筑波大学陸域環境研究センター報告, No.10, 51 〜 60, 2009

ダイナミカルダウンスケール手法による
過去 20 年の気候再現性及び冬季積雪量予測の評価
Reproducibility of Past 20 Years Climate Using Dynamical Downscaling Method andFuture Prediction of Snow Cover in Winter足立

幸穂 *・木村富士男 *・田中美紀 **Sachiho A. ADACHI*, Fujio KIMURA* and Miki TANAKA**
Abstract
This study conducted dynamical downscaling for Japan using a regional atmosphericmodel (TERC-RAMS) to create the spatially detailed meteorological data for the impact
assessment of global warming on the surrounding fields including farming andhydrological cycle. In the first half of this paper, the downscaling for past 20-years climate
was conducted and compared with the observational data. The simulated temperature
was higher (lower) than the observed one in summer (winter) season, although the bias oftemperature in most areas was less than 1℃ throughout year. Precipitation calculated by
the model tended to overestimate, except for the summer rainfall in Kyushu and Okinawa.
However, the simulated climate by the model was able to reproduce the past climate. In
the second half, the snow cover change in 2070s was estimated by using the pseudo global
warming method with regard to the low and high snow-cover years. The model results
showed that the snow cover decreased over a large area. The snow cover in the low snowcover year remained only in a part of Hokkaido. The snow cover in the high snow-coveryear was limited in the regions with an altitude higher than 500 m. This result agrees withthat of Hara et al. (2008). This study indicates that TERC-RAMS is available to predict

inter-annual variation of snow cover. However, the results suggest that the simulation on
the coarser horizontal resolution tends to underestimate the amount of snow cover andoverestimate the impact of global warming on snow cover change.
要旨

本研究は，将来の気候変化が農業や水循環に及ぼす影響を評価するための空間詳細な気象データを作成するため，日本域を対象として領域気候モデル（TERC-RAMS）を用いた力学的ダウンスケールを行った．論文の前半では，過去 20 年の気候を対象としたダウ*

筑波大学大学院生命環境科学研究科**

筑波大学大学院生命環境科学研究科大学院生− 51 −

ンスケール実験を行い，観測データとの比較を行った．夏季（冬季）の気温は観測に比べ高温（低温）バイアスであるが，多くの地域で気温バイアスは 1℃以内であった．モデル
で再現された降水量は九州と沖縄の夏季を除き，過大評価する傾向にあった．しかしながら，モデルは過去の気候を比較的よく再現出来ることが確認された．
論文後半では，疑似温暖化手法を用いて，2070 年代の積雪量変化を評価した．積雪は広い範囲で減少がみられ，2070 年代の少雪年は北海道を除くほとんどの地域で積雪が見られなかった．多雪年でも積雪は標高 500 m 以上の地域に限定される．この結果は Haraet al.（2008）と一致するものの，TERC-RAMS は積雪量の年々変動は再現できるが，空間解像度が 20 km と粗いため積雪量を少なく見積もる傾向があることが示された．

Introduction

to evaluate crop productivity in future climate

Temperature rising following the increase of

the resolution of GCM is mainly 250 km. The

anthropogenic green house gases was observed

second one is that the important variables forall over the world. The Intergovernmental Panelthe assessment fields are not always provided inon Climate Change (IPCC)'s 'Fourth Assessment

appropriate frequency. This is because the saving

Report (AR4)' shows the results of future climate

frequency is not enough and the saved variables

projections estimated using general circulation

are limited due to storage limitation, since themodels (GCMs), based on several future emission

GCM output needs too large content to save. In

scenarios involving greenhouse gases and aerosol

such a case, impact assessment researcher must

precursor (IPCC, 2007). Most of them indicatedestimate the needed value using another valuable.

Ⅰ(Iizumi et al., 2008 ; Okada et al., 2009 ), while

Dynamical downscaling and statistical

the global warming trend would continue.
Surface temperature had increased at rate ofdownscaling methods are utilized as methods for

1.11℃ per 100 years, during the 111 years frombridging the gap between GCM and the impact

1898 to 2008 in Japan (JMA, 2008). It is suspectedassessment study. Both are the methods forthat the global warming causes not only aestimating spacial and temporal high resolution

higher frequency of extremely hot days and adata from the coarse resolution data of GCM.

change in the distribution of precipitation, but

Our study carried out dynamical downscaling

also influences on farming, fishing and forestry.

simulation with a regional climate model, in order

Recently, a lot of papers have reported on theto create detailed meteorological data in the future

impacts of global warming on surrounding

for Japan. We will evaluate the reproducibility offields, such as those mentioned above, using

past climate in the simulation in section III. Then,future climate data projected by GCMs. However,

we perform the future climate simulation of

the following problems with this approachsnowfall and snow cover, which are important as

are worth noting. The first one is a scale gap

water resource, in 2070s and discuss the projectedin spacial resolution between GCM and thechange of distribution of snow cover.

impact assessment. For example, climate datawith at least 1km or 10km resolution is needed− 52 −

Ⅱ

Tremback and Kessler (1985) and the vegetation

Data and methodmodel constructed by Avissar and Pielke (1989).

1. Dynamical downscaling simulation with using a
regional climate model

The calculation of longwave and shortwave
radiation was done by following the Nakajima

The Terrestrial Environment Research Centerradiation scheme (Nakajima et al., 2000).

(TERC) Regional Atmospheric Modeling System

Numerical simulations were conducted for(RAMS) (Sato et al., 2007 ; Inoue and Kimura,two cases listed in Table 2. First case involves

2007 ) was adopted for the climate simulation.

The original RAMS was developed by Pielke etal. (1992). Model settings are described in Table 1.

The model domain has 130 x 140 grids with a 20
km horizontal interval, and covers the while ofJapan as shown in Fig. 1. The vertical grid systemis terrain following coordinate system, which has

30 layers with depth of 65 m at the lowest layerand stretching depth at maximum of 1100 m.
Arakawa-Schubert convective parameterization(Arakawa and Schubert, 1974) and microphysics

parameterization (Walko et al., 1995) were used

to calculate precipitation. Fluxes between air andland at ground surface were evaluated by Louis(1979). Soil and vegetation temperature, moisture
are calculated by the soil model developed by

Fig. 1

Calculation domain. Horizontal grid
number is 130 x 140 grids with a 20 km
horizontal interval. The inside squareindicates the illustrated area in Figs. 3-5.

Table 1 Description of regional climate model
Horizontal grid

Vertical grid
Soil layers
Vegetation type
Soil texture
Sea surface temperature

130 x 140 grids
Center coordinate 137.5°E, 36.0°N
20 km horizontal resolution
30 layers with 65 m thickness in lowest layer,
maximum thickness is 1100 m
0.00, 0.02, 0.11, 0.18, 0.30, 0.50, 0.70, 0.90, 1.80, 2.50,
2.75 m below ground
Tall grass
Silt loam
10 days mean SST of JRA25

Table 2 List of numerical experiments
Run name
CTL20
Hindcast using reanalysis data (JRA25/JCDAS)
PGW-LS Pseudo global warming experiment in low snow-cover year
PGW-HS Pseudo global warming experiment in high snow-cover year

− 53 −

Calculation Period
1985-2004
1993
2000

a 20 -year present climate simulation (CTL 20 )from January 1979 to December 2004. Japanese

future emission scenarios in IPCC Special Reporton Emissions Scenarios (SRES). The scenario

25-year ReAnalysis (JRA25)/JMA Climate Data

assumes that social economy will develop under

Assimilation System (JCDAS) (hereafter JRA

the concept of self-reliance and preservation

together) was used for initial and boundaryof local identities. Fou r dimensional data

conditions (Onogi et al., 2007 ). Atmosphericassimilation by the newtonian relaxation methodb o u n d a r y d a t a w a s g i ve n b y a 6 - h o u rl yinterval with 1.25 x 1.25 horizontal resolution,

including variables of: RH (relative humidity), T(temperature), U (the x-component of velocity),V (the y-component), and Z (elevation). Sea
surface temperature (SST) on T 106 Gaussian

coordinate was converted to 1.25 x 1.25 lat/lon

coordinate and averaged over 10 days. During thesimulation, SST was replaced in an interval of

10 days to next one. The 20-year simulation was

calculated by 60 time-slice experiments. Each

was applied to all experiments to avoid the biasof calculated variables in a regional climate
model. The outermost 8 grids were nudged witha 10 minute time constant, while the inner areaused the weak nudging time constant of 5 days.

2. Validation tool for model results
The evaluation tool for past climate
experiments was developed by Tanaka (2008).
The tool calculates model biases of temperatureand precipitation on every prefecture or river-simulation period was 6 months; from November

system basis. That enables us to check the model

to April, from March to August, and from Julybiases as mosaic map. The observation data

to December. The first two months are a spin-up

provided by the Automated Meteorological Data

period and the last four months are analyzed.

Acquisition System (AMeDAS), distributed with

The second experiment is a future climate

an interval of about 17 km throughout Japan,

simulation. In this study, the Pseudo Global

was used as an evaluation data. In the first stage

Warming (PGW) downscale method was adoptedof the tool, the AMeDAS station located in each

(Kimura and Kitoh, 2007; Sato et al., 2007; Kawase

model grid is detected. If several AMeDAS

et al., 2008 ) instead of the direct downscaling

stations are found in a certain grid, the averagemethod. The difference between the two methodsof the usable data except for missing data isrelates to how they provide the boundary

defined as the evaluation data. When there iscondition. The PGW data is obtained by theno observation point in a model grid, the grid isreanalysis data adding the difference between the

excluded from the validation process.

monthly mean of future climate in the 2070s andthat of present climate in the 1990s simulated by

In the second stage, the model biases arecalculated. From both model and observation

GCM. The climate data used in this study wasdata, the 20 year means of monthly temperature

gained from the MIROC-medres output following

and monthly accumulated precipitation are

the A 2 scenario, provided from the Wo rld

calculated, when both data are available. The

Climate Research Programs (WCRP) Coupledmodel temperature is corrected for the difference

Model Intercomparison Project (CMIP 3) multi-

of elevation from the observation point. The

model dataset. The A 2 scenario is one of thebias of temperature is defined as the difference− 54 −

between the monthly mean temperature simulated

has negative bias, while the one in summer showsby model and the one provided from actual

positive bias. However, the biases in most areas

observation. The bias of precipitation is a ratio ofare limited to 1℃, except for June, November, andthe monthly accumulated precipitation calculated

December (Fig. 2a). There are higher temperatureby the model to one provided via observation.

biases in Hokkaido and Tohoku regions inJanuary and February. The reason presumed for

Ⅲ Reproducibility of present climate

this is that the model weakly estimates the effectof radiation cooling enhanced by snow cover.

The biases of 20 year means of simulated

The bias of precipitation is indicated intemperature and precipitation are shown in Fig. 2.

Fig. 2 b. The color shows the ratio of model

The prefecture with the negative bias more thanto observation. White and light gray mean

− 1 and − 0 . 1 ℃ are shown by white and lightunderestimate , while g ray and dark g ray

gray, respectively, while one with the positive bias

indicate overestimate. The TERC-RAMS tends tomore than 0.1 and 1℃ are indicated by gray andunderestimate precipitation in Shikoku, Kyushu,

dark gray, respectively. The temperature in winterand Okinawa in Baiu-summer season, that isFig. 2 Biases of 20 year means of (a) simulated temperature and (b) simulated precipitation by
TERC-RAMS. The bias of temperature is defined as the difference between the monthly
mean temperature simulated by model and the one provided from observation. The biasof precipitation is a ratio of the monthly accumulated precipitation of model to the one
provided from observation.

− 55 −

eguivalent to about half of the observations. Thisestimated by model was able to reproduce theis because the model reproduces a relatively small

present climate, although the simulated results

amount of rain associated with the baiu raininclude the biases described above.

band and typhoons.
The temperature bias has seasonal dependence,
however the dependence of the bias on prefectureⅣ

Future prediction of winter snow
cover change

is small. In addition, the precipitation bias issmall throughout Japan. Thus, the climatology

Fig. 3 shows the observed and simulated

Fig. 3 Observed and simulated snow cover at 24 JST on 28th February in the low snow-coveryear (1993) and the high-snow cover year (2000); (a) and (b) observed by AMeDAS and (c)and (d) simulated by TERC-RAMS.

− 56 −

snow cover at 24 JST on 28th February in thewas distributed from Hokkaido to the Chugoku

low snow-cover year (1993) and the high snow-

region, while the areas with snow cover of moresnow-cover year was 1 〜 1.5℃ higher than the

Tohoku, and Hokuriku (Fig. 3a). In the high snow-

cover year ( 2000 ). The temperature in the low

one in the high snow-cover year (Fig. 4). Snow

than 100 cm are limited to part of Hokkaido,

cover year, the area with snow cover of more thancover depth of model was calculated from the

100 cm is widely distributed in the Sea of Japanwater equivalent of the snow cover under theside. The snow cover evaluated by the model isassumption that snow cover density is 300

largely underestimated compared to AMeDAS,

kg/m . In the low snow-cover year, snow coverboth in low and high snow-cover years. This is3

Fig. 4 Seasonal averaged temperature in DJF in the low snow-cover year (1993) and the high snowcover year (2000); (a) and (b) observed by AMeDAS and (c) and (d) simulated by TERC-RAMS.
The plus signs in (a) and (b) indicate the stations with temperature more than 4℃.

− 57 −

because the 20km horizontal resolution of RAMS

equals snow cover change in the 2070s compared

has a lower peak of elevation and cannot expresswith the 1990 s. A decrease of snow cover isdetailed topography. The smooth topography

detected over a large area. The snow cover ofmakes the ratio of snow to rain decrease and

the PGW-LS run (Fig. 5a) remains only in a partthe snow more soluble. However, RAMS canof Hokkaido. The PGW-HS run indicates snow

reproduce the characteristics of interannual

cover in the high snow-cover year is distributed

variation in each year.

in Hokkaido, Aomori and Hokuriku, although

The difference of snow cover between the
CTL run and PGW run is shown in Fig. 5, whichsnow cover in Honshu island is less than 10 cm

and decreases about 50 cm from the CTL run.

Fig. 5 Snow cover in future climate of 2070s simulated in (a) PGW-LS, (b) PGW-HS, and snow
cover change in (c) the low snow-cover year and (d) high snow-cover year.

− 58 −

The areas with snow cover in the 2070s (Figs. 5a

impact of global warming on snow cover change.

higher than 500 m. This result agrees with that of

Acknowledgments

warming on the amount of snow cover (snowThis study was supported by the Global

cover change from the CTL run to the PGW run)

Environment Research Fund (S- 5 - 3 ) of theand 5b) are confined to regions with an altitude

Hara et al. (2008). However, the impact of global

is extremely large compared to Hara et al. (2008).

Ministr y of the Environment, J ap an. The

It is speculated that coarser horizontal resolution

datasets used for this study are provided from theevaluates smaller snowfall, and smoothercooperative research project of the JRA-25 long-

topography enhances melting of accumulated

term reanalysis by Japan Meteorological Agency

snow.

（JMA) and Central Research Institute of ElectricPower Industry（CRIEPI).

Ⅴ

Conclusion
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