SOLA, 2008, Vol. 4, 073 076, doi:10.2151/sola.2008 019

73

Downscaling of the Climatic Change in the Mei-yu Rainband
in East Asia by a Pseudo Climate Simulation Method
Hiroaki Kawase1, Takao Yoshikane1, Masayuki Hara1, Borjiginte Ailikun2,
Fujio Kimura1, 3, and Tetsuzo Yasunari1, 4
1
Frontier Research Center for Global Change, JAMSTEC, Yokohama, Japan
2
Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, P.R. China
3
Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
4
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan

Southern China.

Abstract
This study focuses on the reproducibility of theclimatic change in the early summer rainfall in East
Asia (Mei-yu rainband) by the Pseudo Climate
Simulation (PCS) using a regional climate model. In the
PCS, the lateral boundary condition is given by the composite of six hourly reanalysis data and the difference
between two decadal climate means. The daily variationon the lateral boundary of the PCS is similar to that of
the control hindcast in one decade, but its climate is thesame as the other decadal mean. The PCS accurately
reproduces the change in the Mei-yu rainband overSouthern China between the 1960s and the 1990s,
suggesting that the climatic features of the Mei-yu
rainband are controlled by the climatic change in alarge-scale circulation.
The PCS reduces the uncertainty caused by theinterannual variability in case of the downscaling of
global warming projected by the General Circulation
Models (GCMs) even if the number of sample years isthe same as that in the conventional dynamical downscaling. The PCS can also exclude model biases in thepresent climatology reproduced by each GCM.

2. Mei-yu rainband in Southern China
A large rainband, called the Mei-yu rainband in
China, brings a large amount of rainfall over Southern
China in summer (Kurashima and Hiranuma 1971), especially over the Yangtze River Basin (YRB) in earlysummer (June). Recently, the total summer rainfall has
been increasing over the YRB (Endo et al. 2005). Gongand Ho (2002) speculated that the extension and intensification of the subtropical northwestern Pacific High(hereafter, Pacific High) since 1980 were closely related
to the increase in precipitation using the six-hourly
National Center for Environmental Prediction (NCEP)/
National Center for Atmospheric Research (NCAR)
reanalysis data.
Figure 1a shows the ten-year mean precipitation
observed in June during the 1990s. In situ observation

1. Introduction
To project the climatic change in a specific area,some methods have been proposed, such as the dynamical and statistical downscaling (Murphy 1999; Woodet al. 2004; Wang et al. 2004). The conventional methodof dynamical downscaling (hereafter; referred to as the
direct dynamical downscaling) consists of some numerical simulations using a regional climate model (RCM)
assuming the initial and lateral boundary conditions
given by the General Circulation Models (GCMs). A newconcept of the dynamical downscaling has been introduced by Kimura and Kitoh (2007) and Sato et al. (2007).
This method is almost the same as the direct dynamical
downscaling but the lateral boundary condition of the
RCM is a composite of the present reanalysis data andthe difference of monthly mean values between thepresent climate and the future climate simulated by a
GCM. Sato el al. (2007) conducted the future climate projection in Mongolia using the new downscaling method.
However, it has not been confirmed that this new
downscaling method can simulate the past climaticchange. The purpose of this study is to investigate thereproducibility of the past climatic change by the newdownscaling method. We call it the Pseudo Climate
Simulation (PCS) in this paper. We focused on the
climatic change in the early summer rainfall overCorresponding author: Hiroaki Kawase, Frontier ResearchCenter for Global Change, Japan Agency for Marine-Earth
Science and Technology, 3173-25, Showa-machi, Kanazawa-ku,
Yokohama, Kanagawa, 236-0001, Japan. E-mail: kawase@
jamstec.go.jp. ©2008, the Meteorological Society of Japan.

Fig. 1. Distributions of ten-year mean monthly precipitation(unit: mm month 1 ) in June: (a) observations at the stations in
the 1990s and (c) simulation by the HC-1990s. Differences inprecipitation (b) between observations in the 1960s and the1990s, (d) between the HC-1960s and the HC-1990s, and (e)between the PCS-1960s and the HC-1990s. In Figs. 1b, 1d, and1e blue (red) shading indicates that precipitation increased (decreased) from the 1960s to the 1990s.

74

Kawase et al., Downscaling by a Pseudo Climate Simulation Method
Table 1. Specifications of simulations.

a

Run Names

Initial and lateral boundary conditions

PeriodDescription

HC-1960s
HC-1990s
PCS-1960s

ERA40
ERA40
pseudo ERA40a

1960s
1990s
pseudo 1960s

Hindcast
Hindcast
Pseudo Climate Simulation

The change of large-scale atmospheric circulation between the 1960s and the 1990s is subtracted from every six-hour ERA40 in
the 1990s.

Fig. 2. Differences in the monthly mean moisture flux in June (unit: g kg 1 m s 1) (a) between the 1960s and 1990s derived from the
ERA40, (b) between the HC-1960s and HC-1990s, and (c) between the PCS-1960s and HC-1990s. The grid intervals in Figs. 2b and2c are interpolated to 2.5 degrees, as are those of the ERA40. The region enclosed by black lines in Figs. 2a, 2b, and 2c represents
the model domain. Figures 2d and 2e show the differences in the mean monthly precipitation (shade; unit: mm month 1 ) andmoisture flux (vector; unit: g kg 1 m s 1) in the regions enclosed by red lines in Fig. 2b and in Fig. 2c.

data were provided by the China Meteorological Administration. Heavy precipitation exceeding 350 mm/
month occurred south of the Yangtze River (27.5N,
117.5E) and around the southern coast of China. Figure
1b shows the differences in the ten-year mean precipitation in June between the 1960s and the 1990s. Precipitation increased around the Yangtze River but
decreased in the southern adjacent area.
The vertically integrated water vapor flux (hereafter, moisture flux) is estimated in June during the 1990s
by the ECMWF 40-year Re-Analysis Data (ERA40). The
distribution of the moisture flux shows the typical
pattern in the Mei-yu season suggested by Kurashima
and Hiranuma (1971) and Ninomiya (2000) (seeSupplement 1a). Figure 2a shows the difference in the
moisture flux derived from the ERA40 between the
1960s and the 1990s. A clear anti-cyclonic circulation
anomaly is found over the northwestern Pacific Ocean
in the 1990s, which is consistent with a report by Gongand Ho (2002). The extension of the Pacific Highappears not only in the NCEP/NCAR reanalysis data
but also in the ERA40.

3. Design of the Pseudo Climate Simulation
The Advanced Research of Weather Research andForecasting (WRF) model, version 2.2 (Skamarock et al.
2005), was used in this study. The domain of the nu-

merical simulation was covered by 100 × 130 grids at
40-kilometer grid intervals (Fig. 2a). In order to simulate
the precipitation in June, the simulation was executedeach year from May 22 to July 1. Both the Kain-Fritschconvective parameterization scheme (Kain and Fritsch1993) and the WRF single-moment microphysics
scheme (WSM6) were activated.
The procedure of the past PCS is as follows. Ten-year
monthly mean fields of the ERA40, including the wind,temperature, geopotential height, and sea surface temperature, were estimated in June in the 1960s and the1990s. The component of climatic change between the
1960s and the 1990s was obtained as the difference
between them. The components of climatic change weresubtracted from each six-hour ERA40 in the 1990s, andthe pseudo reanalysis data corresponding to the 1960s(hereafter, P-ERA40-1960s) were then obtained. The
relative humidity of the P-ERA40-1960s was assumed tobe equal to that of the ERA40 in the 1990s, whichmeans that the amount of water vapor was expected tochange only due to the change in temperature because
the saturation vapor pressure increases with temperature (i.e., the Clausius-Clapeyron relation).
We conducted the PCS run in the 1960s (PCS-1960s)
adapting the P-ERA40-1960s to the lateral boundary
conditions of the WRF. This paper assumes that theinterannual variability does not change in a different
climate. The hindcast runs for the 1960s and the 1990s(HC-1960s and HC-1990s, respectively) were also con-

SOLA, 2008, Vol. 4, 073 076, doi:10.2151/sola.2008 019

75

ducted in comparison with the PCS run. The specifications of the simulations are summarized in Table 1.

4. Simulated precipitation and moisture flux
The distribution of precipitation was simulated wellin the hindcast run, HC-1990s (Fig. 1c). The maximum
precipitation in the domain was consistent with the observation (Fig. 1a), while precipitation was overestimated for all of Southern China. Figure 1d shows thedifference between the HC-1960s and the HC-1990s,
which indicates an increase in precipitation around the
YRB and a decrease in precipitation to the south of the
YRB from the 1960s to the 1990s. These results agreewith the observed precipitation change shown in Fig.
1b.
Figure 1e shows the difference between the precipitation in the PCS-1960s and that in the HC-1990s. The
difference of the precipitation is similar to the observed
one, as shown in Fig. 1b, and also similar to the difference between the two hindcast runs, as shown in Fig.
1d, especially for the increase in precipitation aroundthe YRB and the decrease to the south of the YRB.
These results indicate that the precipitation change
between the 1960s and the 1990s can be simulated wellby the PCS run.
The decadal mean large-scale moisture fluxes inJune are simulated well by the HC-1960s and HC-1990s
although they are slightly overestimated (see Supplement 1). Figure 2b shows the difference of the moisture
flux between the HC-1960s and the HC-1990s. The anticyclonic circulation anomaly can be reproduced overthe northwestern Pacific Ocean as well as the ERA40(Fig. 2a). This indicates that the RCM does not strongly
modify the large-scale system.
Figure 2c is the same as Fig. 2b but shows the difference between the PCS-1960s and the HC-1990s. The
anti-cyclonic circulation anomaly is also simulated overthe northwestern Pacific Ocean, which is quite similar
to Fig. 2a and Fig. 2b. It is noteworthy that the daily
variation of the lateral boundary in the PCS-1960s
differs from that in the HC-1960s but the decadal
climates of their lateral boundaries are the same. These
results suggest that the decadal change in the moisture
flux in the model domain is strongly affected by theclimatic forcing from the lateral boundary of the RCM.
Ninomiya (2000) suggested that the differential
advection of equivalent potential temperature, which
consists of a poleward moisture flux and cold air
advection by the upper-level subtropical jet, is theprimary factor in the formation of the active Mei-yu
rainband. Figures 2d and 2e show the differences in the
moisture flux and precipitation between the HC-1960s
and the HC-1990s and between the PCS-1960s and theHC-1990s, respectively. The northeastward componentof the moisture flow increases in the YRB in both cases.
Precipitation increases in the northern part of thedomain and decreases in the southern part of thedomain. The northward shift of the upper subtropicaljet at 200hPa is also simulated over Southern China
between the PCS-1960 and the HC-1990s (figure notshown). The simulations suggest that the change in the
moisture flux modulates the differential advection in association with the upper subtropical jet and results inthe change in precipitation over Southern China.

5. Discussion
The climatic change was sufficiently simulated by
the PCS on the early summer rainfall in China between
the 1960s and the 1990s. To confirm the reliability,
another PCS run (PCS-1980s) was conducted for theperiod of the 1980s when precipitation was less thanthat in the 1990s in most of Southern China. The results

Fig. 3. Time series of the regional mean precipitation (unit: mm)over the enclosed area in Figs. 2d and 2e.
(a) Variations of the daily precipitation in June 1965 simulatedby the HC-1960s (orange), pseudo 1965 simulated by the PCS1960s (light blue), and 1995 simulated by the HC-1990s (gray).
(b) Interannual variation of total precipitation in June. (c) Tenyear mean precipitation in June.

showed that the difference between the PCS-1980s andthe HC-1990 agreed well with the observed difference
between the 1980s and the 1990s as well as with thatbetween the HC-1980s and HC-1990s although the PCS1980s underestimated the magnitude of the differences(see Supplement 2).
Figure 3 is a time series of regional mean precipitation over the enclosed area in Figs. 2d and 2e. The upper
panel (a) shows the variation of the daily precipitationin June 1965 (HC-1960s), pseudo 1965 (PCS-1960s), and1995 (HC-1990s). Lower panels (b) and (c) show theinterannual variation of the monthly total precipitationand ten-year mean precipitation in June, respectively.
The daily variation of precipitation in the HC-1990s is
quite similar to that in the PCS-1960s (Fig. 3a), whose
correlation coefficient is 0.878. The large positive correlation is considered to be induced by the common shortterm components in the boundary conditions using in
the HC-1990s and the PCS-1960s. On the other hand, theHC-1960s shows different behavior from the other simulations. The behavior in the HC-1960s is almost independent from that in the HC-1990s, as expected. In thesame way, the interannual variations of precipitation in
the PCS-1960s are similar to those in the HC-1990s,
whose correlation coefficient is 0.835 (Fig. 3b), although
the amount of precipitation in the PCS-1960s is always
less than that in the HC-1990s. The large correlation coefficient results from the strong relationship between
the HC-1990s and the PCS-1960 (see Section 3). In
contrast, the correlation coefficient between the twohindcasts, the HC-1960s and the HC-1990s, is quite low.
On the other hand, the amount of ten-year mean precipitation in the PCS-1960s is 261.6 mm, which is comparable to that estimated in the HC-1960s (234.3 mm)
but much less than that in the HC-1990s (353.8 mm)(Fig. 3c). As reported in Section 4, the horizontal distribution of the difference in precipitation between the
HC-1960s and the HC-1990s is quite similar to thatbetween the PCS-1960s and the HC-1990s (Figs. 1d and1e), as is the difference in the moisture flux (Figs. 2b and2c). These facts indicate that the climatic change estimated by the PCS-1960s is similar to that estimated by
the HC-1960s. We concluded that the time evolution of
the PCS-1960s was different from that of the HC-1960s
but the characteristics of the climate of the PCS-1960s
were similar to those of the HC-1960s.

76

Kawase et al., Downscaling by a Pseudo Climate Simulation Method

These characteristics of the PCS run bring two
major advantages for the case of the downscaling of
global warming projected by GCMs. One is the reduction of the uncertainty caused by the interannual variability, and the other is the reduction of the model bias
contained in the projection by GCMs, although the reduction is limited in the component of the climate mean.
If we assume no correlation between the two groupsof annual mean values sampled from two datasets, theestimated standard deviation of the difference between
the two mean values depends only on the number ofsample years and the standard deviations of the original
datasets. In the case of the direct dynamical downscaling, e.g., the hindcast runs in this paper, the two datagroups, the HC-1960s and the HC-1990s, are expected tohave a low correlation; thus, a larger number of sample
years are required for the smaller uncertainty caused by
the interannual variability. In case of the PCS, e.g., thePCS-1960s and the HC-1990s, however, the estimatedstandard deviation of the difference becomes smaller
because of the strong correlation between the twodatasets even if the number of sample years is the sameas that in the direct dynamical downscaling.
The second advantage was also reported by Satoet al. (2007). The direct dynamical downscaling is contaminated by the model biases of the GCM. On the other
hand, the lateral boundary condition of the PCS consistsof the present reanalysis data and the components ofclimatic change estimated by the GCMs. The PCS is contaminated only by the uncertainty of the climaticchange estimated by the GCM since the present climate
is replaced by the reanalysis data.
The PCS has disadvantages, however. In the PSC
runs, the frequency of the large-scale disturbance would
be difficult to change, although their intensity may
change. The PCS assumes that the interannual variability does not change in a different climate. The PCS,
however, would contribute to the reduction of theuncertainty of the downscaling method as one of thedifferent tools from the direct dynamical downscaling.
When the PCS is applied to the future projection, thelateral boundary condition is given by the composite of
the six hourly present reanalysis data and the difference
between the present climate and the future climate projected by GCMs. The difference would be better to be estimated by the more than ten-year means of the present
and future climates or by the model ensemble means inorder to reduce the natural variabilities, such as interannual and decadal variabilities. Even in these cases,
short-term integrations, e.g., the two ten-year integrations in this paper, by the PCS method would reduce theuncertainty caused by the natural variability in comparison with the integrations by the direct dynamical
downscaling methods.

6. Conclusion
The reproducibility of the climatic change was
evaluated by the PCS method. The PCS method can reproduce some decadal changes in the rainfall observed
over Southern China. This indicates that the change inthe large-scale circulation contributes to the climaticchange in the rainfall associated with the Mei-yu rainband through the climatic forcing from the lateralboundary of the RCM.
The PCS method has some advantages to evaluate
the future regional climatic change. This method would
reduce the uncertainty caused by the natural variability
in the simulated climatic change in comparison with thedirect dynamical downscaling method because theinterannual variation of the PCS correlates well withthat of the control hindcast. The PCS can also exclude
model biases in the present climatology reproduced byeach GCM. In short, the PCS would contribute to the reduction of the uncertainty of the downscaling method

as one of the different tools from the direct dynamical
downscaling.

Acknowledgments
This work was supported by the Global Environment Research Fund (B-061) of the Ministry of theEnvironment, Japan. This work was partly supportedby the Ministry of Education, Culture, Sports, Scienceand Technology (MEXT). It was also supported by
MEXT through a Grant-in-Aid for Scientific Research(Category B, 17310003). One of the authors (Kimura)
was supported by the Global Environment Research
Fund (S-5-3) of the Ministry of the Environment, Japan.
This research was jointly supported by the China
National Key Planning Development for Basic Research(Grant No.2006CB400504). The authors thank Dr. Endoof the Institute of Observational Research for Global
Change for his meaningful comments about the precipitation dataset. We thank two anonymous reviewers fortheir helpful comments.

Comments and supplements
1. Monthly mean moisture fluxes in June.
2. Distributions of the ten-year mean monthly precipitation in June during the 1980s.

References
Endo, N., B. Ailikun, and T. Yasunari, 2005: Changes in precipitation amounts and the number of rainy days and heavy
rainfall events during summer in China. J. Meteor. Soc.
Japan, 83, 621 631.
Gong, D., and C. Ho, 2002, Shift in the summer rainfall overthe Yangtze River valley in the late 1970s, Geophys. Res.
Lett., 29(10), 1436, doi:10.1029/2001GL014523.
Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain-Fritsch scheme.
The representation of cumulus convection in numericalmodels, Amer. Meteor. Soc., K. A. Emanuel and D. J.
Raymond, Eds., 246 pp.
Kimura, F., and A. Kitoh, 2007: Downscaling by Pseudo Global
Warming Method, The Final Report of ICCAP, 43 46.
Kurashima, A., and Y. Hiranuma, 1971: Synoptic and climatological study on the upper moist tongue extending fromSoutheast Asia to East Asia. Water Balance of Monsoon
Asia, University of Tokyo Press, M. M. Yoshino, Ed., 153
169.
Murphy, J., 1999: An evaluation of statistical and dynamical
techniques for downscaling local climate. J. Climate, 12,2256 2284.
Ninomiya, K., 2000: Large- and meso--scale characteristics of
Meiyu/Baiu Front associated with intense rainfalls in
1.10 July 1991. J. Meteor. Soc. Japan, 78, 141 157.
Sato, T., F. Kimura, and A. Kitoh, 2007: Projection of global
warming onto regional precipitation over Mongolia usinga regional climate model. J. Hydrol., 333, doi:10.1016/
j.jhydrol.2006.07.023.
Skamarock, W. C., J. Dudhia, D. O. Gill, D. M. Barker, W. Wang,and J. G. Powers, 2005: A description of the Advanced
Research WRF version 2, NCAR Tech. Rep., TN-468, 100
pp.
Wang, Y. Q., L. R. Leung, J. L. McGregor, D.-K. Lee, W.-C. Wang,
Y. H. Ding, and F. Kimura, 2004: Regional climate
modeling: progress, challenges, and prospects. J. Meteor.
Soc. Japan, 82, 1599 1628.
Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier,
2004: Hydrologic implications of dynamical and statistical approaches to down-scaling climate model outputs.
Clim. Change, 62, 233 256.
Manuscript received 4 June 2008, accepted 28 July 2008
SOLA: http://www.jstage.jst.go.jp/browse/sola/