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
CAM4-Oslo
GCM
2.5˚×1.8˚
26
CAM5.1
GCM
2.5˚×1.8˚
30
Produced by CAM4 atmospheric physics with CAM4-Oslo
cloud tuning and boundary data from the data ocean and
sea-ice models of CCSM4.
CAM5.1
GEOS_CHEM
CTM
5.0˚×4.0˚
47
GEOS-5, reanalysis??
GISS-MATRIX
GCM
2.5˚×2.0˚
40
Nudged to NCEP winds
GISS-modelE
GCM
2.5˚×2.0˚
40
Nudged to NCEP winds
GMI
CTM
2.5˚×2.0˚
72
GEOS-5 MERRA reanalysis, nudged
GOCART
CTM
2.5˚×2.0˚
30
IMPACT
CTM
5.0˚×4.0˚
26
GEOS-4 DAS (Goddard Earth Observing System version 4
Data Assimilation System), reanalysis
DAO assimilation fields for 1997), reanalysis.
INCA
CTM
3.8˚×1.9˚
19
ECMWF reanalysis from the Integrated Forecast System
(IFS) model
HadGEM2
MPIHAM
GCM
GCM
1.8˚×1.2˚
1.8˚×1.8˚
38
31
NCAR-CAM3.5
GCM
2.5˚×1.8˚
26
ERA Interim data for 2006, nudged
model nudged with ERA-Interim reanalysis for the year
2006, nudged
GCM-generated
OsloCTM2
CTM
2.8˚×2.8˚
60
ECMWF reanalysis from the Integrated Forecast System
(IFS) model for year 2006
SPRINTARS
GCM
1.1˚×1.1˚
56
NCEP/NCAR reanalysis, nudged
Hua Zhang
Zhili Wang
Alf Kirkevåg
Trond Iversen
Øyvind Selan
X. Liu
R. C. Easter
Steve Ghan
P. J. Rasch
J.-H. Yoon
Fangqun Yu
Gan Luo
Xiaoyan Ma
Susanne Bauer
Kostas Tsigaridis
Kostas Tsigaridis
Susanne Bauer
Huisheng Bian
Stephen D Steenrod
Mian Chin
Thomas Diehl
Mian Chin
Guangxing Lin
Joyce Penner
Yves Balkanski
Michael Schulz
Didier Hauglustaine
Nicolas Bellouin
Kai Zhang
Philip Stier
Jean-Francois
Lamarque
Gunnar Myhre
Ragnhild B. Skeie
Terje Berntsen
Toshihiko Takemura
Table 2: Model description, aerosol information.
Model
S
BC
OC
BB
SOA
NO3
Aerosol microphysics
References for
aerosol module
BCC
Y
Y
Y
Y
--
--
Zhang et al. (2012)
CAM4-Oslo
Y
Y
Y
Y
--
--
CAM5.1
GEOS_CHEM
Y
Y
Y
Y
Y
Y
Y
--
Y
Y
-Y
GISS-MATRIX
Y
Y
Y
Y
--
Y
12 bin sizes for each aerosol with radii between 0.0050.01, 0.01-0.02, 0.02-0.04, 0.04-0.08, 0.08-0.16, 0.160.32, 0.32-0.64, 0.64-1.28, 1.28-2.56, 2.56-5.12, 5.1210.24, and 10.24-20.48 µm.
Mass conc. of SO4, BC, OM, sea-salt and dust
in four size-classes are tagged according to production
mechanism. Based on 44 sectional size bins and
lognormal distributions at the point of emission, lookup tables yield physical properties of the processed
aerosols.
3 internally-mixed log-normal modes
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.
Aerosol microphysical scheme
GISS-modelE
Y
Y
Y
Y
Y
Y
Aerosol mass based scheme
GMI
Y
Y
Y
Y
--
Y
GOCART
Y
Y
Y
Y
Y
--
IMPACT
Y
Y
Y
Y
Y
*
INCA
Y
Y
Y
Y
--
Y
HadGEM2
MPIHAM
Y
Y
Y
Y
Y
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
distributions.
Parameterized with prescribed dry particle 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
4 bin sizes for sea-salt and mineral dust, pure sulfate
treated using 2 modes with predicted size and
coagulation and condensation of SO4 with other
aerosols explicitly resolved.
Soluble and insoluble aerosol treated separately,
modal assumptions with log-normal size distributions.
We distinguish between accumulation, coarse and
super coarse modes
Log-normal size distribution in the optics?
Modal method, log-normal size distributions, 7 modes
(4 soluble, 3 insoluble)
NCAR-CAM3.5
Y
Y
Y
Y
-¿?-
-¿?-
OsloCTM2
Y
Y
Y
Y
Y
Y
SPRINTARS
Y
Y
Y
Y
--
--
Bulk-aerosol model, except 4-bins for sea-salt and
mineral dust
8 bin sizes for sea-salt and mineral dust, aerosol mass
scheme for other aerosols with log-normal size
distributions in calculations of optical properties
Modal assumption, log-normal size distributions
Kirkevåg et al. (in
prep).
Liu et al., 2012
Yu and Luo (2009) , Yu
(2011), Ma et al.
(2012)
Bauer et al. (2010;
2008)
Koch et al. (2007;
2006), Bauer et al.
(2007) , Tsigaridis et
al. (in prep)
Bian et al. (2009)
Chin et al. (2009;
2002; 2000), Ginoux et
al. (2001)
Wang and Penner
(2009), Lin et al.
(2012); Xu and Penner
(2012)
Balkanski et al. (2004),
Schulz (2007),
Balkanski, (2011),
Szopa et al. (2012)
Bellouin et al. (2011)
Vignati et al. (2004),
Stier et al. (2005),
Zhang et al. (2012)
Lamarque et al. (2012)
Myhre et al. (2007;
2009), Skeie et al.
(2011)
Takemura et al. (2009;
2005)
Note: GOCART only includes SOA from biogenic sources (terpene oxidation)
* The NO3 values 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
NOx chemistry described in Feng and Penner (2007). The resolution was 2.5x2.0.
Table 3: Global mean anthropogenic value for all-sky and clear sky RF, normalized RF (NRF) with respect to AOD for clear
sky, atmospheric 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
[W/m2]
[W/m2]
[W/m2]
[W/m2]
[W/m2]
[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
0.69
470
0.0148
0.123
0.0015
0.901
-0.0064
0.64
0.72
451
CAM5.1
-0.016
-0.35
-23.6
GEOS_CHEM
-0.49
-0.67
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
MPIHAM
-0.15
-0.44
-17.8
0.0244
0.218
0.0016
0.936
-0.0101
0.63
NCAR-CAM3
-0.28
-0.74
-24.7
0.47
360
0.0298
0.277
0.0013
0.956
OsloCTM2
-0.43
-1.18
-27.4
1.11
508
0.0432
0.252
0.0022
0.949
-0.0078
0.63
0.0016
SPRINTARS
-0.14
-0.71
-27.4
0.85
685
0.0260
0.272
0.0012
0.952
-0.0071
0.60
Mean
-0.30
-0.72
-27.1
0.81
535
0.0312
0.238
0.0016
0.946
-0.0056
0.60
Median
-0.31
-0.72
-23.7
0.73
479
0.0298
0.252
0.0015
0.952
-0.0064
0.63
Stddev
0.17
0.22
15.1
0.40
167
0.0120
0.067
0.0007
0.022
0.0045
0.06
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
BCC
CAM4-Oslo
CAM5.1
GEOS_CHEM
GISS-MATRIX
GISS-modelE
GMI
GOCART
HadGEM2
IMPACT-Umich
INCA
MPIHAM
NCAR-CAM3
OsloCTM2
SPRINTARS
Mean
Median
Stddev
Load
[mg/m2]
1.29
2.78
1.69
1.87
1.54
1.03
2.14
1.87
1.59
1.42
2.26
2.25
MEC
[m2/g]
5.4
12.3
5.6
10.7
13.2
38.6
12.0
12.2
8.9
10.3
12.5
9.1
AOD
[1]
0.0069
0.0342
0.0095
0.0200
0.0203
0.0398
0.0256
0.0228
0.0142
0.0146
0.0283
0.0204
2.61
2.13
1.89
1.87
0.50
11.2
10.3
12.3
11.2
7.9
0.0293
0.0220
0.0220
0.0220
0.0091
RF
[W/m2]
-0.14
-0.48
-0.18
-0.49
-0.30
-0.32
-0.42
-0.44
-0.31
-0.16
-0.41
-0.28
-0.45
-0.58
-0.37
-0.35
-0.37
0.13
NRFB
[W/g]
-108
-173
-104
-263
-196
-307
-195
-238
-193
-113
-180
-125
0
-223
-172
-185
-180
60
NRFA
[W/m2]
-20.0
-14.0
-18.4
-24.6
-14.9
-8.0
-16.3
-19.5
-21.7
-11.0
-14.4
-13.8
0.0
-19.9
-16.6
-16.6
-16.3
4.4
Table 5: Same as Table 4 for BC from FF and BF emissions.
Model
BCC
CAM4-Oslo
CAM5.1
GEOS_CHEM
GISS-MATRIX
GISS-modelE
GMI
GOCART
HadGEM2
IMPACT-Umich
INCA
MPIHAM
NCAR-CAM3
OsloCTM2
SPRINTARS
Mean
Median
Stddev
Load
[mg/m2]
0.076
0.21
0.074
0.12
0.075
0.16
0.14
0.21
0.31
0.09
0.15
0.10
MEC
[m2/g]
4.2
8.2
18.6
6.4
1.4
13.8
12.0
10.4
5.4
14.0
9.5
11.2
AOD
[1]
0.0003
0.0017
0.0014
0.0008
0.0001
0.0023
0.0017
0.0021
0.0016
0.0013
0.0015
0.0011
0.17
0.16
0.15
0.15
0.07
13.2
7.7
9.7
10.4
4.6
0.0022
0.0012
0.0014
0.0015
0.0007
RF
[W/m2]
0.05
0.37
0.20
0.20
0.19
0.21
0.17
0.18
0.19
0.14
0.18
0.14
0.15
0.38
0.21
0.20
0.19
0.08
NRFB
[W/g]
650
1763
2661
1693
2484
1253
1208
874
612
1467
1160
1453
0
2271
1322
1491
1453
633
NRFA
[W/m2]
155.1
216.0
143.3
266.1
1798.4
90.9
100.4
84.3
114.1
104.6
122.5
130.2
0.0
172.5
170.8
262.1
143.3
445.1
Table 6: Same as Table 4 for OA from FF and BF emissions
Model
BCC
CAM4-Oslo
CAM5.1
GEOS_CHEM
GISS-MATRIX
GISS-modelE
GMI
GOCART
HadGEM2
IMPACT-Umich
INCA
MPIHAM
NCAR-CAM3
OsloCTM2
SPRINTARS
Mean
Median
Stddev
Load
[mg/m2]
0.35
0.28
0.31
0.23
0.19
0.46
0.30
0.42
0.24
0.20
0.62
0.35
MEC
[m2/g]
3.7
5.8
4.6
3.0
11.1
6.1
6.6
4.9
7.0
14.1
7.5
1.6
AOD
[1]
0.0013
0.0017
0.0014
0.0007
0.0021
0.0028
0.0020
0.0021
0.0017
0.0028
0.0046
0.0006
0.45
0.22
0.33
0.31
0.12
6.7
0.0030
6.4
6.1
3.3
0.0021
0.0020
0.0011
RF
[W/m2]
-0.03
-0.03
-0.02
-0.02
-0.02
-0.03
-0.06
-0.06
-0.04
-0.03
-0.05
-0.01
-0.01
-0.08
-0.02
-0.04
-0.03
0.02
NRFB
[W/g]
-97
-118
-69
-100
-129
-76
-189
-144
-145
-141
-76
-41
0
-187
-102
-115
-102
44
NRFA
[W/m2]
-26.3
-20.1
-15.0
-33.5
-11.6
-12.4
-28.5
-29.4
-20.6
-10.0
-10.1
-25.0
0.0
-28.0
0.0
-20.8
-20.6
8.2
Table 7: Same as Table 4 for SOA
Model
BCC
CAM4-Oslo
CAM5.1
GEOS_CHEM
GISS-MATRIX
GISS-modelE
GMI
GOCART
HadGEM2
IMPACT-Umich
INCA
MPIHAM
NCAR-CAM3
OsloCTM2
SPRINTARS
Mean
Median
Stddev
Load
[mg/m2]
MEC
[m2/g]
AOD
[1]
RF
[W/m2]
NRFB
[W/g]
NRFA
[W/m2]
0.27
0.44
8.0
4.8
0.0022
0.0021
-0.01
-0.01
-45
-29
-5.6
-6.1
0.090
6.3
0.0006
0.97
18.9
0.0184
-0.21
-218
-11.5
0.15
10.9
0.0016
-0.02
-139
-12.8
0.36
6.9
0.0025
-0.07
-183
-26.4
0.38
0.36
0.32
9.3
8.0
5.1
0.0046
0.0022
0.0068
-0.06
-0.02
0.09
-123
-139
83
-12.5
-11.5
8.4
Table 8: Same as Table 4 for nitrate
Model
BCC
CAM4-Oslo
CAM5.1
GEOS_CHEM
GISS-MATRIX
GISS-modelE
GMI
GOCART
HadGEM2
IMPACT-Umich
INCA
MPIHAM
NCAR-CAM3
OsloCTM2
SPRINTARS
Mean
Median
Stddev
Load
[mg/m2]
MEC
[m2/g]
AOD
[1]
RF
[W/m2]
NRFB
[W/g]
NRFA
[W/m2]
0.90
0.44
0.16
0.76
7.4
23.8
151.4
8.0
0.0066
0.0104
0.0246
0.0061
-0.17
-0.10
-0.11
-0.08
-191
-240
-684
-103
-25.9
-10.1
-4.5
-12.9
0.44
0.78
0.44
11.8
11.2
0.0051
0.0088
-0.11
-0.12
-0.05
-249
-155
-110
-21.1
-13.8
0.0
0.16
11.3
0.0018
-0.03
-191
-16.9
0.51
0.44
0.28
32.1
11.3
52.9
0.0091
0.0066
0.0074
-0.10
-0.10
0.04
-240
-191
187
-15.0
-13.8
7.1
Table9: Same as Table 4 for combined OA and BC from BB emissions
Model
BCC
CAM4-Oslo
CAM5.1
GEOS_CHEM
GISS-MATRIX
GISS-modelE
GMI
GOCART
HadGEM2
IMPACT-Umich
INCA
MPIHAM
NCAR-CAM3
OsloCTM2
SPRINTARS
Mean
Median
Stddev
Load
[mg/m2]
0.46
2.96
0.24
MEC
[m2/g]
3.9
5.4
7.0
AOD
[1]
0.0018
0.0159
0.0017
0.21
7.1
0.0015
0.48
0.88
1.76
0.07
8.0
6.4
8.4
22.6
0.0038
0.0056
0.0147
0.0015
0.72
6.1
0.0044
0.86
0.48
0.93
8.3
7.0
5.5
0.0057
0.0038
0.0057
RF
[W/m2]
-0.03
0.07
0.04
NRFB
[W/g]
-65
24
145
NRFA
[W/m2]
-16.8
4.5
20.7
-0.08
-0.06
-0.02
-0.07
0.07
-0.03
0.02
0.02
-0.02
0.00
-0.01
-0.02
0.05
0
-291
0
-143
84
-16
294
0
-25
0
1
-16
168
0.0
-40.9
0.0
-17.8
13.1
-1.9
13.0
0.0
-4.1
0.0
-3.4
-1.9
19.3
Figure 1: Zonal mean top of the atmosphere short wave (TOA) albedo (left) and effective broadband surface albedo (right)
shown for all the models. CERES TOA albedo data is shown together with the models.
Figure 2: Zonal mean DAE RF for all-sky (left) and RF for clear sky (right).
Figure 3: Anthropogenic AOD (upper left), anthropogenic AAOD (upper right), anthropogenic single scattering albedo
(lower left), and change in single scattering albedo from pre-industrial to present conditions (lower right). All values are
taken at 550nm. Between 80S and 30S 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.
Figure 4: Radiative forcing from the six components, overlain with the (unmodified) model total forcing (yellow bars).
Figure 5: Model total RFs. Black bars show the bare modelled forcing, the colored bars show the forcing modified for
untreated components (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.
Figure 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.
Figure 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.
Figure 8: Zonal mean SO4 RF, burden, AOD at 550nm, normalized RF with respect to AOD (NRF(A))
Figure 9: Zonal mean BC RF, burden, AOD at 550nm, NRF(A). For GEOS-CHEM the RF of BC has been derived as the total
DAE minus the contribution from the non-BC aerosol components. This treatment has been applied due to the mixing
assumption in this model makes removal of all anthropogenic BC unrealistic in relations to the total DAE.
Figure 10: Zonal mean OAFF RF, burden, AOD at 550nm, NRF(A)
Figure 11: Zonal mean SOA RF, burden, AOD at 550nm, NRF(A)
Figure 12: Zonal mean NO3 RF, burden, AOD at 550nm, NRF(A)
Figure 13: Model mean RF (left) and standard deviation (right).
Figure 14: Aerosol forcing partial sensitivities for the AeroCom models. The partial senistivities are calculated as P x,n = xn /
<x> * <RF>, where n is model, x is 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).
Figure 15: 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.
Figure 16: Correlations between burdens (left column), RF (middle) and normalized forcing (right), for SO4 vs BCFF (top row)
and OCFF vs BCFF (bottom row).
Figure 17: 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.
Supplementary figure 1: Zonal mean BB RF, burden, AOD at 550nm, NRF(A)
Balkanski, Y., L‘Influence des Aérosols sur le Climat. Thèse d‘Habilitation à Diriger des 871 Recherches Thesis, Université
Versailles Saint Quentin, 2011.
Balkanski, Y., Schulz, M., Moulin, C. and Ginoux, P., The formulation of dust emissions on global scale: formulation and
validation using satellite retrievals. In: Emissions of Atmospheric Trace Compounds. C. Granier, P. Artaxo and C.
Reeves (Editors), Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 239-267, 2004.
Bauer, S. E., Koch, D., Unger, N., Metzger, S. M., Shindell, D. T. et al.: Nitrate aerosols today and in 2030: a global simulation
including aerosols and tropospheric ozone, Atmospheric Chemistry and Physics, 7(19), 5043-5059, 2007.
Bauer, S. E., Menon, S., Koch, D., Bond, T. C. and Tsigaridis, K.: A global modeling study on carbonaceous aerosol
microphysical characteristics and radiative effects, Atmospheric Chemistry and Physics, 10(15), 7439-7456, 2010.
Bauer, S. E., Wright, D. L., Koch, D., Lewis, E. R., McGraw, R. et al.: MATRIX (Multiconfiguration Aerosol TRacker of mIXing
state): an aerosol microphysical module for global atmospheric models, Atmospheric Chemistry and Physics, 8(20),
6003-6035, 2008.
Bellouin, N., Rae, J., Jones, A., Johnson, C., Haywood, J. et al.: Aerosol forcing in the Climate Model Intercomparison Project
(CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate, Journal of Geophysical ResearchAtmospheres, 116, D20206, 2011.
Bian, H., Chin, M., Rodriguez, J. M., Yu, H., Penner, J. E. et al.: Sensitivity of aerosol optical thickness and aerosol direct
radiative effect to relative humidity, Atmospheric Chemistry and Physics, 9(7), 2375-2386, 2009.
Chin, M., Diehl, T., Dubovik, O., Eck, T. F., Holben, B. N. et al.: Light absorption by pollution, dust, and biomass burning
aerosols: a global model study and evaluation with AERONET measurements, Annales Geophysicae, 27(9), 34393464, 2009.
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N. et al.: Tropospheric aerosol optical thickness from the GOCART
model and comparisons with satellite and Sun photometer measurements, Journal of the Atmospheric Sciences,
59(3), 461-483, 2002.
Chin, M., Rood, R. B., Lin, S. J., Muller, J. F. and Thompson, A. M.: Atmospheric sulfur cycle simulated in the global model
GOCART: Model description and global properties, Journal of Geophysical Research-Atmospheres, 105(D20),
24671-24687, 2000.
Feng, Y. and Penner, J. E.: Global modeling of nitrate and ammonium: Interaction of aerosols and tropospheric chemistry,
Journal of Geophysical Research-Atmospheres, 112(D1), D01304, 2007.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B. et al.: Sources and distributions of dust aerosols simulated with
the GOCART model, Journal of Geophysical Research-Atmospheres, 106(D17), 20255-20273, 2001.
Koch, D., Bond, T. C., Streets, D., Unger, N. and van der Werf, G. R.: Global impacts of aerosols from particular source
regions and sectors, Journal of Geophysical Research-Atmospheres, 112(D2), D02205, 2007.
Koch, D., Schmidt, G. A. and Field, C. V.: Sulfur, sea salt, and radionuclide aerosols in GISS ModelE, Journal of Geophysical
Research-Atmospheres, 111(D6), D06206, 2006.
Lamarque, J. F., Emmons, L. K., Hess, P. G., Kinnison, D. E., Tilmes, S. et al.: CAM-chem: description and evaluation of
interactive atmospheric chemistry in the Community Earth System Model, Geoscientific Model Development, 5(2),
369-411, 2012.
Ma, X., Yu, F. and Luo, G.: Aerosol direct radiative forcing based on GEOS-Chem/APM and uncertainties, Atmos. Chem. Phys.
Discuss., 12, 193-240, 2012.
Myhre, G., Bellouin, N., Berglen, T. F., Berntsen, T. K., Boucher, O. et al.: Comparison of the radiative properties and direct
radiative effect of aerosols from a global aerosol model and remote sensing data over ocean, Tellus Series BChemical And Physical Meteorology, 59(1), 115-129, 2007.
Myhre, G., Berglen, T. F., Johnsrud, M., Hoyle, C. R., Berntsen, T. K. et al.: Modelled radiative forcing of the direct aerosol
effect with multi-observation evaluation, Atmospheric Chemistry and Physics, 9(4), 1365-1392, 2009.
Schulz, M., Constraining model estimates of the aerosol radiative forcing. Habilitation Thesis Thesis, Universit ´e Pierre et
Marie Curie, Paris VI, 2007.
Skeie, R. B., Berntsen, T. K., Myhre, G., Tanaka, K., Kvalevag, M. M. et al.: Anthropogenic radiative forcing time series from
pre-industrial times until 2010, Atmospheric Chemistry and Physics, 11(22), 11827-11857, 2011.
Stier, P., Feichter, J., Kinne, S., Kloster, S., Vignati, E. et al.: The aerosol-climate model ECHAM5-HAM, Atmospheric
Chemistry and Physics, 5, 1125-1156, 2005.
Szopa, S., Balkanski, Y., Cozic, A., Déandreis, C., Dufresne, J.-L. et al.: Aerosol and Ozone changes as forcing for Climate
Evolution between 1850 and 2100, Clim. Dyn., in press 2012.
Takemura, T., Egashira, M., Matsuzawa, K., Ichijo, H., O'Ishi, R. et al.: A simulation of the global distribution and radiative
forcing of soil dust aerosols at the Last Glacial Maximum, Atmospheric Chemistry and Physics, 9(9), 3061-3073,
2009.
Takemura, T., Nozawa, T., Emori, S., Nakajima, T. Y. and Nakajima, T.: Simulation of climate response to aerosol direct and
indirect effects with aerosol transport-radiation model, Journal of Geophysical Research-Atmospheres, 110(D2),
D02202, 2005.
Vignati, E., Wilson, J. and Stier, P.: M7: An efficient size-resolved aerosol microphysics module for large-scale aerosol
transport models, Journal Of Geophysical Research-Atmospheres, 109(D22), 2004.
Wang, M. and Penner, J. E.: Aerosol indirect forcing in a global model with particle nucleation, Atmospheric Chemistry and
Physics, 9(1), 239-260, 2009.
Yu, F.: A secondary organic aerosol formation model considering successive oxidation aging and kinetic condensation of
organic compounds: global scale implications, Atmospheric Chemistry and Physics, 11(3), 1083-1099, 2011.
Yu, F. and Luo, G.: Simulation of particle size distribution with a global aerosol model: contribution of nucleation to aerosol
and CCN number concentrations, Atmospheric Chemistry and Physics, 9(20), 7691-7710, 2009.
Zhang, H., Wang, Z. L., Wang, Z. Z., Liu, Q. X., Gong, S. L. et al.: Simulation of direct radiative forcing of aerosols and their
effects on East Asian climate using an interactive AGCM-aerosol coupled system, Climate Dynamics, 38(7-8),
1675-1693, 2012.