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 60 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 72 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 157 needed, and should focus on both of these types of region. It is however clear that while the 158 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 1 167 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 171 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 174 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, 181 OsloCTM227 and SPRINTARS28. From AeroCom P1, participating models are UiO_GCM29 182 (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. 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# Understanding black carbon radiative forcing uncertainty due to