Ishii and Mori Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00367-7(2020) 7:58

Progress in Earth andPlanetary Science

REVIEW

Open Access

d4PDF: large-ensemble andhigh-resolution climate simulations for
global warming risk assessment
Masayoshi Ishii1,2* and Nobuhito Mori3

Abstract
A large-ensemble climate simulation database, which is known as the database for policy decision-making for futureclimate changes (d4PDF), was designed for climate change risk assessments. Since the completion of the first set ofclimate simulations in 2015, the database has been growing continuously. It contains the results of ensemble
simulations conducted over a total of thousands years respectively for past and future climates using high-resolution
global (60 km horizontal mesh) and regional (20 km mesh) atmospheric models. Several sets of future climate
simulations are available, in which global mean surface air temperatures are forced to be higher by 4 K, 2 K, and 1.5 K
relative to preindustrial levels. Nonwarming past climate simulations are incorporated in d4PDF along with the past
climate simulations. The total data volume is approximately 2 petabytes. The atmospheric models satisfactorily
simulate the past climate in terms of climatology, natural variations, and extreme events such as heavy precipitationand tropical cyclones. In addition, data users can obtain statistically significant changes in mean states or weather andclimate extremes of interest between the past and future climates via a simple arithmetic computation without any
statistical assumptions. The database is helpful in understanding future changes in climate states and in attributing
past climate events to global warming. Impact assessment studies for climate changes have concurrently been
performed in various research areas such as natural hazard, hydrology, civil engineering, agriculture, health, andinsurance. The database has now become essential for promoting climate and risk assessment studies and fordevising climate adaptation policies. Moreover, it has helped in establishing an interdisciplinary research community
on global warming across Japan.
Keywords: Global warming, d4PDF, Ensemble climate simulation, Atmospheric model, Dynamical downscaling,
Detection and attribution, Impact assessment, Climate change adaptation, Natural hazard, Storm surge

1 Introduction
Following the series of the Intergovernmental Panel on
Climate Change (IPCC) Fifth Assessment Report published in 2013 and 2014, the Paris Agreement was adopted
in December 2015. Globally, governments started designing adaptation measures for future increases in global*Correspondence: maish@mri-jma.go.jp
Department of Atmosphere, Ocean and Earth System Modeling Research,
Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052,
Japan
2
Climate and Environment Research Promotion, Japan MeteorologicalBusiness Support Center, Kanda-Nishikicho, Chiyoda-ku, Tokyo 101-0054, Japan
Full list of author information is available at the end of the article
1

warming. To devise consistent adaptation measures forflood control, agriculture, city planning, and risk prevention in human lives and social infrastructures, it isessential for governmental agencies and political decisionmakers to adopt a future scenario commonly used acrosssocio-economic and environmental fields between adjacent geographical regions. At present, people are particularly concerned for the severe weather and extreme
hazards that are caused by global warming. To address
this concern, the climate information should be detailed
in space and time.

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Ishii and Mori Progress in Earth and Planetary Science(2020) 7:58

1.1 A brief history

In 1989, Meteorological Research Institute (MRI) started
researches on global warming. They first conducted climate simulations by an atmospheric model coupled to aslab ocean model (Noda and Tokioka 1989), and the result
was provided to the first IPCC assessment report. Since
then, MRI has contributed to all IPCC assessment reports
published so far and has developed atmosphere-ocean
coupled models (Tokioka et al. 1995; Yukimoto et al. 2001)
and earth system models (Adachi et al. 2013; Yukimoto etal. 2019) in parallel. From 2002 to 2016, enhanced global
warming studies were conducted under three Japanese
research programs supported by the Japanese ministry,
MEXT, in which the University of Tokyo, JAMSTEC, MRI,NIES, Kyoto University, and other institutes participated.
These programs developed climate models (Hasumi andEmori 2004; Nozawa et al. 2007; Watanabe et al. 2010;
Tatebe et al. 2019), a high-resolution coupled atmosphereand ocean model (Sakamoto et al. 2012), and ambitious
earth system models (Kawamiya et al. 2005; Watanabe etal. 2011; Hajima et al. 2020) that were used for future climate projections. Under these programs, climate studies
were also performed on the global and regional scales,
along with interdisciplinary climate impact assessment
studies (Nakakita et al. 2018). Further, event attribution
studies requiring large-ensemble model simulations (e.g.,
Watanabe et al. 2013) were conducted by the Programfor Risk Information on Climate Change (SOUSEI, from
FY2012 to FY2016).
During the SOUSEI program, there were strong
demands for the contribution to society utilizing theirresearch results obtained during the programs. Concurrently, it was pointed out that existing model outputs
for future climate states were insufficient for devising
suitable adaptation measures in Japan based on impact
projections and assessments (Mori et al. 2016; Takemi etal. 2016). In the programs, time-slice projection (Kitoh
et al. 2016), in which high-resolution AGCMs are integrated for past and future periods of several tens ofyears, had been used to obtain impact projections andassessment of climate change. However, the number of
events sampled from the available climate simulations wasinsufficient to estimate extreme natural hazards in particular regions. Furthermore, reliable probabilistic information on the extremes of 1/20 to 1/200-year return
periods, for instance, was required for hardware countermeasure design for extreme natural hazards. Here,an N-year extreme refers to an exceedance probabilityof once per N years. When the sampling size is small,the uncertainty in estimated occurrence frequency of theextremes is large. Therefore, increasing the climate simulation period was highly desirable for the impact projection and assessment, and for adaptation to future extreme
hazards.

Page 2 of 22

Therefore, they developed future scenarios at several
warming levels in the twenty-first century by performing
numerous members of climate simulations using highresolution global and regional atmospheric models forcedby observed and future sea surface temperatures projected by CMIP5 participating models. The simulation
experiments were designed such that past climate signals
and future changes in extreme events such as heat wave,
heavy rainfall, and tropical cyclones as well as in climate
extremes are detectable with high statistical confidence,
based on the climate simulations as total of thousands
years. The database containing the simulation outputs wasnamed d4PDF. Further details of d4PDF will be presented
below.
The future climate simulations of d4PDF were first conducted in 2015 (Mizuta et al. 2017). In the simulations,the global surface air temperatures (SATs) are set to climatologically constant at +4 K relative to preindustrial
levels, corresponding to the end of the twenty-first century in the RCP8.5 scenario experiments of the CMIP5
participating models. In December 2015 when the firstd4PDF was released, the Paris Agreement was adopted,
in which efforts to maintain the temperature below +2
K was promoted with global cooperation. To meet thisgoal, the Japanese cabinet decided to plan policies for climate change adaptation with an outline report entitled“National Plan for Adaptation to the Impacts of Climate
Change in November 2015.” Under this background, aclimate science and social implementation program of
MEXT called SI-CAT (from FY2015 to FY2019) decided
to conduct further future climate simulations at a warming level of +2 K as a subset of d4PDF (Fujita et al. 2019).
Adaptation or countermeasures against global warming
are not only governmental issues but also social problems. Since August 2018, d4PDF has become open to bothcommercial and noncommercial applications.
A new research program for Integrated Research Program for Advancing Climate Models (TOUGOU, from
FY2017 to FY2021) has started in 2017. As one of research
themes in TOUGOU, an update of d4PDF is currently
planned and designed.
1.2 Aims of d4PDF

The main purpose of the database is to become a standard for use in devising policies at Japanese ministries andagencies, as well as in global warming and impact assessment studies. Governments and stakeholders are particularly concerned with future severe atmospheric extremes
and their uncertainties. To realize this, d4PDF is constituted by the high-resolution and large-ensemble climate
simulations, aiming at re-examining previous results on
climate changes including past and future extreme eventsand at drawing highly reliable conclusions in both climate and assessment studies. Climate researchers focus

(2020) 7:58

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on physical backgrounds and mechanisms of climatechanges with d4PDF. The large-ensemble simulations alsoenable them to perform factor analyses of climate variations such as detection and attribution and event attribution on a near real-time basis. Such research activities
would contribute to the progress of operational climate
monitoring, for instance, done by World Meteorological Organization (2019) and related operational centers.
Researchers on impact and risk assessments conduct
their assessment models using the d4PDF. Bias corrections of the model-generated atmospheric variables areoften required to stabilize the performances in the assessment results. Information engineering approaches are
sometimes effective to extract statistical characteristics ofatmospheric events from the large database. To handle
the large database, brand-new techniques on data service
must be introduced. All these research activities form aninterdisciplinary community on global warming research(Fig. 1). This community hopefully functions for realization of effective countermeasure against global warming
and for progress in science, engineering, agriculture, etc.
In addition, all results of the community contribute toimprovement in a future version of climate simulation andprediction databases.
Internationally, several similar databases exist. In 2009,
the UK produced the global warming prediction databasein collaboration with national agencies (UKCP09; Jenkins et al. 2009), in order to assess future climate risks.
This is an epoch-making database since probability distribution functions (PDFs) of future climate states canbe drawn from the database. The database was updated
in 2018 (UKCP18; Met Office 2018). Both UKCP18 andd4PDF enable probabilistic evaluations of occurrence of
extreme events, although the former treats a wider rangeof uncertainties in future climates by using a coupledmodel rather than an atmospheric model, more CMIP5
model states, and physics parameter ensemble. The spatial resolution of the UKCP18 regional climate modelis 12 km. In addition, UKCP18 includes perspectives ofsea level rise diagnostically estimated. Another databasecalled ClimEx (Leduc et al. 2019) has been produced
as a large-ensemble regional forecast database for North
American and European regions by using the Canadianregional climate model of 12 km resolution, dynamically downscaled from 50-member simulations of a single
global climate model. The Max Planck Institute Grand
Ensemble (MPI-GE; Maher et al. 2019) is an extension of
CMIP5 historical and four RCP scenario experiments bya single low-resolution earth system model. In MPI-GE,
the sets of 100-member simulations for each experiment
provide robust estimates of forced response separated
with internal variability. An international research projectcalled CORDEX is a part of CMIP5 (and ongoing CMIP6
as well), and the project focuses on regional-scale global

warming projections, adopting a multi-model ensemble
approach (Giorgi et al. 2009).

Ishii and Mori Progress in Earth and Planetary Science

2 Review
Now we review the use of d4PDF in climate and assessment studies on future climate changes. More than 70
papers related to d4PDF have been published since 2015,
and all of these are presented in this review. The purpose of this review is to summarize the present d4PDF
studies and to obtain perspectives of future climate simulation databases. First, atmospheric models and various
climate simulations in d4PDF are described, and thenpast and future climate simulations are evaluated focusing on atmospheric phenomena on global and regional
scales. Second, we introduce how d4PDF has been applied
to impact assessment studies and social implementation.
These are the planned goals of the database, that is, to provide high-resolution and large-ensemble data required forclimate studies and risk assessments for various sectors
in human society and the earth environment. Throughout this review, it is stressed that the high-resolution
and large-ensemble climate simulations are effective fordetecting signals of atmospheric extreme events and forpractical applications to assessments of future climate
risks. Data users in a wide range of research fields haveraised new problems in the database. The future perspective on the database will be discussed in the final section.
Figure 1 shows a schematic view of this review.
2.1 Variants of climate simulation · · · (1)Table 1 lists various climate simulations at different global
warming levels, which constitute d4PDF. Global andregional atmospheric climate models of 60 km and 20 km
meshes, respectively, are used for past and future climate
simulations. The 60 km mesh resolution is approximately
twice as high as the majority of CMIP5-participating models. The global model is the MRI atmospheric generalcirculation model version 3.2 (MRI-AGCM3.2; Mizuta etal. 2012) of a low-resolution setup, and the regional modelis the nonhydrostatic regional climate model (NHRCM;
Sasaki et al. 2011; Murata et al. 2013) covering JapaneseIslands, the Korean Peninsula, and a part of the Asian continent. Spatiotemporally detailed simulation outputs were
produced by dynamical downscaling with NHRCM using
lateral boundary conditions given by MRI-AGCM3.2.
These models are the same as the operational models formerly used at the Japan Meteorological Agency (JMA),
although the physics schemes of the models were replacedby those suitable for climate simulations. The ensemble
sizes of the experiments were more than 50, and a pair ofboth global and regional simulations was available in eachsubset.
All simulations listed in Table 1 were conducted byintegrating the models forced by prescribed sea surface

Ishii and Mori Progress in Earth and Planetary Science(2020) 7:58

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Fig. 1 A schematic view of this review. The numbers denote subsections of the main section

temperature (SST), sea ice concentration (SIC), and sea
ice thickness (SIT). The greenhouse gas concentrationsof CMIP5 (Taylor et al. 2012) were fed into the models.
In the past climate simulation, observed SSTs, SIC, and
SIT were used, where the SSTs and SIC are of COBE-

SST2 (Hirahara et al. 2014). Time-varying SIT is constructed and given as SIT monthly climatology (Bourkeand Garrett 1987) multiplied by the observed SIC. Future
simulations were performed assuming climatologicallyconstant warming conditions of +1.5 K (Nosaka et al.

Table 1 Subsets of climate simulation experiments in d4PDF under different climate conditions. T in the forth column means
differences in past and future climatologies of SST and SIC computed from CMIP5 RCP8.5 experiments by six climate models (CCSM4,
GFDL-CM3, HadGEM2-AO, MIROC5, MPI-ESM-MR, and MRI-CGCM3). Two numbers in brackets following T indicate the year range for
RCP8.5 SST trend used in the future climate simulation. The baseline of nonwarming SSTs is the climatology for the period from 1900 to
1919
Experiment

Ensemble members

Integration years/period

Boundary conditions

References

Past

100

From 1951 onward

Mizuta et al. (2017)

Nonwarming+1.5 K future+2 K future+4 K future

100
54
54
90

From 1951 onward
29 years*1
60 years
60 yearsObserved SSTs and SIC(Hirahara et al. 2014)
Detrended observed SSTs and SIC : δ(2020-2039) + δ(2030–2049) + δ(2080–2099) + δ*

1:the global model was integrated for 32 years

Mizuta et al. (2017), Kawase et al. (2019)
Nosaka et al. (2020)
Fujita et al. (2019)
Mizuta et al. (2017)

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Page 5 of 222020), +2 K (Fujita et al. 2019), and +4 K (Mizuta et al.
2017), relative to preindustrial levels. Future SSTs used inthese simulations were detrended observations added tothe monthly SST trends of CMIP5 RCP8.5 experiments
done by six different climate models : CCSM4 (Gent etal. 2011), GFDL-CM3 (Delworth et al. 2006; Donner et
al. 2011), HadGen2-AO (Collins et al. 2011; Martin et al.
2011), MIROC5 (Watanabe et al. 2010), MPI-ESM-MR(Gioegetta et al. 2013), and MRI-CGCM3 (Yukimoto etal. 2011; Yukimoto et al. 2012), as shown in Fig. 2 andTable 2. The individual SST trends were rescaled by multiplying by a factor (Fujita et al. 2019; Mizuta et al. 2017;
Nosaka et al. 2020; Shiogama et al. 2010) that forces MRIAGCM3.2 to simulate the global mean SATs equivalent
to each warming level. Future SIC varies consistently with
SSTs at geographical locations, which is determined byusing quadratic relationships between SST and SIC as
used in Hirahara et al. (2014). Coefficients of the quadratic
equations were determined separately for each CMIP5
model so that climatological hemispheric sea ice areas are
the same as those of multi-model ensemble mean SIC of
the six CMIP5 models. Why the multi-model mean wasused here is because the uncertainties of CMIP5 future
SIC are quite large. The future SIT was given similarly
to the case of the past SIT. Future changes in particular atmospheric variables are given by differences between
the past and future climate simulations. This is sometimes
referred to as a time-slice experiment (Kitoh et al. 2016).
In d4PDF, two sources of uncertainties are taken into
account. One is the uncertainties in the CMIP5 scenario
experiments accounted for by the future trends of the six
CMIP5 models (Table 2). The other is those in internal(natural) variability induced by different initial and lower

boundary conditions. The latter is perturbations of SST,
SIC, and SIT which are represented as continuous fluctuations in space and time based on the uncertainty information of COBE-SST2. Although the magnitudes of theuncertainty vary in space and time depending on observation distributions, the magnitudes of the uncertainty
are set constant in time. The SIC perturbations are made,being consistent with those of SST by using the quadratic
relationship mentioned above, and SIT perturbations arecomputed as SIC perturbations multiplied by the SIT climatology. Merits in using SST, SIC, and SIT perturbations
are to consider observational uncertainties in climate simulations and to optimize SST-force climate simulations.
Or these are closely equivalent to using different SST and
SIC analyses other than COBE-SST2. The global atmospheric model was integrated from several different initialconditions with the perturbed oceanic boundary conditions for 1 year before starting the main simulation runs.
In the +4 K experiment, the 90 ensemble members wereconstituted of combinations of the six SST trends and the
15 sets of oceanic perturbations, and the 6 trends times 9
perturbations for the 1.5 K and +2 K experiments.
The global model simulations provide lateral boundary
conditions for dynamical downscaling climate simulations
in East Asia (Mizuta et al. 2017). A spectral nudging
method is used for consistency between the global andregional large-scale climate states and for stabilization of
the NHRCM integration (Murata et al. 2013; Sasaki et al.
2011), as adopted in other regional climate models (Giorgi2019). The oceanic boundary conditions are the same asin the global model. An error in SIC used by NHRCM wasreported by Nosaka et al. (2020), that is, no sea ice in the
Okhotsk Sea given for all NHRCM simulations. The error

Ishii and Mori Progress in Earth and Planetary ScienceFig. 2 Geographical distributions of sea surface temperature differences (K) of six CMIP5-participating climate models between two periods from1991 to 2010 and from 2080 to 2099. The values indicate mean changes in climatological SSTs for January in years from 2080 to 2099 relative tothose from 1991 to 2010

Ishii and Mori Progress in Earth and Planetary Science(2020) 7:58

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Table 2 List of CMIP5 models whose future climate projections are used in d4PDF. Please refer to Appendix 9.A of the IPCC Fifth
Assessment Report (the Working Group I) for further details
AcronymResolution (horizontal resol., vertical lev.s)

Center/institute, country

Refereces

CCSM

AGCM: 0.9◦ × 1.25◦ , 27

National Centre for Atmospheric Research, USA

Gent et al. (2011)

Geophysical Fluid Dynamics Laboratory, USA

Delworth et al. (2006)

OGCM: nominal 1◦ , 60
GFDL-CM3

AGCM: ∼ 200 km, 48
OGCM: 1◦ , 50

HadGEM2-AO

AGCM: 1.875◦ × 1.25◦ , 60

Donner et al. (2011)
National Institute of Meteorological Research, Korea

OGCM: 1.875◦ × 1.25◦
MIROC5

AGCM: 1.41◦ × 1.41◦ , 40

Collins et al. (2011)
Martin et al. (2011)

Univ. Tokyo and JAMTEC, Japan

Watanabe et al. (2010)

Max Planck Institute for Meteorology, Germany

Giorgetta et al. (2013)

OGCM: 1.4◦ × 0.5 − 1.4◦ , 50
MPI-ESM-MR

AGCM: approx. 1.8◦ , 95
OGCM: approx. 0.4◦ , 40

MRI-CGCM3

AGCM: approx. 110 km, 48

Meteorological Research Institute Japan

OGCM: 1◦ × 0.5◦ , 50

caused large biases in simulated daily minimum temperatures in northern winters. Further information will beavailable at the d4PDF website and others.
2.2 Large-ensemble simulations · · · (2)Each subset contains the global and regional model
simulations for 29 to 60 years with 50 to 100 different initial and boundary conditions (Table 1). Hence,a total of thousands-year simulations at each warming
level provides an ample supply of samples of weather
extremes and rare events, as introduced later. The
total volume of d4PDF is approximately 2 petabytes.
Hereafter, the climate simulations by the global andregional models are referred to d4PDF-G and d4PDF-R,
respectively.
Figure 3 displays all experiments of d4PDF-G. Global
mean surface air temperatures (SAT) of the d4PDF past
simulations display a global warming trend as in thetime series of the CMIP5 experiments. By contrast, theSAT time series of the other experiments fluctuate ateach warming level. The uncertainties of the future climate simulation are much smaller than those of the six
CMIP5 models, because of the adjustment procedure forthe future SST trends introduced above. The interannual
variations of the d4PDF simulations are mostly the same
between each other, since observed internal variations of
the ocean boundary conditions are commonly used in all
the experiments.
In general, large-ensemble simulations yield robust
statistics on both means and extremes. Large-ensemble
database d4PDF was designed to be able to estimate futurechanges in severe weather and climate events occurring
rarely, such as heavy precipitation and tropical cyclones,
as well as changes in mean climate states. To achievethis, it is furthermore necessary to confirm how well the

Yukimoto et al. (2011)
Yukimoto et al. (2012)

models reproduce mean climate states and severe eventsin the past climate simulations.
With d4PDF outputs, users can obtain robust statistics on, for instance, return periods of more than 100
years of heat waves, heavy precipitation, storm surges,
etc., which affect social infrastructures, via simple arithmetic computations without any statistical assumptions.
Such nonparametric approaches are powerful in both climate and assessments studies. Statistics on events lying
close to the tails of the PDF can also be at confidence levels (e.g., Shiogama et al. 2016; Kawase et al. 2016), andspatially or temporally smooth images of characteristicsof rare events can be obtained as desired in many cases(e.g., Mizuta et al. 2017; Matsueda and Endo 2017; Yoshida
et al. 2017).
Several studies have reported on the ensemble members that are required for attribution of atmosphericphenomena. How many ensemble members is sufficient
for robust estimates depends on targets or on temporaland spatial averaging scale. Hibino and Takayabu (2016)
demonstrated detectable signals of future precipitation atlow, middle, and high latitudes, changing temporal andspatial scales for averaging. With 100 ensemble simulations of MPI-GE, Maher et al. (2019) showed estimationof ensemble sizes needed for isolating forced trends infuture sea level pressure from internal variations of large
amplitude. For detecting year-to-year variations of annual
hurricane frequency forced by SST, Mei et al. (2019)concluded that a twenty ensemble was sufficient, using
d4PDF-G.
Figure 4 demonstrates ensemble sizes required for
detecting future changes in extremes of annual maximum 1-day precipitation (Rx1d) of d4PDF-R at Tokyo.
The histograms of Rx1d become smoother and less uncertain as many ensemble members are used (Fig. 4a). The

Ishii and Mori Progress in Earth and Planetary Science(2020) 7:58

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Fig. 3 Time series of global mean surface air temperature anomalies of d4PDF-G for past (light blue), nonwaming (blue), +1.5 K (green), +2 K
(orange), and +4 K (red) climate simulations and CMIP5 historical and RCP8.5 experiments with six climate models. Shading indicates two-sigma
uncertainties. Time series are drawn relative to the averages for the period from 1975 to 2005

Rx1d extremes locating in the thin-tailed PDF somewhat
largely depend on the number of ensemble members (blueand red circles along the abscissas). For future changes
in 95-percentile Rx1d extremes, the uncertainties are notchanged substantially when the members more than 30
are used, while all members should be taken into accountfor the 99-percentile extremes (Fig. 4b).
2.3 Attributing extreme and severe events · · · (3)How well past weather and climate events on seasonalto-decadal time scales are reproduced responding toobserved SSTs is one of the key factors when attributing
weather or climatic events to global warming. In addition, better reproduction of past climate and severe eventsshould be a minimum requirement for confidence in estimating the future changes because the adopted model
physics has been proven feasible for the present climate
system of the earth. Moreover, high-resolution and largeensemble members of climate simulation help reduce
uncertainties in estimates of atmospheric responses toanthropogenic forcing and given SST variations.
Prior to d4PDF, MRI-AGCM3.2 was applied to timeslice experiments using the same model setups as in

d4PDF; however, a different experimental design was
adopted before, particularly with respect to lower boundary conditions: a different SST and SIC analysis of
HadISST (Rayner et al. 2003) used, and no SST and SIC
perturbations given (Mizuta et al. 2012). Through theseexperiments, model climatology was confirmed to be
sufficient to reproduce observed distributions of precipitation and tropical cyclone genesis, as reported by Mizuta
et al. (2012) and Murakami et al. (2012). Figures 5 and6 are drawn using d4PDF-G and indicate that the past
climate simulations are almost identical to those of theformer studies quantitatively.
The global model outputs of d4PDF for the past 60 years
have been examined in many studies, focusing on extreme
events of SATs and precipitation and typical atmosphericphenomena. These studies reported the reproducibilityof the past climate by MRI-AGCM3.2 in various pointsof view. Shiogama et al. (2016) most early conducted astudy with d4PDF-G. They attributed historical changes
in daily temperature and precipitation extremes to global
warming and pointed out that spatial distributions andanthropogenic impacts of record-breaking events are sensitive to climate models and boundary conditions used inIshii and Mori Progress in Earth and Planetary Science(2020) 7:58

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Fig. 4 a Histograms of annual maximum 1-day precipitation (mm/day) of each year in the past (blue) and +4 K (red) regional climate simulations(d4PDF-R) at Tokyo, for cases of a certain set of 6, 18, 42, and 90 members as aligned from the top to the bottom. Members plotted are chosen
evenly from 15 simulations with six different SST trends. Colored circles along the abscissas in each histogram indicate positions at the 95, 97, and 99
percentiles for past (blue) and +4 K (red) climates. b The mean future changes (line) and the one-sigma uncertainties (shade) in the 95 (red) and 99(blue) percentile maximum daily precipitations estimated nonparametrically from the database

the experiments. Endo et al. (2017) examined monsoonal
precipitation in East-Asian summer and confirmed a good
agreement of climate (Fig. 5), daily strong precipitation,
and atmospheric circulations with corresponding satellite observations and reanalysis. Duan et al. (2019) had
another comparison study with the d4PDF-G precipitation and station data over China. They obtain rather highcorrelation coefficients above 0.7 between d4PDF-G andground-based observations for all seasons in China, and amaximum of 0.93 for spring. The frequency of the modelgenerated tropical cyclones (TCs) agrees with the observations (Fig. 6; Yoshida et al. 2017). For example, a correlation coefficient between d4PDF-G and the observations
is 0.84 for time series of annual hurricane frequency in
the North Atlantic basin for recent decades (Mei et al.
2019). However, the model-generated TCs are generally
weak (Fig. 6b). By correcting simulated wind speeds (Sugiet al. 2017), Yoshida et al. (2017) showed a realistic distribution of occurrence frequency of very intense TCs(categories 4 and 5), and the future changes for the latetwenty-first century agree with Knutson et al. (2015). Notethat the SST-forced AGCM abnormally enhances tropical
cyclones because of the lack of air-sea interaction (Ogata
et al. 2016), although the genesis frequency agrees wellwith the observations at midlatitudes (Fig. 6c).

According to studies with d4PDF-G, MRI-AGCM3.2
is superior in reproducing atmospheric phenomena in
response to tropical SST variations, in particular, El
Niño and Southern Oscillation (Ueda et al. 2018; Xieet al. 2018), and decadal climate variations such as
Pacific Decadal Oscillation (PDO), Interdecadal PacificOscillation (IPO; Imada et al. 2017), and Atlantic Multidecadal Oscillation (AMO) (Kamae et al. 2017a). A
couple of researches reported reproducibility of specificatmospheric phenomena: Northern Hemisphere blocking(Matsueda and Endo 2017) and atmospheric river (Kamae
et al. 2017b). In addition, the large-ensemble members yield robustness of atmospheric responses forcedby observed SSTs together with probabilistic information (Kamae et al. 2017b; Mei et al. 2019; Yoshida etal. 2017), and reduce atmospheric noise (Kamae et al.
2017b). Using the nonwarming simulations, contributions
from anthropogenic radiative forcing and observed SSTs
are successfully isolated from signals in the past climate
simulation. Imada et al. (2017) confirmed that enhancement of warm summers and cold winters after year 2000
in Japan resulted from atmospheric anomalies associatedwith a decadal La Niña-like condition of IPO, separating
anthropogenic warming, implying that future heat wave
risks in summer and autumn over Japan will intensify byIshii and Mori Progress in Earth and Planetary Science(2020) 7:58

Fig. 5 a Precipitation climatology (mm/day) for June in the past
climate simulations of d4PDF-G and b corresponding observations(GPCP; Adler et al. 2003)

a coincidence of global warming and a decadal phase oftropical Pacific conditions. Similarly, Kamae et al. (2017b)
examined summer climate over western North America forced by radiative forcing response and showed theinternal decadal variations depending on phases of AMO
and PDO.
The large-ensemble and high-resolution outputs support studies approaching mechanisms of the climate system. The model simulations resolve, for example, the tropical precipitation system (Xie et al. 2018), tropical cyclone
activity (Ueda et al. 2018), and internal atmosphere-landinteractions (Sato and Nakamura 2019). Recent observations suggest arctic warming instigates Eurasian cold
anomalies in winter. This is sometimes referred to as
Warm Arctic/Cold Eurasian (WACE; Mori et al. 2014).
Hori and Oshima (2018) studied WACE using the pastand nonwarming simulations and presented one sight ofPage 9 of 22

the recent trend in WACE associated with the Arctic
warming.
Regional model NHRCM has an advantage in reproducing spatially detailed climate states in the East Asian
region, which are highly required by assessment studies.
Higher-resolution regional models perform better in representing heavy precipitations, resolving complex topography, coastal lines, and dynamical process on mesoscales(Giorgi 2019). The model of the 20 km spatial resolution
used in d4PDF satisfactorily simulated temperature andprecipitation variations on the daily time scale (Nosaka
et al. 2020; Takayabu and Hibino 2016). In addition,
the global model provided NHRCM with good-quality
lateral boundary conditions for dynamical downscaling.
The model having detailed topography shows SAT and
precipitation climatologies (Nosaka et al. 2020), snow
coverage (Kawase et al. 2016), and extreme precipitation (Hatsuzuka and Sato 2019; Osakada and Nakakita
2018b) more realistically than the global model. Meanwhile, seasonal biases unavoidably exist in the NHRCM
simulations. Assessment studies often require unbiased
inputs of atmospheric variables on local and short-term
scales, whereas any methodologies do not always satisfy the requirements. Watanabe et al. (2020) proposeda bias correction method specifically designed for largeensemble data, and they demonstrated how to obtain lesserroneous estimates of hydrographic extremes.
Nonwarming simulations with both global and regionalmodels are available in d4PDF. This is a quite unique feature of d4PDF. Examining climate states together with the
nonwarming simulations provides an additional interpretation of past climate variations. Typically, this explains
the extent to which anthropogenic forcing affected theweather and climate events by comparing the past andnonwarming climate simulations, as shown by the aforementioned studies (Imada et al. 2017; Kamae et al. 2017b;
Shiogama et al. 2016). If one focuses on a specific weather
event, the approach is often termed event attribution(EA) technique. The American Meteorological Society
have issued annual special reports collecting materials on
world-wide extreme events (e.g., Herring et al. 2019), in
which the EA technique is widely used. An EA study with
d4PDF was conducted on the 2018 heat wave event inJapan (Imada et al. 2019). In general, severe events accompanying anomalous precipitation over Japanese Islands
are hardly attributed by global model outputs owning
to the lack of resolution for topographic complexity,
mesoscale dynamics and physics, and so on. Therefore, the nonwarming simulations by NHRCM createda new value in interpreting regional climate systems
under global warming. An EA study for a heavy precipitation event occurring in Japan is currently conducted.
Another study with the regional nonwarming simulations
by Kawase et al. (2019) found a topographic contrast ofIshii and Mori Progress in Earth and Planetary Science(2020) 7:58

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Fig. 6 a Observed (Knapp et al. 2010) and b simulated tropical cyclone trajectories. The simulation of d4PDF-G is of one member under the past
climate condition. The trajectories are colored depending on wind speed. c Genesis frequencies of a and b as a function of latitude (Yoshida et al.
2017)

global warming impacts on regional climates on a southern island of Japan, which is called Kyushu island. Namely,
the area of the island is about 37,000 km2 , and ranges ofmountains in the island cause different impacts of global
warming on synoptic phenomena between the westernand eastern parts of the island.
2.4 Global future climate changes · · · (4)Future +1.5 K, +2 K, and +4 K climates treated in d4PDF
correspond to those around 2030, 2040, and 2090, respectively, under the CMIP5 RCP8.5 scenario (Fig. 3). The
database exhibits detailed view of future changes particularly in atmospheric extremes because its resolution is
twice as high as those of the CMIP5-participating models.
Because of the large number of ensemble members available in the database, future changes obtained by simple
arithmetic computations are statistically robust in mostcases. As mentioned in the previous section, the reproducibility of past weather and climatic events is necessarily confirmed in the past climate simulations for reliable
estimations of future climate changes.
The database contains many samples of rare weather
and climatic events concerned by risk assessment studies. Using global model outputs, future changes in tropicalstorm (Yoshida et al. 2017), blocking (Matsueda and Endo2017), atmospheric river (Kamae et al. 2019), and precipitation extremes (Duan et al. 2019; Endo et al. 2017)

have been examined. In these studies, rare events sampled from past climate simulations were compared withthe observations, and the future changes were judged to
be robust, however not necessarily for all aspects of thetargets. One scientific finding is that very intense tropical
cyclones (categories 4 and 5) will occur more frequently
in elongated areas from the south of Japan to Mexico via
Hawaii compared to those under the past climate conditions (Yoshida et al. 2017). As a result, heavy precipitation,
90- and 99-percentile values of daily precipitation, will
possibly increase in these regions as discussed by Kitoh
and Endo (2019) who examined precipitation extremes
in the tails of PDF separately for tropical cyclones andsynoptic events.
Further, the slow translation speed of tropical cyclones
would possibly cause high disaster risks due to longterm heavy rainfall. Whether translation speeds of pastand future tropical cyclones reduce is a current recent
research topic. Yamaguchi et al. (2020) showed a 10%
slowdown at midlatitudes in the +4 K climate scenario of
d4PDF-G because of weakening westerly winds due to thedecrease in baroclinicity in the future atmosphere; however, no signals were found in the past climate simulation,
which is different from an observational study (Kossin
2018). Kanada et al. (2020) also reported a similar slowdown (about 13%) of future TCs passing east of Japan byconducting further dynamical downscaling experiments

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from d4PDF-R with a high-resolution (4 km) nonhydrostatic regional model called the Cloud Resolving Storm
Simulator (CReSS).
Future climate simulations involve uncertainties causedby the different future SST warming patterns and the perturbations of initial and lower boundary conditions. The
six SST trend patterns caused some varieties of futurechanges in frequencies of blocking events that can cause
abnormal weather (Matsueda and Endo 2017), atmospheric river that can cause serious floods (Kamae et al.
2019), and North-Pacific clear-air turbulence concernedby aircraft pilots (Watanabe et al. 2019). Using a conventional approach for detecting SST-forced signals (Rowell
et al. 1995; Sugi et al. 1997), Endo et al. (2017) obtained aconclusion; climatological mean changes in annual maximum 1-day precipitation amounts over Japanese Islands
are affected by uncertainties originating equally from thetwo sources of uncertainty with respect to external forcing
and internal variability, whereas internal variability is the
major source of uncertainty in those over inland China.
This contrast between future changes over Japan and
China is likely affected by future tropical cyclone activity
modulated under the six different climate conditions (Ideet al. 2018).

the season. Osakada and Nakakita (2018a) and Nakakita
and Osakada (2018) studied future change in the frequency of heavy rainfall during Baiu. Two studies commonly confirmed that the frequency of extreme rainfall
events in Baiu will increase in the future simulationsof d4PDF-R. In particular, the former study focused onchanges in typical atmospheric synoptic patterns andreported that Baiu heavy rainfall events mainly occur inwestern Japan in the past climate; however, the areas
extended eastward in future climates with increasing
trends of accumulated precipitation amount per rainfall
event.
Information engineering approaches are effective forhandling large gridded data. A method of self-organizing
maps (SOM) was applied to detecting future changes inextreme rainfall during the Baiu season (Ohba and Sugimoto 2019) and heavy snowfall in Japan (Ohba and Sugimoto 2020; Ohba and Kawase 2020) related to synopticscale weather patterns. Another study by Matsuoka etal. (2019) proposed a neural network system for extract
stationary fronts over Japan from d4PDF-R weather variables.
Heavy precipitation events induced by tropical cyclones
are frequently observed in Japan. Due to the lack ofconsensus on future changes in tropical cyclone (TC)
activity reported in the IPCC Fifth Assessment Report(Working Group I; Stocker et al. 2013), it is not easy toconclude how much TC-induced accumulated precipitation amounts change in the future on average. However,
it is valuable to examine changes in extreme precipitation due to TCs as Hatsuzuka et al. (2020) did. They
tried to find a relationship between TC intensity and90th percentile values of maximum daily precipitation of
more than 4000 TC samples taken from the d4PDF simulations. Consequently, the strong precipitation amounts
increase in the future, and there is no meaningful relationship between precipitation extreme and TC intensity except in areas with wind-facing slopes. However,a future increase in TC-induced precipitation amount is
expected due to the aforementioned slowdown of TCs at
midlatitudes.

Ishii and Mori Progress in Earth and Planetary Science

2.5 Regional future climate changes · · · (5)The climate simulations of d4PDF-R are helpful to evaluate future changes in mean climatological states andrare events governed by mesoscale dynamics and physics,
resolving topographical effects over Japan. As Nosaka
et al. (2020) summarized, future changes in SATs arelarger in higher latitudes during both winter and summer.
These latitudinal contrasts are possibly due to reducing
snow cover and prescribed ocean warming. Precipitation changes appear large on the Pacific side both during
winter and summer owing to weakening monsoonal circulations.
Heavy snowfall particularly on the Sea-of-Japan sideof Japanese Islands is caused by strong winter monsoon winds across the warm ocean. Kawase et al. (2016)
reported increased heavy daily snowfall in central Japaneven in the future +4 K climate. Owing to global warming,
the occurrence of snowfall reduces particularly in coastal
areas. However, heavy snowfall appears to be intensified
at high altitudes, where temperatures are subzero, accompanied by enhanced polar air mass convergence over the
Sea of Japan (Nagata et al. 1986). Recently, Kawase etal. (2020) obtained more detailed images of future snow
cover changes particularly for mountainous areas withdynamical downscaling by NHRCM of 5 km and 1 km
resolution.
Japan has a rainy season called Baiu lasting from
June to July, which accompanies a frontal structure andstrong precipitations particularly in the latter half of

2.6 Scalability in future climate changes · · · (6)Using simulations at three or four levels of warming in thepast and future climate conditions, several studies investigated the relationships between future changes in climate
variables of interest and increases in global mean SAT. If
the former vary linearly with the latter, the climate system
is considered scalable. In this case, the system is rather
simple in terms of effects of global warming. Otherwise,some nonlinear mechanisms have to be considered. In
addition, scalable relationships help us determine futurechanges at any warming levels without additional model
simulations.

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Fujita et al. (2019) compared future changes in precipitation indices between +2 K and +4 K simulations
separately for wet and dry seasons across the globe using
d4PDF-G. Nosaka et al. (2020) investigated the scalability of various aspects in future regional climate changes
using simulation results at four warming levels of d4PDFR. In many cases, future changes of climatological statesand extremes have scalability. According to these studies, increasing rates of heavy precipitation to temperaturechange mostly agree with the Clausius-Clapeyron rate
(7%/K) although they sometimes depend on regions andseasons (Nosaka et al. 2020). For rare events located at thetails of probabilistic distribution functions, scalability may
not always be possible. With regard to tropical cyclones,
the genesis frequency for all categories is scalable; however, scalability is unlikely for categories 4 and 5 (Yoshida
2019, pers.comm.).

In SI-CAT, several dynamical downscaling studies for
spatially refined risk assessments were performed withNHRCM of 5 km resolution. The 5 km model resolves
hourly changes of precipitation with complex topography(Murata et al. 2017). Uemura et al. (2018) demonstrated
the impact of the downscaling on river discharge in
Hokkaido. In general, the 5 km NHRCM gives better
results than the 20 km NHRCM, but these are not always
enough for impact assessment in areas of complex local
topographies and any basin sizes. The projections for disaster assessment of extreme river flooding caused by Baiu
and typhoon events using d4PDF revealed that future
river discharges significantly change in highly urbanizedbasins in Japan and Asia as follows. Hoshino and Yamada(2018a) analyzed spatiotemporal distributions of annual
maximum rainfall in the Tokachi River basin in Hokkaido
and reported that the annual maximum heavy rainfall
will be spatially concentrated in the future climate. Furthermore, Hoshino and Yamada (2018b) expanded theiranalysis to the first-class rivers, which are directly managed by the central government, using d4PDF-R. They
found that annual maximum precipitation amounts averaged in each basin increase in the future whose increasing
ratios depend on geographical locations, and suggested
usefulness of d4PDF for analysis of precipitation extremes,
serious river flood disasters of the more frequent future
extremes, and the necessity of flood control measures.
Figure 8 shows that future changes in the first-class river
basin averaged 60-year maximum precipitation in Japan,
and statistically significant increases in basin-averaged
precipitation ranging from 10 to 30% appear in northern
Japan. Another study by Watanabe and Utsumi (2018) presented the regional characteristics of precipitation basedon a clustering approach, highlighting the accuracy ofclustering classification and frequency for precipitation.
These river-basin-based analyses associated with heavy
precipitation using the d4PDF-R downscaling are useful
to discuss the future changes in river discharge or flood
risks.
The projection of extreme river flooding is directly
applicable to adaptation or economic risk analysis.
Tokioka et al. (2018) examined different countermeasuresfor devising a flood control plan as a case study of the
Hokkaido region. In addition, Tanaka et al. (2018) studied
flood risks via probabilistic rainfall modeling targeting the
Yodo River basin in Japan. Figure 9 shows the exceedance
probability of the economic damage caused by the flooding of the Yodo River based on d4PDF-R. The economic
damage caused by flood risk is clearly higher in the +4
K climate condition. This increase in risk is caused by
the increased frequencies of heavy rainfall in this river
basin. Furthermore, the variability of the damage causedby floods increases as the probability of their occurrence
decreases. Combining the probabilistic rainfall model

Ishii and Mori Progress in Earth and Planetary Science

2.7 Assessment of risks due to heavy precipitation andriver flooding · · · (7)Climate change caused by global warming is expected
to have a major impact on extreme water-related hazards. Specifically, Baiu-related heavy precipitation andtyphoons are major natural disasters in East Asia (Moriand Takemi 2016; Nakakita and Osakada 2018; Osakada
and Nakakita 2018a). To assess the impact of heavy
precipitation and typhoons, quantitative large-ensemble
projections are critically important to capture intensity
and the numbers of events. Therefore, d4PDF is applicable for extreme hazard projections and related impact
assessments. Here, we review typical natural hazard studies using d4PDF in both English and Japanese language
journals.
Tachikawa et al. (2017) projected future changes ofd4PDF-R in extreme river discharge for three major basinsin Japan. Figure 7 shows the nonexceedance probabilities
of river discharge. The 1/200-year occurrence probability events for the annual maximum 24 h rainfall of the+4 K simulations in major river basins of Japan are 1.3
to 1.4 times larger than those of the past climate simulations. Therefore, they concluded that, in general, changes
that are 1.5 to 1.7 times larger are expected for annual
maximum river discharge over Japan. The 200-year annual
maximum 24 h rainfall event in the +4 K future climate
is equivalent to the 900-year event in the past climate
simulation. Hence, the return period of future extreme
river discharge will significantly decrease (more frequent)
due to climate change. Such analyses relevant to extremes
are possible using the large ensembles without statistical
hypothesis. Similar results were obtained in different river
basins in Japan and other countries: the Nagara river basin
by Harada et al. (2018), the Hokkaido region by Kimura
et al. (2018) and (Masuya et al. 2018), and Indochinese
Peninsula by Hanittinan et al. (2018).

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Fig. 7 Nonexceedance probabilities of river discharge for three major rivers in Japan (left: Arakawa river, middle: Shonai river, right: Yodo river; blue:
past climate, red: +4 K climate) based on d4PDF-R, after Tachikawa et al. (2017)

Fig. 8 Future changes in the maximum precipitation averaged over the first-class river basins (future minus past, unit: %), after Hoshino and Yamada(2018b)Ishii and Mori Progress in Earth and Planetary Science(2020) 7:58

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Fig. 9 Exceedance probability of economic damage to Yodo river flooding by d4PDF-R (past: blue, future: red), after Tanaka et al. (2018)

and large-ensemble climate data, process-based ensemble flood risk curves are derived without assuming any
parametric error distributions.
The large ensemble helps in developing statistical methods for river flood risk assessment studies. Kim et al.
(2017) developed a statistical downscaling technique for
precipitation based on d4PDF-G. The statistically downscaled results show a plausible mimic to the 20 km AGCM
output (Mizuta et al. 2012) in the frequency of daily precipitation amounts, which can be applied to statistical
weather generator systems. Meanwhile, Watanabe et al.
(2018) applied two bias correction methods for hourly
precipitation to d4PDF-R. The overestimation bias in precipitation was reduced by the bias correction, and theprobability of historical extremes was reproduced in biascorrected outputs. A similar bias correction method was
examined for the cumulative rainfall duration by Kojima
et al. (2018). Furthermore, Kitano et al. (2017) used d4PDF
precipitation data for validating an extreme value theory.
They demonstrated an advantage of using large ensemble
for the convergence of extreme values, which cannot bevalidated by observed data owing to the limited number ofsamples. Kuzuha and Senda (2018) performed an uncertainty of extreme value analysis for 100-year precipitation
around Japan.
2.8 Assessment of coastal risks · · · (8)The projection of future changes in storm surges is important for estimating the risk of coastal flooding. As severe
storm surges excite depending on the combination of thetropical cyclone intensity, translation speed, and track to aparticular bay, their probability of occurrence is expected
to be equal to or less than once per 100 years. Unless large-

ensemble simulations were available, probabilistic determination of the occurrence of tropical cyclone-induced
storm surges is difficult. Mori et al. (2019) projected
future changes in regional storm surges targeting Tokyo
and Osaka Bays using d4PDF. The projected storm surgeheights in Tokyo and Osaka Bay will increase by 0.3 to 0.45
m for a 100-year event. Mori et al. (2019) indicated thatboth frequency and intensity changes of tropical cyclones
will significantly give impacts on storm surge heights. The
increase in 100-year storm surge height was first discussed
in the IPCC special report on the Ocean and Cryospherein a Changing Climate (SROCC; Pörtner et al. 2019). Similar case studies were conducted for the Korean Peninsula
by Yang et al. (2017) and for Bangladesh by Al Mohit etal. (2018). For example, Fig. 10 shows the projection of
future storm surge heights at Yeosu in Korea (Yang et al.
2018), clearly demonstrating that the confidence intervals
become narrower as the number of ensembles increases.
Winter extratropical cyclones also generate storm surges
and storm waves in the Sea of Japan. Taka et al. (2018);
Taka et al. (2019) analyzed changes in winter extratropical cyclones using d4PDF-R. The intensity of the strongest
extratropical cyclones will increase by approximately 12
hPa in the +4 K climate; however, the climatological average will remain unchanged. The tracks of extratropical
cyclones will also be shifted northeastward, and the number of events will decrease on the main island of Japan, as
shown in Fig. 11.
In the meantime, extreme ocean waves is important forthe coastal, ocean, and environmental engineering. As afirst step, the future projections of the global wave climate
under a global warming scenario using a dynamical wave
model were conducted by Shimura et al. (2015); Shimura

Ishii and Mori Progress in Earth and Planetary Science(2020) 7:58

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Fig. 10 Projection of future storm surge heights at Yeosu, Korea (10 ensembles: green, 50 ensembles: blue, 100 ensembles: red, shade indicates the
95% confidence intervals), based on d4PDF-R, after Yang et al. (2018)

et al. (2016); Shimura et al. (2017) and Morim et al. (2019).
Robust changes in wave characteristics are projected overlarge sectors off of the world’s coasts (covering approximately 50% of the coastline; Morim et al. 2019). In thewestern North Pacific region, future projected changes insummertime mean wave heights, which are influenced bytropical cyclone (TC) changes, significantly vary depending on the spatial SST pattern over the tropical Pacific.
Future projections of 10-year return period wave heights
generated by TCs show both increases and decreases fromthe past climate ranging from ± 4 m for different regions
over the ocean (Shimura et al. 2015).
For using the model-generated TCs for coastal assessment studies, Ide et al. (2018) conducted a spatiallydetailed analysis for the future TC characteristics inthe western North Pacific sector, applying a bias correction of TC intensity similar to Sugi et al. (2017).
They showed a large uncertainty in minimum surfacepressure changes among the future SST trend patterns.
This should be paid attention to when applying d4PDF’s
TCs to impact assessments of future storm surges inthis region. Webb et al. (2019) focused on TCs in theSouthern Hemisphere and developed a TC tracking
method which well resolves TC decay latitudes necessary
for coastal impact assessments even at middle-to-highlatitudes. The statistical characteristics of observed TCs
needed by impact assessments are rather poorly known,whereas d4PDF enables us to assess hazards related toTCs, as seen above. In addition, the use of TCs in thelarge-ensemble simulations could improve stochastic

Fig. 11 Future changes in severe winter storm tracks around Japan, based on d4PDF-R (future minus past, unit: numbers per year in each
1°-longitude and 1°-latitude box), after Taka et al. (2018)

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TC models commonly used in engineering fields(Umeda et al. 2019).

in October 2019 (MLIT 2019). Furthermore, MLIT
released a new policy of flood control agenda considering climate change based on the above discussion (MLIT
2020). The interministerial committee of MLIT, Ministry of Agriculture, Forestry and Fisheries (MAFF), andForestry Agency was established in October 2019 fordiscussing climate change impact and adaptation along
Japanese coasts (MLIT and MAFF 2020). A study of adaptation measures for coastal defense has begun at thecentral governmental level. Furthermore, Osaka Prefectural Government started to discuss a construction plan
for rebuilding three major storm surge barriers along the
Osaka Bay Area. The construction of this new gate will
begin around 2022 to withstand future storm surges andsea level rise up to 2100. The technical council of river
structure in Osaka Prefecture started to analyze long-term
typhoon characteristics derived from d4PDF for coastal
gate design (Osaka Prefecture 2020). This will be the firstcase of climate scenario application to large-scale infrastructure designed in Japan. Although real applications will
increase, technical engineering and scientific support will
be highly demanded by governmental agencies for conducting national projects requiring a large budget andother resources.
In addition to governmental applications, d4PDF contributes to climate education. Kumamoto Prefecture Environment Center setup interactive display of “Global
warming and climate simulation” based on d4PDF. It isessential that both public and young students understandthe risks of climate change.

Ishii and Mori Progress in Earth and Planetary Science

2.9 Other risk assessment topics · · · (9)The impact of climate change on water resources andagriculture is essential not only to reduce negative consequences but also to increase opportunities. Iizumi et al.
(2018a) estimated economic production losses for major
crops at a global scale during the period from 1981 to
2010, relative to those observed in the early twentiethcentury, based on the d4PDF-G past and nonwarming
simulations. The crop yield simulation demonstrated significant decreases at lower latitudes as well as significant
yield increases at higher latitudes. Furthermore, Iizumi
et al. (2018b) summarized agricultural research topics forclimate change adaptation. Kim et al. (2018) conducteda survey of climate change impact on heating and cooling system for air conditioning by targeting Yokohama
Minatomirai 21 Area, Japan. They showed that electric
power consumption will increase to more than 140% in
the +4 K climate scenario.
The use of large-ensemble climate simulations suchas d4PDF is a big challenge for impact projection andassessment, whereas increasing the number of ensembles makes it difficult to use process models becauseof limited data storage and computational resources. A
novel data service system developed by Nakagawa et al.
(2020) could be a solution to the problem, by which data
users access and download the data of minimum requirement. Also, the system helps realize an integrated system
for impact, adaptation, and vulnerability models highly
desired together with the ensemble projections.
2.10 Social Implementations · · · (10)The extreme natural hazards estimated from a numberof climate extremes in d4PDF showed significant futurechanges. It is important to apply these estimates to adaptation, implementation, and policy decisions. The Japanese
central government released a report entitled “National
Plan for Adaptation to the Impacts of Climate Change”
on November 25, 2015, which triggered the movement ofclimate adaptation in Japan (Ministry of the Environment2018).
Since the release of d4PDF in 2015, the central andlocal government agencies of Japan have started to pay
attention to climate simulation products and d4PDF. The
first real application of d4PDF was an emergent riverflood control management policy at Hokkaido in 2017
in response to typhoon-induced severe river flooding inthe summer of 2017. Since 2018, the technical committee of the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan mainly uses d4PDF
for future river flood control management and released
revised technical implementation of heavy precipitation

3 Conclusions
The large-ensemble and high-resolution climate simulation database called d4PDF was developed for extensive
use in impact/risk assessment and policy decision-making
for adaptation measures. Since the first data release at theend of 2015, it has become a common future scenario
used to realize adaptation measures in Japanese administrations and agencies. Researchers working on global
warming have actively incorporated d4PDF in their studies. Consequently, d4PDF has contributed toward theformation of an interdisciplinary research community on
global warming across Japan. Through the discussion inthis community, physical climate science is expected tosatisfy the needs of risk assessments (Sutton 2019).
Large-ensemble climate simulation studies haveadvanced our understanding of past and future climatechanges and have provided statistical reliability forassessing the impacts of significant weather and climatic
phenomena in the future. Among impact assessment
studies, there has been a “paradigm shift” from parametric statistical approaches to nonparametric approaches.
Namely, only by incorporating simulation results directly
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evaluate risks for the largest class of hazards with return
periods exceeding thousands of years, as well as develop
useful materials when considering adaptation measures.
Although d4PDF comprises climate simulations at several warming levels with uncertainties in the future projections of the CMIP5 participating models, its rangeof uncertainties is limited. It may be desirable to consider the uncertainties of climate prediction experiments
using multiple physics, multiple models, and more social
development scenarios. There is room for ingenuity inexperimental setups. However, because it is not easy toencompass all uncertainties by a single research group, arealistic approach is to provide value-added informationthat incorporates the knowledge and results of existing
researches. An alternative solution is a multi-model intercomparison under an international collaboration as CMIP
(Sutton 2019).
Missing air-sea interaction in climate simulations is critical in some cases. This tends to intensify tropical cyclones(e.g., Ogata et al. (2016)) and fails to present a strong
air-sea interaction between rainfall and sea surface temperature east of the Philippines (e.g., Duan et al. 2008;
Kobayashi et al. 2005). Nonetheless, one of the biggest
challenges of future climate prediction in Japan is toaccount for uncertainties in future ocean changes. Surrounded by the Kuroshio, the Oyashio, and the closed
seas: the Sea of Japan and the Sea of Okhotsk, each ofwhich has complex variations, the past climate over Japan
was affected largely by these. The axis of Kuroshio locatesclosely along 35°N. However, those in the model simulations are shifted north in many models, because theresolution adopted by most CMIP5 climate models is
too coarse to resolve the regional atmosphere and ocean
climates in this region. As a result, future changes inmodel-generated SSTs among the six CMIP5 models showa large variety in seas around Japan (Fig. 12). For theabove reasons, the future changes in SST around JapaneseIslands are seen as lack in persuasiveness. Unless we correctly understand details of the future oceanic changes,
reducing true uncertainties in climate changes over Japancannot be realized. Information on ocean warming is alsostrongly needed for policy decision-making in this country. Now, it is important to take a step toward constructing
a new database in which future states of atmosphere andthe oceans are physically consistent.
Practically, the outputs of regional atmospheric model
are used frequently. User demand for spatiotemporal resolution is extremely high. Such regional climate data are
hopefully defined on the same grid as that of the National
Land Numerical Information of a typically 1 km meshissued by the Ministry of Land, Infrastructure, Transport and Tourism, Japan. Similar products as d4PDF
are available for the Japanese domain of 5 km and 2
km meshes (Murata et al. 2017), although the ensemble
sizes are restricted to approximately four due to limited
computer resources. Validating high-resolution outputs is
not easy even when observations of AMeDAS are used,which is a high-density station data network at a spatial interval of approximately 20 km over Japanese Islands
maintained by the JMA. Therefore, an optimum resolution that guarantees reliability of the outputs is not sohigh. For users who desire high-resolution information,

Ishii and Mori Progress in Earth and Planetary ScienceFig. 12 Same as Fig. 2, but for seas around Japan

Ishii and Mori Progress in Earth and Planetary Science(2020) 7:58

dynamical downscaling to higher resolution is one of thesolutions (Hoshino et al. 2020; Kawase et al. 2018; Kawase
et al. 2020; Sasai et al. 2019). Meanwhile, the models usedin d4PDF are the same as those used operationally at
JMA. Thus, the model performances are verified every
day on an operational basis. The current resolution of
the JMA operational regional model is a 2 km mesh,which is expected to reach 1 km or less in the future. At
present, only products of temperature and precipitation
are satisfactory to users. Other products such as relative
humidity, wind, and solar radiation also have user applications. To improve performance on climatological timescales, detailed land processes and an urban model havebeen introduced to the current model (Murata et al. 2013).
It is inevitable that the regional model becomes an earthsystem model, including the human components (Giorgi2019).
Abbreviations
AGCM: Atmospheric General Circulation Model; AMeDAS: AutomatedMeteorological Data Acquisition System; CMIP5: Climate Model
Intercomparison Project Phase 5; d4PDF: Database for policy decision-making
for future climate changes; DIAS: Data Integration and Analysis System; IPCC:
Intergovernmental Panel on Climate Change; JAMSTEC: Japan Agency for
Marine-Earth Science and Technology; JMA: Japan Meteorological Agency
MEXT: Ministry of Education, Culture, Sports, Science, and Technology, Japan;
MRI: Meteorological Research Institute; NIES: National Institute forEnvironmental Studies; RCP8.5: Representative Concentration Pathway with aradiative forcing level of 8.5 W/m2 ; SI-CAT: Social Implementation Program on
Climate Change Adaptation Technology
Acknowledgements
The Earth Simulator (ES) maintained by JAMSTEC is extremely useful for
conducting the large-ensemble climate simulations, and the continuous
supports of the ES staff members on our global warming studies are thankfully
acknowledged. Their appreciation is extended to Prof. Kimoto, Prof. Nakakita,
Dr. Takayabu, all colleagues of the d4PDF team, and DIAS staff members who
worked together for planning, model integration, post processing, writing
papers, and data management. Figures and processed data were provided byDrs. Y. Imada, R. Mizuta, Y. Okada, Y. Tachikawa, T. Tanaka, and K. Yoshida.
Authors’ contributions
MI designed the review. Topics on climate studies were summarized by MI
and others on risk assessment and social implementations by NM. All authors
read and approved the final manuscript.
Authors’ information
MI and NM have participated respectively in Theme C: Integrated Climate
Change Projection and Theme D: Integrated Hazard Prediction of Integrated
Research Program for Advancing Climate Models (TOUGOU).
Funding
This work was supported by Program for Risk Information on Climate Change(SOUSEI, from FY2012 to FY2016) program, Integrated Research Program for
Advancing Climate Models (TOUGOU, from FY2017 to FY2021; Grant Numbers:
JPMXD0717935561, JPMXD0717935498), Social Implementation Program on
Climate Change Adaptation Technology (SI-CAT, from FY2015 to FY2019), andData Integration and Analysis System (DIAS) funded by the Ministry of
Education, Culture, Sport, Science, and Technology (MEXT), Japan. The Earth
Simulator was used for building up the database under “Strategic Project with
Special Support” (FY2015) of JMASTEC.
Availability of data and materials
The general information is available at http://www.miroc-gcm.jp/~pub/d4PDF/index_en.html except for simulations recently performed. All data ofd4PDF are downloadable from the DIAS repository. The entries of the global

Page 18 of 22

and regional model outputs are http://search.diasjp.net/en/dataset/d4PDF_
GCM and http://search.diasjp.net/en/dataset/d4PDF_RCM, respectively. The
database is open to any users in domestic and foreign countries, regardlessuses for profit or non-profit purposes since 20 August 2018.
Competing interests
The authors declare that they have no competing interest.
Author details
1 Department of Atmosphere, Ocean and Earth System Modeling Research,
Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052,
Japan. 2 Climate and Environment Research Promotion, Japan MeteorologicalBusiness Support Center, Kanda-Nishikicho, Chiyoda-ku, Tokyo 101-0054,
Japan. 3 Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji,
Kyoto 611-0011, Japan.
Received: 24 December 2019 Accepted: 31 August 2020

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