Evaluation of black carbon estimations in global aerosol models

Abstract

We evaluate black carbon (BC) model predictions from the AeroCom model intercomparison project by considering the diversity among year 2000 model simulations and comparing model predictions with available measurements. These model-measurement intercomparisons include BC surface and aircraft concentrations, aerosol absorption optical depth (AAOD) from AERONET and Ozone Monitoring Instrument (OMI) retrievals and BC column estimations based on AERONET. In regions other than Asia, most models are biased high compared to surface concentration measurements. However compared with (column) AAOD or BC burden retreivals, the models are generally biased low. The average ratio of model to retrieved AAOD is less than 0.7 in South American and 0.6 in African biomass burning regions; both of these regions lack surface concentration measurements. In Asia the average model to observed ratio is 0.6 for AAOD and 0.5 for BC surface concentrations. Compared with aircraft measurements over the Americas at latitudes between 0 and 50 N, the average model is a factor of 10 larger than observed, and most models exceed the measured BC standard deviation in the mid to upper troposphere. At higher latitudes the average model to aircraft BC is 0.6 and underestimates the observed BC loading in the lower and middle troposphere associated with springtime Arctic haze. Low model bias for AAOD but overestimation of surface and upper atmospheric BC concentrations at lower latitudes suggests that most models are underestimating BC absorption and should improve estimates for refractive index, particle size, and optical effects of BC coating. Retrieval uncertainties and/or differences with model diagnostic treatment may also contribute to the model-measurement disparity. Largest AeroCom model diversity occurred in northern Eurasia and the remote Arctic, regions influenced by anthropogenic sources. Changing emissions, aging, removal, or optical properties within a single model generated a smaller change in model predictions than the range represented by the full set of AeroCom models. Upper tropospheric concentrations of BC mass from the aircraft measurements are suggested to provide a unique new benchmark to test scavenging and vertical dispersion of BC in global models.

Full text

Page 1

Atmos. Chem. Phys., 9, 9001–9026, 2009
www.atmos-chem-phys.net/9/9001/2009/
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmospheric
Chemistry
and Physics
Evaluation of black carbon estimations in global aerosol models
D. Koch1,2, M. Schulz3, S. Kinne4, C. McNaughton10, J. R. Spackman9, Y. Balkanski3, S. Bauer1,2, T. Berntsen13,
T. C. Bond6, O. Boucher14, M. Chin15, A. Clarke10, N. De Luca24, F. Dentener16, T. Diehl17, O. Dubovik14, R. Easter18,
D. W. Fahey9, J. Feichter4, D. Fillmore22, S. Freitag10, S. Ghan18, P. Ginoux19, S. Gong20, L. Horowitz19,
T. Iversen13,27, A. Kirkev˚ ag27, Z. Klimont7, Y. Kondo11, M. Krol12, X. Liu23,18, R. Miller2, V. Montanaro24,
N. Moteki11, G. Myhre13,28, J. E. Penner23, J. Perlwitz1,2, G. Pitari24, S. Reddy14, L. Sahu11, H. Sakamoto11,
G. Schuster5, J. P. Schwarz9, Ø. Seland27, P. Stier25, N. Takegawa11, T. Takemura26, C. Textor3, J. A. van Aardenne8,
and Y. Zhao21
1Columbia University, New York, NY, USA
2NASA GISS, New York, NY, USA
3Laboratoire des Sciences du Climat et de l’Environnement, Gif-sur-Yvette, France
4Max-Planck-Institut fur Meteorologie, Hamburg, Germany
5NASA Langley Research Center, Hampton, Virginia, USA
6University of Illinois at Urbana-Champaign, Urbana, IL, USA
7International Institute for Applied Systems Analysis, Laxenburg, Austria
8European Commission, Institute for Environment and Sustainability, Joint Research Centre, Ispra, Italy
9NOAA Earth System Research Laboratory, Chemical Sciences Division and Cooperative Institute for Research in
Environmental Sciences, University of Colorado, Boulder, Colorado, USA
10University of Hawaii at Manoa, Honolulu, Hawaii, USA
11RCAST, University of Tokyo, Japan
12Meteorology and Air Quality, Wageningen University, Wageningen, The Netherlands
13University of Oslo, Oslo, Norway
14Universite des Sciences et Technologies de Lille, CNRS, Villeneuve d’Ascq, France
15NASA Goddard Space Flight Center, Greenbelt, MD, USA
16EC, Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
17University of Maryland Baltimore County, Baltimore, Maryland, USA
18Pacific Northwest National Laboratory, Richland, USA
19NOAA, Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
20ARQM Meteorological Service Canada, Toronto, Canada
21University of California – Davis, CA, USA
22NCAR, Boulder, CO, USA
23University of Michigan, Ann Arbor, MI, USA
24Universita degli Studi L’Aquila, Italy
25Atmospheric, Oceanic and Planetary Physics, University of Oxford, UK
26Kyushu University, Fukuoka, Japan
27Norwegian Meteorological Institute, Oslo, Norway
28Center for International Climate and Environmental Research – Oslo (CICERO) Oslo, Norway
Received: 1 July 2009 – Published in Atmos. Chem. Phys. Discuss.: 24 July 2009
Revised: 5 November 2009 – Accepted: 10 November 2009 – Published: 27 November 2009
Published by Copernicus Publications on behalf of the European Geosciences Union.

Page 2

9002D. Koch et al.: Evaluation of black carbon estimations in global aerosol models
Abstract. We evaluate black carbon (BC) model predictions
from the AeroCom model intercomparison project by con-
sidering the diversity among year 2000 model simulations
and comparing model predictions with available measure-
ments. These model-measurement intercomparisons include
BC surface and aircraft concentrations, aerosol absorption
optical depth (AAOD) retrievals from AERONET and Ozone
Monitoring Instrument (OMI) and BC column estimations
based on AERONET. In regions other than Asia, most mod-
els are biased high compared to surface concentration mea-
surements. However compared with (column) AAOD or BC
burden retreivals, the models are generally biased low. The
average ratio of model to retrieved AAOD is less than 0.7
in South American and 0.6 in African biomass burning re-
gions; both of these regions lack surface concentration mea-
surements. In Asia the average model to observed ratio is
0.7 for AAOD and 0.5 for BC surface concentrations. Com-
pared with aircraft measurements over the Americas at lati-
tudes between 0 and 50N, the average model is a factor of
8 larger than observed, and most models exceed the mea-
sured BC standard deviation in the mid to upper troposphere.
At higher latitudes the average model to aircraft BC ratio is
0.4 and models underestimate the observed BC loading in
the lower and middle troposphere associated with springtime
Arctic haze. Low model bias for AAOD but overestimation
of surface and upper atmospheric BC concentrations at lower
latitudes suggests that most models are underestimating BC
absorption and should improve estimates for refractive in-
dex, particle size, and optical effects of BC coating. Retrieval
uncertainties and/or differences with model diagnostic treat-
ment may also contribute to the model-measurement dispar-
ity. Largest AeroCom model diversity occurred in northern
Eurasia and the remote Arctic, regions influenced by anthro-
pogenic sources. Changing emissions, aging, removal, or
optical properties within a single model generated a smaller
change in model predictions than the range represented by
the full set of AeroCom models. Upper tropospheric con-
centrations of BC mass from the aircraft measurements are
suggested to provide a unique new benchmark to test scav-
enging and vertical dispersion of BC in global models.
1Introduction
Black carbon, the strongly light-absorbing portion of car-
bonaceous aerosols, is thought to contribute to global warm-
ing since pre-industrial times.
plete combustion of fossil fuels and biofuels, such as coal,
wood and diesel. Black carbon (BC) has several effects
on climate, primarily warming, but potentially also some
amount of cooling.The “direct effect” is the scattering
It is a product of incom-
Correspondence to: D. Koch
(dkoch@giss.nasa.gov)
and absorption of incoming solar radiation by the BC sus-
pended in the atmosphere. The absorption warms the air
where the BC aerosol is suspended, but the extinction of
radiation results in negative forcing at the earth’s surface
(e.g. Ramanathan and Carmichael, 2008). The “BC-albedo
effect” occurs because black carbon deposited on snow low-
ers the snow albedo and may therefore promote snow and
ice melting (e.g. Warren and Wiscombe, 1980; Hansen and
Nazarenko, 2004). BC may also have important effects on
clouds by changing atmospheric stability and/or relative hu-
midity, and thus affect cloud formation; this has been termed
the “semi-direct effect” (e.g. Ackerman et al., 2000; Johnson
et al., 2004). Finally, BC is a primary aerosol particle and
influences the number of particles available for cloud con-
densation (e.g. Oshima et al., 2009); it may thus play an im-
portant role for the aerosol cloud “indirect effect”. BC may
also affect the indirect effect by acting as ice nuclei (e.g. Co-
zic et al., 2007; Liu et al., 2009).
Quantifying the effects of black carbon on climate change
is hindered by several uncertainties. Emissions are uncertain
because of difficulties quantifying sources and emission fac-
tors (e.g. Bond et al., 2004). Measurements of BC concentra-
tions are uncertain because of instrumental limitations in the
present measurement techniques (Andreae and Gelencser,
2006). Optical properties are uncertain since these vary with
source, morphology, particle age and chemical processing.
Atmospheric column aerosol absorption comes mostly from
black carbon in many polluted and biomass burning regions.
This absorption aerosol optical depth (AAOD) has been re-
trieved from satellite and an array of sunphotometer mea-
surements, and these retrievals also help to constrain column
BC. However the constraint is limited by uncertainties and
assumptions in the retrievals as well as by the fact that other
absorbing species besides BC are present, such as dust and
organic carbon. Model simulation of BC is complicated by
uncertainties in treatment of initial particle size and shape ap-
propriate for initial release in a model gridbox, particle up-
take in liquid or frozen clouds and precipitation, treatment of
mixing state and optical properties. Assumptions influencing
the degree of internal vs. external mixing with water-soluble
particles in the accumulation mode strongly influence the
aerosol absorption (Seland et al., 2008) and CCN-activation.
Internal mixing of BC also affects BC lifetime, decreasing it
relative to hydrophobic BC (Ogren and Charlson, 1983; Stier
et al., 2006, 2007). Furthermore, the BC model predictions
are subject to model uncertainties that apply to any chemical
modelsimulation, suchastheaccuracyofthemodel’smeteo-
rology including transport, clouds, and precipitation (e.g. Liu
et al., 2007).
The aim of this study is to evaluate model-calculated BC
in recent state-of-the-art global models with aerosol chem-
istry and physics, to consider their diversity and compare
them with available observations. There has been concern
that some models may greatly underestimate BC absorption
and therefore BC contribution to climate warming (e.g. Sato
Atmos. Chem. Phys., 9, 9001–9026, 2009www.atmos-chem-phys.net/9/9001/2009/

Page 3

D. Koch et al.: Evaluation of black carbon estimations in global aerosol models9003
et al., 2003; Ramanathan and Carmichael, 2008; Seland et
al., 2008). However it is unclear whether this is a problem
common to all models, whether the problem is regional or
global, and the extent to which the bias is due to BC mass
underestimation possibly linked to emissions underestima-
tion, or to model treatment of optical properties leading to
underestimation of BC absorption. We examine these issues
by comparing the models to a variety of measurements, and
working with a large number of current models. We also in-
vestigate whether biases in some regions are more problem-
atic than in others. Finally we make use of one of the models,
the GISS model (available to the first author of this paper),
to consider the effects of changing BC emissions, aging, re-
moval assumptions and optical properties. We also use the
GISS model to consider the seasonality of model bias and
the spectral dependence of AAOD bias.
We compare the models with several types of observa-
tions. Model surface concentrations are compared with long-
term surface concentration measurements. Model BC con-
centration profiles are compared with aircraft measurements
for several recent aircraft campaigns, spanning the North
American region from the tropics to the Arctic. Column
BC is assessed by comparing model AAOD with that re-
trieved by Dubovik and Kings (2000) inversion algorithm
from AERONET sunphotometer measurements (Holben et
al., 1998), as was done in Sato et al. (2003), and with OMI
satellite retrievals of AAOD. We also compare column bur-
den of BC with the AERONET-based estimation as in Schus-
ter et al. (2005). While the measurements provide constraints
for the models, in the final section we will discuss measure-
mentuncertaintiesandthediscrepanciesamongthemthatare
apparent as we apply them to the models.
2 Model descriptions
2.1 AeroCom models
We evaluate seventeen models from the AeroCom aerosol
model intercomparison, an exercise that has been ongoing
for the past 5 years. Model results, as well as observa-
tion datasets for validation purposes, are available at the Ae-
roCom website (http://nansen.ipsl.jussieu.fr/AEROCOM/).
The AeroCom intercomparison exercises included an exer-
cise “A” with each model using its own emissions, and an
exercise “B” where all models used identical emissions, and
were described in detail in Textor et al. (2006, 2007), Kinne
et al. (2006) and Schulz et al. (2006). Here we work with
exercise A unless only B is available for a particular model
in the database. The models used year 2000 emissions and
in some cases year 2000 meteorological fields. Not all di-
agnostics were available for all models, so we used all those
available for each quantity considered. Many aspects of the
models have been evaluated in previous publications, and we
refer to those for general background information. Textor et
al. (2006) provided a first comparison of the models in ex-
periment A and included basic information on the models
such as model resolution, chemistry, and removal assump-
tions. Textor et al. (2007) described the exercise B model
intercomparison, and showed that model diversity was not
greatly reduced by unifying emissions, indicating that the
greatest model differences result from features such as me-
teorology and aerosol treatments rather than from emissions.
Kinne et al. (2006) discussed the aerosol optical properties
of the models and Schulz et al. (2006) presented the radiative
forcing estimates for the models.
Some of the model features most relevant for the BC sim-
ulations are provided in Table 1. As shown there, nine dif-
ferent BC energy emissions inventories and eleven different
biomass burning emissions inventories were used. The mod-
els had a variety of schemes to determine black carbon aging
from a fresh to aged particle, where aged particles may be
activated into cloud water. Ten models assumed that black
carbon aged from hydrophobic to hydrophilic after a fixed
lifetime; five models had microphysical mixing schemes to
make the particles hydrophilic, in one model the black and
organic carbon are assumed to be mixed when emitted, and
one model had fixed solubility. In three cases the particle
mixing affected optical/radiative properties.
assumptions were made about how frozen clouds removed
aerosols compared to liquid clouds, ranging from identical
treatments for frozen and liquid clouds to zero removal by
ice clouds. Black carbon lifetime ranges from 4.9 to 11.4
days.
We note that the model versions evaluated here were sub-
mitted to the database in year 2005, and many models have
evolved significantly since (e.g. Bauer et al., 2008; Chin et
al., 2009; Ghan and Zaveri, 2007; Liu et al., 2005, 2007;
Myhre et al., 2009; Stier et al., 2006, 2007; Takemura et al.,
2009). Thus this study provides a benchmark at the time of
the 2005 submission.
A variety of
2.2 GISS model sensitivity studies
We use the GISS aerosol model to study sensitivity to fac-
tors that could impact the BC simulation. The GISS aerosol
scheme used here includes mass of sulfate, sea-salt (Koch
et al., 2006), carbonaceous aerosols (Koch et al., 2007) and
dust (Miller et al., 2006; Cakmur et al., 2006). The sensitiv-
ity studies are described below and listed in Table 2. All
simulations are performed and averaged for 3 years, after
a 2-year model spin-up. The standard GISS model version
for these sensitivity studies is slightly different than the ver-
sion in the AeroCom database. This version does not include
dust-nitrate interaction, and does not include enhanced re-
moval of BC by precipitating convective clouds as was in-
cluded in the AeroCom-database GISS model version, and
therefore has a somewhat larger BC load.
www.atmos-chem-phys.net/9/9001/2009/ Atmos. Chem. Phys., 9, 9001–9026, 2009

Page 4

9004D. Koch et al.: Evaluation of black carbon estimations in global aerosol models
Table 1. AeroCom model black carbon characteristics.
AeroCom modelsEnergy
Emis(1)
BB Emis(1)
Aging(2)
BC
lifetime
days
Ice/snow
removal(3)
Mass median
diameter of
emitted
particle(4)
BC
density
gcm−3(6)
Refractive
index at
550nm(6)
MABS
m2g−1(5,6)
References for aerosol module
GISS 99B04 GFEDA7.2 12% 0.081.6 1.56–0.5i8.4 Koch et al. (2006, 2007),
Miller et al. (2006)
ARQM 99 C99L00,
L96
I 6.7T 0.1 1.54.1 Zhang et al. (2001);
Gong et al. (2003)
CAM C99L96ALXXXCollins et al. (2006)
DLRCW96 CW96I 5%
accum,
strat
0.08, 0.75 FF
0.02,
0.37 BB
XXXAckermann et al. (1998)
GOCARTC99GFED,
D03
A 6.6T0.078 1.01.75–0.45i10.0Chin et al. (2000, 2002),
Ginoux et al. (2001)
SPRINTARSNK06NK06BCOCL 0.0695 FF,
0.1 others
1.25 1.75–0.44i2.3Takemura et al. (2000, 2002,
2005)
LOA B B04GFEDA 7.3LI 0.0118 1.01.75–0.45i8.0 #Boucher and Anderson (1995);
Boucher et al. (2002);
Reddy and Boucher (2004);
Guibert et al. (2005)
LSCE G03G03A 7.5L0.141.61.75-0.44i 3.5
(4.4 #)
Claquin et al. (1998, 1999);
Guelle et al. (1998a, b, 2000);
Smith and Harrison (1998);
Balkanski et al. (2003); Bauer et
al. (2004); Schulz et al. (2006)
MATCH L96L96AL0.1XXX Barth et al. (2000);
Rasch et al. (2000, 2001)
MOZGNC99,
O96
M92AL 0.11.01.75–0.44i 8.7Tie et al. (2001, 2005)
MPIHAM D06 D06I # 4.9S 0.069 (FF, BF)
0.172 (BB)
2.0 1.75–0.44i7.7 # Stier et al. (2005)
MIRAGEC99 CW96,
L00
I #6.1L0.19, 0.0251.7 1.9-0.6i3 aitk,
6 acc
Ghan et al. (2001); Easter et al.
(2004); Ghan and Easter (2006)
TM5 D06D06 A 5.720%0.0341.61.75-0.44i 4.3Metzger et al. (2002a, b)
UIOCTMC99CW96A5.5 L 0.1 (FF),
0.295,
0.852 (BB)
1.01.55-0.44i 7.2 # Grini et al. (2005); Myhre et al.
(2003); Berglen et al. (2004);
Berntsen et al. (2006)
UIOGCM 99 IPCCIPCCI # 5.5none0.0236–0.4 2.02.0–1.0i 10.5 #Iversen and Seland (2002);
Kirkevag and Iversen (2002);
Kirkevag et al. (2005)
UMI L96P93N5.8L 0.1452 (FF),
0.137 (BB)
1.5 1.80–0.5i6.8 # Liu and Penner (2002)
ULAQ 99 IPCCIPCCA 11.4L0.02–0.321.02.07–0.6i7.5 #Pitari et al. (1993, 2002)
(1)BB = biomass burning; B04 = Bond et al. (2004); C99 = Cooke et al. (1999); L00 = Lavoue et al. (2000); L96 = Liouse et al. (1996); CW
= Cooke and Wilson (1996); GFED = Van der Werf et al. (2003); NK06 = Nozawa and Kurokawa (2006); G03 = Generoso et al. (2003);
R05 = Reddy et al. (2005); D03 = Duncan et al. (2003); D06 = Dentener et al. (2006); M92 = Mueller (1992); O96 = Olivier et al. (1996);
IPCC = IPCC-TAR (2000); P93 = Penner et al. (1993)
(2)Aging as it affects particle solubility. A= aging with time; I= aging by coagulation and condensation, particles are internally mixed;
BCOC = BC assumed mixed with OC; N= none; # indicates that mixing/aging also affects particle optical properties.
(3)T= Temp dependence, L= as liquid, LI = As liquid for in-cloud removal only; S= Stier et al. (2005); % is relative to water.
(4)FF = fossil fuel
(5)MABS = BC mass absorption coefficient at 550nm; # taken from Schulz et al. (2006).
(6)X: models did not simulate optical properties.
Atmos. Chem. Phys., 9, 9001–9026, 2009 www.atmos-chem-phys.net/9/9001/2009/

Page 5

D. Koch et al.: Evaluation of black carbon estimations in global aerosol models9005
Table 2. GISS model sensitivity studies.
DescriptionEmission
Tgyr−1
Burden
mgm−2
Lifetime, d AAOD x100
550nm
Standard run,
see text
7.2 (4.4 energy,
2.8 biomass
burning)
7.5
0.36 9.20.55
EDGAR32
emission
IIASA emission
BB 1998
2x
(Faster aging)
2x
(Slower aging)
2x More ice-out
2x Less ice-out
Reff =0.1µm
Reff =0.06µm
0.379.30.58
8.1
8.2
7.2
0.41
0.38
0.29
9.5
8.7
7.6
0.60
0.58
0.50
7.20.51 130.67
7.2
7.2
7.2
7.2
0.33
0.38
0.35
0.36
8.5
9.8
9.1
9.3
0.52
0.57
0.47
0.70
2.2.1Emissions
The standard GISS model uses carbonaceous aerosol energy
production emissions from Bond et al. (2004).
burning emissions are based on the Global Fire Emissions
Database (GFED) v2 model carbon estimates for the years
1997–2006 (van der Werf et al., 2003, 2004), with the car-
bonaceous aerosol emission factors from Andreae and Mer-
let (2001). One sensitivity case had fossil and biofuel emis-
sions from the Emission Database for Atmospheric Research
(EDGAR V32FT2000, called “EDGAR32” below; Olivier
et al., 2005) combined with emission factors from Bond et
al. (2004) and in a second those of the International Institute
for Applied Systems Analysis (IIASA) (Cofala et al., 2007).
In a third we used the largest biomass burning year from the
GFED dataset, 1998.
Biomass
2.2.2 Aging and removal
In the standard GISS model, energy-related BC is assumed to
be hydrophobic initially and then ages to become hydrophilic
with an e-fold lifetime of 1 day. Biomass burning BC is as-
sumed to have 60% solubility, so that if a cloud is present,
60% these aerosols are taken into the cloud water for each
half-hour cloud timestep. One sensitivity test assigned a
shorter lifetime with a halved e-folding time for energy BC
and 80% solubility for biomass burning. A second test as-
sumes a longer lifetime, with doubled e-folding time for en-
ergy BC and 40% solubility for biomass burning.
Treatment of BC solubility is particularly uncertain for
frozen clouds. In our standard model, BC-cloud uptake for
frozen clouds is 12% of that for liquid clouds. A sensitivity
run allowed 24% ice-cloud BC uptake, and another case 5%.
2.2.3Aerosol size
The standard GISS model assumes the BC effective radius
(cross section weighted radius over the size distribution;
Hansen and Travis, 1974) is 0.08µm. One sensitivity case
increased this to 0.1µm, and another decreased it to 0.06µm.
The size primarily affects the BC optical properties. For BC
sizes 0.1, 0.08 and 0.06µm, the model global mean BC mass
absorption efficiencies are 6.2, 8.4 and 12.4 and BC single
scattering albedos are 0.31, 0.27 and 0.21.
3 Model evaluation
3.1 Surface concentrations
Annual average BC surface concentration measurements are
shown in the first panel of Fig. 1. The data for the United
States are from the IMPROVE network (1995–2001), those
from Europe are from the EMEP network (2002–2003);
some Asian data from 2006 are from Zhang et al. (2009);
additional data, mostly from the late 1990s, are referenced in
Koch et al. (2007). These data are primarily elemental car-
bon, or refractory carbon, which can be somewhat different
than BC (Andreae and Gelencser, 2006). The data were not
screened according to urban, rural or remote environment,
all available data were used; however the IMPROVE sites
are generally in rural or remote locations. There are broad re-
gional tendencies, with largest concentrations in Asia (1000–
14000ngm−3), then Europe (500–5000ngm−3), then the
United States (100–500ngm−3), then high northern latitudes
(10–100ngm−3)andleastatremotelocations(<10ngm−3).
Figure 1 also shows BC surface concentrations from the
GISS model sensitivity studies.The biggest impact for
www.atmos-chem-phys.net/9/9001/2009/ Atmos. Chem. Phys., 9, 9001–9026, 2009

Page 6

9006D. Koch et al.: Evaluation of black carbon estimations in global aerosol models
Observed BC surface concentrationstandardEDGAR
IIASA BB 1998 lifex2
life/2ice/2icex2
0
10
25
100
200
500
1000
5000
14500
Fig. 1. Observed BC surface concentrations (upper left panel) and GISS sensitivity model results (annual mean; ngm−3).
remote regions comes from increasing BC lifetime, either by
doubling the aging rate or by reducing the removal by ice.
Decreasing the BC lifetime has a smaller effect. The larger
1998 biomass burning emissions mostly increase BC in bo-
real Northern Hemisphere and Mexico. EDGAR32 emis-
sions increase BC in Europe, Arabia and northeastern Africa;
IIASA emissions increase south Asian BC.
Figure 2 show the AeroCom model simulations of BC sur-
face concentration, using model layer one from each model.
Figure 2 also shows the average and standard deviations of
the models. The standard deviation distribution is similar to
the average. Regions of especially large model uncertainty
occur where the standard deviation equals or exceeds the av-
erage, such as the Arctic. Overall the models capture the
observed distribution of BC “hot spots”. SPRINTARS is the
only model that successfully captures the large BC concen-
trations in Southeast Asia (Table 3), however it overestimates
BC in other regions. Unfortunately there are no long-term
measurements of BC in the Southern Hemisphere biomass
burning regions.
Table 3 shows the ratio of modeled to observed BC in re-
gionswheresurfaceconcentrationobservationsareavailable.
The regional ratios are based on the ratio of annual mean
model to annual mean observed for each site, averaged over
each region. Thirteen out of seventeen AeroCom models
over-predict BC in Europe. Sixteen of the models underes-
timate Southeast Asian BC surface concentrations; however
most of these measurements are from 2004–2006 and emis-
sions have probably increased significantly since the 1990s
(Zhang et al., 2009). Nine of the models overestimate re-
mote BC; in the United States about half the models over-
estimate and half underestimate the observations. Overall,
the models do not underestimate BC relative to surface mea-
surements. None of the GISS model sensitivity studies show
significant improvement over the standard case. The longer
lifetime cases improve the model-measurement agreement in
polluted regions but worsen the agreement in remote regions.
3.2Aerosol absorption optical depth
The aerosol absorption optical depth (AAOD), or the non-
scattering part of the aerosol optical depth, provides another
test of model BC. AAOD is an atmospheric column measure
of particle absorption, and so provides a different perspec-
tive from the surface concentration measurements. Both BC
and dust absorb radiation, so AAOD is most useful for test-
ing BC in regions where its absorption dominates over dust
absorption. Therefore we focus on regions where the dust
Atmos. Chem. Phys., 9, 9001–9026, 2009www.atmos-chem-phys.net/9/9001/2009/

Page 7

D. Koch et al.: Evaluation of black carbon estimations in global aerosol models9007
AVE ST DEVGISS CAM
GOCARTUIO GCMSPRINTARS MPI HAM
MATCH UIO CTMULAQLOA
LSCE MOZART TM5UMI
ARQMMIRAGE DLR
0 10
25
100200 50010005000
13000
Fig.2. AeroCommodelsannualmeanBCsurfaceconcentrations(ngm−3). Firstpanelshowsaverage, secondpanelshowsstandarddeviation
of models.
load is relatively small, for example Africa south of the Sa-
hara Desert. However since some sites within these regions
still have dust, we work with model total AAOD, including
all species.
Figure 3 shows AERONET (1996–2006) sunphotometer
(e.g. Dubovik et al., 2000; Dubovik and King, 2000) and
OMI satellite (2005–2007, from OMAERUV product; Tor-
res et al., 2007) retrievals of clear sky AAOD. A scatter
plot compares the AERONET and OMI retrievals at the
AERONET sites. Table 4 (last 5 rows) provides regional av-
erage AAOD for these retrievals. The two retrievals broadly
agree with one another. However, the OMI estimate is larger
than the AERONET value for South America and smaller for
Europe and Southeast Asia.
TheAeroCommodelAAODsimulationsareinFig.4. The
standard deviation relative to the average is similar to the sur-
face concentration result; it is less than or equal to the aver-
age, except in parts of the Arctic. Table 4 gives the average
ratio of model to retrieved AAOD within regions. For the
ratio of model to AERONET we average the model AAOD
over all AERONET sites within the region and divide by the
average of the corresponding AERONET values. For OMI
we average over each region in the model and divide by the
OMI regional average. The average model agrees with the
www.atmos-chem-phys.net/9/9001/2009/Atmos. Chem. Phys., 9, 9001–9026, 2009

Page 8

9008D. Koch et al.: Evaluation of black carbon estimations in global aerosol models
Table 3. Average ratio between model and observed BC surface
concentrations within regions for AeroCom models and GISS sen-
sitivity studies. Number of measurements is given for each region.
Bottom row is observed average concentration in ngm−3. Regions
defined as NAm (130W to 70W; 20N to 55N), Europe (15W to
45E; 30N to 70N), Asia (100E to 160E; 20N to 70N).
AeroCom models N Am
#26
Europe
#16
Asia
#23
Rest of
World
#12
GISS
ARQM
CAM
DLR
GOCART
SPRINTARS
LOA
LSCE
MATCH
MIRAGE
MOZGN
MPIHAM
TM5
UIOCTM
UIOGCM
UMI
ULAQ
AeroCom Ave
AeroCom Median
GISS
sensitivity
std
r=.1
r=.06
EDGAR32
IIASA
BB1998
Lifex2
Life/2
Ice/2
Icex2
Observed
(ngm−3)
0.81
0.29
1.6
3.0
1.2
7.7
0.89
0.61
1.3
1.2
2.4
1.5
1.8
0.72
0.88
0.81
0.75
1.6
1.2
0.65
0.49
2.2
3.1
2.1
9.7
1.2
3.0
3.0
1.7
3.8
0.73
1.0
1.6
2.9
4.8
3.0
2.6
2.2
0.43
0.12
0.40
0.37
0.48
1.0
0.23
0.43
0.25
0.32
0.76
0.56
0.76
0.37
0.53
0.65
0.82
0.50
0.43
2.4
0.55
1.8
1.4
1.2
4.4
0.50
0.81
1.0
0.76
2.2
0.44
1.2
0.41
1.7
1.0
2.2
1.4
1.2
0.81
0.82
0.82
0.70
0.70
0.81
0.88
0.78
0.83
0.79
0.88
0.90
0.91
1.1
0.86
0.93
0.98
0.80
0.93
0.88
0.42
0.41
0.42
0.34
0.50
0.42
0.43
0.38
0.41
0.41
1.9
1.9
2.0
1.7
1.9
1.8
2.9
1.5
2.1
1.7
29011705880 750
retrievals in eastern North America and with AERONET in
Europe (ratios of modeled to AERONET in these regions are
0.86 and 0.81); it underestimates Asian (ratio is 0.67) and
biomass burning AAOD (about 0.5–0.7 for AERONET and
0.4–0.5 for OMI).
AAOD depends not just on aerosol load but also on op-
tical properties, such as refractive index, particle size, den-
sity and mixing state. In Fig. 3 we show how the GISS
model AAOD changes with assumed effective radius. The
global mean AAOD decreases/increases 15%/27% for an in-
crease/decrease of 0.02µm effective radius. Note that the
AeroCom model initial particle diameters (Table 1) span be-
yond this range (0.01 to 0.9µm) and in some cases grow
as the particles age. Increasing particle density from 1.6
to 1.8gcm−3in the GISS model decreases AAOD about as
much as increasing particle size from 0.08 to 0.1µm (calcu-
lated but not shown). Thus the AAOD is highly sensitive to
small changes in these optical properties.
Note that models generally underestimate AAOD but not
surface concentration. As we will discuss below, this could
result from inconsistencies in the measurements, from model
under-prediction of BC aloft, or from under-prediction of ab-
sorption. In this connection most models in the 2005-version
of AeroCom did not properly describe internal mixing with
scattering particles in the accumulation mode. Such mixing
increases the absorption cross section of the aerosols com-
pared to external mixtures of nucleation- and Aitken-mode
BC particles.
3.3 Wavelength-dependence
Black carbon absorption efficiency decreases less with in-
creasing wavelength compared with dust or organic carbon
(Bergstrom et al., 2007). Therefore comparison of AAOD
with retrievals at longer wavelength indicates the extent to
which BC is responsible for biases. In Fig. 5 we compare
AERONET AAOD at 550 and 1000nm with the GISS model
AAOD for the wavelength intervals 300–770nm and 860–
1250nm respectively. Table 5 shows the ratio of the GISS
model to AERONET within source regions for 1000nm and
550nm, for three different BC effective radii. In all regions
except Europe and Asia, the ratio is even lower at the longer
wavelength, confirming the need for increased simulated BC
absorption, rather than other absorbing aerosols that absorb
relatively less at longer wavelengths.
3.4 Seasonality
Our analysis has considered only annual mean observed and
modeled BC. Here we present the seasonality of observed
AAOD compared with the GISS model to explore how the
bias may vary with season.
for AERONET, OMI and the GISS model in Fig. 6. As
in most of the models, the GISS model BC energy emis-
sions do not include seasonal variation. Biomass burning
emissions do, and dust seasonality is also very pronounced.
However, transport and removal seasonal changes also cause
fluctuations in model industrial source regions. Note that
more AERONET data satisfy our inclusion criteria for the
3 month means compared with annual means (see figure cap-
tions), so coverage is better in some regions and seasons than
in the annual dataset in Fig. 3. Table 4 (bottom 4 rows)
gives regional seasonal retrieved mean AAOD. The seasonal
model-to-measurement ratios are also provided in the middle
portion of Table 4.
Seasonal AAOD are shown
Atmos. Chem. Phys., 9, 9001–9026, 2009www.atmos-chem-phys.net/9/9001/2009/

Page 9

D. Koch et al.: Evaluation of black carbon estimations in global aerosol models9009
AAOD AER 550AAOD OMI 500
0.1 0.1 0.10.10.1 0.1
0.20.2 0.20.2 0.2 0.2
0.5 0.50.5 0.5 0.50.5
111111
222222
555555
10 10 1010 1010
OMI
0.1 0.2 0.1 0.20.1 0.2 0.1 0.20.1 0.20.1 0.20.5 1
AERONETAERONETAERONET AERONET AERONET AERONET
22222255555510 1010 1010 10

OMI
0.5 10.5 10.5 10.5 10.5 1
OMIOMI OMIOMI
+ North America
+ Europe
+ Southeast Asia
o South America
o South Africa
+ Other
standard r=0.08 0.55 r=0.1 0.47 r=0.06 0.70
0.0
0.1
0.2
0.5
1.0
2.0
3.0
4.0
5.0
6.0
20.0
Fig. 3. Top: Aerosol absorption optical depth, AAOD, (x100) from AERONET (at 550nm; upper left), OMI (at 500nm; upper right);
middle: scatter plot comparing OMI and AERONET at AERONET sites; and bottom: GISS sensitivity studies for effective radius 0.08, 0.1,
and 0.06µm for 300–770nm. The AERONET data are for 1996–2006, v2 level 2, annual averages for each year were used if >8 months
were present, and monthly averages for >10 days of measurements. The values at 550nm were determined using the 0.44 and 0.87µm
Angstrom parameters. The OMI retrieval is based on OMAERUVd.003 daily products from 2005–2007 that were obtained through and
averaged using GIOVANNI (Acker and Leptoukh, 2007).
Biomass burning seasonality, with peaks in JJA for central
Africa (OMI) and in SON for South America, is simulated in
the model without clear change in bias with season. In Asia
both retrievals have maximum AAOD in MAM, which the
model underestimates (i.e. ratio of model to observed is low-
est in MAM). The MAM peak may be from agricultural or
biomass burning not underestimed by the model emissions.
The other industrial regions do not have apparent seasonal-
ity. However the model BC is underestimated most in Eu-
rope during fall and winter suggesting excessive loss of BC
or missing emissions during those seasons.
Summertime pollution outflow from North America seems
to occur in both OMI and the model. The large OMI AAOD
in the southern South Atlantic during MAM-JJA may be a
retrieval artifact due to low sun-elevation angle and/or sparse
sampling; however if it is real, then the model seasonality in
this region is out of phase.
3.5Column BC load
Model simulation of column BC mass (Fig. 7) in the atmo-
sphere should be less diverse than the AAOD since it con-
tains no assumptions about optical properties. However there
is no direct measurement of BC load. Schuster et al. (2005)
developed an algorithm to derive column BC mass from
AERONET data, working with the non-dust AERONET cli-
matologies defined by Dubovick et al. (2002). The Schuster
algorithm uses the Maxwell Garnett effective medium ap-
proximation to infer BC concentration and specific absorp-
tion from the AERONET refractive index. The Maxwell
Garnett approximation assumes homogeneous mixtures of
small insoluble particles (BC) suspended in a solution of
scattering material. Such mixing enhances the absorptivity
of the BC. Schuster et al. (2005) estimated an average spe-
cific absorption of about 10m2g−1, a value larger than most
www.atmos-chem-phys.net/9/9001/2009/Atmos. Chem. Phys., 9, 9001–9026, 2009

Page 10

9010D. Koch et al.: Evaluation of black carbon estimations in global aerosol models
AVEST DEVGISSGOCART
UIO GCM ARQMSPRINTARS MPI HAM
MIRAGE UIO CTM ULAQLOA
LSCEMOZART TM5 UMI
0.0
0.1
0.2
0.5
1.0
2.0
3.0
4.0
5.0
6.0
20.0
Fig. 4. Annual average AAOD (x100) for AeroCom models at 550nm. First panel is average, second panel standard deviation.
1216
Figure 5. Annual average AAOD (x100) at AERONET stations for 550 nm and 1000 nm (top
Fig. 5. Annual average AAOD (x100) at AERONET stations for 550nm and 1000nm (top left and right), and for the GISS model for
300–770nm (bottom left) and 860–1250nm (bottom right).
1217
left and right), and for the GISS model for 300-770nm (bottom left) and 860-1250nm (bottom 1218
right). 1219
Atmos. Chem. Phys., 9, 9001–9026, 2009www.atmos-chem-phys.net/9/9001/2009/

Page 11

D. Koch et al.: Evaluation of black carbon estimations in global aerosol models 9011
Table 4. Average ratio of model to retrieved AERONET and OMI Aerosol Absorption Optical Depth at 550nm within regions for AeroCom
models and GISS sensitivity studies. Number of measurements is given for AERONET. Annual and seasonal measurement values are given
in last 5 rows. Regions defined as NAm (130W to 70W; 20N to 55N), Europe (15W to 45E; 30N to 70N), Asia (100E to 160E; 30N to
70N), SAm (85W to 40W; 34S to 2S), Afr (20W to 45E; 34S to 2S).
AER
N Am
#44
AER
Eur
#41
AER
Asia
#11
AER
S Am
AER
Afr
AER
Rest of
World #40
OMI
N Am
OMI
Eur
OMI
Asia
OMI
S Am
OMI
Afr
OMI
Rest of
World#7#5
GISS
ARQM
GOCART
SPRINTARS
LOA
LSCE
MOZGN
MPIHAM
MIRAGE
TM5
UIOCTM
UIOGCM
UMI
ULAQ
Ave
GISS
sensitivity
studies
std
r=.1
r=.06
EDGAR32
IIASA
BB1998
Lifex2
Life/2
Ice/2
Icex2
Std DJF
Std MAM
Std JJA
Std SON
Retrieved
x100
Annual
Average
DJF
MAM
JJA
SON
1.0
0.79
1.4
1.4
0.57
0.42
1.5
0.39
0.73
0.41
0.62
1.3
0.32
1.4
0.86
0.83
0.36
1.5
0.48
0.56
0.55
1.3
0.21
0.55
0.32
0.67
1.1
0.29
2.6
0.81
0.49
0.30
1.4
0.44
0.42
0.48
0.99
0.29
0.49
0.29
0.46
0.75
0.29
2.1
0.67
0.59
0.42
0.72
1.8
0.44
0.20
0.60
0.43
0.76
0.24
1.1
0.82
0.21
1.1
0.68
0.35
0.25
0.79
1.2
0.70
0.18
0.60
0.35
0.78
0.20
0.61
0.54
0.21
0.52
0.53
0.88
0.44
0.96
0.64
0.44
0.34
0.77
0.21
0.42
0.31
0.57
0.80
0.22
1.1
0.55
0.73
0.50
0.79
0.76
0.32
0.29
0.82
0.21
0.35
0.21
0.37
0.82
0.17
1.1
0.52
1.4
0.61
2.5
0.69
0.95
1.1
2.6
0.29
0.91
0.48
1.1
1.8
0.44
6.7
1.6
0.74
0.40
1.4
0.59
0.44
0.51
1.4
0.32
0.48
0.31
0.53
1.0
0.28
1.5
0.71
0.29
0.22
0.34
0.83
0.25
0.11
0.32
0.22
0.41
0.12
0.57
0.46
0.095
0.62
0.35
0.40
0.23
0.72
1.3
0.48
0.21
0.40
0.35
0.58
0.22
0.54
0.42
0.19
0.48
0.47
0.28
0.19
0.46
0.28
0.18
0.16
0.35
0.082
0.20
0.11
0.19
0.36
0.086
0.71
0.26
1.0
0.86
1.4
1.1
1.2
1.1
1.3
0.93
1.1
0.96
0.85
0.96
0.83
1.2
0.83
0.66
1.1
0.81
0.85
0.81
0.91
0.73
0.83
0.74
0.45
0.86
0.97
0.64
0.49
0.40
0.68
0.46
0.57
0.51
0.54
0.46
0.51
0.48
0.45
0.51
0.64
0.56
0.59
0.49
0.77
0.57
0.59
0.67
0.66
0.58
0.62
0.58
0.29
0.41
0.43
0.51
0.35
0.28
0.47
0.35
0.36
0.40
0.41
0.33
0.36
0.34
0.33
0.38
0.30
0.34
0.53
0.48
0.61
0.58
0.55
0.55
0.58
0.52
0.52
0.52
0.31
0.46
0.66
0.40
0.73
0.60
1.0
0.75
0.82
0.80
0.93
0.65
0.81
0.68
0.40
0.95
0.63
0.63
1.4
1.2
1.8
1.4
1.5
1.4
1.6
1.3
1.5
1.3
0.40
1.2
1.7
0.80
0.74
0.61
1.0
0.73
0.90
0.84
0.88
0.67
0.79
0.71
0.64
0.60
0.93
0.71
0.29
0.24
0.38
0.28
0.29
0.31
0.35
0.28
0.29
0.31
0.22
0.21
0.28
0.20
0.40
0.32
0.53
0.41
0.41
0.45
0.50
0.37
0.41
0.39
0.30
0.33
0.36
0.57
0.28
0.22
0.38
0.29
0.32
0.30
0.39
0.23
0.31
0.24
0.36
0.38
0.36
0.38
0.691.5 3.61.82.02.40.850.68 1.52.21.7 1.2
0.57
0.79
0.88
0.57
1.4
1.6
1.6
1.6
3.3
4.0
3.0
3.0
1.4
1.0
2.4
3.1
0.9
0.8
3.1
3.9
2.6
2.4
2.0
2.2
1.0
0.72
1.0
0.95
1.4
0.97
0.71
1.0
1.4
2.2
1.2
1.4
1.5
1.4
2.7
4.7
1.2
0.82
2.7
1.4
0.9
1.4
1.8
1.1
of the models (see Table 1); however a lower value would in-
crease the retrieved burden and worsen the comparison with
the models.
An updated version of the AERONET-derived BC col-
umn mass is shown in Figs. 7 and 8. For this retrieval, a
BC refractive index of 1.95–0.79i was assumed, within the
range recommended by Bond and Bergstrom (2006), and
BC density of 1.8gcm−3.In the retrievals, most conti-
nental regions have BC loadings between 1 and 5mgm−2,
with mean values for North America (1.8mgm−2) and
Europe (2.1mgm−2) being somewhat smaller than Asia
(3.0mgm−2) and South America (2.7mgm−2). The current
www.atmos-chem-phys.net/9/9001/2009/ Atmos. Chem. Phys., 9, 9001–9026, 2009

Page 12

9012D. Koch et al.: Evaluation of black carbon estimations in global aerosol models
AAOD 550 DJF MAMJJASON
OMI
model
0.0 0.10.2 0.51.0 2.03.04.0 5.06.0 20.0
Fig. 6. Seasonal average AAOD (x100) for AERONET 550nm (top), OMI 500nm (middle), standard GISS model 550nm (bottom).
Table 5. The average ratio of GISS model to AERONET within regions for 1000nm and 550nm.
Effective
Radius, µm
AAOD
Nam 44
AAOD
Eur 41
AAOD
Asia 11
AAOD
S Am 7
AAOD
Afr 5
AAOD
Rest 21
1000nm
Std r=0.08
r=0.1
r=0.06
550nm
Std r=0.08
r=0.1
r=0.06
0.85
0.72
1.1
0.87
0.73
1.1
0.55
0.47
0.73
0.42
0.36
0.55
0.28
0.23
0.36
0.54
0.50
0.61
1.0
0.86
1.4
0.83
0.66
1.1
0.49
0.40
0.68
0.59
0.49
0.77
0.35
0.28
0.47
0.53
0.48
0.61
industrial region retrievals are larger than the previous esti-
mates of Schuster et al. (2005), which were 0.96mgm−2for
North America, 1.4mgm−2for Europe and 1.6mgm−2for
Asia. The biomass burning estimates are similar to the previ-
ous retrievals. The differences may be due to the larger span
of years and sites in the current dataset.
Figure 7 shows the AeroCom model BC column loads.
The model standard deviation relative to the average is sim-
ilar to the surface concentration (Fig. 2) and the AAOD
(Fig. 4). The model column loads are smaller than the
Schuster estimate. Some models agree quite well in Europe,
Southeast Asia or Africa (e.g. GOCART, SPRINTARS,
MOZGN, LSCE, UMI). Model to retrieved ratios within re-
gions are presented in Table 6. This ratio is generally smaller
than model to retrieved AAOD in North America and Eu-
rope. The inconsistencies among the retrievals would benefit
from detailed comparison with a model that includes particle
mixing and with model diagnostic treatment harmonized to
the retrievals.
Figure 8 has GISS BC column sensitivity study re-
sults. The load is affected differently than the surface
concentrations (Fig. 1). The Asian IIASA emissions are
Atmos. Chem. Phys., 9, 9001–9026, 2009www.atmos-chem-phys.net/9/9001/2009/

Page 13

D. Koch et al.: Evaluation of black carbon estimations in global aerosol models9013
Schuster BC load
AVE ST DEVGISS
CAMGOCARTUIO GCMARQM
SPRINTARSMPI HAMMATCHMIRAGE
UIO CTMULAQLOADLR
LSCEMOZARTTM5UMI
0.00.10.2 0.5 1.02.0 5.010.0
13.0
mg/m2
Fig. 7. Annual mean column BC load for 9 AeroCom models, mgm−2. The Schuster BC load is based on AERONET v2 level 1.5; annual
averages require 12 months of data, data include all AERONET up to 2008.
larger than Bond (Bond et al., 2004) or EDGAR32, so that
the outflow across the Pacific is greater. The large-biomass
burning case (1998) also results in greater BC transport to
Northwestern US in the column. Increasing BC lifetime in-
creases both BC surface and column mass more than the
other cases; however it has a larger impact on Southern
Hemisphere load than surface concentrations. The reduced
ice-outcasehassomewhatsmallerimpactonthecolumnthan
at the surface, especially for some parts of the Arctic. The
reduced ice-out thus has an enhanced effect at low levels, be-
low ice-clouds, in the Arctic, while having a relatively small
impact on the column. Modest model improvements relative
to the retrieval occur for the case with increased lifetime and
for the IIASA emissions (Table 6).
3.6 Aircraft campaigns
We consider the BC model profiles in the vicinity of recent
aircraft measurements in order to get a qualitative sense of
how models perform in the mid-upper troposphere and to
see how model diversity changes aloft. The measurements
were made with three independent Single Particle Soot ab-
sorption Photometers (SP2s) (Schwarz et al., 2006; Slowik
et al., 2007) onboard NASA and NOAA research aircraft at
tropical and middle latitudes (Fig. 9) and at high latitudes
www.atmos-chem-phys.net/9/9001/2009/Atmos. Chem. Phys., 9, 9001–9026, 2009

Page 14

9014 D. Koch et al.: Evaluation of black carbon estimations in global aerosol models
standard 0.36 EDGAR 0.37 IIASA 0.41
BB 1998 0.38 lifex2 0.51 life/2 0.29
0.0
0.1
0.2
0.5
1.0
2.0
5.0
10.0
13.0
mg/m2
ice-out/2 0.38 ice-outx2 0.33
Schuster BC load
Fig. 8. Annual mean column BC load for GISS sensitivity simulations and the Schuster BC retrieval (see Fig. 7).
(Fig. 10) over North America. Details for the campaigns
are provided in Table 7. The SP2 instrument uses an intense
laser to heat the refractory component of individual aerosols
in the fine (or accumulation) mode to vaporization. The de-
tected thermal radiation is used to determine the black carbon
mass of each particle (Schwarz et al., 2006). The U. Tokyo
and the NOAA data have been adjusted 5–10% (70% dur-
ing AVE-Houston) to account for the “tail” of the BC mass
distribution that extends to sizes smaller than the SP2 lower
limit of detection. This procedure has not been performed
on the U. Hawaii data, however this instrument was config-
ured to detect smaller particle sizes so that the unmeasured
mass is estimated to be less than about 13% (3% at smaller
and 10% at larger sizes). The aircraft data in each panel
of Figs. 9 and 10 are averaged into altitude bins along with
standard deviations of the data. When available, data mean
as well as median are shown. For cases in which signifi-
cant biomass smoke was encountered (e.g. Figs. 9d, 10d and
e), the median is more indicative of background conditions
than the mean. However, for the spring ARCPAC campaign
(Fig. 10c), four of the five flights encountered heavy smoke
conditions, so in this case profiles are provided for the mean
of the smoky flights and the mean for the remaining flight
which is more representative of background conditions. The
ARCPAC NOAA WP-3D aircraft thus encountered heavier
burning conditions (Fig. 10c; Warneke et al., 2009) than the
other two aircraft for the Arctic spring (Fig. 10a and b).
Model profiles shown in each panel are constructed by
averaging monthly mean model results at several locations
along the flight tracks (map symbols in Figs. 9 and 10).
We tested the accuracy of the model profile-construction ap-
proach using the U. Tokyo data and the GISS model, by com-
paring a profile constructed from following the flight tracks
within the model fields with the simpler profile construction
shown in Fig. 10a. The two approaches agreed very well
except in the boundary layer (the lowest 1–2 model levels).
Potentially more problematic is the comparison of instan-
taneous observational snapshots to model monthly means.
Nevertheless the comparison does suggest some broad ten-
dencies.
The lower-latitude campaign observations (Fig. 9) indicate
polluted boundary layers with BC concentrations decreas-
ing 1–2 orders of magnitude between the surface and the
mid-upper troposphere. Some of the large data values can
be explained by sampling of especially polluted conditions.
For example, the CARB campaign (Fig. 9d) encountered
Atmos. Chem. Phys., 9, 9001–9026, 2009www.atmos-chem-phys.net/9/9001/2009/

Page 15

D. Koch et al.: Evaluation of black carbon estimations in global aerosol models 9015
50
100
200
500
1000


a
AVE Houston
NASA WB-57F
November

b
CR-AVE
NASA WB-57F
February
50
100
200
500
1000
Pressure (hPa)
0.512510 20
BC(ng/kg)
50 100 200500

c
TC4
NASA WB-57F
August
0.512510 20
BC(ng/kg)
50 100 200 500

d
CARB
NASA DC-8, P3-B
June
0
20
40
60
80
-180 -120-600

Campaigns
(a) AVE Houston
(b,c) CR-AVE, TC4
(d) CARB
Models
ARQM
CAM
GISS
GOCART
SPRINTARS
LOA
LSCE
MATCH
MOZART
MPI
MIRAGE
UIO CTM
UIO GCM (dash)
ULAQ (dash)
UMI (dash)
TM5 (dash)
DLR (dash)
Fig. 9. Model profiles in approximate SP2 BC campaign locations in the tropics and midlatitudes, averaged over the points in the map
(bottom). Observations (black curves) are average for the respective campaigns, with standard deviations where available. The Houston
campaign has two profiles measured two different days. Mean (solid) and median (dashed) observed profiles are provided for (d). The
markers in the map inset denote the location of model profiles in these comparisons with the aircraft measurements that are detailed in
Table 7.
unusually heavy biomass burning. The models used climato-
logical biomass burning and would not have included these
particular fire conditions. Nevertheless, overall the datasets
show remarkably consistent mid-tropospheric mean BC lev-
els of about 0.5–5ngkg in the tropics and midlatitudes. With
the exception of the CARB campaign, the models generally
exceed the upper limit of the standard deviation of the data.
For CARB, most models are within the data standard devi-
ations up to about 500mb (Fig. 9d), while about half ex-
ceed the upper limit of the observed standard deviation above
500mb.
The spring-time Arctic campaigns observed maximum BC
above the surface (Fig. 10a–c), which may occur from two
mechanisms. First, background “Arctic haze” pollution is
thought to originate at lower latitudes, and is transported to
the Arctic by meridionally lofting along isentropic surfaces
(Iversen, 1984; Stohl et al., 2006). Most of the observed
profiles and the model results would reflect those conditions.
Alternatively, BC could be injected into the mid-troposphere
near its source by agricultural or forest fires and then ad-
vected into the Arctic. This is apparently the case for the
ARCPAC measurements (Fig. 10c) that probed Russian fire
smoke (Warneke et al., 2009). In both cases, the pollution
levels aloft during springtime are substantial and compara-
ble to those levels observed in the polluted boundary layer at
midlatitudes. Thus at the lower latitudes BC decreases with
altitude, whereas at these higher latitudes it increases toward
the middle troposphere during springtime. Model profile di-
versity is especially great in the Arctic, as discussed in previ-
ous sections. Many of the models do have profile maximum
BC above the surface, but most of the springtime peak val-
ues are smaller in magnitude than the aircraft measurements.
The three spring campaign measurements have mean BC of
about 50–200ngkg−1at 500mb; 10 of the 17 models are
www.atmos-chem-phys.net/9/9001/2009/Atmos. Chem. Phys., 9, 9001–9026, 2009