Text S1
1.
Method
Model description
The Integrated Massively Parallel Atmospheric Chemical Transport (IMPACT)
model simulates the emissions, chemistry, transport, and deposition of major aerosol
species, including sulfate, nitrate, ammonium, black carbon, particulate organic matter,
mineral dust and sea salt aerosols, and their precursor gases [Rotman et al., 2004; Liu et
al., 2005; Feng and Penner, 2007; Ito and Feng, 2010; Ito, 2011, 2012]. The IMPACT
model is driven by assimilated meteorological fields for the year 2001 from the Goddard
Earth Observation System (GEOS) of the NASA Global Modeling and Assimilation
Office (GMAO). The GEOS-4 meteorological fields were archived with a 6-h temporal
resolution (3-hour for surface quantities and mixing depths) on a horizontal resolution of
1.0° × 1.25° with 55 vertical layers [Bloom et al., 2005]. Simulations were performed at a
horizontal resolution of 2.0° × 2.5° with 42 vertical layers after a 2-month spin-up time
period.
The emission data sets of primary aerosols and precursor gases compiled by
Dentener et al. [2006] were used for the simulations except for dust [Ginoux et al., 2001]
and combustion-generated iron aerosols such as those from biomass and fossil fuel
burnings [Ito and Penner, 2005; Ito et al., 2007; Ito, 2011]. The emissions of dust are
calculated on-line, based on the surface wind speed and soil wetness from the GEOS-4
assimilated meteorological fields. The mass flux at emission is immediately distributed
among the aerosol bins according to an initial source aerosol size distribution determined
by the emission scheme (Table 1). However, the size distribution to be chosen for the
atmospheric transport model needs to be representative of source grid boxes [Schulz et
al., 1998]. In our model these boxes have a considerable extent in height and area and a
large vertical mass gradient is to be expected, as a consequence of the immediate removal
by sedimentation. Thus we resorted to a background size distribution of mineral dust
particles within each size bin [Jaenicke, 1988] in the simulation of aerosol transport and
removal processes and for the calculation of AOD. The fractional contribution of each
IMPACT size bin to the total dust mass concentration varies in space and time as it is
altered by the atmospheric processes.
The aerosol optical depth (AOD) is calculated on-line for an external mixture of
dust, ammonium sulfate, sea salt and carbonaceous aerosols, based on the simulated
aerosol mass concentrations, the mass specific extinction coefficient, and the relative
humidity from the GEOS-4 assimilated meteorological fields. The mass specific
extinction for dry aerosol particles was calculated off-line based on Mie theory [Grant et
al., 1999; Liu et al., 2007]. Hygroscopic growth for ammonium sulfate, sea salt and
carbonaceous aerosols is accounted for based on the Köhler theory [Grant et al., 1999].
The monthly mean of the model AOD is calculated for a comparison with observation by
sampling the model AOD at each day at each grid where the MODIS retrievals exist
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[Remer et al., 2005] and weighting each model clear-sky AOD with the MODIS
non-cloudy fraction [Platnick et al., 2003].
The total iron is derived from the dust sources and the combustion sources such as
those from biomass and fossil fuel burning. The iron content in size-resolved mineral
aerosols [Ito, 2012] was estimated from a compilation of measurements [Meskhidze et al.,
2005; Journet et al., 2008]. The calculated total percentage value of the iron content
(7.9% for clay and 4.3% for silt) is higher than the average value of 3.5% in upper crustal
materials [Taylor and McLennan, 1985]. Therefore, we fit the total percentage value of
the iron content in dust sources to the average value. Then, the total mass flux of iron is
distributed among the aerosol bins according to the source aerosol size distribution, while
maintaining the relationship between particle size and iron content for each mineral (i.e.,
hematite, illite, and smectite). The iron solubility changes from that in the originally
emitted dust aerosols (which is 0.45%) due to reactions with acidic species (mainly
sulfuric acid formed from oxidation of SO2). Our explicit iron dissolution scheme for dust
aerosols was based on Meskhidze et al. [2005], but was improved by taking into account
variations in the mixing of alkaline dust with iron-containing minerals [Ito and Feng,
2010; Ito, 2012]. The mineral iron in the fine mode (diameter < 2.5 μm) dissolves more
efficiently due to the preferential acidification of smaller particles, which are externally
mixed with alkaline carbonate minerals [Sullivan et al., 2007]. On the other hand, the
coarse-mode dust (> 2.5 μm) does not generally acidify due to the alkaline buffering
3
ability of carbonate minerals [McNaughton et al., 2008]. In addition, soluble component
of iron from the combustion sources is readily released into solution in aerosols [Schroth
et al., 2009; Chang-Graham et al., 2011]. We assume constant iron solubility in
combustion aerosol at 4% [Luo et al., 2008], based on the soluble iron measurements at
Dunhuang in China and Gosan in the Republic of Korea [Chuang et al., 2005].
We adjusted the global scaling constant for dust emissions (the variable C in
equation (2) of Ginoux et al., 2001) for each simulation in order to produce a reasonable
agreement with AOD at 550-nm wavelength [Remer et al., 2005] (Figures S1–S2). The
differences in AOD for dust at the grid level between the five different experiments and
the reference experiment (Exp5) are generally within 10% near the dust source regions
such as the tropical Atlantic and the Southern Atlantic where the annually averaged dust
AOD is larger than 0.001 (Figure S2). The dust AOD with the theoretical expression is
higher (1.1–1.2 for Exp5/Exp2) in the vicinity of sources such as Patagonia, southern
Africa, and Australia but lower (0.5–0.8 for Exp5/Exp2) far from sources such as over
the tropical oceans. These results reflect different lifetimes of the clay and silt particles.
Note that clay and silt particles (the two major soil texture classes) are assigned to fine
(diameter < 2.5 μm) and coarse mode (> 2.5 μm) aerosols, respectively. Our global
scaling constant for dust emissions in Exp1 (0.375 μg s2 m–5) is identical to that in the
GEOS-4 model [Nowottnick et al., 2010]. Our global dust emission simulated from Exp1
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for the year 2001 (1579 Tg) is comparable to the mean value (1970 Tg) from the GEOS-4
model for the period 2000–2006 [Colarco et al., 2010].
Since our model treatment of deposition depends on the size distribution (i.e., faster
deposition for larger particles), the sub-bin size distribution is equally treated for the
optical calculation according to the background size distribution of mineral dust particles
[Jaenicke, 1988]. To examine the effect of this assumption on simulated AOD, we
calculated the mass-weighted averages of dust extinction efficiency at 550nm (in unit of
m2/g) for the four size bins based on the size distribution of Kok [2011], for comparison
analysis with those based on the size distribution of Jaenicke [1988]. Refractive index of
1.48–0.0014i [Myhre et al., 2003] is used. Then, the two different data sets of the dust
extinction efficiency are applied to the model calculation of the AOD with the dust
emissions from Exp5. The sensitivity simulation results show that the differences in AOD
for dust at the grid level are generally within 5% (Figure S3). Thus the same sub-bin size
distribution for mineral aerosols is an acceptable approximation for this study.
Our model results showed reasonable agreement of total iron (Fe) and soluble iron
(SFe) concentrations in the fine aerosols (diameter < 2.5 μm) and the coarse aerosols
(diameter > 2.5 μm) with observations of total iron and “labile” iron (to within a factor of
10) (Figure S4). The measurement target is termed labile iron, because the measurement
of SFe components is operationally defined by the extraction and analytical procedures
[Chen and Siefert, 2003, 2004; Baker et al., 2006]. The daily average of model results
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was calculated from hourly output at the surface along the cruise tracks in order to
compare with the compilation of ambient measurements [Chen and Siefert, 2003, 2004;
Baker et al., 2006]. In Table S1, we show the results of a regression analysis between the
simulated concentration and measurements from cruises in the Atlantic and Pacific in
2001 [Chen and Siefert, 2003, 2004; Baker et al., 2006]. The overall high correlations
indicate that the model successfully reproduces the wide range of daily mean surface
concentration observed at different locations. The results of the regression analysis for Fe
and SFe concentrations between different simulations and observations are similar to
each other (Table S1), mainly because the differences between different simulations are
much smaller than those between model results and observations [Ito and Feng, 2010; Ito,
2012]. Further research is needed to improve the predictions. The correlation coefficients
for SFe (0.69–0.70) are smaller than those for Fe (0.77–0.79). These results reflect the
fact that the model does not capture specific events or have sufficient variability in iron
solubility as that observed over the Atlantic and Pacific Oceans [Ito, 2012]. Further
research is needed to improve our understanding of the processes that increase iron
solubility in different aerosol particles.
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