Annual Journal of Hydraulic Engineering, JSCE, Vol.54, 2010, February
Annual Journal of Hydraulic Engineering, JSCE, Vol.54, 2010, February

PRECIPITATION CHANGES IN JAPAN UNDER
THE A1B CLIMATE CHANGE SCENARIO
Sunmin KIM 1, Eiichi NAKAKITA2, Yasuto TACHIKAWA3 and Kaoru TAKARA4
1Member of JSCE, Dr. Eng., Assistant Professor, DPRI, Kyoto University(Gokasho, Uji 611-0011, Japan) E-mail:sunmin@hmd.dpri.kyoto-u.ac.jp
2Member of JSCE, Dr. Eng., Professor, DPRI, Kyoto University (Gokasho, Uji 611-0011, Japan)
3Member of JSCE, Dr. Eng., Associate Professor, Dept. of Urban and Env. Eng., Kyoto University(Nishikyo-ku, Kyoto 615-8540, Japan)
4Fellow of JSCE, Dr. Eng., Professor, DPRI, Kyoto University (Gokasho, Uji 611-0011, Japan)

Output of a super-high-resolution atmospheric general circulation model was analyzed to understandchanges of precipitation pattern over Japan in the coming century. AGCM20 output, which is providing
climate condition of near-future (2015~2039) and future term (2075~2099) under the A1B scenario,
shows that annual precipitation increase slightly (2~5% in the most region) in the next century and moreincreases in summer season. However, evapo-transpitation amount is also likely to be increased in the
future, thus net-water-resources in most regions beside Kyushu and Hokkaido is going to be decreased
down to -5.7%. Analysis on extreme event changes are given, and several limitations and characteristics
on the AGCM20 precipitation output are discussed.
Key Words: Climate change, Japan, AGCM, precipitation, seasonal pattern, extreme events

1. INTRODUCTION
Rapid evolution of general circulation models(GCMs) in the last three decades allows us to expect
reasonable hydrologic dataset from the model
output. In 2007, Japan’s Ministry of Education,
Culture, Sports, Science, and Technology (MEXT)
launched the Innovative Program of Climate Change
Projection for the 21st Century (Kakushin21), andhave developed a super-high-resolution atmosphericmodel having 20-km spatial and 1-hour temporal
resolution (hereafter AGCM20). This is the finest
resolution output available by the time this paper iswritten. The AGCM20 provides two terms of future
projection run output based on the A1B climatechange scenario, which are near-future term
(2015~2039) and future term (2075~2099). The
controlled run using an observed sea surface
temperature (SST) provides the present term (1979~2003) atmospheric data1), 2), 3). The AGCM20 has
finished producing its first run output in 2009, and ispreparing for the second run.
This paper provides preliminary analysis resultson the precipitation output using the first run outputof the AGCM20. Firstly, the controlled simulation
output was evaluated using the AMeDAS observed
precipitation data to check the reproducibility of themodel. Recoding duration of the AMeDAS data iscovering the controlled run period with very fineobservation network, thus it may provide reasonableindex to evaluate the AGCM20 precipitation output.
Secondly, changes of future precipitation pattern
in Japan were examined using the AGCM20 output.
The evaluation was fulfilled simply by comparing
the present term output and the near-future (future)term output, and it is focusing on two points, water
resources change and extreme events change.
Lastly, this paper describes some characteristics
and limitations of the AGCM20 output according to
the analysis results given in this paper as well as theprevious studies of Kim et al.4) and Takara et al.5).
The information can be useful for possible futureusers who want to carry out hydrological impact
analysis research with the AGCM20 output data.
The organization of this paper is as follow:
Section 2 describes about the AGCM20 output data
including description on the model and simulation
conditions. Evaluation on the controlled simulation
output (1979~2003) is also given in this section.
Section 3 provides examination results on the
projection run output, and illustrates precipitation
changes in Japan focusing on seasonal variation andextreme events. Section 4 describes several
characteristics and limitations of the AGCM20
output, and the last section concludes this paperwith summarizing the analysis results.

- 127 -

2. AGCM20 OUTPUT(1) Super-High Resolution Atmospheric Model
AGCM20 is the state-of-the-art atmosphericmodel in a sense of its fine resolution. The model
conducts simulation using triangular truncation at
wave number 959 with a linear Gaussian grid
(TL959) in the horizontal based on 1920 × 960 gridcells about 20 km in size and 60 levels in thevertical. Test running results during the model
development showed advantages in simulating
orographic rainfall and frontal rain bands by its veryfine spatial resolution2).
AGCM20 uses the HadISST1 dataset6) as
observed monthly mean climatologic sea surface
temperature (SST) for a boundary condition of thecontrolled simulation. HadISST1 provides global
sea ice and sea surface temperature (GISST)
datasets from 1871 uniquely combining monthly,
globally complete fields of SST and sea ice
concentration on a 1° latitude × 1° longitude grid6).
Future SST for the projection simulation was
estimated from the ensemble mean of GCM
simulation output under the A1B emission scenario7)
from the model output of the Coupled Model
Intercomparison Project Phase 3. According to theA1B scenario of Special Report on Emissions
Scenarios (SRES)7), IPCC, the global averagetemperature is expected to increase 2.5°C and theCO2 concentration to become 720 ppm by 2100.
The A2 scenario that was based on the RCM20 of
the Kyousei program was expecting 860 ppm of
CO2 concentration by 2100.
To provide more realistic condition in the
AGCM20 projection simulation, ensemble mean of
the SST was additionally composed with an annual
variation of the current HadISST1 SST. Refer to
Mizuta et al.1) and Kitoh and Kusunoki2) for details
on AGCM20 and Kusunoki and Mizuta3) for
simulation environment details.
(2) Evaluating the Controlled Simulation Output
using the AMeDAS Observation Data
Reliance on GCM output, especially on the
projection run output can be achieved through anevaluation of the model reproducibility for thecurrent climate condition. Before the projection
output was analyzed in this paper, the 25 years ofcontrolled run output of AGMC20 was evaluatedusing the AMeDAS observation data. For areasonable comparison, the point-gauged AMeDAS
data was converted into the 20-km grid-based
spatially averaged data as the AGCM20 output
format. Inverse-distance weighting factor method
was adopted for the AMeDAS data conversion.

Fig. 1(a) Annual mean precipitation of Japan, observedby the AMeDAS (1975~2003) and converted into 20
km grid data, showing 1684.3mm of mean value.

Fig. 1(b) Annual mean precipitation from the controlled
run output (1975~2003) of the AGCM20. It shows
0.78 of spatial pattern correlation to the observation,and the mean annual precipitation is 1703.8mm.

First of all, annual mean precipitation was
estimated from the converted AMeDAS observation
data and the AGCM20 output data (see Fig. 1).
According to the AMeDAS observation, annual
mean precipitation during 1979 and 2003 is 1684.3
mm, and the AGCM20 output shows 1703.8 mm ofannual mean for the same duration, which shows
very good consistency. Spatial distribution patternof the annual mean precipitation also shows
considerably good match between the AGCM20
output and the AMeDAS observation, showing 0.78
of pattern correlation.
Winter precipitation such as heavy snowfall in
Hokuriku mountainous area along the northernseashore of Kanto and Tohoku region is showing
successful reproducibility. Summer heavy rainfall in
Kyushu, Shikoku and Kansai region, which ismostly due to frontal rain-band and Typhoon, is also
well presented in the model output. However, it isnoticeable that the clear spatial pattern of theobserved precipitation is presented in somewhatsmoothen way in the AGCM20 precipitation output.
It is mainly because of topographic information in
the AGCM20, which also has 20-km spatiallyaveraged elevation values. The 20-km resolution
topographic data has rather flattened shapecomparing to the original topography. Even though
there is several physical parameterization schemes
are applied in the AGCM20 to properly consider the
influence of flattened sub-grid scale topographic
data1), the performance of the atmospheric model
still shows some limitations. However, the model
performance is an encouraging result consideringthat the output is from a global atmospheric model.

- 128 -

Fig. 2 Examples of the regression coefficient calculation
with the daily and hourly maximum precipitation
values of the AGCM20 output and the AMeDAS.

Reproducibility of daily and hourly maximum
precipitation of each grid was evaluated by checking
100 maximum precipitation of the AGCM20
controlled run output and the AMeDAS observed
one within the same period. The 100 maximum
values of daily and hourly precipitation wereselected by choosing 4 maximum values of eachyear during 25 years. As shown in Fig. 2, aregression coefficient was calculated using one pairof 100 maximum values on each grid. Desirable
reproducibility on the extreme value will beshowing 1.0 regression coefficient. If it is less than
1.0, it means that the AGCM20 output has generally
underestimated extreme values compared to theobservation and vice versa. Regression coefficient isdepending on the choosing sample numbers
however, this simple evaluation method provides
direct and clear understanding on the overall model
performance related to extreme values.
From the Fig. 3, which expresses the regression
coefficients on each grid, it is very clear that theAGCM20 output has underestimated daily andhourly maximum in most part of Japan. The samecharacteristics on the controlled run output of theAGCM20 was also found in detailed analysis of
Takara et al.5) using the precipitation output dataover the Tone River basin. This underestimation on
the extreme precipitation values reveals that the
20-km spatial resolution might be still insufficient tosimulate sophisticated sub-grid scale orographic
rainfall.
To understand the model performance on
seasonal variation, monthly mean precipitation was
estimated, and correlation of each month’s spatial
distribution pattern were calculated (see Fig. 4).
Except the late summer season, from July to
October, the AGCM20 precipitation output shows
very high performance level. The reason of the low
spatial pattern matches in the late summer season isdue to difficulty in the simulation of unstable
atmospheric conditions in summer season.

3. CHANGES IN PRECIPITATIONPATTERN AND ITS AMOUNT

Fig. 3(a) Regression coefficient of 100 daily maximum
precipitations from the AGCM20 controlled run
output to the AMeDAS observed one.

Fig. 3(b) Regression coefficient of 100 hourly maximum
precipitations from the AGCM20 controlled run
output to the AMeDAS observed one.

Fig. 4 Spatial pattern correlation of AGCM20 monthly
mean precipitation to the AMeDAS observation.
Summer season shows lower spatial pattern matches.

Based on the evaluation results on the controlled
simulation, it was able to understand that the modelshows considerably good performance and provide
reliable precipitation output, even though it has
some limitations in some aspects. In this section, theoverall analysis on the projection simulation results
is given using the near-future and future term outputof the AGCM20 model. Analysis on water resources
is firstly given, and then analysis on the extreme
event is provided.
(1) Seasonal Pattern
Annual mean precipitation amount of the nearfuture term and future term were calculated andchanging ratios of those amount compared to thepresent term output are shown Fig. 5(a) and 5(b)with percentage unit. In the near-future term, mostregions in Japan show increased annual precipitation
amount and this increasing will be more apparent in
the future term. The most significant changes are
there in Hokkaido with 6.1 % and 10.6% of increasein the near-future and future term, respectively. The
Kanto region is showing rather mild changes with
1.8% and 2.0% of increases in the respected terms.

- 129 -

Fig. 5(a) Annual precipitation amount changes in near
future compared to present (both are AGCM20).

Fig. 5(b) Annual precipitation amount changes in futurecompared to present (both are AGCM20).

Fig. 6(a) Summer (Jul~Sep) rainfall amount changes innear future compared to present.

Fig. 6(b) Summer (Jul~Sep) rainfall amount changes infuture compared to present.

Fig. 7(a) Snowfall amount changes in near futurecompared to present.

Fig. 7(b) Snowfall amount changes in future compared topresent.

Summer season rainfall amount during July and
September (see Fig. 6(a) and 6(b)) shows more
severe changes, but with similar spatial pattern of
the annual mean precipitation changes. Wintersnowfall amount is going to be decreased according
to the AGCM20 projection simulation output (seeFig. 7(a) and 7(b)). Snowfall amount in the regions
between Kyushu and Kansai becomes negligible in
the near-future, and most regions in Japan, except
Hokkaido, show significant decrease of snowfall in
the next century. Hokkaido area also shows somedecreased winter snowfall (17.8% of decrease in the
future term, however, some inland area of Hokkaido
shows increased snowfall due to increased water
vapor in the atmosphere.
Not only for the precipitation, but also evapotranspiration loss was examined using the AGCM20
output data. The model provides the information of
evaporation and transpiration amount from the soil
layer. According to this information, net-waterresources amount of each region were estimated andpresented in Table 1. Many previous studies havepredicted that climate change would affect thehydrologic cycle in a way of accelerating the current
water cycle with more precipitation and more
evapo-transpiration8). AGCM20 output for thenear-future and the future term also show this kindof water cycle change in a manner of increased
precipitation and evapo-transpiration as well.

Table 1. Net-water-resources amount in each region(annual amount averaging 25 years)
RegionHokkaido
Tohoku
Kanto
Chubu
Kinki
Shikoku
Chugoku
KyushuJapan

Present

NearFuture

Future(unit: mm)(changes: %)(changes: %)

607.6

648.2
( 6.68)838.0( 0.43)1043.7(0.50)
1369.0( 0.71)1180.1( 0.58)1256.5(-0.91)1091.4( 4.80)1264.8( 0.14)1027.25(1.70)

645.2( 6.19)789.7(-5.72)1002.0(-3.51)1296.7(-4.61)1152.7(-1.76)1254.3(-1.09)1063.8( 2.15)1290.9( 2.20)997.79(-1.22)834.4
1038.51359.41173.41268.11041.41263.11010.1

As a result, available fresh water resources insome regions of Japan, such as Chubu, Kanto andTohoku is expecting 3~5% of water resources
decrease. Drought index investigation in the Tone
River basin also showed that the area has higher
possibility of mild but long duration of water
shortages in the future5).

- 130 -

Fig. 8(a) Regression coefficients of near future to present
using 100 daily maximum values of each grid.

Fig. 8(b) Regression coefficients of future to present
using 100 daily maximum values of each grid.

Fig. 9(a) Regression coefficients of near future to present
using 100 hourly maximum values of each grid.

Fig. 9(b) Regression coefficients of future to present
using 100 hourly maximum values of each grid.

(2) Extreme Events
To understand the possible changes in extreme
event in the future, the regression analysis method,which was introduced in the section 2.2, is utilizedagain using the near-future (future) term maximum
precipitation and the present term maximum values.
Regression coefficient of 100 (= 4maximum×
25yrs) daily (hourly) maximum values from thenear-future (future) and the present term on eachgrid was calculated and presented in Fig. 8 (Fig. 9).
From the results, it is able to see that the changeof extreme events of future is showing regional
dependency. Chugoku and Shikoku region shows
apparent increase of daily and hourly maximum inthe near-future and the future term as well. Also
Hokkaido shows increased maximum events in the
future. However, Kanto and some part of Tohoku
region shows decreased maximum events in bothnear-future and future term. Analysis on the Tone
River basin using the river discharge, which wasestimated using the AGCM20 data through a
distributed hydrologic model, was also showed
mildly decreased peak flow in the future9).
However, it should be noted here that the
maximum data is highly affected by several extreme
conditions within the analyzing durations. Even
though the regression coefficient analyzing method
was introduced to avoid undesirable effect byseveral abnormally extreme events, such as typhoon
events, still it is not confident enough to decide
whether the analysis results are expressing
generalized changing pattern of future extreme
values. Understanding that the model provides justone possible climate behavior under the designed
climate change scenario, it is highly desirable tohandle several GCM or ensemble simulation output
to achieve more confident extreme event analysis.

4. UNDERSTANDING AGCM20 OUTPUT(1) Scale Variant Reproducibility
GCM output becomes more valuable information
to hydrologists when the atmospheric data isconverted into river discharge data through areasonable hydrologic model. The presenting
AGCM20 output was converted into river discharge
data in the previous study of Kim et al.4)9), andsurveyed water resources change in the Tone Riverbasin considering dam reservoir operation. In the
study, severe bias of AGCM20 was a big problem in
the Yagisawa dam basin (167 km2 of basin area).
However, the bias level was alleviated when theanalyzing area is getting larger. As shown in Fig.
10, simulated discharge for the Yattajima gauging
point (covering basin area is 5,134 km2) in the Tone
River basin shows good coincide with the observed
discharges. Considering that the covering area of
one grid of the AGCM20 output is around 400 km2,
it is able to understand that the AGCM20 output (atleast the precipitation output) shows highreproducibility when it is analyzed on a large basin
scale. More details on the scale variant
reproducibility are given in Kim et al.9).

- 131 -

Fig. 10 Observed river discharge (25 years average) at
the Yattajima st. of the Tone River and simulated
discharge using the AGCM20 controlled run output

analysis, future extreme events seem to be increasedin most regions except some inland part of Kanto.
Spatial scale dependency of the model
performance and drawbacks due to 20-km spatiallyaveraged topographic information in the model arealso discussed. Further research is under processing
to achieve complete understanding on the model
performance related to the sub-scale orographic
effects in the AGCM20.
Fig. 11 Summer season rainfall amount (mean of 25
years) in Kyushu, corresponding to 20-km gridelevation. AGCM20 controlled run output was notable to show high correlation with elevation.

(2) Smoothen Orographic Effects
Orographic effect and elevation dependency of
precipitation is well known phenomena, especiallyin mountainous area of Japan10),11). As it was alreadyexplained in section 2, the AGCM20 output shows alimitation to simulate clear orographic effects
mainly due to its 20-km resolution topographic data.
As another proof of this limitation, the AGCM20
precipitation output shows low dependency on
topographic elevation, as shown in Fig. 11. The
figure shows mean rainfall amount during summer
season (July~ September) in Kyushu, corresponding
to the elevation information of 20-km grid in the
AGCM20. While the AMeDAS observed rainfall
amount shows clear dependency on elevation (eventhough it is spatially averaged within the 20-kmgrid), the AGCM20 output was not able to show
reasonable elevation dependency of precipitation.
One who wants to carry out hydrologic impact
analysis using the AGCM20 output on a certain
mountainous
area
shouldconsiderthischaracteristics in his/her analysis.

5. CONCLUING REMARKSThis paper provides analysis results on the
precipitation output from the AGCM20. The
controlled run output was firstly evaluated using theAMeDAS observed data, and reproducibility of themodel was discussed in two aspects, seasonal andspatial pattern of precipitation and extreme events.
The model performance was generally good and it
provides reasonable output, and thus it is goodenough to understand future precipitation changes.
Changes of future precipitation pattern show
slight increases (2~5%) of annual precipitation andmore increases in summer season. Evapotranspitation is also likely to be increased in the
future, thus net-water-resources in most regions,
except Kyushu and Hokkaido, is going to be
decreased down to -5.7%. A simple method using a
regression coefficient was introduced to understandoverall extreme event patterns. According to the

ACKNOWLEDGEMENTS
This work was done under the framework of the"Integrated assessment of climate change impacts onwatersheds in a disaster environment (PI: Prof.
Nakakita at DPRI, Kyoto Univ.)" supported by
KAKUSHIN Program of MEXT.
REFERENCES

1) Mizuta, R., Oouchi, K., Yoshimura, H., Noda, A., Katayama,
K., Yukimoto, S., Hosaka, M., Kusunoki, S., Kawai, H. andNakagawa, M.: 20-km-mesh global climate simulations
using JMA-GSM model -Mean climate states-, J. of the
Meteorological Soc. of Japan., Vol. 84, pp. 165–185, 2006.
2) Kitoh, A. and Kusunoki, S.: East Asian summer monsoon
simulation by a 20-km mesh AGCM, Climate Dynamics,
DOI 10.1007/s00382-007-0285-2, 2007.
3) Kusunoki, S. and Mizuta, R.: Future Changes in the Baiu
Rain Band Projected by a 20-km Mesh Global Atmospheric
Model: Sea Surface Temperature Dependance, Scientific
Online Letters on the Atmosphere (SOLA), The
Meteorological Society of Japan, 4, pp. 85-88, 2008.
4) Kim, S., Tachikawa, Y., Nakakita, E., and Takara, K.:
Reconsideration of Reservoir Operations under Climate
Change: Case Study with Yagisawa Dam, Japan, Annual
Journal of Hydraulic Engineering, JSCE, Vol. 53, pp.
597-611, 2009.
5) Takara, K., Kim, S., Tachikawa, Y., and Nakakita, E.:
Assessing Climate Change Impact on Water Resources in
the Tone River Basin, Japan, Using Super-High-Resolution
Atmospheric Model Output, Journal of Disaster Research,
Vol. 4, No. 1, pp. 12-23, 2009.
6) Rayner, N.A., Parker, D.E., Horton, E.B., Folland, C.K.,
Alexander, L.V., Rowell, D.P., Kent, E.C., and Kaplan, A.:
Global analyses of sea surface temperature, sea ice, andnight marine air temperature since the late nineteenth
century, Journal of Geophysical Research, 108, No. D14,
4407, doi:10.1029/2002JD002670, 2003.
7) Nakicenovic, N., Alcamo, J., Davis,G., deVries, B., Fenhann,J., Gaffin, S., Gregory, K., Grübler, A. et al.: Special Reporton Emissions Scenarios, Working Group III, IPCC,
Cambridge University Press, Cambridge, 2000.
8) Milly, P.C.D., Dunne, K.A. and Vecchia, A.V.: Global
pattern of trends in streamflow and water availability in achanging climate, Nature, 438, pp. 347-350, 2005.
9) Kim, S., Tachikawa, Y., Nakakita, E., and Takara, K.:
Climate Change Impacts on Water Resources Managementin the Tone River Basin, Annuals of Disaster Prevention
Research Institute, Kyoto Univ., no. 52B, 2009 (in printing).
10) Nakakita, E., Suzuki, Y., and Ikebuchi, S.: Hierarchicaltime-scale structure in the dependence of rainfall
distribution on topography, J. Hydrosci Hydraul Eng., Vol.
19, no.2, pp.1-10, 2001.
11) Oki, T., Musiake, K. and Koike T.: Spatial Rainfall
Distribution at a Storm Event in Mountainous Regions,
Estimated by Orography and Wind Direction, Water
Resources Research., Vol. 27, no. 3, pp. 359–369, 1991.

- 132 -

(Received September 30, 2009)