GEOPHYSICAL RESEARCH LETTERS, VOL. 38, L24802, doi:10.1029/2011GL049697, 2011
Vertical dependence of black carbon, sulphate and biomass
burning aerosol radiative forcing
Bjørn H. Samset1 and Gunnar Myhre1
Received 15 September 2011; revised 1 November 2011; accepted 6 November 2011; published 16 December 2011.
[1] A global radiative transfer model is used to calculate the
vertical profile of shortwave radiative forcing from a
prescribed amount of aerosols. We study black carbon (BC),
sulphate (SO4) and a black and organic carbon mixture typical
of biomass burning (BIO), by prescribing aerosol burdens in
layers between 1000 hPa and 20 hPa and calculating the
resulting direct radiative forcing divided by the burden
(NDRF). We find a strong sensitivity in the NDRF for BC
with altitude, with a tenfold increase between BC close to the
surface and the lower part of the stratosphere. Clouds are a
major contributor to this dependence with altitude, but
other factors also contribute. We break down and explain
the different physical contributors to this strong sensitivity.
The results show a modest regional dependence of the
altitudinal dependence of BC NDRF between industrial
regions, while for regions with properties deviating from
the global mean NDRF variability is significant. Variations
due to seasons and interannual changes in cloud conditions
are found to be small. We explore the effect that large
altitudinal variation in NDRF may have on model estimates
of BC radiative forcing when vertical aerosol distributions are
insufficiently constrained, and discuss possible applications
of the present results for reducing inter-model differences.
Citation: Samset, B. H., and G. Myhre (2011), Vertical dependence of black carbon, sulphate and biomass burning aerosol radiative forcing, Geophys. Res. Lett., 38, L24802, doi:10.1029/
2011GL049697.
of the CALIOP Layer Product to evaluate the vertical distribution of aerosols estimated by global models. Part 1:
AeroCom phase I results, submitted to Journal of Geophysical Research, 2011]. Recently the climate impact of
black carbon as function of altitude has received increased
attention, e.g., in the work by Zarzycki and Bond [2010]
which uses a column model, and that by Ban-Weiss et al.
[2011] which studies dynamical climate response in a full
GCM. State-of-the-art model descriptions of aerosol vertical
distributions are summarized and discussed by Schwarz et al.
[2010]. Radiative forcing uncertainties associated with sulphate profiles is discussed by Goto et al. [2011]. The present
study aims to better understand the factors that contribute to
changes in normalized direct radiative forcing (NDRF) with
altitude, for a set of absorbing and scattering aerosol species.
[4] NDRF is a measure of the amount of energy scattered
or absorbed by the atmosphere per unit weight. While it is not
always a good predictor of surface temperature change, in
particular for absorbing aerosols such as BC [Hansen et al.,
2005; Lohmann et al., 2010], it can be an excellent diagnostic for understanding global RF results from threedimensional aerosol models. The global and regional NDRF
curves and vertical sensitivity figures given here can be taken
together with the vertical aerosol distributions from models,
and used to determine what fraction of model differences
they cause.
2. Method and Inputs
1. Introduction
[2] Atmospheric aerosols affect the energy balance of the
Earth through scattering and absorption of solar and thermal
radiation. Important examples are black carbon (BC), which
both absorbs and scatters solar radiation, and sulphate compounds (SO4) which only scatter. Their total effect on the
top-of-atmosphere radiation balance is however influenced
by external factors such as surface albedo, absolute humidity,
the presence of other aerosols, and of clouds above or below
the aerosol layer [Haywood and Shine, 1997]. It is thought
that such factors are major contributors to the variation in
total anthropogenic radiative forcing from aerosols found by
different climate models [Schulz et al., 2006].
[3] The radiative forcing (RF) induced by a given atmospheric load of aerosols also varies with altitude. However
aerosol vertical profiles are not well measured, and their
descriptions vary significantly among global aerosol climate
models [Textor et al., 2006, 2007; B. Koffi et al. Application
1
Center for International Climate and Environmental Research - Oslo
(CICERO), Oslo, Norway.
Copyright 2011 by the American Geophysical Union.
0094-8276/11/2011GL049697
[5] In this section we present the models and methods
used, and introduce a measure for NDRF vertical sensitivity.
We use a state of the art 3-D four band, eight stream radiative transfer model [Myhre et al., 2007] based on the discrete
ordinate method [Stamnes et al., 1988], in T42 (2.8°) horizontal resolution and 3 hour time steps. For the annual mean
results in the present study, we run the model for one day per
calendar month to save computation time. We have verified
that the uncertainty introduced by this approximation is ≤1%
for the final NDRF profiles. A background of aerosols
(notably sulphate, organic and black carbon from fossil fuel
and biomass burning, mineral dust and sea salt) are provided
by the OsloCTM2 global aerosol and chemical transport
model [Myhre et al., 2009], using emissions based on those
of Lamarque et al. [2010]. OsloCTM2 is among the global
aerosol models with vertical profiles closest to the CALIPSO
satellite retrieved aerosol extinction profiles (Koffi et al.,
submitted manuscript, 2011) and close to the mean of
the global aerosol models in terms of BC vertical profiles in remote regions [Schwarz et al., 2010]. Meteorological data, including clouds, were generated through the
Integrated Forecast System at the ECMWS. For details, see
Myhre et al. [2009]. The main study uses year 2006
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Figure 1. Annual mean BC aerosol burden (in mg/m2) for the background fields used, and regions selected for the regional
study (boxes).
meteorology, while years 2003–2007 were used to study
cloud sensitivity.
[6] Three aerosol species were studied, each of which
have distinct parametrizations in the model. In the following BC refers to black carbon particles from fossil fuel
burning, SO4 refers to sulphate compounds resulting from
SO2 emissions, and BIO refers to a mixture of black and
organic carbon from biomass burning. The aerosol optical
properties are calculated with Mie theory. For BC we
increase the total absorption by 50% for aged (hydrophilic)
BC to take into account internal mixing with other aerosols
as suggested by Bond et al. [2006], whereas for fresh
(hydrophobic) BC we use the simulated absorption coefficient of around 7.5 m2/g (at 550 nm) in accordance with
measurements [Bond and Bergstrom, 2006]. Biomass burning aerosols are typically a mixture of absorbing black carbon
and strongly scattering organic carbon compounds. In addition they are to a large extent internally mixed, and have a
lower light absorption coefficient than soot particles emitted
from higher temperature combustion processes. Hygroscopicity is treated separately for each species. BC is assumed to
be in a 50/50 mixture of hydrophilic and hydrophobic states.
For SO4, the relative humidity is important due to its effect on
the radiative properties through hygroscopic growth. We
performed two experiments – one where relative humidity
was kept constant at 80% for all pressure levels, and one
where we used the OsloCTM2 ambient relative humidity. For
a complete discussion of size distributions, optical properties
and other parametrizations, see Myhre et al. [2007].
[7] To calculate radiative forcing for a given species, an
atmospheric pressure was chosen and a prescribed amount of
aerosol was introduced here. The model was run twice, with
and without the added aerosol, and we define instantaneous
direct radiative forcing (DRF) as the difference in outgoing
top-of-atmosphere shortwave flux between these two model
runs. This procedure is repeated for 20 levels in the vertical. Pressures of (1000, 950, 900, … 100, 50, 20)hPa were
used. Finally, NDRF is calculated as the DRF divided by
the prescribed aerosol load. The amounts prescribed were
0.3 mg/m2 for BC, 3.0 mg/m2 for SO4 and 0.15 mg/m2 for
BIO. A detailed study of NDRF variability with the
prescribed load is beyond the scope of the present work,
but has been found in simple tests to be small (not shown).
[8] Figure 1 shows the annual mean aerosol burden from
the fossil fuel BC background field, and indicates some
regions that will be used below. Blue boxes show indicative
regions with properties that are expected to deviate from the
global mean, e.g., the Pacific or the Arctic, while red boxes
show industrial regions with high emissions of one or more
aerosol species.
[9] Figure 2 shows the annual mean vertical distribution of
cloud fraction (Figure 2, left), ice (Figure 2, middle) and
liquid water content (Figure 2, right). Solid lines show
global means, while conditions over the regions selected in
Figure 1 are indicated with various dashed lines. The global
distribution can be seen to be composed of liquid water
clouds concentrated at lower altitudes, and ice water clouds
spread out over higher altitudes up to 20 hPa which is the top
point of the present study.
Figure 2. Global, annual mean vertical distributions of
(left) cloud fraction, (middle) cloud liquid water content,
(right) cloud ice water content. Global, annual means, year
2006, from ECMWF reanalysis data.
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Figure 3. Global annual mean vertical NDRF distributions
broken down into contributing components, for (a) BC, (c, d)
SO4 and (e) BIO aerosols. (b) Variability with seasons and
cloud year. Figure 3c shows SO4 with hygroscopic growth
based on ambient relative humidity, Figure 3d with relative
humidity prescribed to 80% (right). The vertical axis shows
the pressure level where an aerosol load was introduced.
[10] For the discussion below, we define vertical sensitivity as the derivative of NDRF with respect to atmospheric
pressure:
sv ¼ dNDRF
½W =ðg hPaÞ
dP
A high sV will indicate a rapidly changing NDRF with altitude. The minus sign is included to make a positive sV
indicate that NDRF strengthens with increasing altitude.
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[14] Turning on simulation of Rayleigh scattering
(Figure 3, orange dot-dashed line) strengthens the NDRF
notably with altitude. Ozone and water vapour effects (yellow and blue dashed lines) weaken the NDRF due to
absorption of solar radiation by these gases rather than the
aerosol, while the background of other aerosol species
strengthens the NDRF again due to scattering effects. Ozone,
water vapour and background aerosols do not significantly
alter the vertical NDRF profile (see discussion below).
[15] Finally adding cloud effects we see a strong effect
on the NDRF, as expected from previous column model
studies [e.g., Zarzycki and Bond, 2010]. The global cloud
profile (see Figure 2a) shows a peak in cloud fraction above
800 hPa, followed by a slow increase down to 200 hPa and
subsequent drop to zero cloud fraction. These features are
clearly reflected in the NDRF distribution for BC. Similar
to introduction of Rayleigh scattering, clouds increase the
NDRF with altitude, and the difference in the NDRF for BC
in the lower stratosphere or upper troposphere to close to
the surface is a factor of ten.
[16] Figures 3c and 3d show similar simulations for sulphate aerosols. The general trends seen from BC are still
present, but the final vertical sensitivities are much lower.
For the SO4 experiment using ambient relative humidity
(Figure 3c), the bare aerosol NDRF changes significantly at
low altitudes. Progressively adding the other terms, NDRF is
strongly weakened at all altitudes, and the sensitivity seen at
low altitudes is masked. The SO4 experiment with constant
relative humidity (Figure 3d) illustrates this weakening more
clearly, from about 800 W/g for bare sulphate aerosols to
about 500 W/g for clear sky conditions. Adding clouds
further weakens the NDRF to 250 W/g close to surface,
with modest vertical sensitivity to around 350 W/g in the
upper troposphere. This shows that even though the sensitivity is low, treatment of scattering effects, relative humidity and clouds significantly affect SO4 NDRF and hence also
modelled sulphate direct radiative forcing.
[17] Figure 3e shows BIO aerosol NDRF. Here we see a
shift from a strong and negative NDRF for the bare aerosol,
3. Results
[11] Here we present global and regional NDRF profiles
for BC, SO4 and BIO aerosols, and discuss changes with
underlying factors such as cloud cover, season and region.
[12] Figure 3 shows global annual mean NDRF vertical
profiles, calculated as described in the previous section. For
each species (BC, SO4, BIO) we ran a baseline simulation
where only the scattering and absorption properties of the
aerosol itself were active in the model. We then progressively turned on modelling of Rayleigh scattering, atmospheric ozone, water vapour, of a background of other
aerosols, and finally of clouds.
[13] In Figure 3a the thick, solid line shows NDRF for BC
including all effects. A variation is seen from 380 W/g at
1000 hPa, up to 3800 W/g at 20 hPa. The “aerosol only”
simulation (red dashed line) shows a small strengthening
near the surface, up to a constant value of 950 W/g below
800 hPa. This increase is due to BC being both scattering
and absorbing, leading to the possibility of competing
extinction processes near the surface. Purely absorbing BC
has a fully constant NDRF profile (not shown).
Figure 4. Vertical NDRF sensitivities for BC, SO4 and
BIO aerosols. Dashed lines show aerosol-only simulations, solid lines show simulations with all effects included.
For BC clear sky simulations are also shown. The vertical
axis shows the pressure level where an aerosol load was
introduced.
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Figure 5. Vertical NDRF distribution for BC, for selected regions. (left) Illustrative regions (Sahara, Pacific, Arctic). (right)
Industrial regions with varying degrees of BC emissions (North America, Europe, Africa, China). Results for global and
annual mean are shown in both panels (thick black line). The vertical axis shows the pressure level where an aerosol load
was introduced.
to a vertical profile that goes from negative to positive at
around 700 hPa when all effects are included.
[18] Figure 4 shows the vertical sensitivity sV, as defined
above, for bare aerosols (dashed lines) and all effects
included (solid lines). For BC the clear sky sensitivity is also
shown (dotted line). For all species sensitivities are highest
at low altitudes, and change little above 700 hPa. While BIO
and SO4 do have significant, non-zero sensitivities, the BC
sensitivities are far greater in magnitude. The correlation of
BC vertical sensitivity with cloud fraction can clearly be
seen, and in the region with high water cloud fraction we
find sensitivities of up to 10 W/(g hPa). For this reason, we
will focus on BC in the following discussion.
[19] Several factors may influence the global mean NDRF
distributions, including the meteorology for the chosen year,
seasonal variations, and most prominently regional differences. To study the effects of varying cloud distributions, we
have run the BC simulations for the years 2003–2007. Also,
seasonal variations were studied by extracting BC NDRF for
four three-month periods rather than a full year. Figure 3b
shows the result from one cloud year divided by a chosen
baseline year (black lines), and the same for one season
(three-month period) divided by a chosen baseline season
(green lines). We assume these to be uncorrelated errors, and
indicate their total effect (one standard deviation) on the
global annual mean profiles as a grey band in Figure 3a. Of
the two, seasonal variations can be seen to be of greatest
magnitude.
[20] Figure 5 shows the variability of BC NDRF with
region. In Figure 5 (left) we have chosen regions with significantly different conditions – the Sahara desert, the Pacific
Ocean and the Arctic. Unlike for clouds and seasonal variations, we here see significant changes in NDRF. Despite a
large fraction of high cloud cover over the Pacific, BC
causes lower forcing due to the low albedo of the ocean.
Over the Arctic, with its high albedo and high solar zenith
angle, the impact of BC is conversely very high. Above the
Sahara, where there are very few clouds (see Figure 2) but
Figure 6. Vertical distribution of RF from fossil fuel BC in OsloCTM2. (left) BC NDRF (present work) interpolated to the
model resolution of 61 levels (solid line), and global, annual mean fossil fuel BC concentration (dotted green line). (middle)
Fossil fuel BC RF per height, calculated from the NDRF in the present work (solid blue line), and multiplied by the
OsloCTM2 layer heights to show the actual RF per model layer (dashed black line). (right) The integrated RF in OsloCTM2
as function of pressure.
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high surface albedo, the NDRF values are close to the global
mean but the vertical sensitivity is lower as expected from an
almost cloud-free sky. Figure 5 (right) shows the BC NDRF
for industrial regions with high aerosol emissions – North
America, Europe, Southern Africa and China (see Figure 1a
for region definitions). Here the variability is less pronounced than for the indicative regions, but still significantly
higher than for changes in cloud cover or season. Notably, at
pressures above 800 hPa, where BC concentrations are
highest for realistic vertical profiles (see, e.g., Figure 6), all
industrial regions show lower NDRF than the global mean.
Similar statements hold for BIO and SO4 aerosols (not
shown). This shows that the variability in NDRF on regional
scales can affect the RF uncertainty on a global scale, and
one should therefore be careful using globally averaged
NDRF values to study regional effects.
4. Application to Model Results
[21] As shown, e.g., by Koffi et al. (submitted manuscript,
2011) and Schwarz et al. [2010], the actual vertical distribution of aerosols is poorly determined and varies significantly between aerosol models. The NDRF calculations in
the previous section can be used to extract the contributions
to the total aerosol forcing from different altitudes within a
model, and thus to quantify the difference introduced by
poor understanding of the vertical distributions.
[22] In Figure 6, we have taken recent output from the
OsloCTM2 model [Skeie et al., 2011] and combined with
the present results. The solid line in Figure 6 (left) shows the
global, annual mean BC NDRF distribution, interpolated to
the full resolution of OsloCTM2 (61 layers) and assuming a
constant NDRF for pressures below 20 hPa. The dotted line
shows the global and annual mean BC concentration from
OsloCTM2. In Figure 6 (middle), the solid line shows the
RF per height as calculated from the present analysis, while
the dashed line shows the actual BC direct radiative forcing
vertical profile when multiplied by the model layer heights.
We note a sharp rise from sea level and up to about 800 hPa,
followed by a slow decline up to around 200 hPa where the
BC RF rapidly vanishes. Hence for most of the vertical
profile the declining BC concentration is counteracted by the
strengthening NDRF. In Figure 6 (right) we show the normalized integrated RF from BC (dot-dashed line). More than
half of the BC RF from this model arises from atmospheric
pressures lower than 500 hPa, while only about 30% of the
aerosol mass is found in this region. This shows that
uncertainties in BC profiles higher in the atmosphere can
have a significant impact on the modelled forcing, even if
the loads are low, due to the strong vertical sensitivity of BC
aerosol forcing documented above.
[23] While studying a single model is interesting, the full
potential of the present results becomes clear when discussing intermodal comparisons. The AeroCom direct
aerosol experiment [Schulz et al., 2006] found significant
variations between models, both for total aerosol forcing and
for individual components. Schwarz et al. [2010] show that
one reason for the variations in BC could be the wide range
of vertical profiles. For the ongoing Phase 2 of this global
aerosol model intercomparison, the vertical mass-mixingratio distributions provided by the model groups will be
combined with NDRF distributions like the ones reported
above, to quantify the differences in model RF due to this
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variability. For such an exercise model differences in NDRF
and vertical sensitivities would also be useful to know. We
therefore encourage other model groups to repeat the exercise reported here and publish their NDRF vertical distributions and sensitivities.
[24] Acknowledgments. We acknowledge the support of the Research
Council of Norway through the SLAC programme. Computations were run
via a NOTUR grant on the TITAN cluster. We thank the two anonymous
reviewers for thorough comments that have improved the quality of the
paper.
[25] The Editor thanks two anonymous reviewers for their assistance in
evaluating this paper.
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G. Myhre and B. H. Samset, Center for International Climate and
Environmental Research - Oslo (CICERO), PO Box 1129 Blindern,
N-0318 Oslo, Norway. (b.h.samset@cicero.uio.no)
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