1
Understanding black carbon radiative forcing uncertainty due to vertical distributions
2
3
4
5
6
Samset, B.H., Myhre, G., Schulz, M., Balkanski, Y., Bauer, S., Bellouin, N., Berntsen, T.K., Chin, M.,
Diehl, T., Easter, R.E., Ghan, S.J., Iversen, T., Kirkevåg, A., Lamarque, J.-F., Lin, G., Penner, J.,
Seland, Ø., Skeie, R.B., Stier, P., Takemura, T., Tsigaridis, K., Zhang, K.
7
warming effect of BC has led to suggestions, both in the scientific community1-4 and
8
among policy makers, for reduction of BC emissions to mitigate global warming. The
9
uncertainties in the radiative forcing (RF) of the direct aerosol effect of BC are however
10
large5-7, making clear conclusions from mitigation studies difficult8. Among the causes of
11
these model uncertainties is differences in the vertical concentration profiles of BC9,
12
where there are also significant discrepancies between models and observations10. Here
13
we show, based on a large set of global aerosol models, that the diversity in the vertical
14
profile of BC contributes between 25% and 50% of the variability in modeled BC RF. A
15
significant fraction of this variability comes from high altitudes, as we find that, globally,
16
models simulate more than 40% of their total BC RF above 5km. BC emission regions
17
and areas with transported BC are found to have differing characteristics. These
18
insights into the importance of the vertical profile of BC will benefit the understanding
19
of the differences among the global aerosol models, and may guide the initiation of
20
observational studies with the aim of improving the models in a focused way.
21
Aerosol radiative forcing is a measure of the extra amount of energy absorbed in the
22
atmosphere by a given change in aerosol concentrations. The efficiency with which BC can
23
induce RF is however dependent on external factors such as surface albedo, water vapor,
24
background aerosol distributions and, most notably, the presence of clouds9,11. The total BC
25
forcing is therefore highly sensitive to the full 3D distribution of BC concentration12.
26
Anthropogenic direct BC forcing estimated by the current major global aerosol models varies
27
between 0.05 W/m2 and 0.38 W/m2
Unlike most atmospheric aerosols, black carbon (BC) absorbs solar radiation. This
13
. This range is similar to previous estimates6,7.
28
Understanding the causes of this variability in BC forcing would be a good step towards
29
reducing the total uncertainty in the total anthropogenic aerosol forcing.
30
Several studies have previously indicated that the vertical transport is one area where the
31
models still differ significantly14-17. This study uses input from 11 global aerosol models
32
participating in the AeroCom model intercomparison proect to compare and study the impacts
33
of modeled BC vertical forcing profiles. By combining the models’ own concentration
34
profiles with a common efficiency profile (EP) of RF per gram of BC (see Methods), we are
35
able to recalculate the induced RF of the BC direct aerosol effect at various altitudes and
36
spatial regions. We isolate contributions from the cloud field by comparing all-sky and clear
37
sky conditions, and study variations due to regional differences.
38
We first quantify the fraction of the total modeled RF variability attributable to vertical
39
profiles alone. Figure 1a shows the global mean total BC burden from 11 models from
40
AeroCom Phase 2, in addition to three models from AeroCom Phase 1. See Methods for
41
details. We find a multi-model mean of 0.19 mg/m2, but with a relative standard deviation
42
(RSD) of 32% and a model spread from 0.09 to 0.37 mg/m2. Supplementary Table 1 lists the
43
numbers for individual models. Under an assumption of equal forcing per gram, this alone
44
will cause a large diversity in the total BC forcing predicted by the models. AeroCom Phases
45
1 and 2 found a similar burden range, as shown by the whisker boxes in Figure 1a. Note that
46
the AeroCom P2 results are for BC from fossil and biofuel burning (BCFF) only. Figure 1b
47
shows the RF for each model, recalculated from the concentration profiles using a common
48
EP as described in Methods. That the average is stronger than for previous estimates is due to
49
the strength of the profile used (see Methods for discussion). The RF values are also highly
50
correlated (95%) with burden values, again due to the use of a common BC EP. However,
51
dividing the recalculated RF by the global mean burdens gives new global estimates of BC RF
52
per gram, shown in Figure 1c. If all model vertical profiles were identical, this spread would
53
vanish. It therefore carries information on the impact of the vertical profiles of BC
54
concentration on the model spread, separate from the variability due to burden differences. To
55
quantify this impact, analyse the multi-model RSDs and correlations, as shown in Methods.
56
We find that of the total model variability of BC RF, no more than 50% but likely more than
57
25% is attributable to differences in vertical profiles.
58
Next we compare global and regional BC vertical profiles and EPs. As also shown in previous
59
model comparisons15-17, there are large differences in the modeled BC concentrations. Figure
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2a-c shows the annual mean vertical profiles of BC concentration from all models, globally
61
and for two selected regions (Arctic and China; see Methods for definitions). Globally, the
62
concentrations quickly decrease with altitude while the forcing efficiency increases. The
63
Arctic is a region with negligible indigenous BC emissions, but where the effects of
64
transported aerosols are often discussed18. While all models show a significant BC
65
contribution at high altitudes the model spread in the Arctic is quite large, ranging from
66
virtually constant below 200hPa to double-peaked structures with maxima at 900 hPa and
67
200hPa. This highlights the differences in transport and wet removal schemes between the
68
climate models. There is no discernible trend between the AeroCom P1 and P2 models. China
69
has heavy surface emissions of BC, and also sees contributions from transport at high
70
altitudes, as is evident from Figure 2c.
71
Figure 2d-f shows the corresponding recalculated RF per meter of height, divided by the
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model global mean burden. The resulting vertical profiles show at what altitudes RF is
73
generated. Globally, RF can be seen to be mostly induced in a range around 800hPa for most
74
models, with a secondary peak between 400hPa and 200hPa for some models. The China
75
region follows the global pattern, but with the low altitude peak closer to the ground,
76
reflecting the emissions there. The Arctic region, however, has a strong peak at high altitudes
77
for most models, evidencing the relative importance of high altitude BC RF there.
78
Figure 2g-i illustrates the model spread in forcing at a given altitude due to vertical profiles,
79
by integrating the absolute forcing per model layer from the surface and upwards. Globally,
80
the majority of models reach 50% of their forcing around 500hPa (approximately 5km);
81
however there is a large spread around this value. For the regions, this picture is quite
82
different. In the Arctic all models have a significant forcing component above 5km, while for
83
the industrial regions of China the opposite is true.
84
We find a significant fraction of modeled BC RF from high altitudes, with a notable regional
85
pattern. Figure 3 maps the model mean fraction of aerosol mass (3a) and induced forcing (3b)
86
from model layers of altitude above 500hPa, or approximately 5km. Firstly, the distinction
87
between BC emission regions and regions with mostly transported BC is evident. The former
88
have small mass and RF fractions at high altitudes, typically 10-20%, while transport regions
89
show RF fractions up to 80%. Figure 3c compares the global and regional (Arctic, China and
90
Europe) mean values of the mass and RF fractions. Note that the fraction of RF above 5km is
91
systematically higher than the mass fraction, due to the strongly increasing shape of the RF
92
efficiency profiles. Globally, more than 40% of the model simulated RF from BC comes from
93
model layers above 5km, while only 24% of the mass is found in this region. This illustrates
94
the importance of validating model vertical profiles and transport codes not only in industrial
95
regions, but also in transport regions with low indigenous BC emissions.
96
Supplemental figure 1 shows the RF and mass fractions in four altitude bands, globally and
97
for the three regions. Such information is useful for comparing models with observational
98
data, which can aid future constructions of best estimates for global BC forcing.
99
We have shown that, even on global mean, a significant fraction of the variability in the BC
100
forcing per gram is due to differences in vertical profiles. It is however instructive to further
101
divide this variability into the components that make up the efficiency profiles. In Figure 4a
102
we show the zonal mean RF per gram for all models, and investigate the relative importance
103
of the cloud field and of regional differences in albedo for the resulting spread. In Figure 4c
104
we have run the analysis using a global, annual mean efficiency profile instead of the full time
105
dependent 3D profile, and in addition used clear sky conditions. The magnitude has a weak
106
but non-vanishing model spread, and is only weakly dependent on latitude since the effects of
107
varying surface albedo, which normally strongly increases the forcing efficiency at the poles,
108
are averaged out in the global profile. Not all vertical sensitivity of BC forcing is due to the
109
aerosols being above or below clouds12. The remainder, caused predominantly by Rayleigh
110
scattering, water vapor and the competing effects of other aerosols, is the cause of the
111
variability here. RSD on global mean values is 10% (see Figure 4b). Figure 4d shows the
112
same analysis using the full 3D efficiency profile. The RSD increases to 13%, and we now
113
see the latitudinal effects of the high polar planetary albedo. Figure 4e shows the analysis
114
using the global mean efficiency profile again, but under all-sky conditions. Again the RSD
115
increases to 13%, due to the cloud field. The model variability in forcing efficiency that is due
116
to vertical profile differences can therefore be decomposed into three factors: Equal and
117
significant contributions from the cloud field and from regional differences, and a major
118
contribution from the underlying sensitivity of BC forcing to altitude even in the absence of
119
clouds and albedo differences. Harmonizing model treatment of clouds and albedo is therefore
120
not sufficient to remove uncertainties in BC forcing due to vertical profiles. Combining the
121
effects of clouds and regional variations with the intrinsic vertical variability of the forcing
122
efficiency yields the total variability picture seen in Figure 4a.
123
Different models will likely have different BC efficiency profiles. To quantify the sensitivity
124
of the present analysis to the shape of the profile used, we reran the analysis with an EP that
125
was changed by 20% at the top of the atmosphere unchanged at the surface, with a linear
126
interpolation in between, resulting in a weaker EP. This changed the global fraction of RF
127
above 5km by less than 5%, indicating that the results are relatively stable within reasonalbe
128
variations of the EP.
129
Results in the present study are given for total BC aerosol emissions only. However, due to
130
different source regions, the vertical profiles of BCFF could be different from BC. Six models
131
also provided concentration profiles for BCFF. Using the same efficiency profiles we
132
performed the analysis also for BCFF, and found results consistent with what we have
133
presented for BC (not shown). While the absolute forcing numbers differ due to lower total
134
burdens for BCFF, the variability in vertical profiles is very similar to that for total BC. Our
135
conclusions here are therefore also applicable to model comparison results on BCFF only.
136
Results presented here have all used emissions from year 2000. One model (CAM4-Oslo) also
137
provided simulations for year 2006. While the BC burden was 60% higher for 2006 emissions,
138
the forcing per burden and vertical profiles were invariant. This gives confidence that the
139
variability due to vertical profiles can indeed be regarded as independent of that due to the
140
present day emissions dataset.
141
Results are also similar for the P1 and P2 submissions of the IMPACT model, where no major
142
aerosol microphysical changes have been performed. For OsloCTM2, however, where both an
143
ageing scheme and modifications to the washout of BC were added, forcing per burden
144
changes between P1 and P2 by as much as half of the full range observed. Hence, the
145
transport scheme and model treatment of BC are crucial factors in determining the modeled
146
value of forcing per burden, and both of these factors are closely linked to the vertical
147
distribution.
148
In conclusion, we have shown that the previously documented large spread in BC aerosol
149
concentration profiles is enhanced for vertical BC RF profiles. Using 11 global aerosol
150
models, we show that most models globally simulate 40% of their BC forcing above 5km, and
151
that regionally the fraction can be above 70%. The spread between models is however quite
152
large, and we have indications that between 25% and 50% of the differences in modeled BC
153
RF can be attributed to differences in vertical profiles. Harmonizing the treatment of clouds
154
and albedo between models is found to not be sufficient to remove this variability. To propose
155
efficient mitigation measures for BC, its radiative forcing needs to be well understood both in
156
emission and transport regions. Further model improvements and comparisons with data are
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needed, and should focus on both of these types of region. It is however clear that while the
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BC vertical profiles are important, they are not sufficient to explain the remaining differences
159
between global aerosol models.
160
Methods
161
Model simulations
162
The AeroCom initiative asks models to simulate the direct aerosol radiative effect under as
163
similar conditions as possible. All models run single year simulations using the same base
164
years for aerosol emissions (2000 and 1850), with unchanged meteorology to exclude indirect
165
effects. Both circulation models and transport models have participated. Two sets of model
166
intercomparisons have been performed, here labeled AeroCom Phase 1
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Phase 2 13. See the references or the main AeroCom website (aerocom.met.no) for details.
168
For the present study, monthly 3D BC concentration fields from 11 global aerosol models
169
participating in AeroCom Phase 2 are used. For six of the models, concentration fields of BC
170
from fossil and biofuel burning only (BCFF) are also used. Three of the same modeling
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groups also provided BC concentration profiles to AeroCom Phase 1, and these fields are also
172
included here. The IMPACT model has not undergone major changes between the AeroCom
173
phases, while the OsloCTM2 model has been heavily revised with the addition of both an
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ageing scheme for BC and improved treatment of BC washout. Changes in treatment of BC
6,16,19
and AeroCom
175
fom CAM4-Oslo between P1 and P2 are small. The concentration fields and the 3D model
176
resolutions are the only inputs taken from each separate model. The 1850 concentrations are
177
subtracted from the 2000 ones, leaving the contribution due to anthropogenic BC or BCFF
178
emissions.
179
From AeroCom P2, participating models are NCAR-CAM3.520, CAM4-Oslo, CAM5.121,
180
GISS-modelE22, GOCART-v423, HadGEM224, IMPACT25, INCA, ECHAM5-HAM26,
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OsloCTM227 and SPRINTARS28. From AeroCom P1, participating models are UiO_GCM29
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(earlier version of CAM4-Oslo), IMPACT30 and OsloCTM231.
183
RF recalculation
184
Samset and Myhre 2011 presents a set of vertical profiles of the direct radiative forcing per
185
gram of aerosol for BCFF, i.e. the amount of top-of-atmosphere shortwave radiative forcing
186
seen by a particular model (OsloCTM2) per gram of aerosols at a given altitude. Here we term
187
these efficiency profiles (EP). In the specialized literature, the term normalized radiative
188
forcing is more commonly used. We here employ the full time dependent 3D efficiency
189
profiles, either for all sky or clear sky conditions as described above. For each model grid
190
point and time step, we multiply the modeled BC concentration by the EP to get the
191
contribution to the the total shortwave, top-of-atmosphere BC radiative forcing. This gives us
192
intercomparable 3D RF fields, something that is not immediately available from each model.
193
We label this the recalculated RF, to emphasize that it is heavily correlated with the burden
194
and should not be taken directly as an estimate of BC forcing of each model. Since the model
195
that was used to produce the profiles of RF per gram has among the strongest global mean
196
forcings per gram13, most recalculated RFs can be expected to be stronger than their host
197
model would predict. However, by dividing the recalculated RF by the total aerosol burden of
198
the host model, we extract the variability in forcing per gram from each model that is due only
199
to vertical profiles, and can subsequently use the vertical RF profiles to study it.
200
As our method removes contributions to the spread in BC RF due to differences in model
201
specific treatment of clouds, water uptake and microphysics, we are left solely with variations
202
due to the concentration profiles and the total aerosol burden. Dividing by the burden, we can
203
study the contribution to the variability due to regional differences (high or low surface
204
albedo, indigenous emissions, transport region or both), and due to the presence of clouds. To
205
study the latter effects, we ran parallel analyses using only a global mean profile of RF per
206
gram (labeled GP above), a distinct profile of RF per gram for clear sky conditions (labeled
207
CS), or both (labeled CSGP).
208
Region definitions
209
“Europe” is defined as the box covering longitudes -10 to 30, latitudes 38 to 60. “China”
210
covers longitudes 105 to 135, latitudes 15 to 45. “Arctic” covers latitudes 70 to 90, all
211
longitudes. Europe and China represent regions with high industrial BC emissions and
212
significant fractions of the global BCFF emissions, making their total BC forcing sensitive to
213
pure vertical transport and wet scavenging. The Arctic is a region with low indigenous
214
emissions, but important BC contributions at high altitudes transported from other regions. Its
215
total BC forcing is therefore sensitive to model differences in vertical and long range transport.
216
Estimating the variability caused by vertical distributions
217
We show that there is a residual variability in the forcing per burden that is due to the
218
variations in vertical distributions, with a RSD of 16%. This is approximately 40% of the
219
variability on forcing per burden in AeroCom P1 and P2. Three models in P2 have mass
220
extinction coefficients that deviate significantly from recommendations given in the
221
literature32. Removing contributions from these three models reduces the RSD on the forcing
222
per burden to 32%, subsequently increasing the estimate of the variability due to vertical
223
profiles to 50%.
224
The burden and forcing per burden in P1 and P2 have approximately equal RSDs, so if they
225
were uncorrelated the vertical distribution could be said to contribute 25% of the variability
226
on RF. This is however not the case. We first note that in both AeroCom P1 and P2, the
227
burden and forcing per burden are weakly anticorrelated. This is apparent from the fact that
228
the RSD on the RF (shown in Figure 1) is lower what it would be if the errors on burden and
229
forcing per burden were uncorrelated. Quantifying this effect using numbers from AeroCom
230
P2, we find a weak Pearson correlation coefficient of =-0.46 (or -0.40 if we again remove
231
the three outlier models).
232
In the present analysis, we can estimate the impact of the vertical distributions by the fraction
233
of mass simulated above 5km (M5k), shown in Supplementary table 1. We observe that M5k
234
is strongly correlated with the recalculated RF (=0.70) and forcing per burden (=0.97), as
235
expected due to the efficiency profile used for the present analysis. However M5k is also
236
weakly positively correlated with the total burden (=0.48). The vertical distribution
237
variability therefore does not contribute to the observed anticorrelation between burden and
238
forcing per burden in AeroComP1 and P2.
239
We can assume that forcing per burden is determined by a combination of the vertical profile
240
(positive correlation with burden) and a set of uncorrelated global variables such as BC
241
optical properties (which must then have a combined negative correlation with burden). The
242
variability on BC RF would be higher if the models had not compensated for high burden
243
with a low forcing efficiency and vice versa. Since the vertical variability rather leads to a
244
high efficiency for a high burden, its impact on the RF variability is likely stronger than the
245
estimate of 25% above. The present analysis does not allow for rigorous quantification, but it
246
unlikely to be larger than the 50% of the variability on total BC RF caused by forcing per
247
burden. Hence we have a range of 25% to 50% of the variability of modeled BC RF caused by
248
differences in vertical distributions.
249
250
Author contributions
251
252
253
254
255
256
257
258
B.H.Samset performed the analysis and wrote the paper. G. Myhre contributed to the paper
and analysis. M. Schulz coordinates the AeroCom project, and contributed to discussions.
Model data were provided by G. Myhre, R.B. Skeie, T.K. Berntsen (OsloCTM2), A. Kirkevåg, T.
259
Iversen, Ø. Seland (CAM4-Oslo), S. J. Ghan, R. C. Easter (CAM5.1), S. Bauer ,K. Tsigaridis (GISSModelE), T. Diehl, M. Chin (GOCART), G. Lin, J. Penner (IMPACT), Y. Balkanski, M. Schulz
(INCA), N. Bellouin (HadGEM2), K. Zhang, P. Stier (ECHAM-HAM), J.-F. Lamarque, (NCARCAM3), T. Takemura (SPRINTARS)
260
261
262
263
264
265
Tables
Supplementary Table 1: Modeled BC burden, RF calculated by use of full 3D efficiency profiles (RF) or by a global mean
profile (RFGP), and forcing per gram (NRF). All numbers shown for global mean and for three selected regions.
RF_fraction shows the fraction of the total BC forcing simulated within the stated region. M>5km and RF>5km show the
fractions of aerosol mass and RF, respectively, simulated above an altitude of 5km (500hPa).
Global
Model
NCAR-CAM3.5
CAM4-Oslo
CAM5.1
GISS-modelE
GOCART-v4
HadGEM2
IMPACT
INCA
ECHAM5-HAM
OsloCTM2
SPRINTARS
CAM-Oslo-P1
IMPACT-P1
OsloCTM2-P1
Mean
Std.dev
Europe
Model
NCAR-CAM3.5
CAM4-Oslo
CAM5.1
GISS-modelE
GOCART-v4
HadGEM2
IMPACT
INCA
ECHAM5-HAM
OsloCTM2
SPRINTARS
CAM-Oslo-P1
IMPACT-P1
OsloCTM2-P1
Mean
Std.dev
266
267
Burden
[mg/m2]
0.14
0.22
0.09
0.21
0.19
0.37
0.17
0.23
0.18
0.20
0.19
0.17
0.19
0.19
0.19
0.06
RF
[W/m2]
0.25
0.54
0.16
0.40
0.38
0.81
0.24
0.41
0.26
0.40
0.37
0.34
0.30
0.27
0.37
0.16
NRF
[W/g]
1801
2445
1655
1905
1982
2185
1450
1833
1492
2003
1959
1955
1546
1477
1835
290
RFGP
[W/g]
0.27
0.57
0.18
0.44
0.41
0.86
0.28
0.45
0.31
0.44
0.41
0.36
0.34
0.33
0.40
0.16
RF fraction
[%]
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
0
Mass>5km
[%]
20.3
46.0
18.1
30.1
27.1
33.6
5.8
28.9
10.8
30.1
30.3
26.4
14.2
11.7
23.8
10.8
RF>5km
[%]
39.3
64.7
36.4
56.4
46.3
50.6
13.1
53.8
25.7
48.3
54.2
46.9
30.7
24.2
42.2
14.5
Burden
[mg/m2]
0.20
0.36
0.21
0.62
0.30
0.58
0.36
0.45
0.39
0.30
0.23
0.36
0.50
0.38
0.37
0.13
RF
[W/m2]
0.27
0.73
0.25
0.79
0.48
1.09
0.37
0.54
0.41
0.47
0.38
0.52
0.54
0.45
0.52
0.22
NRF
[W/g]
1386
2029
1230
1276
1626
1864
1036
1206
1052
1557
1658
1425
1081
1189
1401
308
RFGP
[W/g]
0.32
0.80
0.30
0.91
0.54
1.20
0.45
0.63
0.47
0.54
0.43
0.59
0.63
0.53
0.60
0.24
RF fraction
[%]
1.15
1.41
1.70
2.03
1.34
1.40
1.61
1.37
1.62
1.23
1.08
1.62
1.87
1.71
1.51
0.27
Mass>5km
[%]
16.2
36.2
9.1
16.5
21.6
27.3
4.3
17.4
5.8
19.5
27.9
16.5
8.5
5.9
16.6
9.5
RF>5km
[%]
40.8
60.1
25.0
43.6
44.0
47.4
13.0
49.6
19.4
41.0
58.7
38.5
24.9
16.0
37.3
15.2
Arctic
Model
NCAR-CAM3.5
CAM4-Oslo
CAM5.1
GISS-modelE
GOCART-v4
HadGEM2
IMPACT
INCA
ECHAM5-HAM
OsloCTM2
SPRINTARS
CAM-Oslo-P1
IMPACT-P1
OsloCTM2-P1
Mean
Std.dev
China
Model
NCAR-CAM3.5
CAM4-Oslo
CAM5.1
GISS-modelE
GOCART-v4
HadGEM2
IMPACT
INCA
ECHAM5-HAM
OsloCTM2
SPRINTARS
CAM-Oslo-P1
IMPACT-P1
OsloCTM2-P1
Mean
Std.dev
Burden
[mg/m2]
0.05
0.20
0.02
0.16
0.14
0.34
0.05
0.07
0.03
0.07
0.08
0.12
0.11
0.02
0.10
0.09
RF
[W/m2]
0.20
0.79
0.07
0.64
0.52
1.19
0.16
0.29
0.11
0.27
0.34
0.46
0.35
0.07
0.39
0.31
NRF
[W/g]
3907
3877
4187
3889
3746
3465
3214
4016
4078
3879
4413
3716
3266
3637
3806
335
RFGP
[W/g]
0.16
0.60
0.06
0.49
0.38
0.86
0.11
0.24
0.08
0.21
0.26
0.34
0.26
0.05
0.29
0.23
RF fraction
[%]
2.47
4.41
1.41
4.74
4.15
4.40
1.95
2.12
1.21
2.03
2.77
4.10
3.48
0.74
2.85
1.34
Mass>5km
[%]
63.2
60.9
81.6
65.7
53.2
40.3
35.9
78.8
75.5
71.0
83.1
48.9
39.4
63.0
61.5
15.9
RF>5km
[%]
76.1
71.0
88.5
77.2
64.1
52.1
48.1
89.2
89.2
82.4
89.9
61.1
52.9
80.4
73.0
15.0
Burden
[mg/m2]
0.89
0.84
0.65
1.60
1.12
2.08
0.83
1.06
1.31
1.27
1.33
0.67
0.96
0.84
1.10
0.39
RF
[W/m2]
1.17
1.35
0.74
1.89
1.50
3.29
0.94
1.27
1.39
1.95
1.66
0.89
0.87
1.00
1.42
0.66
NRF
[W/g]
1312
1612
1139
1185
1340
1581
1138
1198
1061
1544
1248
1336
904
1189
1270
202
RFGP
[W/g]
1.42
1.55
0.93
2.43
1.82
3.82
1.21
1.53
1.80
2.26
2.04
1.06
1.20
1.27
1.74
0.75
RF fraction
[%]
8.88
4.66
8.87
8.78
7.46
7.61
7.32
5.70
9.85
9.17
8.30
4.96
5.41
6.82
7.41
1.69
Mass>5km
[%]
9.7
22.7
8.1
10.4
11.3
15.9
3.3
13.7
2.8
15.4
10.2
10.5
5.6
6.6
10.4
5.3
RF>5km
[%]
25.0
48.3
24.4
30.5
28.4
34.2
9.2
40.8
9.1
33.0
28.3
27.4
19.8
18.1
26.9
10.9
268
Figures
269
270
271
272
Figure 1: Modeled BC global mean (a) burden, (b) RF and (c) RF per gram BC. Yellow boxes indicate mean, one standard
deviation and max/min values. Mean values and spreads for AeroCom P1 and P2 (hatched whisker boxes) are taken from
Schulz et al 2006 and Myhre et al 2012 respectively.
273
274
275
276
277
278
Figure 2: Comparison of modeled concentration and RF profiles. (a-c) BC concentration vertical profiles, global mean and
for two selected regions. Overlain is the annual mean forcing efficiency profile for the selected region (grey dashed line).
Solid lines show AeroCom P2 submissions, dashed lines show P1. (d-f) BC RF per height, divided by the modeled global
mean BC burden, globally and for three selected regions. (g-i) Vertical profile of integrated absolute BC RF. Lines indicate
the 50% mark and 500hPa altitude.
279
280
281
282
283
284
285
Figure 3: Black carbon mass and induced forcing at high altitudes. (a) Fraction of modeled BC mass above 5km. (b)
Fraction of modeled BC RF originating above 5km. (c) Mean fraction of mass (orange) and RF (yellow) globally and for
three selected regions. Boxes indicate one standard deviation on the model spread, whiskers show maximum and
minimum values.
286
287
288
289
290
291
292
293
Figure 4: Breakdown of spread in BC RF per gram. (a) Zonal mean of BC RF per gram using 3D efficiency profiles and all
sky conditions, for all models. (c-e) Model mean (solid line) and maximum/minimum (dashed lines) when instead using
(c) a global, annual mean efficiency profile and clear sky conditions, (d) 3D profile and clear sky conditions, and (e) a
global profile and all sky conditions. (b) shows the global mean values for the four cases. Boxes show one standard
deviation on the model spread, whiskers show maximum/minimum values.
294
295
296
297
Supplementary figure 1: Mass and forcing fractions in four altitude bands, globally and for three regions. Yellow boxes
show mass fractions, orange boxes show RF fractions. (a) Global mean, (b) Arctic, (c) Europe, (d) China.
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
1
2
316
317
318
Doi 10.1029/2010gl044555 (2010).
10
Koch, D. et al. Evaluation of black carbon estimations in global aerosol models. Atmos Chem Phys 9, 9001-9026
(2009).
3
4
5
6
7
8
9
Time for early action. Nature 460, 12-12, doi:Doi 10.1038/460012a (2009).
Grieshop, A. P., Reynolds, C. C. O., Kandlikar, M. & Dowlatabadi, H. A black-carbon mitigation wedge. Nat Geosci 2,
533-534, doi:Doi 10.1038/Ngeo595 (2009).
Shindell, D. et al. Simultaneously Mitigating Near-Term Climate Change and Improving Human Health and Food
Security. Science 335, 183-189, doi:DOI 10.1126/science.1210026 (2012).
Hansen, J., Sato, M., Ruedy, R., Lacis, A. & Oinas, V. Global warming in the twenty-first century: An alternative
scenario. P Natl Acad Sci USA 97, 9875-9880 (2000).
Ramanathan, V. & Carmichael, G. Global and regional climate changes due to black carbon. Nature Geoscience 1,
221-227, doi:10.1038/ngeo156 (2008).
Schulz, M. et al. Radiative forcing by aerosols as derived from the AeroCom present-day and pre-industrial
simulations. Atmospheric Chemistry and Physics 6, 5225-5246 (2006).
Feichter, J. & Stier, P. Assessment of black carbon radiative effects in climate models. doi:DOI: 10.1002/wcc.180
(2012).
Koch, D. et al. Soot microphysical effects on liquid clouds, a multi-model investigation. Atmos Chem Phys 11,
1051-1064, doi:DOI 10.5194/acp-11-1051-2011 (2011).
Zarzycki, C. M. & Bond, T. C. How much can the vertical distribution of black carbon affect its global direct
radiative forcing? Geophys Res Lett 37, L20807, doi:Artn L20807
319
320
321
322
11
323
324
325
326
Doi 10.1029/2011gl049697 (2011).
13
Myhre, G. Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations. (2012).
14
Koffi, B. et al. Application of the CALIOP layer product to evaluate the vertical distribution of aerosols estimated
by global models: AeroCom phase I results. J Geophys Res-Atmos 117, doi:Artn D10201
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
Doi 10.1029/2011jd016858 (2012).
15
Schwarz, J. P. et al. Global scale black carbon profiles observed in the remote atmosphere and compared to
models. Geophys Res Lett 37 (2010).
16
Textor, C. et al. Analysis and quantification of the diversities of aerosol life cycles within AeroCom. Atmos Chem
Phys 6, 1777-1813 (2006).
17
Textor, C. et al. The effect of harmonized emissions on aerosol properties in global models - an AeroCom
experiment. Atmos Chem Phys 7, 4489-4501 (2007).
18
Shindell, D. T. et al. A multi-model assessment of pollution transport to the Arctic. Atmos Chem Phys 8, 5353-5372
(2008).
19
Kinne, S. et al. Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets
for AeroCom. Atmos Chem Phys 6, 4321-4344 (2006).
20
Lamarque, J. F. et al. CAM-chem: description and evaluation of interactive atmospheric chemistry in the
Community Earth System Model. Geosci Model Dev 5, 369-411, doi:DOI 10.5194/gmd-5-369-2012 (2012).
21
Liu, X. et al. Toward a minimal representation of aerosols in climate models: description and evaluation in the
Community Atmosphere Model CAM5. Geosci Model Dev 5, 709-739, doi:10.5194/gmd-5-709-2012 (2012).
22
Koch, D. et al. Coupled Aerosol-Chemistry-Climate Twentieth-Century Transient Model Investigation: Trends in
Short-Lived Species and Climate Responses. J Climate 24, 2693-2714, doi:Doi 10.1175/2011jcli3582.1 (2011).
23
Chin, M. et al. Light absorption by pollution, dust, and biomass burning aerosols: a global model study and
evaluation with AERONET measurements. Ann Geophys-Germany 27, 3439-3464 (2009).
24
Bellouin, N. et al. Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2ES and the role of ammonium nitrate. J Geophys Res-Atmos 116, doi:Artn D20206
348
349
350
351
352
353
354
355
356
Doi 10.1029/2011jd016074 (2011).
25
Lin, G., Penner, J., Sillmann, S., Taraborrelli, D. & Lelieveld, J. Global modeling of SOA formation from dicarbonyls,
epoxides, organic nitrates and peroxides. Atmos Chem Phys 12, 4743-4774, doi:10.5194/acp-12-4743-2012 (2012).
26
Zhang, K. et al. The global aerosol-climate model ECHAM-HAM, version 2: sensitivity to improvements in process
representations. Atmos. Chem. Phys. Discuss. 12, 7545-7615, doi:10.5194/acpd-12-7545-2012 (2012).
27
Skeie, R. B. et al. Black carbon in the atmosphere and snow, from pre-industrial times until present. Atmos Chem
Phys 11, 6809-6836, doi:10.5194/acp-11-6809-2011 (2011).
28
Takemura, T., Nozawa, T., Emori, S., Nakajima, T. Y. & Nakajima, T. Simulation of climate response to aerosol
direct and indirect effects with aerosol transport-radiation model. J Geophys Res-Atmos 110, doi:Artn D02202
357
358
359
Doi 10.1029/2004jd005029 (2005).
29
Kirkevag, A. & Iversen, T. Global direct radiative forcing by process-parameterized aerosol optical properties. J
Geophys Res-Atmos 107, doi:Artn 4433
360
361
362
Doi 10.1029/2001jd000886 (2002).
30
Liu, X. H., Penner, J. E. & Herzog, M. Global modeling of aerosol dynamics: Model description, evaluation, and
interactions between sulfate and nonsulfate aerosols. J Geophys Res-Atmos 110, doi:Artn D18206
363
364
365
Doi 10.1029/2004jd005674 (2005).
31
Myhre, G. et al. Modeling the solar radiative impact of aerosols from biomass burning during the Southern African
Regional Science Initiative (SAFARI-2000) experiment. J Geophys Res-Atmos 108, doi:Artn 8501
366
367
368
Doi 10.1029/2002jd002313 (2003).
32
Bond, T. C., Habib, G. & Bergstrom, R. W. Limitations in the enhancement of visible light absorption due to mixing
state. J Geophys Res-Atmos 111, doi:Artn D20211
369
Doi 10.1029/2006jd007315 (2006).
370
371
12
Haywood, J. M. & Shine, K. P. Multi-spectral calculations of the direct radiative forcing of tropospheric sulphate
and soot aerosols using a column model. Q J Roy Meteor Soc 123, 1907-1930 (1997).
Samset, B. H. & Myhre, G. Vertical dependence of black carbon, sulphate and biomass burning aerosol radiative
forcing. Geophys Res Lett 38, doi:Artn L24802