Received: 22 May 2018

Revised: 4 December 2018

Accepted: 5 December 2018

DOI: 10.1002/asl.877

RESEARCH ARTICLE

An evaluation method for uncertainties in regional climate
projections
Sachiho A. Adachi1,2
Ryuji Yoshida1,3| Seiya Nishizawa1| Kazuto Ando1 | Tsuyoshi Yamaura1 || Hisashi Yashiro1 | Yoshiyuki Kajikawa1,3 | Hirofumi Tomita1,21Computational Climate Science Research Team,RIKEN Center for Computational Science, Kobe,
Japan
2
Mathematical Climatology Laboratory, RIKEN
Cluster for Pioneering Research, Kobe, Japan
3
Research Center for Urban Safety and Security,
Kobe University, Kobe, Japan

Correspondence
Sachiho A. Adachi, RIKEN Center for
Computational Science, 7-1-26 Minatojimaminami-machi, Chuo-ku, Kobe 650–0047, Japan.
Email: sachiho.adachi@riken.jp
Present address
Ryuji Yoshida, CIRES, University of Colorado
Boulder, Boulder CO, 80309.NOAA Earth System
Research Laboratory, Boulder CO, 80305.
Funding information
Foundation for Computational Science
Establishing Super-computing Center of
Excellence; Japan Science and Technology Agency(JST), Core Research for Evolutional Science andTechnology (CREST), Grant Number:
JPMJCR1312; Japan Society for the Promotion of
Science (JSPS), KAKENHI, Grant Number:
JP17K12975

Regional climate projections inevitably inherit uncertainties from general circulation models (GCMs). We therefore propose a new approach for identifying thedominant uncertainties. This approach employs the downscaling procedure by Adachi et al. to the uncertainty problem using multiple GCM projections. The meanstate of the large-scale atmospheric states and the deviation from this mean state
are the two uncertainty factors considered here, which are provided by a GCM.
These two factors are referred to as climatology and perturbation components,
respectively. To demonstrate the effectiveness in identifying these uncertainty factors using the proposed approach, a regional projection of summertime climate inwestern Japan is conducted using four different future climate data that are calculated using an atmospheric GCM with different sea surface temperatures. Results
show that the variability in surface air temperature projections is reasonably
derived from the climatology uncertainty, whereas the variability in precipitation
projections is equally influenced by the climatology and perturbation uncertainties.
Both the climatology and perturbation uncertainties should therefore be consideredwhen analysing regional climate projections.
KEYWORDS

dynamical downscaling, regional climate model, regional climate projection
uncertainties, uncertainty evaluation

1 | INTRODUCTION
It is important to elucidate uncertainties that persist in
regional climate projections when designing adaptation plansto anticipate future climate change. The sources of uncertainty in future climate projections are primarily divided into
three categories (Hawkins and Sutton, 2009, 2011): internalvariability, the greenhouse gas emission scenario and imperfections in general circulation models (GCMs). Uncertainty
related to internal variability is present in the projections
regardless of global warming and its influence decreases relative to climate change signal due to global warming whenlonger prediction lead times are considered. Differences in
the greenhouse gas emissions affect the radiative balance of
the atmosphere. Uncertainties due to GCM imperfections
occur because GCMs generally exhibit different climate sensitivities to certain external forces (e.g., greenhouse gases)
and exhibit different spatial patterns in the atmospheric fields
even if the global climate sensitivities are of the same
degree. Uncertainties due to imperfections in regional climate models (RCMs) should also be considered in regional
climate projections that employ downscaling simulations(Wilby and Dessai, 2010) because they can show different
downscaled climates even if the RCMs use the same

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,provided the original work is properly cited.
© 2018 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.
Atmos Sci Lett. 2019;20:e877.
https://doi.org/10.1002/asl.877

wileyonlinelibrary.com/journal/asl2

1 of 8ADACHI ET AL.

2 of 8boundary conditions and similar model configurations(e.g., domain and grid spacing).
Regional-scale comparisons of these uncertainties havebeen performed using multi-GCMs (e.g., Giorgi and Francisco, 2000; Hawkins and Sutton, 2011), multi-RCMs(e.g., Ishizaki et al., 2012), multi-GCM/RCM combinations(e.g., van der van der Linden and Mitchell, 2009; Inatsu
et al., 2015; Sørland et al., 2018) and a large number ofensemble simulations (Mizuta et al., 2017). Hawkins andSutton (2011) analysed multiple GCM projections andshowed that the GCM model uncertainty was larger than the
scenario uncertainty for both global and regional precipitation projections, except for the polar regions, at the end of
21st century. Inatsu et al. (2015) performed multi-model
downscaling experiments with three GCMs and three RCMs
and subsequently compared the uncertainty contributions
derived from GCMs and RCMs to a regional climate projection. They showed that the downscaling results primarily
depended on the GCM boundary conditions. A welldeveloped RCM for a target region has the advantages of
reducing GCM climate biases and quantitatively modifying
the GCM climate-change signals (Sørland et al., 2018);
however, an RCM still qualitatively inherits the tendency ofthe climate change signals projected by a GCM(e.g., Rowell, 2006; Déqué et al., 2007). This study focuseson the uncertainties due to GCM projections in regional climate projections since GCM projection differences are oneof the factors that lead to variations in regional climate
projections.
Adachi et al. (2017) (henceforth, A2017) proposed a
new downscaling procedure to quantitatively evaluate thecontributions of three factors to regional climate change.
A2017 basically used the same mathematical approach as
the “factor separation method” proposed by Stein and Alpert(1993) and extended this approach to study regional climate
projections. The three factors are the changes in two largescale atmospheric components provided by a GCM, the climatology (mean state) and perturbation (deviation from themean) components and the nonlinear effects between thesetwo component changes. The thermodynamic and dynamic
TABLE 1 Examples and meanings of the climatology and perturbation
changes from the viewpoints of thermodynamic and dynamic changes

Thermodynamic change

Dynamic change

Climatology
changeIncrease in atmosphericmoisture content due towarming temperatures(Clausius–Clapeyron
relationship)

Changes in large-scale flow
patterns associated with
global circulation(e.g., width of the Hadley
cell, position and strengthof jets)

Perturbation
changeChanges in temperature andspecific humidity in thesurrounding areas of a
disturbance due tochanges in the track of
the disturbance

Changes in the frequency,
intensity and track of the
disturbances, such as
tropical and extratropical
cyclones

effects of these two component changes on regional climate
are explained in A2017 and summarised in Table 1.
Here, we propose a new method for evaluating theuncertainties derived from GCM projections when assessing
regional-scale projections. Specifically, we apply the procedure of A2017 to multiple GCM projections and evaluate
how each of the three factors influences the variability inregional climate projections. Section 2 briefly introduces theconcept and procedure of A2017 and then describes theexperimental design of this study, along with the GCM climate data and the RCM employed here. The proposed
method is then applied to summertime climate projections inwestern Japan (Section 3), with focus on surface air temperature and precipitation. Section 4 provides a summary anddiscussion of our results.

2 | METHODS
2.1 | Description of the principal concept andprocedureThis subsection briefly introduces the procedure proposedby A2017, which is summarised in Figure 1a. Here we consider the changes in the large-scale atmospheric state
between the present (p) and future (f ) climates. We express
p, which is provided by a GCM, as the sum of two components: the temporally averaged component, hpi, and its devi0
0
ation, p such that p = hpi + p . We refer to the former andlatter components as the climatology and perturbation components, respectively, following A2017. We similarly0
express f as f = hfi + f . These large-scale climate states areshown as diamonds on the climatology–perturbation phasespace in Figure 1a, according to A2017 and Nishizawa et al.
(2018), and they are used as RCM boundary conditions in
our analysis. The vertical axis indicates the changes in aregional climate target variable that are estimated fromdownscaling experiments.
The regional climate change, Δ, is estimated from two
direct dynamical downscaling (DDS) experiments, which
are labelled PDDS (present DDS) and FDDS (future DDS)
in Figure 1a. The boundary conditions for the DDS experiments are taken directly from the GCM output, with p for
PDDS and f for FDDS.
Two additional downscaling experiments are conducted
to extract the contributions of climatology and perturbation
changes to regional climate, the pseudo climatology change
downscaling (Pseudo-Clim-DS) and pseudo perturbation
change downscaling (Pseudo-Perturb-DS) experiments. The
boundary conditions for these experiments are prepared byaltering either the climatology or perturbation component,0
where hfi + p for the Pseudo-Clim-DS experiment and0
hpi + f for the Pseudo-Perturb-DS experiment. The difference between the Pseudo-Clim-DS and PDDS experiments
corresponds to the regional climate change due toADACHI ET AL.

3 of 8(a)(b)
Perturbation

Variable

Perturbation

PseudoPerturb-DS
f′

FDDS

f0′

Δcp
ΔP+ΔC

ΔP

Δ

f3′
f1′
<f2>

p′

PDDS
p′
<p>

ΔC
<f>

Climatology

Pseudo-Clim-DS

Actual climate of f
Expected climate of f<f1> <f0>

Climatology

<f3>

f2′
<p>
ColorsMarks
PDDS
FDDS_nPseudo-Clim-DS_n
Pseudo-Perturb-DS_n: p (Present): fn (Future)n=0~3

Schematics of the (a) basic downscaling procedure and (b) boundary condition relationships among the downscaling experiments conducted inthis study. The subscript n (n = 0  3) uniquely identifies the future climate projections, which are calculated using MRI-AGCM3.2S with different SSTs.
The experiments are the same as those described in Table 2. Note that the subscripts that identify the future climate projections are omitted in (a) because(a) illustrates the basic concept of the procedure proposed by A2017

FIGURE 1

climatology changes (ΔC), whereas that between the
Pseudo-Perturb-DS and PDDS experiments corresponds to
the regional climate change due to perturbation
changes (ΔP).
We refer to the total regional climate change Δ as theactual climate change, with the sum of ΔC and ΔP representing the expected climate change. Therefore, the difference between the actual and expected climate changes
corresponds to the nonlinear effect Δcp between ΔC and
ΔP. This procedure allows us to estimate the total regional
climate change between the two large-scale states, and thenevaluate the contributions of these three factors on the total
regional climate change: Δ = ΔC + ΔP + Δcp.
2.2 | Experimental design for uncertainty evaluation
Here we apply the above procedure to evaluate the uncertainties derived from the boundary conditions. Although it isdesirable to investigate the uncertainties using as many
GCM projections as possible, it is not easy to obtain GCM
outputs with the required temporal resolution for an RCM
boundary condition. We therefore utilise one present climate
and four future climate data from a single GCM for our
uncertainty analysis. The details of these climate datasetsand the GCM are described in following subsection.
When N sets of present and future climate datasets areavailable, 4 N large-scale atmospheric states can be constructed. If a reference climate (i.e., present climate) is common for N future climates, 3 N + 1 large-scale atmosphericstates are constructed. Since one present and four future climate datasets are available in this study, we perform
13 downscaling experiments for our uncertainty analysis:
one present climate DDS (reference state, PDDS), four
future climate DDS (FDDS), four Pseudo-Clim-DS and four
Pseudo-Perturb-DS experiments (Table 2). A conceptual diagram of the relationship between the boundary conditions
for the 13 experiments is illustrated in Figure 1b.

TABLE 2 Experimental design for estimating the contributions of thechanges in the climatology and perturbation components, and theirnonlinearity to the variability in regional climate changes. DDS stands for0
direct dynamical downscaling. h i and indicate the climatology andperturbation components, respectively, of the large-scale boundary
condition. p and f represent the present and future climate data, respectively,
which are provided by MRI-AGCM3.2S. The subscript n (n = 0  3)
uniquely identifies the future climate projections, which are calculated using
different SSTs

Boundary
condition

Run name

Description of experiment

PDDS

Present climate experiment via
the DDS method

hpi + p

FDDS_nFuture climate experiment via the
DDS method for futureclimate fn

hfni + fn

Pseudo-Clim-DS_n

Pseudo climatology change
downscaling experiment for
future climate fn

hfni + p

Pseudo-Perturb-DS_n

Pseudo perturbation change
downscaling experiment for
future climate fn

hpi + fn

0

0

0

0

ADACHI ET AL.

4 of 8If we consider two DDSs (one PDDS and one FDDS),
one Pseudo-Clim-DS and one Pseudo-Perturb-DS as a set ofdownscaling experiments, then N sets of experiments provide N sets of regional climate change information for Δ,
ΔC, ΔP and Δcp. The difference between the estimated Δ
values corresponds to the uncertainty in the regional climate
projections estimated from multiple GCM projections. The
variability among the estimated Δ values is broken into thevariabilities derived from the climatology and perturbation
components, which are estimated from the differences
between the ΔC and ΔP values, respectively.

28 CGCMs (Mizuta et al., 2014). Here, the future climate
data, which are projected using the mean SST of the
28 CGCMs, are defined as f0, whereas the others are labelled
as f1, f2 and f3. The present climate data are expressed as p.
The boundary conditions for the downscaling experiments
are prepared using combinations of the climatology and perturbation components from the present and future climate
datasets provided by MRI-AGCM3.2S, as shown in Table 2.
Note that the specific humidity in the Pseudo-Clim-DS and
Pseudo-Perturb-DS experiments is determined by the relative
humidity in the present and future climates, respectively,
where the relative humidity is assumed to be dependent on theperturbation component of each experimental climate. Therefore, the specific humidity for these experiments is calculated
from the temperature, which is derived from the combinationof the two components, and the relative humidity in the climate providing the perturbation component.
What does our investigation of the variability in downscaled climates due to differences in the climatology and perturbation changes mean? The difference in the experimental
settings between the four MRI-AGCM3.2S future climate
projections is the only forcing dataset on the SSTs. Our analysis therefore focuses on the regional climate projection uncertainties due to the GCM imperfections, which are recognised
through the resultant SST patterns in the CMIP5 models.
The Scalable Computing for Advanced Library andEnvironment-Regional model (SCALE-RM) (Nishizawa
et al., 2015; Sato et al., 2015; Yoshida et al., 2017) was used
to perform the downscaling experiments listed in Table 2,with the calculation domains shown in Figure 2a. The innerand outer domains are modelled using 2.5- and 7.5-km-gridintervals, respectively. The results from the inner domain,
excluding lateral boundary nudging region, were used for the
analysis. The physical schemes that are used in the experiments are listed in Table S1 of Appendix S1, Supporting
Information. The calculation periods extend from June to

2.3 | Description of the GCM climate data and RCM
model settings
Our method was applied to the changes in summertime precipitation and surface air temperature in western Japan to demonstrate its effectiveness in uncertainty evaluation. The climate
data used as the RCM boundary conditions were provided by
MRI-AGCM3.2S (Mizuta et al., 2012), which is an atmospheric GCM. The present climate was simulated using a Hadley Centre Sea Ice and Sea Surface Temperature (SST) dataset(HadISST) (Rayner et al., 2003) as forcing data. The four
future climates were estimated using four different SSTs. The
SST data for the future climates were constructed by summing
the fluctuation, trend and climatological mean, and the climatological mean was further divided into the present climate
and its future change (Mizuta et al., 2008). The fluctuation
was obtained from the HadISST dataset, which is the same asthat for the present climate. The trend and future change of theclimatological mean are obtained from the (a) multi-model
mean of 28 coupled GCMs (CGCMs) under the RCP8.5 scenario, which are provided by the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Taylor et al., 2012), and(b) multi-model means for three model groups, classified using
a cluster analysis of the dominant SST patterns among the(a) Model domains

200

500

(b) Precipitation

1000

[m]

2

4

6

8

(c) Surface air temperature

10

12

14

16

[mm/day]

18

19

20

21

22

23

24

[˚C]

(a) Calculation domains and model topography, and PDDS distributions of the 25-year means of (b) precipitation and (c) surface air temperature
at 2 m. The solid lines in (a) outline the entire calculation domains, with the dashed lines indicating the inner regions with no lateral boundary nudging region.
The results in the inner region of the second domain are used for the analyses

FIGURE 2

ADACHI ET AL.

5 of 8September 1979–2003 for the present climate and from Juneto September 2075–2099 for the future climates. The
4-month downscaling simulation for each year is divided
into 31 runs, with a run conducted every 4 days. The integration time of one run is 5 days, which includes 1 day formodel spin-up. The PDDS precipitation and surface air temperature climatology is shown in Figure 2b,c, respectively.
Their correlation coefficients are 0.85 and 0.92 for precipitation and temperature, respectively, based on 705 and459 observation stations in the target region, respectively.
The high degree of correlation between the observations andpresent climate model results indicates the sufficient model
performance to demonstrate the proof-of-concept for evaluating the uncertainties in future regional climate projections.

indices of the surface air temperature at 2 m (T2) for the
analysis: mean temperature, daily maximum temperature andnumber of tropical-night days as well as three precipitation
indices: mean precipitation, maximum 1-day precipitationand maximum number of consecutive dry days. A tropicalnight day is defined as a day when the daily minimum T2≥20 C, and a dry day is defined as a day when the daily precipitation <1 mm/day. All the indices are calculated in eachyear, and then temporally averaged over the 25-year period
and spatially averaged across the analysis domain.
All the FDDS experiments indicate that the mean anddaily maximum temperatures increase by 3.5–4.0 C in the
future climates (Figure 3a,b). The number of tropical-nightdays also increases by 20 days per year (Figure 3c). The
common trend among the three temperature indices is thatthe difference between the four projections, which is shown
as |Max-Min| in each panel, is smaller than the future change
in each instance. However, the precipitation results differ
from the T2 results. The mean precipitation is projected todecrease in each future climate (Figure 3d), whereas each

3 | RE SUL TS
The predicted future changes (Δ) between the PDDS andfour FDDS experiments are shown in Figure 3. We use three

(a)
[K]

(b)
Mean:
3.79
|Max–Min|: 0.41

[K]

4

4

3

3

2

2

1

1

0

0

(c)
Mean:
3.69
|Max–Min|: 0.42

Mean:
20.74
|Max–Min|: 0.84

[Days]

20

(*)

(*)

(*)

_2

_3
DS

FD

FD

DS

(*)

_1

(*)

_0

DS

DS

FD

(*)

_3

FD

FD

Tropical-night
days

(f)
[Days]
Mean:
7.74|Max–Min|: 15.55

_3

_2
DS

FD

DS

(*)
FD

FD

DS

_1

[mm/day](*)[mm/day]
_0

(e)

DS

−10

Daily max
temp

(d)

FD

0

DS

(*)

_2

FD

DS

(*)

_1

Mean temp

20

4

Mean:
2.31
|Max–Min|: 1.35

3
2

10

FD
DS
_2
(*)
FD
DS
_3

0

_1
DS
FD

−10

FD

Mean:
-0.55
|Max–Min|: 0.83

DS

_0

0
−1.0

−1.5

−1

DS
_0
(*)
FD
DS
_1
(*)
FD
DS
_2
(*)
FD
DS
_3
(*)

1

−0.5

FD

0.0

DS

_0
FD

DS

(*)

−2

−2

0.5

FD

(*)

_3

−1

FD

DS

(*)

_2

FD

DS

(*)

_1
FD

FD

DS

DS

−1

_0

10

−2
Mean prep

Max 1-day
prepConsecutive
dry days

Differences between the PDDS and four FDDS precipitation and surface air temperature indices: 25-year-means of (a) surface air temperature,(b) daily maximum temperature, (c) tropical-night days, (d) daily precipitation, (e) maximum 1-day precipitation and (f) maximum consecutive dry days. A
tropical-night day is a day when the daily minimum surface air temperature ≥20 C, and a dry day is a day when the daily precipitation <1 mm/day. The
asterisks after the experimental name indicate that the difference is statistically significant within the 95% confidence level. The “mean” in the panels indicatesthe mean value of the four projections, whereas “|Max-Min|” indicates the difference between the maximum and minimum values among the four projections

FIGURE 3

ADACHI ET AL.

6 of 8projection shows an increase in the maximum 1-day precipitation. The common trend among the two precipitation indices is that the spread of the four downscaled projections,shown as |Max-Min|, is larger than the absolute value ofmean future change. Each future climate predicts an increasein consecutive dry days, with the spread of the four projections being smaller than the mean future change. These
results imply that a single regional climate projection is
insufficient to yield a statistically reliable projection, especially for precipitation, as suggested in previous studies(e.g., Rowell, 2006; Déqué et al., 2007).
The contributions of each component in a large-scaleatmospheric state to the variability in future climate changes
are shown in Figure 4. Each mark in Figure 4 corresponds to
its respective future projection in Figure 1b, with the exception that results are shown as the differences from the PDDS
experiment (Δ, ΔP and ΔC). Each bar represents the meanof the four downscaling experiments. The Δ values (redsymbols) are a condensed representation of the Figure 3
results.
The results for the three temperature indices (Figures 4a–c) show that Δ is primarily influenced by ΔC. The spread in
the four Δ values of the mean and daily maximum temperature indices is also influenced by the variability in ΔC
because the variance in ΔC is larger than that in ΔP. The
variabilities in ΔC and ΔP are within the same range for thetropical-night days. These results show that the difference in

(a)

(b)

[K]

[K]

5

5

4

4

3

3

2

2

1

1

0

0
(*)

[mm/day](c)[Days]
30

(e)

10
0

[mm/day]

Tropical-night
days

(f)

[Days]

1.5

30

4

1.0

20

3

10

2

0.5
0.0

0

−0.5
−1.0

−10

−1.5

−20

Mean prep

Δ
ΔP
ΔC
Δcp

20

Daily max(*)
temp

Mean temp(d)climatology projected by a GCM has a larger influence thanthe difference in perturbation on the downscaled temperaturechange and its variability in the climate projections.
The precipitation results show a completely different
trend compared to the temperature results. The decrease inmean precipitation (Δ) is primarily explained by the precipitation decrease due to the perturbation changes (ΔP) in thefour experiments. ΔP and ΔC exhibit the same degree of
spread, and therefore equally influence the variability in Δ
for mean precipitation. The spread in Δcp is smaller thanthat in the other two factors, but it is not negligible. The precipitation increase due to ΔC is generally larger than that
due to ΔP for the maximum 1-day precipitation, yielding a
projected increase in Δ. As with mean precipitation, both
ΔP and ΔC provide similar contributions to the variability in
Δ for the maximum 1-day precipitation, and Δcp is not negligible. The influence of ΔP and its variability is large compared to that of ΔC for the consecutive dry days, with the
spread in Δ primarily due to the difference in ΔP. Our
results therefore show that the main factor controlling future
regional climate and its variability is not always the climatology of a GCM, as perturbation differences can also exert astrong influence on regional climate projections.
All four FDDS simulations yield a decrease in mean precipitation, which is primarily attributed to ΔP. A2017
explained the negative ΔP in mean precipitation for the
Pseudo-Perturb-DS_0 experiment as a drastic decrease in

1
0

Max 1-day
prepConsecutive
dry days

Variability among the future climate projections for the precipitation and surface air temperature indices. The indices are the same as thoseshown in Figure 3. Each mark represents an individual future projection and corresponds to its respective projection in Figure 1b. Δ is the total change
estimated from the PDDS and FDDS experiments. ΔP and ΔC indicate the perturbation and climatology change contributions, respectively. Δcp is thecontribution of the nonlinear effect between the two component changes. The evaluation indices with asterisks indicate that the variance of ΔC is larger thanthe variance of ΔP within a 95% confidence level

FIGURE 4

ADACHI ET AL.

precipitation associated with typhoons. Here, we apply thesame analysis to other cases and observe that the changes inprecipitation associated with typhoons account for more than
60% of ΔP for all the Pseudo-Perturb-DS experiments. To
determine the reason for the changes in ΔP that are associated with typhoons, we analysed the results from the viewpoint of the changes in event frequency and precipitation
intensity of an event. We assumed that the precipitation (R)can be decomposed into the number of events (E) and mean
precipitation intensity (I) of an event, where R = E × I.
Here we note that “precipitation intensity” is not an exact
representation of the cyclone intensity but rather its impact
0
0
0
on the target area. Given that R = E × I and R = E × I =(E + ΔE)(I + ΔI) for the PDDS and Pseudo-Perturb-DS
experiments, respectively, the precipitation change (ΔR =0
R − R ) can be approximated as ΔR  ΔE × I + E × ΔI,
where ΔE × I and E × ΔI represent the influences due to thechanges in the number of events and the changes in the precipitation intensity, respectively. We find that the primaryfactor for the changes in ΔP is due to the changes in thenumber of typhoons, that is, ΔE × I of typhoons. On theother hand, the increase in mean precipitation due to ΔC isdetermined by the balance between the increase in precipitation associated with typhoons and non-typhoon cyclones,
and the decrease in precipitation associated with other rain
events. The former can be explained by rich moisture in theatmosphere due to soaring temperatures under the assumption of the same relative humidity, with the latter explainedby the stabilisation of the atmosphere in the future climate.

4 | CO N CL U S I ON S AN D D I S CU S S I ON
We propose a new method to evaluate the uncertainties
derived from GCM boundary conditions in regional climate
projections by extending the procedure introduced by
A2017. We consider the climatology and perturbation differences of the GCM projections as uncertainty sources. Our
results show that the primary factor that influences the
spread of future climate projections is the climatology component for surface air temperature indices, whereas it is theperturbation component for the consecutive dry days. For
the mean and maximum 1-day precipitation indices, both theclimatology and perturbation components are equally important factors. Improving the reliability of both the climatology
and perturbation responses in a GCM is important for
obtaining more accurate projections of regional climatechange, especially for quantities with large nonlinearities
such as precipitation. Therefore, considering both climatology and perturbation uncertainties on large-scale future climate projections are necessary when assessing future
regional climate.
GCMs generally comprise different numerical schemes,
especially for physical processes, and exhibit different
responses to certain external forcings. Because the7 of 8demonstration in this study used climate data produced by asingle GCM, many experimental conditions are common
among the four future climates such as the modelled climate
response, model schemes, greenhouse gases and ground surface condition. We therefore consider our estimated variability
in the downscaled climate projections to represent only aportion of those estimated using multiple climate projections
from multiple GCMs. The proposed method needs to be
applied to multiple future climate projections from different
GCMs to properly assess the influences of these two components. Such results would improve our understanding of theuncertainties derived from GCM projections for the regional
climate projections of a target region and provide valuable
information on the regional climate itself.

ACKNOWLEDGEMENTS

We thank Dr Takayabu, Dr Mizuta and Dr Arakawa for providing the MRI-AGCM3.2S data, which are from the Program for Risk Information on Climate Change (SOUSEI),
Ministry of Education, Culture, Sports, Science and Technology of Japan, and produced by the MeteorologicalResearch Institute (MRI). The results of this study wereobtained using the computational resources of the K computer at RIKEN/R-CCS. The downscaling data, which were
produced by SCALE-RM, are stored at RIKEN/R-CCS andare available from the corresponding author upon reasonable
request. The MRI-AGCM3.2S data were provided to theauthors by MRI under the condition that the authors can only
use the MRI-AGCM3.2S data for the current and related
studies, such that the authors cannot make these data publicly available. However, the data may be provided upon reasonable request and with permission from MRI.

CONFLICTS OF INTEREST

The authors declare no potential conflict of interests.
OR CID
Sachiho A. Adachi
Seiya Nishizawa
Ryuji Yoshidahttps://orcid.org/0000-0002-3066-6175
https://orcid.org/0000-0001-9457-7457

https://orcid.org/0000-0003-3238-2595

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SUPPORTING I NFORMATION

Additional supporting information may be found online in
the Supporting Information section at the end of the article.
How to cite this article: Adachi SA, Nishizawa S,
Ando K, et al. An evaluation method for uncertainties
in regional climate projections. Atmos Sci Lett. 2019;20:e877. https://doi.org/10.1002/asl.877