Oshima et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00348-w(2020) 7:38

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Global and Arctic effective radiative forcingof anthropogenic gases and aerosols in
MRI-ESM2.0
Naga Oshima* , Seiji Yukimoto, Makoto Deushi, Tsuyoshi Koshiro, Hideaki Kawai, Taichu Y. Tanaka
and Kohei Yoshida

Abstract
The effective radiative forcing (ERF) of anthropogenic gases and aerosols under present-day conditions relative topreindustrial conditions is estimated using the Meteorological Research Institute Earth System Model version 2.0(MRI-ESM2.0) as part of the Radiative Forcing Model Intercomparison Project (RFMIP) and Aerosol and Chemistry
Model Intercomparison Project (AerChemMIP), endorsed by the sixth phase of the Coupled Model Intercomparison
Project (CMIP6). The global mean total anthropogenic net ERF estimate at the top of the atmosphere is 1.96 W m−2
and is composed primarily of positive forcings due to carbon dioxide (1.85 W m−2), methane (0.71 W m−2), andhalocarbons (0.30 W m−2) and negative forcing due to the total aerosols (− 1.22 W m−2). The total aerosol ERF
consists of 23% from aerosol-radiation interactions (− 0.32 W m−2), 71% from aerosol-cloud interactions (− 0.98 Wm−2), and slightly from surface albedo changes caused by aerosols (0.08 W m−2). The ERFs due to aerosol-radiationinteractions consist of opposing contributions from light-absorbing black carbon (BC) (0.25 W m−2) and from lightscattering sulfate (− 0.48 W m−2) and organic aerosols (− 0.07 W m−2) and are pronounced over emission source
regions. The ERFs due to aerosol-cloud interactions (ERFaci) are prominent over the source and downwind regions,caused by increases in the number concentrations of cloud condensation nuclei and cloud droplets in low-levelclouds. Concurrently, increases in the number concentration of ice crystals in high-level clouds (temperatures < –38 °C), primarily induced by anthropogenic BC aerosols, particularly over tropical convective regions, cause bothsubstantial negative shortwave and positive longwave ERFaci values in MRI-ESM2.0. These distinct forcings largely
cancel each other; however, significant longwave radiative heating of the atmosphere caused by high-level ice
clouds suggests the importance of further studies on the interactions of aerosols with ice clouds. Total
anthropogenic net ERFs are almost entirely positive over the Arctic due to contributions from the surface albedo
reductions caused by BC. In the Arctic, BC provides the second largest contribution to the positive ERFs after
carbon dioxide, suggesting a possible important role of BC in Arctic surface warming.
Keywords: Radiative forcing, Aerosols, Short-lived climate forcer, Black carbon, Greenhouse gas, Arctic, Earth systemmodel, CMIP6, RFMIP, AerChemMIP* Correspondence: oshima@mri-jma.go.jp
Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052,
Japan© 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
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

(2020) 7:38

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Introduction
Anthropogenic gases and aerosols affect radiation balance on the Earth and therefore cause climate changeover the industrial era. Well-mixed greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous
oxide (N2O), and halocarbons, have lifetimes that aremuch longer than a few years and accordingly impact
the climate over long time scales (Myhre et al. 2013). Atmospheric aerosols have a typical lifetime of 1 day to 2
weeks in the troposphere and significantly influence
reginal and global climates. Aerosol particles modify theradiation balance directly through scattering and the absorption of shortwave (SW) radiation (aerosol-radiationinteractions) and indirectly through cloud modification
by serving as cloud condensation nuclei (CCN) and ice
nucleating particles (INPs) for both SW and longwave(LW) radiation (aerosol-cloud interactions) (Boucher
et al. 2013). The deposition of light-absorbing aerosols,
such as black carbon (BC), on snow and ice can also
affect the radiation due to reduction of the surface albedo (e.g., Hansen and Nazarenko 2004; Flanner et al.
2007). Because BC strongly absorbs SW radiation andtherefore leads to atmospheric heating, climate responses to BC in the Earth system are complex (e.g.,
Jacobson 2002; Bond et al. 2013; Stohl et al. 2015; Kaiho
et al. 2016; Kaiho and Oshima 2017; Suzuki and
Takemura 2019; Takemura and Suzuki 2019), and therole of BC has been recognized as being particularly important in the Arctic (e.g., Arctic Monitoring and Assessment Programme (AMAP) 2015; Sand et al. 2015;
Mahmood et al. 2016).
Effective radiative forcing (ERF) has been recognized
as a useful indicator of the eventual temperature response, especially for aerosols, because ERF accounts for
rapid adjustments (Myhre et al. 2013). In the Intergovernmental Panel on Climate Change fifth Assessment
Report (IPCC AR5; Myhre et al. 2013), the total anthropogenic net (SW plus LW) ERF at the top of the atmosphere (TOA) over the industrial era (years 1750–
2011) was estimated to be 2.3 (1.1 to 3.3) W m−2, where
the uncertainty values in the parenthesis represent the
5–95% (90%) confidence range. The total well-mixed
greenhouse gas ERF was estimated to be 2.83 (2.26 to3.40) W m−2. Aerosols partially offset the well-mixed
greenhouse gas ERF in the total anthropogenic ERF. In
IPCC AR5, the aerosol ERF was distinguished by forcing
processes arising from aerosol-radiation interactions(ERFari) and aerosol-cloud interactions (ERFaci). The
total aerosol net ERF (ERFari+aci, excluding the effect oflight-absorbing aerosols on snow and ice) was estimatedto be − 0.9 (− 1.9 to 0.1) W m−2, and ERFari was estimated to be − 0.45 (− 0.95 to 0.05) W m−2. The total
aerosol ERFaci, which is defined as ERFari+aci minus
ERFari in the IPCC AR5 case (Myhre et al. 2013), wasestimated to be − 0.45 (− 1.2 to 0.0) W m−2. Zelinka
et al. (2014) conducted a systematic intercomparison of
ERFs across the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models. The present-day(year 2000) net ERFari+aci was estimated to be − 1.17 ±0.30 W m−2, consisting of an ERFari of − 0.25 ± 0.22 W
m−2 and an ERFaci of − 0.92 ± 0.34 W m−2. The large
intermodel spread in the ERFaci values was dominatedby differences among models in how the aerosols affected the cloud albedo (Zelinka et al. 2014). Previous
studies have indicated that aerosols and clouds are still
the largest source of uncertainty in estimates of radiative
forcing of the climate (Boucher et al. 2013), and further
improvements to aerosol and cloud processes in models
are required from CMIP5.
Recently, we developed the Meteorological ResearchInstitute Earth System Model version 2.0 (MRI-ESM2.0;
Yukimoto et al. 2019) as a major update of our previous
MRI coupled global climate model version 3, MRICGCM3 (Yukimoto et al. 2012), which participated in
CMIP5 (Taylor et al. 2012). We implemented multiple
modifications in the model with particular emphasis on
improving the aerosol and cloud processes (Yukimoto
et al. 2019; Kawai et al. 2019). For example, significant
improvements to the cloud representations in MRIESM2.0 led to a remarkable reduction in errors in theSW, LW, and net radiation at the TOA, and the score of
the spatial pattern of the radiative fluxes at the TOA for
MRI-ESM2.0 is better than the 48 CMIP5 models(Kawai et al. 2019). MRI-ESM2.0 participated in thesixth phase of CMIP (CMIP6; Eyring et al. 2016). To estimate ERF and quantify the climate impacts of anthropogenic gases and aerosols using MRI-ESM2.0, we
participate in the Radiative Forcing Model Intercomparison Project (RFMIP; Pincus et al. 2016) and the Aerosol
and Chemistry Model Intercomparison Project (AerChemMIP; Collins et al. 2017), which are endorsed by
CMIP6.
In this study, we perform all time-slice perturbation
experiments from the anthropogenic forcing agentsplanned in RFMIP and AerChemMIP using MRIESM2.0, which enables a comprehensive estimation of
the present-day ERFs from individual anthropogenic
agents in a consistent manner with the same physicaland chemical processes in the model (e.g., the model including the aerosol effects on ice clouds). We estimate
the global mean SW, LW, and net ERFs induced by all
anthropogenic agents, including well-mixed greenhousegases, short-lived gases, aerosols and their precursors,and land use. Particularly, we focus on the role of aerosols and quantify the contributions to the aerosol ERFs
from aerosol-radiation interactions (ARI), aerosol-cloudinteractions (ACI), and changes in the surface albedo
due to the aerosols. The ERF estimations are also

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conducted over the Arctic. In addition, we compare theaerosol ERF estimates to those derived using different
diagnostic approaches and those calculated by our previous model and other CMIP6 models.

the physical and chemical processes (e.g., emission,transport, diffusion, chemical reactions, and dry and wet
depositions) of the atmospheric aerosols and treats thefollowing species: non-sea-salt sulfate, BC, organic carbon (OC), sea salt, mineral dust, and aerosol precursor
gases (e.g., sulfur dioxide (SO2) and dimethyl sulfide).
The size distributions of sea salt and mineral dust are divided into 10 discrete bins, while the sizes of the otheraerosols are represented by lognormal size distributions.
The model assumes external mixing for all aerosol species; however, in the radiation process in MRIAGCM3.5, it is assumed that hydrophilic BC is internally
mixed with sulfate with a shell-to-core volume ratio of2, and the optical properties of hydrophilic BC are calculated based on Mie theory with a core-shell aerosol
treatment, in which a concentric BC core is surrounded
by a uniform coating shell composed of other aerosol
compounds (Oshima et al. 2009a, 2009b). MRI-ESM2.0
employs a BC aging parameterization (Oshima andKoike 2013) that calculates the variable conversion rateof BC from hydrophobic BC to hydrophilic BC, in which
the conversion rate generally depends on production
rate of condensable materials such as sulfate. Note thatthe BC aging parameterization implementation could reproduce seasonal variations of the BC mass concentrations observed over the Arctic (Mori et al. submitted;
Koike et al. submitted; Oshima et al. in preparation).
The deposition fluxes of BC and mineral dust calculated
in the aerosol component model are provided to a physically based snow albedo model in MRI-AGCM3.5,
which calculates the broadband albedos and the solar
heating profile in the snowpack as functions of the snow
grain size and concentrations of snow impurities (Aokiet al. 2011). In the radiation and cloud processes in
MRI-ESM2.0, sulfate is assumed to be (NH4)2SO4, andOC is assumed to be organic matter (OM) by lumping
OC species using an OM-to-OC factor of 1.4.
MRI-ESM2.0 represents the activation of aerosols into
cloud droplets based on the parameterizations of AbdulRazzak et al. (1998), Abdul-Razzak and Ghan (2000),
and Takemura et al. (2005). For the temperature range
from − 38 to 0 °C, the deposition nucleation is calculated based on the study of Meyers et al. (1992), and theimmersion and condensation freezing are calculated
based on the studies of Bigg (1953), Murakami (1990),
Levkov et al. (1992), and Lohmann (2002). In these parameterizations, the aerosol concentrations are not explicitly considered. Therefore, in this temperature range,
aerosol concentrations do not directly affect the number
concentrations of the ice crystals in the model except in
the case of contact freezing (Lohmann and Diehl 2006;
Cotton et al. 1986). Ice nucleation for cirrus clouds isrepresented using a parameterization from Kärcher et al.
(2006), when the temperature is less than – 38 °C;

Oshima et al. Progress in Earth and Planetary Science

Methods
ModelsWe use MRI-ESM2.0 to estimate present-day ERFs due
to anthropogenic forcing agents. Detailed descriptions
and evaluations of MRI-ESM2.0 are given by Yukimoto
et al. (2019). Brief descriptions of the model and the processes related to this study are given below.
MRI-ESM2.0 consists of four major component
models: an atmospheric general circulation model withland processes (MRI-AGCM3.5), an ocean–sea-ice general circulation model (OGCM), an aerosol chemicaltransport model, and an atmospheric chemistry model;
however, we do not couple OGCM in this study. MRIESM2.0 uses different horizontal resolutions in each atmospheric component model but employs the same vertical resolution, i.e., TL159 (approximately 120 km),
TL95 (approximately 180 km), and T42 (approximately
280 km) are used in MRI-AGCM3.5, the aerosol chemical transport model, and the atmospheric chemistrymodel, respectively; all models employ 80 vertical layers(from the surface to the model top at 0.01 hPa) in a hybrid sigma-pressure coordinate system. A coupler(Yoshimura and Yukimoto 2008) is used to interactively
couple each component model in MRI-ESM2.0; this enables an explicit representation of the effects of the gases
and aerosols on the climate system (e.g., the interactionsof aerosols with radiation, clouds, and the surface albedo
of snow and ice).
The atmospheric chemistry component model used in
MRI-ESM2.0 is the MRI Chemistry Climate Model version 2.1 (MRI-CCM2.1), which calculates evolution anddistribution of the ozone and other trace gases in the
troposphere and middle atmosphere. The model calculates a total of 90 gas-phase chemical species and 259
chemical reactions in the atmosphere. The chemical reactions of well-mixed greenhouse gases (CO2, CH4,
N2O, and halocarbons) are treated in MRI-CCM2.1;
however, MRI-CCM2.1 does not provide the concentrations of these species to MRI-AGCM3.5, which calculates the radiative effects of the greenhouse gases
according to the concentrations given by the boundary
conditions. Although sulfur chemistry is not treated in
MRI-CCM2.1, the chemical species required for sulfur
chemistry (e.g., ozone, hydrogen oxide radicals (HOx)and hydrogen peroxide (H2O2)) are provided from MRICCM2.1 to the aerosol component model. The aerosol
component model used in MRI-ESM2.0 is the Model of
Aerosol Species in the Global Atmosphere mark-2 revision 4-climate (MASINGAR mk-2r4c), which calculates(2020) 7:38

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homogeneous nucleation (Kärcher and Lohmann 2002)
for sulfate, OM, and sea salt aerosols and heterogeneous
nucleation (Kärcher and Lohmann 2003) for BC andmineral dust aerosols. Although aerosol-cloud interactions in anvil clouds are considered, the interactions
within the cores of convections are not incorporated(i.e., aerosol-cloud interactions are not calculated in theconvection scheme). The updraft velocities that are used
for the aerosol activations are represented as the sum of
the grid-scale vertical velocity and the velocity fluctuations due to turbulence, which can be obtained from aturbulence scheme (e.g., Lohmann et al. 1999; Takemura
et al. 2005) (note that a lower limit of 0.12 m s−1 is set
for the velocity fluctuations). More detailed descriptions
and evaluations of the cloud processes and cloud representations in MRI-ESM2.0 are given by Kawai et al.
(2019).
We compare the results calculated by our previous
model, MRI-CGCM3, in the CMIP5 experiments to
those obtained by MRI-ESM2.0 in this study. Details
concerning MRI-CGCM3 and the evaluations are given
by Yukimoto et al. (2012). MRI-CGCM3 is a previous
version of MRI-ESM2.0, and the basic framework (e.g.,interactive coupled system of each component model by
the coupler and the horizontal resolutions of each component model) of the two models are the same, although
there are major updates to multiple components of
MRI-ESM2.0 (Yukimoto et al. 2019; Kawai et al. 2019).
The major differences in the framework of the twomodels are that MRI-CGCM3 employed 48 verticallayers and that the atmospheric chemistry component
model was not coupled in MRI-CGCM3 in the CMIP5
experiments.

Oshima et al. Progress in Earth and Planetary Science

Model experiments

We perform 31-year time-slice experiments with the prescribed preindustrial climatology of sea surface
temperature (SST) and sea ice using MRI-ESM2.0 within
the frameworks of RFMIP (Pincus et al. 2016) and AerChemMIP (Collins et al. 2017) as summarized in Table 1.
The monthly climatology of the SST and sea ice data weretaken from the 500-year average of those calculated in thepreindustrial control (piControl, one of the Diagnostic,
Evaluation, and Characterization of Klima (DECK) experiments in CMIP6) experiment conducted by the atmosphere–ocean–chemistry–aerosol coupled version of MRIESM2.0. A detailed description of the piControl experiment performed by MRI-ESM2.0 is given by Yukimoto
et al. (2019).
The control simulation (piClim-control experiment) is
performed for 31-year time slices with the preindustrial(year 1850) conditions of well-mixed greenhouse gas
concentrations, short-lived gas and aerosol emissions,and land use in the prescribed preindustrial SST and sea
ice configurations (Table 1). The piClim-control experiment serves as a baseline for estimations of the ERFs inthis study. The piClim-control experiment is initiated

Table 1 Summary of the experiments performed by MRI-ESM2.0
Experiment name

Anthropogenic emissions and drivers
CO2

CH4

CFC/HCFCMIP name
N2O

CO/VOC

NOx

BC

OC

SO2

Land Use

piClim-control

RFMIP/AerChemMIP

piClim-ghg

2014

piClim-4xCO2a

4xCO2 (1850)

piClim-CH4

2014

2014

2014

RFMIP
RFMIP

2014

piClim-HC

AerChemMIP
2014

piClim-N2O

AerChemMIP
2014

piClim-VOC

AerChemMIP
2014

piClim-NOx

AerChemMIP
2014

AerChemMIP

piClim-O3

2014

2014

AerChemMIP

piClim-NTCF

2014

2014

2014

2014

2014

AerChemMIP

piClim-aer

2014

2014

2014

RFMIP/AerChemMIP

piClim-BC

2014

piClim-OC

AerChemMIP
2014

piClim-SO2

AerChemMIP
2014

piClim-lu
piClim-anthro

2014

2014

2014

2014

2014

2014

2014

2014

2014

AerChemMIP
2014

RFMIP

2014

RFMIP

Blank spaces in the table indicate the preindustrial conditions for the year 1850. The values “2014” in the table indicate the present-day conditions for theyear 2014a
Carbon dioxide concentrations in the piClim-4xCO2 experiment are set to four times the preindustrial values for the year 1850

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from the output of a 10-year spin-up run with the same
preindustrial conditions.
A series of perturbation experiments is performed foreach 31-year time slice using the same prescribed preindustrial SST and sea ice conditions but with the presentday (year 2014) conditions for each species as follows:present-day well-mixed greenhouse gas concentrations(piClim-ghg), four times the preindustrial CO2 concentrations (piClim-4xCO2), present-day CH4 concentrations (piClim-CH4), present-day chlorofluorocarbons(CFCs) and hydrochlorofluorocarbon (HCFC) concentrations (piClim-HC), present-day N2O concentrations(piClim-N2O), present-day carbon monoxide (CO) andvolatile organic compound (VOC) emissions (piClimVOC), present-day nitrogen oxide (NOx) emissions(piClim-NOx), present-day CO/VOC/NOx emissions(piClim-O3), present-day emissions of near-term climate
forcers (NTCFs, i.e., tropospheric ozone and aerosols
and their precursors, not including methane here)(piClim-NTCF); present-day BC/OC/SO2 emissions(piClim-aer); present-day BC emissions (piClim-BC);present-day OC emissions (piClim-OC); present-day
SO2 emissions (piClim-SO2); present-day land use(piClim-lu); and all present-day anthropogenic forcers(piClim-anthro) as summarized in Table 1. The outputof the 10-year spin-up run for the piClim-control experiment is used as the initial state for these perturbation
experiments, except for the CH4, HC, and N2O cases,
which use the outputs of 10-year spin-up runs performed with their respective conditions. Note that thechemical species required for the production reactionsof sulfate (e.g., HOx, H2O2, and ozone) are calculated
under the preindustrial conditions for the piClim-SO2
and piClim-aer experiments due to the experimental
configuration.
We use the results from the 30-year prescribed SST
and sea ice experiments by MRI-CGCM3 conducted aspart of CMIP5 (Taylor et al. 2012) in this study. The
control run (sstClim experiment) was performed for 30year time slices with the prescribed preindustrial climatology of the SSTs and sea ice derived from the preindustrial control run by MRI-CGCM3 in the CMIP5
preindustrial (year 1850) conditions. The perturbationrun (sstClimAerosol experiment) was identical to that in
the sstClim experiment but used the emissions of theanthropogenic aerosols and their precursors at year 2000
from the CMIP5 historical experiment.

between the perturbation experiment (present-day conditions) and the piClim-control experiment (preindustrial conditions). The signs of the radiative fluxes aredefined as downward positive in this study. Although
ERFs are generally defined at the TOA, we also estimate
the ERFs at the surface, and these values denote the surface radiative flux changes between the perturbed andcontrol experiments. Note that the changes in the surface latent and sensible heat fluxes are not regarded aspart of the forcing in this study, although they may significantly impact the surface temperature response. The
ERFs for the present-day CO2 (ΔFCO2) are obtained fromthe results of the piClim-4xCO2 experiment using thefollowing approximation (Ramaswamy et al. 2001):

Oshima et al. Progress in Earth and Planetary Science

ERF estimates and decomposition into ARI, ACI, andsurface albedo effects

We use the last 30 years of each experiment for the analysis in this study. The ERFs at the TOA are calculated
as the differences of the 30-year annual mean net(downward minus upward) TOA radiative fluxes

ΔFCO2 ¼ ΔF4xCO2 ln ðC=C 0 Þ= ln ð4Þ;

ð1Þ

where C and C0 are the CO2 concentrations at thepresent and preindustrial levels, respectively, and
ΔF4xCO2 is the radiative flux difference between thepiClim-4xCO2 experiment and the piClim-control
experiment.
The aerosol ERF can be separated into forcings due tothe aerosol-radiation interactions, aerosol-cloud interactions, and changes in the surface albedo due to aerosols.
A direct method is proposed by Ghan (2013), which requires additional aerosol-free radiation calls in themodel, to calculate these forcing components. We perform the aerosol-free radiation calls in MRI-ESM2.0 todiagnose the instantaneous radiative forcing of the aerosols in the experiments related to the aerosols. In thisstudy, we employ the method of Ghan (2013) to decompose the ERFs at the TOA into aerosol-radiation interactions (ERFari), aerosol-cloud interactions (ERFaci), andsurface albedo effects (ERFalbedo) as follows:
ERFari ¼ Δð F − Faf Þ;ð2Þ

ERFaci ¼ ΔðFaf − Fcsaf Þ;

ð3Þ

ERFalbedo ¼ ΔFcsaf;

ð4Þ

where F is the net radiative flux, Faf is the flux calculated
neglecting the scattering and absorption of radiation by
aerosols, Fcsaf is the flux calculated neglecting the scattering and absorption of radiation by both clouds andaerosols, and Δ indicates the difference between the perturbation experiment and the piClim-control experiment(af indicates aerosol-free and cs indicates clear-sky). Thisapproach is applied to both the SW and LW radiation.
The approximate partial radiative perturbation (APRP)
method can also decompose the ERFs into ARI, ACI,
and surface albedo effects (Taylor et al. 2007; Zelinka
et al. 2014). The APRP method can diagnose the ERF
components using standard model outputs in climate
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comparisons, although this method is approximate andmay induce biases in the SW ERFs due to the ARI andACI (Zelinka et al. 2014). We compare the decompositions of the aerosol radiative effects derived using themethod of Ghan (2013) and the APRP method of Taylor
et al. (2007).

anthropogenic ERF by 0.33 W m−2 and more negative
total aerosol ERF by 0.4 W m−2. In the following, we
focus on the influences of anthropogenic gases and aerosols on the ERFs.

Oshima et al. Progress in Earth and Planetary Science

Results and discussion
Global ERFs

Figure 1 shows a summary of the global mean net ERF
estimates at the TOA induced by the anthropogenic forcing agents (in year 2014 relative to year 1850). The ERF
induced by the total anthropogenic components is estimated to be 1.96 W m−2, composed of a positive ERF
due to the well-mixed greenhouse gases (3.02 W m−2),
negative ERFs due to the NTCFs (− 1.08 W m−2), which
are mostly due to aerosols (− 1.22 W m−2), and slightly
negative ERFs due to land-use changes (− 0.19 W m−2).
In comparison to the best ERF estimates in IPCC AR5(in year 2011 relative to year 1750), the ERF estimates by
MRI-ESM2.0 in this study show lower total

Well-mixed greenhouse gases and short-lived gases

Figure 2 shows the global mean LW and SW ERFs andtheir net values at the TOA as estimated by each experiment. The global mean ERF values at the TOA and at
the surface are also summarized in Table 2. The net
ERFs at the TOA from the well-mixed greenhouse gases
are all positive, i.e., 1.85 W m−2 for CO2, 0.71 W m−2 for
CH4, 0.30 W m−2 for halocarbons, and 0.16 W m−2 forN2O (Figs. 1 and 2). These forcings are dominated by
LW radiation due to greenhouse effects; approximately
80% LW and 20% SW contributions are seen for the net
ERFs of the total well-mixed greenhouse gases (Fig. 2).
Note that the negative SW ERF value from halocarbons
is primarily due to the decrease in the absorption ofsolar radiation due to halocarbon-induced stratosphericozone depletion.

Fig. 1 Summary of the effective radiative forcing (ERF) estimates in the year 2014 relative to the year 1850 in MRI-ESM2.0. The estimates are theglobal annual mean net ERF values at the top of the atmosphere (TOA) caused by the emitted compounds and processes. The ERFs due toaerosols are separated into three radiative effects as follows: aerosol-radiation interactions, aerosol-cloud interactions, and the surface albedo
changes due to aerosols. The values of the net ERFs are also shown on the left side of the figure (positive and negative values are indicated by
red and blue, respectively). The ERFs caused by individual species or their precursors are shown by color crossbars with their values (e.g., CO2,
CH4, halocarbons, N2O, sulfate, OM, and BC). The ERFs from the total aerosols (estimated by the piClim-aer experiment) are shown by black bars
and are not equal to the sum of the individual ERFs from each aerosol. The ERFs of the other compounds are shown by red (positive) and blue
(negative) crossbars, respectively. Values are given in units of W m−2

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Fig. 2 Global and annual mean ERF estimates at the TOA in each experiment in MRI-ESM2.0. The longwave (red) and shortwave (blue) ERFs andthe sum (net, black diamonds) are shown. The net ERF values are also shown on the left side of the figure (positive and negative values areindicated by red and blue, respectively) and are given in units of W m−2. The experiment names are shown on the left axis. The ERFs from CO2 atpresent-day concentrations are estimated from the piClim-4xCO2 experiment. The ERF values are given in Table 2

The magnitudes of the global mean net ERFs of theshort-lived gases, which are precursors of the tropospheric ozone, are small and exhibit both positive andnegative signs, i.e., − 0.03 W m−2 for CO/VOC; − 0.02
W m−2 for NOx; and 0.07 W m−2 for net CO/VOC/NOx(piClim-O3) (Figs. 1 and 2). Tropospheric ozone should
enhance the greenhouse effect; however, the LW ERFs
caused by NOx and CO/VOC/NOx are negative (Fig. 2
and Table 2). This result indicates that troposphericozone may influence clouds and convective activity, particularly over the tropics, and that the greenhouse effectof ozone can be masked by cloud changes, which complicates the net radiative effects. The changes in the atmospheric circulation caused by short-lived gases andaerosols are discussed in another paper (Deushi et al. in
preparation).
Aerosols

The global mean net ERF estimate of the total aerosols
at the TOA is − 1.22 W m−2 and is composed of positive
forcing from BC (0.24 W m−2) and negative forcingsfrom sulfate (− 1.38 W m−2) and OM (− 0.33 W m−2)
(Fig. 1). The net ERF of the total aerosols is composedof partial offsets of the large negative SW and positive
LW ERFs (Fig. 2). The net ERF of the total aerosols (−1.22 W m−2) consists of a 23% contribution from ARI (−0.32 W m−2), a 71% contribution from ACI (− 0.98 Wm−2), and a small contribution from the surface albedo
effect (0.08 W m−2) (Fig. 1), where these percentages are
estimated using the absolute value of each effect.
Becauseaerosols
areregionally
distributed(Supplementary Fig. 1), the geographic distributions of
the ERF are important. Figure 3 shows the spatial distributions of the SW, LW, and net ERFs at the TOA by the
total aerosols and each aerosol species or precursor (e.g.,
BC, OC, and SO2). The SW ERF of the total aerosols(Fig. 3a) is generally negative and indicates strong forcing in the emission source regions and their downstream regions predominantly over the northern
midlatitude, which is primarily due to the sulfate forcing(Fig. 3j). In addition, there is a strong negative SW ERF
in the tropical convective regions. The LW ERF of thetotal aerosols (Fig. 3b) is generally positive and exhibits
strong positive forcing in the tropical convective regions,particularly over the maritime continent via the eastern
tropical Indian Ocean, where BC LW forcing (Fig. 3e)dominates the total aerosol LW ERF (Fig. 3b). In the following, we describe the ERF magnitudes and their spatial
distributions by distinguishing the ARI, ACI, and surface
albedo effects for the SW and LW radiation.

(2020) 7:38

Oshima et al. Progress in Earth and Planetary Science

Page 8 of 21

Table 2 Global annual mean effective radiative forcings of each experiment
ERF at TOA

ERF at surface

Experiment name SW
ARI

LW
ACI

ALB

Total

ARI

NET
ACI

ALB

Total

ARI

ACI

ALB

SW

LW

NET

Total

Total

Total

Total

− 0.031.33

1.30

piClim-ghg

---

---

---

0.63

---

---

---

2.40

---

---

---

3.02

piClim-4xCO2a

---

---

---

1.97 (0.48) ---

---

---

5.69 (1.38) ---

---

---

7.66 (1.85) − 0.79 (− 0.19) 3.36 (0.81) 2.57 (0.62)

piClim-CH4

---

---

---

0.09

---

---

---

0.62

---

---

---

0.71

− 0.060.33

0.27

piClim-HC

---

---

---

− 0.22

---

---

---

0.52

---

---

---

0.30

0.05

0.23

0.29

piClim-N2O

---

---

---

− 0.06

---

---

---

0.22

---

---

---

0.16

0.06

0.03

0.09

piClim-VOC

---

---

---

− 0.09

---

---

---

0.07

---

---

---

− 0.03− 0.190.12− 0.06

piClim-NOx

---

---

---

0.27

---

---

---

− 0.29

---

---

---

− 0.02− 0.020.06

0.04

piClim-O3

---

---

---

0.14

---

---

---

− 0.07

---

---

---

0.07

− 0.160.20

0.05

piClim-NTCF

---

---

---

2.53

---

---

---

1.45

---

---

---

piClim-aer− 0.32 − 2.47 0.05

− 2.740.00

1.49 0.03

1.52− 0.32 − 0.98 0.08

− 1.63 0.15

− 1.22− 0.00 1.54 − 0.08 1.45

− 0.09 0.07

− 3.390.87− 2.52− 3.260.68− 2.57− 1.950.78− 1.170.05− 0.470.26− 0.07 − 0.43 − 0.02 − 0.52

piClim-SO2− 0.49 − 1.36 − 0.05 − 1.90

0.00

0.42 0.09

0.52− 0.48 − 0.94 0.05

− 1.38− 1.770.21− 1.56

piClim-lu

---

---

---

− 0.21

---

---

---

0.03

---

---

---

− 0.19− 0.16− 0.25− 0.41

piClim-anthro

---

---

---

− 2.21

---

---

---

4.16

---

---

---

1.96

− 3.562.01− 1.560.21 − 0.03 0.18

0.24

− 0.52

piClim-BC
piClim-OC

0.00

0.25

− 1.08− 1.22− 0.07 − 0.21 − 0.05 − 0.33

Values are given in units of W m−2
ERF effective radiative forcing, TOA top of the atmosphere, SW shortwave, LW longwave, NET SW + LW, ARI aerosol-radiation interactions, ACI aerosol-cloudinteractions, ALB surface albedo effects, NTCF near-term climate forcersaValues in parentheses are the ERFs of CO2 at present-day concentrations as estimated from the piClim-4xCO2 experiment(a) ARI Figure 4 shows the spatial distributions of the
SW ERFs at the TOA due to the ARI, ACI, and surface
albedo effects for the total aerosols and each species
using the method of Ghan (2013). Figures 5 and 6 show
the spatial distributions of the aerosol ERFs at the TOA
due to the ARI, ACI, and surface albedo effects for the
LW and net radiation, respectively. The global mean
SW, LW, and net ERFs of the aerosols due to each of
the three components are summarized in Table 2. The
SW ERFari values due to the total aerosols are mostly
negative, particularly over the source regions (e.g., East
Asia and South Asia; Fig. 4a and Supplementary Fig. 1a).
BC aerosols efficiently absorb solar radiation and cause
globally positive ERFs and largely positive ERFs over thesource regions (Fig. 4d and Supplementary Fig. 1b). The
ERFs of the scattering aerosols are globally negative andare largely negative over the source regions, particularly
for sulfate (Fig. 4 g and j). The smaller magnitude of the
ERFs for OM compared to those for sulfate, which isconsistent with the smaller aerosol optical depths of OM
compared to those of sulfate (Supplementary Figs. 1cand 1d), is primarily due to the lower OC emission
amounts, the lower hygroscopic growth of organic compounds, and the slight light-absorbing effects of OC as
treated in the model based on recent observations andexperiments (Yukimoto et al. 2019). The greater negative
ERFs from the scattering effects compensate for the
positive ERFs from light absorption, resulting in negative
SW ERFari values for the total aerosols (Fig. 4a). The
LW ERFari values of the aerosols are mostly negligible(Table 2 and Fig. 5a, d, g, and j). As a result, the global
mean net ERFari value at the TOA (Figs. 1 and 6) is estimated to be − 0.32 W m−2 for the total aerosols and consists of BC (0.25 W m−2), OM (− 0.07 W m−2), andsulfate (− 0.48 W m−2) components, which comprise 23%of the net ERF from the total aerosols (Fig. 1).
(b) ACI and aerosol effects on high-level ice clouds
The SW ERFs due to ACI from the total aerosols areglobally negative and are substantially negative over thetropical convective regions (particularly over the maritime continent and the eastern Indian Ocean) and thesource and downwind regions (Fig. 4b). The global mean
SW ERFaci values of the individual aerosol species are
all negative (Fig. 4e, h, and k) and contribute to the large
negative ERFaci values of the total aerosols. Over thesource and downwind regions, a large number of aerosols could influence clouds by serving as CCN and by increasing the cloud droplet number concentrations ofliquid clouds (Supplementary Fig. 2). The SW ERFaci isdominated by sulfate, primarily due to higher CCN activity and greater sulfate amounts (Supplementary Fig. 1), rather than BC and OM (Fig. 4k). Over the tropicalconvective regions, BC aerosols are vertically transported
into the upper troposphere by deep convection and serve
as INPs at high altitude and influence ice clouds, resulting
in substantially negative SW ERFaci values (Fig. 4e).
On the other hand, the LW ERFs due to ACI from thetotal aerosols are globally positive and are significantly
positive over the tropical convective regions and over

Oshima et al. Progress in Earth and Planetary Science(2020) 7:38

Page 9 of 21

Fig. 3 Horizontal distributions of the annual mean ERFs (W m−2, filled contours) at the TOA for shortwave (SW, left); longwave (LW, middle); andtheir sum (net, right) in the (a–c) piClim-aer (aer); (d–f) piClim-BC (BC); (g–i) piClim-OC (OC); and (j–l) piClim-SO2 (SO2) experiments in MRIESM2.0. The global mean ERF values are shown near the upper-right corner of each panel and are given in Table 2

the oceans along storm tracks in the Northern Hemisphere(e.g., in the Pacific and Atlantic oceans) (Fig. 5b). In suchregions, the influences of aerosols on high-level clouds,
which have efficient greenhouse effects, are responsible forthe positive LW ERFaci values. Positive LW ERFaci values
are present for all aerosol species (Fig. 5e, h, and k) and arelargest for BC (Fig. 5e). The LW ERFs at the TOA are
mostly dominated by ACI (Fig. 5 and Table 2), leading tosimilar distributions between the total LW ERFs (Fig. 3b)
and the LW ERFaci (Fig. 5b).
Here, we discuss the mechanism for the pronounced
SW and LW ERFaci values over the tropical convective
regions (Figs. 4b and 5b). In terms of the LW radiation,
increases in aerosols at high altitudes where high-levelclouds are frequently present (i.e., regions with low outgoing LW radiation) are essential for the LW ERFaci, although one may simply consider that the impacts ofaerosols on ERFaci depend only on the aerosol concentrations. Figure 7 shows the spatial distributions of thehigh-level cloud cover and the column-integrated number concentration of ice crystals in the piClim-aer experiment and the changes in the column-integrated
number concentration of ice crystals due to the total
aerosols. Note that the effects of the aerosols on thecloud cover, liquid water path, and ice water path aresmaller than those on the number concentrations of thecloud droplets and ice crystals (Supplementary Figs. 2
and 3). The increases in the number concentrations ofOshima et al. Progress in Earth and Planetary Science(2020) 7:38

Page 10 of 21

Fig. 4 Horizontal distributions of the annual mean shortwave (SW) ERFs (W m−2, filled contours) at the TOA separated into the aerosol-radiationinteractions (ARI, left); aerosol-cloud interactions (ACI, middle); and surface albedo effects (Albedo, right) in the (a–c) piClim-aer (aer); (d–f) piClimBC (BC); (g–i) piClim-OC (OC); and (j–l) piClim-SO2 (SO2) experiments in MRI-ESM2.0. The values of the global mean SW ERFs of each component
are shown near the upper-right corner of each panel and are given in Table 2

ice crystals due to the total aerosols are pronounced in
the upper troposphere (Supplementary Fig. 4) at higher
altitudes (150–300 hPa) over the tropical convective regions (i.e., the maritime continent via the eastern tropical Indian Ocean and over central Africa) and at lower
altitudes (250–500 hPa) over the midlatitude Pacificalong the storm tracks (Fig. 7c). The results clearly indicate that regions with pronounced LW ERFaci values(Fig. 5b) correspond to regions where both the number
concentrations of ice crystals and the high-level cloud
cover are large (Fig. 7). In the upper troposphere, thenumber concentrations of the total aerosols, which cancontribute to the formation of ice clouds by serving as

INPs, increase over the source and downwind regions in
the midlatitude Pacific and modestly increase over thetropical convective regions (Supplementary Figs. 5 and 6)
due to the upward transport of aerosols by cyclones(which occur frequently in boreal winter and spring) andcumulus convections (in boreal summer). Note that thelarge aerosol number concentrations influenced by BC in
the piClim-BC experiment (Supplementary Fig. 5b) are
due to the smaller prescribed size distribution of BC compared to those of sulfate and OM in MRI-ESM2.0, as wellas the nonlinear effects of BC, as discussed below. In boreal summer, high-cloud regions (e.g., regions enclosed
within the thick black lines in Supplementary Fig. 6) are(2020) 7:38

Page 11 of 21

shifted north (i.e., close to the source regions) and partiallyoverlap regions with large aerosol number concentrations,leading to increases in the number concentrations of ice
crystals in the vicinity of the maritime continent and theeastern tropical Indian Ocean (Supplementary Fig. 7).
However, despite the larger number concentrations ofaerosols over the midlatitude regions, ice clouds overthese regions are less effective for the LW radiation thanthose over tropical regions because the high-level cloud
cover is smaller and the ice clouds are present at lower altitudes (250–500 hPa) over the midlatitude regions. Thesemechanisms are responsible for the pronounced LW
ERFaci over the tropical convective regions (Fig. 5). In
terms of the SW radiation, anvil clouds generally have asimilar magnitude of positive LW and negative SW cloudradiative effects due to reflection of the SW radiation,
which leads to pronounced negative SW ERFaci values
over the tropical convective regions (Figs. 4 and 5).
The significantly negative SW ERFaci values (Fig. 4b)
and significantly positive LW ERFaci values (Fig. 5b) in
the high-level clouds, which are particularly pronounced
over the tropical convective regions, mostly cancel eachother out. On the other hand, the negative SW ERFacivalues in the low-level clouds (Fig. 4b) are unlikely to beoffset by LW radiation (Fig. 5b), resulting in globally
negative net ERFaci values (Fig. 6b), which are pronounced over the source and downwind regions. The
global mean net ERFaci value at the TOA (Figs. 1 and 6)
is estimated to be − 0.98 W m−2 for the total aerosols,
consisting of BC (− 0.09 W m−2), OM (− 0.21 W m−2),

Oshima et al. Progress in Earth and Planetary ScienceFig. 5 Same as Fig. 4 but for longwave (LW) radiation

Oshima et al. Progress in Earth and Planetary Science(2020) 7:38

Page 12 of 21

Fig. 6 Same as Fig. 4 but for the net (net, shortwave + longwave) radiationand sulfate (− 0.94 W m−2) components, which accountfor 71% of the net ERF from the total aerosols (Fig. 1).
Note that the sum of the ERFaci values for each aerosol
compound (− 1.25 W m−2) does not equal the ERFacivalue for all the aerosols (− 0.98 W m−2), as shown in
Fig. 1, due to the strong nonlinearities in the ACI
process. Carslaw et al. (2013) demonstrated that the sensitivity of the cloud albedo to anthropogenic emissions ismuch higher under preindustrial conditions (lower aerosol concentrations) than under present-day conditions(higher aerosol concentrations). The increase in the insensitivity of the cloud albedo with increasing emissions
explains in part why the change in the indirect radiative
forcing caused by the present-day emissions, with all

species combined, is smaller than the sum of the forcings caused by the individual species.
Our results suggest that the interactions of the aerosols with ice clouds can cause both substantial SW and
LW radiative forcing anomalies in MRI-ESM2.0. The
parameterization of the interactions of the aerosols withice clouds used in the model leads to INP production by
the BC particles and influences the high-level clouds(temperatures < – 38 °C). Recent experimental studies
suggest that BC particles are not as efficient for INPs in
mixed-phase clouds under supercooled conditions as
was previously thought (Vergara-Temprado et al. 2018).
However, the high altitude temperatures near the tropical deep convective cloud tops can be less than − 38 °C

Oshima et al. Progress in Earth and Planetary Science(2020) 7:38

Fig. 7 Horizontal distributions of the 30-year annual mean (a) highlevel (above 440-hPa level) cloud cover (%) and (b) columnintegrated number concentration of ice crystals (109 m−2) in thepiClim-aer experiment and (c) the difference in the columnintegrated number concentration of ice crystals between the piClimaer and piClim-control experiments (109 m−2)

(Supplementary Fig. 4), and most aerosol particles would
serve as INPs in such cold conditions (DeMott et al.
1999; Mahrt et al. 2018), suggesting the possible occurrence of interactions of aerosols with high-level ice
clouds over the tropical convective regions. In terms of
the radiation budget at the TOA, the net ERFs would
mostly cancel out between the negative SW and positive

Page 13 of 21

LW radiation; however, strong radiative heating of theatmosphere due to LW absorption by the high-level clouds
could change the vertical atmospheric temperature profile(Supplementary Fig. 8a), leading to modifications in thelarge-scale atmospheric circulation and the hydrologicalcycle. A global modeling study implementing aparameterization of the aerosol effects on ice clouds estimated that the forcing due to anthropogenic BC was − 0.3
W m−2 (Penner et al. 2018), which is consistent with our estimate of the net ERFaci of BC, − 0.09 W m−2 (Table 2). Because there remain large uncertainties in the processes
involved in the interactions of aerosols with ice clouds andthe representations of the aerosol concentrations in theupper troposphere over the tropics in the model, the SW
ERFaci and LW ERFaci values calculated in MRI-ESM2.0
might include quantitative uncertainties. Nevertheless, ourresults indicate the possible importance of the interactionsof aerosols with ice clouds on atmospheric circulation andthe hydrological cycle and suggest the importance of further studies of these interactions in high-level clouds over
the tropics.
The piClim-BC experiment exhibits a positive net ERF
at the TOA (0.24 W m−2) and a negative ERF at the surface (−1.17 W m−2) (Table 2); therefore, the difference
has a positive value (1.41 W m−2). This large radiative
heating in the atmosphere is balanced by turbulent (latent and sensible) surface heat flux decreases, which correspond to the global mean precipitation decrease seenin the piClim-BC experiment. Although the spatial distributions of BC and precipitation are globally inhomogeneous, precipitation decreases could extend the BC
lifetimes due to the weaker wet deposition processes because BC lifetimes are primarily controlled by wet removal from the atmosphere by precipitation (Oshima
et al. 2012). Longer BC lifetimes could cause further radiative heating effects due to high-level clouds, which
could represent a positive feedback process. On theother hand, stabilized atmosphere by radiative heating
leads to a weakening of the upward mass flux of cumulus convection (Supplementary Fig. 8b), which could
partially reduce the upward transport of BC to the upper
troposphere. Because the climate responses to BC arecomplex, it is important to quantify the global energy
budget perturbations caused by BC (e.g., Suzuki and
Takemura 2019), particularly for models that represent
the interactions of aerosols with ice clouds; however, theenergy balance is not the focus of this study and will
therefore be discussed in a future study.
Note that the aerosol processes controlling the
spatial distributions of BC have nonlinearities in MRIESM2.0. For example, the conversion rate of BC fromhydrophobic BC to hydrophilic BC (i.e., BC aging; see
the “Methods” section) depends on the sulfate production rate. In the piClim-BC experiment, smaller

Oshima et al. Progress in Earth and Planetary Science(2020) 7:38

levels of preindustrial SO2 emissions led to lower sulfate production, which caused slower BC aging rates,
followed by a reduced wet removal of BC and therefore longer BC lifetimes because hydrophobic BC isinefficiently removed from the atmosphere by precipitation. This is consistent with the longer global mean
BC lifetime of 7.1 days in the piClim-BC experimentcompared to that of 5.9 days in the piClim-aer experiment. These nonlinearities in the aerosol processes
tend to lead to greater effects due to BC in thepiClim-BC experiment.
(c) Surface albedo The SW ERFs due to changes inthe surface albedo induced by BC are significantly
positive over the Tibetan Plateau and the entire Arctic region (Fig. 4f) and are caused by the surface albedo reduction due to BC deposition on snow andsea ice. The SW ERFalbedo induced by OM and sulfate are smaller (Fig. 4i and l). As a result, the SW
ERFalbedo induced by the total aerosols is primarily
controlled by BC (Fig. 4c). The global mean net
ERFalbedo from the total aerosols is slightly positive(0.08 W m−2) due to the large contribution from BC(0.07 W m−2) (Figs. 1 and 6). The influence of BC on
radiation over the Arctic is described in the next
section.

Page 14 of 21

Arctic ERFs

The Arctic region is strongly influenced by climatechange. Figure 8 shows the LW and SW ERFs, and their
net values averaged over the Arctic region (60–90°N) at
the TOA estimated by each experiment. The Arctic
mean ERF values at the TOA and at the surface aresummarized in Table 3. The large positive Arctic mean
net ERFs at the TOA are estimated to be 1.47 W m−2 for
CO2; 0.61 W m−2 for BC; 0.35 W m−2 for CH4; 0.32 W
m−2 for the tropospheric ozone (piClim-O3); and 1.94
W m−2 for the total anthropogenic agents (Fig. 8 andTable 3). In the Arctic, BC and CH4 are responsible forthe second and third largest positive net ERFs, respectively, after CO2 (Table 3 and Fig. 8). Note that the SW
ERF of BC at the TOA is positive in the Arctic mean but
is negative in the global mean (Fig. 2). The Arctic mean
SW ERFs of BC due to the ARI, ACI, and surface albedo
effects are estimated to be 0.28 W m−2, − 0.77 W m−2,
and 1.08 W m−2, respectively, while those of the total
aerosols are − 0.07 W m−2, − 1.01 W m−2, and 0.24 W
m−2, respectively, indicating the large contribution to the
SW ERFs from the reduction in the surface albedo due
to BC deposition on snow and sea ice over the Arctic.
The large positive net ERFs at the TOA (e.g., CO2, BC,
CH4, and the total anthropogenic agents) may be responsible for the increases in the surface air

Fig. 8 Same as Fig. 2 but for the Arctic mean ERF estimates. The Arctic region is defined as the latitude range of 60–90°N. The ERF values are
given in Table 3

Oshima et al. Progress in Earth and Planetary Science(2020) 7:38

Page 15 of 21

Table 3 Arctic (60–90°N) annual mean effective radiative forcings and surface air temperatures of each experiment
Experimentname

ERF at TOA

ERF at surface

SAT

SW

LW

NET

SW

LW

NET

0.89

1.19

2.08

0.18

1.07

1.26

0.41

piClim-4xCO2

2.51 (0.61)

3.57 (0.86)

6.09 (1.47)− 0.02 (− 0.00)

2.78 (0.67)

2.76 (0.67)

1.25

piClim-CH4

0.02

0.33

0.35

− 0.13

0.29

0.16

0.09

piClim-ghg
apiClim-HC− 0.360.42

0.06− 0.05

0.21

0.16

0.02

piClim-N2O− 0.190.13− 0.07− 0.080.09

0.01− 0.00piClim-VOC

0.00− 0.06− 0.05− 0.140.11− 0.030.04

piClim-NOx

0.55− 0.230.31− 0.02− 0.01− 0.02− 0.04piClim-O3

0.38− 0.060.32− 0.09

0.11

0.02

0.04

piClim-NTCF− 0.460.35− 0.11− 1.030.40− 0.63− 0.09piClim-aer− 0.850.25− 0.59− 0.970.46− 0.510.03

piClim-BC

0.60

0.01

0.61

0.09

0.36

0.45

0.22

piClim-OC− 0.530.23− 0.29− 0.500.03− 0.47− 0.01

piClim-SO2− 1.190.34− 0.85− 0.970.34− 0.63− 0.10piClim-lu− 0.05

0.06

0.01

0.02

−0.09

− 0.07− 0.02

piClim-anthro− 0.011.95

1.94− 0.95

1.58

0.63

0.49

SAT values are the differences in the 30-year annual mean values between each experiment and the piClim-control experiment. ERF and SAT values are given inunits of W m−2 and K, respectively
ERF effective radiative forcing, TOA top of the atmosphere, SAT surface air temperature, SW shortwave, LW longwave, NET SW + LW, NTCF near-termclimate forcersaValues in parentheses are the ERFs of CO2 at the present-day concentrations as estimated from the piClim-4xCO2 experimenttemperatures over the Arctic (Table 3). Note that thetemperature changes are limited because the experiments in this study were conducted using the prescribed
SST and sea ice conditions. An understanding of the
Arctic climate response requires more quantitative estimations of the ERFs taking into account the latent andsensible heat fluxes and the energy transport from lower
latitudes in the atmosphere.
Figure 9 shows the spatial distributions of the net ERFs
at the TOA over the Arctic and surrounding regions.
The total anthropogenic net ERFs at the TOA exhibit
substantially positive values over the entire Arctic region(Fig. 9a). The net ERFs of the well-mixed greenhousegases at the TOA are composed of 57% LW and 43%
SW radiation over the Arctic (Fig. 8) and are generally
spatially homogeneous both inside the Arctic and in thesurrounding regions (Fig. 9b). The net ERFs of BC at the
TOA are generally positive over the entire Arctic region,
particularly around the edge of the springtime snow
cover areas and the coastal region areas in Greenland(Fig. 9e) because BC deposition on snow over land andsea ice decreases the snow albedo, resulting in a decreasein the snow cover due to snow melting. Scattering aerosols, primarily sulfate aerosols, induce negative net ERFs
both inside the Arctic and in the surrounding regions(Fig. 9f), and the spatial distributions of the ERFs generally
depend on those of the sulfate in the atmosphere. These
spatially dependent influences of greenhouse gases andaerosols (BC and sulfate) on the radiation are responsible
for the positive net ERFs of the total anthropogenic agentsover the entire Arctic region (Fig. 9a).
The results obtained in this study indicate the possible importance of greenhouse gases (e.g., CO2 andCH4) and BC aerosols on the Arctic climate. In particular, the interactions of BC aerosols with snow andice could impact the radiative effects over the entire
Arctic region. Decreases in the surface albedo due to
BC deposition could influence the ERFs at the surface(Fig. 10); the net ERFs from BC at the surface aresubstantially positive inside the Arctic region andnegative outside the Arctic region (Fig. 10e), which
could be partially responsible for the sharp contrasts
in the total anthropogenic net ERFs at the surface between the areas inside (positive ERFs) and outside(negative ERFs) the Arctic regions (Fig. 10a). In the
Arctic, BC and CH4 are responsible for the second
and third largest positive net ERFs at the surface, respectively, after CO2 (Table 3). Because the Arctic climate has a significantly higher sensitivity to BC
emitted within the Arctic compared to BC emitted in
the surrounding regions (Sand et al. 2013), the results
obtained in this study suggest that increases in direct
BC emissions in the Arctic in the near future (e.g.,
ship emissions from new shipping routes and flaring
emissions from oil and gas activities) could acceleratesurface warming.

Oshima et al. Progress in Earth and Planetary Science(2020) 7:38

Page 16 of 21

Fig. 9 Horizontal distributions of the annual mean net (net, shortwave + longwave) ERFs (W m−2, filled contours) at the TOA in the (a) piClimanthro, (b) piClim-ghg, (c) piClim-CH4, (d) piClim-aer, (e) piClim-BC, and (f) piClim-SO2 experiments in MRI-ESM2.0. The ERF values averaged over
the Arctic region (60–90°N) are shown near the upper-right corner of each panel and are given in Table 3. The black circles denote a latitudeof 60°N

Comparison of ERFs from different methods and previous
and other models

In this study, we use the method of Ghan (2013) to decompose the aerosol ERFs into the ARI, ACI, and surface albedo effects for SW and LW radiation; however,this method requires outputs from additional aerosolfree radiation calls in the model and additional computational time, making it difficult to conduct long calculations. Here, we compare our results to those derived
using the APRP method of Taylor et al. (2007). Although
the APRP method is an approximate technique, it isavailable for most of the climate model outputs, whichfacilitates comparisons to evaluations of the radiative effects of aerosols estimated by other models.
A comparison of the global mean ERF estimates at the
TOA for the total aerosols (piClim-aer experiment) due
to the ARI, ACI, and surface albedo effects in MRIESM2.0 using both methods is given in Table 4. Using
the APPR method, the SW ERFari value is 0.17 W m−2
too negative and the SW ERFaci value is 0.23 W m−2 too

positive, and they offset each other, and the total SW
ERFari+aci value is similar (within a difference of 3%) to
those obtained using the method of Ghan (2013). The
total LW ERFari+aci values derived by both methods are
similar (within a difference of 3%). The net ERFari+aci
values are estimated to be − 1.30 W m−2 using themethod of Ghan (2013) and − 1.21 W m−2 using the
APRP method; this 7% difference is primarily due to the
SW radiation. The biases in the SW ERFs using the
APRP method are consistent with the results in Zelinka
et al. (2014), who described possible reasons for the disagreements seen between the two methods in detail. The
similar ERF estimates obtained using the two different
methods lends credence to the validity of our decomposition results.
To quantify the impacts of the improvements to theaerosol and cloud processes in MRI-ESM2.0 on ERFs,
we estimate the global mean ERFs and diagnose the
ERFari, ERFaci, and ERFalbedo values using the APRP
method in the sstClimAerosol experiment, which was(2020) 7:38

Oshima et al. Progress in Earth and Planetary Science

Page 17 of 21

Fig. 10 Same as Fig. 9 but for the net (net, shortwave + longwave) ERFs at the surface

Table 4 Global annual mean effective radiative forcings of aerosols estimated by different methods and other models
Model name(Method)

SW ERF at TOA

LW ERF at TOA

ARI
scat

ACI
abs

sum

scat +
abs

ALB
amt

ARI +
ACI

ARI

ACI

NET ERF at TOA
ARI +
ACI

ARI

ACI

ALB

ARI +
ACI

sum

---

− 0.32------− 2.470.05 − 2.79

0.00

1.49

1.49

− 0.32− 0.980.08− 1.30

MRI-ESM2.0(APRP)− 0.760.28− 0.49− 1.86− 0.38− 2.24−− 2.73
0.00

0.04

1.48

1.52

− 0.45− 0.76---− 1.21

MRI-CGCM3(APRP)− 0.110.14

0.03− 2.20− 0.44− 2.640.04 − 2.60

0.01

1.14

1.15

0.05

− 1.50---− 1.46

12 CMIP6 models − 0.59 ±(APRP) Smith0.17
et al. (2020)

0.26 ±
0.12− 0.33 ±0.20− 0.84 ±0.49− 0.15 ±0.26− 0.99 ±0.49---− 1.33 ±0.50

0.12 ±
0.06

0.16 ±
0.42

0.27 ±
0.40− 0.21 ±0.18− 0.84 ±0.30---− 1.05 ±0.22

4 CMIP6 models(G13) Thornhill
et al. (2020)

---

---

---

---

---

---

---

---

---

---

− 0.17 ±0.15− 0.96 ±0.25

0.05 ±
0.05− 1.12 ±0.25

MRI-ESM2.0 (G13) ------Values estimated by MRI-CGCM3 are the forcings for the year 2000 relative to the year 1850 with CMIP5 emissions and those estimated by MRI-ESM2.0, the 12
CMIP6 models (Smith et al. 2020), and the 4 CMIP6 models (Thornhill et al. 2020) are the forcings at the year 2014 relative to the year 1850 with CMIP6 emissions(the piClim-aer experiment). Values estimated by the CMIP6 models are the multi-model means and standard deviations. All estimates are given in units of W m−2.
The ACI of the LW ERF is estimated to be the change in the LW cloud radiative effect, and the ARI of the LW ERF is estimated to be the residual of the total ERF
minus the ACIERF effective radiative forcing, TOA top of the atmosphere, SW shortwave, LW longwave, NET SW + LW, ARI aerosol-radiation interactions, ACI aerosol-cloudinteractions, ALB surface albedo effects, scat scattering, abs absorption, amt cloud amount, G13 the method of Ghan (2013), APRP the approximate partial radiative
perturbation (APRP) method of Taylor et al. (2007)

(2020) 7:38

Page 18 of 21

performed using our previous model, MRI-CGCM3, in
CMIP5. Note that the sstClimAerosol experiment did
not include outputs from the aerosol-free radiation calls
and that the ERFs in the experiment in CMIP5 were estimated for the year 2000 relative to the year 1850. The
global mean ERF estimates for the total aerosols using
the APRP method for MRI-ESM2.0 and MRI-CGCM3
are compared in Table 4. The SW ERFari value at the
TOA is a slightly positive 0.03 W m−2 in MRI-CGCM3
compared to − 0.49 W m−2 in MRI-ESM2.0 due to theinappropriate treatment of the aerosol-radiation
parameterization in MRI-CGCM3, which leads to a verysmall scattering effect of − 0.11 W m−2. In MRI-ESM2.0,
this issue is corrected, and the scattering effect is − 0.76
W m−2. The SW ERFaci value at the TOA is reduced by
0.40 W m−2, from − 2.64 W m−2 in MRI-CGCM3 to −2.24 W m−2 in MRI-ESM2.0. On the other hand, the LW
ERFaci value at the TOA is increased by 0.34 W m−2,
from 1.14 W m−2 to 1.48 W m−2, as a result of the enhanced greenhouse effect of the tropical high-levelclouds in MRI-ESM2.0 (Fig. 3b, not shown for MRICGCM3). As a result, the net ERFari+aci value at the
TOA is 17% lower, from − 1.46 W m−2 in MRI-CGCM3
to − 1.21 W m−2 in MRI-ESM2.0. These results indicatethat improvements to the aerosol, radiation, and cloud
processes in MRI-ESM2.0 are responsible for the increases in the ARI effects (shifts to more negative) anddecreases in the ACI effects (shifts to more positive).
The global mean ERF estimates at the TOA for the
total aerosols due to the ARI, ACI, and surface albedo
effects in MRI-ESM2.0 using the APRP method and themethod of Ghan (2013) are compared to other CMIP6
model results using the respective methods reported in
the literature (Table 4), i.e., 12 CMIP6 models using the
APRP method (Smith et al. 2020) and 4 CMIP6 models
using the method of Ghan (2013) (Thornhill et al. 2020).
In comparison to the multi-model means of the 12
CMIP6 models using the APRP method, the SW ERFarivalue in MRI-ESM2.0 is 0.16 W m−2 too negative due tothe greater scattering effects in MRI-ESM2.0 and thesimilar degree of absorption effects (Table 4), whichleads to greater negative net ERFari value in MRIESM2.0 (− 0.45 W m−2). The SW ERFaci and LW ERFacivalues in MRI-ESM2.0 are more negative (− 2.24 W m−2)
and positive (1.48 W m−2), respectively, compared to the
12 CMIP6 model means (− 0.99 W m−2 and 0.16 W m−2,
respectively) because the aerosol effects on the highlevel ice clouds in the tropics contribute to the large
ERFaci values in MRI-ESM2.0 and only a few of the 12
CMIP6 models treated the aerosol effects on the ice
clouds (Smith et al. 2020). However, the net ERFacivalue in MRI-ESM2.0 (− 0.76 W m−2) is close to the 12
CMIP6 model mean value (− 0.84 W m−2) because thelarge negative SW ERFaci and positive LW ERFaci

mostly cancel each other out. In total, the net ERFari+aci value in MRI-ESM2.0 (− 1.21 W m−2) using the
APRP method is approximately 15% more negative than
the 12 CMIP6 model mean (− 1.05 W m−2), and this difference is within the range of the standard deviation of
the 12 CMIP6 models (Table 4). In addition, we compare the ERF values in MRI-ESM2.0 to the four CMIP6
model results using the method of Ghan (2013), although the number of models conducting the aerosolfree radiation calls was limited. The net ERFari value in
MRI-ESM2.0 is 0.15 W m−2 too negative; however, thenet ERFaci value (− 0.98 W m−2) and the net ERF value
due to the surface albedo changes (0.08 W m−2) are similar to the four CMIP6 model mean values (− 0.96 W m−2
and 0.05 W m−2, respectively, and both are within a difference of 0.03 W m−2). In total, the net ERFari+aci valuein MRI-ESM2.0 (− 1.30 W m−2) using the method of
Ghan (2013) is approximately 15% more negative than
the four CMIP6 model mean (− 1.12 W m−2).
We also compare the global mean net ERFs at the
TOA induced by the well-mixed greenhouse gases, aerosols, land-use change, and total anthropogenic components in MRI-ESM2.0 to the 12 CMIP6 model results(Smith et al. 2020); these ERF values are 3.02 W m−2, −1.22 W m−2, − 0.19 W m−2, and 1.96 W m−2, respectively,in MRI-ESM2.0 (Table 1 and Fig. 1) and 2.87 ± 0.18 W
m−2, − 1.04 ± 0.23 W m−2, − 0.08 ± 0.14 W m−2, and1.97 ± 0.26 W m−2, respectively, in the 12 CMIP6 multimodel means. The differences between these values arewithin the ranges of the standard deviations in the 12
CMIP6 models; therefore, the overall net ERFs in MRIESM2.0 are consistent with the CMIP6 multi-modelmeans.

Oshima et al. Progress in Earth and Planetary Science

Conclusions
We use MRI-ESM2.0 to estimate the ERFs from the anthropogenic forcing agents for present-day (year 2014)
conditions relative to preindustrial (year 1850) conditions based on a suite of 30-year time-slice experimentswith the prescribed preindustrial climatology of the SSTs
and sea ice within the framework of RFMIP and AerChemMIP in CMIP6. The global mean total anthropogenic net ERF estimate at the TOA is 1.96 W m−2 and iscomposed of positive ERF from the well-mixed greenhouse gases (3.02 W m−2), negative ERF from the NTCFs(− 1.08 W m−2), and slightly negative ERF from land-use
changes (− 0.19 W m−2). The ERF of the well-mixed
greenhouse gases (3.02 W m−2) consists of forcings fromCO2 (1.85 W m−2), CH4 (0.71 W m−2), halocarbons (0.30
W m−2), and N2O (0.16 W m−2). The ERF value due to
the NTCFs (− 1.08 W m−2) consists of slightly positive
ERFs from the tropospheric ozone (0.07 W m−2) andlarge negative ERFs from the total aerosols (− 1.22 Wm−2), which consists of positive forcing from BC (0.24(2020) 7:38

Page 19 of 21

W m−2) and negative forcings from sulfate (− 1.38 Wm−2) and OM (− 0.33 W m−2).
We quantify the contributions to the ERFs at the
TOA from the aerosol-radiation interactions, aerosolcloud interactions, and changes in the surface albedo
due to aerosols using the method of Ghan (2013).
The global mean total aerosol net ERF at the TOA (−1.22 W m−2) consists of a 23% contribution from
ERFari (− 0.32 W m−2), a 71% contribution from
ERFaci (− 0.98 W m−2), and a small contribution from
ERFalbedo (0.08 W m−2). The total aerosol net ERFari(− 0.32 W m−2) is dominated by SW radiation andconsists of opposing contributions from lightabsorbing BC aerosols (0.25 W m−2), light-scattering
sulfate (− 0.48 W m−2), and organic aerosols (− 0.07
W m−2), which are pronounced over the emission
source regions. The total aerosol net ERFaci at the
TOA (− 0.98 W m−2) consists of ERFaci contributions
due to BC (− 0.09 W m−2), OM (− 0.21 W m−2), andsulfate (− 0.94 W m−2) and is pronounced over thesource and downwind regions, primarily due to thegreater CCN activity of sulfate aerosols and theresulting large increases in the cloud droplet number
concentrations in the low-level liquid clouds. The global mean total aerosol ERFalbedo at the TOA (0.08
W m−2) is dominated by the BC contribution (0.07 Wm−2), which is caused by the decrease in the surface
albedo due to BC deposition on snow and sea ice.
MRI-ESM2.0 shows the large influence of aerosols on
high-level ice clouds and LW radiation. In particular, increases in the number concentration of ice crystals inhigh-level clouds (temperatures < – 38 °C) primarily due
to anthropogenic BC aerosols by serving as INPs, particularly over the tropical convective regions, induces
both substantial positive LW ERFaci and negative SW
ERFaci in MRI-ESM2.0 (e.g., 1.54 W m−2 and − 1.63 W
m−2, respectively, due to BC). In terms of the radiationbudget at the TOA, these distinct SW ERFaci and LW
ERFaci can mostly cancel each other out, resulting in asmaller net ERFaci value (e.g., − 0.09 W m−2 due to BC).
However, high-level ice clouds over the tropical convective regions can cause significant LW radiative heating of
the atmosphere, leading to modifications in the largescale atmospheric circulation and the hydrological cycle.
This suggests the importance of anthropogenic INPinduced high-level ice cloud modifications on LW radiative heating. Note that the SW ERFaci and LW ERFaci
estimated by MRI-ESM2.0 might include quantitative
uncertainties because there remain large uncertainties in
the parameterizations of the aerosol effects on ice clouds
and the aerosol concentrations in the upper tropospherein the model. Nevertheless, our results suggest the potential importance of the interactions of aerosols withice clouds at high altitudes over the tropics where high-level clouds are frequently present and that further studies on these interactions are required.
In the Arctic, BC and CH4 can provide the second andthird largest contributions, respectively, to the positive
ERFs after CO2 both at the TOA and at the surface. The
reduction in the surface albedo due to BC deposition onsnow over land and sea ice, which leads to a decrease in
the snow cover due to snow melting, plays a major rolein the Arctic mean SW ERFs of BC. The total anthropogenic net ERFs at the TOA exhibit substantially positive
values over the entire Arctic region. The combination of
the spatially homogeneous distributions of the net ERFs
of the well-mixed greenhouse gases and the spatially inhomogeneous distributions of the net ERFs of the aerosols, primarily BC and sulfate, is responsible for the
distributions of the total anthropogenic net ERFs over
the Arctic. The greenhouse gases (e.g., CO2 and CH4)
and BC aerosols likely have large impacts on the radiative effects and surface warming over the entire Arctic
region.
The global mean ERF estimates at the TOA for the
total aerosols in MRI-ESM2.0 are compared to other
CMIP6 model results using the APRP method and themethod of Ghan (2013). The comparisons indicate that
MRI-ESM2.0 has particularly large positive LW ERFaciand negative SW ERFaci values due to the aerosol effects
on high-level ice clouds over the tropical convective regions; however, these LW and SW ERFaci values mostly
cancel each other out in the net ERFaci. Accordingly,
the overall net ERFs in MRI-ESM2.0 are consistent with
the CMIP6 multi-model means.

Oshima et al. Progress in Earth and Planetary Science

Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s40645-020-00348-w.
Additional file 1. Supplementary Figure 1. Horizontal distributions of
the differences in the 30-year annual mean aerosol optical depths (AODs)
between the (a) piClim-aer, (b) piClim-BC, (c) piClim-OC, and (d) piClimSO2 experiments and the piClim-control experiment. Note that the color
scales are different in each panel. Supplementary Figure 2. Horizontal
distributions of the 30-year annual mean (a-c) mid-level (440–680 hPa)
cloud cover (%), (d-f) low-level (below 680-hPa level) cloud cover (%), (gi) liquid water path (g m−2), and (j-l) column-integrated number concentration of cloud droplets (109 m−2) in the piClim-control experiment (left),the piClim-aer experiment (middle), and their differences (right). Notethat the color scales are different in each panel. Supplementary Figure 3.
Horizontal distributions of the 30-year annual mean (a-c) high-level(above 440-hPa level) cloud cover (%), (d-f) ice water path (g m−2), and(g-i) column-integrated number concentration of ice crystals (109 m−2) inthe piClim-control experiment (left), the piClim-aer experiment (middle),
and their differences (right). Note that the color scales are different ineach panel. Supplementary Figure 4. Vertical distributions of the 30-year
annual zonal mean (a-c) cloud fraction (%), (d-f) in-cloud ice watercontent (mg kg−1), and (g-i) in-cloud number concentration of ice crystals(L−1) in the piClim-control experiment (left), the piClim-aer experiment(middle), and their differences (right). The solid black lines (left andmiddle panels) denote temperatures of 0 °C and −38 °C. The solid blacklines (right panels) denote the values shown by the color coding in theOshima et al. Progress in Earth and Planetary Science(2020) 7:38

piClim-control experiment (left panels). Note that the color scales aredifferent in each panel. Supplementary Figure 5. Horizontal distributionsof the differences in the 30-year annual mean number concentrations of
the aerosols averaged within the range of 150–420 hPa between the (a)piClim-aer, (b) piClim-BC, (c) piClim-OC, and (d) piClim-SO2 experiments
and the piClim-control experiment (cm−3). The thick black lines denote
30-year annual mean high-level cloud cover of 70% for the corresponding experiments. Note that the color scales are different in each panel.
Supplementary Figure 6. Same as Supplementary Figure 5a but for boreal(a) winter, (b) spring, (c) summer, and (d) fall (cm−3). Supplementary Figure 7. Same as Figure 7b but for boreal (a) winter, (b) spring, (c) summer,and (d) fall (109 m−2). Supplementary Figure 8. Vertical distributions of thedifferences in the 30-year annual global mean (a) temperature (K) and (b)upward mass flux of cumulus convection (g s−1 m−2) between thepiClim-aer (black), piClim-BC (red), piClim-OC (green), and piClim-SO2(blue) experiments and the piClim-control experiment.
Abbreviations
ACI: Aerosol-cloud interactions; AerChemMIP: Aerosol and Chemistry Model
Intercomparison Project; AGCM: Atmospheric general circulation model;
AMAP: Arctic Monitoring and Assessment Programme; APRP: Approximate
partial radiative perturbation; AR: Assessment report; ARI: Aerosol-radiationinteractions; BC: Black carbon; CCN: Cloud condensation nuclei;
CMIP: Coupled Model Intercomparison Project; DECK: Diagnostic, Evaluation,
and Characterization of Klima; ERF: Effective radiative forcing; ESGF: Earth
System Grid Foundation; ESM: Earth System Model; INPs: Ice nucleating
particles; IPCC: Intergovernmental Panel on Climate Change; JMA: Japan
Meteorological Agency; LW: Longwave; MRI: Meteorological ResearchInstitute; NTCFs: Near-term climate forcers; OC: Organic carbon;
OGCM: Ocean–sea-ice general circulation model; OM: Organic matter;
RFMIP: Radiative Forcing Model Intercomparison Project; SST: Sea surface
temperature; SW: Shortwave; TOA: Top of the atmosphere; VOC: Volatileorganic compound
Acknowledgements
We thank Mr. Satoru Koizumi sand Mr. Masaru Suga for their assistance indata processing.
Authors’ contributions
NO designed the study, performed the model experiments and analysis, andwrote the manuscript incorporating input from all authors. SY performed themodel experiments and analysis and gave many useful comments forwriting the manuscript. MD performed the model experiments and analysis.
TK, TT, and KY performed the model experiments. HK performed the analysis
and helped in the interpretation of the results. All authors read andapproved the final manuscript.
Funding
This work was supported by the Integrated Research Program for Advancing
Climate Models (TOUGOU) grant number JPMXD0717935561 from the
Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan;
the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant
numbers JP26701004, JP15H05816, JP16H01772, JP18H03363, JP18H05292,
JP19K03977, JP19H05699, and JP20K04070); the Environment Research andTechnology Development Fund (JPMEERF20172003, JPMEERF14S11203,
JPMEERF20202003, and JPMEERF20205001) of the Environmental Restoration
and Conservation Agency of Japan; the Arctic Challenge for Sustainability II(ArCS II) grant number JPMXD1420318865; and a grant for the Global
Environmental Research Coordination System from the Ministry of theEnvironment, Japan.
Availability of data and materials
The data used in this study (RFMIP and AerChemMIP data) is freely available
from the CMIP6 repository on the Earth System Grid Foundation (ESGF)
server (https://esgf-node.llnl.gov/search/cmip6/). The CMIP5 data is freely
available from the CMIP5 repository on the ESGF server (https://esgf-node.
llnl.gov/search/cmip5/). The source codes of MRI-ESM2.0 and MRI-CGCM3 are
the property of MRI/JMA and not available to the general public. Access tothe code can be granted upon request, under a collaborative framework
between MRI and related institutes or universities.

Page 20 of 21

Competing interests
The authors declare that they have no competing interest.
Received: 31 December 2019 Accepted: 3 July 2020

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