Nosaka et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00341-3(2020) 7:27

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Scalability of future climate changes acrossJapan examined with large-ensemble
simulations at + 1.5 K, +2 K, and + 4 K
global warming levels
Masaya Nosaka1* , Masayoshi Ishii1, Hideo Shiogama2, Ryo Mizuta1, Akihiko Murata1, Hiroaki Kawase1
and Hidetaka Sasaki1

Abstract
Large-ensemble climate experiments were performed to simulate future climates for a + 1.5 K rise in the global
mean surface air temperatures relative to preindustrial levels as a subset of the database for Policy Decision making
for future climate change (d4PDF), using the Non-Hydrostatic Regional Climate Model (NHRCM) with 20 km grid
spacing. Along with present climate, + 2 K and + 4 K experimental outputs from the d4PDF already available, weinvestigated responses of surface air temperature (SAT) and precipitation on regional scales over Japan to global
warming. The reproducibility of the present climate experiment is satisfactory to investigate future changes in theJapanese climate, and dynamical downscaling from the global to the regional climate states improves thefrequency of heavy daily precipitation. In the future, SAT over Japan rises linearly with and faster than the global
mean SAT. The meridional contrast of SAT rises becomes more pronounced as global warming progresses. Winterprecipitation decreases/increases linearly in the western/eastern Japan, reflected by weakening of future wintermonsoons. Annual maximum daily precipitation (R1d) shows a closely linear increase with the global SAT rise, but
annual precipitation is mostly unchanged. The global mean SAT change from + 1.5 to + 2 K enhances R1d by 2.7%
over the Japanese Islands, although the increase of R1d varies by regions. The increase in R1d is 5% in northern
Japan, where the SAT increases are greater than those in other regions.
Keywords: Climate change, Regional climate model, Large-ensemble climate experiments, Paris Agreement, Surface
air temperature, Precipitation

Introduction
The Meteorological Research Institute has been developing a non-hydrostatic regional climate model (NHRCM,
Sasaki et al. 2008) to predict the future climate for Japan
based on an operational non-hydrostatic model from theJapan Meteorological Agency. We performed highresolution climate simulations using 5 km grid spacing
and using 2 km grid spacing NHRCMs nested from the* Correspondence: snosaka@mri-jma.go.jp
1
Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052,
Japan
Full list of author information is available at the end of the article

global model with 20 km grid spacing (Mizuta et al.
2012; Murata et al. 2017). Increasing the resolution of
the NHRCM improves the representation of the terrain
and leads to the elucidation of detailed physical processes. Murata et al. (2017) showed that the NHRCM
better represents the extremes of surface air temperature(SAT) and precipitation by increasing the horizontal
resolution from 5 to 2 km. However, since the highresolution model integration requires sizable computational resources, it is not easy to conduct large-ensemble
simulations for estimations of the uncertainties in themodel-simulated variables.

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(2020) 7:27

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Mizuta et al. (2017) created a climate simulation dataset, called the database for Policy Decision making for
future climate change (d4PDF), using the MeteorologicalResearch Institute atmospheric general circulation model(MRI-AGCM, Mizuta et al. 2012) with 60 km grid spacing and the NHRCM with 20 km grid spacing. The
database contains the outputs of large-ensemble experiments over a 5000 year span under present and futureclimate conditions; in the latter, the global average SAT
is 4 K higher relative to preindustrial levels. Fujita et al.
(2019) created a dataset for + 2 K conditions as a subdataset of the d4PDF for risk assessments of extreme
weather events based on the 2 K goal of the Paris Agreement. The resolution of the model used for the d4PDF
is coarse compared to the experiments using 2 km or 5km grid spacings, but many model-simulated samples of
infrequent phenomena are available thanks to largeensemble simulations. Kawase et al. (2016) showed thatrare and strong snowfall amounts in mountainous areas
became stronger under a + 4 K climate scenario thanunder the present climate. Fujita et al. (2019) investigated the future changes of the relationship between extreme hourly precipitation events and daily mean SATs
in Japan, and found that hourly precipitation will become stronger for higher-SAT days and slightly weaker
for lower-SAT days in the future.
The Paris Agreement aims to pursue efforts to limit
the SAT rise to + 1.5 K, and to at least keep the global
temperature increase below + 2 K compared to preindustrial levels. Researchers have been investigating thedifferences of changes in extreme events and impacts onhuman society and on ecosystems between the + 1.5 and+ 2 K warming cases (Masson-Delmotte et al. 2018). In a
research project, the HAPPI (half a degree additional
warming, projections, prognosis, and impacts) multimodel experiments (Mitchell et al. 2016; Mitchell et al.
2017) provided an assessment framework and a simulation dataset for climates at the + 1.5 K and + 2 K levels.
Liu et al. (2018) showed that a moderation of droughtrisks appears between + 1.5 and + 2 K levels. Lee et al.
(2018) indicated that warming by more than half a degree brings more intense and more frequent precipitation events to the Asian monsoon region. Shiogama
et al. (2019) suggested that regions with relatively large
increases of extreme hot days, heavy rainfall, highstreamflow and labor capacity reductions related to heatstress coincide with those countries that are characterized by low CO2 emissions, low income, and high vulnerability. Limiting global warming to 1.5 K, comparedto 2 K, lowers these inequalities.
Most studies of the + 1.5 K climate simulation haveused global models, but von Trentini et al. (2019) andLeduc et al. (2019) used regional climate models for theEuropean and North American domains, respectively.

However, there was no regional climate dataset that represented spatially detailed climate states around Japan
under the + 1.5 K climate scenario.
The purpose of this study is to investigate the regional
characteristics of the increase rate of surface airtemperature and precipitation using the large ensemble
past and future climate simulation assuming + 1.5 K, +2 K, and + 4 K relative to preindustrial period. For thispurpose, over a 1500 year span of ensemble + 1.5 K climate simulations with the NHRCM was performed as asubset of the d4PDF. We discuss the linearity of regional
climate states for the present, + 1.5 K, + 2 K, and + 4 K
climates, that is, scalability. In addition, we present results which suggest the necessity of additional mitigation
measures for the + 2 K climate case rather than for the +1.5 K climate case.

Nosaka et al. Progress in Earth and Planetary Science

Methods/Experimental
Experimental design

We used the MRI-AGCM with a 60 km grid spacing(Mizuta et al. 2012) and the NHRCM20 with a 20 km
grid spacing (Sasaki et al. 2008). Using the MRI-AGCM
outputs as boundary conditions, dynamical downscaling
was performed with the NHRCM20. The experimental
design followed Mizuta et al. (2017) and Fujita et al.
(2019), who conducted large-ensemble experiments
under the past climate condition (PAST), + 2 K (d2), and+ 4 K (d4) global mean SAT (gSAT) conditions. In thisstudy, we conducted experiments for the + 1.5 K gSAT
case relative to preindustrial levels (d1.5).
One hundred simulations of the present climate experiments had already been conducted for the period from1951 to 2010 (Mizuta et al. 2017), in which the monthly
sea surface temperature observations (COBE-SST2,
Hirahara et al. 2014) were prescribed. In each simulation, the atmospheric models were perturbed by different initial conditions and by spatio-temporally varying
sea surface temperatures (SST) perturbations. The SST
perturbations reflect the observational uncertainties.
In the + 1.5 K experiment, we prescribed the SSTs that
represent future SST patterns (ΔSSTs) obtained from six
models of the Coupled Model Intercomparison Project
Phase 5 (CMIP5) plus the detrended COBE-SST2. The
future SST patterns are the differences between the averages for 1991–2010 in the historical experiments andthose for 2020–2039 in the Representative Concentration Pathway 8.5 (RCP8.5) scenario experiments. To
force the gSATs of the + 1.5 K experiment with MRIAGCM to become + 1.5 K, each ΔSST was multiplied bya scaling factor (Table 1; Shiogama et al. 2010) which
was estimated from the gSAT changes in the CMIP5 experiments (Mizuta et al. 2017). For the greenhouse gas
and aerosol concentrations, the values for 2030 in the
RCP8.5 scenario were used. Nine ensemble simulations

Nosaka et al. Progress in Earth and Planetary Science(2020) 7:27

Table 1 Scaling factors for future SST trend patterns obtained
from CMIP5 RCP8.5 runs by six models
Model

Scaling factor

CCSM4

1.05828

GFDL-CM3

0.638859

HadGEM2-AO

1.17874

MIROC51.01876

MPI-ESM-MR

1.09218

MRI-CGCM3

1.22361

The six climate models are CCSM4, GFDL-CM3, HadGEM2-AO, MIROC5, MPIESM-MR, and MRI-CGCM. See the “Abbreviations” section for their full forms

starting from different initial conditions were performed
with the aforementioned future SSTs added to the SST perturbations. The integration period of the + 1.5 K climate
simulations was 29 years, in which the interannual variations of the SSTs are those from 1982 to 2010. The total
number of ensembles is 1566 years. Unless otherwise noted,
1450 years are used for analysis of the PAST and 1566 years
are used for analysis of the future climate experiments.

Results and Discussion
Reproducibility of the present climate over the JapaneseregionDynamical downscaling of the NHRCM improves theexpressions of topographic effects and the reproductionof extreme phenomena, and maintains the synoptic
fields of the MRI-AGCM. Areas of complex mountainous terrain along the center of Japan are more realistically reproduced by the NHRCM20 than by the MRIAGCM (Fig. 1). The distributions of the climatologicalsea-level pressures (SLP) and the SATs simulated byPage 3 of 13

NHRCM20 under the present climate condition are
similar to those of the MRI-AGCM (Fig. 2a–b). The precipitation amounts in the mountainous areas of Honshu
and on the Sea of Japan side are greater for the NHRCM
than for the AGCM because of the realistic expressionof topography in the NHRCM. The frequency of daily
precipitation events is more realistic in the NHRCM20
than in the MRI-AGCM (Fig. 2c). The NHRCM20 has afrequency of daily precipitation that is below 300 mm,
close to the observations, and it can reproduce strong
daily precipitation amounts greater than 500 mm.
Figure 3a–c shows the scatter plots of the simulated
and observed daily surface air temperatures (SAT). Here,the Automated Meteorological Data Acquisition System(AMeDAS) is used as the observation data, and themodel grid data closest to the AMeDAS stations areused for comparison. The daily mean SATs (Tmean)
agree with the observations so that the model-simulated
values are located mostly along the identical line. The
bias, root mean square difference (RMSD) and correlation coefficient between the model-simulated and observed Tmean values are 0.48, 1.0 K, and 0.98,
respectively (Table 2). The annual minimum SATs
(Tmin) values are also reproduced well by the
NHRCM20, especially at grid points where the temperatures are greater than – 15 °C and are distributed mostly
along identical lines. Although the annual maximum
SATs (Tmax) are distributed near identical lines, themodel-simulated temperatures are generally slightly
underestimated and are overestimated at some grid
points in those specific areas where the topographic effects are great. The Tmin values of the NHRCM20 are
highly biased for observed minimum temperatures below

Fig. 1 Model topographies of a MRI-AGCM and b NHRCM20. The right panel shows several area names, which are referred to in the text. Redline indicates the Pacific Ocean side in Honshu, and purple line indicates the Sea of Japan side in Honshu

Nosaka et al. Progress in Earth and Planetary Science(2020) 7:27

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Fig. 2 Annual accumulated precipitation, SLPs, and frequencies of daily precipitation. a and b show annual precipitation (mm, shaded) andannual mean SLP (hPa, contours) by a the MRI-AGCM and b the NHRCM20. c shows the frequencies of daily precipitation for all grids on theJapanese Islands (black, AMeDAS observations; blue, AGCM; and red, NHRCM20)– 15 °C. This is due to a “no sea-ice condition” in the
Okhotsk Sea during the entire simulation, which is a
mistake in all experiments of the d4PDF with the
NHRCM20. As a result, the Tmin values in eastern Hokkaido are overestimated in the simulations. The simulated Tmax values are biased by − 1.2 K compared to theobservations (Fig. 3b and Table 2). This is speculated tobe due to the land-cover category definition and the land
surface scheme currently adopted in the NHRCM20.
This improvement is a future work.
Figure 3d shows a scatter plot of the annual accumulated
precipitations. The annual precipitation is reasonably well
reproduced by the NHRCM20 with bias of 9%, an RMSD of
28%, and a correlation coefficient of 0.78. The biases are
smaller than the standard deviations of the interannual variations for 60% of all stations. Figure 3e shows a scatter plotof the annual maximum daily precipitation (R1d). The annual maximum daily precipitation is well reproduced by the

NHRCM20 with slightly better values for the bias, theRMSD, and the correlation coefficients than those of the annual averages. The biases at 90% of all stations are smaller
than the standard deviations of the interannual R1d’s.
Murata et al. (2017) conducted present-climate simulations with a 5 km-mesh NHRCM (NHRCM05) andwith a 2 km-mesh NHRCM using an urban canopy
model (NHRCM02) over a 20 year span. The NHRCM20
shows good performance for the daily scale precipitationand temperatures compared to the NHRCM05 and the
NHRCM02, although an increase in the horizontal resolution slightly improves the reproducibility of the SATs
and of the precipitation levels due to the effects of topography (Table 2). This result is consistent with that of
Takayabu and Hibino 2016. We cannot conduct largeensemble experiments with high-resolution models due
to limitations of computational resources. Note that thelateral boundary conditions of the NHRCM05 and theNosaka et al. Progress in Earth and Planetary Science(2020) 7:27

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Fig. 3 Scatter plots of observed vs. model simulated daily temperature and precipitation. The x and y axes indicate the AMeDAS observations
and the model simulations, respectively. In order to match the elevations between the model grid and the AMeDAS, the height correction of thesurface air temperature is applied to simulated temperatures using a single lapse rate of 0.0065 Km−1 (a annual mean temperature, b Tmin, cTmax, d annual precipitation, and e R1d)

NHRCM02 are the MRI-AGCM, with an approximate
20 km horizontal resolution, which are different fromthat of the NHRCM20 conducted in this study. In thissense, the NHRCM20 has an advantage in performing
large-ensemble simulations. Note that a great advantageof the 2 km- and 5 km-resolution NHRCMs is the highreproducibility of sub-daily heavy precipitation events
required by the assessment studies (Sasaki et al. 2011).

Future changes in SATs

Figure 4 shows the future changes in the SATs. The
north-south contrasts of SAT warming are seen clearlyin d2 and d4. The SAT increases in winter are larger
than in summer. In winter, the temperature increases
are greater in the mountainous areas of Honshu compared to the surrounding plains. Such features are notseen in summer.

Table 2 Biases, RMSDs, and correlation coefficients
Bias

RMSE

Correlation coefficient

Resolution

20 km

5 km

2 km

20 km

5 km

2 km

20 km

5 km

2 km

Tmean

0.48 K− 0.38 K

0.36 K

1.0 K

0.66 K

0.77 K

0.98

0.99

0.99

Tmax

− 1.2 K− 2.1 K

− 0.31 K

2.5 K

2.3 K

2.0 K

0.69

0.86

0.78

Tmin

0.27 K− 1.5 K

− 1.1 K

3.1 K

3.4 K

2.9 K

0.91

0.91

0.93

Annual precipitation

9.0%

11%

1.0%

28%

22%

14%

0.75

0.83

0.91

R1d

1.1%

5.6%

7.8%

25%

21%

22%

0.77

0.87

0.89

Tmean, Tmin, Tmax, annual precipitation, and R1d in NHRCM20, NHRCM05, and NHRCM02 are compared to the AMeDAS observations

Nosaka et al. Progress in Earth and Planetary Science(2020) 7:27

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Fig. 4 Seasonal mean SATs and future SAT changes. Seasonal mean SATs (K) of PAST (a and e) and the future SAT changes of d1.5 (b and f), d2(c and g), and d4 (d and h). Values for DJF and JJA are shown in the left and right columns, respectively. The mean values are based on 1450
years for PAST and 1566 years for the future climate

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Fig. 5 Scatter plots of increases in seasonal mean SAT relative to the present climate as a function of latitude. a DJF and b JJA. Values at all land
grids on the Japanese Islands were used and were based on 1450 years average for PAST and 1566 years average for the future climate. The
green, yellow, and red circles indicate d1.5, d2, and d4, respectively

Figure 5 shows the relationships between latitude andSAT increases. As mentioned above, the distributions of
the SAT warming over Japan are caused by the ΔSST
distributions (Fig. S1) and the anthropogenic forcing
given in the d4PDF experiments (Mizuta et al. 2017).
The meridional gradient for d1.5 is generally zero, while
greater warming in the northern region is clear in d2
and in d4. As decreases in snow accelerate SAT increases due to snow-albedo feedback (Matsumura andSato 2011; Scherrer et al. 2012), future SAT increases
appear abnormally large in winter for those grid points
covered by snow in PAST. In summer, the relationshipbetween latitude and the SAT increases is clear for d2
and d4 with meridional gradients (± standard deviations)of + 0.029 ± 0.020 K°−1 and + 0.089 ± 0.025 K°−1, respectively. The change for d1.5 is + 0.013 ± 0.022 K°−1

and is considered as insignificant, according to the student’s t test.
How future SATs change as the gSATs increase is discussed using Fig. 6. Here, Tmean, Tmax, and Tmin are
the averages for all grid points located over the JapaneseIslands. The Tmean increases are 1.2 K, 1.9 K, and 4.6 K
for the d1.5, d2, and d4 cases, respectively, relative tothose of PAST. Namely, they are approximately linearly
1.3 times larger than the gSAT increases. Therefore, it isconcluded that future changes in the Tmean values are
generally scalable with respect to global warming levels.
Similarly, the Tmax and Tmin increases are scalable
with factors of 1.2 and 1.6 to the gSAT, respectively.
Due to reductions of snow coverage, the Tmin rate ofincrease is higher than that of the Tmean. Since the
Tmean is approximately average of the Tmax and theFig. 6 Future changes in Tmean, Tmax, and Tmin. a Tmean, b Tmax, and c Tmin are averaged on all land grids on the Japanese Islands relative
to those of PAST as a function of gSAT increases. The bars with solid line indicate standard errors of the ensemble mean, and the bars withdashed line indicate standard deviations. Linearly fitted line is shown in each panel. The black, green, yellow, and red circles indicate PAST, d1.5,
d2, and d4, respectively

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Tmin, the increase rate of the Tmean is larger than thatof the Tmax. The standard errors of ensemble-meantemperatures are small, and therefore, the futurechanges in the mean value of the Tmean, Tmax, andTmin are regarded to be significant. The standard deviations of the Tmean are smaller than that of the Tmax
and Tmin. The spread of the Tmean, Tmax, and Tmin
has no apparent response to the gSAT rises. Since theSAT in the Japanese Islands rises linearly, assuming 3 K
rise in gSAT, Tmean, Tmax, and Tmin is estimated tobe 3.2 K, 2.9 K, and 3.9 K, respectively.

Table 3 shows the correlation coefficient patterns ofprecipitation changes in the Japanese Islands. The correlation coefficients for the changes in annual and seasonal average precipitation amounts between futureclimate experiments are very high. This indicates thatthe changes in precipitation amounts appear similarly
regardless of the temperature increase, thus responding
to the future SST trend patterns prescribed. In JJA, thechanges in precipitation amounts are highly correlated
between d1.5 and d2. In contrast, there are lower correlation coefficients between d1.5 and d4 and between d2
and d4 than for the other cases. As presented later, these
are unlikely to respond simply to future SLP changes(Fig. 7).
One of the characteristics of the Japanese climate is
the seasonal contrast of precipitation events on the Sea
of Japan side and on the Pacific Ocean side, which areseparated by the central mountains. Figure 8 shows the
future changes of precipitation amounts on the two sidesof Honshu.
In winter, dry climate states generally appear on the
Pacific Ocean side in the present climate conditions, but
the precipitation amounts increase in the future (redsquares in Fig. 8). By contrast, precipitation events, including snowfall, decrease on the Sea of Japan side (bluesquares). These future tendencies are statistically significant and scalable. The possible reasons are as follows.
Because the southeasterly winds from the Pacific Ocean
to the Japanese Islands become stronger due to futurechanges in the circulation field in winter, precipitation
amounts increase in the areas on the Pacific side, locatedon the windward side of the mountains. Conversely, precipitation decreases on the Sea of Japan side, where thenorthwesterly winds from the Sea of Japan become
weaker.
On the other hand, in summer, there is a large jump
between d4 and the other cases. The areas of Honshu
with reduced precipitation become wider in d4 than in
the other two experiments. Concurrently, in d4, the subtropical high around Okinawa is stronger than in d1.5
and d2, and extends to around Kyushu (Fig. 7f–h) andweakens east of Japan. As a result, the surface windchanges show divergence around Honshu due to boththe strengthening of the southwesterly winds on the Sea
of Japan side and the strengthening of northerly winds
on the Pacific Ocean side. Therefore, summer precipitation amounts decrease on both the Sea of Japan side andon the Pacific side in Honshu (Fig. 8). However, Ose(2019) pointed out that future changes in summer precipitation around Japan are highly uncertain although
the decrease in summer precipitation around Japan ispredicted by several models.
Although future precipitation changes vary depending
on seasons and regions, the annual precipitation

Nosaka et al. Progress in Earth and Planetary Science

Future changes in precipitation

Figure 7 shows 3 month accumulated precipitation
amounts and the average SLP. In winter, d1.5, d2, andd4 all show trend patterns of the changes in the SLP
similar to those seen in previous studies (Kimoto 2005,
Hori and Ueda 2006, and Kawase et al. 2015): low SLPs
in the west and high SLPs in the east. It is thought thatthe continental SLPs become lower because thetemperature increases are greater on the Eurasian continent than for the oceans (Fig. 4).
Commonly, in d1.5, d2, and d4, future summer precipitation levels tend to decrease around Honshu and toincrease outside of this area (Fig. 7e–h). Winter precipitation tends to increase over the Sea of Japan and to decrease south of the Japanese Islands, while the Sea ofJapan side of Honshu shows decreases in winter precipitation (Fig. 7a–d).
Here, we define the SLP difference between two grid
points, (130°E, 37°N) and (140°E, 37°N) as an index of
the strength of the characteristic atmospheric SLP distribution in winter, i.e., high SLPs in the west and low SLPs
in the east. Large indices are reflected by stronger wintermonsoons, and the pattern is opposite to that of the future changes mentioned above. The index is 3.98 hPa in
PAST and decreases by 0.16 hPa, 0.26 hPa, and 0.47 hPa
in d1.5, d2, and d4, respectively. The changes in the indices are closely scalable with respect to gSAT increases.
In summer, the SLPs south of Japan become higher andthe SLPs east of Japan become lower in d1.5, d2, and d4(Fig. 7). The subtropical high pressure zone, which
covers the Japanese Islands in summer, retreats in theeast of Japan and extends to the areas south of Japan in
the future climate compared to the present climate. Thechanges in SLPs in the rectangular region from 27°N to
38°N and from 145°E to 152°E are − 0.19, − 0.29, and −0.78 hPa in d1.5, d2, and d4, respectively, and these aremostly scalable. Those in the area from 24°N to 26°N
and from 125°E to 135°E are also scalable: + 0.25, + 0.31,
and + 0.70 hPa for d1.5, d2, and d4, respectively. In summary, the SLP changes in winter and summer appear as
scalable changes to global warming.

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Fig. 7 Seasonal precipitation amounts and seasonal average SLPs and wind vectors. The shading, contours, and vectors denote precipitation
amounts (mm), SLPs (hpa), and wind vectors (ms−1), respectively, for (a, b, c, and d) DJF and (e, f, g, and h) JJA. a and e show the climatologies
of PAST and the future changes of (b and f) d1.5, (c and g) d2, and (d and h) d4 are presented

Nosaka et al. Progress in Earth and Planetary Science(2020) 7:27

Table 3 Pattern correlation coefficients of future changes inprecipitation amounts
d1.5 vs d2

d1.5 vs d4

d2 vs d4

ANNUAL

0.94

0.88

0.94

DJF

0.96

0.92

0.96

MAM

0.94

0.95

0.95

JJA

0.91

0.59

0.64

SON

0.90

0.95

0.89

The values are the pattern correlation coefficients of future changes in annual
and seasonal precipitation amounts for all grid points in the Japanese Islands
between two selected levels of warming

amounts in Japan have no clear relationship with gSAT
increases (Fig. 9a). The future precipitation changes of
d4 seen largely in Fig. 8 are mostly canceled out. The future annual precipitation amounts appear to significantly
increase in the future climate simulations by approximately 0.8%. Whereas the increases in mean value of annual precipitation due to moisture increase with thegSAT rises are canceled out by changes in circulation
fields, the standard deviations of annual precipitation become large because of the moisture increase

Fig. 8 Future changes (%) in seasonal precipitation amounts
separately for the Pacific area side and the Japan Sea side areas in
Honshu. The red and blue colors indicate the Pacific side and theJapan Sea side, respectively. The squares and triangles indicate DJF
and JJA, respectively. The error bars denote the standard errors of
the ensemble mean

Page 10 of 13(Pendergrass et al. 2017). The future R1d values increase
approximately linearly with the global warming levels(Fig. 9b), and the increase rates are 7% K−1, 6% K−1, and
5% K−1 at d1.5, d2, and d4 respectively. These values
agree with the theoretical values from the ClausiusClapeyron (CC) relationship of 6 to 7% K−1 for strong
precipitation (e.g., Wang et al. 2017; Gao et al. 2018).
The increase rates of R1d become slightly smaller as thegSAT rises due to the development of the divergence
fields in summer (Fig. 7). The standard errors ofensemble-mean R1d indicate that the increase of R1d
due to global warming is regarded to be significant. The
standard deviations of R1d become large as the gSAT
rises and this result is caused by moisture increases andoccurrence frequency increases in intense tropical cyclones in the future climate (Yoshida et al. 2017; Kitoh
and Endo 2019).
Impacts of a 0.5° increase on regional climates

By implementing additional mitigation measures which
limit the gSAT rise by + 1.5 K, it is expected that the
Tmean increases over Japan would be reduced by 0.7 K(Fig. 6) compared to those of the present climate. As
shown in Fig. 5, the north-south contrast in SAT increases is small in d1.5, while it becomes larger in d2
and d4. The impact of a 0.5 K rise from d1.5 in thegSAT on the summer temperatures in Japan is 0.85 K in
the north and 0.51 K in the south. Similarly, the R1d
precipitation amounts over the Japanese Islands increasesignificantly between + 1.5 and + 2 K (Fig. 9). Changes
in the circulation fields cause a significant increase in
winter precipitation on the Pacific side of 2.5% (Fig. 8),
while the winter precipitation amounts on the Sea ofJapan side do not exhibit a significant difference between+ 1.5 and + 2 K.
The increase in R1d due to gSAT rise of 0.5 K is about
4.0% K−1 on average for all land grids on the JapaneseIslands, which is smaller than the theoretical value of 6
to 7% K−1. There are regional characteristics in the differences in R1d, and it is small at latitudes from 34°N to
41°N (Fig. 10). The differences of R1d in Hokkaido, located north of 41°N, are larger than other regions due tolarger increases in the SATs and close to the theoreticalvalue (Table 4). In Kyushu, located south of 34°N, theincrease rate of R1d, within the standard error range,
does not deviate from the theoretical value. In contrast,
the increase rate of R1d in Honshu is significantly
smaller than the theoretical value because the divergencetendency with the progression of global warming prevents increase in R1d (Fig. 7).

Conclusions
In this study, we confirmed the reproducibility seen in
the regional climate model outputs of the d4PDF andNosaka et al. Progress in Earth and Planetary Science(2020) 7:27

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Fig. 9 Changes in annual mean precipitation and R1d. Changes in a annual mean precipitation relative to the gSAT increases and in b R1d
relative to the gSAT increases. Values are averaged on all land grids on the Japanese Islands. The solid bars indicate standard errors of theensemble mean, and the dashed bars indicate standard deviations. The blue color denotes linear fitting. The black circle, green circle, yellow
circle, and red circle indicate PAST, d1.5, d2, and d4, respectively

investigated the scalability of future changes in SATs,
SLPs, and precipitation amounts. The present climate
reproducibility of the regional climate model in thed4PDF was confirmed, and it was found that the
NHRCM20 performs reasonably comparably to thehigher-resolution models for SATs and precipitation at
daily time scales.
As a subset of the d4PDF, future climate simulationsof the + 1.5 K gSATs were performed. The scalability,that is, the linearity of future changes with gSAT

increases, was examined by comparing with the PAST,d2, and d4 (already available) cases. The future SATchanges in Japan showed a scalable relationship with respect to the gSAT increases for faster rates of warming
than that of the gSAT. The meridional warming contrastof the SATs is enhanced as global warming becomes severe. In winter, the zonal SLP differences over Japan decrease, while in summer, the SLPs increase (decrease)south (east) of Japan. These future SLP changes in winter and summer are scalable and weaken the seasonalmonsoons. The average precipitation amounts aremostly unchanged regardless of the gSAT increases.
Winter precipitation amounts on the Pacific side of theJapanese Islands increase linearly with gSAT increases,
while those on the Sea of Japan side decrease linearly.
The changes of summer precipitation are not scalable
because the areal expansion of the divergence of surface
winds around Japan reduces precipitation. Futurechanges in heavy daily precipitation amounts are scalable
to gSAT increases, showing rates of 5–7% K−1. If thegSAT increase is reduced from + 2 to + 1.5 K by implementing mitigation measures, the Tmean rise in Japan
would be reduced by 0.7 K. Heavy precipitation would
also be decreased by 2.7% over the Japanese Island. The
Table 4 Differences in R1d (dR1d) and SAT (dSAT) between
d1.5 and d2 with standard errors of the ensemble mean
ALL

Fig. 10 Differences in R1d (%) between d1.5 and d2 on land grids in
the Japanese IslandsdR1d(%)dSAT(K)

dR1d/dSAT(%K-1)

2.7 ± 0.4

0.67 ± 0.28

4.0 ± 1.1

Hokkaido

5.0 ± 0.8

0.74 ± 0.26

6.8 ± 1.3

Kyushu3.3 ± 0.8

0.62 ± 0.26

5.3 ± 1.1

Honshu2.0 ± 0.5

0.65 ± 0.27

3.1 ± 0.5

The right column shows the increase rate of the R1d per 1 K (dR1d/dSAT). All
land grids in the Japanese Islands are used for ALL

(2020) 7:27

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increase rate of heavy precipitation has regional characteristics. In Hokkaido, where the SAT warming is thegreatest in Japan, the increase rate is larger than otherregions, and in Honshu, where SLP have divergent tendency under the global warming conditions, the increase
rate is smaller than other regions.
Downscale experiments with fine-resolution models
are useful for discussing detailed terrain effects on futureclimate states. To deal with a wide range of uncertainties
in future predictions, ensemble experiments using various regional climate models and ocean-coupled simulations are required. In addition, performing experiments
using different levels of global warming will help to elucidate those phenomena that are not scalable. All ofthese will be future works.

Author details
1
Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052,
Japan. 2National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba,
Ibaraki 305-8506, Japan.

Nosaka et al. Progress in Earth and Planetary Science

Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s40645-020-00341-3.
Additional file 1: Figure S1. The distribution of ΔSST in DJF and JJA
averaged for all ensemble members. The first row (a, b, and c) shows the
distributions in JJA and second row (d, e, and f) shows the distributions
in DJF. The future changes of d1.5, d2, and d4 are presented in the left (aand d), center (b and e), and right (c and f) columns, respectively.
Abbreviations
CCSM4: Community Climate System Model version 4; CMIP5: Coupled Model
Intercomparison Project Phase 5; d4PDF: Database for Policy Decision
making for future climate change; GFDL-CM3: Geophysical Fluid Dynamics
Laboratory; HadGEM2-AO: Hadley Centre Global Environmental Modelversion 2 AO; MIROC5: Model for Interdisciplinary Research on Climate
version 5; MPI-ESM-MR: Max Planck Institute for Meteorology Earth System
Model MR; MRI-AGCM: Meteorological Research Institute Atmospheric
General Circulation Model; MRI-CGCM: Meteorological Research InstituteCoupled Atmosphere-Ocean General Circulation Model; NHRCM: NonHydrostatic Regional Climate Model; RCP8.5: Representative Concentration
Pathway with a radiative forcing level of 8.5 Wm−2; CC: Clausius-Clapeyron
Acknowledgments
This work was conducted under the SOUSEI and TOUGOU Programs of theJapan MEXT.
Authors’ contributions
MN, MI, and HS proposed the topic. MN carried out the experiments,
analyzed the data and drafted the manuscript. MI provided SST data and sea
ice distribution data. HS provides the scaling factors. RM supported thenumerical design environment. MI, RM, AM, HK, and HS discussed theanalyses and the results. All of the authors read and approved the final
manuscript.
Funding
This work was conducted under the SOUSEI and TOUGOU Programs of the
Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of
Japan.
Availability of data and materials
The experiments under the present climate conditions, + 2 K, and + 4 K ofthe d4PDF data set are available from the DIAS website (http://www.diasjp.
net/en/), and the experiment under + 1.5 K climate conditions will beavailable from the DIAS website.
Competing interests
The authors declare that they have no competing interests.

Received: 25 December 2019 Accepted: 22 May 2020

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