AeroCom - aerosol emission data-sets
recommendations for years 2000 and 1750
Frank Dentener, Julian Wilson, Luisa Marelli, Jean-Philippe Putaud (IES, JRC, Italy)
Tami Bond (Univ. of Illinois-Champagne, USA)
Judith Hoelzemann, Stefan Kinne (MPI Hamburg, Germany)
Sylvia Generoso, Christiane Textor, Michael Schulz (LSCE Saclay, France)
Sylvia Generoso (EPFL-ENAC, Lausanne, Switzerland)
Guido van der Werf (Univ. of Amsterdam, Netherlands)
Sunling Gong (ARQM Met Service Toronto, Canada)
Paul Ginoux (NOAA-GFDL Princeton, USA)
Janusz Cofala (IIASA Laxenburg, Austria)
Olivier Boucher (Met Office, Exeter, Great Britain )
Akinori Ito, Joyce Penner (Uni of Michigan Ann Arbor, USA)
document change record
Issue
Date
Modified Items / Reason for Change
0.0
10 Sep 2004
0. version by F. Dentener
0.1
10.Oct 2005
1. version by S. Kinne
0.2
24.Oct 2005
2. version by S. Kinne
0.3
29. Oct 2005
3. version by S. Kinne
0.4
15.Nov 2005
4. version by S. Kinne
… in preparation for ACP techn. notes
1
Emissions of primary aerosol and precursor
gases in the years 2000 and 1750
prescribed data-sets for AeroCom
F. Dentener, S. Kinne, T. Bond, O. Boucher, J. Cofala, S. Generoso, P. Ginoux,
S. Gong, J.J. Hoelzemann, A. Ito, L. Marelli, J. Penner, J.-P. Putaud, C. Textor,
M.Schulz, G. van der Werf and J. Wilson
Abstract
Inventories for global aerosol emissions have been developed for the year 2000 (present-day
conditions) and for the year 1750 (pre-industrial conditions). The choices were based on
available data for the year 2000. The overall goal was to keep it simple, with the primary goal
to harmonize aerosol emission input for model inter-comparisons. Although large
uncertainties are expected with respect to carbonaceous aerosol, these data-sets are considered
an initial stepping stone to even better aerosol emission inventories in the future.
1 Introduction
Large uncertainties in climate modeling are introduced by aerosol (IPCC, 2001). The Aerosol
Inter Comparison project AeroCom (http://nansen.ipsl.jussieu.fr/AEROCOM/) has been
initiated in 2003 to understand the nature of these uncertainties by performing a systematic
analysis of detailed aerosol module output of more than 15 global models, focusing on
comparisons to available data from in-situ sampling and remote sensing. Initial model
comparisons under the umbrella of AeroCom Experiment ‘A’ illustrate at times significant
model diversity (Kinne et al., 2005; Textor et al. 2005). In these initial submissions for
modelers were allowed to use aerosol emissions (an essential model input) of their choice,
without a clear connection to any particular. Thus, these model comparisons are of limited
value. In order to harmonize aerosol input, two additional AeroCom simulations were
requested, that applied common emission data for primary aerosol and for aerosol precursor
2
gases. AeroCom Experiment ‘B’ was established to simulate conditions of the year 2000, if
possible with actual met-data (via nudging in GCMs) in an effort to establish stronger ties to
available observational data. AeroCom Experiment ‘Pre’ was designed to establish a preindustrial modeling reference, around the year 1750, to extract anthropogenic impacts in
conjunction with Experiment ‘B’ (Schulz et al., 2005). For both experiments, ‘B’ and Pre’,
global data sets for aerosol emissions were defined. Background and details to these data-sets
are introduced in this report.
2 General
For the years 2000 and 1750, global aerosol emission fields were developed at a spatial
resolution of 1°latitude *1°longitude (in units of kg per grid-box – note that the grid-box area
varies with latitude). Temporal resolution ranges from daily to yearly depending on the
species. Information was given for injection height and for sizes of particulate emissions.
Emissions were categorized by their origin as either natural or anthropogenic.
Natural emissions are assumed identical for Experiment ‘B’ (year 2000) and Experiment ‘Pre’
(year 1750). Details of the natural emission data-sets are introduced in Chapter 3.
Anthropogenic emissions for years 1750 and 2000 differ. Thus, anthropogenic emission datasets are explained separately, for Experiment ‘B’ in Chapter 4 and for Experiment ‘Pre’ in
Chapter 5. Temporal resolution of aerosol data-sets is summarized in Chapter 6, injection
heights are explained in Chapter 7, and size recommendations are addressed in Chapter 8. All
AeroCom emission data-sets are available (in netcdf data format) via a dedicated file transfer
site at the Joint Research Center in Ispra, Italy: ftp://ftp.ei.jrc.it/pub/Aerocom/. To help locate
specific data, ftp-site subdirectories and their content are listed in Appendix A.
3 Natural emissions
Natural emissions include wind-blown contributions of mineral dust (DU) and sea-salt (SS),
sulfur (S) contributions from volcanoes and DiMethyl Sulphide (DMS, mainly oceanic) and
(Secondary) Organic Aerosol (SOA) formed from natural Volatile Organic Compound (VOC)
emissions. Most of S and all of SOA are formed within the atmosphere from precursor gases.
Hereby, SOA is added to the flux of Particulate Organic Matter (POM). An overview of
annual global amounts and recommendations for injection height are given in Table 1.
3
Table 1. AeroCom natural emissions
temporal aerosol
resolution type*
amount
[Tg /yr]
injection
altitude
size ln size ln
rm [m]
reff [m]

dust
daily
DU
1678
surface
.650 C
2.0 C
2.10
sea-salt
daily
SS
7925
surface
.740 C
2.0 C
2.50
DMS
daily
S+
18.2
surface
.040
1.8
.095
volcanic,
explosive
yearly
S+
2.0
(VTop+500m)(VTop+1500m
.040 50%
.015 50%
1.8
.059
volcanic,
continuous
yearly
S+
12.6
(.67 *VTop)(1.0 *VTop)
.040 50%
.015 50%
1.8
.059
monthly
POM
19.1
surface
SOA
* DU-dust, SS-sea-salt, S-sulfur (=0.50*SO2,or 0.33*S04), POM-particulate org matter (=1.40*organic carbon)
+
C
2.5% of sulfur (S) is emitted as particulate SO4
for the cross-section area dominating coarse mode
Table 1 also lists size-recommendations in terms of log-normal size-distribution parameters rm
(number mode radius) and  (standard deviation), plus the resulting reff (this radiatively eff.
radius is defined by the ratio of sums of the third and the second radii moments, r3/ r2).
3.1
Dust
Dust (DU) data are based on simulations with the year 2000 near surface winds generated by
the NASA Goddard Earth Observing System Data Assimilation System (GEOS DAS). Daily
average DU fluxes were provided at 1o(latitude) *1o(longitude) resolution (Ginoux et al.,
2001, 2003). The original data were distributed over four size-bins (radii ranges of 0.1-1.0,
1.0-1.8, 1.8-3.0, 3.0-6.0m). In order to accommodate modal size schemes, dust flux data
were stratified according to size into two domains (assuming equal number within each
original size-bin): accumulation size (radii: 0.05-0.5m) and coarse size (radii > 0.5m).
Then for each size domain the flux was distributed over a log-normal function, which is
defined by the three parameters of mode-radius rm (radius at the peak concentration), standard
deviation  (distribution width) and number N. Assuming a DU density of 2.5g/cm3 and
prescribing the standard deviation (1.59 for the accumulation size mode and 2.0 for the coarse
size mode) the number mode-radius was determined at each grid-point and time-step (as data
for number N and mass-flux were provided by the original data). Daily global fields for
4
mode-radius and number (along with prescribed values for density and standard deviation)
establish to recommended emission input for dust. As this input is tailored to size-modal
schemes an extra (IDL) routine is provided that determines the distribution equivalent
emission flux models with (size-) bin schemes. Based on the modal approach 98.6% of the
dust flux mass is assigned to the coarse domain (and 1.4% to accumulation size domain). The
spatial distributions of dust-emissions on an annual and monthly basis are given in Figure 1.
Figure 1. Global fields of annual and monthly dust emission flux (based on year 2000 simulations by Ginoux)
The characteristic dust maximum dimension is about 4 m (as a mode radius of 0.63 m in
conjunction with a standard deviation of 2 for the coarse mode translates into an effective
‘radius’ of 2.1 m). Dust emissions are prescribed to take place in the lowest model layer.
Biases may have been introduced by limitations associated with the GEOS DAS meteorology
and simplifying assumption. In particular the coarse (daily) temporal resolution and injections
into the lowest layer of the model only are expected to contribute to dust flux underestimates.
Inaccuracies are introduced with the modal size representation and uncertainties will be
introduced during model implementations (e.g. inconsistencies with respect to boundary layer
mixing (e.g. dry convection) and/or adaptations to different model resolutions).
5
3.2
Sea-salt
Sea-salt (SS) daily emission data are based on year 2000 ECMWF near surface winds. Mass
fluxes are provided over 24 size bins (considering sea-salt radii from 0.005 to 20.48m) at
1.175o(latitude) *1.175o(longitude) horizontal resolution (Gong et al., 2003). Here, flux
contributions associated with radii larger 10m are ignored. The data were re-gridded to 1o*1o
resolution and redistributed over three size domains (assuming equal number within each
original size-bin): Aitken size (radii < 0.05m), accumulation size (radii: 0.05-0.5m) and
coarse size (radii > 0.5m). Then for each size domain sea-salt fluxes were distributed over a
log-normal function, which is defined by the three parameters of mode-radius rm, standard
deviation  and number N. Assuming a SS density of 2.2g/cm3 and prescribing the standard
deviation (1.59 for Aitken and accumulation size mode and 2.0 for the coarse size mode) the
number mode-radius was determined at each grid-point and time-step (as data for number N
and mass-flux were provided by the original data). Daily global fields for mode-radius and
number (along with prescribed values for density and standard deviation) establish to
recommended emission input for sea-salt. These daily SS-fluxes were weighted by the
monthly ECMWF sea-ice-free-fractions of the year 2000, to avoid SS-emissions over sea-ice.
As this input is tailored to accommodate size-modal schemes, an extra (IDL) routine is
provided that determines the distribution equivalent emission flux models with (size-) bin
schemes. Distributions of sea-salt emissions are displayed in Figure 2.
Figure 2. Global fields of annual and monthly sea-salt emission flux (based on year 2000 simulations by Gong)
6
The radiatively characteristic sea-salt diameter is at 5m (a mode radius of 0.74m in
conjunction with a standard deviation of 2 for the coarse mode translates into an effective
‘radius’ of 2.5m). Sea-salt emissions are prescribed to take place in the lowest model layer.
Uncertainty issues are similar to those for dust, including the blurring by the modal
representation. For example, even though sea-salt fluxes associated with radii larger than
10m were rejected, by fitting with the rather large standard deviation for the coarse lognormal size-distribution radii up to 25m in size will contribute to the sea-salt mass flux.
3.3
DMS
Daily DMS emission data are based on six hourly data of simulations with the LMDZ general
circulation model LOA (Boucher et al. 2003) at 2.5o(latitude) *3.75o(longitude) resolution.
Oceanic DMS emissions are derived by applying a parameterization for air-sea transfer
velocities (Nightingale, 2000) to simulated climatology tied to measurement samples (Kettle
and Andreae, 2000). Continental DMS emission of biogenic origin (Pham et al, 1995) are
generally much lower and in order to exclude unrealistic high contributions over land, oceanic
contributions are only allowed over ocean only model grid regions. Finally, daily average data
are interpolated onto a 1o*1o grid. Distributions of DMS emissions are given in Figure 3.
Figure 3. Global fields of annual and monthly DMS (sulfur) emission (based on model simulations by Boucher)
7
3.4
Volcanic Emissions
Yearly volcanic (sulfur) emissions data consider both continuous degassing and explosive
volcanos. Volcanic sulfur is emitted to 97.5% as SO2 and to 2.5% as SO4. The AEROCOM
dataset is based on the GEIA inventory (http://www.igac.noaa.gov/newsletter/22/sulfur.php;
http://www.geiacenter.org/) (Andres and Kasgnoc, 1998). However, since there are a number
of ambiguities in the description and use of these data; we provide an interpreted and updated
dataset, which is summarized next.
Explosive emissions are based observational techniques (including TOMS). They occur at
only a few locations which are probably not representative for the regions of active explosive
volcanism as shown by more recent data. The total emission (at 2 TgS/year) from explosive
volcanos is therefore equally distributed over all active volcanoes that had been active over
the last 100 years (Halmer et al., 2002). The emissions are continuously released, because
only about 1/3 of degassing occurs during highly explosive phases. Injections are placed
between 500 and 1500m above each volcano peak. It should be noted that explosive
emissions are not specific for the year 2000, as data were not available.
Continuous degassing presents significant larger contributions. The annual continuous
emissions recommend by GEIA is for various reasons considered to be an underestimate
(Graf et al. 1998, Textor et al. 2004). Thus, continuous sulfur-containing emissions of GEIA
are multiplied by a factor of 1.2. The new estimate (at 12.6 TgS/year) is still conservative,
since many volcanoes are not monitored, or can not be accurately detected by the satellite
sensors presently at use. Volcano locations are taken from GEIA. The emissions occur in the
upper third of the volcano altitude to account for degassing at the flanks of the volcanoes.
3.5
Secondary Organic Aerosol
Secondary Organic Aerosol (SOA) emissions are provided on monthly basis. They are based
on the assumption that 15% of natural terpene emissions form SOA, although actual SOA
production is much more complicated. It is assumed that SOA is formed on time scales of a
few hours and that SOA precursor emissions condense on pre-existing aerosol. In reality,
substantial SOA formation can occur at higher altitudes (Kanakidou et al., 2005). Terpene
emissions of 127 Tg/year were taken from GEIA (Guenther et al. 1995). This translates into
an annual global average of 19.1 Tg POM/year, which is within bounds of other estimates of
10 to 60 Tg POM/year (Kanakidou et al., 2005).
8
4 Anthropogenic emissions – year 2000
Anthropogenic emissions consider sulfur contributions mainly in terms of sulfate (SO2) and
carbonaceous contributions, where a distinction is made between organic carbon in terms of
Particulate Organic Matter (POM) and strongly absorbing black (or elementary) carbon (BC).
Sources are from large scale (wildland) fires (which are also in part natural), bio fuel burning
and fossil fuel burning. For the ladder a distinction is made between sources from road traffic,
shipping, off-road, industry and power-plants, to account for differences in injection height
and particle size, as indicated in Table 2, which summarizes recommendations of individual
anthropogenic contributions for the year 2000. Details of the data-sets for the AeroCom
Experiment B (year 2000) are introduced next.
Table 2. AeroCom anthropogenic emissions for the year 2000
type
data
temporal aerosol amount
[Tg /yr]
source resolution type
injection
altitude
wildland fire
GFED
monthly
BC
3.1
6 layers H
.040
1.8
.095
GFED
monthly
POM
34.7
6 layers H
.040
1.8
.095
GFED
monthly
S+
2.1
6 layers H
.040
1.8
.095
SPEW
yearly
BC
1.6
surface
.040
1.8
.095
SPEW
yearly
POM
9.1
surface
.040
1.8
.095
IIASA
yearly
S+
4.8
surface
.015
1.8
.036
SPEW
yearly
BC
3.0
surface
.015
1.8
.036
SPEW
yearly
POM
3.2
surface
.015
1.8
.036
Roads
IIASA
yearly
S+
1.0
surface
.015
1.8
.036
Shipping
IIASA
yearly
S+
3.9
surface
.500
2.0
1.66
off-road
IIASA
yearly
S+
0.8
surface
.015
1.8
.036
yearly
S
+
19.6
100-300m
.500
2.0
1.66
S
+
24.2
100-300m
.500
2.0
1.66
biofuel
domestic
fossil-fuel
Industry
power-plant
IIASA
IIASA
yearly
size (ln) size
rm [m] 
reff
[m]
* S-sulfur (=0.50*SO2,or 0.33*S04), POM-particulate org matter (=1.40*organic carbon), BC-black carbon
+
2.5% of sulfur (S) is emitted as particulate SO4, H 0-100m, 100-500m, 500-1000m, 1-2km, 2-3km, 3-6km
9
4.1
Large-scale (wildland) fire emissions of BC, POM and SO2
Monthly data for large-scale (wildland) fire emissions of carbon and sulfur are based on the
Global Fire Emission Database (GFED) inventory (van der Werf et al., 2003, 2004,
Randerson et al. 2005). In this data-set satellite derived burnt area products from ATSR and
MODIS are combined with present day vegetation cover (Olsen et al, 1985). The data
reproduce seasonal variations in biomass burning activities. Annual totals are in overall
agreement to other estimates (e.g. Generoso et al., 2003, Bond et al, 2004). Due to strong
year-to-year variations, two data-sets are offered: An estimate tied specifically to the year
2000 and a 6-year (1997-2002) average for climatological simulations. Monthly emissions
into the atmosphere are distributed over six ecosystem-dependent altitude regimes between
the surface and 6km (see section 7). Emission patterns of POM and BC for the year 2000
from large scale biomass burning (wildland fires) are displayed in Figure 4.
Figure 4. Organic matter (POM) and black carbon (BC) wildland fire emissions for year 2000
4.2
Biofuel emissions of BC, POM, and SO2
Yearly average data (no annual cycle) for biofuel organic emissions are based on the
Speciated Particulate Emissions Wizard (SPEW) inventory (Bond et al., 2004). Biofuel
emissions include the burning of charcoal, dung and crop residues and charcoal making.
Emission patterns of POM and BC for the year 1996 are displayed in Figure 5.
10
Figure 5. Organic matter (POM) and black carbon (BC) biofuel emissions for the year 1996.
Sulfur (-dioxide) biofuel emissions for the year 2000 are based on energy statistics for the
year 2000 (Cofala et al., 2005). Country and regional estimates (see Appendix B) were
gridded following EDGAR distribution patterns (Dentener, 2005). Emission Patterns of offroad and domestic sulfur dioxide emissions for the year 2000 are displayed in Figure 6.
Figure 6. Sulfur emissions of domestic and off-road (rural) activities for the year 2000.
11
4.3
Fossil-fuel emissions of BC, POM, and SO2
Yearly average data (no annual cycle) for fossil-fuel organic emissions are based on the
Speciated Particulate Emissions Wizard (SPEW) inventory (Bond et al., 2004). Emission
patterns of fossil-fuel POM and fossil-fuel BC of the year 1996 are displayed in Figure 7.
Sulfur-dioxide fossil-fuel emissions are based on energy statistics for the year 2000; using
technology controlled emission factors from IIASA/RAINS (Cofala et al., 2005). The country
and region estimates (see Appendix 2) were gridded following EDGAR (Olivier et al., 2002)
distribution patterns (Dentener, 2005).
Figure 7. Organic matter (POM) and black carbon (BC) fossil-fuel emissions for year 1996.
Ship traffic for the year 2000 is assumed to have increased by 1.5% per year since 1995 over
the EDGAR3.2 values. Patterns of industry and power-plant associated sulfur-dioxide
emissions for the year 2000 are displayed in Figure 8.
12
Figure 8. Sulfur dioxide emissions off power-plants and related to industry for the year 2000.
4.4
Other Emissions
For other emission data (e.g. for full chemistry simulations), it is recommended to use the
EDGAR 3.2, 1995 data-base (Olivier et al., 2002; http://arch.rivm.nl/env/int/coredata/edgar).
No specific recommendations are given for oxidant fields.
5 Anthropogenic emissions – year 1750
In pre-industrial times, here represented by the year 1750, anthropogenic emissions were not
zero, though only a fraction of those for the year 2000. While fossil-fuel emissions are
neglected, reduced contributions from wildland fires (open burning) and biofuel emissions are
expected. In the absence of observational data, anthropogenic emission estimates for
AeroCom Experiment ‘Pre’ are derived on educate-guess assumptions, which are explained
next. An overview of resulting annual global amounts and recommendations for injection
height and of log-normal size parameters (number-) mode radius rm and standard deviation 
(plus the associated radiatively effective radius reff ) is given in Table 3.
13
Table 3. AeroCom anthropogenic emissions for the year 1750
temporal
resolution
wildland fire
biofuel
aerosol amount
[Tg /yr]
type*
injection
altitude
size ln size ln
rm [m] std.dev. reff [m]
monthly
BC
1.02
6 layers H
.040
1.8
.095
monthly
POM
12.8
6 layers H
.040
1.8
.095
monthly
S+
0.7
6 layers H
.040
1.8
.095
yearly
BC
0.26
surface
.040
1.8
.095
yearly
POM
2.5
surface
.040
1.8
.095
yearly
S+
0.06
surface
.040
1.8
0.95
* S-sulfur (=0.50*SO2,or 0.33*S04), POM-particulate org matter (=1.40*organic carbon), BC-black carbon
+
H
2.5% of sulfur (S) is emitted as particulate SO4,
0-100m, 100-500m, 500-1000m, 1-2km, 2-3km, 3-6km
5.1
Large-scale (wildland) fire emissions of BC, POM and SO2
Pre-industrial wildland fire emissions are based on scaled 5 year averages (1998-2002) of
monthly data of the Global Fire Emission Database (GFED) inventory (van der Werf et al.,
2003, 2004; Randerson et al. 2005). Central to a rescaling is the “year1750-to-year2000”
population ratio from HYDE data-set (www.rivm.nl/hyde, see also Table C3 in Appendix C).
Actual scaling corrections are then performed according to present day land cover (Olsen et
al, 1985). Wet forest emissions scale by population whereas emissions over all other landsurfaces (e.g. grassland, shrub/bush, agricultural activity) scale only to 60% by population (as
it is assumed that 40% burns anyhow). Forest emissions in high latitudes of the northern
hemisphere (Europe, N.America, Russia) are doubled over current estimates, to account for
less fire suppression in the past (Brenkert et al., 1997).
5.2
Biofuel emissions of BC, POM, and SO2
Pre-industrial biofuel estimates are derived separately for carbonaceous aerosol and sulfur
emissions. The BC and POM contributions are scaled back to the year 1750 based on statistics
for population and crop production in developing countries and in developed countries based
on wood consumption, where the switch from electricity or natural gas as predominant
cooking fuel back to wood is considered (Ito and Penner, 2005). For sulfate-dioxide a CO
biofuel inventory for the year 1890 (van Aardenne et al., 2001) is multiplied by 0.00346,
14
based on the ratio of emission factor estimates (Andreae an Merlet, 2001) for SO2 and CO
(0.27g and 78g per kg of burned dry biomass, respectively). A “year1750-to-year1890”
population ratio from the HYDE data-set (see Appendix C) establishes the emissions for the
year 1750. Emissions at high latitudes in the northern hemisphere (Europe, N.America and
Russia) are doubled to account for a higher per person use (Brenkert et al., 1997).
6 Temporal resolution
The temporal resolution of the individual datasets is given in Tables 1 to 3. For simplification
anthropogenic emissions (with the exception of large scale wildland fires) are constant and
inter-annual variations are only prescribed for natural aerosol anthropogenic emissions.
However, even the daily resolution for dust, sea-salt and DMS (given the dependence on
surface winds) constitutes an approximation.
7 Injection height
Recommended injection heights of the individual emission datasets are given in Tables 1 to 3.
Most emissions are defined to occur evenly distributed in the lowest model-layer (‘surface’).
Fossil fuel emissions from industry and power-plants should be injected between 100 and
300m above the surface, because these emissions are usually released at the top of chimneys.
Large-scale wildland fire emissions are provided for six altitude regimes: 0-100m, 100-500m,
500-1km, 1-2km, 2-3km, 3-6km. Emissions are expected to be distributed evenly within each
layer. The maximum emission height for wildland fire emissions is given in Figure 9.
15
Figure 9. Maximum emission height (in meter) for (large-scale) wild-fire aerosol
The most complex altitude assignment is for volcanic emission. It is based on (a provided list
for) volcanic location and volcano top altitude (VTOP). For each volcano, explosive
contributions should be evenly placed between 500 and 1500m above VTOP and continuous
degassing should occur in the upper 1/3 altitude of each volcano.
8 Size choices for primary emissions
Recommended aerosol sizes for particles of the individual emission datasets are given in
Tables 1 to 3. Size recommendations are given in terms of log-normal distributions
parameters, where the mode radius (rm) describes the peak concentration and the standard
deviation () describes the distribution width. From both log-normal values a (radiatively)
characteristic size has been determined (reff, as the ratio between the sums of the third and the
second moment of the radius: r3/ r2.
For wildland fire (open burning) and biofuel aerosol the recommended characteristic size of
reff ~0.1m is based on an analysis of numerous field-measurements (see Appendix C). For
fossil-fuel two different sizes a given. A large size of reff ~1.6m is recommended for powerplant and industrial emissions (representing fly-ash, and components formed on it). A
16
relatively small size of reff ~.04m, which is recommended for other fossil fuel emissions (e.g.
traffic) is based on kerbside measurements in several EU-cities (Putaud et al. 2004, van
Dingenen et al., 2004). For particles from volcanic emissions, half of the mass is assigned
each to the small fossil-fuel size (reff ~0.1m) and to the biofuel size (reff ~.04m). For dust
and sea-salt, size recommendations are more complex, because they are defined by two and
three size-modes, respectively, with variations on a daily basis. However, since the mass flux
is dominated by contributions of the coarse size domain, the average characteristic size of the
coarse mode seems relevant with reff ~2.1m for dust and at reff ~2.5m for sea-salt.
9 Discussion and Conclusion
The above emissions, recommended for AeroCom, represent the state-of-the-art for global
aerosol emissions inventories in the year 2003. In some cases several alternative datasets were
available, such as three large-scale burning wildland fire inventories (van der Werf et al.,
2003; Generoso et al. 2003; Hoelzemann et al. 2004; see Appendix D). This often necessitates
choices. The overall goal was to keep it simple, with the primary goal to harmonize aerosol
emission input for model inter-comparison exercises (e.g. AeroCom Experiment ‘B’).
There are certainly avenues for improvement, such as (1) reliance on identical meteorological
fields, when deriving emission for sea-salt and dust, (2) higher temporal resolution for all
components (in particular by considering at least seasonal cycles) or (3) consistency to tracegas emissions relevant to atmospheric chemistry. Thus, these data-sets are considered an
initial stepping stone for better and improved aerosol emission inventories that will be
developed in the future.
Acknowledgements
AeroCom was sponsored by the European Communities FP5 project “Phoenics”
EVK2-CT-2001-00098
17
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21
Appendix A
Names and location of data-files on IES, JRC ftp-web site
All AeroCom emission data-sets are available (in netcdf data format) via a dedicated file
transfer site at the Joint Research Center in Ispra, Italy: ftp://ftp.ei.jrc.it/pub/Aerocom/. To
help locate specific data ftp-site subdirectories and their content is outlined below:
dust data
‘dust200001.nc’ (Jan 2000), …, ‘dust200012.nc’ (Dec 2000)
/seasalt_ncf salt data
‘salt200001.nc’ (Jan 2000), … , ‘salt200012.nc’ (Dec 2000)
/DMS_ncf
‘dms200001.nc’ (Jan 2000), … , ‘dms200012.nc’ (Dec 2000)
/dust_ncf
DMS data
/other_ncf_2000
all other emissions for the year 2000
- BC-biofuel
‘BC1bfuel.nc’
- BC-fossil fuel
‘BC1ff.nc’
- BC-veg.fire
‘GFED_2000_BC.nc’
- POM-vwg.fire
‘GFED_2000_POM.nc’
- SO2-veg.fire
‘GFED_2000_SO2_nc’
- POM-biofuel
‘POMbfuel.nc’
- POM-fossil fuel ‘POMff.nc’
- SO2-domestic
‘SO2_Domestic_2000bau.nc’
- SO2-industry
‘SO2_Industry_2000bau.nc’
- SO2-ships
‘SO2_International_Shipping_2000bau.nc’
- SO2-off raod
‘SO2_Off-road_2000bau.nc’
- SO2-powerplant ‘SO2_Powerplants_2000bau.nc’
- SO2-traffic
‘SO2_RoadTransport_2000bau.nc’
- POM-SOA
‘SOA.nc’
- volc. degassing ‘continuous_volc.nc’
- volc, explosive ‘explosive_volc.nc’
/other_ncf_1750
all other emissions for the year 1750
- BC-biofuel
‘BC1_1750_bfuel.nc’
- BC-veg. fire
‘GFED_1750_BC.nc’
- POM-veg. fire
‘GFED_1750_POM.nc’
- SO2-veg. fire
‘GFED_1750_SO2_nc’
- POM-biofuel
‘POM_1750_bfuel.nc’
- SO2-domestic
‘SO2_Domestic_1750bau.nc’
- POM-SOA
‘SOA.nc’
- volc. degassing ‘continuous_volc.nc’
- volc, explosive ‘explosive_volc.nc’
22
Appendix B
Regional aspects of AeroCom emissions
For a quick reference on regional contributions of BC, (P)OM and SO2, annual AeroCom
emissions are stratified into 18 regions according to the IMAGE project, as illustrated in
Figure B1 (courtesy of Bas Eickhout at RIVM, Netherlands).
Figure B1. Regional choices for continental regions by the IMAGE project (by Bas Eickhout)
Regional AeroCom emissions by species and source are summarized for current conditions
(year 2000) in Table B1 and for pre-industrial conditions (year 1750) in Table B2. Hereby,
differences between year 2000 and year 1750 emissions provide estimates on anthropogenic
contributions.
23
Table B1. Regional distributions of AeroCom emissions for the year 2000
category
BC
ofire
BC
biof
BC
fos.f
BC
all
OM
ofire
OM
biof
OM
fosf
OM
SOa
OM
all
SO2
ofire
SO2
v.ex
SO2
v.co
SO2
hom
SO2
road
SO2
pow
SO2
off-r
SO2
ship
SO2
indu
SO2
all
CANADA
USA
C AMERICA
S AMERICA
N AFRICA
W AFRICA
E AFRICA
S AFRICA
OECD EU
EAST EU
F. USSR
MID EAST
SOUTH ASIA
EAST ASIA
SE ASIA
OCEANIA
JAPAN
GREENLAND
OCEAN
0.01
0.07
0.13
0.73
0
0.73
0.25
0.56
0.01
0.01
0.08
0
0.04
0.01
0.20
0.23
0
0
0
0.01
0.06
0.03
0.08
0
0.18
0.08
0.07
0.03
0.03
0.02
0.01
0.40
0.41
0.15
0
0
0
0.07
0.03
0.27
0.06
0.22
0.05
0.01
0.01
0.05
0.25
0.1
0.17
0.12
0.18
1.01
0.14
0.03
0.14
0
0.21
0.05
0.40
0.22
1.03
0.05
0.92
0.34
0.69
0.28
0.14
0.27
0.13
0.61
1.42
0.50
0.26
0.15
0
0.27
0.18
1.11
1.5
8.3
0
7.65
2.65
5.81
0.11
0.12
1.81
0
0.38
0.21
2.24
2.56
0.01
0
0
0.05
0.45
0.18
0.5
0.03
0.97
0.44
0.4
0.21
0.28
0.14
0.06
2.14
2.03
0.8
0.02
0
0
0.39
0.02
0.2
0.14
0.24
0.06
0.04
0.01
0.09
0.17
0.1
0.17
0.24
0.14
0.93
0.19
0.02
0.09
0
0.35
0.69
1.23
0.62
6.58
0.03
2.53
0.7
0.94
0.33
0.08
0.98
0.12
0.69
0.69
1.49
0.97
0.05
0
0.4
0.95
2.99
2.45
15.6
0.12
11.2
3.80
7.24
0.81
0.58
3.10
0.42
3.35
3.87
4.72
3.57
0.15
0
1.14
0.02
0.11
0.14
0.85
0
1.01
0.35
0.83
0.01
0.01
0.15
0
0.05
0.02
0.21
0.33
0
0
0
0
0.22
0.23
0.47
0
0.02
0.17
0.03
0.08
0
0.14
0.03
0
0.02
0.44
0.11
0.22
0
1.83
0
1.01
2.24
4.76
0
0
0.02
0.02
0
0
0
0
0
0
0.79
4.52
2.57
0
9.29
0.07
0.31
0.04
0.16
0.05
0.14
0.09
0.12
0.44
0.67
1.16
0.49
0.59
4.75
0.38
0.01
0.07
0
0
0.01
0.17
0.08
0.22
0.07
0.04
0.01
0.05
0.14
0.03
0.06
0.25
0.44
0.12
0.15
0.04
0.04
0
0
0.54
12.4
1.84
0.54
0.68
0.06
0.03
1.79
3.47
4.2
5.61
2.8
3.49
8.76
1.05
0.85
0.25
0
0
0.05
0.11
0.08
0.12
0.01
0.03
0.01
0.02
0.19
0.04
0.12
0.06
0.13
0.44
0.06
0.04
0.04
0
0
0
0
0
0.01
0
0
0
0
0.08
0
0
0.07
0
0
0.02
0
0
0
7.58
1.19
3.12
1.36
1.61
0.64
0.14
0.08
0.64
2.05
1.01
3.99
2.44
2.89
15.2
1.6
0.81
0.48
0
0
1.88
17.5
6
8.72
1.45
1.43
0.77
3.5
6.47
5.96
11.2
6.14
7.6
29.3
4.7
6.71
3.66
0
18.7
WORLD
3.04
1.63
3.04
7.72
34.7
9.09
3.2
19.1
66.1
4.1
4
25.2
9.55
1.92
48.4
1.56
7.75
39.2
142.
Table B2. Regional distributions of AeroCom emissions for the year 1750
category
BC
ofire
BC
biof
BC
fos.f
BC
all
OM
ofire
OM
biof
OM
fosf
OM
SOa
OM
all
SO2
ofire
SO2
v.ex
SO2
v.co
SO2
hom
SO2
road
SO2
pow
SO2
off-r
SO2
ship
SO2
indu
SO2
all
CANADA
USA
C AMERICA
S AMERICA
N AFRICA
W AFRICA
E AFRICA
S AFRICA
OECD EU
EAST EU
F. USSR
MID EAST
SOUTH ASIA
EAST ASIA
SE ASIA
OCEANIA
JAPAN
GREENLAND
OCEAN
0.04
0.00
0.01
0.23
0
0.22
0.08
0.20
0.00
0.00
0.09
0
0.01
0.01
0.06
0.06
0
0
0
0
0.01
0
0.01
0.01
0.04
0.04
0.03
0.01
0.01
0.02
0.01
0.06
0.10
0.03
0
0.01
0
0.07
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.04
0.02
0.01
0.24
0.01
0.27
0.12
0.22
0.01
0.01
0.11
0.01
0.07
0.10
0.10
0.07
0.01
0
0
0.85
0.06
0.15
2.57
0
2.39
0.88
2.05
0.04
0.01
2.10
0
0.08
0.10
0.74
0.78
0.01
0
0
0
0.06
0.01
0.03
0.02
0.11
0.11
0.07
0.05
0.03
0.13
0.02
0.29
0.46
0.13
0.01
0.03
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.69
1.23
0.62
6.58
0.03
2.53
0.7
0.94
0.33
0.08
0.98
0.12
0.69
0.69
1.49
0.97
0.05
0
0.4
1.54
1.35
0.78
9.17
0.06
5.03
1.68
3.06
0.41
0.12
3.21
0.15
1.06
1.26
2.36
1.77
0.08
0
0.40
0.07
0.01
0.01
0.28
0
0.32
0.12
0.29
0
0
0.17
0
0.01
0.01
0.06
0.10
0
0
0
0
0.22
0.23
0.47
0
0.02
0.17
0.03
0.08
0
0.14
0.03
0
0.02
0.44
0.11
0.22
0
1.83
0
1.01
2.24
4.76
0
0
0.02
0.02
0
0
0
0
0
0
0.79
4.52
2.57
0
9.29
0
0
0
0
0
0
0
0
0.01
0
0
0
0.03
0.05
0.01
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.07
1.24
2.49
5.51
0
0.34
0.31
0.34
0.10
0
0.31
0.03
0.04
0.08
1.30
4.72
2.79
0
11.1
WORLD
1.03
0.39
0
1.41
12.8
1.56
0
19.1
33.5
1.46
4.00
25.2
0.12
0
0
0
0
0
30.8
24
A central role, when back-scaling current emission to obtain estimates on pre-industrial
emissions has been the population ratio of the HYDE data-set (www.rivm.nl/hyde). For the 18
IMAGE regions (see Figure B1) these population ratios are listed in Table B3.
Table B3. Population by region for the years 1750 and 2000 (by HYDE) – and ratios
Canada
USA
C.America
S.America
N.Africa
W-Africa
E-Africa
S-Africa
OECD-Europe
E-Europe
F.USSR
M-East
S-Asia
E-Asia
SE-Asia
Oceania
Japan
world
year 1750
year 2000
(in million)
(in million)
0.3
2.1
5.5
5.0
4.5
22.1
8.0
10.7
113.7
31.6
25.3
9.3
172.0
268.9
28.5
0.3
24.9
722.3
27.8
254.1
145.1
292.8
118.2
242.0
151.8
117.5
377.1
122.2
289.6
192.4
1132.1
1247.1
442.0
26.4
123.5
5301.8
2000 / 1750
pop - ratio
93
121
26
59
26
11
19
11
3.3
3.9
11
21
6.5
4.6
16
88
5.0
7.34
1750 / 2000
pop - ratio
0.011
0.0083
0.038
0.017
0.038
0.091
0.053
0.091
0.30
0.26
0.087
0.048
0.15
0.22
0.065
0.011
.20
.136
25
Appendix C
Background to the sizing of primary aerosol from biomass burning
Biomass burning is one of the main sources for carbonaceus aerosol in the atmosphere.
Globally it contributes to about 40% of CO, 32% of CO, 38% of tropospheric ozone, 7% of
total particulate matter and 39% of particulate organic carbon. The majority of biomass
aerosol (ca 80%) occurs in tropics as seasonal event (e.g. Aug-Oct: S. Africa and S.America).
To provide background on choices for the sizing of freshly emitted (young) biomass aerosol,
Luisa Marelli (JRC) has compiled a summary of measurements (Allen and Miguel, 1995;
Anderson et al., 1996; Andreae and Merlot, 2001; Cachnier et al., 1996, LeCanut et al., 1996,
Radke et al., 1991; Scholes et al., 1996, Suscott et al., 1991). Measured size distributions
have been fitted to a multi-model lognormal distribution, which is defined by
dN

d log r
 (ln r  ln rm ) 2 
N ln 10
 exp 

2(ln  ) 2 
2 ln 

where N is total particle number, rm is the mode radius and  is the standard deviation. A
comparison of fits is presented in Figure C1.
normalized number distributions of biomass in different
burning areas
Brazil - aged
Africa - aged
1.E+01
Africa - young
1.E+00
N.Americaflaming
N.Americasmoldering
N. Americayoung
Brazil-young
1.E-01
1.E-02
1.E-03
Brazil - aged
1.E-04
South Atlantic aged
Canada - aged
1.E-05
Brazil - aged
1.E-06
10
100
1000
10000
100000
Diameter (nm)
Figure C1. Multi-modal log-normal fits to measured biomass size samples
26
The accumulation size-mode (radii smaller than 0.5m) usually contains more then 90% of
the biomass burning aerosol mass. Figure C2 presents the log-normal parameters data-pairs
for rm (actually the diameter is shown) and  of only the accumulation size-mode associated
with the size distributions of Figure C1.
Accumulation mode diameter vs standard deviation
350
300
250
200
150
y = -426.51x + 837.27
R2 = 0.882
100
50
0
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
standard deviation from lognormal fitting
Figure C2. Relationship between (number-) mode diameter (in nm) and standard deviations
from log-normal fits to the size-distributions of Figure A1. Data points in the lower right refer
to young biomass aerosol while pairs towards the upper left refer to aged biomass aerosol.
Data points in the lower right of Figure C2 indicate log-normal parameters from fits to young
biomass aerosol. As the biomass aerosol ages, the mode radius increases and the distribution
width narrows, resulting in an increase to effective radius (from 0.10 to 0.15m), which is
small in the context of orders of magnitudes among different aerosol sizes. As biomass
burning emissions are associated with young aerosol, recommendations are 0.04m for
(number-) mode radius rm and 1.8 for standard deviation (as listed in Tables 1 to 3). These
values are consistent with estimates of volatility analysis on young aerosol emitted by a North
American fires (Reid et al, 2003), where sampled aerosol (rm =0.55m, =1.7) has been preheated to 310° C, to bake off volatile compounds (organics, sulfate, nitrate, water etc.),
resulting in core aerosol (mainly BC and low volatility organics) of rm =0.32m and1.9.
27
Appendix D
Comparison of available wildland fire inventories
By 2003, three different global large-scale wildland fire inventories had been developed (van
der Werf et al, 2003; Generoso et al, 2003; Hoelzemann et al, 2004). When comparing the
inventories on a regional basis, as for POM in Figure D1, there is good agreement in a relative
sense, although estimates by Hoelzemann (GWEM 1.2) are smaller, especially in South
America. This lower estimate is erroneous and associated with a failure in the underlying area
burnt satellite product, so that the impact of small but intense fires is underestimated in that
region. A more recent version of GWEM (version 1.4) is now more consistent with the other
inventories. For more discussion on the uncertainty of the POM/EC inventories, the reader is
referred to the description of SPEW (Bond et al 2004). A particular problem is the uncertainty
of emission factors, which for BC can exceed an order of magnitude (C. Liousse, private
communication). Thus, the certainty for carbonaceous emissions can not be expected to be
better than a factor of 2.
Comparisons of POM estimates
35
POM in Tg of OM/yr
30
25
GLOBAL
Africa
20
South America
Central America
North America
Oceania
Europe + Russia
South East Asia
15
10
East Asia
5
0
1
2
1 : S.Generoso 2 : J.J. Hoelzeman
3
3 : G. van der Werf
Figure D1. Comparison of three different global large-scale burning (wildland fire)
inventories for regional POM emission estimates by S.Generoso et al. (2003), J.J.Hoelzemann
et al. (2004) and G.van der Werf et al. (2003).
28