Nakajima et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00351-1(2020) 7:33

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open AccessA development of reduction scenarios ofthe short-lived climate pollutants (SLCPs)
for mitigating global warming andenvironmental problems
Teruyuki Nakajima1*, Toshimasa Ohara1, Toshihiko Masui1, Toshihiko Takemura2, Kei Yoshimura3, Daisuke Goto1,
Tatsuya Hanaoka1, Syuichi Itahashi4, Gakuji Kurata5, Jun-ichi Kurokawa6, Takashi Maki7, Yuji Masutomi1,
Makiko Nakata8, Tomoko Nitta3, Xerxes Seposo9, Kengo Sudo10,11, Chieko Suzuki12, Kentaroh Suzuki13,
Haruo Tsuruta14, Kayo Ueda15, Shingo Watanabe11, Yong Yu16, Keiya Yumimoto2 and Shuyun Zhao17

Abstract
This study presents the results of the ERTDF S-12 project for searching an optimum reduction scenario of the shortlived climate pollutants (SLCPs) to simultaneously mitigate the global warming and environmental problems. The
study utilized REAS emission inventory, Asia-Pacific Integrated Model-Enduse (AIM/Enduse), MIROC6 climate model,
NICAM non-hydrostatic atmospheric model, and models for estimating environmental damages to health,agriculture, and flood risks. Results of various scenario search indicate that it is difficult to attain simultaneous
reduction of global warming and environmental damages, unless a significant reduction of CO2 is combined withcarefully designed SLCP reductions for CH4, SO2, black carbon (BC), NOx, CO, and VOCs. In this scenario design, it isimportant to take into account the impact of small BC reduction to the surface air temperature and complex
atmospheric chemical interactions such as negative feedback between CH4 and NOx reduction. We identified two
scenarios, i.e., B2a and B1c scenarios which combine the 2D-scenario with SLCP mitigation measures using End-ofPipe (EoP) and new mitigation technologies, as promising to simultaneously mitigate the temperature rise by about
0.33 °C by 2050 and air pollution in most of the globe for reducing damages in health, agriculture, and flood risk. In
Asia and other heavy air pollution areas, health-care measures have to be enhanced in order to suppress themortality increase due to high temperature in hot spot areas caused by a significant cut of particulate matter. For
this situation, the B1b scenario is better to reduce hot spot areas and high-temperature damage to the publichealth.
Keywords: Short-lived climate pollutants, Global warming, Mitigation scenario, Climate change, Air pollution,
Environmental problem* Correspondence: terry-nkj@nifty.com
1
National Institute for Environmental Studies, 16–2 Onogawa, Tsukuba,
Ibaraki 305–8506, Japan
Full list of author information is available at the end of the article© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
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Introduction
The concept of the short-lived climate pollutants(SLCPs) has been created in the development of global
warming mitigation policies to identify anthropogenic
atmospheric compositions that produce positive radiative forcing. Black carbon (BC), methane, and tropospheric ozone are key constituents among the SLCPs.
UNEP and WMO (2011) proposed a scenario of future
global warming mitigation by which a surface airtemperature decrease of about 0.5 °C can be attained by
the introduction of SLCP reduction measures along withthat of long-lived greenhouse gases (LLGHGs). An international initiative of the Climate and Clean Air Coalition(CCAC) was launched in 2012 with a scope of simultaneous mitigation of global warming and health problem
through the reduction of SLCPs and wastes. Such a
combined mitigation strategy of LLGHG and SLCP has
become more important after the Paris Agreement of
COP21 in 2015 set a goal of the global surface airtemperature not exceeding 2 °C and an effort goal of
1.5 °C relative to the pre-industrial era. According to the
IPCC special report on global warming of 1.5 °C (IPCC
2018), these global warming mitigation goals require asignificant reduction of LLGHG emission at a level of almost zero by 2050, which demands the maximum effortof the society. To meet the goals, SLCP reduction is useful to contribute to the mitigation effort, particularly
given that a short lifetime of SLCP assures a quick reduction of surface temperature on the order of 0.5 °C inless than 10 years right after the action of SLCP reduction made. On the other hand, the temperature reduction by LLGHGs takes several 10 years due to their long
lifetime in the atmosphere.
The design of effective mitigation measures criticallydepends on our understanding of the complex climate
effects of the short-lived atmospheric compositions including SLCPs and their precursors. This motivated various research efforts in the past taken to challenge theproblem, e.g., UNEP ABC project (Ramanathan and
Crutzen 2003; Nakajima et al. 2007), AeroCom (Schulz
et al. 2006) and AeroCom-II (Myhre et al. 2013), Atmospheric Chemistry and Climate Model Intercomparison
Project (ACCMIP) (Shindell et al. 2013), European
Union Seventh Framework Programme projectEvaluating the Climate, and Air Quality Impacts ofShort-Lived Pollutants (ECLIPSE, Stohl et al. 2015), Asia
Pacific Clean Air Partnership (APCAP) (Akimoto et al.
2015; UNEP 2018), NAPEX PDRMIP (Myhre et al.
2017), among others. Ministry of Environment of Japan
carried out two SLCP-related strategic researches, i.e., S7 and S-12 by the Environment Research and Technology Development Fund (ERTDF).
This paper summarizes the key results of the S-12 project that aimed at searching an optimum mitigation pathof SLCP reduction, conducted in Japanese fiscal years of
2014–2018, through four research theme activities, i.e.,(1) analysis of air quality change and development ofemission inventories, (2) selection of mitigation technologies and scenario development, (3) investigation of
SLCP impacts on the climate and environment, and (4)development for shared tools and a high-resolution atmospheric modeling to link a large variety of scales fromregional to global. In this paper, depending on the context, we use the term SLCP to simply identify anthropogenic short-lived atmospheric constituents including
scattering aerosols and their precursors as well as absorbing aerosols.

Nakajima et al. Progress in Earth and Planetary Science

Methods/experimental
Evaluation of the SLCP effects on the surface airtemperature

One particular issue in designing the effective SLCP reduction scenario is the fact that anthropogenic atmospheric compositions of different species have different
signs of the radiative forcing (abbreviated as RF, hereafter). Figure 1 shows direct RFs in China and Asia in aperiod of 1980–2010 and RF changes caused by 50% reduction of all the emissions. These are obtained fromclimate change simulations with the MIROC5/MIROC6
climate models (Watanabe et al. 2010; Tatebe et al.
2019), implemented with SPRINTARS aerosol model(Takemura et al. 2005) and CHASER atmospheric chemistry model (Sudo and Akimoto 2007). The figure indicates that a simple 50% reduction of the air pollutants
produces a net positive forcing of + 10 mW m− 2 as a result of the cancelation of a negative forcing due to reduction in black carbon (BC), tropospheric ozone (TO3,hereafter), and methane (CH4) by a positive forcing due
to sulfate reduction, suggesting that a simple 50% reduction is not an optimal scenario to simultaneously mitigate the global warming and air pollution problem.
Another difficulty that complicates the scenario designis the presence of indirect effects between SLCPs andthe climate system. Several key indirect effects have been
identified in the past studies regarding sulfate, BC, andmethane. Figure 2 shows contributions of methane andTO3 to the net RFs as functions of emission change factors of nitrogen oxides (NOx), carbon monoxide (CO),and volatile organic compounds (VOCs) including indirect RFs caused by feedbacks among atmospheric compositions. Reference concentrations for the emission factor
were assumed to be the 2008 values of the HTAP2
multi-model study (Stjern et al. 2016). The figure indicates that a decrease in NOx emission produces a negative RF caused by indirect reduction of TO3 and apositive RF due to indirect change of methane, resulting
in a net positive RF that acts to warm the globe. This enhanced methane increase is caused by a prolonged

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Fig. 1 Instantaneous direct RFs of SLCP emissions in the period of 1980–2010 in Asia (solid right bars) and China (dotted left bars). The Asianbudget of RFs caused by 50% cut of the emissions is also shown aside to the box with a star symbol to show the net total RF

Fig. 2 RF responses to emission change of a NOx and b CO and VOC. Solid and broken lines show net RF of all the atmospheric constituents,
and thick and thin bars show the contributions from methane and TO3, respectively. The reference emission is assumed to be that in 2008 of the
HTAP2 scenario

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lifetime of methane due to the reduction of OH radical(Shindell et al. 2009; Akimoto et al. 2015). On the other
hand, CO emission reduction can produce a negative
forcing of methane. This experiment suggests that acareful design of reduced rates of NOx and CO is required to attain a reduction of TO3 without increasing
methane concentration.
Another process of large uncertainty is the indirect climate effects of sulfate and BC aerosols, which exert asignificant impact on the SLCP-induced climate response. Figure 3, cited from Takemura and Suzuki(2019), shows simulated equilibrium global surface airtemperature changes as a function of the instantaneous
direct RF, Fi, of sulfate, and BC applied to the tropopause. The figure indicates that a sulfate reduction generates a significant temperature rise in a manner mostly
proportional to the sulfate loading, whereas a BC reduction produces a much smaller temperature decrease withthe sensitivity or the slope far smaller than that of sulfate. The large climate sensitivity of sulfate is considered
to result from the superposition of direct RF and indirect
RF due to induced cloud RF of same signs, whereas thesmall climate sensitivity of BC is caused by the cancelation of direct RF by indirect RF as also found by previous studies (Hansen et al. 2005; Hodnebrog et al. 2014;
Stohl et al. 2015; Samset et al. 2016). Tables 1 and 2 andFig. 4 compare the values of reported RFs and associatedsurface air temperature changes caused by CO2 and
SLCPs. Here, following Hansen et al. (2005), we define
the instantaneous direct RF as the earth radiation budget
perturbation at tropopause for RF computation, or simply referred to as the Top Of the Atmosphere (TOA),
that occurs immediately when a climate change factor isadded to the system, and the effective RF (ERF), Fe, isdefined as the net radiation imbalance with the Sea Surface Temperature (SST) fixed. The latter contains the
rapid adjustment of the climate system and is considered
to be a direct driver of the slow climate response tocause the global-mean surface air temperature change
ΔTs. In this definition, the first and other aerosol indirect effects and semi-direct effects are included in the
rapid adjustment. We performed 150-year run for equilibrium climate response using MIROC6 with ocean
coupled to estimate ΔTs and feedback parameter β defined as,

Nakajima et al. Progress in Earth and Planetary ScienceF ðt Þ  − β ΔT s ðt Þ;

ð1Þ

where the radiative imbalance at TOA (denoted by F)
and Ts are represented to depend on time (t). The βvalue thus defined is known to be similar to that derived
from the regression analysis by the method of Gregory(2004) without a large dependence of β on the time
period of the analysis. We therefore selected theFig. 3 Relations between instantaneous RFs by sulfate and BC and resulted equilibrium surface air temperature change (°C). Cited from Takemura
and Suzuki (2019)

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Table 1 Reported values of instantaneous and effective RFs (Wm-2), Fi and Fe, and their ratio γ= Fe/Fi*2 × CO2Fi

γ = Fe/Fi

Sources, notes

MIROC5

4.08

3.68

0.91

Provided by M. Yoshimori, AORI, University of Tokyo

Hansen

4.52

4.11

0.91

Hansen et al. (2005)

Smith

2.62

3.7

1.41

Smith et al. (2018)

SpreadFe

Spread*Aerosols
Shindell-1− 0.26IPCC aerosol-2− 0.42

Shindell-2− 0.39GISS-E2-R− 0.49IPCC aerosol-1− 0.45− 1.17±0.24− 1.92± 1.0− 1.17− 1.1± 0.50− 0.9

aci: − 0.71 to 0

4.50

Shindell et al. (2013), ACCMIP model

4.57

IPCC (2013), Fig. 7.19, aeosol RFari (CMIP5)

3.00

Shindell et al. (2013), including missing components(nitrate and SOA)

2.24

Shindell et al. (2013)

2.00

IPCC (2013), Figs. TS6 and 7

3.02

Takemura and Suzuki (2019)

2.12

IPCC (2013), Fig. TS7 sulfate, 1850–2000; Aci of aerosol;ari + aci uncertainty of aerosols*Sulfate
This study− 0.2– 0.61

IPCC sulfate− 0.41

Hansen− 0.63− 1.492.36

Hansen et al. (2005), 2× sulfate; divided by 2 and added
indirect

GISS-E2-R− 0.37− 0.61

1.65

Shindell et al. (2013)This study

0.148

0.008

0.51

Takemura and Suzuki (2019), Suzuki and Takemura (2019)

Hansen

1.28

0.99

0.77

Hansen et al. (2005)

GISS-E2-R

0.24

0.46

1.92

Shindell et al. (2013)± 0.21− 0.87± 0.9*BC

Hodnebrog

0.35

0.09

0.25

Hodnebrog et al. (2014)

NICAM (×10)

0.77

0.32

0.42

This study, Goto et al. (2020)

NICAM (×5)

0.38

0.14

0.36

This study, Goto et al. (2020)

equilibrium climate sensitivity parameter, λ = 1/β, andthe forcing ratio,
γ¼

Fe
;
Fi

ð2Þ

as useful parameters in our analysis of the SLCP impact
for comparison among present and past studies that
adopt different methods to evaluate Fe with different
time scales to separate rapid and slow climate responses.
We compare the reported values of γ and λ in Fig. 4.
Figure 4a shows that the magnitude of Fe for sulfate isγ = 1.7 to 3 times larger than that of Fi. The γ value forthe total aerosol is in a range of γ = 2 to 4.5 and is noticeably larger than that for sulfate. This phenomenon
can be explained by the fact that the magnitude of Fi forthe total aerosol is smaller than that for sulfate due tolight absorption by absorbing materials externally andinternally mixed in the total aerosol. The major contribution to the large γ value for sulfate is caused by the
first indirect effect of sulfate aerosol acting as effective
cloud condensation nuclei (CCN) that enhances lowlevel cloud albedo. On the other hand, the magnitude of
Fe of BC is significantly smaller by 25 to 75% than themagnitude of Fi with an exception of GISS-E2-R model
which produces a forcing larger than Fi. Recent studies
proposed that the low climate sensitivity of BC occurs as
a result of the cancelation of negative RF due to BC reduction by a positive RF arising from the cloud system
response generated by decreased atmospheric stability
due to reduced heating by BC (Stjern et al. 2017; Suzuki
and Takemura 2019). There is also a RF due to decreased semi-direct effect associated with decreasing
solar radiation absorption by decreased BC. In this regard, it is important to recognize the existence of a large
inter-model spread of the ERF induced by BC and thefact that the MIROC model, used in this study, belongs
to the group of small ERF for BC (Shindell et al. 2013).
This small γ value by the MIROC model is, however,similar to or even larger than the result of 14 km global
simulations by NICAM (Tomita and Satoh 2004; Goto
et al. 2020) implemented with more first-principle modeling of cloud formation and aerosol-cloud interaction
processes. On the other hand, the γ value exceeding theunity in the GISS-ES-R model case also suggests that theBC-induced cloud response might be more complicatedto cause cloud forcing that could amplify, rather thandampen, the initial forcing Fi (Shindell et al. 2013).

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Table 2 Reported values of Fi, Fe, equilibrium surface air temperature changes dTs (°C), and climate sensitivity, λ = dTs/Fe
Fe

ΔTs

ΔTs/Fiλ = ΔTs/Fe

Sources, notes

MIROC5

4.1

3.68

2.7

0.66

0.73

IPCC AR5, fixed SST

GISS-E2-R

2.6

2.62

2.4

0.92

0.92

IPCC AR5, fixed SST

IPCC mean

3.7

3.7

3.2

0.86

0.86

IPCC AR5, fixed SST

Hansen

4.5

4.11

2

0.43

0.49

Hansen et al. (2005)

Samset

3.8

3.8

2.1

0.55

0.55

Smith et al. (2018), Table S2*2× CO2Fi*Sulfate

1.31

This study− 0.2− 0.61

− 0.4

2.00

0.66

Suzuki and Takemura (2019)

Hansen− 0.6− 1.485

− 1.1

1.82

0.74

Hansen et al. (2005), 2× sulfate; divided by 2 and added indirect

Stohl− 0.86

− 0.7

0.81

Stohl et al. (2015), model mean of land temperature change

Samset

− 3.6

− 2.3

0.64

Samset et al. (2016)

Smith

− 0.7

− 0.5

0.71

Smith et al. (2018), 5× sulfate; divided by 5*BC
This study

0.15

0.076

0.02

0.16

0.26

Takemura and Suzuki (2019)

Hansen

1.28

0.99

0.40

0.33

0.40

Hansen et al. (2005), BC

Stohl

0.33

0.05

0.15

Stohl et al. (2015), model mean of land temperature change, FF + BB, all land emission

Myhre

0.096

0.08

0.83

Myhre et al. (2017), Stjern et al. (2017), PDRMIP (mean), 10 BC; divided by 10

Smith

0.12

0.07

0.58

Smith et al. (2018), 10 BC; divided by 10

Fig. 4 Reported values of a forcing ratio γ = Fe/Fi and b climate sensitivity λ = ΔT/Fe for CO2 and SLCPs listed in Tables 1 and 2

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Future studies are needed to conclude this issue, especially in the atmospheric moist convective adjustment
process.
Figure 4b shows that the λ value ranges from 0.6 to
0.8 for sulfate similar to that of doubling CO2 ranging
from 0.5 to 0.9 with the model spread smaller for sulfate
than for CO2. On the other hand, the slow response to
BC is large model dependent from 0.1 to 0.8. Although
it is early to lead a conclusion before more future modelcomparisons, the figure suggests that CO2, sulfate andBC have different effective forcing sensitivities to theslow climate response with different forcing mechanisms, i.e., thermal infrared forcing by CO2, solar forcing
to the earth’s surface by sulfate, and solar absorption by
BC in the atmosphere. Sulfate causes a unique large
cooling of the surface through direct effect and aerosolcloud interaction to increase the cloud optical thickness.
MIROC has a large value of the forcing ratio γ and smallvalue of λ, so that the resulted temperature rise per instantaneous forcing is similar to those models with small
γ and large λ values.
There are several issues to improve the large model
uncertainties in the RF evaluation used in the modelcomparisons shown in Fig. 4. A well-known problem is anegative model bias of AOT and a part of which is attributed to the insufficient model treatment of missing
nitrate and secondary organic aerosol (Shindell et al.
2013; Shrivastava et al. 2017). Another error source is
the inter-model difference in the representation of themixed state of BC with other atmospheric compositions(Koch et al. 2009; Oshima and Koike 2013; Matsui et al.
2018) that significantly influences the RF estimate. Also,
some of absorption by colored pollution aerosols, suchas brown carbon (Kirchstetter et al. 2004) and combustion iron (Moteki et al. 2017), is approximated by BC absorption in a simplified model treatment. The BC
vertical profile is also a key factor affecting the RF and
ERF estimates and could be constrained by aircraft measurements through calibrating the wet removal process
as claimed by Hodnebrog et al. (2014). Undefined BC
sources in the common emission inventories are still aproblem, for example, in India (e.g., Goto et al. 2011).
Additional numerical experiments would also be useful
to better quantify regional responses to SLCP perturbations, including a shift of the tropical precipitation beltthat occurs via both atmosphere and ocean energy transports (Zhao and Suzuki 2019) and modulations of thehydrological cycle over the tropical Asian monsoon regions (Takahashi et al. 2018). High-resolution modelingis also important. Sato et al. (2016) reported an increasein BC transportation to a polar region with their highresolution modeling, and there is also a recent argument
regarding the anti-Twomy response of low clouds as revealed by active satellite remote sensing and high-resolution modeling (Michibata et al. 2016; Sato et al.
2018; Sato and Suzuki 2019). These studies suggest that
we need high-resolution modeling that better represents
the aerosol and cloud processes for more reliable estimates of RF and climate sensitivity due to the aerosolcloud interaction phenomenon in the scenario impact
study.
Figure 5 schematically illustrates various SLCP-related
processes affecting the global air temperature and environmental problems (water resource, flooding, draught;
health and agricultural damage). In the figure, solid anddashed lines respectively represent positive and negativeinteractions to produce increasing and decreasing outputs with increasing input. The thickness of the line indicates the strength of the process. As discussed in
Figs. 3 and 4, a decrease in sulfate causes an increase in
the global surface air temperature by both direct andcloud RF-related indirect effects, but at the same time, it
decreases the public health damage, implying a need fora balanced cut of sulfate for reducing the health problem
without accelerating the global warming. In contrast, a
BC decrease causes a small net increase in the surface
air temperature as a balance of positive direct and negative cloud RF-related indirect effects. It also decreases
the environmental problems, so that a substantial cut of
BC is desirable for simultaneous mitigation of global
warming and environmental problems. Furthermore, aNOx decrease causes a decrease in TO3, but at the sametime, it causes a negative indirect effect to increase methane as discussed in Fig. 2. In order to weaken thenegative effect, we can combine CO and VOC reductions as a mitigation measure that cancel the NOx negative feedback and also serve to reduce TO3.

Nakajima et al. Progress in Earth and Planetary Science

Results and discussion
Construction of SLCP scenarios

The S-12 project adopted the REAS 2.1 emission inventory (Kurokawa et al. 2013) and the Asia-Pacific Integrated Model-Enduse (AIM/Enduse) to relate themitigation technologies with emissions of SLCPs. Table 3
shows the codes and names of SLCP scenarios constructed by the present project using AIM/Enduse. Details of scenario design are given in Hanaoka and Masui(2020). For this work, the AIM/Enduse has been implemented with several SCLP processes to get a better
consistency with the REAS inventory and to output scenario parameters for climate simulations. The reference
scenario (Ref-scenario) is a modified version of the SSP2
scenario (O’Neill et al. 2015), in which the future mitigation policies and technologies are taken place with thecurrent trends. The medium and maximum End-of-Pipe(A1 and A2 scenarios) scenarios are scenarios of enhancing EoP-diffusion both in developed and developing
countries by 2050 for reducing SO2, NOx, BC, OC,

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Fig. 5 A diagram to show interactions among processes related with SLCP effects to global surface air temperature and environmental problems(water resource, flooding, draught; health and agricultural damage). Solid and broken lines respectively indicate positive and negative interactions
to produce increasing and decreasing outputs with increasing input. Thickness of line illustrates the strength of the effectPM2.5, and PM10. The 2D-scenario is designed for significant emission reductions of CO2 and methane, by
utilizing decarbonization mitigation measures towardthe 2 °C target of the Paris Agreement with reaching acarbon price at 400 US$/tCO2 in 2050. The CCSscenario is one of 2D-scenarios with energy shift to coaland biomass power and CCS technology rather thanusing renewable energies. RES- and BLD-scenarios are
2D-scenarios with energy shift to renewables and withenhancing electrification in the building sector acrossthe world, respectively. The TRT-scenario is for enhancing EV in the passenger transport sector across theworld by 2050. We mainly use the abbreviated scenario
codes in this paper, rather than full scenario names, forconcise presentation.

Resulted time series of global emissions from 2010 to2050 are shown in Fig. 6 and their relations in Fig. 7 between (a) CO2-SO2, (b) BC-SO2, (c) CO-NOx, and (d)VOC-NOx. An appendix also presents global spatial distributions of SLCP emission changes. First of all, we notethat the simulated emissions at 2010 by the improved
AIM model are consistent with those of EDGAR4.3 andHTAP without large gaps in the current condition at2010. Secondly, Fig. 6 shows that Ref, A2, and A1 scenarios result in 43% increase of CO2 emission and 53%
increase in methane emission from 2010 to 2050. On
the other hand, the introduction of 2D-scenarios reduces
CO2 emission by 50% and methane emission by 44%
from 2010 to 2050. SLCP emission changes depend on
how new mitigation technologies are selected among

Table 3 SLCP reduction scenarios developed by the S-12 project
Scenario group Scenario code/
scenario name

Reference

Ref/SSP2-based reference

End-of-pipe
only

A1/EoPmid
A2/EoPmax

Major combinations of mitigation measures on GHGs, air pollutants, and SLCPs
Enhancement
2 °C target Enhancement of Enhancement of
renewable energyof end-of-pipe
measures carbon capture
technology (EoP) (2D)and storage (CCS) (RES)

Intensive
Intensive
electrification in electrificationbuilding (BLD) transport (TRT)

Mid
Max

2 °C target and B1a/2D-EoPmid-CCSBLD
end-of-pipe
B2a/2D-EoPmax-CCSBLD

Mid

✔

✔

✔

Max

✔

✔

B1b/2D-EoPmid-RESTRT

Mid

✔

✔

B2b/2D-EoPmax-RESTRT

Max

✔

✔

B1c/2D-EoPmidRESBLDTRT

Mid

✔

✔

✔

✔

B2c/2D-EoPmaxRESBLDTRT

Max

✔

✔

✔

✔

✔
✔
✔

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Fig. 6 Time series of global CO2 and SLCP emissions simulated with the scenarios listed in Table 3 from 2010 to 2050. The values of EDGAR and
HTAP databases are also added

CCS, RES, BLD, and TRT technologies. The figures
show that the EoP-technology is needed for reducing
SO2, BC, and NOx, and we need technologies of CCS,
RES, BLD, and TRT to reduce CO, methane, BC, andVOC. These technologies are especially required for a
substantial reduction of BC emission, which is needed tocompensate for the small climate sensitivity of BC as discussed in the previous section. The figures indicate that

the S-12 scenarios cover wide combinations of SO2 andBC emissions. On the other hand, the methane emission
shown in Fig. 6 does not have much variety in the scenarios because the major sources of methane are fromagriculture and waste sectors for which we do not posemany options for the new mitigation technologies. Future studies of larger emission cuts are needed to besought by the innovation of new methane reduction

Fig. 7 Same as in Fig. 6 but in planes of a CO2-SO2, b BC-SO2, c CO-NOx, and d VOC-NOx

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technologies. A study of Fig. 7 for characteristic patternsof relations among SLCP compositions suggest B1a andB1c scenarios are advantageous for large BC cut andmedium-cut of SO2, while B1c scenario is useful for alarge cut of CO and VOC and medium-cut for NOx.
These figures show that there is a large room forbringing different future air compositions by adopting
different combinations of the new mitigation technologies. This means that the present study serves as atestbed for the search of the scenarios with results representing a part of the future full search which needs more
simulations with climate and environmental models. It isalso noted that the cost of introducing new mitigation
technologies is larger than that of the EoP technology,
that implies a careful discussion considering cost-benefit
and trade-offs has to be included in the future scenario
search.
Figure 8 shows atmospheric concentrations of TO3 inthe Dobson Unit (DU) simulated with the constructed
SLCP scenarios. The figure shows that the TO3 concentration will increase in the Ref-scenario with increasing
NOx as indicated in Fig. 6. It is interesting to find that
A1 and A2 scenarios increase TO3 even though NOx isdecreased in these scenarios. This phenomenon isunderstood by increases in CO and VOC in these scenarios as shown in Fig. 6. Scenarios of new mitigation
technologies are successful to reduce TO3, especially by
2.6 DU from 2010s to 2040s with B2a and B1c scenarios.
This situation is similar to the indirect effect to the methane concentration as shown in Fig. 9. The methane
emission is prescribed in the S-12 scenarios as given in
Fig. 6, but the atmospheric concentration of methane isstrongly affected by the indirect effect of SLCP compositions as discussed in the preceding section. Dotted lines

in Fig. 9 indicate that the methane concentration will increase by 0.2 ppmV for the Ref-scenario more than simulated without the feedback process as presented bysolid lines. We found more increase of methane by 0.3
ppmV for A2 scenario caused by a large decrease in OH
radical with decreasing NOx in the scenario. On theother hand, the figure shows that the reduction of COand VOC in the new technology scenarios successfully
suppresses the methane increase due to NOx reduction.
Figure 10 shows simulated aerosol optical thickness(AOT) and PM2.5 in the globe and East Asia (20°N–
50°N, 100°E–150°E). The figure indicates that the A1
scenario does not improve AOT and PM2.5, but the A2
scenario can reduce AOT by 0.01 and PM2.5 by 0.3 μg
m− 3 in the globe from the current state. It is important tofind that the B1c scenario can perform good mitigation
similar to B2a, i.e., AOT by 0.02 and PM2.5 by 0.9 μg m− 3from the current state, even when the A1 scenario is
adopted but combined with the new mitigation technologies of RES, BLD, and TRT. The East Asian region is morepolluted than the global average, so that the role of EoP
technology is more important. The Ref-scenario canmaintain the present values of AOT and PM2.5, and A2
scenarios will decrease AOT by 0.14 and PM2.5 by 5.3 μg
m− 3, respectively, from 2010. And the B1c scenario candecrease AOT by 0.17 and PM2.5 by 7.3 μg m− 3.
The analyses in this section are especially important tostudy the role of the new mitigation technologies to easethe problem that EoP-scenarios (A1 and A2) is beneficial
for improving health problem but increases global
warming. One conclusion from Figs. 8, 9, 10 is that apollution control measure to introduce the maximum
EoP-technology alone will not lead us to clean andwarming mitigation goals, and therefore, we need to

Nakajima et al. Progress in Earth and Planetary ScienceFig. 8 Time series of global mean TO3 concentrations (DU) simulated with the seven scenarios

Nakajima et al. Progress in Earth and Planetary Science(2020) 7:33

Page 11 of 21

Fig. 9 Time series of global mean methane concentrations (ppmV) simulated with Ref, A2, and B1c scenarios. Dotted lines show simulated
concentrations including the indirect effect of OH radical reactions

combine measures of EoP and new mitigation technologies to attain the goal.
SLCP impacts on the earth’s climate and environment

Emission scenarios constructed by the AIM/Enduse wereused for the MIROC6 climate model to perform sevenensemble runs of coupled atmosphere-ocean model
simulation for each scenario with a grid resolution of
1.4° (T85) for standard simulations and with 0.56° resolution for high-resolution simulations. In detail, atmospheric concentrations of CO2 and methane were
simulated by the MAGICC6 model (Meinshausen et al.

Fig. 10 Time series of mean values of a AOT and b PM2.5 in globe and East Asia simulated with the seven scenarios. The numbers attached to
the right ordinate are scenario numbers in the order of magnitude at 2050

(2020) 7:33

Page 12 of 212011) and given to the climate model, while other emissions are directly inputted to the climate model. With
this experimental setup, it should be noted that the methane concentration in the climate model is prescribedwith AIM/Enduse values depending on the scenario,
which neglects the interaction of CH4 and OH radical or
feedbacks from changes in NOx, CO, and VOCs to alter
the methane lifetime.
Figure 11 shows the simulated time series of the global
mean surface air temperature changes relative to Refscenario. The figure indicates that Ref-scenario will increase the global mean surface air temperature by about
1.0 °C by 2050. The introduction of EoP-technology (A2and A1 scenarios) alone increases temperatures morethan Ref-scenario about 0.07 °C with a large positive RF
generated by a decrease of sulfate. This increase will beenhanced if we introduce the indirect effect of methane
as discussed in Fig. 9. On the other hand, the introduction of 2D-measures and new mitigation technologies
will produce a global mean temperature decrease ofabout 0.3 °C from Ref-scenario by 2050. The best scenarios for warming mitigation are B1a and B1c which will
decrease the temperature by 0.31 °C and 0.33 °C by 2050,
respectively. These scenarios select the A1 scenario withless reduction of sulfate than A2 scenario indicating aneffectiveness of a combination of technologies that simultaneously decreases SO2, BC, methane, NOx, CO, and
VOC in a suitable amount so as not to produce a large
warming by sulfate.
Figures 12 presents global distributions of temperatureand precipitation changes by A1 and B1c scenarios. The
figures show that the A1 scenario produces warming ofabout 0.4 °C and precipitation change of about 0.8 mm
day− 1 in wide areas of the globe relative to the situationof Ref-scenario. On the other hand, the B1c scenario
produces a temperature decrease less than 0.4 °C in mostof the globe without a large perturbation of precipitation
relative to the Ref-scenario. Among the scenarios in Asia
as shown in Fig. 13, B1a and B1c scenarios are useful to
reduce the surface air temperature by 0.4 °C in areas

Nakajima et al. Progress in Earth and Planetary ScienceFig. 11 Time series of a global mean surface air temperature simulated with Ref-scenario and b temperature changes simulated with the six
scenarios relative to Ref-scenario. The numbers attached to the right ordinate are scenario numbers in the order of magnitude at 2050

(2020) 7:33

Page 13 of 21

other than hot spot areas in India, Myanmar, and China,
where the scenarios cannot avoid a temperature increase
reaching 0.3 °C due to a large positive RF by sulfate
reduction.
Figure 14 shows global annual excess mortalities due
to PM2.5 and high temperature for Ref2010 and seven
scenarios in the 2040s. The excess mortality due toPM2.5 was estimated using the health impact assessment
framework of Seposo et al. (2019) as follows:of the mortality that is attributable to PM2.5 and was
calculated by using relative risks (RRPM2.5),

Nakajima et al. Progress in Earth and Planetary Science

ΔMPM2:5 ¼ PAFPM2:5  N;

ð3aÞ

N ¼ Pop  bmort

ð3bÞ

PAFPM2:5 ¼

RRPM2:5 − 1;
RRPM2:5

ð3cÞ

where ΔMPM2.5 is the excess mortality due to PM2.5, aproduct of population attributable fraction (PAFPM2.5)
and the baseline mortality (N) for each grid; N was calculated by multiplying the population (Pop) by the baseline mortality rate (bmort); PAFPM2.5 is the proportion



γ 
RRPM2:5 ¼ 1 þ α 1 − exp ð − β Z q − Z 0

RRPM2:5 ¼ 1

if Z q > Z 0 ;if Z q ≤ Z 0 ;

ð4aÞ
ð4bÞ

where Zq is the scenario-specific PM2.5 concentration(annual mean) with varying theoretical minima Z0 relative to the health outcomes, and an integrated exposureresponse function parameter of α, β, γ (Burnett et al.
2014). In this study, we estimated RRs for stroke, ischemic heart disease, chronic obstructive pulmonary disease, lung cancer, and acute respiratory infection. We
applied the same framework in estimating the excess
mortality due to a high temperature. To derive the heatrelated risks (RRtemp) above the optimum temperature(Top), we applied a V-shaped temperature-mortality riskfunction as,
RRtemp ¼ exp δ T q − T op ; if T q > T op ;
ð5ÞRRtemp ¼ 1; if Tq ≤ T op ;where δ is the parameter of the risk function above Top;Tq is the scenario-specific daily maximum temperature.

Fig. 12 Global distributions of changes in surface air temperature and precipitation in the 2040s with A1 and B1c scenarios relative
to Ref-scenarios

Nakajima et al. Progress in Earth and Planetary Science(2020) 7:33

Page 14 of 21

Fig. 13 Asian distribution of surface air temperature changes in the 2040s with the six scenarios relative to Ref-scenario

Based on the previous study (Honda et al. 2014), Top
was defined as 84th percentile value of daily maximum
temperature for each grid. We summed the daily excess
mortalities through the year. We assumed no adaptation
measures in the estimation. The figure indicates Ref and
A1 scenarios cannot reduce the mortality due to PM2.5
from the situation in the 2010s, because the expected future population increase in polluted areas overcomes the
air pollution mitigation effort in these scenarios. We
need new mitigation technologies for a substantial decrease in the mortality. The introduction of B2a and B1c
scenarios will prevent the mortality by 1.2 to 2 million
persons per year in the 2040s relative to that of Ref2010.
On the other hand, although the magnitude is not aslarge as that of the PM2.5 impact, the annual mortality
due to high temperature will increase from the present
with all the scenarios, i.e., 0.022 million persons with
B1b scenario to 0.037 million persons with Ref-scenario
in the 2040s depending on the global mean temperaturerises of 0.6 °C and 1.0 °C, respectively, as shown in
Fig. 11. It is important to note that the mortality also increases in hot spot areas of temperature rise which remain in polluted areas, e.g., India, Myanmar, and China
as shown in Fig. 13, caused by a large positive RF by sulfate reduction. This is the reason why B2a and B1c scenarios sit next to the B1b scenario for mitigation of themortality, as understood by hot spots higher than thoseof B1b. An alternative solution of trade-off is to adopt
B2a or B1c scenario for mitigation of health damage due
to PM2.5 with enhanced anti-high temperature healthcare in the heavy air pollution areas in Asia where are
expected to experience a hot spot-type temperature increase due to significant cut of particulate matter.
Figure 15 presents changes in the rice production due
to climate change and ozone estimated by a crop growthsimulation model, MATCRO (Masutomi et al. 2016a,
2016b), and a flux-based model for ozone damage on
rice (Mills et al. 2018). Annual losses of the rice yield

Nakajima et al. Progress in Earth and Planetary Science(2020) 7:33

Page 15 of 21

Fig. 14 Global excess mortalities (million persons) due to PM2.5 (ordinate) and high temperature (abscissa) for Ref2010 and seven scenarios inthe 2040s

Fig. 15 The same as in Fig. 14 but for global loss of the rice yield by ozone effect (ordinate) and the rice yield by climate other than the ozone
effect (abscissa)

(2020) 7:33

Page 16 of 21

with increasing ozone amount with Ref and A1 scenarios
are found in the figure. The loss decreases from thecurrent state (Ref2010) when we introduce EoP technology and new mitigation technologies by 0.10 ton ha− 1with B2a and 0.07 ton ha− 1 with B1b and B1c scenarios.
On the other hand, the climate change decreases the riceproduction by 0.21 ton ha− 1 to 0.80 ton ha− 1 with all
the scenarios depending on the temperature rise. Different from the case of health impact due to hightemperature, however, the least production loss is realized by B2a and B1c, not B1b, because the rice production is less affected by the hot spot in the polluted areas.
Figure 16 shows the global population change underrisk of flooding and precipitation change when BC andSO2 concentration change from 0.5 to 10 times less ormore than present (Yoshimura et al. 2018). Though the
SLCP impacts on globally averaged land precipitation
are rather straightforward as decreasing with increasing
sulfate or BC, the impact on peak river discharge (i.e.,
flooding) is not simple because of various impact factors
regarding hydrological processes over land including
runoff, evaporation, snowmelt, river routing, etc. The
figure indicates a V-shaped dependence of the risk witha minimum around the present state, suggesting that thehuman society has been adapted so as to minimize thedisaster risk in the present climate condition. This result
indicates the disaster prevention measures in the decreasing global mean precipitation should also be awareof. Although the magnitude of BC is small and noisy, we
found that there is an increasing tendency of flood

damage, partly because the precipitation increases insome parts of the land area.

Nakajima et al. Progress in Earth and Planetary Science

Conclusions
Figure 17 summarizes changes of CO2, SLCPs, PM2.5,
AOT, and environmental parameters relative to Refscenario in the 2040s. The present study concludes that
it is possible to create a CO2 and SLCP reduction scenario that simultaneously mitigates global warming anddamages on health, agriculture, and water risk by a welldesigned combination of various mitigation technologies
as discussed in Figs. 12, 13, 14, 15, and 16. Among theseven scenarios developed in this study, B2a and B1c
scenarios are the best to simultaneously mitigate
temperature rise, mortality due to PM2.5, and loss ofrice production by ozone and climate change, whereas
B1b scenario is the best to mitigate the mortality increase due to high temperature because the scenario
eases high temperatures in the hot spots caused by positive RF due to sulfate decrease in the polluted area in
Asia.
Figure 18 summarizes changes of ERFs of CO2 and
SLCPs relative to the Ref-scenario in the 2040s. The figure indicates that the main contributors are emission
cut of CO2 and CH4 by the 2D-scenario producing RFsof about − 0.87 Wm − 2. The introduction of the EoPtechnology for air quality control will produce a small
positive RFs of about + 0.05 Wm − 2 and will not help
mitigation of the global warming. Furthermore, theintroduction of new mitigation technologies of CCS,

Fig. 16 Relationship between the proportion of the global population exposed to flood risk and changes in terrestrial precipitation due to BC(orange) and sulfate (blue) emissions changes (0.5 to 10 times, respectively). Error bars represent the standard deviation of interannual variation
during the 20-year simulations

Nakajima et al. Progress in Earth and Planetary Science(2020) 7:33

Page 17 of 21

Fig. 17 Changes of global CO2, SLCP concentrations, and environmental parameters in the 2040s with the six scenarios relative to Ref-scenario

RES, BLD, and TRT reduces sulfate for improving air
quality more than EoP-scenarios, and it produces positive ERF more than + 1 Wm-2. The figure indicates, however, the advantage of the new mitigation technologies is
to compensate for the positive sulfate ERF by negative
ERFs by TO3 and BC reductions to result in a larger net
reduction of the total ERF about − 1.4 Wm-2. The figure
indicates the best scenario of reducing the total ERF is

the B1c scenario with − 0.7 Wm-2 better than that of
B2b. Also, it is important to note that the small value of
BC ERF does not mean small climate impacts of BC, because of the reduced amount of ERF from instantaneous
RF, Fi/Fe shown in Fig. 4a, is used to induce changes in
the fast component of the climate system including global cloud and precipitation changes (Suzuki and Takemura 2019).

Fig. 18 Changes of ERFs in the 2040s with the six scenarios relative to Ref-scenario

(2020) 7:33

Page 18 of 21

Although these results suggest recommendable scenarios for simultaneous mitigation of global warming andenvironmental problems, we feel that the knowledge obtained by the present study is still limited to find the ultimate best scenario, and several undone works are left
for future works. First, we should continue the effort ofevaluating BC forcing because estimates of γ and λvalues are largely scattered as shown in Fig. 4. Indirect
effects on methane and TO3 are also important processes in the scenario construction, because they increase scenario differences between EoP-scenarios and
2D-scenarios than presented in Figs. 17 and 18. Furthermore, emission change scenarios in Figs. 6 and 7 suggestthat there is still a large room for bringing different future air compositions by adopting different combinationsof the new mitigation technologies, especially for thoseof methane. The cost of introducing new mitigation
technologies is larger than that of EoP technology, sothat a careful discussion considering cost-benefit and thetrade-off has to be included in the scenario search.
As found by Fig. 4, large model uncertainties of RFs
still exist and can be an Achilles tendon for reliable scenario construction. Future studies are, therefore, needed
for improving the model treatment of missing nitrateand secondary organic aerosol, representation of themixed state of BC, and colored aerosols, BC vertical profile, and undefined BC sources in Asia, and other areas.
Also, future detailed ensemble simulations with coupledatmosphere-ocean models and multi-model experiments
are needed to investigate the BC impact in the situationof large model dependency as described in Section 2.
Such additional experiments would also be useful to better quantify regional responses to SLCP perturbations.

Another important challenge for the effective SLCP
mitigation is to cycle the scenario construction and theirassessment by monitoring the air quality and climate
state. One of significant problems met in the S-12 project is the long turnaround time of emission inventory
construction by the bottom-up method based on various
census data, which became a bottleneck in the projectfor understanding the emission gap between emission
inventories and constructed scenarios. In order to easethis problem, we introduced NOx emissions in the Asian
region retrieved from the satellite data (Yumimoto et al.
2015). Fig. 19 compares estimates of the annual NOx
emission from the Chinese area using updated versionsof REAS 2.2 and satellite retrieval (Kurokawa et al. 2017;
Itahashi et al. 2019). The figure shows the emission inventory significantly exceeds the satellite-based estimate
by about 2 Mton/year after 2011. We found the differenceis caused by problems both in the inventory and the satellite methods. As for the bottom-up emission inventory
REAS, introduction rates of denitrification equipment tolarge power plants were underestimated. On the other
hand, the rapid increase in Chinese emissions caused
negative biases in the satellite values. To overcome thisproblem, we developed a sequential update technique (Itahashi et al. 2019). The revised inventory and satellite
values get closer to each other as illustrated in Fig. 19.
This update helped reducing the gap between REAS emission and AIM/Enduse scenario in Asia as reported by
Hanaoka and Masui (2020). This experience clearly indicates shortening the turn-around time of the scenario
construction and air quality monitoring cycle is useful tohealthy SLCP mitigation measures along with the global
stock-take initiatives by UNFCCC.

Nakajima et al. Progress in Earth and Planetary ScienceFig. 19 Estimates of the annual NOx emission from Chinese area using REAS emission inventories and satellite retrievals. Dashed line and open
bars show the original values before improvements of REAS and satellite algorithm, and solid line and closed bars are for after improvements

Nakajima et al. Progress in Earth and Planetary Science(2020) 7:33

Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s40645-020-00351-1.
Additional file 1: Figure A1 shows global spatial distributions of SLCP
emissions in the 2010s (2010–2019) with the Ref-scenario (Ref2010) andemission changes with the seven scenarios in the 2040s (2040–2049)
relative to Ref2010. It is found from the figure that the reduction rates of
SLCP compositions depend on regions, so that large regional differencesin the mixing state of SLCPs are expected resulting in different
magnitudes and signs of RF. BLD- and TRT-technologies are effective to
reduce BC and NOx emissions, especially in Asia, Africa, and SouthAmerica. Large increase in NOx in Asia can be eased by combination of
CCS-, BLD-, and TRT-technologies. Fig. A1(a). Global distributions ofemissions (g m− 2 yr− 1) with Ref2010 and emission changes in the 2040s
with the seven scenarios relative to Ref2010 for SO2. Fig. A1(b). Same as
Fig. A1(a), but for NOx. Fig. A1(c). Same as Fig. A1(a), but for BC. Fig.
A1(d). Same as Fig. A1(a), but for OC.

Abbreviations
AIM: Asia-Pacific Integrated Model; CHASER: Chemical AGCM for Study ofAtmospheric Environment and Radiative Forcing; BC: Black carbon;
AOT: Aerosol optical thickness; CO: Carbon monoxide; ERF: Effective radiative
forcing; ERTDF: Environment Research and Technology Development Fund;
MIROC: Model for Interdisciplinary Research on Climate; NICA
M: Nonhydrostatic ICosahedral Atmospheric Model; OC: Organic carbon;
REAS: Regional Emission inventory in ASia; RF: Radiative forcing; SLCP: Shortlived climate pollutants; SPRINTARS: Spectral radiation-transport model foraerosol species; SSP: Shared socioeconomic pathways; TO3: Troposphericozone; VOC: Volatile organic compounds

Page 19 of 21

Competing interests
The authors declare that they have no competing interests.
Author details
1
National Institute for Environmental Studies, 16–2 Onogawa, Tsukuba,
Ibaraki 305–8506, Japan. 2Research Institute for Applied Mechanics, Kyushu
University, Fukuoka, Japan. 3IIS Chiba Experimental Station, The University ofTokyo, 5–1-5 Kashiwanoha, Kashiwa, Chiba 277–8574, Japan. 4Central
Research Institute of Electric Power Industry, 1646 Abiko, Chiba 270–1194,
Japan. 5Department of Environmental Engineering, Graduate School ofEngineering, Kyoto University, Katsura, Nishikyo-ku, Kyoto 615–8540, Japan.
6
Atmospheric Research Department, Asia Center for Air Pollution Research,
1182 Sowa, Nishi-ku, Niigata 950–2144, Japan. 7Meteorological ResearchInstitute, Nagamine, Tsukuba, Ibaraki 305–0052, Japan. 8Faculty of Sociology
Department of Environment, Kindai University, 3–4-1 Kowakae, Higashiosaka
577–8502, Japan. 9Nagasaki University School of Tropical Medicine andGlobal Health, 1–12-4 Sakamoto, Nagasaki 852–8523, Japan. 10Graduate
School of Environmental Studies, Nagoya University, Nagoya 464–8601,
Japan. 11Research Institute for Global Change, Japan Agency for Marine-Earth
Science and Technology, 3173–25 Showa, Kanazawa-ku, Yokohama 236–
0001, Japan. 12Research Institute for Value-Added-Information Generation,
Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa,
Kanazawa-ku, Yokohama 236-0001, Japan. 13Atmosphere and Ocean
Research Institute, The University of Tokyo, 5–1-5 Kashiwanoha, Kashiwa,
Chiba 277–8568, Japan. 14Remote Sensing Technology Center of Japan, 3–17-1 Toranomon, Minato-ku, Tokyo 105–0001, Japan. 15Graduate School of
Global Environmental Studies, Kyoto University, Yoshida-Honmachi, Sakyo-ku,
Kyoto 606–8501, Japan. 16School of Geographical Science, Nanjing Universityof Information Science & Technology, No.219, Ningliu Road, Nanjing, Jiangsu,
China. 17Department of Atmospheric Science, China University ofGeosciences, No. 388 Lumo Road, Wuhan, China.
Received: 16 February 2020 Accepted: 8 July 2020

Acknowledgements
The Japanese information of the S12 project is given at http://157.82.240.16
7/~S12/moej-s12/index.html. Model simulations were performed on the
supercomputer system of JAXA/JSS2, NIES/NEC SX-ACE, RIKEN/K computer(hp140046, hp150156, hp160004, hp170017, hp180012), JAMSTEC/Earth
Simulator, and University of Tokyo/PRIMEHPC FX10.
We express our deep condolence to the passing away of Gakuji Kurata,
Kyoto University, who was an active member of the S-12 project.
Authors’ contributions
TN was the principal investigator of the S-12 project. TO, DG, SI, JK, TM, and
KY contributed to the analysis of air quality change and development ofemission inventories; TM, GK, and TH to the selection of mitigation
technologies and scenario development; TT, YM, MN, XS, CS, KS, KU, SW, and
YY to the investigation of SLCP impacts on the climate and environment; KY,
TN, KS, HT, and SZ to development for shared tools and a high resolution
atmospheric and hydrologic modeling. The authors read and approved thefinal manuscript.
Authors’ information
Correspondence and requests for materials should be addressed to TN(terry-nkj@nifty.com).
Funding
The S-12 (JPMEERF14S11200) was one of the projects in the Strategic
Research and Development Area of the Environment Research andTechnology Development Fund (ERDF), Environmental Restoration andConservation Agency (ERCA), Japan. Parts of this research were also
supported by projects of JST/CREST/EMS/TEEDDA (JPMJCR15K4), NIES/GOSAT&GOSAT-2 projects, and “Integrated Research Program for Advancing
Climate Models (TOUGOU)” Grant Number JPMXD0717935457 from the
Ministry of Education, Culture, Sports, Science, and Technology (MEXT),
Japan.
Availability of data and materials
The datasets used and analyzed during the current study are available fromthe corresponding author on reasonable request.

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