ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-14775-2016Modeling investigation of light-absorbing aerosols in the Amazon Basin
during the wet seasonWangQiaoqiaoq.wang@mpic.deSaturnoJorgehttps://orcid.org/0000-0002-3761-3957ChiXuguangWalterDavidhttps://orcid.org/0000-0001-6807-5007LavricJost V.https://orcid.org/0000-0003-3610-9078Moran-ZuloagaDanielDitasFlorianhttps://orcid.org/0000-0003-3824-9373PöhlkerChristopherhttps://orcid.org/0000-0001-6958-425XBritoJoelhttps://orcid.org/0000-0002-4420-9442CarboneSamaraArtaxoPaulohttps://orcid.org/0000-0001-7754-3036AndreaeMeinrat O.https://orcid.org/0000-0003-1968-7925Biogeochemistry Department, Max Planck Institute for Chemistry, 55131
Mainz, GermanyDepartment of Biogeochemical Systems, Max Planck Institute for
Biogeochemistry, 07745 Jena, GermanyICOS ERIC Head Office, Helsinki, FinlandDepartment of Applied Physics, University of São Paulo, São
Paulo 05508, Brazilnow at: School of Atmospheric Sciences, Nanjing University, Jiangsu,
Chinanow at: Laboratory for Meteorological Physics, University Blaise
Pascal, Aubière, Francenow at: Institute of Agrarian Sciences, Federal University of Uberlandia, Uberlandia, BrazilQiaoqiao Wang (q.wang@mpic.de)28November2016162214775147946July201620July201627October20165November2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/14775/2016/acp-16-14775-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/14775/2016/acp-16-14775-2016.pdf
We use a global chemical transport model (GEOS-Chem) to
interpret observed light-absorbing aerosols in Amazonia during the wet
season. Observed aerosol properties, including black carbon (BC)
concentration and light absorption, at the Amazon Tall Tower Observatory
(ATTO) site in the central Amazon have relatively low background levels but
frequently show high peaks during the study period of January–April 2014. With
daily temporal resolution for open fire emissions and modified aerosol
optical properties, our model successfully captures the observed variation
in fine/coarse aerosol and BC concentrations as well as aerosol light
absorption and its wavelength dependence over the Amazon Basin. The source
attribution in the model indicates the important influence of open fire on
the observed variances of aerosol concentrations and absorption, mainly from
regional sources (northern South America) and from northern Africa. The
contribution of open fires from these two regions is comparable, with the
latter becoming more important in the late wet season. The analysis of
correlation and enhancement ratios of BC versus CO suggests transport times
of < 3 days for regional fires and ∼ 11 days for African
plumes arriving at ATTO during the wet season. The model performance of
long-range transport of African plumes is also evaluated with observations
from AERONET, MODIS, and CALIOP. Simulated absorption aerosol optical depth
(AAOD) averaged over the wet season is lower than 0.0015 over the central
Amazon, including the ATTO site. We find that more than 50 % of total
absorption at 550 nm is from BC, except for the northeastern Amazon and the
Guianas, where the influence of dust becomes significant (up to 35 %).
The brown carbon contribution is generally between 20 and 30 %. The
distribution of absorption Ångström exponents (AAE) suggests more
influence from fossil fuel combustion in the southern part of the basin (AAE
∼ 1) but more open fire and dust influence in the northern
part (AAE > 1.8). Uncertainty analysis shows that
accounting for absorption due to secondary organic aerosol (SOA) and primary
biogenic aerosol (PBA) particles could result in differences of
< 8 and 5–40 % in total absorption, respectively.
Introduction
Light-absorbing aerosols (LAAs) are of climatic interest as strong absorbers
of solar radiation in the atmosphere. They alter the radiative balance of
the atmosphere through a complex web of processes, leading to a positive
top-of-atmosphere forcing, heating of the atmosphere, and surface dimming
(Ramanathan and Carmichael, 2008). Light absorption in the atmosphere is
dominated by black carbon (BC), emitted from combustion sources, with a
smaller contribution from mineral dust (Bond et al., 2013; IPCC, 2013;
Jacobson, 2001; Ramanathan and Carmichael, 2008). Recent work has shown that
light-absorbing organic aerosols, known as brown carbon (BrC), may also be
important, contributing 20–40 % of total carbonaceous aerosol absorption
globally (Andreae and Gelencsér, 2006; Bahadur et al., 2012; Chung et
al., 2012a, b; Lin et al., 2014; Saleh et al., 2015).
However, the poor knowledge of the LAA's atmospheric concentrations and
optical properties leads to large uncertainties in estimating the absorption
attributable to LAA species, and consequently in estimating the radiative
forcing of total aerosols (Andreae and Ramanathan, 2013; Bond et al., 2013;
Kim et al., 2014; Kinne et al., 2006; Myhre et al., 2013; Saleh et al.,
2015; Schulz et al., 2006; Sinyuk et al., 2003; Wang et al., 2014a).
The Amazon Basin, being the world's largest rainforest area, plays an
important role in Earth's climate system. Scientific concern has been
shifting from regional climate change over the Amazon Basin to the
interactions of global climate change with the functioning of the Amazon
Basin's ecosystem (Andreae et al., 2015). Therefore, understanding sources,
concentrations, and properties of aerosols in the Amazon Basin is important
from both regional and global points of view. During the wet season
(January–April), the Amazon Basin is in near-pristine condition, and
therefore this unique environment provides a baseline understanding against
which to assess anthropogenic effects (Andreae, 2007; Martin et al., 2010a).
Over the past decades, numerous field campaigns with measurements of
aerosols and their optical properties have been carried out in the Amazon
Basin. A summary of sources and properties of Amazonian aerosols can be
found in the reviews of Martin et al. (2010a, b) and Andreae et al. (2015). Briefly, Amazonian aerosols are dominated by local and regional
biogenic aerosols in the wet season, with remarkably low concentrations (a
few µg m-3) and absorption coefficients (∼ 0.5 Mm-1 at a wavelength of 550 nm) (Guyon et al.,
2003a, b; Rizzo et al., 2011, 2013). The near-pristine conditions
are episodically interrupted by long-range transport of Saharan dust and
African biomass burning and fossil fuel combustion aerosols, which
significantly elevate the absorption coefficients (Andreae et al., 2015;
Baars et al., 2011; Ben-Ami et al., 2010; Formenti et al., 2001; Guyon et
al., 2003b; Martin et al., 2010a, b; Rizzo et al., 2013).
While species other than BC, such as biogenic aerosols, are suggested to be
responsible for a substantial fraction to the total absorption (Guyon et
al., 2003b, 2004; Rizzo et al., 2010), the contribution of the different
species to total observed aerosol absorption is not clear yet.
Attempts to model Amazonian aerosols are limited, with focus either on dust
deposition (e.g., Ridley et al., 2012; Yu et al., 2015) or biomass burning
aerosols in the dry season (e.g., Castro Videla et al., 2013; Martins and
Pereira, 2006; Rosário et al., 2013). Here we present a detailed
simulation of LAA in the Amazon Basin in the wet season with the GEOS-Chem
chemical transport model (CTM). We show that the GEOS-Chem model successfully captures the observed
variation and magnitude of LAA mass concentration as well as the associated
absorption and its wavelength dependence. In addition to long-range
transport of African dust and open fire aerosols, we show that regional open
fires are also responsible for observed enhancement of aerosol mass and
absorption in wet season.
Model description
We use the GEOS-Chem CTM version 9-02 (http://www.geos-chem.org/), a global 3-D model of atmospheric composition
driven by assimilated meteorological data GEOS-5 FP from the NASA Global
Modeling and Assimilation Office (GMAO). GEOS-5 FP is the current
operational meteorological data, which is now produced with version 5.13.0
of the GEOS-Data Assimilation system (Lucchesi, 2013). The GEOS-5 FP data
have 1-hourly and 3-hourly temporal resolution, 47 vertical layers, and
0.25–0.3125∘ horizontal resolution. We
degrade the horizontal resolution to 2–2.5∘ for input to GEOS-Chem. We initialize the model with a 1-year
spin-up followed by a full chemistry simulation of January–April 2014. We also run
additional simulations with a 6-month spin-up for black carbon and primary
organic aerosol to isolate the contributions from different sources by
tagging them in the model.
Aerosol types simulated in GEOS-Chem include carbonaceous aerosols,
sulfate–nitrate–ammonium aerosols, fine- and coarse-mode sea salt, and
mineral dust in four size classes (aerosols other than sea salt and dust are
treated as fine-mode aerosols). The simulation of carbonaceous aerosols was
originally described by Park et al. (2003). Black carbon and primary organic
aerosol (POA) are emitted by fuel (fossil fuel and biofuel) combustion and
open fires. Note that “black carbon” used here implies particles having
optical properties and composition similar to “soot carbon” as defined by
Andreae and Gelencsér (2006). We assume that 80 % of BC and 50 %
of POA are emitted as hydrophobic particles and convert them to hydrophilic
in the atmosphere with an e-folding time of 1 day, which yields a good
simulation of BC export efficiency in continental outflow (Park et al.,
2005). To account for the non-carbon mass in POA, we apply a factor of 1.7
to the simulated organic carbon (OC) mass concentration, to be consistent
with the measurements in the Amazon Basin (Chen et al., 2009, 2015). The simulations of BC and POA in GEOS-Chem are linear, with
concentrations proportional to sources. We isolate the contributions from
different sources by tagging them in the model. Secondary organic aerosol
(SOA) is produced in the atmosphere as oxidation products of biogenic
(monoterpenes, sesquiterpenes, and isoprene) and aromatic precursors (Chung
and Seinfeld, 2002; Henze and Seinfeld, 2006; Henze et al., 2008; Pye et
al., 2010). Note that the simulation of carbonaceous aerosol does not
include primary biogenic aerosol (PBA), which likely dominates the coarse
aerosols in the Amazon (Pöschl et al., 2010). We discuss the
uncertainties associated with PBA particles later in this paper.
The simulation of mineral dust was described in detail by Fairlie et al. (2007). Briefly, the model uses the dust entrainment and deposition (DEAD)
mobilization scheme of Zender et al. (2003) for calculation of sources and
emission. Dust particles are emitted in four size bins (radii 0.1–1.0,
1.0–1.8, 1.8–3.0, and 3.0–6.0 µm), with corresponding mass fractions
of 12.2, 25.3, 32.3, and 30.2 %. The smallest size bin is
further divided into four sub-bins (radii 0.1–0.18, 0.18–0.3,0.3–0.6, and
0.6–1.0 µm) for optical properties, which are strongly size dependent
for sub-micron aerosols. The mass fraction in each bin is 6, 12,
24, and 58 % from smallest to largest, constrained from aircraft
PCASP (passive cavity aerosol spectrometer probe) measurements of Saharan dust (Ridley et al., 2012).
Dry deposition in GEOS-Chem follows a standard resistance-in-series scheme
(Wesely, 1989) as implemented by Wang et al. (1998), accounting for
gravitational settling and turbulent dry transfer of particles to the
surface (Zhang et al., 2001). Wet deposition in GEOS-Chem was initially
based on the scheme of Liu et al. (2001), which includes scavenging in
convective updrafts, as well as in-cloud and below-cloud scavenging from
convective and large-scale precipitation. We adopt the update of the scheme
by Wang et al. (2011, 2014a), which accounts for scavenging by cold (ice)
clouds and snow as well as the impaction scavenging during convective
updrafts.
Aerosol optical depth (AOD) and absorption aerosol optical depth (AAOD) are
calculated online assuming lognormal size distributions of externally mixed
aerosols and accounting for hygroscopic growth (Martin et al., 2003).
Aerosol optical properties used in the calculation are based on the Global
Aerosol Data Set (GADS) data (Koepke et al., 1997), with modifications in
size distribution (Drury et al., 2010; Jaeglé et al., 2011; Wang et al.,
2003a, b), hygroscopic growth factors, and the refractive
index of dust (Sinyuk et al., 2003). The hygroscopic growth factor of OC at
90 % RH in the model is 1.25, consistent with the range of 1.0–1.3 for
Amazonian fine aerosols at 90 % RH reported by Rissler et al. (2006) and
Zhou et al. (2002).
Treatment of black carbon and brown carbon
Table 1 lists the optical properties used in GEOS-Chem for LAA at a
wavelength of 550 nm and dry conditions. The standard GEOS-Chem does not
account for the absorption enhancement due to the mixing state of BC and
thus has a low value of 5.9 m2 g-1 for BC mass absorption
efficiency (MAE) at 550 nm. The MAE value is proportional to the AAOD value,
and is a fundamental factor in the estimation of directive radiative
forcing. Recent studies have shown that the MAE of BC should be increased
from 7.5 ± 1.2 m2 g-1 at 550 nm for freshly generated BC to
9–13 m2 g-1 as BC becomes internally mixed with other aerosols
chemical components, which is also supported by ambient measurements and the
comparison between models and observations (Bond and Bergstrom, 2006; Bond
et al., 2013; Wang et al., 2014a). In this work, we thus scale the MAE at
550 nm to 12 m2 g-1 assuming thick coating due to the abundance of
SOA in the Amazon Basin (Chen et al., 2009; Pöschl et al., 2010). We
also update the absorption Ångström exponents (AAE) for BC from 1.3
to 0.5 based on the study by Chung et al. (2012b) and Bahadur et al. (2012),
assuming that this lowest observed value represents BC, without too much
contamination by brown carbon or dust.
In addition to the update in BC properties, we add the contribution of brown
carbon (BrC) in the model. Brown carbon absorbs solar radiation,
particularly at UV wavelengths, and thus its absorption spectrum shows a
strong wavelength dependence. The standard GEOS-Chem, however, does not
separate BrC from “white” carbon (non-absorbing carbon) but assumes
slight absorption for all kinds of organic aerosol (OA) and weak wavelength
dependence with AAE of 0.8 between wavelength of 550 and 400 nm. In this
work, we assume that all POA from biofuel and open fires is BrC. The
refractive index is based on the work of Saleh et al. (2015), who
parameterized the absorptivity of BrC as a function of emission ratio of BC
versus OA. The derived MAE at 550 nm and imaginary part of the refractive index are respectively 2.5 m2 g-1 0.023 for biofuel OA and 3.1 m2 g-1 0.017 for open fire OA.
The refractive index of dust at 550 nm in GEOS-Chem is 1.56–0.0014i,
yielding an MAE from 0.017 to 0.028 m2 g-1
depending on the size distribution. The MAE for total dust aerosol increases
as it is transported away from the source regions, because coarse aerosols
are more susceptible to deposition.
Optical properties of light-absorbing aerosol at 550 nm in
GEOS-Chem.
a Mass absorption efficiency. b Absorption Ångström exponent, estimated between wavelength of 400 and 550 nm.
GEOS-Chem emissions of carbonaceous aerosols in January–April 2014.
RegionsBlack carbon (Tg a-1)Organic aerosolsa (Tg a-1)FuelbOpen firecFuelbOpen firecSOAdNorth America (170–17.5∘ W, 24–88∘ N)0.42 (12 %)0.031.12 (44 %)0.640.54 (84 %)Central America (107–61∘ W, 0–24∘ N)0.13 (23 %)0.180.64 (48 %)2.841.90 (11 %)Northern South America (61–45∘ W, 0–10∘ N & 82–34.5∘ W, 24–0∘ S)0.18 (34 %)0.040.63 (71 %)0.704.06 (3 %)Southern South America (76–45∘ W, 56–24∘ S)0.09 (30 %)0.010.27 (65 %)0.220.52 (7 %)Europe (10∘ W–40∘ E, 37.5–71∘ N)0.55 (18 %)0.021.55 (62 %)0.290.30 (46 %)Northern Africa (17.5∘ W–40∘ E, 15–37.5∘ N & 17.5∘ W–52∘ E, 0–15∘ N)0.32 (63 %)0.621.50 (82 %)10.656.40 (9 %)Southern Africa (8–51∘ E, 35–0∘ S)0.16 (69 %)0.030.80 (85 %)0.491.13 (11 %)Rest of world2.70 (36 %)1.709.53 (68 %)28.816.77 (37 %)Total4.55 (34 %)2.6416.03 (67 %)44.6421.61 (19 %)
a Primary organic aerosol is estimated using an OA / OC ratio
of 1.7. b Fuel combustion includes fossil fuel and biofuel. Figures in brackets refer to
the fraction of biofuel contribution. c Biomass burning emissions without biofuel
contribution.
d Secondary organic aerosols. Figures in brackets refer to the contribution of SOA oxidized from aromatics.
GEOS-Chem emissions of black carbon (BC) and organic
aerosol (OA) during January–April 2014 over 120∘ W–60∘ E and
60∘ S–60∘ N. Red lines show the limits of the seven
selected regions as defined in Table 2. The black asterisk is the location
of ATTO.
Emissions of carbonaceous aerosols
Table 2 gives regional and global totals for BC and OA during January–April 2014
in GEOS-Chem and Fig. 1 shows the distribution of BC and OA emissions over
120∘ W–60∘ E during the same period. We tagged BC and
POA tracers from either fuel combustion or open fire from seven regions as
defined in Table 2: NA (North America), CA (Central America), NSA (northern
South America), SSA (southern South America), Europe, NAF (northern Africa),
and SAF (southern Africa). Note that NSA includes the Amazon Basin as well
as a large portion of Brazil. The emissions of BC and POA are from Bond et al. (2007) for fossil fuel and biofuel combustion and from the Fire Inventory from
NCAR version 1.5 (FINNv1.5) (Wiedinmyer et al., 2011) with daily resolution
for open fire. Emissions of SOA precursors are from MEGAN (Model of
Emissions of Gases and Aerosols from Nature) v2.1 (Guenther et al., 2006)
for biogenic VOC and from RETRO (REanalysis of the TROpospheric chemical
composition) (Schultz et al., 2007) for aromatics.
Global total emissions of BC are 7.2 Tg a-1, 37 % of which are from
open fire. NAF has the highest BC emission (0.93 Tg a-1) and open fire
contribution (66 %) among all defined seven regions. In contrast,
emissions of BC over NSA are quite small (0.23 Tg a-1), 20 % of
which are from open fire. We note that the Bond et al. (2007) emission
inventory accounts for urban emissions in NSA (e.g., Ceará and Bahia in
the northeastern coast of Brazil and São Paulo) as seen from the
distribution of BC emissions in Fig. 1. We also compared the BC fossil fuel
and biofuel emissions from Bond et al. (2007) with those from the HTAP
emission inventory (http://edgar.jrc.ec.europa.eu/htap_v2/index.php?SECURE=123), finding that the two have very
similar distributions but that the emissions from Bond et al. (2007) are
twice as high as those from HTAP over NSA. Total emissions of OA are about
82 Tg a-1 (61 Tg a-1 for POA and 22 Tg a-1 for SOA).
The distribution of POA emissions is similar to that of BC, but with more
open fire contribution (74 % of POA on global average). NAF again has the
highest POA emissions (12 Tg a-1) and open fire contribution (88 %
of POA) among all seven regions. NAF also has the highest SOA emissions of 6.4 Tg a-1, which contribute 34 % of total OA emissions over the region.
Total OA emissions over NSA are 5.4 Tg a-1, with SOA being the largest
source (accounting for 75 % of total), followed by open fires (13 %).
As expected from its location and land cover, SOA in NSA is mainly
biogenic.
Emissions of mineral dust
Figure 2 shows the distribution of dust emissions during January–April 2014 over
120∘ W–60∘ E and 60∘ S–60∘ N in
GEOS-Chem. The model has a global emission of 1.2 Pg a-1, 78 % of
which is located in NAF as it contains most of the Sahara. There is also a
small dust emission (4 % of total) in Argentina in SSA. The emissions are
highly variable from month to month. In NAF, the emission in February is 30 % higher than the seasonal average, followed by the emission in March,
10 % higher than the average.
Due to a reduction in surface winds in the dust source regions in Africa,
there is a significant downward trend from 1982 to 2008 in dustiness over
the eastern mid-Atlantic (Ridley et al., 2014). Here we assess the model
performance with regard to the dust loading over Africa during the study
period of January–April 2014 by Aerosol Robotic Network (AERONET) surface
observations and Moderate Resolution Imaging Spectroradiometer (MODIS) data.
Figure 3 shows the time series of simulated and observed AOD at 550 nm at
four AERONET sites situated at the southern border of the Sahel region (see
purple dots in Fig. 2). The observations are from AERONET level 2.0 daily
data (Dubovik et al., 2002) during January–April 2014. For the comparison, we
interpolate AERONET AOD at 673 nm to AOD at 550 nm based on its extinction
Ångström exponent. We find six sites with valid data covering more
than 30 % of the time period. To exclude the influence of sea salt, we
only use those four where dust dominates the AOD from coarse aerosol
(particles with diameter > 1 µm) in the model. The observed
AOD at the four sites ranges widely from 0.05 to 2.0. The model successfully
captures the variation in observed AOD at each site, with correlation
coefficients r of 0.64–0.81. We also diagnose for each site the
normalized mean bias, NMB=∑(Mi-Oi)/∑Oi, where
sums are over the entire period and Mi and
Oi are the simulated and observed values. There is no
systematic bias between the model and AERONET AOD with NMB in the range of
-21 to +13 %.
GEOS-Chem emissions of dust during January–April 2014 over
120∘ W–60∘ E and 60∘ S–60∘ N. Blue
lines show the limits of the four selected regions for the dust sensitivity
runs (see text). The purple dots and triangles are the AERONET sites used in
the text. The purple dots are the AERONET sites used in Fig. 3.
Time series of observed (black lines for AERONET and blue lines for
MODIS) and simulated (red lines) AOD at 550 nm during January–April 2014.
Model results of dust AOD are also shown as green dashed lines. Normalized
mean bias (NMB) and correlation (r) statistics between the model and AERONET
(red) and between the MODIS and AERONET (blue) are shown as inset.
Spatial distributions of observed and simulated AOD at
550 nm over the rectangle region between 60∘ W–40∘ E and
5∘ S–35∘ N averaged over January–April 2014. Observations
are from MODIS level 3 daily data. The left panels are for MODIS and model
AOD using the color bar on the left and the right panels are for the bias
(model - MODIS) and relative bias (model/MODIS - 1) using the color bar
on the right. The red box in the upper left panel indicates the dusty regions
defined in the text.
Figure 4 shows the spatial distribution of simulated and MODIS AOD averaged
over January–April 2014 over both Africa and the ocean. In this study we use MODIS
level 3 data (daily gridded 1∘× 1∘ product)
from both Terra and Aqua satellites (Remer et al., 2005) and degrid the data
over the model grid. Over bright surfaces, where no successful standard
retrieval is available, we use the deep blue retrieval. Over Africa, MODIS
shows strong sources in the Bodélé region, with obvious influence
over its outflow regions. The model captures the spatial distribution
over the dusty region well (17.5∘ W–40∘ E and 13–35∘ N, shown in Fig. 4) with r of 0.76, but it tends to
overestimate observed AOD with NMB of 13 %. The distribution of the bias
has a median of 13 % with a 25th percentile of -5.8 % and a
75th percentile of 31 %. The major underestimation in the model is
over the Bodélé region, which may be due to insufficiently high
resolution to represent the high wind speeds encountered between the
mountain ranges in this relatively small region (Ridley et al., 2012).
However, the NMB is generally in the range of 20–50 % over the outflow
region. The model also shows two large AOD spots (in Sierra Leone and Congo)
with a significant contribution from open fires (20–80 %), while MODIS
only shows slight enhancement in AOD compared to the surrounding areas.
We also sampled the MODIS data at the four AERONET sites to check the
consistence between the MODIS and AERONET AOD. The results are given in Fig. 3. The time series plot shows that the MODIS retrieval underestimates AOD at
most sites with NMB of -12 to -36 %, except at the Banizoumbou site,
with NMB of 7 %. The negative bias in MODIS AOD in the outflow region (at
the Ilorin site in particular) partly explains the large difference between
the model and MODIS AOD discussed above.
Because AOD is a product of dust loading and its mass extinction efficiency
(MEE), the bias could be due to either or both of these factors. While dust
loading over source regions is mainly driven by emissions, MEE is related to
dust optical properties. To evaluate the optical properties assumed in the
model, we also estimate dust MEE using long-term data of size distributions
and refractive indices for African dust from AERONET sites (purple dots and
triangles in Fig. 2) as input for Mie calculations. For this calculation, we
use level 2.0 daily data from January to April for years 2005 to 2014. In
addition, we only use data strongly influenced by dust with coarse aerosols
accounting for more than 90 % of total aerosol volume so that the
refractive index is representative of dust aerosols.
Applying the same density (2.6 g cm-3) as used in the model, we obtain a refractive index of 1.47(±0.027)-0.0033i (±0.0021i) and MEE of 0.51 ± 0.045 m2 g-1 at 550 nm. Numbers in the brackets
are variation from all sites. By comparison, the refractive index at 550 nm
used in the model is 1.56–0.0014i. Our value for the real part is 6 %
higher than the retrieved data, which consequently results in slightly
higher dust MEE (0.60 ± 0.026 m2 g-1) averaged over the dusty
region than the AERONET-derived value. However, the value of 1.56 is
consistent with the range of 1.51–1.56 in the literature (McConnell et al.,
2010). In contrast to the real part, the imaginary part of the refractive
index of AERONET sites shows a large variation, which yields a wide range of
dust mass absorption efficiency (MAE) of 0.038 ± 0.016 m2 g-1. The imaginary part of the refractive index and the MAE
(0.021 m2 g-1) over the dusty region in the model is at the lower end of
the range of AERONET sites. Considering that the AERONET retrieval may be
influenced by aerosols other than dust and that the difference between the
model and AERONET retrieved data is not significant, in particular for MAE,
we keep the model dust refractive index unchanged for the following analysis
in this work.
Measurements at the ATTO site
The Amazon Tall Tower Observatory (ATTO) site is located 150 km northeast of
Manaus, Brazil (59∘0.335∘ W, 2∘8.752∘ S, asterisk in Fig. 1). The site was chosen to have as
little human perturbation as possible. It provides continuous observations
of trace gases and aerosol properties from two 80 m towers initiated in 2012
(Andreae et al., 2015). The regionally representative measurements can
provide important constraints on sources, sinks, and optical properties of
aerosols in the central Amazon.
Measurements related to LAA at the ATTO site used in this paper include
aerosol concentrations, carbon monoxide (CO) concentrations, and aerosol
light absorption. The measurements are generally for air sampled at 60 m
above ground, unless noted otherwise. Aerosol concentrations are from a
scanning mobility particle sizer (SMPS) for fine particles (dp≤1µm) and from an optical particle sizer (OPS) for coarse aerosols
(dp > 1 µm). We convert the measured number
concentrations to mass concentrations assuming spherical particles with a
density of 1.5 g cm-3 (Pöschl et al., 2010). The SMPS has a size
range of 10–430 nm. We extrapolate the range to 1 µm by fitting the
data to a Gaussian distribution, which yields a scale factor of 1.03 on
average. There is also a single-particle soot photometer (SP2) that has been measuring
refractory black carbon (rBC) in the size range of 70–280 nm since late
February 2014. The results are scaled up by 39 % to cover the range of
70–470 nm based on an intercomparison campaign where our four-channel SP2 was
compared to a revision-D eight-channel SP2. Dry air mole ratios of CO are from a
CRDS instrument (Picarro G1302) measuring air sampled at five levels: 79,
53, 38, 24, and 4 m a.g.l. The measurement setup is in essence a copy of
the system described in Winderlich et al. (2010). As the inlets for the
aerosol instruments are at 60 m a.g.l., we use in this study CO dry air mole
ratios averaged between 79 and 53 m a.g.l.
Aerosol light attenuation is measured by a seven-wavelength Aethalometer
(λ=370, 470, 520, 590, 660, 880, and 950 nm) at dry condition (RH < 40 %). In order to obtain absorption coefficients from
Aethalometer attenuation measurements, we apply to our data the correction
algorithm from Schmid et al. (2006). We also use the multi-angle absorption
photometer (MAAP) measurements at 637 nm as a reference, since this
instrument accounts for multiple scattering effects. The impact of the
filter loading effect on the correction calculations is usually negligible
due to the low light-absorbing aerosol concentrations at the ATTO site. For
the comparison, we interpolated the absorption at λ of 400 and 550 nm based on AAE calculated through a linear fit of multiple wavelength
absorption. For further information on the measurements at the ATTO site,
the reader is referred to Andreae et al. (2015).
Transatlantic transport
Several studies have shown transatlantic transport of African mineral dust
to the Amazon during the wet season (Abouchami et al., 2013; Baars et al.,
2011; Ben-Ami et al., 2010; Formenti et al., 2001; Kumar et al., 2014; Swap
et al., 2011; Talbot et al., 1990). Here we use satellite observations to
test the model's ability to capture this feature. The pattern of outflow
over the ocean is obvious in Fig. 4, with elevated AOD between
0 and 15∘ N in both MODIS and model data. The
model captures well the spatial variation in AOD over the ocean with
r of 0.96. There is no significant bias between the model and MODIS
data, with NMB of 0.35 %. The distribution of bias shows a median of
-6.4 %, with a 25th percentile of -23 % and a 75th
percentile of 16 %.
Figure 5 shows the seasonal AOD along a transect from 20
to 60∘ W averaged over 5∘ S–25∘ N and
January–April 2014. Both MODIS and the model show a declining trend from the west of
Africa towards the east of South America. However, the AOD decline is faster
in the model than in MODIS, especially when approaching South America.
Therefore, negative model bias is generally found in the western Atlantic
Ocean. One factor contributing to this difference is the relatively low
background AOD in the model (0.047) compared to MODIS (0.088). This low
background might imply an underestimation of sea salt in the model.
The bias could also mean that there is too much removal in the model. Ridley
et al. (2012) estimated the lifetime of dust based on the gradient of the
logarithm of the AOD against time assuming first-order removal of aerosol
and no significant change in aerosol size distribution and scattering
efficiency across the Atlantic. We apply the same method and find a lifetime
of 3.2 days in the model, a 14 % underestimate relative to MODIS (3.7 days).
This comparison is better than the results in Ridley et al. (2012), who
shows an underestimate of 25–50 % in GEOS-Chem. The better performance
here may be due to different sets of meteorological fields and/or updates in
wet scavenging by Wang et al. (2011).
If we separate the transect trend into two parts, 20 to
50∘ W (ocean) and 50 to
60∘ W (ocean–land transition), we find a similar lifetime of
dust over ocean between the model (4.2 days) and MODIS (4.3 days) but an
underestimate of 19 % in the model (1.7 days) compared with MODIS (2.1 days)
over the transitional zone. The retrieval method for MODIS is different
between land and ocean, which results in discontinuities in AOD observed in
the coastal region with higher AOD over the land than the ocean (Levy et
al., 2005). Such discontinuities could result in a slower decrease in averaged
AOD from 50 to 60∘ W and thus contribute
to the relatively longer lifetime compared to the model results. It is also
possible that the model overestimates the removal over the transitional
zone.
Seasonal AOD at 550 nm along transects from
20 to 60∘ W from MODIS (black) and model
(red), averaged over 5∘ S–25∘ N and January–April 2014. The
solid lines are averaged AOD and the dashed lines are the logarithmic trend
line.
Figure 6 shows the daily distribution of latitudinally averaged AOD over the
region between 60∘ W–20∘ E and 5∘ S–25∘ N from MODIS and the model. Observations from MODIS show
about five events of transatlantic transport of aerosol plumes from Africa
to South America. The most obvious plume originated on 1 March from the east
of the Atlantic (20∘ W) and arrived at the west of the
Atlantic (50∘ W) around 3–4 March. We find that the model is
able to reproduce the outflow events across the Atlantic Ocean with an
r of 0.81.
The lifetime of aerosols along the transport path depends not only on
precipitation intensity but also the aerosols' vertical position.
Aerosols aloft at higher altitude tend to be transported farther. Figure 7
shows curtain plots of observed and simulated extinction from
60∘ W to 20∘ E averaged over
5–10∘ N and the period January–April 2014. The
observation is from Cloud-Aerosol Lidar With Orthogonal Polarization
(CALIOP), which provides vertical profiles of aerosol extinction at 532 nm
in addition to total column AOD during both day and night (Young and
Vaughan, 2009). We use CALIOP level 2, version 3.30 lidar data of aerosol
profiles at a resolution of 5 km along the track. Because of the
interference of sunlight during day, we only use nighttime high-quality
data. We further screen the data with extinction uncertainty and quality
flags including CAD (cloud–aerosol discrimination) score, Ext_QC (extinction quality control flag), and AVD (atmospheric volume
description). In this study, we only consider cloud-free retrievals and use
aerosol layers with Ext_QC values of 0, 1, 18, and 16 and
CAD scores between -20 and -100. For the comparison, observations are
averaged over the model grid in both horizontal and vertical dimensions, and
the model results are sampled to only use days and grids when CALIOP data
are available.
Both observations and model results show high extinction extended to 3 km
close to Africa, descending to below 1 km when crossing the Atlantic Ocean.
Although the model in general captures the vertical distribution of the
extinction, it tends to underestimate it at low altitude over both Africa
and the ocean. The underestimation over Africa is opposite to the comparison
with MODIS discussed above. The magnitude of the underestimation over ocean
also exceeds the comparison with MODIS.
Daily distribution of latitudinally averaged AOD at 550 nm
from MODIS and model over the rectangle region between 60∘ W–20∘ E and 5∘ S–25∘ N from January–April 2014.
The y axis is the day of year (DOY), starting with day 1 on 1 January 2014.
Dashed lines indicate the starting dates of five transatlantic transport
events from the east of the Atlantic (20∘ W).
Figure 7 also shows non-sampled model results (i.e., those for which no CALIOP
data are available) covering the full period and the whole region. While
having similar vertical distribution, the sampled results generally have
higher extinction compared with those non-sampled. This difference reflects
the limited spatial and temporal coverage of CALIOP. While part of the
sample bias is reduced in comparison with the sampled model results, the
substantial remaining bias could be due to the narrow swath of CALIOP and
the relatively coarse model resolution. Therefore, we only use the CALIOP
data for qualitative evaluation of the vertical distribution in the model,
in addition to constraints by MODIS and AERONET data.
Source regions of the dust arriving at Amazon Basin
While it is well recognized that Saharan dust does reach the Amazon Basin,
there is an ongoing discussion about the relative importance of
Bodélé dust as a dominant source (Abouchami et al., 2013; Koren et
al., 2006; Kumar et al., 2014). We thus ran sensitive tests to check the
influence of dust from four regions (see Fig. 2): northwestern Sahara
(17.5∘ W–12.5∘ E and 21–35∘ N), northeastern Sahara (12.5–40∘ E and 21–35∘ N), western Sahel
(17.5∘ W–12.5∘ E and 13–21∘ N), and Bodélé (12.5–40∘ E and 13–21∘ N). Emissions from
the above regions account for 35, 14, 8, and 20 % of total
dust emission during January–April 2014, respectively.
Curtain plots of extinction from CALIOP and model (sampled
and non-sampled) averaged over 5–10∘ N and January–April 2014.
Column burden of total dust (top panel) and the
contribution (middle panels) to total dust burden from four defined source
regions (a, northwestern Sahara; b, northeastern Sahara; c, western Sahel;
d,
Bodélé) over the rectangle region between 80∘ W–40∘ E and 25∘ S–40∘ N from January to April 2014.
The bottom panels are the sensitivity of the dust burden to the emission
from the four source regions, with high values indicating high sensitivity
(see text).
Figure 8 shows the column burden of total dust and the contribution of these
four regions over 80∘ W–40∘ E and 25∘ S–40∘ N during the Amazonian wet season. We find that more than
half of the dust over Central America and northern South America is from the
northwestern Sahara, followed by the contribution of the western Sahel (20–30 %). The
emission from Bodélé is important in eastern Brazil (east of
45∘ W and south of 0∘) but contributes
less than 20 % of the total dust over other Amazonian regions. Such a low
contribution from Bodélé is in contrast with the results by Koren et al. (2006), who suggested that Bodélé emissions are the main source
for the dust transported to the Amazon Basin.
Time series of observed and simulated aerosol
concentrations at ATTO during January–April 2014. Observed total aerosol
concentrations are the sum of coarse aerosol (dp > 1µm)
from OPS and fine aerosol (dp≤ 1 µm) from SMPS. Model
results are separated into different species. Vertical dashed lines indicate
the end of each month.
Figure 8 also gives the sensitivity of the contribution from the above
regions to the corresponding emissions: sij=(cij/∑jcij)/(ej/∑jej), where sij is the
sensitivity of dust at grid i to emissions from region
j, cij is dust concentration at grid i
originating from region j, and ej are dust emissions
from region j. It shows that dust over the Amazon Basin is most
sensitive to emissions in the western Sahel, followed by northwestern Sahara. High
sensitivity to Bodélé emissions is limited to eastern Brazil.
Therefore, even with doubled emissions, Bodélé would not dominate
dust over the Amazon Basin. The dust burden over most of the Amazon Basin
would be increased by less than 20 %, except over eastern Brazil, where
the burden would be increased by 20–50 %.
However, there is also intraseasonal variability for the sources of dust
arriving over the Amazon Basin. We find that the transatlantic transport of
Bodélé dust becomes the largest contributor over the Amazon Basin in
January (see Fig. S1 in the Supplement). This difference is not
introduced by the variation in dust emissions, as the relative contributions
of the four dust source regions are quite similar in January (35,
10, 7.4, and 19 % for northwestern Sahara, northeastern Sahara, western
Sahel, and Bodélé, respectively) compared with February–April. The bottom panel in
Fig. S1 also shows more sensitivity of the Amazon Basin to Bodélé
emissions in January. A detailed analysis of the meteorological fields (wind
fields and precipitation; see Fig. S2 in the Supplement) suggests
that the difference is mainly due to the changes in precipitation fields,
which removes more northwestern Sahara and western Sahel dust but less
northeastern Sahara and Bodélé dust along the transport towards South America. We
also analyzed the interannual variability in wind fields and precipitation
for the period of 2013–2015 and find that the difference between 2014 and the
average of 2013–2015 along the dust transport path (see Fig. S3) is
relatively smaller than the intraseasonal variability (Fig. S2). This wide
spatiotemporal variability in sources to some extent explains the divergence
in the literature.
Aerosols in the Amazon Basin Chemical components of aerosols at ATTO
Figure 9 shows time series of observed and simulated aerosol mass
concentrations at ATTO from January to April 2014. The observed aerosols had a mean
concentration of 13 ± 8.6 µg m-3, dominated by coarse
aerosols with a mean contribution of 90 %. The coarse aerosol
contribution is at the higher end of the previously reported range (66–78 %) at nearby sites (Artaxo et al., 1990; Formenti et al., 2001). However,
the coarse aerosols defined in the latter are aerosols with dp > 2 µm, in contrast with 1 µm as used in this work.
Applying the same criterion would yield a mean contribution of 75 % from
coarse aerosols at ATTO, consistent with the reported range. The model shows
similar aerosol concentrations (11 ± 9.1 µg m-3),
with r=0.75, but has less contribution of coarse aerosols (54 %).
One reason for this difference is the missing sources of PBA particles in
the model, which has been found to dominate the coarse aerosols at
background conditions in previous studies (Formenti et al., 2001). The
observed background concentrations for coarse aerosols are around 2.8 µg m-3, while the model shows only 0.27 µg m-3,
implying concentrations of about 2.5 µg m-3 for PBA at ATTO.
This background is within the reported range of 1.2–3 µg m-3 for
coarse particles excluding dust at nearby sites (Formenti et al., 2001;
Pöschl et al., 2010) and over southern Amazon (∼ 10∘ S) (Fuzzi et al., 2007) during the wet season.
In addition, the model also shows a higher background for fine aerosols
(2.8 µg m-3) compared with observations (0.41 µg m-3).
The difference of 2.4 µg m-3 in fine aerosol concentrations
could be explained by the model bias in SOA production, which dominates fine
aerosol concentrations. Barkley et al. (2011) found that GEOS-Chem has
underestimated hydroxyl concentrations, while overestimating isoprene and its
oxidation products over tropical South America. The mean concentration of
SOA in the model is 3.0 ± 0.85 µg m-3, while measurements
from AMS (aerosol mass spectrometry) show an average of 0.6 µg m-3 during the wet season (Chen et al., 2009). Correcting the bias in
background for both coarse and fine aerosols would yield better agreement in
the contribution of coarse aerosol while not degrading the comparison of
total concentrations.
During the wet season, the model shows a mean concentration of 6.3 µg m-3 for dust, 91 % of which is in coarse mode. Dust contributes to
59 and 85–92 % of total aerosol mass on average and during dusty
periods, respectively. We find that model dust correlates well with observed
coarse aerosols (r=0.73), indicating strong influence of dust
on the observed variation in coarse aerosols. The two highest peaks on 18–19
February and 5–9 March (with dust concentrations up to 30–50 µg m-3)
could also be expected from the daily distribution of transatlantic AOD in
Fig. 6, again implying the long-range transport of dust from Africa to the
ATTO site.
Model sea salt has a mean concentration of 0.31 µg m-3 and
accounts for about 3 % of total aerosol mass on average. There is a
moderate correlation (r=0.49) between model sea salt and dust.
Previous studies also show mixed transport of marine aerosol and dust to the
Amazon Basin with sea salt contribution of up to 10 % (Artaxo et al.,
1990; Ben-Ami et al., 2010; Formenti et al., 2001; Talbot et al., 1990).
After correcting the bias in SOA concentrations, OA has a mean concentration
of 1.3 ± 1.6 µg m-3 and is still the largest contributor
(54 %) to fine aerosols, followed by fine dust (23 %),
sulfate–nitrate–ammonium (11 %), fine sea salt (9.4 %), and BC (2.8 %). The low sulfate contribution (3 % of total aerosol mass) is
consistent with previous results (Talbot et al., 1990). Simulated POA has a
mean concentration of 0.70 ± 0.91 µg m-3 and could explain
50 % of observed variance of fine aerosol.
POA is generally present with co-emitted BC, with r=0.99 in the
model. Figure 10 shows time series of observed rBC from the SP2 and
simulated BC during January–April 2014. Observed rBC has a background
concentration of 1.8 ng m-3. This “clean” condition is frequently
interrupted and thus has median and 75th percentile concentrations of
16 and 53 ng m-3, respectively. There are two peaks with
observed BC higher than 100 ng m-3 on 8–9 March and 9–11 April. The
model reproduced the observed variation well, with r of 0.73, but
generated a higher background concentration of 28 ng m-3. The linear
regression analysis between the model and rBC also indicates a positive bias of
26 ng m-3 in simulated background concentrations. Source attribution in
the model shows that open fires make a median contribution of 39 % (14 and 68 % for 25th and 75th percentile) and account for
most of the variance. The overestimate in the background is probably driven
by fossil fuel and biofuel combustion BC in the model, mainly from NSA with
a mean concentration of 28 ng m-3 during the period. Figure 10 also
shows that open fire in NSA and NAF is equally important for BC at ATTO
during January–April 2014, with the latter becoming more important in the late wet
season.
It is interesting to note that BC peaks generally coincide with coarse
aerosol peaks, with r of 0.70 and 0.52 in the observed and
simulated data, respectively. Long-range transport of dust mixed with open
fire aerosols from Africa to the Amazon Basin has been reported in previous
studies (Baars et al., 2011; Ben-Ami et al., 2010; Guyon et al., 2003b;
Rizzo et al., 2013; Talbot et al., 1990). Source attribution in the model,
however, also points out events of African dust mixed with NSA fire plumes,
which is consistent with the HYSPLIT back trajectories passing over NSA fire
spots to the east/northeast of ATTO (Andreae et al., 2015).
Figure 11 shows scatterplots of observed and simulated BC vs. CO
concentrations for high BC events in Fig. 10 (cases a–e). Both observations
and model results show good correlation between CO and BC during those
events, with r of 0.52–0.91 and 0.93–0.99, respectively, implying
a similar origin for the two species. The model captured the observed slope
for case (c) well but underestimated it for cases (d) and (e) by 30
and 60 %, respectively.
Time series of rBC and model BC concentrations at ATTO in
January–April 2014. The top panel compares rBC with model BC separated into fuel
(fossil fuel and biofuel) and open fire sources. The bottom panel further
separates model fire BC by source regions. Circled peaks (a–e) are used for
BC–CO analysis in Fig. 11.
Scatterplots of observed (top) and simulated (bottom) BC
vs. CO concentrations at ATTO for (a)–(e) peaks in Fig. 10.
Reduced-major-axis (RMA) regression statistics and linear fits are shown inset.
Because of the relatively longer lifetime of CO in the atmosphere, the
enhancement ratio of BC versus CO can indicate the age of an air mass, with
lower ratios in more aged air. Assuming first-order removal of BC, the age
of an air mass can be estimated using Eq. (1):
t=-L⋅lnffe,
where L is the lifetime of BC, f is enhancement ratio of
BC versus CO, and fe is the emission ratio of BC versus
CO from sources. For this calculation, we average fe from the FINN inventory over NSA and NAF during January–April 2014. The derived
fe is 7.7 ± 0.76 and
8.3 ± 0.75 ng m-3 ppb-1 for open fires in NSA and NAF, respectively. The simulated global
mean lifetime of tropospheric BC is 4.3 days, consistent with the range of
4.2–4.4 days that was derived from model simulations constrained by the
ensemble of observations (Wang et al., 2014a, b). BC from
different sources also has different lifetimes, ranging from 3 to 6 days
depending on the surrounding meteorological conditions after it is emitted
to the atmosphere. For cases (a)–(c), the model shows a dominant
contribution from NSA open fire with a simulated BC lifetime of 3.1 days.
The corresponding age of the air mass is 1–3 days for the simulated peaks.
For case (d), the model shows a dominant contribution from NAF open fires.
Applying a simulated BC lifetime of 5.6 days for NAF open fires, we derive a
corresponding age of air mass of 11 days, consistent with the range of 8–12 days for the transport time of African plumes arriving in South America
estimated by Ben-Ami et al. (2010). Model source attribution for case (e)
indicates a combined contribution from NAF (80 %) and NSA (20 %) open
fires for BC. Thus, the slope in case (e) is also a similar combination of
case (d) and case (a)–(c). The above calculation implies an infinite lifetime
for CO. Applying an average CO lifetime of 2 months against oxidation by
the hydroxyl radical (Fisher et al., 2010), less than 20 % of CO would be
lost at a timescale of 11 days. With this CO loss accounted for, the derived
transport time would be increased by 10 %.
The model bias in the BC background affects the intercept of the line but
not the slope. The difference between observed and simulated slopes could be
due to variation in emission ratios from individual fires. The variation
(10 %) in the emission factors for NSA and NAF fires from the FINN inventory
is relatively small as they are already averaged for different land
cover/vegetation type. The uncertainty could be much larger when accounting for
individual fires. For example, the emission ratio of BC to CO reported by
Andreae and Merlet (2001) has an uncertainty of 50 % for savanna and
grassland fires, which would result in 50 % variation in the slope. The
difference in aerosol removal time could also contribute to the bias in the
slopes. As discussed in Sect. 4, the simulated aerosol lifetime is 14 %
shorter than that derived from MODIS AOD over the ocean, which could result
in a difference around 30 % in the slope. It is also possible that the
observed slope for case (e) is due to fires nearby which are missed or
underestimated in the model. Also, model results are for average conditions
and individual cases may be quite different from the average.
Aerosol absorption at ATTO
Figure 12 shows time series of observed and simulated aerosol absorption at
550 and 400 nm at ATTO during January–April 2014. The observed absorption has a
mean value of 1.5 ± 1.5 and 2.3 ± 2.3 Mm-1 at wavelengths
of 550 and 400 nm, respectively. The highest absorption is found in early
January, with values higher than 10 Mm-1 at 550 nm. The simulated
absorption at 550 and 400 nm is 1.3 ± 1.2 and 2.3 ± 2.2 Mm-1,
respectively, agreeing with the observations with an r of
0.64–0.66.
The model absorption is further separated into BC, BrC, and dust in Fig. 12.
BC contributes 63 % of the absorption at 550 nm, followed by BrC (27 %, mainly from open fires) and dust (10 %) on average. The
contribution of BrC to total carbonaceous aerosol absorption
(25 ± 8.8 %) is within the range 20–40 % on a global scale proposed by previous
authors (Chung et al., 2012b; Saleh et al., 2015; Wang et al., 2014b).
Although the mean contribution is small, dust could occasionally be
important. One such example occurred in early March, as expected from Fig. 9, with a dust contribution of up to 50 %. Due to stronger wavelength
dependence, a relatively higher contribution to absorption at 400 nm is found
from BrC (41 %) and dust (17 %) compared to the absorption at 550 nm.
Figure 13 shows scatterplots of absorption at 550 and 400 nm vs. BC
concentrations during the same period. There is high correlation between
observed absorption at both wavelengths and SP2 rBC data with r of
0.96–0.98, implying a major contribution of BC and/or species with similar
origin, such as BrC, consistent with the model source attribution of the
aerosol absorption discussed above. The slope increases from 0.023 for
absorption at 550 nm to 0.038 for absorption at 400 nm, reflecting the
strong wavelength dependence of BrC absorption. We find that the model
successfully captures observed correlation and slopes at both wavelengths.
Time series of Aethalometer and model absorption at 550 nm (top) and 400 nm (bottom) at ATTO during January–April 2014. The model results
are further separated into BC, brown carbon, and dust.
Observed and simulated AAE during the period has mean values of 1.3 ± 0.33 and 1.6 ± 0.37, respectively. One uncertainty in the AAE
estimation is due to its wavelength dependence. AAE estimated directly from
Aethalometer data at 370 and 520 nm is 1.5 ± 0.42, which is slightly
higher than the value (1.3 ± 0.33) based on a linear fit of multiple
wavelength absorption. In addition, Rizzo et al. (2011) reported a typical
uncertainty of 20 % in Aethalometer AAE, mainly due to the choice of
scattering Ångström exponent.
Note that the contribution from BrC is directly affected by the OA / OC ratio
assumed for POA in the model. In this work, we adopt a ratio of 1.7 measured
in the Amazon Basin during the wet season of 2008 (Chen et al., 2009), which
is lower than the value of 2.1 ± 0.2 for aged aerosol suggested by
Turpin and Lim (2001). Using the latter ratio would increase BrC and total
aerosol absorption at 550 nm by 25 and 6 % at ATTO during the wet
season. The corresponding AAE would also be increased to 1.7 ± 0.37.
Scatterplot of observed (black) and simulated (red)
absorption at 550 nm (top) and 400 nm (bottom) vs. BC concentrations from at
ATTO in January–April 2014. Correlation coefficients and slopes of
reduced-major-axis (RMA) regressions are shown as inset.
Model AAOD at 550 nm (left top) and the contribution to
total AAOD from BC, brown carbon, and dust over the Amazon Basin averaged
over January–April 2014.
Aerosol light absorption over the Amazon Basin
Figure 14 shows the distribution of simulated AAOD at 550 nm between
80–35∘ W and 24∘ S–8∘ N averaged over January–April 2014. During the wet season,
AAOD over the central Amazon, including the ATTO site, is generally lower than
0.0015, reflecting a minimal influence of human activities over this region.
High AAOD (up to 0.01) over Colombia is caused by open fires, whereas over
southern Brazil it originates from fossil fuel and biofuel combustion near
São Paulo. Fossil fuel and biofuel emissions over the northeastern coast
of Brazil, although not as high as those from São Paulo, also result in
slightly enhanced AAOD (0.0015–0.003). Due to the high cloud fraction in the
wet season, there is little AERONET level 2.0 data available for the model
evaluation in the Amazon Basin. On the other hand, we compared the
distribution of simulated AOD averaged over January–April 2014 with MODIS AOD
data. We find that the model is able to reproduce the observed spatial
variation with an r of 0.76 but has a negative bias of -32 %.
The background and mean AOD is 0.033 and 0.082 in the model and 0.059 and
0.12 in MODIS data, respectively. Keep in mind that high cloud fractions in
the Amazon Basin in the wet season combined with coarse model resolution
could also introduce significant sample bias in the comparison, which
presents a challenge for the model evaluation.
Figure 14 also shows the separate contributions from BC, BrC, and dust to
total absorption. We find that more than 50 % of AAOD is from BC except
for Guyana, Suriname, French Guiana, and northern Brazil, where the influence
of dust becomes significant with up to 35 %. However, the dust influence,
which could represent the influence of African plumes, becomes negligible
for regions south of 10∘ S. The contribution of BrC is
generally between 20 and 30 % and could be up to 40 % when it is more
affected by open fires, such as in Colombia and Venezuela.
Figure 15 shows model AAE estimated from wavelength 550 and 400 nm over the
Amazon Basin for the period of January–April 2014. Simulated AAE values cover a
wide range from ∼ 1 to more than 2. The distribution of AAE
indicates the distribution of LAA types over the basin. The regions with AAE
less than 1 are fossil fuel combustion dominated, while the regions with AAE
higher than 1.5 are more affected by open fires and dust.
Note that the model bias in SOA production does not affect the simulated
absorption, as the contribution of SOA to brown carbon is not accounted for.
A recent study by Lambe et al. (2013) shows that the absorptivity of SOA
depends on both precursor type and oxidation level. The derived MAE at 405
nm is less than 0.02 m2 g-1 for biogenic SOA and less than 0.08 m2 g-1 for aromatic SOA. These values are 1 to 2 orders of
magnitude smaller than those for biofuel and open fires (1.4 and 1.3 m2 g-1, respectively). Adding SOA absorption using the upper limit values
only increases total absorption at 400 nm by less than 8 % over the
Amazon Basin. The influence is even smaller if we correct the model positive
bias in SOA production to be consistent with observations in the Amazon
Basin.
Due to limited information on the detailed components/concentrations of PBA
and associated absorptivity (Despres et al., 2012), the contribution of PBA
to absorption is not accounted for in the above results. However, assuming
that the background of absorption at 550 nm is from PBA due to its
relatively constant concentrations (Huffman et al., 2013; Pauliquevis et
al., 2012), we obtain an absorption of 0.2 Mm-1 from observations at
ATTO for PBA, accounting for 13 % of total absorption at 550 nm on
average. This value is consistent with the intercept (0.24 Mm-1) of the
regression line between the absorption at 550 nm and the SP2 data in Fig. 13,
which can represent the upper limit assuming all absorption not correlated
with rBC is from PBA. Our estimate is also consistent with Pauliquevis et al. (2012), who reported an average background of 50 ng m-3 of
equivalent black carbon (BCe), corresponding to an absorption around
0.3 Mm-1 for PBA in the central Amazon.
Assuming well-mixed PBA below a height of 2 km with absorption of 0.2 Mm-1, we find that PBA could account for 5–40 % of total AAOD at
550 nm over the Amazon Basin, with relatively lower contributions over regions
with more influence from open fires (e.g., the northern Amazon). This
estimate is roughly consistent with the results of Guyon et al. (2004), who
reported 35–47 % of absorption attributed to biogenic particles during
the wet-to-dry/wet season in the Amazon Basin.
Model absorption Ångström exponent
(AAE) based on AAOD at 400 and 550 nm over the
Amazon Basin averaged over January–April 2014.
Conclusions
We used the GEOS-Chem chemical transport model to interpret observed aerosol
concentrations and associated absorption over the Amazon Basin during
January–April 2014. To better understand the source types and contributions to
light-absorbing aerosols and their optical properties over the Amazon Basin,
we modified the aerosol optical properties in the model to account for the
coating enhancement of BC light absorption. We also added brown carbon as a
light absorber, with its absorptivity depending on emission ratios of BC / OA
based on the most recent study (Saleh et al., 2015). Our simulation used
FINNv1.5 (Fire INventory from NCAR version 1.5) with daily resolution
(Wiedinmyer et al., 2011) for open fire so as to capture the observed daily
variance over the Amazon Basin.
The observations show relatively low aerosol concentrations and light
absorption over the Amazon Basin during the wet season. However, this low
background is frequently interrupted by sources not fully quantified
previously. Several studies have reported long-range transport of dust
plumes from Africa to the Amazon Basin during the wet season (Baars et al.,
2011; Ben-Ami et al., 2010; Formenti et al., 2001). To investigate the
influence of the transatlantic transported plumes, we first evaluated the
emissions and optical properties of dust over Africa through comparison with
AERONET and MODIS AOD data. The comparison shows no systematic bias between
the model and AERONET AOD but a positive model bias (∼ 13 %
over the dusty region and 20–50 % over the outflow region) compared with
MODIS. Comparison with dust optical properties derived from Mie calculation
using long-term AERONET observations suggests slightly positive model bias
(18 %) in the mass extinction efficiency (MEE) of dust and consistence
between simulated mass absorption efficiency (MAE) and MAE derived from
AERONET data.
We further examined the simulated transatlantic transport constrained by
MODIS and CALIOP observations. We find that GEOS-Chem reproduces well the
spatial/vertical distribution as well as the time variance of the African
outflows. Based on the gradient of the logarithm of the AOD over the
Atlantic Ocean, we estimated a lifetime of dust of 3.2 days in the model, which
is slightly underestimated (14 %) when compared with MODIS data (3.7 days).
This comparison is better than in the study by Ridley et al. (2012),
probably due to difference in meteorology and updates in wet scavenging
taken from the previous study by Wang et al. (2014a).
During January–April 2014, both the model and MODIS observations show five events
of transatlantic transport plumes from Africa to the Amazon Basin. Model
sensitivity tests against sources show that dust over most of the Amazon
Basin is dominated by emissions from the northwestern Sahara, followed by the
contribution from the western Sahel during the period. On the other hand, the
Bodélé emissions can dominate the dust over the Amazon Basin in
January and in eastern Brazil over the whole period.
Finally, we evaluated the model performance in the Amazon Basin through
comparison with observations at the ATTO site, which represents the Amazon
Basin with minimal human perturbation (Andreae et al., 2015). The
observations show an average aerosol concentration of 13 ± 8.6 µg m-3 for the study period, 90 % of which is from coarse aerosol.
The model is able to reproduce the mean concentrations (11 ± 9.1 µg m-3 with r=0.75) but has less contribution from
coarse aerosols. Detailed analysis indicates a positive model bias of 2.4 µg m-3 in SOA production and a missing source of primary
biogenic aerosol (PBA) of 2.5 µg m-3.
Source attribution in the model suggests that open fires are responsible for
most variance of observed aerosol concentrations (BC in particular). Open
fires from both northern South America and northern Africa are comparably
important, with the latter more important in the late wet season. Based on
the enhancement ratio of BC versus CO, fire emissions from northern South
America generally arrive at ATTO within 1–3 days, whereas those from Africa
have a transport time of 11 days, consistent with the range of 8–12 days found by
Ben-Ami et al. (2010).
With modified optical properties, the model reproduces the observed light
absorption and its wavelength dependence. The simulated absorption
Ångström exponent (AAE) of 1.6 ± 0.37 for total aerosols is
within the uncertain range of AAE (1.5 ± 0.42) estimated directly from
the Aethalometer data at 370 and 520 nm. Source attribution indicates a soot
BC contribution of 63 %, followed by brown carbon (27 %) and dust
(10 %) to the total absorption at 550 nm.
Expanding the model results to the Amazon Basin, we find more than 50 %
of total absorption at 550 nm is from BC except for Guyana, Suriname, French
Guiana, and northernmost Brazil, where the influence of dust becomes
significant with up to 35 %. The brown carbon contribution is generally
between 20 and 30 %. The spatial distribution of AAE suggests more fossil
fuel combustion in the southern part of the Amazon (with AAE ∼ 1), whereas more open fire and dust influence is found in the northern part
of the basin (with AAE > 1.8).
In this study, we only considered OA from biofuel and open fires as brown
carbon. Therefore, the model bias in SOA production as well as the missing
representation of PBA does not affect the model results in total absorption.
However, we also discussed the uncertainties of absorption due to SOA and
PBA in the Amazon Basin. Applying the upper limit of the MAE value for SOA,
we find a difference of less than 8 % in absorption at 400 nm over the
Amazon Basin, even with the positive model bias in SOA production.
Absorption due to PBA is important under background conditions. Assuming all
background absorption of 0.2 Mm-1 at 550 nm is due to PBA, we find that
PBA contributes 13 % of absorption at 550 nm averaged over the whole wet
season at ATTO site. Applying this constant background absorption to the
whole Amazon Basin and boundary layer, PBA could account for 5–40 % of
total AAOD at 550 nm over the Amazon Basin.
Data availability
The AERONET date are available online at http://aeronet.gsfc.nasa.gov/
(NASA, 2016a). The MODIS product is available online on the Goddard Earth
Sciences Data and Information Services Center (GES DISC, 2016) at
http://giovanni.sci.gsfc.nasa.gov/giovanni/. The CALIOP product is
available online at https://www-calipso.larc.nasa.gov/ (NASA, 2016b).
The ATTO data used in this work are not available in any public repository
but are available upon request.
The Supplement related to this article is available online at doi:10.5194/acp-16-14775-2016-supplement.
Acknowledgements
The work was supported by the Max Planck
Society (MPG). We acknowledge the support by the Instituto Nacional de
Pesquisas da Amazônia (INPA). We would like to thank all the people
involved in the technical, logistical, and scientific support of the ATTO
project, in particular Jürgen Kesselmeier, Reiner Ditz, Matthias Sörgel, Thomas Disper, Uwe Schultz, Steffen Schmidt, Thomas Seifert,
Andrew Crozier, Antonio Ocimar Manzi, Hermes Braga Xavier, Elton Mendes da Silva, Nagib Alberto de Castro Souza, Adir Vasconcelos Brandão,
Amauri Rodriguês Perreira, Antonio Huxley Melo Nascimento, Thiago de Lima
Xavier, Josué Ferreira de Souza, Roberta Pereira de Souza, Bruno
Takeshi, and Wallace Rabelo Costa. We acknowledge the support by the German
Federal Ministry of Education and Research (BMBF contract 01LB1001A) and the
Brazilian Ministério da Ciência, Tecnologia e Inovação
(MCTI/FINEP contract 01.11.01248.00) as well as the Amazon State University
(UEA), FAPEAM, LBA/INPA, and SDS/CEUC/RDS-Uatumã. We thank the AERONET
staff for their effort in establishing and maintaining the AERONET sites
used in this study. The MODIS data were obtained from NASA Goddard Data
Center and CALIPSO data were obtained from the NASA Langley Research Center
Atmospheric Sciences Data Center. The GEOS-5 FP data used in this
study/project have been provided by the Global Modeling and Assimilation
Office (GMAO) at NASA Goddard Space Flight Center. This paper contains
results of research conducted under the Technical/Scientific Cooperation
Agreement between the National Institute for Amazonian Research, Amazonas
State University, and the Max-Planck-Gesellschaft e.V. The work was
conducted under scientific licenses 001030/2012-4, 001262/2012-2, and
00254/2013-9 of the Brazilian National Council for Scientific and
Technological Development (CNPq). The opinions expressed herein are the
entire responsibility of the authors and not of the participating
institutions.
The article processing charges for this open-access publication were covered by the Max Planck Society.
Edited by: Gilberto Fisch
Reviewed by: two anonymous referees
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