Understanding black carbon radiative forcing uncertainty due to

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Understanding black carbon radiative forcing uncertainty due to vertical distributions
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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.
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warming effect of BC has led to suggestions, both in the scientific community1-4 and
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among policy makers, for reduction of BC emissions to mitigate global warming. The
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uncertainties in the radiative forcing (RF) of the direct aerosol effect of BC are however
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large5-7, making clear conclusions from mitigation studies difficult8. Among the causes of
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these model uncertainties is differences in the vertical concentration profiles of BC9,
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where there are also significant discrepancies between models and observations10. Here
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we show, based on a large set of global aerosol models, that the diversity in the vertical
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profile of BC contributes between 25% and 50% of the variability in modeled BC RF. A
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significant fraction of this variability comes from high altitudes, as we find that, globally,
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models simulate more than 40% of their total BC RF above 5km. BC emission regions
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and areas with transported BC are found to have differing characteristics. These
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insights into the importance of the vertical profile of BC will benefit the understanding
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of the differences among the global aerosol models, and may guide the initiation of
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observational studies with the aim of improving the models in a focused way.
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Aerosol radiative forcing is a measure of the extra amount of energy absorbed in the
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atmosphere by a given change in aerosol concentrations. The efficiency with which BC can
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induce RF is however dependent on external factors such as surface albedo, water vapor,
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background aerosol distributions and, most notably, the presence of clouds9,11. The total BC
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forcing is therefore highly sensitive to the full 3D distribution of BC concentration12.
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Anthropogenic direct BC forcing estimated by the current major global aerosol models varies
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between 0.05 W/m2 and 0.38 W/m2
Unlike most atmospheric aerosols, black carbon (BC) absorbs solar radiation. This
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. This range is similar to previous estimates6,7.
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Understanding the causes of this variability in BC forcing would be a good step towards
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reducing the total uncertainty in the total anthropogenic aerosol forcing.
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Several studies have previously indicated that the vertical transport is one area where the
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models still differ significantly14-17. This study uses input from 11 global aerosol models
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participating in the AeroCom model intercomparison proect to compare and study the impacts
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of modeled BC vertical forcing profiles. By combining the models’ own concentration
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profiles with a common efficiency profile (EP) of RF per gram of BC (see Methods), we are
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able to recalculate the induced RF of the BC direct aerosol effect at various altitudes and
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spatial regions. We isolate contributions from the cloud field by comparing all-sky and clear
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sky conditions, and study variations due to regional differences.
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We first quantify the fraction of the total modeled RF variability attributable to vertical
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profiles alone. Figure 1a shows the global mean total BC burden from 11 models from
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AeroCom Phase 2, in addition to three models from AeroCom Phase 1. See Methods for
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details. We find a multi-model mean of 0.19 mg/m2, but with a relative standard deviation
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(RSD) of 32% and a model spread from 0.09 to 0.37 mg/m2. Supplementary Table 1 lists the
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numbers for individual models. Under an assumption of equal forcing per gram, this alone
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will cause a large diversity in the total BC forcing predicted by the models. AeroCom Phases
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1 and 2 found a similar burden range, as shown by the whisker boxes in Figure 1a. Note that
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the AeroCom P2 results are for BC from fossil and biofuel burning (BCFF) only. Figure 1b
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shows the RF for each model, recalculated from the concentration profiles using a common
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EP as described in Methods. That the average is stronger than for previous estimates is due to
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the strength of the profile used (see Methods for discussion). The RF values are also highly
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correlated (95%) with burden values, again due to the use of a common BC EP. However,
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dividing the recalculated RF by the global mean burdens gives new global estimates of BC RF
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per gram, shown in Figure 1c. If all model vertical profiles were identical, this spread would
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vanish. It therefore carries information on the impact of the vertical profiles of BC
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concentration on the model spread, separate from the variability due to burden differences. To
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quantify this impact, analyse the multi-model RSDs and correlations, as shown in Methods.
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We find that of the total model variability of BC RF, no more than 50% but likely more than
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25% is attributable to differences in vertical profiles.
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Next we compare global and regional BC vertical profiles and EPs. As also shown in previous
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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
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and for two selected regions (Arctic and China; see Methods for definitions). Globally, the
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concentrations quickly decrease with altitude while the forcing efficiency increases. The
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Arctic is a region with negligible indigenous BC emissions, but where the effects of
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transported aerosols are often discussed18. While all models show a significant BC
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contribution at high altitudes the model spread in the Arctic is quite large, ranging from
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virtually constant below 200hPa to double-peaked structures with maxima at 900 hPa and
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200hPa. This highlights the differences in transport and wet removal schemes between the
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climate models. There is no discernible trend between the AeroCom P1 and P2 models. China
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has heavy surface emissions of BC, and also sees contributions from transport at high
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altitudes, as is evident from Figure 2c.
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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
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generated. Globally, RF can be seen to be mostly induced in a range around 800hPa for most
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models, with a secondary peak between 400hPa and 200hPa for some models. The China
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region follows the global pattern, but with the low altitude peak closer to the ground,
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reflecting the emissions there. The Arctic region, however, has a strong peak at high altitudes
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for most models, evidencing the relative importance of high altitude BC RF there.
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Figure 2g-i illustrates the model spread in forcing at a given altitude due to vertical profiles,
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by integrating the absolute forcing per model layer from the surface and upwards. Globally,
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the majority of models reach 50% of their forcing around 500hPa (approximately 5km);
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however there is a large spread around this value. For the regions, this picture is quite
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different. In the Arctic all models have a significant forcing component above 5km, while for
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the industrial regions of China the opposite is true.
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We find a significant fraction of modeled BC RF from high altitudes, with a notable regional
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pattern. Figure 3 maps the model mean fraction of aerosol mass (3a) and induced forcing (3b)
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from model layers of altitude above 500hPa, or approximately 5km. Firstly, the distinction
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between BC emission regions and regions with mostly transported BC is evident. The former
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have small mass and RF fractions at high altitudes, typically 10-20%, while transport regions
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show RF fractions up to 80%. Figure 3c compares the global and regional (Arctic, China and
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Europe) mean values of the mass and RF fractions. Note that the fraction of RF above 5km is
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systematically higher than the mass fraction, due to the strongly increasing shape of the RF
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efficiency profiles. Globally, more than 40% of the model simulated RF from BC comes from
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model layers above 5km, while only 24% of the mass is found in this region. This illustrates
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the importance of validating model vertical profiles and transport codes not only in industrial
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regions, but also in transport regions with low indigenous BC emissions.
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Supplemental figure 1 shows the RF and mass fractions in four altitude bands, globally and
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for the three regions. Such information is useful for comparing models with observational
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data, which can aid future constructions of best estimates for global BC forcing.
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We have shown that, even on global mean, a significant fraction of the variability in the BC
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forcing per gram is due to differences in vertical profiles. It is however instructive to further
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divide this variability into the components that make up the efficiency profiles. In Figure 4a
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we show the zonal mean RF per gram for all models, and investigate the relative importance
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of the cloud field and of regional differences in albedo for the resulting spread. In Figure 4c
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we have run the analysis using a global, annual mean efficiency profile instead of the full time
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dependent 3D profile, and in addition used clear sky conditions. The magnitude has a weak
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but non-vanishing model spread, and is only weakly dependent on latitude since the effects of
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varying surface albedo, which normally strongly increases the forcing efficiency at the poles,
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are averaged out in the global profile. Not all vertical sensitivity of BC forcing is due to the
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aerosols being above or below clouds12. The remainder, caused predominantly by Rayleigh
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scattering, water vapor and the competing effects of other aerosols, is the cause of the
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variability here. RSD on global mean values is 10% (see Figure 4b). Figure 4d shows the
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same analysis using the full 3D efficiency profile. The RSD increases to 13%, and we now
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see the latitudinal effects of the high polar planetary albedo. Figure 4e shows the analysis
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using the global mean efficiency profile again, but under all-sky conditions. Again the RSD
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increases to 13%, due to the cloud field. The model variability in forcing efficiency that is due
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to vertical profile differences can therefore be decomposed into three factors: Equal and
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significant contributions from the cloud field and from regional differences, and a major
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contribution from the underlying sensitivity of BC forcing to altitude even in the absence of
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clouds and albedo differences. Harmonizing model treatment of clouds and albedo is therefore
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not sufficient to remove uncertainties in BC forcing due to vertical profiles. Combining the
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effects of clouds and regional variations with the intrinsic vertical variability of the forcing
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efficiency yields the total variability picture seen in Figure 4a.
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Different models will likely have different BC efficiency profiles. To quantify the sensitivity
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of the present analysis to the shape of the profile used, we reran the analysis with an EP that
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was changed by 20% at the top of the atmosphere unchanged at the surface, with a linear
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interpolation in between, resulting in a weaker EP. This changed the global fraction of RF
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above 5km by less than 5%, indicating that the results are relatively stable within reasonalbe
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variations of the EP.
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Results in the present study are given for total BC aerosol emissions only. However, due to
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different source regions, the vertical profiles of BCFF could be different from BC. Six models
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also provided concentration profiles for BCFF. Using the same efficiency profiles we
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performed the analysis also for BCFF, and found results consistent with what we have
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presented for BC (not shown). While the absolute forcing numbers differ due to lower total
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burdens for BCFF, the variability in vertical profiles is very similar to that for total BC. Our
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conclusions here are therefore also applicable to model comparison results on BCFF only.
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Results presented here have all used emissions from year 2000. One model (CAM4-Oslo) also
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provided simulations for year 2006. While the BC burden was 60% higher for 2006 emissions,
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the forcing per burden and vertical profiles were invariant. This gives confidence that the
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variability due to vertical profiles can indeed be regarded as independent of that due to the
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present day emissions dataset.
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Results are also similar for the P1 and P2 submissions of the IMPACT model, where no major
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aerosol microphysical changes have been performed. For OsloCTM2, however, where both an
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ageing scheme and modifications to the washout of BC were added, forcing per burden
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changes between P1 and P2 by as much as half of the full range observed. Hence, the
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transport scheme and model treatment of BC are crucial factors in determining the modeled
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value of forcing per burden, and both of these factors are closely linked to the vertical
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distribution.
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In conclusion, we have shown that the previously documented large spread in BC aerosol
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concentration profiles is enhanced for vertical BC RF profiles. Using 11 global aerosol
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models, we show that most models globally simulate 40% of their BC forcing above 5km, and
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that regionally the fraction can be above 70%. The spread between models is however quite
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large, and we have indications that between 25% and 50% of the differences in modeled BC
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RF can be attributed to differences in vertical profiles. Harmonizing the treatment of clouds
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and albedo between models is found to not be sufficient to remove this variability. To propose
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efficient mitigation measures for BC, its radiative forcing needs to be well understood both in
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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
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between global aerosol models.
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Methods
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Model simulations
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The AeroCom initiative asks models to simulate the direct aerosol radiative effect under as
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similar conditions as possible. All models run single year simulations using the same base
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years for aerosol emissions (2000 and 1850), with unchanged meteorology to exclude indirect
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effects. Both circulation models and transport models have participated. Two sets of model
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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.
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For the present study, monthly 3D BC concentration fields from 11 global aerosol models
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participating in AeroCom Phase 2 are used. For six of the models, concentration fields of BC
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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
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included here. The IMPACT model has not undergone major changes between the AeroCom
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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
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fom CAM4-Oslo between P1 and P2 are small. The concentration fields and the 3D model
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resolutions are the only inputs taken from each separate model. The 1850 concentrations are
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subtracted from the 2000 ones, leaving the contribution due to anthropogenic BC or BCFF
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emissions.
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From AeroCom P2, participating models are NCAR-CAM3.520, CAM4-Oslo, CAM5.121,
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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.
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RF recalculation
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Samset and Myhre 2011 presents a set of vertical profiles of the direct radiative forcing per
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gram of aerosol for BCFF, i.e. the amount of top-of-atmosphere shortwave radiative forcing
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seen by a particular model (OsloCTM2) per gram of aerosols at a given altitude. Here we term
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these efficiency profiles (EP). In the specialized literature, the term normalized radiative
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forcing is more commonly used. We here employ the full time dependent 3D efficiency
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profiles, either for all sky or clear sky conditions as described above. For each model grid
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point and time step, we multiply the modeled BC concentration by the EP to get the
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contribution to the the total shortwave, top-of-atmosphere BC radiative forcing. This gives us
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intercomparable 3D RF fields, something that is not immediately available from each model.
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We label this the recalculated RF, to emphasize that it is heavily correlated with the burden
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and should not be taken directly as an estimate of BC forcing of each model. Since the model
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that was used to produce the profiles of RF per gram has among the strongest global mean
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forcings per gram13, most recalculated RFs can be expected to be stronger than their host
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model would predict. However, by dividing the recalculated RF by the total aerosol burden of
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the host model, we extract the variability in forcing per gram from each model that is due only
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to vertical profiles, and can subsequently use the vertical RF profiles to study it.
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As our method removes contributions to the spread in BC RF due to differences in model
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specific treatment of clouds, water uptake and microphysics, we are left solely with variations
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due to the concentration profiles and the total aerosol burden. Dividing by the burden, we can
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study the contribution to the variability due to regional differences (high or low surface
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albedo, indigenous emissions, transport region or both), and due to the presence of clouds. To
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study the latter effects, we ran parallel analyses using only a global mean profile of RF per
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gram (labeled GP above), a distinct profile of RF per gram for clear sky conditions (labeled
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CS), or both (labeled CSGP).
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Region definitions
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“Europe” is defined as the box covering longitudes -10 to 30, latitudes 38 to 60. “China”
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covers longitudes 105 to 135, latitudes 15 to 45. “Arctic” covers latitudes 70 to 90, all
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longitudes. Europe and China represent regions with high industrial BC emissions and
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significant fractions of the global BCFF emissions, making their total BC forcing sensitive to
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pure vertical transport and wet scavenging. The Arctic is a region with low indigenous
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emissions, but important BC contributions at high altitudes transported from other regions. Its
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total BC forcing is therefore sensitive to model differences in vertical and long range transport.
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Estimating the variability caused by vertical distributions
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We show that there is a residual variability in the forcing per burden that is due to the
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variations in vertical distributions, with a RSD of 16%. This is approximately 40% of the
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variability on forcing per burden in AeroCom P1 and P2. Three models in P2 have mass
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extinction coefficients that deviate significantly from recommendations given in the
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literature32. Removing contributions from these three models reduces the RSD on the forcing
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per burden to 32%, subsequently increasing the estimate of the variability due to vertical
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profiles to 50%.
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The burden and forcing per burden in P1 and P2 have approximately equal RSDs, so if they
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were uncorrelated the vertical distribution could be said to contribute 25% of the variability
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on RF. This is however not the case. We first note that in both AeroCom P1 and P2, the
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burden and forcing per burden are weakly anticorrelated. This is apparent from the fact that
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the RSD on the RF (shown in Figure 1) is lower what it would be if the errors on burden and
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forcing per burden were uncorrelated. Quantifying this effect using numbers from AeroCom
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P2, we find a weak Pearson correlation coefficient of =-0.46 (or -0.40 if we again remove
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the three outlier models).
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In the present analysis, we can estimate the impact of the vertical distributions by the fraction
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of mass simulated above 5km (M5k), shown in Supplementary table 1. We observe that M5k
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is strongly correlated with the recalculated RF (=0.70) and forcing per burden (=0.97), as
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expected due to the efficiency profile used for the present analysis. However M5k is also
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weakly positively correlated with the total burden (=0.48). The vertical distribution
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variability therefore does not contribute to the observed anticorrelation between burden and
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forcing per burden in AeroComP1 and P2.
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We can assume that forcing per burden is determined by a combination of the vertical profile
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(positive correlation with burden) and a set of uncorrelated global variables such as BC
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optical properties (which must then have a combined negative correlation with burden). The
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variability on BC RF would be higher if the models had not compensated for high burden
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with a low forcing efficiency and vice versa. Since the vertical variability rather leads to a
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high efficiency for a high burden, its impact on the RF variability is likely stronger than the
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estimate of 25% above. The present analysis does not allow for rigorous quantification, but it
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unlikely to be larger than the 50% of the variability on total BC RF caused by forcing per
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burden. Hence we have a range of 25% to 50% of the variability of modeled BC RF caused by
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differences in vertical distributions.
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Author contributions
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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.
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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)
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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.
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