101

Improvements to Statistical Climate Downscaling Simulations by
Incorporating the APHRO_JP Advanced
Gridded Daily Precipitation Dataset
Toshichika IIZUMI* and Motoki NISHIMORI
Agro-Meteorology Division, National Institute for Agro-Environmental Sciences,3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan*e-mail: iizumit@affrc.go.jp

Abstract
In this study, we compared the daily precipitation indices derived from two types of statistical downscaling simulations in Japan. One simulation was conducted by applying a cumulative distribution function-based downscaling method (CDFDM) to a state-of-the-art gridded daily precipitation dataset for Japan,
APHRO_JP (Kamiguchi et al., 2010). The other simulation was conducted by applying the same downscaling
method to a simple dataset, namely a set of linearly interpolated rain gauge data. We then highlighted theadded values for the coarse-resolution general circulation model (GCM) outputs achieved by theincorporation of the APHRO_JP dataset into the statistical downscaling simulations, relative to thesimulations using the simple dataset. The evaluated indices were mean precipitation, number of wet days,
90th percentile of daily precipitation, and maximum number of consecutive dry days. A comparison of thetwo approaches shows that the relative improvements achieved using the APHRO_JP dataset vs. the simple
dataset are most pronounced for mean precipitation in mountainous areas, precipitation frequency on theleeward side of mountains, and intensity and frequency of heavy precipitation. These relative improvements
over the regional climate change scenarios derived from the CDFDM demonstrate a significant benefit fromcombining advanced gridded observation datasets with statistical downscaling methods. On a regional scale,the bias of the daily precipitation indices for the simple dataset, relative to the APHRO_JP dataset, isoccasionally comparable in amplitude to the projected change. This finding suggests the importance of
higher-quality gridded observation datasets in assessing climate impacts in various fields, particularly,hydrological regimes, irrigation planning, and risk assessments of water-related disasters.
Key words: climate change scenario, gridded dataset, precipitation extremes, statistical downscaling method

1. Introduction
High-resolution climate change scenarios are essential to project possible impacts of future climate change
on various sectors and in examining potential adaptation
practices on a regional scale. Statistical downscaling
methods (SDMs) are frequently used to generatespatially-fine and less biased climate data from coarseresolution general circulation model (GCM) outputs
and/or regional climate model (RCM) outputs (e.g.,
Mearns et al., 2003; Semenov et al., 2010; Iizumi et al.,2011; Sharma et al., 2011).
Statistical downscaling methods are techniques for
estimating the local conditions of climate elements at a
station from the large-scale conditions of the atmosphere,
as represented by GCM outputs using empirical
relationships established from long-term historical data
(Wilby et al., 1998). A few decades of quality-controlled
Global Environmental Research
15/2011: 101-108
printed in Japanobservations are required for using SDMs to calibrate
statistical models. The quality of climate data derived
from SDMs, especially precipitation extremes, is sensitive to the observed data used for statistical model
calibration. An additional well-known limitation of
SDMs is that the spatial resolution and the coverage ofclimate data derived from these methods depend on thedensity of observation networks, as SDMs typicallygenerate climate data station by station (Wilby et al.,2004).
For these reasons, high-quality and high-resolution
gridded daily precipitation datasets are called for. Suchdatasets can be obtained by applying advanced spatial
interpolation methods to accurate long-term observation
data. Recently, a gridded daily precipitation dataset witha grid interval of 0.05° has been released for Japan(APHRO_JP; Kamiguchi et al., 2010). This dataset isbased on gauge data collected at over 1300 Automated©2011 AIRIES102

T. IIZUMI & M. NISHIMORI

Meteorological Data Acquisition System (AMeDAS)
stations. The data are further refined using a combinationof spatial interpolation methods to reduce the estimation
error in mountainous areas and in frequency of heavy
precipitation.
In this study, we compared the daily precipitation
indices in the present-day and future climate conditions
derived from the two types of statistical downscaling
simulations in Japan: one simulation was achieved byapplying the cumulative distribution function-based
downscaling method (CDFDM; Iizumi et al., 2010;2011) to the state-of-the-art gridded dataset APHRO_JP,
and the other simulation was conducted by applying thesame downscaling method to the ‘simple’ gridded daily
precipitation dataset, that is, the linearly interpolated
AMeDAS data. We then highlighted the added values forthe coarse-resolution GCM outputs achieved by theincorporation of the APHRO_JP dataset into the statistical downscaling simulations, through a comparison
between the simulations using the two types of observation datasets.

2. Data and Methods
2.1 Observed daily precipitation datasets
The AMeDAS used for this study include a dense
gauge observation network, of which the average distance between the stations was 17 km. For the statisticalmodel calibration, we used two types of gridded daily
precipitation datasets over Japan, the linearly interpolated AMeDAS (simple) dataset and the APHRO_JP
dataset. The simple dataset, with a grid interval of 0.2°,
was provided by applying the inverse distance weighted
average method to the daily precipitation data collectedfrom 742 AMeDAS stations. These stations are lessdensely arrayed in the mountainous areas of Japan(Fig. 1). Stations to be used were selected from over
1,300 AMeDAS stations for which less than 20% of thedata were missing for every month during the 20-year
study period (1985-2004) to avoid errors in estimating
the daily precipitation indices. The average horizontal
distance between selected stations was over 25 km. The
precipitation value at a grid point was calculated from theprecipitation values at eight neighboring stations (and notfrom the ratio of the precipitation value to the climatological mean). Orographic effects on precipitation were
not considered in the spatial interpolation, and very small
amounts of interpolated precipitation were not truncated.
For these reasons, the AMeDAS dataset is referred to as
the ‘simple’ dataset.
The APHRO_JP dataset is a quality-controlled,
high-resolution gridded daily precipitation dataset forJapan that covers the more than 100-year period since
1900 (Kamiguchi et al., 2010). The APHRO_JP dataset
was obtained by applying a combination of spatial
interpolation methods, i.e., the Spheremap and mountainmapper methods (Willmott et al., 1985; Schaake et al.,2004), to the gauge data of a dense network of observation stations while considering the effects of orography

Fig. 1 AMeDAS stations used for providing the simple dataset(see Section 2.1; black dots). Areas are indicated bydifferent colors: North Japan/Japan Sea side (NJ); North
Japan/Pacific side (NP); East Japan/Japan Sea side (EJ);
East Japan/Pacific side (EP); West Japan/Japan Sea side
(WJ); and West Japan/Pacific side (WP). Altitudes areindicated by red lines spaced every 500 meters.

and network bias on precipitation in mountainous areas.
For the time period analyzed in this study (1985-2004),
the APHRO_JP dataset includes gauge data from surface
observation stations of the Japan Meteorological Agency(JMA) (the number of stations varied by year up to a
maximum of 158) as well as from over 1,300 AMeDAS
stations. Thus, the APHRO_JP dataset utilized a substantially larger amount of data than the simple dataset.
The quality of daily precipitation data was substantiallycontrolled, based on the system developed in the
APHRODITE project (Asian Precipitation - HighlyResolved Observational Data Integration Towards
Evaluation of Water Resources; available at http://www.
chikyu.ac.jp/precip/). In this study, the APHRO_JP data
were spatially aggregated to a grid interval of 0.2° fromthe original interval of 0.05° for consistent comparison
with data from the simple dataset.
2.2 Statistical downscaling method
For this study, we used the CDFDM (Iizumi et al.,2010; 2011), which is a bias correction-type statistical
downscaling method. Bias correction-type SDMs estimate the condition of a climate element at a station
through a statistical bias correction of the same climate
element derived from a GCM. These methods are generally simpler than ‘sophisticated’ statistical downscaling
methods (e.g., weather typing methods, regression-type
methods, and weather generator techniques; Wilby et al.,2004). Due to this simplicity, there is some controversy
as to whether bias correction-type SDMs fall into theStatistical Downscaling Methods and a Gridded Dataset

category of statistical downscaling methods (seeSchmidli et al., 2006; Iizumi et al., 2011). However,these methods certainly bridge the coarse-resolution
GCM outputs and climate inputs required for impact
models. Therefore, in this study, we consider bias
correction-type methods to be statistical downscaling
methods in a broader sense.
The CDFDM uses daily precipitation data from a
GCM (or reanalysis data) as the predictor of local
precipitation. In the CDFDM algorithm, the GCM error
is defined for each position of cumulative distribution, F,
on the basis of the empirical cumulative distributionfunctions (CDFs) of daily GCM outputs and observed
data for the long-term calibration period. The defined
GCM error (referred to as the F-e relationship) is thenremoved from the empirical CDF of daily GCM outputs
for a downscaling period, with the assumption that theF-e relationship does not change with time. This assumption is similar to the stationarity assumption used inother SDMs. Compared to quantile-quantile mapping(Wood et al., 2004), which is a CDF-based statistical bias
correction method, the CDFDM method can incorporatechanges in maximum precipitation values on the basis of
GCM projections. In contrast, the maximum precipitation values derived from quantile-quantile mapping areconstrained to be equal to those of the existing observations, even in altered climate conditions.
In this study, the F-e relationship was provided forwarm (May-October) and cold (November-April) seasons, respectively, after confirming the results in which
the 6-month periods produced a slightly better representation of daily precipitation indices than other time
periods (monthly and yearly). We applied the CDFDM toeach grid point of the re-gridded APHRO_JP dataset andsimple one, respectively. The statistical downscaling
simulations of daily precipitation were performed using
the high-resolution version of the Model for Interdisciplinary Research On Climate version 3.2 (K-1 model
developers, 2004; referred to as MIROCHI) outputs
under the 20th Century Climate in Coupled Model(20C3M) experiment and the Special Report on Emissions Scenarios (SRES: Nakicenovic & Swart, 2000)
A1B experiment. The downscaling period corresponded
to two 20-year periods, i.e., 1981-2000 and 2081-2100
for the 20C3M and A1B downscaling simulation,
respectively. The calibration period was commonly set
for 1985-2004 for the two simulations, although there
was a slight discrepancy between the calibration anddownscaling periods for the 20C3M experiment. We
believe the error associated with this discrepancy to besmall.
2.3 Daily precipitation indices
Four types of diagnostics of daily precipitation (called
daily precipitation indices) listed in Table 1 were evaluated. Namely, (1) mean precipitation, MEA; (2) number
of wet days, FRE; (3) 90th percentile of daily precipitation, Q90; and (4) maximum number of consecutive dry
days, CDD. A wet day was defined as a day with

103

Table 1 Diagnostics of daily precipitation (daily precipitation
indices) used in this study.
Index
MEA
FRE
Q90
CDD

Description
Mean precipitation
Number of wet days (≥1 mm/day)90th percentile of daily precipitation
Maximum number of consecutive dry days(< 1 mm/day)Unit
mm/dayfraction
mm/daydays

precipitation ≥1 mm/day. All indices except for Q90
were calculated for each month and the index values forthree months were averaged to obtain a seasonal value.
The calculated seasonal value was averaged for the
20 years and used in the analysis. We used a simple
approach to calculate Q90. In practice, Q90 was calculated for each month from the empirical CDF of daily
precipitation, consisting of 28-31 days x 20 years. No
fitting of a parametric probability distribution was conducted to avoid any fitting errors. Seasonal mean Q90
values were calculated by averaging monthly Q90 values
for the corresponding months.

3. Results
3.1 Bias of the simple dataset
Figure 2 shows the geographical pattern of mean
winter (December, January, and February; DJF) andsummer (June, July, and August; JJA) precipitation(MEA) calculated from the two types of observation
datasets and of the GCM outputs downscaled by the
CDFDM using each of the observation datasets. The two
types of observation datasets have comparable geographic patterns (Fig. 2a-b). However, compared to the
APHRO_JP dataset, the simple dataset underestimatedthe mean winter precipitation by over 10% on the northwest (windward) side of the mountains facing the Sea ofJapan and overestimated it by over 10% on the southeast(leeward) side of the mountains (Fig. 2c). The simple
dataset also underestimated the mean summer precipitation in the mountains and overestimated it in the basins
and on the plains (Fig. 2i). On the other hand, comparedto the APHRO_JP dataset, the simple dataset overestimated the number of wet days (FRE) on the southeast(leeward) side of the mountains facing the Pacific Oceanin winter (Fig. 3c). A similar geographical pattern of bias
for the simple dataset was found for Q90 and CDD(figures not shown).
These differences between the two observation datasets were attributed to the following factors. In the simple
dataset, the maximum value of daily precipitation in themountains is constrained by the observed values at
nearby stations that are often located in the plains: theresult is a lower value of daily precipitation on the windward side of the mountains for the simple dataset. In
addition, the simple dataset spatially interpolated precipitation events in the plains to the leeward side of themountains without accounting for the mountains’ rain
shadows: this causes more frequent precipitation events
to be reported in such areas. We therefore emphasize that

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T. IIZUMI & M. NISHIMORI

the APHRO_JP dataset is more realistic than the simple
one from the viewpoint of the orographic effects on precipitation.
The CDFDM accurately estimated the geographicalpattern of the mean precipitation data from MIROCHI20C3M output. The mean precipitation bias of the GCM
output downscaled using the simple dataset, relative to
the GCM output downscaled using the APHRO_JP
dataset, varied little regardless of the season (Fig. 2c, f, i,and l). This indicates that the downscaled GCM output
has statistical characteristics similar to the observation
dataset used for the statistical model calibration. Thisfinding is also applicable to the other daily precipitation
indices, such as the number of wet days FRE (Fig. 3c, f, i,and l).
The area-mean bias of the daily precipitation indicesfor the simple dataset, relative to the APHRO_JP dataset,
was calculated for six areas in Japan and summarized inFig. 4 (see Fig. 1 for the areas). Compared to the
APHRO_JP dataset, the simple dataset frequently underestimated the mean precipitation (MEA) and 90th
percentile of daily precipitation (Q90) in most areas,
whereas the simple dataset showed more frequent wetdays (FEA) and shorter consecutive dry days (CDD) in
all areas for both winter and summer. For all daily
precipitation indices, the downscaled GCM output under
the present-day climate condition (20C3M) took over thebias of the simple dataset. Similar results were found forFig. 2 Geographical pattern of mean winter (DJF) and summer(JJA) precipitation (MEA) for (a and g) the APHRO_JP
dataset, (b and h) the simple dataset, MIROCHI-20C3M
outputs downscaled using (d and j) the APHRO_JP or (eand k) simple dataset, and (c, f, i, and l) the bias of the
MEA for the simple dataset, relative to the APHRO_JP
dataset (presented in percentages).

Fig. 3 Geographical pattern of mean winter (DJF) and summer(JJA) number of wet days (FRE) for (a and g) the
APHRO_JP dataset, (b and h) the simple dataset,
MIROCHI-20C3M outputs downscaled using (d and j)
the APHRO_JP or (e and k) simple dataset, and (c, f, i,and l) the bias of the FRE for the simple dataset, relative
to the APHRO_JP dataset (presented in percentages).

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105

the downscaled GCM output under the future emission
scenario (A1B).
3.2 Probability density function of daily precipitation
Figure 5 shows the probability density functions(PDFs) of summer daily precipitation (≥1 mm/day) provided from the two types of observation datasets andfrom the MIROCHI-20C3M output downscaled using
each of the observation datasets. The PDFs were provided from the daily data for the period 1985-2004. The
frequency was calculated for every 1 mm/day bin. The
PDF was provided for each grid and then averaged acrossthe grids along the bins.
The provided PDF was similar to that of Kamiguchi
et al. (2010), but the PDF presented here showed asmaller amount of daily precipitation because of thespatial aggregation through re-gridding. The simple
dataset showed a shorter tail on the heavy precipitationside as compared to the APHRO_JP dataset. In addition,
the simple dataset produced a higher frequency of
low precipitation (around 1 mm/day). The downscaled
MIROCHI-20C3M outputs obviously took over thestatistical characteristics of the observation dataset used
for the statistical model calibration in terms of PDF. The
daily precipitation characteristics for the simple dataset
reported here were consistent with the other results. For
instance, the simple dataset had more frequent wet days
(FRE) and lower 90th percentile values of daily
precipitation (Q90) (Fig. 4c, d, e, and f).

Fig. 4 Bias of the simple dataset, relative to the APHRO_JP
dataset, for mean winter (DJF) and summer (JJA) daily
precipitation indices for six areas in Japan. The bias of thesimple dataset (simple minus APHRO_JP) was calculated from the observation datasets (OBS), MIROCHI20C3M and -A1B outputs, downscaled using each of theobserved datasets. (a and b) Mean precipitation, MEA; (cand d) number of wet days, FRE; (e and f) 90th percentileof daily precipitation, Q90; and (g and h) maximum
number of consecutive dry days, CDD. The unit is apercentage relative to the 20-year mean value of daily
precipitation indices calculated from the APHRO_JP
dataset or the GCM outputs downscaled using the
APHRO_JP dataset.

3.3 Projected future changes and bias
Figure 6 shows the projected change in daily precipitation indices for the 2081-2100 period, relative to
1981-2000 (referred to as the 2090s and 1990s, respectively), as calculated from the MIROCHI-A1B and-20C3M outputs, downscaled using the APHRO_JP
dataset. In the downscaled GCM projection, the mean
winter (DJF) precipitation will decrease by up to 13% in
the western areas and on the Japan Sea side of the easternarea (WJ, WP, and EJ). The number of wet days (FRE)will decrease in most areas except for the Pacific side of
the northern area (NP) in winter. Heavy precipitation(represented by Q90) will increase over Japan in winter.

Fig. 5 Mean probability density function of summer (JJA) daily precipitation (≥1 mm/day) averaged
over the grids for the two types of observation datasets (the APHRO_JP and simple datasets)
and MIROCHI-20C3M outputs downscaled using each of the observation datasets.

106

T. IIZUMI & M. NISHIMORIThe maximum number of consecutive dry days (CDD) in
winter will be increased in all areas except for the Pacificside of the northern area (NP) as a result of the decreasein the number of wet days.
For summer (JJA), the mean precipitation (MEA) andheavy precipitation (Q90) will increase by over 19% andby up to 23% in all areas, respectively. The number ofwet days will increase mainly in the northern areas (NJ
and NP) in summer. The change in the maximum numberof consecutive dry days (CDD) in summer is not
remarkable, but it will decrease by 10% on the Pacificside of the eastern area (EP). For both winter and summer,
the downscaled projected change in mean precipitation isconsistent with the results of the original GCM outputs(Kimoto et al., 2005) in terms of the geographical pattern,
amplitude, and tendency of change.
Figure 7 summarizes the projected changes in thedaily precipitation indices and bias of the simple dataset,

Fig. 6 Geographical pattern of the projected change in daily precipitation indices in the 2090s, relative to the1990s, calculated from the MIROCHI-20C3M and -A1B outputs downscaled using the APHRO_JP
dataset. (a and b) Mean precipitation, MEA; (c and d) number of wet days, FRE; (e and f) 90th
percentile of daily precipitation, Q90; and (g and h) maximum number of consecutive dry days, CDD.
Bluer colors indicate wetter conditions in future, relative to the present-day, whereas redder colors
correspond to drier conditions.

Fig. 7 Bias of the simple dataset, relative to the APHRO_JP dataset, and projected change in mean winter(DJF) and summer (JJA) daily precipitation indices for six areas in Japan. The bias of the simple
dataset was calculated from the MIROCHI-20C3M outputs downscaled using each of the observation
datasets (simple minus APHRO_JP). The projected change was calculated from the MIROCHI20C3M and -A1B outputs downscaled using the APHRO_JP dataset. The unit is a percentage relative
to the 20-year mean value of MIROCHI-20C3M outputs downscaled using the APHRO_JP dataset.
Open bars are used when the bias or projected change is negative in order to facilitate a comparison of
the absolute values of these data.

Statistical Downscaling Methods and a Gridded Dataset

107

relative to the APHRO_JP dataset, for the six areas inJapan. The projected change was calculated from the
MIROCHI-20C3M and -A1B outputs downscaled using
the APHRO_JP dataset, whereas the bias of the simple
dataset was calculated from the MIROCHI-20C3M outputs downscaled using each of the observation datasets.
Both change and bias are presented in percentages relative to the same reference, i.e., the 20-year mean valueof the daily precipitation indices calculated from
the MIROCHI-20C3M output downscaled using the
APHRO_JP dataset. From Fig. 7, the amplitude of thebias was found to be smaller in many cases, but it wasoccasionally close to half of the projected change,
indicating the possibility that the bias could be comparable in amplitude to the projected change. This possibility
seems be higher for FRE and CDD than MEA and Q90.

As demonstrated in this study, the quality-controlled,
high-resolution gridded daily dataset represented by theseries of precipitation products developed in the
APHRODITE project (Yatagai et al., 2008) is a highly
beneficial data source for statistical downscaling simulations, and enables modelers to overcome many of the
known limitations of SDMs. Gridded datasets for climate
elements other than precipitation over Japan and otherregions could support achievement of more realistic
statistical downscaling simulations and be beneficial in
conducting climate impact and adaptation studies on aregional scale. To examine the stationarity assumption of
the F-e relationship across various climate zones and at
various time scales may be a further study.

4. Discussion

In this study, we highlight the improvements to thestatistical downscaling simulations of coarse-resolution
GCM outputs by incorporating the APHRO_JP advanced
gridded daily precipitation dataset. These improvements
are significant relative to the results achieved by simulations using a simple dataset. Specifically, the APHRO_JP
dataset produced improved data quality in terms of thestatistical characteristics of daily precipitation data compared to the results obtained using the simple dataset: theimprovements are most pronounced for mean precipitation in mountainous areas, precipitation frequency on theleeward sides of mountains, and the intensity and frequency of heavy precipitation.
These improvements, resulting from the incorporation of the APHRO_JP dataset, yield better results forclimate change scenarios derived from SDMs (CDFDM).
On a regional scale, the bias of the daily precipitation
indices for the simple dataset, relative to the APHRO_JP
dataset, is occasionally comparable in amplitude with the
projected change. The bias associated with the choice ofobservation datasets potentially affects the results ofimpact and adaptation studies on hydrological regimes,
irrigation planning, and risk assessments of water-related
disasters. It is therefore recommended that statistical
downscaling simulations of coarse-resolution GCM outputs incorporate gridded observation datasets createdusing advanced quality control algorithms and spatial
interpolation methods. Such gridded observation datasets
will help reduce errors in simulating climate impacts andadaptation practices, particularly errors associated withunrealistic precipitation data derived from SDMs calibrated using biased observation datasets.

As shown in Section 3, the GCM outputs downscaledby the CDFDM take on the statistical characteristics ofthe observation dataset, demonstrating the sensitivity ofclimate data derived from SDMs to the calibration data.
The bias of the daily precipitation indices for the simple
dataset, relative to the APHRO_JP dataset, is occasionally non-negligible in amplitude compared to the projected change; though this conclusion was derived from asingle GCM analysis. However, Okada et al. (2009)
provided climate change scenarios over Japan for the
agricultural sector using the six GCM outputs under the
SRES-A1B and -A2 emission scenarios, and they
showed that some GCMs projected smaller changes inmean winter and summer precipitation compared to the
MIROCHI-A1B. If we consider this result, the bias of thesimple dataset, relative to the APHRO_JP dataset, could
be comparable to, or larger than, that of the projectedchange for some GCMs other than MIROCHI-A1B.
For precipitation characteristics in mountainous areas,
the APHRO_JP dataset showed relative improvements indata quality compared to the simple dataset. The relative
improvements in the APHRO_JP dataset are pronounced
for mean precipitation values in mountainous areas, forprecipitation frequency on the leeward side of mountains,and for the intensity and frequency of heavy precipitation.
These relative improvements are taken over the climatechange scenario derived from the SDMs. The incorporation of the APHRO_JP dataset into statistical downscaling simulations thereby offers a better representationof daily precipitation indices under the present-day
climate condition as well as a possibility to produce
climate change scenarios based on more realistic estimates of historical precipitation. The incorporation of the
APHRO_JP dataset can significantly improve the findings of impact and adaptation studies in the field where
precipitation in mountainous areas plays a central role:for instance, in studies of hydrological regimes, irrigation
planning, and risk assessments of water-related disasters(floods, droughts and land slides).

5. Conclusions

Acknowledgements
This study was supported by the Global Environmental Research Fund (S-5-3) and Research Program on
Climate Change Adaptation (RECCA program).

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T. IIZUMI & M. NISHIMORI

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Toshichika IIZUMIDr. Toshichika IIZUMI is a researcher of theAgro-Meteorology Division at the National
Institute for Agro-Environmental Sciences. He
completed his PhD in 2007 in the Doctoral
Program in Geoenvironmental Sciences, theGraduate School of Life and Environmental
Sciences, University of Tsukuba. He has expertise in the fields ofagricultural meteorology, applied climatology, and large-scale cropmodeling. His recent works focus on the generation of regional climatechange scenarios for the Asian region including Japan using statistical
downscaling methods; the development of a process-based large-scale cropmodel; and the assessment of climate change impacts on crop productivity
in Japan and major production countries in the world. He is a winner of the
UK-Japan Collaboration Development Awards (Crop Modeling) in 2010
under the Global Partnership Fund offered by the Science & Innovation
Section, British Embassy. Tokyo.

Motoki NISHIMORI
Dr. Motoki NISHIMORI is a senior researcher inthe Agro-Meteorology Division at the National
Institute for Agro-Environmental Sciences. He
previously served as a research associate at theInstitute of Geoscience, University of Tsukuba.
He is interested in various research fields on
agriculture-related environmental problems, ranging from geographicaland geophysical sciences to organo-chemical (pesticide) sciences. He has
published many books including Global Warming: Potential Impacts on
Japan, Third Assessment Report (Kokon-Shoin, Tokyo) and Climate inJapan I & II (Ninomiya-Shoten, Tokyo). He is currently leading the research project entitled “Climate change scenarios derived from statistical
downscaling methods for basin zones to support river management andenvironmental policy-making in Kochi Prefecture” funded by the Ministryof Education, Culture, Sports, Sciences and Technology (FY2010-2014).

(Received 8 February 2011, Accepted 4 August 2011)