of the MRI 20 km AGCM
under the SRES-A1B scenario [Yasunaga et al., 2006]. The
Baiu front was likely to stay over the southern Japan Islands
and did not move northward in summer in the global
warming climate [Yoshizaki et al., 2005].
[7] A new concept of the DDS called pseudo global
warming downscaling method (hereafter PGW-DS) was
proposed by Kimura and Kitoh [2007] and adopted forsome regions [Sato et al., 2007; Hara et al., 2008]. The
PGW-DS is almost the same as the conventional DDS, but
the boundary condition of the RCM is a composite of
6-hourly reanalysis data and the differences in monthly
mean variables between the present and the future climates
simulated by CGCMs. Knutson et al. [2008] applied asimilar method to the Atlantic hurricanes and found that
Atlantic hurricane and tropical storm frequencies would be
reduced and near-storm rainfall rates would increase substantially under the pseudo atmospheric state and sea
surface temperature (SST) in the 21st century. Frei et al.
[1998] conducted a similar DDS to the PGW-DS to investigate heavy precipitation processes in a warmer climate inEurope, but they considered only the effects of uniform
temperature rise and relevant changes in other atmosphericvariables.
[8] Kawase et al. [2008] applied the PGW-DS to the past
climatic change in the distribution of the Baiu rainband over
China between the 1960s and 1990s during early summer.

D24110

They discussed two advantages of the PGW-DS. One is
the reduction of the model bias contained in the GCMs,
and the other is the circumventing of the uncertainty causedby the interannual variability even in the case of a shorter
integration period, such as 10 years. It is, however, difficult
to evaluate the changes in the interannual variability and thefrequencies of disturbances because the PGW-DS assumes
unchanged variability even in the future climate. The
uncertainty caused by the change in the variability still
remains.
[9] The PGW-DS easily enables multiple DDSs undermultiple CGCMs because it requires only monthly mean
atmospheric variables and sea surface temperature, both ofwhich can be much more easily obtained than 6-hourly data.
This reduces the need to archive and access CGCM data.
The purpose of this study is to assess future changes in theBaiu rainband using the PGW-DS. We focus on June when
the stable Baiu rainband appears around Southern China
and Japan.

2. Reproducibility of Present Climate
and Projections by CGCMs[10] The CGCMs have various biases in the simulation ofpresent regional climate, e.g., in East Asia, which would
result in a large uncertainty in the projections of regional
climate change owing to global warming. The PGW-DS
relies on the differences between present and future climates
simulated by CGCMs. The selection of CGCMs with highperformance of the present climate is essential to conduct
the PGW-DS, although this method reduces the effects of
the CGCM bias in the present climate more than theconventional DDS.
[11] A new type of diagram (hereafter, the Taylor diagram) and skill scores were proposed by Taylor [2001] toevaluate CGCM performances. The Taylor diagram relates
the centered Root Mean Square Error (RMSE), the patterncorrelation, and the standard deviation. The performances of
CGCMs in Asia have also been evaluated using the Taylor
diagram in previous studies [Min et al., 2004; Kusunoki etal., 2006; Inoue and Ueda, 2009]. The Taylor diagram andskill score make it possible to evaluate the location andstrength of the Baiu rainband simulated by the CGCMs.
[12] Table 1 shows the 15 CGCMs with the necessary
atmospheric and surface variables to conduct the PGW-DS,
which are available at the CMIP3 multimodel data set. The
present climate is obtained from the 20th Century Climate
in Coupled Models (20C3M). References of precipitation
and 850 hPa wind are derived from CPC Merged Analysis
of Precipitation (CMAP) [Xie and Arkin, 1997] andECMWF 40-year Re-Analysis (ERA40) Data [Uppala etal., 2005], respectively. We define the analyzed period as
1980– 1999 for comparison with CMAP precipitation data.
The analyzed area is region A, shown in Figure 1. All
models are interpolated into the same 2.5°  2.5° grids as
the reference data.The skill score (S) is defined as

2 of 14S¼

4ð1 þ RÞ4ðSDR þ 1=SDRÞ2 ð1 þ R0 Þ4;

D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

D24110

Table 1. Fifteen CMIP3 Multimodel Data Sets Having Enough Atmospheric Variables to Conduct the PGW-DS
Model

CMIP3 I.D.

Originating Group(s)Country

CCCMA
CCSM
CNRM
GFDL20
GFDL21
GISS-EH
GISS-ER
IAP
INGV
INM
IPSL
MIROCH
MIROCM

CGCM3.1(T47)
CCSM3
CNRM-CM3
GFDL-CM2.0
GFDL-CM2.1
GISS-EH
GISS-ERFGOALS-g1.0
INGV-SXG
INM-CM3.0
IPSL-CM4
MIROC3.2(hires)
MIROC3.2(medres)

Canadian Centre for Climate Modeling and Analysis
National Center for Atmospheric Research
Meteo-France, Centre National deRecherches Météorologiques
U.S. Department of Commerce, NOAAGeophysical Fluid Dynamics Laboratory

CanadaUnited States
FranceUnited States

NASA Goddard Institute for Space Studies

United States
China
Italy
RussiaFrance
Japan

MRI
PCM

MRI-CGCM2.3.2
PCM

LASG/Institute of Atmospheric Physics
Instituto Nazionale di Geofisica e Vulcanologia
Institute for Numerical Mathematics
Institut Pierre Simon Laplace
Center for Climate System Research,
University of Tokyo,
National Institute for Environmental Studies,and Frontier Research Center for Global Change (JAMSTEC)
Meteorological Research InstituteNational Center for Atmospheric Researchwhere R, R0, and SDR are the correlation coefficient, themaximum correlation attainable, which is equal to the meanof the intraensemble correlation values, and the model
standard deviation divided by the standard deviation of thecorresponding observed field [Taylor, 2001], respectively.
Here, we assume R0 = 1.0. High skill scores mean that the
spatial patterns of the realistic climate are well simulated by
the CGCMs. More detailed information is provided in thework of Taylor [2001]. We select seven CGCMs, i.e.,
CCCMA, CNRM, GFDL21, INGV, MIROCM, MIROCH,
and MRI, which have higher skill scores for precipitation
than the average of all models. Both the MIROCM and the
MIROCH are selected to focus on the difference inresolutions, but we only use one later version of the GFDL
model, GFDL21.
[13] The seven selected CGCMs show higher correlation
coefficients for precipitation in the range between 0.6 and0.8 than the other CGCMs (Figure 2a). However, the standard
deviations of the selected CGCMs are smaller than those of
the reference data, except for the MIROCH and the MRI. The
skill score of the ensemble mean of the seven CGCMs(hereafter, MME-CGCM) is the highest in Figure 2a, asreported in previous studies [e.g., Min et al., 2004].
[14] The selected CGCMs, except for CCCMA, showquite high correlation coefficients of about 0.9 for the 850
hPa zonal wind component (Figure 2b). Their standard
deviations are, however, scattered. The standard deviationsof the MRI and the GFDL are similar to those of thereference data. The selected CGCMs are plotted more
scattered on the Taylor diagram for the meridional windcomponent (Figure 2c) than for the zonal wind component.
The MME-CGCM shows the highest skill score for thezonal and meridional winds as well as the precipitation.
[15] Figure 3 shows 30-year mean precipitation in Junefrom 2060 to 2089 simulated by the MME-CGCM and theseven selected CGCMs under the SRES-A1B scenario. The
rainband corresponding to the Baiu rainband is located fromsouthern China to Japan in the MME-CGCM (Figure 3a).
The MIROCM is similar to the MIROCH, but the amountof precipitation in the MIRCOM is much weaker thanthat in the MIROCH (Figures 3g and 3h), which depends
on their resolutions [Kawatani and Takahashi, 2003].

Japan
United States

Most CGCMs do not simulate the Baiu rainbands clearly(Figures 3b –3g), but only the MIROCH does (Figure 3h).
Some CGCMs could not accurately simulate the Baiu rainband in the present climate (figure not shown), resulting in
the obscure Baiu rainband in the future climate. Consideringthat we selected the CGCMs with higher performance in
East Asia from the CMIP3 multimodel data set, the other
CGCMs simulate the Baiu rainbands more poorly.
[16] Figure 4 shows the differences in the 30-year mean
June precipitation between the 20C3M for the present
climate (1970– 1999) and the SRES-A1B scenario for the
future climate (2060 – 2089). The MME-CGCM indicates anincrease in precipitation over middle- and high-latitude
regions, especially near the Baiu rainband, and a decreasein precipitation over the low-latitude region (Figure 4a). Thechange in precipitation of the MME-CGCM is, however,quite different from the changes in the individual CGCMs.
The GFDL and the MRI show a decrease in precipitation(Figures 4c and 4e), while the CCCMA, MIROCM, and
MIROCH show an increase in precipitation over Japan.
Only the INGV shows a decrease in precipitation over
Northern China (Figure 4d). The MIROCH and the

Figure 1. Geographical map of the analysis area. Areas
enclosed by black, red, and blue lines represent the model
domain, region A, and region B, respectively.

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D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

D24110Figure 2. Taylor diagrams for the June climatological precipitation and 850 hPa wind (1980– 1999) for
East Asia. Large colored circles represent the seven selected models, and small black circles represent theother models.
MIROCM project similar changes in precipitation except
for the amount (Figures 4g and 4h).

3. Procedure of a Pseudo Global Warming
Downscaling Method[17] A control run was conducted to simulate the Baiu
rainband in the control period, which is the 1990s in thispaper. We used the Advanced Research of Weather
Research and Forecasting (WRF) model version 2.2[Skamarock et al., 2005]. The ERA40 data was applied tothe initial and boundary conditions of the WRF model(CTRL-ERA40 run). The specifications of the numericalsimulations are summarized in Table 2. The model domain
was covered by 300  200 grids with 20 km grid intervals(Figure 1). The simulation was executed from 22 May to 1
July for each year. The first 10-day period was defined as a
spin-up duration. Physical processes of the surface layer,boundary layer, and land surface were calculated by the
Monin-Obukhov (Janjic Eta) scheme (Z. I. Janjic, The
surface layer in the NCEP Eta model, Preprint, 11th
Conference on Numerical Weather Prediction, pp. 354 –
356, Am. Meteorol. Soc., Norfolk, Va., 1996), the MellorYamada-Janjic (Eta) TKE scheme [Janjic, 2002], and the
Noah land surface scheme [Chen and Dudhia, 2001],
respectively. Both the Kain-Fritsch convective parameterization scheme [Kain and Fritsch, 1993] and the WRF

single-moment microphysics scheme (WSM6) [Hong andLim, 2006] were activated as the precipitation process.
[18] Next, the present climatic fields, including the wind,temperature, geopotential height, surface pressure, and sea
surface temperature, were calculated by the 30-year monthly
mean from 1970 to 1999 in the 20C3M, as in section 2.
Here, we used one ensemble member in each CGCM. The
future climatic fields were calculated by the 30-year monthly mean from 2060 to 2089 under the SRES-A1B scenario.
The pseudo future 6-hourly data set was obtained by adding
the components of the climatic change between the present
and future climates into the 6-hourly ERA40 data in the
1990s. The climate of the pseudo future data set corresponds
to the future climate (2060– 2089), while the variability,
such as short-term disturbances and interannual variability,
is similar to that in the 1990s. The PGW-DS runs wereconducted using the pseudo future 6-hourly data. The
flowchart of the PGW-DS is summarized in Figure 5.
[19] The relative humidity of the future climate wasassumed to be equal to that of the present climate, whichmeans that the absolute amount of water vapor was
expected to increase owing to global warming because thesaturation vapor pressure increases with temperature (i.e.,
the Clausius-Clapeyron relation). It has often been suggested that the distribution of relative humidity should
remain roughly constant in the warming climate because
the global trend of near-surface relative humidity is very

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D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

D24110Figure 3. Thirty-year mean precipitation in June from 2060 to 2089 simulated by seven CGCMs and
MME-CGCM under the SRES A1B scenario.
small during recent decades [Dai, 2006; Trenberth, 2005]
and there is no detectable trend in relative humidity in theupper troposphere [Soden et al., 2005].
[20] We conducted seven PGW-DS runs using the seven
CGCMs selected in section 2, i.e., the PGW-CCCMA,
PGW-CNRM, PGW-INGV, PGW-GFDL, PGW-MIROCM,
PGW-MIROCH, and PGW-MRI runs. In addition, another
PGW-DS run, called the PGW-MME run, was conductedusing the MME-CGCM. We estimated future climaticchanges in the Baiu rainband with a comparison of the
CTRL-ERA40 run with the seven PGW-DS runs and the
PGW-MME run.
[21] Considering the linear approximation, the pseudo
future 6-hourly data satisfies the hydrostatic equilibrium,
the equation of continuity, and the linear terms of dynamical
equations. However, the balance of nonlinear terms, such as
an advection term, is slightly disrupted. The PGW-DS
applies the pseudo future 6-hourly data to only the initialand lateral boundary conditions. The imbalanced error

caused by the nonlinear term seems to be smaller than theimbalanced errors between the lateral boundary conditions
and interior mesh values.
[22] A simple numerical experiment was also conducted
to investigate the impact of only the temperature rise on theBaiu rainband (TEMP-RISE run), as in the work of Frei etal. [1998]. The initial and boundary conditions were provided by the 6-hourly ERA40 data in the 1990s, but 3K inatmospheric temperature at all vertical levels and 2.2K atthe sea surface temperature were added; these were comparable to the temperature rise at lower atmospheric levels andthe sea surface in the MME-CGCM at the end of 21stCentury under the SRES-A1B scenario. The geopotential
height was recalculated according to the temperature rise.

4. Results of Downscalings
4.1. Observations and Control Run[23] Figure 6a shows the distributions of the 10-year
mean precipitation in June during the 1990s observed by

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KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

D24110Figure 4. Difference in the 30-year mean precipitation in June between the 20C3M and SRES A1B
scenario. Blue represents the increase of precipitation under the SRES-A1B scenario.
in situ stations provided by the Meteorological Administration of China and Automated Meteorological Data Acquisition System (AMeDAS) provided by JMA in Japan.
Heavy precipitation exceeding 420 mm/month is observed
over the south of the Yangtze River basin (28°N, 118°E)
Table 2. Specifications of the Simulations
Run Name

Initial and Boundary Conditionsa

Years

CTRL-ERA40
PGW-MIROCM
PGW-MIROCH
PGW-MRI
PGW-GFDL
PGW-CNRM
PGW-CCCMA
PGW-INGVPGW-MME
PGW-MME-80s
CTRL-ERA40-80s
TEMP-RISE

ERA40
ERA40+diff_MIROCM
ERA40+diff_MIROCH
ERA40+diff_MRI
ERA40+diff_GFDL21
ERA40+diff_CNRM
ERA40+diff_CCCMAERA40+diff_INGVERA40+diff_MME-CGCM
ERA40+diff_MME-CGCM
ERA40
ERA40+ATM3.0K_SST2.2K

1990s
1990s
1990s
1990s
1990s
1990s
1990s
1990s
1990s
1980s
1980s
1990s

a
ATM, atmospheric temperature; diff_, difference between the present(1970 – 1999) and future (2060 – 2089) climates.

6 of 14

Figure 5. Flowchart of the PGW-DS.

D24110D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

Figure 6. Distributions of 10-year mean precipitation inJune during the 1990s (a) observed by in situ stations
provided by the Meteorological Administration of China
and AMeDAS provided by JMA in Japan, (b) derived from
CMAP, and (c) simulated by the CTRL-ERA40 run.
and around the southern coast of China. A large amount ofprecipitation is also observed over Kyushu in Japan. The
amount of precipitation drastically decreases to the north of
the Yangtze River. Figure 6b shows the distribution of the
10-year mean precipitation derived from CMAP. The Baiu
rainband extends from southern China to Japan.
[24] The CTRL-ERA40 run accurately simulates the Baiu
rainband extending from southern China to Japan and the
distribution of precipitation in China, e.g., a large amount ofprecipitation over the south of the Yangtze River basin andthe southern coast of China, and a drastic decrease inprecipitation to the north of the Yangtze River (Figure 6c).
Some previous studies focused on the regional climate
modeling without interior or spectral nudging [e.g., Castro
et al., 2005]. The climatic fields of wind and geopotential
height simulated by the CTRL-ERA40 run without interior orspectral nudging have some small differences from theoriginal ERA40 data. The CTRL-ERA40 run, however, wellsimulates the location and strength of the Baiu rainband in the
1990s, as shown in Figure 6, which makes it possible toestimate future changes in the Baiu rainband. It should be
noted that the amount of precipitation is overestimated,
especially around the center of southern China (25°N,
118°E).

D241104.2. PGW-DS Runs
[25] The Baiu rainbands can be simulated from southern
China to Japan in all PGW-DS runs as strongly as in the
CTRL-ERA40 run (Figure 7). Most PGW-DS runs showsthat the amount of precipitation increases over the southernparts of the Baiu rainband but decreases over the northernparts of the Baiu rainband (Figures 7 and 8). This feature isstrongly simulated by the PGW-INGV run (Figure 8d) but is
not clear in the PGW-MIROCH and PGW-MIROCM runs
in which precipitation increases over the Baiu rainband(Figures 8g and 8h).
[26] Figure 9 shows the interannual variability of theregional mean precipitation in June over region B shownin Figure 1. Region B is wide enough for almost of the
precipitation around the Baiu rainband to be detected. The
amounts of precipitation simulated by the PGW-MME and
PGW-DS runs are 10 – 50 mm/month larger than thatsimulated by the CTRL-ERA40 run. The PGW-MME andindividual PGW-DS runs show similar interannual variability to the CTRL-ERA40 run. The PGW-MIROCH, however,simulated more precipitation than the other PGW-DS runs
in 1993 and 1997. The PGW-INGV also simulated more
precipitation in 1991 and 1993. It is noteworthy that the
average of the seven PGW-DS runs (hereafter, MMEPGW-DS) shows quite similar interannual variability to the
PGW-MME run.
[27] Figure 10 shows the latitudinal distributions of
the zonal mean precipitation, which is averaged every
2.5 degrees over region B, in June during the 1990s andpseudo warmed 1990s. The maximum precipitation of about
280 mm/month, which signals the peak of the Baiu rainband, appears at around 27.5°N–30°N in the CTRL-ERA40
run. However, the maxima appear at around 25°N –27.5°N
in the PGW-MME run and most PGW-DS runs. The
precipitation increases over the southern part of the control
Baiu rainband simulated by the CTRL-ERA40 run (20°N–
27.5°N). Only the PGW-MIROCM and PGW-MIROCH
runs show an increase in precipitation over the control Baiu
rainband. The MME-PGW-DS shows quite a similar change
to the PGW-MME run as well as the interannual variabilityof the regional mean precipitation (Figure 9).
4.3. Comparison Between PGW-DS Runs and CGCMs[28] We compare the intermodel variability in the changeof precipitation simulated by the PGW-DS runs (Figures 7
and 8) and projections of the CGCMs (Figures 3 and 4) withthe aid of the Taylor diagram [Taylor, 2001]. The observational data are applied to the reference data in the original
Taylor diagram (see section 2 and Figure 2), while the
MME-CGCM and the MME-PGW-DS are applied to thereference data here. We call this Taylor diagram the ‘‘MME
Taylor diagram.’’ It should be noted that the MME Taylor
diagram cannot be used to evaluate the performances of themodels in the future climate. The skill score, which shows
the skill of CGCM in the original Taylor diagram, refers to
the similarity among the models and the MME mean in the
MME Taylor diagram. The skill score is called a ‘‘similarity
score’’ in the MME Taylor diagram. The models with high(low) similarity scores show similar features to (different
features from) the MME mean. When all models have high(low) similarity scores, the intermodel variability is small(large). When the variance of the MME mean is much

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D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

D24110Figure 7. Distributions of June precipitation simulated by the seven PGW-DS runs and the PGW-MME
run.
smaller than those of the individual models, all models havedifferent features.
[29] Figure 11 shows the MME Taylor diagrams for the
spatial distribution of precipitation in the future climate(Figures 11a and 11c) and the difference in precipitation
among the present and future climates (Figures 11b and11d). Figures 11a and 11b show the projections of the
CGCMs and Figures 11c and 11d show the results of the
PGW-DS runs. Figures 11a, 11b, 11c, and 11d correspondto Figures 3, 4, 7, and 8, respectively. The similarity scoresfor the precipitation in the CGCMs are lower than 0.7(Figure 11a). The CCCMA, MIROCH, and MRI havesmaller similarity scores than the other models, whichindicates that their projections are far from the MMECGCM. The location of the rainband in the CCCMA isquite different from that in the MME-CGCM (Figures 3aand 3b), which results in the lowest correlation coefficientand the smallest similarity scores. The variances in the
MIROCH and the MRI are higher than those of the others

depending on the amount of precipitation around the rainband (Figures 3e and 3h). The similarity scores for thechange in precipitation are quite low in all CGCMs(Figure 11b). They are lower than 0.2 except for the
MIROCM and the CNRM. The variance of the MMECGCM is much smaller than those of the seven CGCMs.
These results indicate that the seven CGCMs show differentchanges in the spatial distribution of precipitation, i.e.,
different changes in the Baiu rainband.
[30] However, all PGW-DS runs show much higher
similarity scores than the CGCMs. Their correlation coefficients exceed 0.90 in most PGW-DS runs (Figure 11c).
The variances of all PGW-DS runs are comparable to that of
the MME-PGW-DS. Even the PGW-MIROCH, PGW-MRI,and PGW-CCCMA show high similarity scores, although
their original CGCMs, i.e., MIROCH, MRI, and CCCMA,
show quite low similarity scores (Figure 11a). Figure 11c
indicates that all PGW-DS runs simulate similar locations
and strengths for the Baiu rainband. Moreover, it is note-

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D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

D24110Figure 8. Difference in June precipitation among the CTRL-ERA40 run and each PGW-DS run. Blue
indicates that the precipitation in the PGW-DS runs is larger than that in the CTRL-ERA40 run. The areas
enclosed by red lines represent the heavy rainfall areas of over 350 mm/month to the east of 110°E
simulated by the CTRL-ERA40 run.
worthy that the PGW-MME run, which is the PGW-DS run
using the MME-CGCM, also shows a high similarity scoreof about 0.96. The similarity scores for the difference inprecipitation in the PGW-DS runs are also much higher thanthose in the CGCMs (Figure 11d). They exceed 0.4, except
for PGW-MIROCH and PGW-MIROCM. As in Figure 11c,the PGW-MME run shows a high similarity score of about
0.73. The variances of the PGW-DS runs are comparable tothat of the PGW-MME run, except for the PGW-INGV run.
The clear contrast of the decrease and increase of precipitation results in the large variance in the PGW-INGV run(Figure 8d). The PGW-MIROCM and PGW-MIROCH runs
have low correlation coefficients and low similarity scoresbecause they simulate different changes in the spatial
distribution of precipitation compared with the other PGWDS runs and the PGW-MME run (Figure 8). Figure 11
suggests that the PGW-DS runs decrease the intermodel

variability in the Baiu rainband relative to the original
CGCMs.

5. Discussion[31] A significant assumption of the PGW-DS is theneglect of changes in variability, such as interannual variability, in the future climate. The future changes in theamplitudes of the interannual variability obtained by selected CGCMs and their uncertainty were investigated in regard
to the geopotential height at three pressure levels (850 hPa,
500 hPa, and 200 hPa) over region B shown in Figure 1. We
estimated the difference in the standard deviation of interannual variability (hereafter, DSDIV) between the present
climate (1970– 1999) and the future climate (2060 – 2089)
simulated by each CGCM as well as the DSDIV between
ERA40 data and each CGCM in the present climate. The

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D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

D24110Figure 9. Time series of the regional mean precipitation in June over region B shown in Figure 1. A
solid black line represents the CTRL-ERA40 run. The solid and dashed red lines represent the PGWMME run and the average of all PGW-DS runs, respectively. The other colored lines represent theindividual PGW-DS runs. The dashed black line represents the TEMP-RISE run.
DSDIVs between the present and the future climates werecomparable to those between ERA40 data and the individual CGCMs in the present climate at all of three pressurelevels. These facts indicate that the uncertainty of CGCMs
is too large to discuss the change in the amplitudes of theinterannual variability. In addition, the mean of the DSDIVs
between the present and future climates among the CGCMs
was much smaller than the standard deviation of the
DSDIVs among the CGCMs. These results suggest thatthe signal of the change in the amplitudes of the interannual
variability is insignificant in the CGCMs.
[32] The PGW-DS based on other decades, such as the
1970s and the 1980s, may simulate the different changes in

the Baiu rainband from those simulated by the PGW-DS
based on the 1990s. We conducted the additional control run(hereafter, CTRL-ERA40-80s run) and the PGW-MME run(hereafter, PGW-MME-80s run) in the 1980s. Here, the
PGW-MME-80s run is expected to represent the average of
all PGW-DS runs using individual CGCMs in the 1980s(see Figures 9 and 10).
[33] The CTRL-ERA40-80s simulates the Baiu rainband
extending from southern China to Japan (Figure 12a). In
comparison with the 1990s, the location of the Baiu rainband is similar to that in the 1990s, while the amount ofprecipitation is smaller than that in the 1990s, which is also
shown in observational data (figure not shown). Figure 12b

Figure 10. Latitudinal distribution of the 10-year mean precipitation in June averaged over 90°E –150°E and every 2.5 degrees. All lines are the same as those in Figure 9.
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D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

D24110Figure 11. MME Taylor diagrams for the 10-year mean precipitation in June projected by (a) the
CGCMs and (c) the PGW-DS runs and changes in precipitation projected by (b) the CGCMs and (d) the
PGW-DS runs.
shows the difference in precipitation in the CTRL-ERA4080s and PGW-MME-80s runs. Precipitation increases overthe southern part of the control Baiu rainband, whichsuggests the southward shift of the Baiu rainband. It isnoteworthy that the change in the Baiu rainband simulatedby the PGW-MME-80s run is quite similar to the PGWMME run based on the 1990s (Figure 8a). In consideration
with the mechanism to form the Baiu rainband, it is unlikelythat the Baiu rainband locates at a quite different place inthe other present decades. The results suggest that the PGWDS is expected to obtain similar results regardless of theselected decade.
[34] These facts also suggest that the selection of reanalysis data, such as ERA40 data, the National Centers forEnvironmental Prediction/National Center for AtmosphericResearch (NCEP/NCAR) reanalysis data, and Japanese
25-year Reanalysis (JRA25) data, would not affect theresults of the PGW-DS run. The differences in the reanalysis
data are much smaller than those between decades, e.g., the1980s and the 1990s.
[35] An increase in temperature owing to global warming
would contribute to the change in the Baiu rainband becauseof the increase of the saturation vapor pressure. The PGW-DS assumes that the relative humidity of the future climate
would be equal to that of the present climate, which results
in an increase in the absolute value of water vapor in thewarm future climate following the Clausius-Clapeyron
relationships. The impact of a temperature rise on the Baiu
rainband is estimated by the TEMP-RISE run assuming
only an atmospheric and sea surface temperature rise (seesection 3).
[36] The dashed black lines in Figures 9 and 10 show theresults of the TEMP-RISE run. The regional mean precipitation increases, as in the PGW-DS runs (Figure 9), while
the amount of precipitation increases at almost the same rate
at all latitudes (Figure 10). The southward shift of the Baiu
rainband, which is shown in most PGW-DS runs, is not
simulated by the TEMP-RISE run. The TEMP-RISE run
suggests that the temperature rise would contribute to theincrease of precipitation around the Baiu rainband, while theother factors of the PGW-DS runs would influence the shiftof the Baiu rainband. It must be noted that we only
indicated the direct effect of the temperature rise in theregional scale shown in Figure 1 without considering theindirect effect accompanied with the SST rise, such as
the global-scale atmospheric response to the El Nino con-

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D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

Figure 12. Distribution of (a) June precipitation simulatedby the CTRL-ERA40-80s run and (b) difference in June
precipitation between the CTRL-ERA40-80s run and the
PGW-MME-80s run. The areas enclosed by red lines
represent the heavy rainfall areas of over 350 mm/month tothe east of 110°E simulated by the CTRL-ERA40-80s run.
ditions of the ocean pointed out in previous studies [e.g.,
Kitoh et al., 2005].
[37] The change in the location of the Baiu rainband isthought to be affected by the change in the large-scale
circulation projected by the CGCMs throughout the lateralboundary of the RCM. Ninomiya [2000] suggested that thedifferential advection of the equivalent potential tempera-

D24110ture, which consists of a poleward moisture flux by the lowlevel jet and cold air advection by the upper-level jet, is a
primary factor in the formation of the active Baiu rainband.
Yoshikane et al. [2001] reported that the Baiu rainband isquite sensitive to the positions of the upper-level jet and the
low-level jet. It is suggested that the future change insummer precipitation in East Asia will be attributable tothe change in the lower moisture flux and its convergenceassociated with the subtropical high [Kurihara et al., 2005].
[38] Figure 13 shows the difference of the moisture flux
at 850 hPa between the CTRL-ERA40 run and the PGWMME run. Contour lines represent the zero vorticity at 200
hPa, which mean the axes of the upper-level jet. The
poleward moisture flux increases around the southwesternboundary of the model domain (15°N, 115°E). The eastward to northeastward moisture flux also increases over thenortheastern Pacific Ocean (115°E– 145°E, 20°N – 30°N).
The increase of the lower-level moisture flux enhances theconvective instability, resulting in more precipitation aroundthe Baiu rainband in the PGW-MME run than that in the
CTRL-ERA40 run. However, the zero vorticity contour line
shifts southward (110– 140°E), which means that the meandering of the upper-level jet is more remarkable in the
PGW-MME run than that in the CTRL-ERA40 run. The
larger amplitude of the meandering brings cold air more
southward in the upper level. It can be summarized that theenhancement and the southward shift of the Baiu rainband
are the results of the change in the differential advectionof equivalent potential temperature caused by two factors:(1) the increase of the northeastward moisture flux over thenorthwestern Pacific Ocean at the lower level, and (2) the
increase of the southward cold air advection in the upper
level. However, detailed mechanisms in the shift of the Baiu
rainband are still unknown. Further studies are needed to
identify them.
[39] One major problem is the projection of typhoons in
the future climate that would sometimes strongly affect the

Figure 13. Difference of the moisture flux at 850 hPa between the CTRL-ERA40 run and the PGWMME run (vector) and the zero vorticity contours at 200 hPa simulated by the CTRL-ERA40 (bolddashed line) and PGW-MME runs (bold solid line) in June. The gray shading indicates that the amount ofprecipitation in the PGW-MME run is 40 mm larger than that in the CTRL-ERA40 run.
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D24110KAWASE ET AL.: ASSESSMENT OF FUTURE CHANGES IN BAIU

amount of precipitation during the Baiu season. The PGWDS may not estimate the impact of a typhoon accurately
because of the assumption of PGW-DS on the boundary
conditions. To reduce the uncertain impacts of a typhoon,
this study focuses on only June, when there are fewer
typhoons than in the other months. Since several typhoons
annually approach Japan and China from July to September,extreme care must be exercised regarding typhoons when
the PGW-DS is conducted in such months. Knutson et al.
[2008] applied a similar method to the simulation of theAtlantic hurricane frequency using a wide domain for the
RCM. The PGW-DS would require a much larger domain
than has been assumed here when the PGW-DS estimatesthe change in the Baiu rainband, including the impact of
typhoons.
[40] The PGW-DS assumes unchanged interannual variability and frequencies of disturbances. The change inextreme precipitation events might be affected by them.
The usefulness of PGW-DS for extreme events should be
examined in future works. We also assume constant relative
humidity between the present and future climates. Rowell
and Jones [2006] suggested that the relative humidity tends
to decrease over land in global warming condition owing to
the change in large-scale land-sea circulation. The change inthe relative humidity in the regional scale would affect theamount and distribution of precipitation. It is necessary to
conduct further PGW-DSs to investigate the impact of thechange in the relative humidity.
[41] The PGW-DS has some limitations in the assessmentof future projections. However, the PGW-DS can reduce theintermodel variability of changes in the Baiu rainband andcontribute to the reduction of uncertainty in the climatechanges projected by multiple CGCMs.

6. Conclusion[42] For the estimation of the change in the Baiu rainband
owing to global warming, multiple PGW-DS runs wereconducted using selected CMIP3 multimodel data set giving
better performance around East Asia in June. Most PGWDS runs and the PGW-MME run, which is the PGW-DS run
using the multiple CGCM ensemble mean, simulated anincrease in precipitation over the Baiu rainband and thesouthward shift of the Baiu rainband. The PGW-MME run
has good similarity to the average of all PGW-DS runs, e.g.,
the change in the regional mean precipitation and the shiftof the Baiu rainband. These facts suggest that the average ofthe multiple PGW-DS runs can be replaced by a single
PGW-DS using the multiple CGCM ensemble mean, whichsignificantly reduces the computational expense. The PGWMME run based on the 1980s simulated the change in theBaiu rainband with considerable similarity to the PGWMME run based on the 1990s, suggesting that the PGW-DS
is expected to obtain similar results regardless of theselection of control decade. The comparison of the
PGW-DS runs and the CGCM projections suggests that
the PGW-DS runs reduce the intermodel variability in theBaiu rainband caused by CGCMs themselves. Although
the PGW-DS assumes an unchanged variability in the
future climate, the PGW-DS can be used as a new method
to assess the regional climate changes owing to global
warming.

D24110[43] Acknowledgments. This work was supported by the Global
Environment Research Fund (B-061) of the Ministry of the Environment
and Ministry of Education, Culture, Sports, Science, and Technology ofJapan. H. Ueda, T. Inoue, and F. Kimura were supported by the Global
Environment Research Fund (S-5-2 and S-5-3) of the Ministry of theEnvironment of Japan in the course of this work. We acknowledge themodeling groups, the Program for Climate Model Diagnosis and Intercomparison, and the World Climate Research Program’s (WCRP) Working
Group on Coupled Modeling for their roles in making the WCRP Coupled
Model Intercomparison Project Phase 3 multimodel data set available.
Support of this data set was provided by the Office of Science, U.S.
Department of Energy. This work was jointly supported by the China
National Key Planning Development for Basic Research (grant
2006CB400500). We appreciate I. Takayabu in the MeteorologicalResearch Institute for his helpful suggestions. We also thank three anonymous reviewers for their precise and useful comments.

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

B. Ailikun, Institute of Atmospheric Physics, Chinese Academy of
Science, Qijiahuozi Huayanli 40, Chaoyang District, PO Box 9804, Beijing
100029, China.
M. Hara, F. Kimura, and T. Yoshikane, Research Institute for Global
Change, Japan Agency for Marine-Earth Science and Technology, 3173-25
Showamachi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan.
T. Inoue and H. Ueda, Graduate School of Life and Environmental
Sciences, University of Tsukuba, Tennoudai 1-1-1, Tsukuba, Ibaraki
305-8577, Japan.
H. Kawase, Atmospheric Physics Section, Atmospheric Environment
Division, National Institute for Environmental Studies, 16-2 Onogawa,
Tsukuba, Ibaraki 305-8506, Japan. (kawase.hiroake@nies.go.jp)
T. Yasunari, Hydrospheric Atmospheric Research Center, Nagoya
University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.

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