1
Understanding black carbon radiative forcing uncertainty due to vertical distributions
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B.H.Samset, Myhre, G., R.B. Skeie, T.K. Berntsen (CICERO/UiO – OsloCTM2)
A. Kirkevåg, (UiO - CAM4-Oslo)
S. J. Ghan, R. C. Easter (PNNL – CAM5.1)
S. Bauer ,K. Tsigaridis (GISS-ModelE)
T. Diehl, M. Chin (GOCART)
G. Lin, J. Penner (UMICH – IMPACT)
Y. Balkanski, M. Schulz (IPSL/met.no –INCA)
N. Bellouin (MetOffice -HadGEM2)
K. Zhang, P. Stier (MPIHAM)
J.-F. Lamarque (NCAR – NCAR-CAM3)
T. Takemura (SPRINTARS)
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Unlike most atmospheric aerosols, black carbon (BC) absorbs solar radiation. This
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warming effect of BC has led to suggestions for mitigation of BC emissions to limit
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global warming both in the scientific community1 and among policy makers. The
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uncertainties in the radiative forcing (RF) of the direct aerosol effect of BC are however
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large2-4. Among the causes of these uncertainties are differences in the vertical profile of
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BC5. Here we show, based on a large set of global aerosol models, that the diversity in
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the vertical profile of BC contributes between 25% and 50% of the variability in
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modeled BC RF. A significant fraction of this variability comes from high altitudes, as
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we find that, globally, models simulate more than 40% of their total BC RF above 5km.
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While BC concentrations quickly decrease with altitude, BC forcing sensitivity increases
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due to the presence of Rayleigh scattering, background aerosols and clouds. Both BC
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emission regions and areas with transported BC are found to be important, but with
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different characteristics. These insights into the importance of the vertical profile of BC
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will benefit the understanding of the differences among the global aerosol models, and
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may guide the initiation of observational studies with the aim of improving the models in
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a focused way.
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The efficiency with which BC can induce radiative forcing is dependent on external factors
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such as surface albedo, water vapor, background aerosol distributions and, most notably,
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clouds5,6. The total BC forcing is therefore highly sensitive to the full 3D distribution of
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aerosol concentration7. Anthropogenic BC forcing estimated by the current major global
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aerosol models varies between 0.05 W/m2 and 0.38 W/m2 8. This range is similar to previous
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AeroCom estimates3, and to other assesments4. Understanding the causes of this variability in
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BC forcing would be a good step towards reducing the total uncertainty on the total
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anthropogenic aerosol forcing.
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Several studies have previously shown that the vertical transport is one area where the models
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still differ significantly9-12. Using input from 11 global aerosol models, we study the fraction
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of model variability in BC forcing attributable to vertical differences alone. By combining the
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models’ own concentration profiles with a common efficiency profile (EP) of RF per gram of
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BC (see Methods), we are able to recalculate the induced RF of the BC direct aerosol effect at
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various altitudes and regions. We can also separate out the contributions from the cloud field
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and variations due to regional differences.
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Figure 1a shows the global mean total BC burden from 11 global aerosol models participating
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in AeroCom Phase 2, in addition to three models from AeroCom Phase 1. Individual numbers
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are shown in Supplementary Table 1. We find a mean of 0.19 mg/m2, but with a relative
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standard deviation (RSD) of 32% and a model spread from 0.09 to 0.37 mg/m2. Under an
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assumption of equal forcing per gram, this alone will cause a large diversity in the total BC
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forcing predicted by the models. AeroCom Phases 1 and 2 found a similar ranges, as shown
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by the whisker boxes in Figure 1a. Note that the AeroCom P2 results are for BC from fossil
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fuel burning (BCFF) only. Figure 1b shows the RF for each model, recalculated from the
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concentration profiles using a common 3D, time dependent RF per gram profile. That the
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average is stronger than for previous estimates is due to the strength of the profile used (see
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Methods for discussion). The spread is highly correlated (95%) with the spread in burden,
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again due to the use of a common BC EP. However, dividing the recalculated RF by the
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global means gives new global estimates of BC RF per gram, shown in Figure 1c. If all model
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vertical profiles were identical, this spread would vanish. It therefore carries information on
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the impact of the vertical profiles of BC concentration on the model spread, separate from the
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variability due to burden differences.
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For AeroCom P1 and P2, the RSDs of the burden and RF per gram (quantifying the amount of
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aerosol and the effect they have, respectively) are of approximately equal value, ranging from
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32% to 46%. The two estimates are however internally correlated (Schulz 2006), such that the
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spread in RF is less than if we simply combine the uncertainties. Our RSD in RF per gram is
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16%, or 40% of that from AeroCom P1 and P2. Vetoing models in the P1 and P2 results that
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have known issues (see Methods) brings this fraction up to 50%. Temporarily ignoring the
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correlations, the vertical distributions can therefore be said to cause at least 25% of the spread
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in BC RF. Correlations are however important here, and an observed antocorrelation between
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BC load and forcing per gram in AeroCom P1 and P2 (see Methods) combined with the fact
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that forcing per gram in the present analysis is positively correlated with the burden, indicates
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that it may be as high as 50%. We therefore conclude that the variability of modeled BC RF
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lies between 25% and 50%. Additional studies would be needed to further quantify this.
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As also shown in previous model comparisons10-12, there are large differences in the modeled
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BC vertical concentration profiles. Figure 2 shows the annual mean vertical profiles of BC
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concentration from all models, globally and for two selected regions (Arctic and China, see
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Methods for definitions). The Arctic is a region with no indigenous BC emissions, but where
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the effects of transported aerosols are often discussed13. While all models show a significant
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BC 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 schemes between the climate models.
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There is no discernible trend between the AeroCom P1 and P2 models. China has heavy
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surface emissions of BC emissions, 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 (2d) and for the two regions (2e-f). The interplay of the concentration
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profiles, declining with altitude, and the RF per gram, increasing with altitude, is evident for
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all regions. 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 illustrate the model spread in forcing at a given altitude due to vertical profiles, by
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integrating the absolute forcing per model layer from the surface and upwards. Globally, the
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majority of models reach 50% of their forcing around 500hPa (approximately 5km), however
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there is a large spread around this value. For the regions, this picture is quite different. In the
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Arctic all models have a significant forcing component above 5km, while for the industrial
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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. Firstly, the distinction between emission regions
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of BC and regions that see mostly transported BC is evident. The former have small mass and
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RF fractions at high altitudes, typically 10-20%, while transport regions show RF fractions up
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to 80%. Figure 3c compares the model mean fractions and spreads globally and for three
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regions (Arctic, China and Europe). Note that the fraction of RF above 5km is systematically
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higher than the mass fraction, due to the strongly increasing shape of the RF efficiency
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profiles. Globally, more than 40% of the model simulated RF from BC comes from model
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layers above 5km, while only 24% of the mass is found in this region. This illustrates the
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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 inherent in the efficiency profiles. In Figure 4a we
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show the zonal mean RF per gram for all models, and investigate the relative importance of
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the cloud field and of regional differences in albedo for the resulting spread. In Figure 4c we
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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 solid line shows the
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model mean forcing per gram, and the dashed lines show the maximum and minimum values
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from individual models. The magnitude can be seen to be only weakly dependent on latitude,
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due to the global profile masking the effects of varying surface albedo, and to have a low but
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non-vanishing spread. Not all vertical sensitivity of BC forcing is due to the aerosols being
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above or below clouds7. The remainder, caused predominantly by Rayleigh scattering, water
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vapor and the competing effects of other aerosols, is the cause of the variability here. RSD on
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global mean values is 10% (see Figure 4b). Figure 4d shows the same analysis using the full
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3D efficiency profile. The RSD increases to 13%, and we now see the latitudinal effects of the
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high polar planetary albedo. Figure 4e shows the analysis using the global mean efficiency
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profile again, but under all-sky conditions. Again the RSD increases to 13%, due to the cloud
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field. The model variability in forcing efficiency that is due to vertical profile differences can
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therefore be decomposed into three factors: Equal and significant contributions from the cloud
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field and from and from regional differences, and a major contribution from the underlying
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sensitivity of BC forcing to altitude even in the absence of clouds and albedo differences.
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Harmonizing model treatment of clouds and albedo is therefore not sufficient to remove
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uncertainties in BC forcing due to vertical profiles. Combining the effects of clouds and
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regional variations with the intrinsic vertical variability of the forcing efficiency yields the
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total variability picture seen in Figure 4a.
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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, which provided a useful sensitivity test. While the BC
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load was 60% higher for 2006 emissions, the forcing per burden and vertical profiles were
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invariant. This gives confidence that the variability due to vertical profiles can indeed be
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regarded as independent of that due to the 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. To propose efficient mitigation
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measures for BC, its radiative forcing needs to be well understood both in emission and
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transport regions. Further model improvements and comparisons with data are needed, and
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should focus on both of these types of region. It is however clear that while the BC vertical
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profiles are important, they are not sufficient to explain the remaining differences between
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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 using the
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same base years (2000 and 1850), with the same meteorology and microphysics. Two sets of
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comparisons have been performed, here labeled AeroCom Phase 13,11,14 and AeroCom Phase
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2 8. 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 fuel burning only (BCFF) are also used. Three of the same models also provided
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BC concentration profiles to AeroCom Phase 1, and these fields are also included here. The
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IMPACT model has not undergone major changes between the AeroCom phases, while the
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OsloCTM2 model has been heavily revised with the addition of both an ageing scheme for
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BC and improved treatment of BC washout. 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-CAM3.5, CAM4-Oslo, CAM5.1, GISS-
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modelE, GOCART-v4,
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SPRINTARS. From AeroCom P1, participating models are CAM4-Oslo, IMPACT and
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OsloCTM2.
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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. Here we employ the full time dependent 3D efficiency
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profiles, either for all sky or clear sky conditions as described above. We find the forcing per
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gram BC for each model grid point and time step, and then calculate the contribution to the
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shortwave, top-of-atmosphere BC radiative forcing by multiplying in the modeled BC
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concentration. This gives us intercomparable 3D RF fields, something that is not immediately
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available from each model. We label this the recalculated RF, to emphasize that it is heavily
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correlated with the burden and should not be taken directly as an estimate of BC forcing of
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each model. Since the model that was used to produce the profiles of RF per gram has among
HadGEM2,
IMPACT,
INCA, MPIHAM,
OsloCTM2 and
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the strongest global mean forcings per gram (Myhre et al 2012), most recalculated RFs can be
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expected to be stronger than their host model would predict. However, by dividing the
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recalculated RF by the total aerosol burden of the host model, we extract the variability in
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forcing per gram from each model that is due only to vertical profiles, and can subsequently
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use the vertical RF profiles to study it.
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As this 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 load. Dividing by the load, we can study
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the contribution to the variability due to regional differences (high or low surface albedo,
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indigenous emissions, transport region or both), and due to the presence of clouds. To study
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the latter effects, we ran parallel analyses using only a global mean profile of RF per gram
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(labeled GP above), a distinct profile of RF per gram for clear sky conditions (labeled CS), or
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both (labeled CSGP).
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Region definitions
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For the present analysis, “Europe”, defined as the box of longitudes -10 to 30, latitudes 38 to
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60, “China” which covers longitudes 105 to 135, latitudes 15 to 45, and “Arctic” which
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covers latitudes 70 to 90. Europe and China represent regions with high industrial BC
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emissions and significant fractions of the global BCFF emissions, making their total BC
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forcing sensitive to pure vertical transport and wet scavenging. The Arctic is a region with
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low indigenous emissions, but important BC contributions at high altitudes transported from
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other regions. Its total BC forcing is therefore sensitive to model differences in vertical and
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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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literature15. 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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257
258
259
260
261
Tables
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
MPIHAM
OsloCTM2
SPRINTARS
CAM4-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
MPIHAM
OsloCTM2
SPRINTARS
CAM4-Oslo-P1
IMPACT-P1
OsloCTM2-P1
Mean
Std.dev
262
263
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
MPIHAM
OsloCTM2
SPRINTARS
CAM4-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
MPIHAM
OsloCTM2
SPRINTARS
CAM4-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
264
Figures
265
266
267
268
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.
269
270
271
272
273
274
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.
275
276
277
278
279
280
281
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.
282
283
284
285
286
287
288
289
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.
290
291
292
293
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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295
296
297
298
299
300
301
302
303
304
1
305
306
307
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