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Atmos. Chem. Phys., 13, 1853–1877, 2013
www.atmos-chem-phys.net/13/1853/2013/
doi:10.5194/acp-13-1853-2013
© Author(s) 2013. CC Attribution 3.0 License.
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Radiative forcing of the direct aerosol effect from AeroCom Phase II
simulations
G. Myhre1, B. H. Samset1, M. Schulz2, Y. Balkanski3, S. Bauer4, T. K. Berntsen1, H. Bian5, N. Bellouin6,*, M. Chin7,
T. Diehl7,8, R. C. Easter9, J. Feichter10, S. J. Ghan9, D. Hauglustaine3, T. Iversen2,11, S. Kinne10, A. Kirkev˚
ag2,
J.-F. Lamarque12, G. Lin13, X. Liu8, M. T. Lund1, G. Luo14, X. Ma14, T. van Noije15, J. E. Penner13, P. J. Rasch9,
A. Ruiz15,16, Ø. Seland2, R. B. Skeie1, P. Stier17, T. Takemura18, K. Tsigaridis4, P. Wang15, Z. Wang19, L. Xu13,20,
H. Yu5, F. Yu14, J.-H. Yoon9, K. Zhang10,9, H. Zhang21, and C. Zhou13
1Center for International Climate and Environmental Research – Oslo (CICERO), Oslo, Norway
2Norwegian Meteorological Institute, Oslo, Norway
3Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
4NASA Goddard Institute for Space Studies and Columbia Earth Institute, New York, NY, USA
5Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
6Met Office Hadley Centre, Exeter, UK
7NASA Goddard Space Flight Center, Greenbelt, MD, USA
8Universities Space Research Association, Columbia, MD, USA
9Pacific Northwest National Laboratory, Richland, WA, USA
10Max Planck Institute for Meteorology, Hamburg, Germany
11Department of Geosciences, University of Oslo, Oslo, Norway
12NCAR Earth System Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
13Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann Arbor, Michigan, USA
14Atmospheric Sciences Research Center, State University of New York at Albany, New York, USA
15Royal Netherlands Meteorological Institute, De Bilt, The Netherlands
16LIFTEC, CSIC-Universidad de Zaragoza, Zaragoza, Spain
17Department of Physics, University of Oxford, Oxford, UK
18Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan
19Chinese Academy of Meteorological Sciences, Beijing 100081, China
20Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USA
21Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China
*now at: Department of Meteorology, University of Reading, Reading, UK
Correspondence to: G. Myhre (gunnar.myhre@cicero.uio.no)
Received: 31 July 2012 – Published in Atmos. Chem. Phys. Discuss.: 30 August 2012
Revised: 9 January 2013 – Accepted: 1 February 2013 – Published: 19 February 2013
Abstract. We report on the AeroCom Phase II direct aerosol
effect (DAE) experiment where 16 detailed global aerosol
models have been used to simulate the changes in the aerosol
distribution over the industrial era. All 16 models have esti-
mated the radiative forcing (RF) of the anthropogenic DAE,
and have taken into account anthropogenic sulphate, black
carbon (BC) and organic aerosols (OA) from fossil fuel,
biofuel, and biomass burning emissions. In addition several
models have simulated the DAE of anthropogenic nitrate
and anthropogenic influenced secondary organic aerosols
(SOA). The model simulated all-sky RF of the DAE from
total anthropogenic aerosols has a range from −0.58 to
−0.02Wm−2, with a mean of −0.27 Wm−2for the 16 mod-
els. Several models did not include nitrate or SOA and mod-
ifying the estimate by accounting for this with informa-
tion from the other AeroCom models reduces the range and
Published by Copernicus Publications on behalf of the European Geosciences Union.
1854 G. Myhre et al.: Radiative forcing of the direct aerosol effect
slightly strengthens the mean. Modifying the model esti-
mates for missing aerosol components and for the time pe-
riod 1750 to 2010 results in a mean RF for the DAE of
−0.35Wm−2. Compared to AeroCom Phase I (Schulz et al.,
2006) we find very similar spreads in both total DAE and
aerosol component RF. However, the RF of the total DAE is
stronger negative and RF from BC from fossil fuel and bio-
fuel emissions are stronger positive in the present study than
in the previous AeroCom study. We find a tendency for mod-
els having a strong (positive) BC RF to also have strong (neg-
ative) sulphate or OA RF. This relationship leads to smaller
uncertainty in the total RF of the DAE compared to the RF of
the sum of the individual aerosol components. The spread in
results for the individual aerosol components is substantial,
and can be divided into diversities in burden, mass extinction
coefficient (MEC), and normalized RF with respect to AOD.
We find that these three factors give similar contributions to
the spread in results.
1 Introduction
Global estimates of the direct aerosol effect (DAE) have im-
proved tremendously over the last two decades, due to im-
provements in processes taken into account and handling of
complexities in the aerosol schemes (Bauer et al., 2010; Chin
et al., 2009; Seland et al., 2008; Stier et al., 2005; Take-
mura et al., 2002). The first study of radiative forcing (RF) of
the DAE with a global aerosol model included only sulphate
aerosols (Charlson et al., 1991). Now multi-model studies
with multi aerosol components and including some complex
aerosol microphysical schemes have been performed (Schulz
et al., 2006).
In the estimate of the RF from the direct aerosol effect
from AeroCom Phase I models (Schulz et al., 2006) an-
thropogenic sulphate, black carbon and organic carbon were
considered. In the interim increased attention has been given
to the direct aerosol effect of anthropogenic nitrate and sec-
ondary organic aerosols (SOA). The formation of ammonium
nitrate is complex and is dependent on sufficient ammonia
and thus competition with sulphate is important (Adams et
al., 2001; Metzger et al., 2002). With the expected reduc-
tion in SO2emission in the future, nitrate may become more
important as a climate forcer (Bauer et al., 2007; Bellouin
et al., 2011). Over the last few years increasing considera-
tion has also been paid to secondary organic aerosols (SOA),
with advanced understanding of their anthropogenic influ-
ence (Hoyle et al., 2011; Robinson et al., 2007). Anthro-
pogenic influence on SOA is now included with varying de-
grees of complexity in some global aerosol models (Hoyle
et al., 2009; Liao and Seinfeld, 2005; Spracklen et al., 2011;
Tsigaridis et al., 2006).
The mean RF of the total direct aerosol effect in Schulz et
al. (2006) from 9 global aerosol models was −0.22Wm−2.
This is much weaker than several observational based esti-
mates (Bellouin et al., 2005, 2008; Chung et al., 2005; Quaas
et al., 2008). Observational based estimates have been made
possible through the advancement of remote sensing from
ground and space (Holben et al., 1998; Remer et al., 2008).
Myhre (2009) showed that estimates of the direct aerosol ef-
fect must rely on global aerosol model estimates, but con-
sistency between models and observational based methods
could be reached by accounting for anthropogenic changes
in the aerosol optical properties in the latter method.
Various factors lie behind the large range in the global
estimates of the DAE, which in AeroCom Phase I was
from −0.41 to +0.04Wm−2(Schulz et al., 2006). Schulz
et al. (2006) showed that the variations in RF could be sepa-
rated into diversities in aerosol residence time, aerosol mass
extinction coefficients, and normalized forcing (RF divided
by aerosol optical depth). Normalized forcing was found to
be the single largest contributor to the variability. Differences
in the aerosol vertical profile, known to be significant be-
tween the models, is among the factors that contribute to dif-
ferences in the normalized RF (Heald et al., 2011; Schwarz
et al., 2010; Textor et al., 2006; Zarzycki and Bond, 2010).
In this paper RF of the DAE from 16 recent state-of-the-
art global aerosol models in the AeroCom Phase II are pre-
sented. Two of the 16 global aerosol models are from Asia,
six from Europe and eight from North America. Additional
models compared to Schulz et al. (2006) are included in
Phase II, and the models have been further developed over
the 7–8 yr since AeroCom Phase I. Results for anthropogenic
nitrate and SOA are included in the present study. Evaluation
of the global aerosol models is an ongoing activity within
AeroCom, with comparisons of various measurements being
performed (Huneeus et al., 2011; Koch et al., 2009; Koffi
et al., 2012). Activities to understand the model differences
are also an important part of AeroCom (Randles et al., 2012;
Samset et al., 2012; Stier et al., 2012; Textor et al., 2007).
2 Methods
The RF of the total anthropogenic DAE is calculated as the
difference between the reflected solar radiation at TOA for
simulations with present (2000, or 2006 for a few models)
and pre-industrial (1850) emissions of aerosols and their
precursors, denoted respectively as CTRL and PRE simu-
lations, but the same cloud and surface conditions. The RF
has been simulated by 16 global aerosol models with vari-
ous complexities with regard to number of aerosol species
included, aerosol microphysical treatment and their aerosol
optical properties. The host model is also of great impor-
tance since aerosol transport and removal is dependent on
the transport scheme and distribution of precipitation, respec-
tively. In the radiative transfer calculations cloud distribution
and properties and surface albedo, among other factors, are
important.
Atmos. Chem. Phys., 13, 1853–1877, 2013 www.atmos-chem-phys.net/13/1853/2013/
G. Myhre et al.: Radiative forcing of the direct aerosol effect 1855
Table 1. Model description, general information. If meteorology was nudged or driven by reanalysis fields, the year 2006 meteorology was
used.
Model Type Resolution Levels Meteorology Responsible
BCC GCM 2.8°×2.8° 26 NCEP/NCAR reanalysis Hua Zhang
Zhili Wang
CAM4-Oslo GCM 2.5°×1.8° 26 Produced by CAM4 atmospheric physics with CAM4-
Oslo cloud tuning and boundary data from the data ocean
and sea-ice models of CCSM4.
Alf Kirkev˚
ag
Trond Iversen
Øyvind Seland
CAM5.1 GCM 2.5°×1.8° 30 CAM5.1 X. Liu
R. C. Easter
Steve Ghan
P. J. Rasch
J.-H. Yoon
GEOS-Chem CTM 5.0°×4.0° 47 GEOS-5, reanalysis, nudged Fangqun Yu
Gan Luo
Xiaoyan Ma
GISS-MATRIX GCM 2.5°×2.0° 40 Nudged to NCEP winds Susanne Bauer
Kostas Tsigaridis
GISS-modelE GCM 2.5°×2.0° 40 Nudged to NCEP winds Kostas Tsigaridis
Susanne Bauer
GMI CTM 2.5°×2.0° 72 GEOS-5 MERRA reanalysis for 2006, nudged Huisheng Bian
Hongbin Yu
GOCART CTM 2.5°×2.0° 30 GEOS-4 DAS (Goddard Earth Observing System version
4 Data Assimilation System), reanalysis for year 2006 Thomas Diehl
Mian Chin
IMPACT CTM 5.0°×4.0° 46 DAO assimilation fields for 1997, reanalysis. Guangxing Lin
Joyce Penner
Li Xu
Cheng Zhou
INCA GCM 3.8°×1.9° 19 ECMWF reanalysis from the Integrated Forecast System
(IFS) model for year 2006. Yves Balkanski
Michael Schulz
Didier Hauglus-
taine
HadGEM2 GCM 1.8°×1.2° 38 ERA Interim data for 2006, nudged Nicolas Bellouin
ECHAM5-HAM GCM 1.8°×1.8° 31 Model nudged with ECMWF analysis for the year 2006 Kai Zhang
Philip Stier
Johann Feichter
NCAR-CAM3.5 GCM 2.5°×1.9° 26 GCM-generated Jean-Francois
Lamarque
OsloCTM2 CTM 2.8°×2.8° 60 ECMWF reanalysis from the Integrated Forecast System
(IFS) model for year 2006 Gunnar Myhre
Ragnhild B. Skeie
Terje Berntsen
SPRINTARS GCM 1.1°×1.1° 56 NCEP/NCAR reanalysis (temperature and horizontal
wind),nudged. Toshihiko Take-
mura
TM5 CTM 3.0°×2.0° 34 ECMWF ERA-Interim reanalysis for year 2006 Twan van Noije
Ping Wang
Ana Ruiz
Tables 1 and 2 show a list of the models included in the
AeroCom Phase II DAE experiment, together with the res-
olutions used, aerosols components treated and comments
on their microphysics scheme. Meteorological data for year
2006 have been used for all the models, except for IMPACT
with meteorological data for 1997 and for CAM4-Oslo,
NCAR-CAM3.5, and CAM5.1 where nudging possibilities
were not available. A protocol describing the data submitted
from the global aerosol models are available from the Ae-
roCom web site (http://aerocom.met.no/aerocomhome.html).
Most of the simulations for this DAE experiment are per-
formed with identical cloud cover and cloud optical proper-
ties in PRE and CTRL, i.e. no indirect aerosol effects are
included, but one (CAM5.1) estimated the DAE from the
CTRL-PRE difference (1) in the direct radiative forcing by
all aerosols in each simulation (Ghan et al., 2012): 1(S-
Sclean), where S is the net downward solar at TOA calculated
www.atmos-chem-phys.net/13/1853/2013/ Atmos. Chem. Phys., 13, 1853–1877, 2013
1856 G. Myhre et al.: Radiative forcing of the direct aerosol effect
Table 2. Model description, aerosol information.
Model S BC OC BB SOA NO3Aerosol microphysics References for aerosol
module
BCC Y Y Y Y – – 12 bin sizes for each aerosol with radii be-
tween 0.005–0.01, 0.01–0.02, 0.02–0.04,
0.04–0.08, 0.08–0.16, 0.16–0.32, 0.32–
0.64, 0.64–1.28, 1.28–2.56, 2.56–5.12,
5.12–10.24, and 10.24–20.48 µm.
Zhang et al. (2012a)
CAM4-Oslo Y Y Y Y – – Mass conc. of SO4, BC, OM, sea-salt and
dust in four size-classes are tagged ac-
cording to production mechanism. Based
on 44 sectional size bins and lognor-
mal distributions at the point of emission,
look-up tables yield physical properties of
the processed aerosols.
Kirkev˚
ag et al. (2012)
CAM5.1 Y Y Y Y Y – 3 internally-mixed log-normal modes Liu et al. (2012)
GEOS-Chem Y Y Y – Y Y 40 bins for secondary particles, 20 bins
for sea salt, 15 bins for dust, 4 log-normal
modes for BC and primary OC. Coating
of primary particles by secondary species
tracked. Extended SOA formation consid-
ering oxidation aging.
Yu and Luo (2009);
Yu (2011); Ma et al. (2012)
GISS-MATRIX Y Y Y Y – Y Aerosol microphysical scheme Bauer et al. (2008, 2010)
GISS-modelE Y Y Y Y Y Y Aerosol mass based scheme Koch et al. (2007, 2006);
Bauer et al. (2007);
Tsigaridis et al. (2013)
GMI Y Y Y Y – Y 5 bin sizes for dust, 4 bin sizes for sea-
salt, 3 bin size for nitrate and sulfate,
all aerosols with log-normal size distribu-
tions.
Bian et al. (2009)
GOCART Y Y Y Y Y – Parameterized with prescribed dry parti-
cle sizes: 8 bins for dust, 4 bins for sea
salt, 1 bin for sulfate, BC, and OA, with
log-normal distributions, particle growth
parameterized as a function of RH
Chin et al. (2000, 2002,
2009); Ginoux et al. (2001)
IMPACT Y Y Y Y Y ∗4 bin sizes for sea-salt and mineral dust,
pure sulfate treated using 2 modes with
predicted size and coagulation and con-
densation of SO4 with other aerosols ex-
plicitly resolved.
Wang and Penner (2009);
Lin et al. (2012);
Xu and Penner (2012)
INCA Y Y Y Y – Y Soluble and insoluble aerosol treated
separately, modal assumptions with log-
normal size distributions. We distinguish
between accumulation, coarse and super
coarse modes.
Balkanski et al. (2004);
Schulz (2007);
Balkanski (2011);
Szopa et al. (2012)
HadGEM2 Y Y Y Y – Y Component aerosol mass transported in
Aitken, accumulation, coarse, and dis-
solved modes. Size distributions assumed
lognormal for interaction with radiation.
Bellouin et al. (2011)
ECHAM5-HAM Y Y Y Y Y – Modal method, log-normal size distribu-
tions, 7 modes (4 soluble, 3 insoluble). In-
ternal mixing is assumed for aerosol com-
positions considered in each mode, while
external mixing is assumed among differ-
ent aerosol modes.
Vignati et al. (2004);
Stier et al. (2005);
Zhang et al. (2012b)
Atmos. Chem. Phys., 13, 1853–1877, 2013 www.atmos-chem-phys.net/13/1853/2013/
G. Myhre et al.: Radiative forcing of the direct aerosol effect 1857
Table 2. Continued.
Model S BC OC BB SOA NO3Aerosol microphysics References for aerosol
module
NCAR-CAM3.5 Y Y Y Y – Y Bulk-aerosol model, except 4-bins for
sea-salt and mineral dust Lamarque et al. (2012)
OsloCTM2 Y Y Y Y Y Y 8 bin sizes for sea-salt and mineral dust,
aerosol mass scheme for other aerosols
with log-normal size distributions in cal-
culations of optical properties
Myhre et al. (2007, 2009);
Skeie et al. (2011)
SPRINTARS Y Y Y Y – – 6 bins for dust, 4 bins for sea salt, 1 bin
for sulfate, BC, and OA, with log-normal
size distributions and particle growth as a
function of relative humidity
Takemura et al.
(2005, 2009)
TM5 Y Y Y Y Y Y Modal method, log-normal size distribu-
tions, 7 modes (4 soluble, 3 insoluble). In-
ternal mixing is assumed for aerosol com-
positions considered in each mode, while
external mixing is assumed among differ-
ent aerosol modes.
Vignati et al. (2004);
Aan de Brugh et al. (2011)
Note: GOCART only includes SOA from biogenic sources (terpene oxidation)
∗The NO3values for forcing from this model were simulated using the same model as the IMPACT model described here, but did not include the chemistry of formation of
SOA and used the simplified NOxchemistry described in Feng and Penner (2007). The resolution was 2.5×2.0.
with all aerosols present, and Sclean is the solar calculated
neglecting scattering and absorption by all aerosols.
Aerosol optical properties are treated differently in the
global aerosol models but in all models the optical proper-
ties are derived using Mie theory; see further description in
model references. All models include treatment of sulphate,
black carbon, primary organic carbon, sea salt, and mineral
dust. Internal mixing between BC and scattering aerosols en-
hances the absorption from BC (Fuller et al., 1999; Hay-
wood and Shine, 1995), and half of the 16 models include
some degree of internal mixture for BC. Most of the models
have used emissions of aerosols and their precursors from
Lamarque et al. (2010). CAM4-Oslo and SPRINTARS have
used these IPCC AR5 emissions for PRE (year 1850), but
for the CTRL simulations emissions for year 2006 from the
AeroCom Phase II dataset has been used (HCA0 v1 or v2
by T. Diehl, seehttp://aerocom.met.no/emissions.html). This
dataset also includes emissions estimates of BC, SO2and
POM from aviation. BCC used year 2000 emissions, but
preindustrial emissions for 1750. GEOS-Chem uses emis-
sions for 2006 which is close to AeroCom emission for
2000 and employs pre-industry emission corresponding to
1750 (Dentener et al., 2006). TM5 used 1850 emissions from
Lamarque et al. (2010) and for 2006 a linear interpolation
between 2005 and 2010 for the representative concentration
pathway RCP4.5 (Thomson et al., 2011).
If the diagnostics for the different components were diffi-
cult to establish, separate model simulations were performed
to allow for an attribution to eg biomass burning (BB) or fos-
sil fuel (FF) and bio fuel (BF) sources. For some of the mod-
els with internally-mixed aerosols the separation of AOD
and RF by aerosol component is challenging. Several groups
have solved this by additional radiation calls. Even with this
method a few models have been unable to properly extract
the component AOD. Where relevant, these results have been
removed from the analysis. All simulations were performed
such that the cloud fields were identical, e.g. by neglecting
the influence of aerosols on clouds or keeping the influence
constant.
Only anthropogenic aerosol effects will be treated in the
present study, and reference to the DAE apply to the anthro-
pogenic change in the aerosol distribution and its influence
on the scattering and absorption of solar radiation. Results
will be presented for sulphate, nitrate, BC from fossil fuel
and biofuel, OA from fossil fuel and biofuel, BC and OA
combined from biomass burning, and SOA.
3 Results
All results are for anthropogenic aerosols only, unless other-
wise stated. Anthropogenic aerosols are defined as the dif-
ference between the CTRL and PRE simulations described
above.
3.1 Total direct aerosol effect
The underlying albedo is crucial for RF of the direct aerosol
effect (Haywood and Shine, 1995). Scattering aerosols are
efficient at producing a RF over dark surfaces and absorb-
ing aerosols are efficient over bright surfaces. Figure 1a
shows the latitudinal variation of the top of the atmosphere
(TOA) albedo of the 16 models and the satellite retrieval from
www.atmos-chem-phys.net/13/1853/2013/ Atmos. Chem. Phys., 13, 1853–1877, 2013
1858 G. Myhre et al.: Radiative forcing of the direct aerosol effect
Fig. 1. Zonal mean top of the atmosphere short wave (TOA) albedo (a) and effective broadband surface albedo (b) shown for all the models.
CERES TOA albedo data is shown together with the models.
Fig. 2. Zonal mean DAE RF for all-sky (a) and RF for clear sky (b).
CERES (Smith et al., 2011; Wielicki et al., 1996). Note that
the albedo at the TOA consists of reflection at the surface
and scattering in the atmosphere from clouds, aerosols, and
Rayleigh scattering, and thus the scattering may occur above
the anthropogenic aerosols. The higher albedo at high lati-
tudes is mainly due to high surface albedo from snow and
ice, but larger cloud fraction also contributes. For most of
the models the albedo compares well to the CERES data,
except between 30◦N and 30◦S where almost all overesti-
mate the albedo. By inspection of the geographical distribu-
tion (not shown) of the albedo the overestimation is largest
in regions with low cloud fraction, indicating problems with
too large cloud fractions in these regions for the models. The
GOCART model has a very low albedo compared to the rest
of the models and the CERES data, which is identified to
be caused by the meteorological data. The agreement be-
tween the simulated latitudinal variation in effective broad-
band surface albedo (computed from surface level down and
up welling short wave radiation fluxes as diagnosed in the
models) is in general excellent (see Fig. 1b), except at high
latitudes due to differences in snow cover or albedo for snow
and for the Northern Hemisphere sea ice coverage and albedo
for sea ice.
Figure 2 shows the latitudinal variation in the RF of the
total DAE for all sky (Fig. 2a) and clear sky (Fig. 2b) condi-
tions. The negative RF is at maximum around 30◦N for most
of the models, since the anthropogenic change in aerosol
burden (see Fig. 3) has its maximum in this region com-
bined with a relatively high solar radiation flux. GISS-Matrix
and GMI have the strongest all-sky RF in the Northern
Hemisphere, but for clear-sky the IMPACT model shows the
strongest RF in most of the Northern Hemisphere. The RF
in the Southern Hemisphere is generally weak. How clouds
affect the RF of the direct aerosol effect depends on a variety
Atmos. Chem. Phys., 13, 1853–1877, 2013 www.atmos-chem-phys.net/13/1853/2013/
G. Myhre et al.: Radiative forcing of the direct aerosol effect 1859
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Fig. 3. Anthropogenic AOD (a), anthropogenic AAOD (b), anthropogenic single scattering albedo (c), and change in single scattering albedo
from pre-industrial to present conditions (d). All values are taken at 550nm. Between 80◦S and 30◦S the anthropogenic AOD is extremely
small for some of the models and anthropogenic single scattering albedo may reach unrealistic values and in such cases values have been
removed from the figure.
of factors. Among them are the degree of aerosol absorp-
tion, vertical position of aerosols in relation to clouds, and
the cloud distribution in general. The all-sky RF is around
half in magnitude of the clear sky RF, except for the mod-
els with close to zero all sky RF. All models turn over into
a positive net RF at high northern latitudes. This is caused
by the higher surface and cloud albedo at high latitudes, as
can be seen in Fig. 1. The surface and cloud albedo is the
main cause of the positive RF since it will be later shown that
aerosol optical properties are not a cause for the positive RF
(see Fig. 3). CAM4-Oslo has the strongest positive all-sky
RF at high latitudes. This model has more anthropogenic ab-
sorbing aerosols at high latitudes than the other models (see
Fig. 3). This is partly due to the different CTRL emission
data sets, which are for year 2006 and have more biomass
burning from forest fires than the 2000 emissions. Assumed
emission height of biomass burning (up to 6km) may be an-
other reason. The treatment of convective mixing of aerosols
and aerosol precursors probably also leads to somewhat over-
estimated aerosol burdens in CAM4-Oslo (see Kirkev˚
ag et
al., 2012).
Figure 3 shows the latitudinal variation in the anthro-
pogenic AOD (550nm) used in the radiative transfer calcu-
lations, anthropogenic absorbing AOD (AAOD) (550 nm),
anthropogenic single scattering albedo (550nm), and the
change in single scattering albedo (550nm) between pre-
industrial (PRE) and current (CTRL). The anthropogenic
AOD in Fig. 3a shows a strong hemispheric difference which
is a consistent model characteristic. CAM4-Oslo has the
highest and CAM5.1 and BCC the lowest anthropogenic
AOD almost at all latitudes. The pattern for the anthro-
pogenic AAOD (Fig. 3b) is rather similar in the models, with
two peaks, one around the Equator where biomass burning
emissions of BC are high, and the second around 30◦N,
where fossil fuel and biofuel emissions of BC are high.
The range in the anthropogenic single scattering albedo (de-
fined as the ratio of anthropogenic AOD-AAOD and AOD)
(Fig. 3c) is large, with most values lying between 0.9 and 1.0
but even lower values than 0.9 seen at high latitudes for a few
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1860 G. Myhre et al.: Radiative forcing of the direct aerosol effect
models. The lowest anthropogenic single scattering albedo
is simulated near the Equator and can be seen in Fig. 3b to
be caused by strong anthropogenic absorption AOD that is
not associated with a similar strong maximum in the anthro-
pogenic AOD in Fig. 3a. This pattern is caused by large BB
emissions of BC and OA in this region. Most models sim-
ulate a reduction in the single scattering albedo during the
industrial era at all latitudes, except GISS-MATRIX, GO-
CART, TM5 and IMPACT. The reduction in the single scat-
tering albedo in the majority of the models is due to stronger
growth in absorbing aerosol (BC) than scattering aerosols
relative to pre-industrial distribution of absorbing and scat-
tering aerosols. GOCART has an increase in the single scat-
tering albedo in the NH due to low single scattering albedo
in the PRE simulation compared to most of the other models.
IMPACT shows a weaker increase in single scattering albedo
than GOCART, but still positive in the NH. As will be shown
later this model has a much larger anthropogenic influenced
SOA than the other models, which is likely the cause of the
increase in single scattering albedo.
Table 3 summarizes the global and annual mean values
for RF, anthropogenic AOD, anthropogenic absorption AOD
(AAOD), atmospheric absorption, anthropogenic single scat-
tering albedo, and cloud fraction. The anthropogenic AOD
has a mean of 0.030, which is slightly higher than the Schulz
et al. (2006) value of 0.029. A main reason for this small
increase is likely the inclusion of additional aerosol com-
ponents in this experiment. The anthropogenic AAOD of
0.0015 is lower than the value of 0.0019 for AAOD attributed
to BC in Schulz et al. (2006). However, here it should be
mentioned that some of the models in this experiment in-
clude absorption by OA which is non-negligible at 550nm
and thus the anthropogenic AAOD is not solely due to BC.
A main reason for the lower AAOD compared to Schulz et
al. (2006) is likely due to the much smaller anthropogenic
BB emissions of BC in a majority of the models using the
Lamarque et al. (2010) data. Figure 4 shows the model simu-
lated global and annual mean total RF and the RF for the six
aerosol components. The simulated mean RF for the DAE
is −0.27Wm−2which is stronger negative than the mean
of −0.22Wm−2found in Schulz et al. (2006). A stronger
negative RF of the DAE in this experiment than in Schulz
et al. (2006) is in accordance with the stronger AOD and
weaker AAOD as noted above. It can also be noted that
the models in this experiment have lower global and annual
mean cloud cover (mean of 57 % for the 13 models reporting
cloud cover) than in the models in Schulz et al. (2006) (mean
of 63 %). As shown in many previous studies of the DAE
the total RF consists of aerosol components causing negative
RF and positive RF. Figure 4 shows clearly that this balance
varies substantially between the models. Further, it shows
that some models have weak RF for the aerosol compo-
nents (CAM5 and BCC) whereas other models have stronger
aerosol component RF (CAM4-Oslo, OsloCTM2 and GMI).
Fig. 4. Radiative forcing from the six components, overlain with the
(unmodified) model total forcing (yellow bars).
Not all models have all six aerosol components included.
Figure 5 shows the total RF where modifications for miss-
ing aerosol components have been taken into account. We
calculate the mean of each aerosol component from all mod-
els that treat it, then add that mean to the total RF for each
component that an individual model doesn’t treat. Since the
missing aerosol components in the models are predominantly
scattering aerosols (nitrate, SOA, and in some few cases BB
aerosols), the modification leads to stronger negative RF for
all models with modification. The mean RF of the DAE
changes from −0.27Wm−2to −0.32 Wm−2with the aerosol
component modification.
Table 3 also shows the normalized clear sky RF (NRF,
clear sky RF divided by AOD), which ranges from
−17Wm−2to −76 Wm−2with a mean of −27Wm−2. The
NRF depends on a variety of factors such as spatial dis-
tribution, optical properties, and the vertical profile. The
weakening of the DAE RF due to clouds (Schulz et al.,
2006) depends on vertical positioning of the aerosols and the
clouds (Chand et al., 2009; Liao and Seinfeld, 1998; Sam-
set and Myhre, 2011) as well as the degree of absorption of
the aerosols (single scattering albedo) (Haywood and Shine,
1995).
Atmospheric absorption by the aerosols is calculated as
the difference between the RF at TOA and radiative ef-
fect of aerosols at the surface. Absorbing aerosols such as
BC are the main cause for the atmospheric absorption by
the aerosols, but scattering aerosols enhance the absorption
by gases in the atmosphere and make a small contribution
(Randles et al., 2012; Stier et al., 2012). Figure 6 shows
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G. Myhre et al.: Radiative forcing of the direct aerosol effect 1861
Table 3. Global mean anthropogenic value for all-sky and clear sky RF, normalized RF (NRF) with respect to AOD for clear sky, at-
mospheric absorption, atmospheric absorption divided by AAOD, AOD, anthropogenic fraction of AOD, AAOD, single scattering albedo
(SSA), combined natural and anthropogenic change in SSA from PRE simulation to CTRL simulation, and present day cloud fraction (CLT).
For NCAR-CAM3 the sum of component forcings is used.
Model RF All-sky RF Clear-sky NRF Clear-sky Atm.abs. Atm.abs/AAOD AOD AOD Ant.fr. AAOD SSA dSSA CLT
[Wm−2] [Wm−2] [W m−2] [W m−2] [W m−2] [1] [1] [1] [1] [1] [1]
BCC −0.18 −0.75 −76.0 0.20 561 0.0099 0.138 0.0004 0.963 −0.0007 0.59
CAM4-Oslo −0.08 1.75 479 0.0527 0.345 0.0037 0.931 −0.0148 0.54
CAM5.1 −0.016 −0.35 −23.6 0.69 470 0.0148 0.123 0.0015 0.901 −0.0064 0.64
GEOS CHEM −0.26 −0.61 −20.7 0.66 387 0.0293 0.231 0.0017 0.942 −0.011 0.59
GISS-MATRIX −0.58 −0.79 −19.9 0.0398 0.229 0.0018 0.955 −0.0005 0.65
GISS-modelE −0.32 −0.46 −20.9 0.0219 0.147 0.0020 0.907 −0.0096 0.65
GMI −0.52 −0.91 −24.7 0.49 387 0.0368 0.271 0.0013 0.965 −0.0033
GOCART −0.36 −0.58 −21.8 0.73 432 0.0267 0.236 0.0017 0.937 0.0005
HadGEM2 −0.31 −0.72 −27.2 0.61 429 0.0265 0.209 0.0014 0.947 −0.0073 0.55
IMPACT-Umich −0.21 −1.01 −23.7 1.10 935 0.0428 0.325 0.0012 0.973 −0.0014 0.66
INCA −0.36 −0.73 −17.4 0.95 723 0.0417 0.295 0.0013 0.968 −0.0046 0.47
ECHAM5-HAM −0.15 −0.44 −17.8 0.0244 0.218 0.0016 0.936 −0.0101 0.63
NCAR-CAM3.5 −0.28 −0.74 −24.7 0.47 360 0.0298 0.277 0.0013 0.956
OsloCTM2 −0.17 −0.69 −25.0 0.82 481 0.0276 0.221 0.0017 0.9389 −0.0078 0.62
SPRINTARS −0.14 −0.71 −27.4 0.85 685 0.0260 0.272 0.0012 0.952 −0.0071 0.60
TM5 −0.32 −0.51 −24.5 0.43 492 0.0208 0.282 0.0009 0.958 −0.0054 0.25
Mean −0.27 −0.67 −26.8 0.75 525 0.0295 0.239 0.0015 0.946 −0.0060 0.57
Median −0.26 −0.71 −23.7 0.69 479 0.0276 0.233 0.0015 0.952 −0.0064 0.62
Stddev 0.15 0.18 14.5 0.38 165 0.011 0.064 0.0007 0.021 0.0044 0.12
Table 4. Anthropogenic load, mass extinction coefficient (MEC), AOD, RF, normalized RF with respect to burden (NRFB), normalized RF
with respect to AOD (NRFA) for sulphate.
Model Load MEC AOD RF NRFB NRFA
[mgm−2] [m2g−1] [1] [Wm−2] [W g−1] [W m−2]
BCC 1.29 5.4 0.0069 −0.14 −108 −20.0
CAM4−Oslo 2.78 12.3 0.0342 −0.48 −173 −14.0
CAM5.1 1.69 5.6 0.0095 −0.18 −104 −18.4
GEOS CHEM 1.57 7.3 0.0114 −0.19 −123 −16.8
GISS-MATRIX 1.54 −0.30 −196
GISS-modelE 1.03 38.6 0.0398 −0.32 −307 −8.0
GMI 2.14 12.0 0.0256 −0.42 −195 −16.3
GOCART 1.87 12.2 0.0228 −0.44 −238 −19.5
HadGEM2 1.59 8.9 0.0142 −0.31 −193 −21.7
IMPACT-Umich 1.42 10.3 0.0146 −0.16 −113 −11.0
INCA 2.26 12.5 0.0283 −0.41 −180 −14.4
MPIHAM 2.25 9.1 0.0204 −0.28 −125 −13.8
NCAR-CAM3.5 1.27 23.2 0.0295 −0.45 −354 −15.1
OsloCTM2 1.82 10.1 0.0183 −0.35 −192 −19.0
SPRINTARS 2.13 10.3 0.0220 −0.37 −172 −16.6
Mean 1.78 12.7 0.0213 −0.32 −185 −16.0
Median 1.69 10.3 0.0212 −0.32 −180 −16.5
Stddev 0.47 8.6 0.0096 0.11 71 3.7
the atmospheric absorption by anthropogenic aerosols as a
function of absorbing AOD. The mean atmospheric absorp-
tion (see Table 3) is 0.75 Wm−2, which is weaker than the
0.82Wm−2reported in Schulz et al. (2006). The normal-
ized values (atmospheric absorption divided by absorbing
AOD) are shown on the figure. Normalized values range from
360Wm−2to 935 Wm−2with a mean of 525Wm−2and the
median 10% lower. Some of the models with lowest anthro-
pogenic atmospheric absorption have the largest deviation
from the mean of the normalized values. The correlation be-
tween anthropogenic absorption AOD and atmospheric ab-
sorption is 0.73 for the 12 models available for this part of the
study. IMPACT has high atmospheric absorption mainly, as
discussed above, due to a large fraction of SOA that absorbs
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1862 G. Myhre et al.: Radiative forcing of the direct aerosol effect
Fig. 5. Model total RFs. Black bars show the bare modelled forc-
ing, the colored bars show the forcing modified for untreated com-
ponents (see text for details). The yellow bar shows the AeroCom
mean of the total RF of DAE. Solid lines inside the boxes show the
model mean, dashed lines show the median. The boxes indicate one
standard deviation, while the whiskers indicate the max and min of
the distribution. The yellow shaded bar shows the AeroCom mean
when aerosol component adjustment is made for missing aerosol
components.
Fig. 6. Correlation between anthropogenic absorption AOD and
atmospheric absorption. Numbers show ratio AtmAbs/AAOD, the
lines indicate the mean and one standard deviation of this ratio.
R2=0.73
Fig. 7. Component and total RF. Total RF has been modified for
missing components in individual models. Solid lines inside the
boxes show the model mean, dashed lines show the median. The
boxes indicate one standard deviation, while the whiskers indicate
the max and min of the distribution.
at short wavelengths, while the absorption is much weaker
at 550nm which is the reported wavelength for absorption
AOD. This leads to the strongest normalized atmospheric ab-
sorption of 935 Wm−2for IMPACT. The mean of the anthro-
pogenic fraction of AOD (Table 3) is 24 %, slightly smaller
than in Schulz et al. (2006), where a different preindustrial
year (here 1850 instead of 1750) is one contributor to this
reduction. Half of the models are close to the mean anthro-
pogenic fraction, whereas two of the models have quite low
anthropogenic fractions of 12–14% and one has 35 %.
Figure 7 summarizes the component and aerosol com-
ponent modified total RFs and their intermodel variability.
Model means are shown as solid lines in the middle of the
boxes, median values as dashed lines. The boxes indicate one
standard deviation, and the bars show the maximum and min-
imum single values.
In the following sections we discuss the individual aerosol
components in detail, highlighting model results that deviate
significantly from the mean.
3.2 RF of sulphate
The RF of the direct aerosol effect of sulphate is shown
in Fig. 4 for all models. In addition Figure 7 shows the
mean, median, standard deviation and the range for sulphate
aerosols and for other aerosol components. Table 4 summa-
rizes the aerosol burden, mass extinction coefficient (MEC)
(550nm), AOD (550nm), RF, and NRF with respect to both
burden and AOD. The mean RF of sulphate from the Ae-
roCom Phase II models of −0.32Wm−2is weaker than the
mean of AeroCom Phase I models of −0.35Wm−2in Schulz
et al. (2006). The range in RF is very similar in the two Aero-
Com DAE experiments. The global mean MEC is calculated
as the ratio of global mean AOD to global mean burden. The
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G. Myhre et al.: Radiative forcing of the direct aerosol effect 1863
Fig. 8. Zonal mean SO4RF (a), burden (b), AOD at 550 nm (c), normalized RF with respect to AOD (NRF(A)) (d).
Table 5. Same as Table 4 for BC from FF and BF emissions.
Model Load MEC AOD RF NRFB NRFA
[mgm−2] [m2g−1] [1] [Wm−2] [W g−1] [W m−2]
BCC 0.08 4.2 0.0003 0.05 650 155.1
CAM4-Oslo 0.21 8.2 0.0017 0.37 1763 216.0
CAM5.1 0.07 18.6 0.0014 0.20 2661 143.3
GEOS CHEM 0.12 8.2 0.0010 0.13 1067 130.0
GISS-MATRIX 0.08 0.19 2484
GISS-modelE 0.16 13.8 0.0023 0.21 1253 90.9
GMI 0.14 12.0 0.0017 0.17 1208 100.4
GOCART 0.21 10.4 0.0021 0.18 874 84.3
HadGEM2 0.31 5.4 0.0016 0.19 612 114.1
IMPACT-Umich 0.09 14.0 0.0013 0.14 1467 104.6
INCA 0.15 9.5 0.0015 0.18 1160 122.5
MPIHAM 0.10 11.2 0.0011 0.14 1453 130.2
NCAR-CAM3.5 0.11 0.15 1364
OsloCTM2 0.13 13.0 0.0017 0.28 2161 166.4
SPRINTARS 0.16 7.7 0.0012 0.21 1322 170.8
Mean 0.14 10.5 0.0015 0.18 1438 133.0
Median 0.14 10.4 0.0015 0.18 1322 130.0
Stddev 0.07 3.9 0.0005 0.07 630 37.1
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1864 G. Myhre et al.: Radiative forcing of the direct aerosol effect
Fig. 9. Zonal mean BC RF (a), burden (b), AOD at 550 nm (c), NRF(A) (d).
MEC depends on various factors, where growth by water up-
take for sulphate aerosols is a major cause of the difference
between the models. To illustrate the importance of water
uptake, the MEC in OsloCTM2 is 4.0m2g−1for low rela-
tive humidity (below 30%) and is 10.1m2g−1as a global
mean with ambient relative humidity. The mean of the MEC
is 12.7m2g−1, but also without the two models with very
high MEC (NCAR-CAM3.5 and GISS modelE ) the MEC
is 9.7m2g−1and 7 % higher than in Schulz et al. (2006).
The weaker normalized RF with respect to AOD and stronger
with respect to burden than in Schulz et al. (2006) could be a
result of sulphate at generally lower altitudes, but other fac-
tors could contribute.
Figure 8 shows the latitudinal variation in RF, burden,
AOD, NRF (with respect to AOD) for sulphate. All models
have a maximum in the burden at around 30◦N with a large
difference in the magnitude. The difference in the magnitude
of the burden is particularly large at high northern latitudes
and is likely linked to transport and scavenging problems in
the models (Rasch et al., 2000). The difference in RF of sul-
phate at high latitudes is not as evident as for the burden (and
AOD), since NRF at high latitudes is weak due to the high
surface albedo, and some of the models with strongest bur-
den (and AOD) have weak NRF. The RF of sulphate is very
weak in the Southern Hemisphere.
3.3 RF of BC
In Fig. 9 the latitudinal variation is shown for RF, burden,
AOD, NRF (with respect to AOD) for BC from FF and BF
emissions. Global numbers are shown in Table 5. The im-
portance of surface and cloud albedo is evident in the NRF
for BC, especially if it is compared to NRF for sulphate
in Fig. 8d, which weakens (for a majority of the models)
rather than strengthens over the poles as for BC. The much
stronger NRF at high latitudes for BC than sulphate and the
relatively large range in burden at high latitudes are also
clearly evident for the RF. Schulz et al. (2006) showed the
latitudinal variation in burden of BC (total and not only FF
and BF as in Fig. 9b). There is no indication of a reduced
range in the BC burden among the AeroCom models from
Phase I to Phase II. For the maximum in RF at around 30◦N
there is almost an order of magnitude difference between
the model with strongest RF (CAM4-Oslo) and the model
with the weakest RF (BCC). The model differences in the
NRF are larger for BC than for sulphate, and main reason
for this is likely a strong dependence on altitude of BC and
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G. Myhre et al.: Radiative forcing of the direct aerosol effect 1865
Table 6. Same as Table 4 for OA from FF and BF emissions.
Model Load MEC AOD RF NRFB NRFA
[mgm−2] [m2g−1] [1] [Wm−2] [W g−1] [W m−2]
BCC 0.35 3.7 0.0013 −0.03 −97 −26.3
CAM4-Oslo 0.28 5.8 0.0017 −0.03 −118 −20.1
CAM5.1 0.31 4.6 0.0014 −0.02 −69 −15.0
GEOS CHEM 0.23 4.9 0.0011 −0.02 −95 −19.4
GISS-MATRIX 0.19 −0.02 −129
GISS-modelE 0.46 6.1 0.0028 −0.03 −76 −12.4
GMI 0.30 6.6 0.0020 −0.06 −189 −28.5
GOCART 0.42 4.9 0.0021 −0.06 −144 −29.4
HadGEM2 0.24 7.0 0.0017 −0.04 −145 −20.6
IMPACT-Umich 0.20 14.1 0.0028 −0.03 −141 −10.0
INCA 0.62 7.5 0.0046 −0.05 −76 −10.1
MPIHAM 0.35 1.6 0.0006 −0.01 −41 −25.0
NCAR-CAM3.5 0.21 −0.01 −48
OsloCTM2 0.26 6.6 0.0017 −0.04 −165 −25.1
SPRINTARS 0.22 −0.02 −102
Mean 0.32 6.1 0.0020 −0.03 −113 −20.2
Median 0.30 6.1 0.0017 −0.03 −102 −20.1
Stddev 0.12 3.0 0.0010 0.01 41 6.9
variations in absorbing properties of BC (Samset and Myhre,
2011; Zarzycki and Bond, 2010). The model mean mass ex-
tinction coefficient is 10.5m2g−1. Unfortunately the model
mass absorption coefficient for BC from FF and BF emis-
sions was not diagnosed. Based on observations, Bond et
al. (2006) recommend a mass absorption coefficient (MAC)
of around 7.5m2g−1for fresh BC particles and 50 % higher
for aged BC. Jacobson (2012) and Chung et al. (2012) use
higher absorption enhancement factors. As the single scat-
tering albedo for BC often is in the range of 0.2 to 0.3 (Bond
and Bergstrom, 2006), the BC MEC should be around 25%
higher than the MAC, implying the models underestimate the
MEC and MAC. The low MEC for a few of the models is
the cause of the generally low mean MEC; but half of the
models have MEC according to the recommendations. Inter-
nal mixing of BC with other aerosol components increases
the absorption, so simulating sufficiently high MEC requires
representing internal mixing of BC and its effects on absorp-
tion. The difference in MEC for models using internal mix-
ture (12.1m2g−1) is on average 34 % higher than for mod-
els assuming external mixture (9.1m2g−1). However, recent
work has questioned the importance of the enhancement of
MEC from internal mixing, at least in certain regions (Cappa
et al., 2012).
The model mean global average burden of BC is 0.14
mgm−2and a RF of 0.18 Wm−2. The RF estimate is similar
to the IPCC AR4 estimate, but much lower than Ramanathan
and Carmichael (2008). However, the estimates here and in
IPCC AR4 (Forster et al., 2007) are solely from FF and BF,
whereas Ramanathan and Carmichael (2008) included also
BC from BB emissions and estimate forcing for all BC, not
just anthropogenic. The spread in NRF with respect to burden
is larger than in NRF with respect to AOD due to variations
in MEC included in the former. For the two NRF measures
the model difference is shown to arise from differences in the
vertical profile of BC (Samset et al., 2012), host model dif-
ference (Stier et al., 2012) and likely differences in optical
properties, especially the single scattering albedo of BC.
3.4 RF of OA
The RF of primary OA from FF and BF is weak and has a
mean of −0.03Wm−2. The RF together with burden, MEC,
AOD and NRF are shown in Table 6. Primary OA from
biomass burning and SOA are described in separate sections
below. The burden of OA is on average about 20 % of the sul-
phate burden; however, this fraction varies between the mod-
els. RF from primary OA from FF and BF is around 10% of
the sulphate RF, and thus the NRF with respect to burden for
OA is much weaker than for sulphate. One main reason for
this is the much smaller water uptake for OA. This is also re-
flected in the MEC. Due to the smaller water uptake for OA
compared to sulphate the particles are smaller, leading to a
larger ˚
Angstrøm exponent and a stronger NRF with respect
to AOD. Compared to BC from FF, OA has a lower burden
in the Arctic (see Fig. 10). Combined with the weak NRF in
the Arctic for scattering aerosols, this causes the quite weak
RF from OA in this region seen in Fig. 10.
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1866 G. Myhre et al.: Radiative forcing of the direct aerosol effect
Fig. 10. Zonal mean OAFF RF (a), burden (b), AOD at 550 nm (c), NRF(A) (d).
Table 7. Same as Table 4 for SOA.
Model Load MEC AOD RF NRFB NRFA
[mgm−2] [m2g−1] [1] [Wm−2] [W g−1] [W m−2]
CAM5.1 0.27 8.0 0.0022 −0.01 −45 −5.6
GEOS CHEM 0.27 2.4 0.0006 −0.01 −45 −19.0
GISS-modelE 0.090 6.3 0.0006
IMPACT-Umich 0.97 18.9 0.0184 −0.21 −218 −11.5
MPIHAM 0.15 10.9 0.0016 −0.02 −139 −12.8
OsloCTM2 0.25 6.5 0.0016 −0.04 −161 −24.6
Mean 0.33 8.8 0.0042 −0.06 −122 −14.7
Median 0.27 8.0 0.0016 −0.02 −139 −12.8
Stddev 0.32 5.7 0.0070 0.09 76 7.3
Another cause for the differences in NRF between the
models as well as between sulphate and OA are differences
in the single scattering albedo for OA. Several models have
single scattering albedo for OA at 550nm at 0.96 with in-
creasing values for higher relative humidity. Some OA com-
ponents have absorption at short solar wavelength with a re-
duction in the absorption with wavelength stronger than for
BC (Jacobson, 2012; Kanakidou et al., 2005). Uncertainties
in the refractive indexes and thus absorption for OA are large.
However, some of the major components of OA do not ab-
sorb (Kanakidou et al., 2005). OsloCTM2 has among the
strongest RF for OA from FF and BF emissions and uses
pure scattering OA aerosols. One additional simulation was
performed with OsloCTM2 using refractive indexes for OA
as in CAM4-Oslo, resulting in a 25 % weakening in the RF of
OA from FF and BF emissions. In the INCA model a larger
effect was found when applying pure scattering OA instead
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Fig. 11. Zonal mean SOA RF (a), burden (b), AOD at 550 nm (c), NRF(A) (d).
Table 8. Same as Table 4 for nitrate.
Model Load MEC AOD RF NRFB NRFA
[mgm−2] [m2g−1] [1] [Wm−2] [W g−1] [W m−2]
GEOS CHEM 0.90 7.4 0.0067 −0.12 −136 −18.4
GISS-MATRIX 0.44 −0.10 −240
GMI 0.76 8.0 0.0061 −0.08 −103 −12.9
HadGEM2 0.44 11.8 0.0051 −0.11 −249 −21.1
IMPACT-Umich 0.78 11.2 0.0088 −0.12 −155 −13.8
INCA 0.44 −0.05 −110
NCAR-CAM3.5 0.32 6.3 0.002 −0.03 −91 −14.5
OsloCTM2 0.14 10.8 0.0015 −0.02 −173 −16.0
Mean 0.56 9.8 0.0056 −0.08 −166 −16.4
Median 0.44 10.8 0.0061 −0.08 −155 −16.0
Stddev 0.27 2.0 0.0027 0.04 59 3.4
of their standard simulation, resulting in a 72 % enhancement
in the RF of OA from FF and BF emissions.
3.5 RF SOA
Five models have explicitly performed RF simulations for
SOA; the two GISS-models have SOA included in the to-
tal DAE simulations and some information is available for
GISS-modelE. The uncertainties in the modelling of SOA
are large (Spracklen et al., 2011), and thus it is not sur-
prising that we find a huge range in the burden of anthro-
pogenic SOA from 0.09 to 0.97mg m−2(see Table 7). The
burden in IMPACT-Umich is substantially higher than the
other models. Figure 11 shows that burden of SOA has a
larger fraction around the Equator than for the other aerosol
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1868 G. Myhre et al.: Radiative forcing of the direct aerosol effect
Table 9. Same as Table 4 for combined OA and BC from BB emissions.
Model Load MEC AOD RF NRFB NRFA
[mgm−2] [m2g−1] [1] [Wm−2] [W g−1] [W m−2]
BCC 0.46 3.9 0.0018 −0.03 −65 −16.8
CAM4-Oslo 2.96 5.4 0.0159 0.07 24 4.5
CAM5.1 0.24 7.0 0.0017 0.04 145 20.7
GEOS CHEM 1.90 2.4 0.0045 0.00 −2−1.0
GISS-modelE −0.08
GMI 0.21 7.1 0.0015 −0.06 −291 −40.9
GOCART −0.02
HadGEM2 0.48 8.0 0.0038 −0.07 −143 −17.8
IMPACT-Umich 0.88 6.4 0.0056 0.07 84 13.1
INCA 1.76 8.4 0.0147 −0.03 −16 −1.9
MPIHAM 0.07 22.6 0.0015 0.02 294 13.0
NCAR-CAM3.5 0.21 0.02 95
OsloCTM2 0.47 6.0 0.0028 0.02 38 6.4
SPRINTARS 0.00 0 0.0
Mean 0.94 7.7 0.0054 −0.00 7 −2.1
Median 0.48 7.0 0.0038 −0.00 24 4.5
Stddev 0.95 5.6 0.0054 0.05 158 18.4
components. IMPACT-Umich and CAM5.1 have a clearer
maximum over latitudes where industrialization is strong,
whereas ECHAM5-HAM and OsloCTM2 have the maxi-
mum in the RF shifted slightly towards the Equator. The
zonal pattern of the NRF is rather similar for the models, but
the magnitude differs substantially, and one major cause here
is assumptions regarding the optical properties and in partic-
ular the degree of absorption at short wavelength similar to
primary OA discussed above. OsloCTM2 has the strongest
NRF and uses pure scattering SOA. CAM5.1 has some pos-
itive NRF at very low SOA abundances and this model in-
cludes weak absorption of SOA at short wavelengths.
3.6 RF nitrate
Eight models have simulated RF of nitrate aerosols, explic-
itly. The resulting range of nitrate burdens has a larger span
among the aerosol components than for sulphate and pri-
mary BC and OA, ranging from 0.14 to 0.90mg m−2(see
Table 8 and Fig. 12). The mean RF is −0.08 Wm−2but, like
the burden, the RF also varies widely among the AeroCom
Phase II models with a range from −0.12 to −0.02Wm−2.
The pattern of the zonal mean burden of nitrate resembles the
sulphate pattern. There is a tendency for models with weak
sulphate burden to have a strong nitrate burden and vice versa
and to some extent this can be expected since ammonium ni-
trate can only be formed if there is an excess of ammonia and
thus it competes with sulphate (Adams et al., 2001). How-
ever, there are some exceptions to this relationship with some
models having both low burden for sulphate and nitrate. The
hygroscopic growth for nitrate is generally stronger than for
sulphate (Fitzgerald, 1975) so one might expect stronger or
at least similar MEC for nitrate compared to sulphate, but
this is not the case for GMI and NCAR-CAM3.5. However,
regional variation in the aerosols and thus water uptake may
influence the results. The NRF with respect to burden and
with respect to AOD are rather similar for nitrate and sul-
phate.
The mean RF of the DAE of nitrate is substantial and
with its potentially larger role in the future it is important
to investigate the model differences and compare with ob-
servations. We encourage studies comparing nitrate surface
concentrations and in the atmosphere from aircraft measure-
ments, which over Europe indicate a large role of nitrate
(Crosier et al., 2007; Putaud et al., 2010), as well as over
ocean where observations show small fractions fine mode ni-
trate (Quinn and Bates, 2005).
3.7 RF BB
The RF for simulated BB aerosol varies between positive and
negative RF, highly dependent on SSA and likely also the al-
titude of the BB aerosols. The mean RF from the models is
close to zero. The BB RF is weak in magnitude, but it is im-
portant to note that the RF of BB consists of a positive RF
from BC and a negative RF from OA of much larger magni-
tude than for the total RF from BB aerosols. The MECs for
BB aerosols given in Table 9 are in the upper range of what
is measured during aerosol campaigns in Africa (Haywood
et al., 2003; Johnson et al., 2008). In Schulz et al. (2006) the
burden of primary carbonaceous was given separately for BC
and OA, but not differentiated by sources. Since RF of OAFF
and BCFF are at least not weaker and the total primary car-
bonaceous burden is lower than in Schulz et al. (2006) this
Atmos. Chem. Phys., 13, 1853–1877, 2013 www.atmos-chem-phys.net/13/1853/2013/
G. Myhre et al.: Radiative forcing of the direct aerosol effect 1869
Fig. 12. Zonal mean NO3RF (a), burden (b), AOD at 550 nm (c), NRF(A) (d).
Fig. 13. Model mean RF (left) and standard deviation (right).
indicates lower BB burden in Phase II compared to Phase I.
The model range in burden of BB is large as illustrated by
the large difference between the mean and the median val-
ues. Zonal mean RF, burden, AOD and NRF with respect to
AOD is shown for BB aerosols in Fig. S1.
4 Discussion
The geographical distributions of the RF of the total DAE and
its uncertainty (given as one standard deviation) are shown
in Fig. 13. As shown in earlier studies the RF of the DAE
is strongly negative in industrialized regions of Asia, Europe
and Northern America and reaches positive RF at high lati-
tudes and in other regions with high albedo (either high sur-
face albedo or high cloud cover).
The model mean RF and standard deviation of DAE for
sulphate, BC, and OA are shown in Fig. S2. Maximum
strength in RF for sulphate, BC, and OA is in Southeast Asia.
For sulphate a quite strong RF is also simulated in Europe
and Eastern US. Over regions with high surface albedo such
as Sahara and Arctic the RF for sulphate and OA are quite
weak, but RF from BC is relatively strong. The standard de-
viations are highest in the regions of high RF.
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1870 G. Myhre et al.: Radiative forcing of the direct aerosol effect
Fig. 14. Aerosol forcing partial sensitivities for the AeroCom models. The partial sensitivities are calculated as Px,n =xn/<×>∗<RF>,
where nis model, xis either burden, MEC, NRF (with respect to AOD) or RF, <>denote mean values. Black dotted line is the mean of
the AeroCom models. (Some of the results from the GISS-models are removed due to unresolved issues). Results shown for sulphate, black
carbon, organic carbon, biomass burning, SOA, and nitrate as given in the figure.
The geographical distribution of RF by nitrate and SOA
(Fig. S3) show similar pattern as for the other aerosol
components (Fig. S2). For BB aerosols the geographical pat-
tern of RF is very different from the other aerosol compo-
nents. In some areas (southeast U. S.) this has been caused
by reduction in BB emissions, but in other regions differing
signs may be caused by the underlying albedo such as south-
ern Africa (Chand et al., 2009; Keil and Haywood, 2003),
where low albedo over land causes negative RF and large
coverage of low lying clouds with BB aerosols above cause
positive RF over ocean to the west of the coast. The highsur-
face albedo at high latitudes is the major cause of the positive
RF at high latitudes.
Schulz et al. (2006) introduced a way to investigate the
contribution to the uncertainty from various key components
in estimates of the RF of the direct aerosol effect. The same
approach is shown in Fig. 14 for burden, MEC, NRF (with re-
spect to AOD) and RF. The figure shows for each of burden,
MEC, NRF and RF the contribution for the individual mod-
els to the diversity in the RF. For example one model may
have a burden that contributes to a strong RF, but a MEC of
opposite contribution. For sulphate the uncertainties in RF
are slightly stronger than for the burden, MEC, NRF. How-
ever, the range for burden, MEC, NRF seems quite similar.
CAM5, IMPACT, and BCC have the weakest RF. Weak MEC
is shown to be a main reason for this for CAM5 and BCC,
whereas for IMPACT burden and NRF are the main factors.
CAM4-Oslo has the strongest sulphate RF. Here the burden
is the main reason.
Whereas Schulz et al. (2006) found that diversity in NRF
clearly dominated the diversity in RF of BC, Fig. 14 shows
that diversities in burden and MEC are at least equally impor-
tant in this study. Illustrative of how differences in burden,
MEC, and NRF may compensate each other, the HadGEM2
model has the highest BC burden, but a RF of BC close to the
mean, which can be seen to be caused by a rather low MEC.
For OA the dominant cause for the spread in RF is differ-
ences in MEC, overwhelming the importance of differences
in burden and NRF. ECHAM5-HAM has the weakest MEC
of 1.6 m2g−1and IMPACT the strongest of 14.1 m2g−1. The
weak MEC for ECHAM5-HAM explains why it shows the
weakest RF among the global aerosol models, since its bur-
den and NRF are close to mean values.
The reasons for the differences in burden, MEC and NRF
are numerous. For differences in NRF Randles et al. (2012)
show that the radiative transfer scheme itself contributes to
the diversity and Stier et al. (2012) further explores that host
model differences such as surface albedo and clouds cause a
substantial part of the diversity for the RF of the DAE. The
differences in the aerosols vertical profile (Koffi et al., 2012;
Schwarz et al., 2010) contribute to differences in NRF, es-
pecially for BC. Samset et al. (2012) show that 20–50% of
the diversity in RF of BC is caused by the differences in the
BC vertical profile. Further studies within AeroCom will ex-
plore other causes of the diversity in the results. Since most
of the models in this study apply similar data sets for anthro-
pogenic emissions of aerosols and their precursors, the un-
certainties in this study do not include the full uncertainty
Atmos. Chem. Phys., 13, 1853–1877, 2013 www.atmos-chem-phys.net/13/1853/2013/
G. Myhre et al.: Radiative forcing of the direct aerosol effect 1871
Fig. 15. Correlations between burdens (aand d), RF (band e) and normalized forcing (cand f), for SO4vs BCFF (a,b, and c) and OCFF vs
BCFF (d,e,f).
in the RF of the DAE. The difference in the background
aerosol concentration (pre-industrial) between the models is
of small importance for the RF of the DAE since RF is
relatively linearly depending on the anthropogenically per-
turbed concentrations at current levels of the aerosol species.
Global aerosol models with strong RF for sulphate have a
tendency to also have strong RF for BC and similar for OA
in relation to BC. The RF sulphate and RF BC have an anti-
correlation of 0.62. Figure 15 shows the relationship between
burden, RF and NRF for sulphate, BC and OA. Aerosol bur-
den is dependent on dry and wet deposition and the transport
schemes in the models, and the simulated burdens of various
aerosol compounds are therefore correlated. Similarly, there
is a tendency also for relationship between the NRF (with
respect to burden) for various aerosol compounds. The total
DAE which is the combined effect of all aerosol components
with both positive RF (BC, and in some cases OA) and nega-
tive RF (rest of components) thus also consists of RF contri-
butions that are correlated. Altogether, this leads to smaller
range in the total DAE than combining the range through er-
ror propagation rules for the individual aerosol components.
This is illustrated in Fig. 16, which shows the PDFs for the
individual aerosol components and their sum, and the mod-
elled total RF. A narrower PDF for the model total DAE than
aerosol component sum is evident. Further, Figure 16 illus-
trates the smaller model diversity when the aerosol missing
component modification is performed compared to the total
DAE directly from the various models.
The emission data used in most of the simulations (Lamar-
que et al., 2010) is generated for the period 1850 to 2000.
Anthropogenic emissions of aerosols and their precursors
Fig. 16. Normalized PDFs of each aerosol component RF (dashed
lines) based on the model spread shown above. The red line shows
the mean of the modeled total aerosol RF, while the black line shows
the spread when correcting for missing components as explained in
the text.
had started in 1850 and emission changes have occurred af-
ter 2000. Skeie et al. (2011) performed RF simulations for
OsloCTM2 for the different aerosol species for several time
intervals for the period 1750 to 2000 based on Lamarque et
al. (2010), as well as for 2010 based on the RCP4.5 scenario.
GISS model simulations in Shindell et al. (2012) were also
performed under the RCP4.5 scenario for years 2000 and
2010. Figure 17 shows a scaling of the AeroCom RF simula-
tion for 1850-2000 to the extended time period 1750–2010.
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1872 G. Myhre et al.: Radiative forcing of the direct aerosol effect
Fig. 17. Model mean RF per component (internal bars), modified
from the study timeperiod of 1850–2000 to 1750–2010, based on
numbers from Skeie et al. (2011). Details to be given in text.
To achieve this, we first calculate the aerosol component spe-
cific ratios of OsloCTM2 RF between 2010 and 1750 (Skeie
et al., 2011) to RF between 2000 and 1850 (present work).
Similar ratios are extracted using the GISS values from 2000
to 2010. The ratios from these two models are combined,
and applied to each of the aerosol components individually.
To scale the total RF, we then take the multi-model mean
from the present work and add the sum of the component
scalings. BB is excluded from this analysis, due to large un-
certainties in BB activity between 1750 and 1850 as well as
the inhomogeneous RF (see Fig. S3). The strengthening in
the RF for 1750–2010 relative to 1850–2000 is particularly
strong for OA from FF and BF and nitrate (40–45%), quite
important for BC from FF and BF and SOA (25–30%), but
rather small for sulphate (5%). For OA from FF and BF, ni-
trate and SOA the largest strengthening occurs for the period
1750–1850 in comparison to the 2000–2010 period. For BC
and sulphate the strengthening is rather similar in the 1750–
1850 and 2000–2010 periods. The increase in BC emission
from 2000 to 2010 is of similar magnitude in other emis-
sion data sets as we have applied in for the scaling (Granier
et al., 2011). In Skeie et al. (2011) the increase in RF from
BC (from FF and BF emissions) for the time period 2000 to
2010 was slightly larger than the associated increase in emis-
sions due to also changes in the spatial pattern of the emis-
sions. The increase in the RF of the primary carbonaceous
aerosols from 2000 to 2010 were very similar in OsloCTM2
and GISS, but somewhat weaker for the secondary compo-
nents in the GISS model compared to OsloCTM2. This in-
dicates that uncertainties in the scaling are largest for ni-
trate and SOA which had a quite large change from 1750 to
1850. Taking into account the changes between 1750–1850
and 2000–2010 for the aerosol components changes the total
DAE from −0.32Wm−2(modified for missing aerosol com-
ponents) for 1850–2000 to −0.35Wm−2for the 1750–2010
period.
5 Conclusions
We have documented the RF of the DAE for anthropogenic
aerosol emissions from 16 global aerosol models, combined
and for sulphate, BC, OA, BB, nitrate and SOA separately.
This AeroCom Phase II DAE experiment shows many sim-
ilarities with the Phase I (Schulz et al., 2006), even though
more models are included here and model development has
taken place over the intervening years. We summarize here
the most important findings:
–The total RF of DAE is more strongly negative in the
current simulations than in AeroCom Phase I. The main
reason for this is addition of new species such as nitrate
and SOA.
–Sulphate and BC from FF and BF emissions are the two
aerosol components with strongest absolute RF. The RF
of the DAE of sulphate is very similar in the simulations
presented here and in Schulz et al. (2006). On the other
hand BC RF from FF and BF emissions is almost twice
as strong compared to in Schulz et al. (2006).
–The global aerosol models have undergone substantial
development over the last 7–8yr, and while there are
some changes relative to AeroCom Phase I (Schulz et
al., 2006), the present analysis shows that the main esti-
mates across AeroCom Phase I and II are quite robust.
This is both with respect to mean estimates and uncer-
tainty range, with the main exception that the BC RF
from FF and BF is substantial stronger in Phase II com-
pared to Phase 1.
–The mean from the 16 models of the RF of the total
DAE is −0.27Wm−2. Several of the AeroCom mod-
els have not included either nitrate aerosols or SOA or
both. Modifying these models with information from
AeroCom models altogether results in a mean RF of
−0.32Wm−2. The model simulations are to a large
extent performed for the period 1850–2000 and using
available information to scale to 1750–2010 results in a
mean RF of the total DAE of −0.35Wm−2.
–The AeroCom Phase II simulated 1850–2000 (four
models use slightly different time period) mean RF of
the DAE for sulphate is −0.32Wm−2, BC from FF
and BF emissions is +0.18Wm−2, OA from FF and
BF emissions is −0.03Wm−2, BB is −0.00 Wm−2, ni-
trate is −0.08Wm−2and SOA is -0.06 Wm−2. When
these numbers were scaled to 1750–2010 numbers the
RF for sulphate is −0.34Wm−2, BC from FF and
BF emissions is +0.23Wm−2, OA from FF and BF
emissions is −0.05Wm−2, BB is −0.00 Wm−2(no
change included), nitrate is −0.11Wm−2and SOA is
−0.08Wm−2.
–The differences between models are large for the DAE
of the aerosol components, as illustrated by the relative
Atmos. Chem. Phys., 13, 1853–1877, 2013 www.atmos-chem-phys.net/13/1853/2013/
G. Myhre et al.: Radiative forcing of the direct aerosol effect 1873
standard deviation of more than 40% for RF of BC and
a range from 0.05 to 0.37Wm−2. A further understand-
ing of the causes of the uncertainties is attempted by
decomposing the results into diversities due to burden,
MEC, and NRF. In general these three factors contribute
equally to the uncertainty in the DAE.
–We find a correlation in the magnitude of RF of BC
(positive) and RF of sulphate or OA (negative) among
the models, due to transport, deposition, and optical
properties being treated similarly in each of the mod-
els. We therefore find a smaller uncertainty in the total
DAE than the sum of the DAE by aerosol components.
Supplementary material related to this article is
available online at: http://www.atmos-chem-phys.net/13/
1853/2013/acp-13-1853-2013-supplement.pdf.
Acknowledgements. S. Ghan, X. Liu, R. Easter, P. Rasch and
J.-H. Yoon were funded by the US Department of Energy, Office
of Science, Scientific Discovery through Advanced Computing
(SciDAC) Program and by the Office of Science Earth System
Modeling Program. Computing resources were provided by the
Climate Simulation Laboratory at NCAR’s Computational and
Information Systems Laboratory (CISL), sponsored by the National
Science Foundation and other agencies. The Pacific Northwest
National Laboratory is operated for DOE by Battelle Memorial
Institute under contract DE-AC06- 76RLO 1830. Simulations of
the ECHAM5-HAM, INCA, CAM4-Oslo and HadGEM2 models
have been supported with funds from the FP6 project EUCAARI
(Contract 34684). A. Kirkev˚
ag, T. Iversen and Ø. Seland (CAM4-
Oslo) were supported by the Research Council of Norway through
the EarthClim (207711/E10) and NOTUR/NorStore projects, by
the Norwegian Space Centre through PM-VRAE, and through
the EU projects PEGASOS and ACCESS. H. Zhang and Z.
Wang were funded by National Basic Research Program of China
(2011CB403405). G. Luo, X. Ma and F. Yu were funded by the US
NSF (AGS-0942106) and NASA (NNX11AQ72G). K. Tsigaridis
and S. Bauer were supported by NASA-MAP (NASA award
NNX09AK32G). Resources supporting this work were provided
by the NASA High-End Computing (HEC) Program through the
NASA Center for Climate Simulation (NCCS) at Goddard Space
Flight Center. N. Bellouin was supported by the Joint DECC/Defra
Met Office Hadley Centre Climate Programme (GA01101). G.
Myhre and B. Samset were funded by the Research Council
of Norway through the EarthClim and SLAC projects and the
EU-project ECLIPSE.
Edited by: E. Highwood
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