Introduction
Open biomass burning is an important source of trace gases and particulate
matter (PM) to the atmosphere (Crutzen and Andreae, 1990; Andreae and Merlet,
2001; Van der Werf et al., 2010). Biomass burning emissions can influence
weather (Kolusu et al., 2015; Gonçalves et al., 2015; Tosca et al., 2015)
and climate (Ramanathan et al., 2001; Tosca et al., 2013; Jacobson, 2014)
directly, by scattering and absorbing solar radiation (Johnson et al., 2008;
Sakaeda et al., 2011), and indirectly, by modifying cloud properties (Andreae
et al., 2004; Feingold et al., 2005; Tosca et al., 2014). The influence of
biomass burning aerosol on surface radiation can have subsequent impacts on
the biosphere. For example, smoke plumes from biomass burning have been
observed to increase plant productivity, through increasing the amount of
diffuse radiation (Oliveira et al., 2007; Doughty et al., 2010), which has
been shown to be a regionally important process over the Amazon (Rap et al.,
2015). PM from biomass burning can substantially degrade regional air
quality, leading to adverse effects on human health (Emmanuel, 2000;
Frankenberg et al., 2005; Johnston et al., 2012; Jacobson et al., 2014;
Reddington et al., 2015). A better understanding of particulate emissions is
needed to improve predictions of the impacts of biomass burning on climate
and air quality. Here we use a global aerosol model with tropical
observations of surface PM and aerosol optical depth (AOD) to better
understand the impact of tropical fires on atmospheric aerosol.
The spatial and temporal distribution of fires depends on climate,
vegetation and human activities. At the global scale, fire emissions are
dominated by burning in the tropics (van der Werf et al., 2010).
Anthropogenic activity can increase the occurrence of fires either directly,
through deforestation fires and agricultural residue burning (van der Werf
et al., 2010), or indirectly, through land-use/land-cover change that acts
to increase the fire susceptibility of the land surface, e.g. forest
fragmentation in the Amazon (Cochrane and Laurance, 2002) and large-scale
drainage of peatlands in Indonesia (Field et al., 2009; Carlson et al.,
2012). Human activity can also reduce the occurrence of fires, directly
through fire suppression and indirectly through reducing and fragmenting
fuel loads, which limits fire spread (Bistinas et al., 2014). Over the
21st century, predicted changes in rainfall and temperature may
increase forest water stress and subsequent fire occurrence in tropical
forests (Cox et al., 2008; Golding and Betts, 2008; Malhi et al., 2009). The
incidence of fire and resulting emissions are therefore sensitive to both
changing climate and changes in land use (Heald and Spracklen, 2015).
High temporal and spatial variability in biomass burning emissions coupled
with the difficulties involved in conducting measurements in remote tropical
regions leads to major challenges for their quantification. In recent years,
global estimates of biomass burning emission fluxes have mostly been obtained
using satellite remote sensing (e.g. van der Werf et al., 2006, 2010; Reid
et al., 2009; Wiedinmyer et al., 2011; Kaiser et al., 2012; Zhang et al.,
2012; Ichoku and Ellison, 2014), which provides long-term observations with
relatively high spatial coverage. A range of satellite products and methods
are utilised to derive fluxes of aerosol- and gas-phase species emitted from
fires. The most common methods use satellite-retrieved burned area, active-fire counts, and/or fire radiative power (FRP) in combination with
biogeochemical models (when using burned area) and/or species-specific
emission factors obtained from laboratory experiments and field observations
(e.g. Hoelzemann et al., 2004; Ito and Penner, 2004, 2005; van der Werf et
al., 2006, 2010; Wiedinmyer et al., 2006, 2011; Schultz et al., 2008; Kaiser
et al., 2012). Large uncertainties are associated with satellite observations
of fires and with the various methods used to calculate emissions fluxes from
the observational data (e.g. Ito and Penner, 2005; Reid et al., 2009;
Konovalov et al., 2014).
Previous studies using satellite-derived emissions and atmospheric models to
investigate the properties and impacts of biomass burning aerosol have found
a persistent underestimation of AOD observed in most tropical biomass
burning regions (Matichuk et al., 2007, 2008; Chin et al., 2009; Petrenko et
al., 2012; Kaiser et al., 2012; Ward et al., 2012; Tosca et al., 2013;
Pereira et al., 2016). In general, modelling studies have required biomass
burning emissions or concentrations of biomass burning aerosol to be
increased by factors ranging from ∼ 1.5 to ∼ 6
in order to match satellite and ground-based observations of AOD (Matichuk
et al., 2007, 2008; Johnson et al., 2008; Sakaeda et al., 2011; Johnston et
al., 2012; Kaiser et al., 2012; Tosca et al., 2013; Marlier et al., 2013).
The underestimation of AOD observed in biomass burning regions has been
attributed to a number of factors (see, e.g., Kaiser et al., 2012), including
(i) underestimation of biomass burning emission fluxes, (ii) errors in
modelling the atmospheric distribution and properties of biomass burning
aerosol, and (iii) uncertainties in the calculation of AOD.
Uncertainties associated with the derivation of emission fluxes arise from
errors present in the satellite detection of active fires or burned area
(e.g. obscuring of the surface by clouds and smoke, satellite spatial
resolution and detection limits, and satellite overpass time), as well as
uncertainties in emission factors and fuel consumption estimates. For
example, Randerson et al. (2012) suggest that emission datasets based on
relatively coarse burned-area data (detection limit of ∼ 100 Ha) result in an underestimation of global area burned by ∼ 35 %, although this error is not sufficient to fully explain the
underestimation of AOD discussed above. Inadequate representation of biomass
burning aerosol in models, including errors in the modelled aerosol size
distribution, chemical composition, ageing processes, vertical and
horizontal transport (including fire emission injection heights) and dry/wet
removal from the atmosphere, could also contribute to an underestimation of
AOD. The contribution of secondary organic aerosol (SOA) from the oxidation
of volatile organic compounds in biomass burning plumes is also a large
uncertainty (Jathar et al., 2014; Shrivastava et al., 2015). In the
calculation of AOD itself, the uncertainties associated with the assumed
optical properties of biomass burning aerosol, e.g. their refractive indices,
hygroscopicity (uptake of water onto the aerosol), and/or mixing state (i.e.
treated as core/shell mixtures, internally/externally mixed etc.), may also
contribute to this negative model bias in AOD.
Using only AOD to evaluate estimates of biomass burning aerosol emissions
can be misleading because AOD depends on many factors in addition to aerosol
abundance. Scaling biomass burning emissions to match observed AOD could
therefore lead to inaccurate model representation of biomass burning aerosol
concentrations and, subsequently, errors in model predictions of the air
quality and climate effects of biomass burning aerosol. Although there has
been extensive use of AOD retrievals to evaluate model predictions of
biomass burning aerosol, thus far there have been relatively few studies to
use aerosol measurements to thoroughly evaluate these models (e.g. Liousse
et al., 2010; Daskalakis et al., 2015).
In this study, we evaluate a global aerosol microphysics model against
observations of aerosol mass concentrations in addition to AOD. Our aim is
to understand the discrepancy between bottom-up and top-down estimates of
particulate emissions from tropical fires. We compare three different
biomass burning emission inventories in our global model, investigating
regional differences between emissions and helping to constrain emissions
for future modelling studies.
Observations
To evaluate the simulated distribution of PM at the surface, we use
long-term in situ measurements of PM2.5 (particulates with
aerodynamic diameters < 2.5 µm) mass concentrations
conducted at four ground stations in the Amazon region (Alta Floresta, Porto
Velho, Santarem and Manaus). The location and observation period are
detailed for each station in Table S1 in the Supplement. Figure S1 shows the measured PM2.5 concentrations at each station between 2003 and
2011, demonstrating the data coverage.
The PM2.5 measurements were made using gravimetric filter analysis and the
measurement duration ranges from less than 1 day to more than 10 days.
Particles were sampled under ambient relative humidity (RH) conditions
(typically in the range of 80–100 % RH). The sampled filters were weighed
after 24 h of equilibration at 50 % RH and 20 ∘C. Amazonian
submicrometre aerosol particles have growth factors of ∼ 1.1–1.3 at 90 % RH (Zhou et al., 2002; Rissler et al., 2006), so we estimate
that water represents roughly ∼ 10–20 % of the PM2.5 mass
concentrations at measurement conditions. Uncertainties related to filter
handling, sampling and analysis are estimated as 15 % of particle mass.
Further information on the measurements conducted at the Manaus and Porto
Velho stations can be found in Artaxo et al. (2013). Our evaluation of PM2.5
is restricted to Amazonia since there are few long-term observations of
PM2.5 in other tropical regions impacted by biomass burning.
The measurement stations at Porto Velho and Alta Floresta are located in the
arc of deforestation and are strongly impacted by fresh biomass burning
emissions (Fig. 1). The Santarem and Manaus stations are located within
forest reservations and are impacted by transported regional biomass burning
emissions in the dry season. The Santarem station is located in Para, where
the number of fire hotspots observed by satellites during the dry season is
typically a factor of ∼ 10 greater than the number observed in
Amazonas, where the Manaus station is located. Thus, in the dry season, PM2.5
concentrations measured at Santarem are typically higher than those measured
at Manaus.
To evaluate the simulated distribution of AOD, we use observations of
spectral columnar AOD measured by the Aerosol Robotic Network (AERONET) using
ground-based Cimel sun photometers (Holben et al., 1998). Specifically, we
use level 2.0 (quality-assured) daily average AOD retrieved at 440 nm from
27 AERONET stations detailed in Table S1. We selected stations located within
regions influenced by tropical biomass burning (Southeast and equatorial
Asia, central and southern Africa, and the Amazon region in South America)
that have more than 1 year of relatively continuous data (automatic cloud
screening leads to gaps in the dataset) between 2003 and 2011. We note that
whilst the majority of cloud-contaminated AOD data are removed, comparisons
with co-located Micro-Pulse Lidar Network observations indicate that some
contamination from thin cirrus clouds may remain, possibly leading to small
positive biases in observed AOD (Huang et al., 2013; Chew et al., 2011).
(a–c) Total annual emissions of organic carbon (OC) in
Gg(C) a-1 averaged over the period of January 2003 to December 2011
from (a) GFED3, (b) GFAS1 and (c) FINN1. Black
circles mark the locations of the four aerosol measurement stations and black
crosses mark the locations of the 27 AERONET stations (see Table S1).
(d–f) Absolute difference in 2003–2011 mean annual OC emissions
between GFAS1, GFED3 and FINN1, (d) GFAS1 minus GFED3
(e), GFAS1 minus FINN1, and (f) GFED3 minus FINN1. The FINN1 OC
emissions (with a 1 km × 1 km horizontal resolution) were
aggregated onto a grid of 0.5∘ × 0.5∘
resolution to compare with GFED3 and GFAS1.
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To compare modelled and observed PM2.5 and AOD, daily mean model output was
linearly interpolated to the location (latitude, longitude and altitude
above sea level) of each ground station. Model data that corresponded to
gaps in the observation datasets were removed prior to calculating
monthly mean values used in the analysis. The modelled PM2.5 concentration
is calculated for dry aerosol, omitting the contribution of water to the
total mass, thus modelled PM2.5 concentrations may be underestimated
compared to the observations, which include some contribution from the mass
of water.
Model description
Global aerosol microphysics model
The global distribution of aerosol was simulated using the 3-D Global Model
of Aerosol Processes (GLOMAP; Spracklen et al., 2005a, b; Mann et al., 2010),
which is an extension to the TOMCAT chemical transport model (Chipperfield,
2006). Simulations were run for the period 2003 to 2011. Large-scale
atmospheric transport and meteorology in TOMCAT are specified from European
Centre for Medium-Range Weather Forecasts (ECMWF) analyses, updated every 6 h and linearly interpolated onto the model time step. The model runs at
a horizontal resolution of 2.8∘ × 2.8∘ with 31 vertical model levels between the surface and 10 hPa. The vertical
resolution in the boundary layer ranges from ∼ 60 m near the
surface to ∼ 400 m at ∼ 2 km above the surface.
GLOMAP has been extensively evaluated in previous studies against aerosol
observations (Mann et al., 2010, 2014; Spracklen et al., 2011a, b; Schmidt et
al., 2012; Scott et al., 2014; Reddington et al., 2011, 2013, 2014). Below
we describe the features of the model relevant for this study, please see
Spracklen et al. (2005a) and Mann et al. (2010) for more detailed
descriptions of the model.
GLOMAP simulates the mass and number of size resolved aerosol particles in
the atmosphere, including the influence of aerosol microphysical processes
on the particle size distribution. These processes include nucleation,
coagulation, condensation, ageing, hygroscopic growth, cloud processing, dry
deposition, and nucleation/impact scavenging. The aerosol particle size
distribution is represented using a two-moment modal scheme with seven
log-normal modes (Mann et al., 2010). Within each mode, aerosol particles
are treated as internally mixed. GLOMAP treats the following aerosol
species: black carbon (BC), particulate organic matter (POM), sulfate
(SO4), sea spray and mineral dust. Biogenic SOA is formed in the model
via the reaction of biogenic monoterpenes with O3, OH and NO3,
which produces a gas-phase oxidation product that condenses with zero vapour
pressure onto pre-existing aerosol (Spracklen et al., 2006, 2008).
Concentrations of oxidants are specified using monthly mean 3-D fields at
6-hourly intervals from a TOMCAT simulation with detailed tropospheric
chemistry (Arnold et al., 2005) linearly interpolated onto the model
time step. Monthly mean emissions of biogenic monoterpenes are taken from
the Global Emissions InitiAtive (GEIA) database (Guenther et al., 1995).
Size-resolved emissions of mineral dust are prescribed from daily varying
emissions fluxes provided for AEROCOM (Dentener et al., 2006).
For this study, anthropogenic emissions of sulfur dioxide (SO2), BC
and organic carbon (OC) were specified using the MACCity emissions inventory
(Lamarque et al., 2010; Granier et al., 2011), which provides annually
varying emissions for the period 1979–2010. For simulations in the year 2011
we used MACCity anthropogenic emissions from 2010. Biomass burning emissions
of SO2, BC and OC were specified using three different
satellite-derived emission datasets, which are described in detail in
Sect. 3.3. We convert OC to POM using a prescribed POM : OC ratio of 1.4,
which is at the lower end of the range prescribed in other global models
(1.4 to 2.6) (Tsigaridis et al., 2014). The fire emissions were injected
into the model over six ecosystem-dependent altitudes between the surface
and 6 km recommended by Dentener et al. (2006). In the regions studied in
this paper (South America, Africa and Southeast Asia), the fire emission
injection heights range between the surface and an altitude of
∼ 3 km a.s.l. The largest fraction of the fire emissions,
ranging from ∼ 99 % of emissions in equatorial Asia to
88 % in Indochina, is injected below 1 km a.s.l. (or at surface level if the
altitude of the model level exceeds 1 km a.s.l.). Analysis of smoke plume
heights has demonstrated that most smoke emissions from fires occur within
the boundary layer (Val Martin et al., 2010).
Primary carbonaceous aerosol particles are assumed to be non-volatile and
are emitted into the model with a fixed log-normal size distribution,
assuming a number median diameter of 150 nm for biomass burning emissions
and 60 nm for fossil fuel emissions and modal width (σ) of 1.59.
Several previous studies have investigated the impacts of the uncertainty in
the assumed emission size distribution on simulated aerosol and cloud
condensation nuclei concentrations (Pierce et al., 2007; Pierce and Adams,
2009; Reddington et al., 2011, 2013; Lee et al., 2013) and aerosol radiative
forcing (Bauer et al., 2010; Spracklen et al., 2011b; Carslaw et al., 2013).
An assumption of a number median diameter of 150 nm for biomass burning
emissions is reasonably consistent with measurements of the size
distributions of fresh biomass burning aerosol from grassland (100–125 nm) and deforestation (100–130 nm) fires (Reid et al., 2005, and
references therein). Once emitted into the model, the components of primary
carbonaceous aerosol (BC and OC) are assumed to mix instantaneously and are
initially treated as non-hygroscopic. Once these particles have accumulated
10 monolayers of soluble material (assumed to be SOA and H2SO4)
through condensation, they are transferred directly to the corresponding
soluble Aitken or accumulation mode to account for ageing. For a discussion
of the treatment of organic aerosol within global aerosol models, see
Tsigaridis et al. (2014).
Calculation of aerosol optical depth
AOD was calculated from the simulated aerosol size distribution using Mie
theory assuming spherical particles (Grainger et al., 2004) that are
externally mixed within each log-normal mode. For this study, modelled
AOD was calculated at a wavelength of 440 nm using
component-specific refractive indices at the closest wavelength available
(468 nm) from Bellouin et al. (2011). Water uptake plays a significant role
in determining AOD, altering the refractive index and the size distribution
of the aerosol. The water uptake for each soluble aerosol component is
calculated online in the model according to Zdanovskii–Stokes–Robinson
(ZSR) theory, which estimates the liquid water content as a function of
solute molarity (Stokes and Robinson, 1966). For POM in the soluble modes,
we assign a hygroscopicity consistent with a water uptake per mole at 65 %
of that of SO4 (Mann et al., 2010). This is an upper estimate of
aerosol hygroscopicity. In Sect. 4.1.3 we explore the sensitivity of
simulated AOD to different assumptions on aerosol hygroscopicity as well as
aerosol refractive indices and aerosol mixing state. The resulting
daily mean wet radii and refractive indices are used to calculate the
daily mean aerosol extinction. Using hourly mean values of water uptake
increased simulated daily AOD on average by less than 1 %.
Summary of biomass burning emission inventories used in this study:
the Global Fire Emissions Database version 3 (GFED3), the National Centre for
Atmospheric Research Fire Inventory version 1.0 (FINN1) and the Global Fire
Assimilation System version 1.0 (GFAS1). For each emission inventory, the
total amounts of black carbon (BC) and organic carbon (OC) aerosol emitted
from fires over the tropical region (defined as 23.5∘ N to
23.5∘ S) are given for the 2003 to 2011 average. Numbers in
parentheses give the ratio to GFED3 emissions.
GFED3
GFAS1
FINN1
Method
MODIS burned area and
MODIS thermal anomaly product
MODIS thermal anomaly product
biogeochemical model
and fire radiative power
and assumed burned area
Spatial resolution
0.5∘ × 0.5∘
0.5∘ × 0.5∘
1 km × 1 km
Temporal resolution
Monthly (1997–2011)
Daily (2001–2015)
Daily (2002–2013)
Daily (2003–2011)
Amount of OC emitted
13.412
11.731 (0.87)
17.282 (1.29)
over tropics (Tg a-1)
Amount of BC emitted
1.705
1.532 (0.90)
1.724 (1.01)
over tropics (Tg a-1)
OC : BC ratio over tropics
7.87
7.66
10.02
Reference
Van der Werf et al. (2010)
Kaiser et al. (2012)
Wiedinmyer et al. (2011)
Biomass burning emissions
In this study we compare three different satellite-derived datasets of
biomass burning emissions: the Global Fire Emissions Database version 3
(GFED3; van der Werf et al., 2010), the National Centre for Atmospheric
Research Fire Inventory version 1.0 (FINN1; Wiedinmyer et al., 2011) and the
Global Fire Assimilation System version 1.0 (GFAS1; Kaiser et al., 2012).
The key aspects of these emission inventories are summarised in Table 1. We
complete GLOMAP simulations for the period 2003 to 2011, where all three
emission datasets are available.
GFED3 provides monthly mean fire emissions of aerosol- and gas-phase species
from 1997 to 2011 at 0.5∘ × 0.5∘ resolution
(van der Werf et al., 2010). GFED3 emissions are derived using the
monthly mean time series of global burned-area estimates from Giglio et al. (2010). For 1997–2000, the fire emissions are based on burned area derived
from the TRMM Visible and Infrared Scanner (VIRS) and Along-Track Scanning
Radiometer (ATSR) active-fire data and estimates of plant productivity
derived from observations from the Advanced Very High Resolution Radiometer
(AVHRR). For November 2000 onwards, the fire emissions are based on
estimates of burned area, active-fire detections, and plant productivity
from the MODerate resolution Imaging Spectroradiometer (MODIS) instrument
on board the Terra and Aqua satellites. To derive total carbon emissions, the
satellite datasets are combined with estimates of fuel loads and combustion
completeness for each monthly time step from the
Carnegie–Ames–Stanford–Approach biogeochemical model. The carbon emission
fluxes are converted to trace gas and aerosol emissions using species-specific emission factors complied by Andreae and Merlet (2001). From 2003
onwards, GFED3 fire emissions are available on a daily time step, developed
using detections of active fires from MODIS (Mu et al., 2011). Daily GFED3
fire emissions were implemented in GLOMAP for the period 2003–2011.
FINN1 provides daily fire emissions of aerosol- and gas-phase species from
2002 to 2012 on a 1 km2 grid (Wiedinmyer et al., 2011). FINN1 fire
emissions are based on detections of active fires (specifically their
location and timing) from the MODIS Fire and Thermal Anomalies products
(Giglio et al., 2003). FINN1 also uses the MODIS Land Cover Type product to
specify land-cover classes and the MODIS Vegetation Continuous Fields
product to identify the fractions of tree and non-tree vegetation, as well as bare
ground. Specifically, the emitted mass (E) of a certain species
(i) is calculated using the following equation (Seiler and Crutzen,
1980):
Ei=Ax,t×Bx×FB×efi,
where A is the area burned at time t and location
x, B is the biomass loading at location x,
FB is the fraction of that biomass that is burned and ef
is the emission factor of species i. For each fire count the area
burned, A, is assumed to be 0.75 km2 for fires detected on
grassland and savannah land-cover classes, and 1 km2 for those detected
on all other land-cover classes following Wiedinmyer et al. (2006) and
Al-Saadi et al. (2008). Adjustments are made to the assumed burned area if
the fire pixel extends partially over bare ground (reducing the burned area
by the percentage of bare area assigned to that pixel). Estimates of biomass
loading, B, are taken from Hoelzemann et al. (2004) and are assumed
to be land-cover-specific. The fraction of biomass assumed to burn,
FB, in each fire pixel is determined as a function of tree cover
using relationships from Ito and Penner (2004) (see Wiedinmyer et al.,
2006). Emission factors, ef, for each species are taken from Akagi
et al. (2011).
GFAS1 provides daily fire emissions of aerosol- and gas-phase species from
March 2000 to 2013 at 0.5∘ × 0.5∘ resolution (Kaiser
et al., 2012). Like FINN1, GFAS1 uses the observed geolocation of active
fires from the MODIS instrument. However, GFAS1 also makes use of the NASA
fire products (MOD14 and MYD14) that provide quantitative information on the
radiative power of detected fires (Justice et al., 2002). The FRP fields are
corrected for observation gaps due to partial cloud cover by assuming the
same FRP areal density throughout the grid cell. Data assimilation is used to
further fill observation gaps using information from earlier FRP observations
(see Kaiser et al., 2012). Spurious signals from volcanoes, gas flares and
other industrial activity are removed from the data. The FRP is converted to
the combustion rate of dry matter using land-cover-specific conversion
factors based on data from GFED3 (Heil et al., 2010; Kaiser et al., 2012). As
for GFED3, species emission rates are calculated using updated emission
factors based on Andreae and Merlet (2001).
Table 1 gives the total annual amounts of BC and OC aerosol emitted from
fires over the tropics for each emission inventory. The total BC and OC
emitted from fires in the tropics make up 77–84 and 66–77 %,
respectively, of the global total emissions. FINN1 has the greatest tropical
OC emission, with emissions being 47 % greater than in GFAS1 and 30 %
greater than GFED3. Emission of BC is more consistent, with FINN1 BC
emissions being 13 % greater than GFAS1 and 1 % greater than GFED3. This
results in different OC : BC emission ratios between the datasets, with the
mean ratio across the tropics varying being 10.0 in FINN1, 7.9 in GFED3 and
7.1 in GFAS1.
Figure 1a–c show the spatial distribution of annual total biomass burning
emissions of OC from each fire inventory averaged over the period of 2003 to
2011. There are similarities in the general spatial distributions of fire
emissions, with all three inventories showing maximum emissions over the
tropical savannah and humid subtropical regions of Africa, the arc of
deforestation in Amazonia, coastal regions of Indonesia (Sumatra and
Kalimantan), northern Australia, and parts of Indochina (particularly
Cambodia, Laos and Myanmar). However, Fig. 1d–f show that there are strong
regional differences between the different emission inventories. Differences
between FINN1 and GFAS1 (Fig. 1e) and FINN1 and GFED3 (Fig. 1f) are more
spatially organised than differences between GFAS1 and GFED3 (Fig. 1d),
which are more spatially heterogeneous.
Over Africa, GFED3 gives higher OC emissions in northern tropical savannah
and southern humid subtropical regions, with GFAS1 and FINN1 giving higher
emissions than GFED3 at the boundaries of these regions and over central
Africa. Over Australia, GFED3 gives the highest OC emissions estimates over
the tropical savannah region of northern Australia, with GFAS1 giving the
highest emissions in the dryer grassland and desert regions further south.
Over South America the picture is more complex. In general, FINN1 and GFAS1
emission estimates are higher in northern and eastern Brazil than GFED3,
with GFAS1 giving the highest emissions over eastern areas and FINN1 over
northern Brazil. FINN1 emissions are generally higher than GFAS1 and GFED3
over the central and southern Amazon region (particularly over the state of
Mato Grosso), Peru and generally over northern South America. GFED3
emissions are higher than FINN1 and GFAS1 in northern parts of Bolivia and
the northern part of the state of Rondônia in the arc of deforestation.
Over South Asia, Indochina and equatorial Asia, FINN1 gives higher emissions
than both GFED3 and GFAS, particularly over Bangladesh, Myanmar and Laos,
with the exception of the coastal peatland regions of Sumatra and Kalimantan,
where GFAS1 and GFED3 give higher emissions than FINN1. The differences in
emissions over Indonesia may be explained by a potentially improved
representation of tropical peat fire emissions in GFED3 and GFAS1 relative
to FINN1 (Andela et al., 2013).
Results
Overview of all comparisons
Particulate matter concentrations in the Amazon region
Figure 2 shows simulated vs. observed multi-annual monthly mean PM2.5
concentrations at each of the four ground stations in the Amazon region (see
Fig. 1 for site locations). To quantify the agreement between model and
observations, we use the Pearson correlation coefficient (r) and normalised
mean bias factor (NMBF) as defined by Yu et al. (2006):
NMBF=∑Mi-∑Oi∑Mi-∑Oiexpln∑Mi∑Oi-1,
where M and O represent the multi-annual monthly mean
model and observed values, respectively, for each month i. A
positive NMBF indicates the model overestimates the observations by a factor
of NMBF + 1. A negative NMBF indicates the model underestimates the
observations by a factor of 1 - NMBF.
Figure 2 demonstrates the important contribution of biomass burning to PM2.5
concentrations across the region: there is a strong improvement in the
agreement between model and observations when biomass burning emissions are
included in the model (Fig. 2b–d; NMBF = -0.62 to -0.25,
r2=0.77–0.83) relative to the simulation without fire emissions (Fig. 2a; NMBF = -1.85, r2=0.44).
The overall bias between model and observations is smallest with FINN1
emissions (NMBF = -0.25) compared to GFED3 (NMBF = -0.49) or GFAS1
(NMBF = -0.62), with simulated monthly mean concentrations mostly within a
factor of ∼ 2 of the observations. The correlation between
model and observations across all sites is relatively similar between the
three emission datasets, with a slightly stronger correlation with GFED3
emissions (r2=0.83) compared to FINN1 (r2=0.77) and GFAS1
(r2=0.79).
Simulated vs. observed multi-annual monthly mean PM2.5
concentrations at each ground station in the Amazon region for the model
(a) without biomass burning emissions, and with (b) GFED3,
(c) GFAS1 and (d) FINN1 emissions. Multi-annual monthly
mean concentrations were calculated by averaging over all years of data
available between January 2003 and December 2011 to obtain an average
seasonal cycle at each station. The normalised mean bias factor (NMBF; Yu et
al., 2006) and Pearson's correlation (r2) between modelled and observed
PM2.5 concentrations are shown in the top left corner.
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Normalised mean bias factor (NMBF; Yu et al., 2006) and Pearson's
correlation coefficient (r2) between modelled and observed multi-annual
monthly mean PM2.5 concentrations at each of the four ground stations in
Amazonia. Results are shown for four model simulations: without fires
(noBBA), and with each of the three biomass burning emissions inventories:
GFED3, GFAS1, and FINN1. (a) No scaling applied to the fire emissions,
(b) particulate (BC / OC) fire emissions scaled up globally by a
factor 1.5, and (c) particulate (BC / OC) fire emissions scaled up
globally by a factor of 3.4. The dashed lines indicate NMBFs of -1 and 1,
which equate to an underestimation and overestimation, respectively, of a
factor of 2. The dotted line indicates an r2 value of 0.5.
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The NMBF and correlation between model and observations are shown for the
individual stations in Fig. 3a. Correlations are calculated between
simulated and observed multi-annual monthly mean concentrations to evaluate
the ability of the model to simulate seasonal variability in aerosol. In
general, the model with fire emissions overestimates observed PM2.5
concentrations at the forest site near Manaus (mean NMBF = 0.57) but
underestimates observed PM2.5 concentrations at the sites that are more
strongly impacted by biomass burning (Porto Velho, Alta Floresta and
Santarem; mean NMBF = -0.60). Figure 3 demonstrates that the relatively
small bias with the FINN1 emissions in Fig. 2 is partly due to an
overestimation of PM2.5 concentrations at Manaus (NMBF = 0.98) but also due
to smaller model biases at the three other sites (-0.51 to -0.11) compared
to GFED3 (-0.76 to -0.48) and GFAS1 (-1.26 to -0.39).
Average seasonal cycles in observed (black) and simulated (colour)
multi-annual monthly mean PM2.5 concentrations at four ground stations
in the Amazon region: (a) Porto Velho (2009–2011),
(b) Manaus (2008–2011), (c) Santarem (2003–2006), and
(d) Alta Floresta (2003–2004). Multi-annual monthly mean
concentrations were calculated by averaging over all years of available
observation data between January 2003 and December 2011. The modelled results
are shown for four simulations: without biomass burning (purple), with GFED3
emissions (red), with GFAS1 emissions (blue) and with FINN1 emissions
(green). The error bars show the standard deviation of the mean of the
observed and simulated values, which represents the inter-annual and
intra-monthly variability in the daily mean PM2.5 concentrations.
![]()
Figure 4 shows the multi-annual average seasonal cycle in observed and
simulated PM2.5 concentrations at the four measurement sites (the full
time series at each site is shown in Fig. S1 in the Supplement).
The model with biomass burning emissions simulates the observed seasonal
variability in PM2.5 concentrations over the Amazon region, characterised by
high concentrations in the local dry season (between ∼ June and
∼ December depending on the site) and relatively low
concentrations in the wet season. At Porto Velho, Santarem and Alta
Floresta, the model underestimates observed PM2.5 concentrations during the
dry season and has relatively good agreement during the wet season. This
suggests that the negative model bias in the dry season is largely due to
uncertainty in the biomass burning emissions rather than anthropogenic
emissions, biogenic SOA or microphysical processes in the model. The model
overestimates PM2.5 concentrations observed at Manaus all year round, but
particularly during the dry season. This positive model bias may be due to
several factors, including a possible overestimation of biogenic SOA over
tropical forests and/or the model resolution, which is not fully capturing
the gradient in PM2.5 concentrations between the arc of deforestation and
the relatively undisturbed forest near Manaus.
In previous work we carried out a detailed model sensitivity analysis that
accounted for the uncertainty in the emissions (including biomass burning)
and in the model processes such as wet removal and dry deposition of aerosol
(Lee et al., 2013). This analysis confirms that the parametric uncertainty
in modelled PM2.5 concentrations at these four stations is dominated by the
uncertainty in the biomass burning emissions flux in the dry season and by
the yield of biogenic SOA in the wet season, rather than the removal
processes in the model.
Figure 4 demonstrates the differences in the spatial and temporal
variability between the three fire emission datasets, with different
emissions capturing the observations better in different months and
locations. The model with GFED3 emissions captures the average seasonal
variability in PM2.5 observed at Alta Floresta (Fig. 4; r2=0.69) and
Porto Velho (r2=0.94) reasonably well, in particular, better
simulating the peak in dry season concentrations at Porto Velho than both
FINN1 (r2=0.72) and GFAS1 (r2=0.85) emissions. However, PM2.5
concentrations observed towards the end of the biomass burning season at
Alta Floresta (September–November) and Porto Velho (October–November)
are not well captured by GFED3 emissions, leading to larger biases at these
sites (NMBF = -0.73 and -0.48, respectively) than with FINN1 emissions
(-0.51 and -0.41, respectively). At Santarem, the model with GFED3 emissions
underestimates observed PM2.5 concentrations throughout the dry season,
leading to a relatively large model bias and poor correlation with the
observations (NMBF = -0.76, r2=0.39). Agreement with the
observations at this site is improved with either FINN1 (NMBF = -0.11,
r2=0.76) or GFAS1 (NMBF = -0.39, r2=0.75) emissions
(discussed further in Sect. 4.2).
Simulated vs. observed multi-annual monthly mean AOD at 440 nm at
each AERONET station. The model is shown (a) without biomass burning
emissions, and with (b) GFED3, (c) GFAS1 and
(d) FINN1 emissions. As for Fig. 2, the multi-annual monthly mean
AODs were calculated using all years of daily mean data available between
January 2003 and December 2011 to obtain an average seasonal cycle at each
station. AERONET stations located in South America are shown in blue,
stations in Southeast Asia are shown in green (stations in equatorial Asia
and Indochina in light and dark green, respectively), and stations in Africa
are shown in orange. The normalised mean bias factor (NMBF) and Pearson's
correlation (r2) between modelled and observed PM2.5 concentrations
are shown in the top left corner.
![]()
If we consider the inter-annual variability in simulated and observed PM2.5
concentrations (Fig. S2), we find that the results are consistent with the
evaluation of the simulated seasonal cycle. The smallest bias between model
and observations is with the FINN1 emissions (NMBF = -0.22) compared to
GFED3 (NMBF = -0.36) or GFAS1 (NMBF = -0.48). One notable point is that
the model with GFED3 emissions simulates the highest PM2.5 concentrations
for the 2010 drought year, relative to the model with GFAS1 or FINN1
emissions, leading to improved agreement with observations at Porto Velho
(see Figs. 3a, 4a and S2).
In summary, the model captures the seasonal cycle and inter-annual
variability in observed PM2.5 reasonably well at biomass-burning-influenced
sites in the Amazon. However, the model underestimates observed
concentrations in the dry season, suggesting that the biomass burning aerosol
emission fluxes in all three emission inventories (GFED3, FINN1, GFAS1) may
be underestimated. We explore this further in Sect. 4.3.
Aerosol optical depth in tropical biomass burning regions
Figure 5 shows the simulated vs. observed multi-annual monthly mean AOD at
440 nm at each of the AERONET sites displayed in Fig. 1 (simulated and
observed annual means are compared in Fig. S3). Agreement between model and
observed AOD is improved substantially when biomass burning emissions are
included in the model (Fig. 5; NMBF = -0.40 to -0.18,
r2=0.62–0.69) compared to the simulation without fire emissions
(NMBF = -0.69, r2=0.22). As for PM2.5, the bias in AOD across
all sites is smallest with the FINN1 emissions (NMBF = -0.18) compared
to GFED3 (NMBF = -0.34) or GFAS1 (NMBF = -0.40). The model with
FINN1 emissions also shows slightly improved correlation with the
observations (r2=0.69) relative to GFED3 (r2=0.67) and GFAS1
(r2=0.62).
Box-and-whisker plots of the normalised mean bias factor (NMBF) and
Pearson's correlation coefficient (r2) between modelled and observed
multi-annual monthly mean AOD at 440 nm for AERONET stations located in
South America (eight sites), equatorial Asia (four sites), Africa (six sites) and
Indochina (nine sites). Results are shown for four model simulations: without
fires (white), and with each of the three biomass burning emissions
inventories – GFED3 (red), GFAS1 (blue), and FINN1 (green). (a) No
scaling applied to the fire emissions, (b) particulate (BC / OC)
fire emissions scaled up globally by a factor of 1.5, and (c) particulate
(BC / OC) fire emissions scaled up globally by a factor of 3.4. The
dashed lines indicate NMBFs of -1 and 1, which equate to an underestimation
and overestimation, respectively, of a factor of 2. The dotted line indicates
an r2 value of 0.5.
![]()
Figure 6a shows the NMBF and correlation between simulated and observed
multi-annual monthly mean AOD at the individual AERONET sites, grouped by
region. In South America, the bias in modelled AOD is smallest with the
FINN1 emissions (mean NMBF = -0.47) compared to GFED3 (-0.69) and GFAS1
(-0.89) emissions, which is consistent with comparisons between modelled and
observed PM2.5 in Amazonia (Sect. 4.1.1). In Indochina, the model with FINN1
emissions also gives the smallest bias (mean NMBF = -0.02), relative to
GFED3 (-0.21) and GFAS1 (-0.23). In Africa, the model bias is smallest with
GFED3 emissions (mean NMBF = -0.78) compared to GFAS1 (-0.90) and FINN1
(-0.96). In equatorial Asia, the model bias is small and does not vary
substantially between the different emission datasets (FINN: 0.02; GFAS:
-0.01; GFED: -0.02). In terms of temporal agreement between model and
observations, the correlation is noticeably stronger with GFED3 (mean
r2=0.52) in Africa and with FINN1 (mean r2=0.75) in
Indochina, relative to the other emission datasets.
In general, the model with fire emissions captures the seasonal variability
in observed AOD best in South America (mean r2=0.90) and captures the
magnitude of observed AOD best in Southeast Asia (equatorial Asia: mean
NMBF = -0.00; Indochina: mean NMBF = -0.14). The agreement between model
and observations in Africa is relatively poor, with substantial
underestimation of observed AOD (mean NMBF = -0.88). The negative model
bias in Africa is unlikely to be solely due to an underestimation of biomass
burning aerosol and is likely complicated by a contribution from dust
(Pandithurai et al., 2001; Sayer et al., 2014; Cesnulyte et al., 2014;
Queface et al., 2011). There is better agreement between the model and
observed AOD at Ascension Island, which observes aged biomass burning
aerosol from the African continent (Sayer et al., 2014), with all three
emission inventories (mean NMBF = -0.38, r2=0.84). This suggests
that the model is able to capture outflow of biomass burning emissions from
Africa.
At the South American sites located in regions of high biomass burning
activity associated with deforestation fires (Abracos Hill, Rio Branco, Ji
Parana SE and Alta Floresta), there is a small improvement in the
correlation with observed AOD with FINN1 (r2=0.96–0.98) and GFAS1
(r2=0.94–0.97) emissions relative to GFED3 (r2=0.79–0.88). At
these sites, AOD observed at the tail end of the biomass burning season
(∼ October–November) is better captured by GFAS1 and FINN1
than GFED3, leading to the improved correlation relative to GFED3. The model
with GFED3 is generally better able to capture observed AOD at the peak of
the biomass burning season (∼ August–September) than GFAS1 and
FINN, which is largely due to relatively high GFED3 emission estimates for
the drought years 2007 and 2010 (see Fig. S1). These results are consistent
with comparisons with observed PM2.5 concentrations at Porto Velho and Alta
Floresta (Sect. 4.1.1).
At the AERONET sites located in equatorial Asia and the Philippines
(Singapore, Bandung, Manila Observatory, ND Marbel University) an improved
performance of either the GFAS1 or GFED3 emission inventories may be
expected over FINN1 (Andela et al., 2013) due to their improved
representation of tropical peatlands (in Indonesia and Malaysian Borneo) in
their biome maps (van der Werf et al., 2010). The agreement between AOD
observed at Bandung, Indonesia, and the model is marginally improved with
GFED3 (NMBF = -0.14, r2=0.52) or GFAS1 (NMBF = -0.15,
r2=0.47) relative to FINN1 (NMBF = -0.18, r2=0.34). However,
at the other sites we find no strong indication of an improved performance
with GFED3 (NMBF = -0.06 to 0.13, r2=0.15–0.24) or GFAS1 (NMBF = -0.03 to 0.14, r2=0.13–0.56) relative to FINN1 (NMBF = 0.04 to 0.17,
r2=0.16–0.42). At most of these sites the model does not simulate a
strong contribution of biomass burning to AOD, likely due to their urban
locations, which may explain why we do not see a substantial difference in
the performances of the three emission datasets. Long-term ground-based
retrievals of AOD located outside the influence of urban environments are
lacking in equatorial Asia.
At the African AERONET sites, observed AODs are generally better captured by
the model with GFED3 emissions (mean NMBF = -0.78, r2=0.52) than
with FINN1 (mean NMBF = -0.96, r2=0.35) or GFAS1 (mean NMBF = -0.90, r2=0.41) emissions. Andela et al. (2013) report that the GFED3
emissions flux of carbon monoxide (CO) is higher than GFAS1 or FINN1 for
humid savannah regions, where the burned-area product may observe more cloud
covered fires than active-fire detection. This feature may explain the
improved simulation of AOD with GFED3 over Africa. Andela et al. (2013) also
report that the FINN1 emission estimates of CO are lower than both GFED3 and
GFAS1 in global savannah regions, with the largest spatial deviation found
in humid savannahs where fire size is large. This may suggest that the
assumed fire size in FINN1 for savannah fires (0.75 km2) could be too
small for humid savannah fires in Africa, contributing to an underestimation
of AOD in this region.
Overview of PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn>2.5</mml:mn></mml:msub></mml:math></inline-formula> and AOD evaluation
In the previous sections we have evaluated the model against ground-based
observations of PM2.5 and AOD. In general, we find that the model is
negatively biased against observations in regions strongly influenced by
biomass burning. However, the model bias in surface PM2.5 concentrations is
generally smaller than for AOD over South America, where observations of
both quantities are available (NMBFPM2.5=-1.85 to -0.25,
NMBFAOD=-2.38 to -0.40; see Figs. 2 and S4). If we compare average
model biases (with fires) in multi-annual monthly mean PM2.5 and AOD (for
2003–2004) at locations where AERONET stations are in close proximity to the
PM2.5 measurement stations, we find a larger model bias in AOD at
Santarem/Belterra (NMBFPM2.5=-0.61, NMBFAOD=-1.15), but
the reverse at Alta Floresta (NMBFPM2.5=-0.64, NMBFAOD=-0.42).
These results suggest that although the negative model bias in PM2.5 and AOD
may be partly due to an underestimation of biomass burning aerosol emissions
(due to uncertainties associated with fire detection and subsequent
calculations of emission fluxes), there are likely to be other factors
contributing to the model discrepancy in AOD that do not affect modelled
surface PM2.5 concentrations. These factors include uncertainties in the
calculation of AOD that are largely associated with assumptions made about
the aerosol optical properties (assumed refractive indices), mixing state
(external/internal mixing) and hygroscopic growth of the aerosol. We
investigate the sensitivity of simulated AOD to these assumptions below.
Simulated vs. observed multi-annual monthly mean AOD at 440 nm at
each AERONET station to demonstrate the sensitivity of simulated AOD to the
calculation of aerosol water uptake. The model is with FINN1 fire emissions
and simulated AOD is calculated assuming internal mixing with
(a) ZSR water uptake scheme (identical to Fig. 5d);
(b) κ-Köhler water uptake scheme:
κSO4=0.53, κPOM=0.1;
(c) κ-Köhler water uptake scheme:
κSO4=1.19, κPOM=0.1; and
(d) κ-Köhler water uptake scheme:
κSO4=1.19, κPOM=0.2. AERONET stations
located in South America are shown in blue, stations in Southeast Asia are
shown in green (stations in equatorial Asia and Indochina in light and dark
green, respectively), and stations in Africa are shown in orange. The
normalised mean bias factor (NMBF) and Pearson's correlation (r2)
between modelled and observed PM2.5 concentrations are shown in the top
left corner.
![]()
As described in Sect. 3.2, to calculate AOD at 440 nm we use
component-specific refractive indices from Bellouin et al. (2011) for a
wavelength of 468 nm (1.500-0.000i for POM and 1.750-0.452i for BC). To test the sensitivity of AOD to the choice of
refractive indices, we applied the refractive indices tested by Matichuk et
al. (2007) for smoke aerosol (1.54-0.025i calculated by Haywood
et al., 2003, for young smoke aerosol over southern Africa; 1.51-0.024i and 1.52-0.019i retrieved by an AERONET station,
Ndola in Zambia, located close to smoke sources) to the BC and POM
components in our model. We find that the modelled AOD is relatively
insensitive to the choice of complex refractive index within the range of
values tested here (altering the magnitude of AOD by less than 5 %), which
is in agreement with Matichuk et al. (2007). Although the range of
refractive indices tested is relatively narrow (Matichuk et al., 2007), this
result suggests that uncertainty in the assumed refractive indices is
unlikely to explain the discrepancy in simulated AOD.
We also find that the AOD is fairly insensitive to the mixing state
assumption, with limited difference in simulated AOD between assuming optical
properties derived from an external mixture of aerosol species and an
internal (volumetrically averaged) mixture. Figure S5 shows the simulated vs.
observed multi-annual monthly mean AOD at AERONET sites when assuming
external and internal mixing and indicates that the difference is less than
5 %, with internal mixing causing slightly higher AOD at the AERONET
sites. However, we note that the internal mixing assumption used in this
study does not take into account the lensing effects of coating BC with
organic aerosol, which has been shown to interact with the aerosol absorption
in a non-linear way (Saleh et al., 2015).
As described in Sect. 3.2, the hygroscopic growth of the aerosol is
calculated in the model using the ZSR scheme. To test the sensitivity of AOD
to aerosol hygroscopic growth, we instead use the κ-Köhler water
uptake scheme, based upon the Köhler equation with a single hygroscopic
parameter, κ, defining the water uptake for different chemical
species (Petters and Kreidenweis, 2007) (see description of method in Sect. S1 of the Supplement). For the SO4 and sea spray components
in the model we used the mean values of κ for ammonium sulfate and
sodium chloride for subsaturated air masses (0.53 and 1.12, respectively)
from Petters and Kreidenweis (2007). BC is considered entirely hydrophobic
in this model when using this scheme. A wide range of κ values have
been reported for organic aerosol (∼ 0.01–0.25; Petters and
Kreidenweis, 2007) and biomass burning particles specifically (0.02–0.8;
DeMott et al., 2009; Petters et al., 2009). Engelhart et al. (2012) reported
κ values of between 0.06 and 0.6 for primary biomass burning aerosol
in a smog chamber (fuels representative of North American wildfires), with
photochemical ageing reducing the range of κ values to 0.08 to 0.3,
with biomass burning SOA having κ values of 0.11. We assume a
κ value for POM (0.1) based upon aerosol samples, largely composed
of SOA, collected at the Manaus ground station (TT34) during the 2008
Amazonian Aerosol Characterization Experiment (AMAZE-08) (Gunthe et al.,
2009). We test the sensitivity of simulated AOD to different κ
values for both SO4 and POM.
Figure 7 shows a comparison between AOD simulated using ZSR and the κ-Köhler scheme. Using the κ-Köhler scheme and κ
defined above, the water uptake is reduced relative to the ZSR scheme,
reducing the simulated AOD on average by a factor of 1.6 (range 1.1 to 2.3)
at AERONET sites (see Fig. 7a and b). This large reduction relative to ZSR is
in part from the assumption that the SO42- component behaves as
ammonium sulfate rather than the more hygroscopic sulfuric acid and the reduced water uptake for POM relative to that in ZSR. To explore the
sensitivity to assumed κ values we increased κ values
separately for SO4 and POM. Assuming a higher κ for sulfate
(1.19 as for sulfuric acid, Fig, 7c) results in simulated AOD being a factor
of 1.25 lower than ZSR. Assuming a higher κ for both sulfate (1.19)
and POM (0.2) results in simulated AOD being a factor of 1.18 lower. Our
results highlight the large uncertainty present in the simulated AOD due to
aerosol hygroscopicity. AOD simulated with ZSR (assuming sulfuric acid and
high water uptake for organics) appears to be an upper estimate for water
uptake. This result is confirmed by comparing simulated AOD and mass
extinction efficiencies for the two water uptake cases against observations
and values from other global aerosol models (see Sect. S2 and Table S2).
Calculated AOD is also sensitive to errors in relative humidity (Myhre et
al., 2007), which are here taken from ECMWF reanalysis. Since water uptake is
not a linear function of RH, calculated AOD will also be sensitive to spatial
resolution of the aerosol and RH fields. Coarse spatial resolution (here
2.8∘) will not capture fine-scale variability in RH that will
influence measurements from AERONET stations. A higher resolution model would
be required to test how sensitive the simulated AOD is to the spatial
resolution of the aerosol and RH fields and whether or not increasing the
resolution improves the agreement with observed AOD (and reduces the
discrepancy between the model performance in AOD and PM2.5). Bian et
al. (2009) showed that increasing the resolution of the RH field from
2∘ × 2.5∘ to 1∘ × 1.25∘ can
increase simulated AOD by ∼ 10 % in biomass burning regions. This
suggests the coarse resolution of our global models may partly explain the
underestimation of AOD and the larger discrepancies with observed AOD
compared to PM2.5.
Errors may also exist in the model representation of biomass burning
aerosol, for example in the modelled particle size distribution, altering
simulated optical properties of the aerosol and thus calculated AOD. In
addition, since AOD is a column-integrated quantity, an underestimation of
AOD may be due to an underestimation of aerosol concentrations aloft since
we have shown that the model agrees relatively well with PM2.5
concentrations observed at the surface.
Further uncertainties in the model representation of biomass burning aerosol
are associated with the conversion of OC to organic matter (OM), which would
affect both PM2.5 concentrations and AOD predicted by the model.
Increasing the assumed OM : OC ratio would increase the total simulated
mass of biomass burning aerosol. In our model we assume a relatively low
OM : OC ratio of 1.4 compared to previous studies on biomass burning
aerosol. Kaiser et al. (2012) use a value of 1.5, but
they note that this ratio is low compared to values
of around 2.2 proposed for aged pollution and biomass burning aerosols by
Turpin and Lim (2001), Pang et al. (2006) and Chen and Yu (2007) and a value
of 2.6 used by Myhre et al. (2003) for biomass burning aerosol in southern
Africa. These larger OM : OC ratios could account for in-plume (sub-grid)
atmospheric oxidation and subsequent SOA formation observed in some biomass
burning plumes (Vakkari et al., 2014). In future work we need to include the
formation of semi-volatile SOA in biomass burning plumes, which has been
shown to be important (Konovalov et al., 2015; Shrivastava et al., 2015).
Small-scale fires
The GFED3 fire emissions are known to underestimate contributions from
small-scale fires (smaller than ∼ 100 ha) that are below the
detection limit of the global burned-area product derived from MODIS
(Randerson et al., 2012). However, many of these small fires generate
thermal anomalies that can be detected by satellites (Randerson et al.,
2012). This means that fire inventories using active-fire detections to
derive emissions (FINN1 and GFAS1) will better capture these small fires
(Kaiser et al., 2012). Kaiser et al. (2012) demonstrate that GFAS1 includes
emissions from small fires that are omitted in GFED3. Some of the
differences between the spatial patterns of emissions seen in Fig. 1 are
likely due to missing small fires in GFED3.
Taylor diagrams (Taylor, 2001) comparing monthly mean modelled and
observed AOD (440 nm) at 9 AERONET stations located in Indochina. The
modelled and observed monthly mean AODs were calculated for every month with
available daily mean data between January 2003 and December 2011. The
observations are represented by a point on the x axis at unit distance from
the y axis. The results are shown for four simulations: without biomass
burning (purple), and with GFED3 (red), GFAS1 (blue) and FINN1 (green) fire
emissions. The model standard deviation and root-mean-square error (RMSE) are
normalised by dividing by the corresponding observed standard deviation. The
normalised standard deviation and RMSE values are marked by the solid grey
and dashed grey lines respectively. The correlation coefficient (r) values
are marked by the dotted grey lines.
![]()
Average seasonal cycles in observed (black) and simulated (colour)
monthly mean AOD at 440 nm at four AERONET stations in the Thailand:
(a) Chiang Mai Met. Station, (b) Mukdahan,
(c) Phimai, and (d) Ubon Ratchathani. Multi-annual monthly
mean concentrations were calculated by averaging over all years of available
daily mean observation data between January 2003 and December 2011. The
modelled results are shown for four simulations: without biomass burning
(purple), and with GFED3 (red), GFAS1 (blue) and FINN1 (green) fire
emissions. The error bars show the standard deviation of the mean of the
observations.
![]()
This result is corroborated by our comparisons between modelled and observed
PM2.5 concentrations at Santarem in the northern region of Brazil (Sect. 4.1.1), where the poor agreement between the observations and model with
GFED3 emissions (NMBF = -0.76, r2=0.39) is substantially improved by
using either of the active-fire-based emission inventories (FINN: NMBF = -0.11, r2=0.76; or GFAS: NMBF = -0.39, r2=0.75). Randerson
et al. (2012) show that in the region surrounding the Santarem station there
is a particularly high small fire fraction of total burned area, which
explains why the GFED3 emissions do not capture the observations in this
region of Brazil. This result is consistent with comparisons between
modelled and observed AOD at the nearby AERONET station, Belterra. At this
station, the model better captures the observed AOD with either FINN1
(NMBF = -0.85, r2=0.84) or GFAS1 (NMBF = -1.02, r2=0.81)
emissions than with GFED3 emissions (NMBF = -1.58, r2=0.29).
The improved representation of small fire emissions in FINN1 and GFAS1 may
also explain the improved agreement between modelled and observed PM2.5
(Sect. 4.1.1) and AOD (Sect. 4.1.2) towards the end of the burning season
(∼ October–November) in Amazonia. Kaiser et al. (2012) report that
GFAS1 exhibits slightly longer fire seasons in South America than GFED3.
Fires occurring at the tail end of the biomass burning season may be smaller
in size and thus better captured by using an active-fire-based emission
inventory (GFAS1 and FINN1 emissions). However, at the peak of the burning season in Amazonia, when fires are
potentially larger, the comparisons in Sect. 4.1.1 and 4.1.2 suggest that
GFED3 emissions capture the observations better than FINN1 or GFAS1.
In Indochina, there is improved agreement between simulated and observed AOD
with FINN1 emissions (Fig. 6a; NMBF = -0.26 to 0.19, r2=0.14–0.98)
relative to both GFED3 (NMBF = -0.54 to -0.08, r2=0.11–0.84) and
GFAS1 (NMBF = -0.51 to -0.08, r2=0.03–0.83). Figure 8 compares the
model with different emissions against observations at the nine AERONET
sites in Indochina. FINN1 emissions lead to an improved correlation with
observations at all sites and a reduced root-mean-square model error at six
sites compared to GFED3 and GFAS1. Figure 9 compares the multi-annual
average seasonal cycle in AOD at four sites in Thailand. The model with
GFED3 and GFAS1 emissions underestimates AOD observed during the dry season
(∼ January–May), whereas the model with FINN1 emissions
captures the magnitude of dry season AOD reasonably well.
AERONET sites in Indochina (located in north and central Thailand and
Vietnam) are influenced by local agricultural burning (Li et al., 2013; Lin
et al., 2013; Sayer et al., 2014) of sugarcane and rice crop residues (Gadde
et al., 2009; Sornpoon et al., 2014). Agricultural fires are typically
smaller than other fire types (e.g. deforestation, grassland/savannah and
forest), with burned areas of ∼ 0.3 to ∼ 16 ha
reported for individual agricultural fires in the US (McCarty et al., 2009)
and Africa (Eva and Lambin, 1998). The prevalence of small fires in
Indochina may explain why FINN1 emissions result in better prediction of AOD
compared to GFED3 in this region.
We do not find an improved prediction of AOD with GFAS1 compared to GFED3 in
this region, although this would be expected since GFAS1 better captures
emissions from small fires than GFED3 (Kaiser et al., 2012). However, the
GFAS1 FRP is converted to dry matter burned using GFED3 data (Heil et al.,
2010; Kaiser et al., 2012), which may lead to an underestimation of small
fire emissions in some regions. Conversely, FINN1 assumes a relatively large
burned area of 1 km2 (100 ha) for individual agricultural fires and
therefore may overestimate emission fluxes in agricultural fire regions.
However, since many small fires may be undetected as fire hotspots by MODIS
(due to factors such as the small size of the fires, orbital gaps,
persistent cloud cover and the timing of satellite overpass, i.e. the
potential to miss fires events), by oversizing the area of individual burns,
the FINN1 emissions may compensate for missing fire detections in this
region (B. Yokelson, personal communication, 2014).
Summary of scaling factors applied in previous modelling studies to
biomass burning emissions or modelled concentrations of biomass burning
aerosol to match observations. Region abbreviations used in the table are
defined in van der Werf et al. (2006): Northern Hemisphere South America
(NHSA), Southern Hemisphere South America (SHSA), Northern Hemisphere Africa
(NHAF), Southern Hemisphere Africa (SHAF), Southeast Asia including the
Philippines (SEAS) and equatorial Asia (EQAS). See van der Werf et al. (2006,
2010) for discussion of differences between GFED versions 1, 2 and 3; on
average GFED3 is 13 % lower than GFED2 van der Werf et al. (2010), with
total GFED2 emissions lower than GFED1 in Central and South America and
southern Africa (van der Werf et al., 2006).
Reference
Biomass burning emission inventory
Region of focus
Details of scaling applied
Myhre et al. (2003)
Biomass burning BC emissions from the Global Emissions Inventory Activity (GEIA), based on Cooke and Wilson (1996); OC emissions from Liousse et al. (1996).
Southern Africa
Used a relatively high OM / OC ratio of 2.6 and increased the modelled aerosol mass by 20 % to account for mass fraction of inorganic components observed to be of 17 % of the total mass.
Matichuk et al. (2007)
GFED1 (van der Werf et al., 2003)
Southern Africa
Multiple sensitivity studies were performed with the model including simulations with halved and doubled fire emissions.
Matichuk et al. (2008)
GFED2 (van der Werf et al., 2006)
South America
Smoke source function was scaled up by a factor of 6.
Johnson et al. (2008)
Biomass burning emissions following Dentener et al. (2006): GFED1 (van der Werf et al., 2004) for year 2000 or a 5-year (1997–2001) average (not specified).
West Africa
Increased mass concentration of biomass burning AOD by a factor of 2.4.
Chin et al. (2009)
Calculated using dry mass burned dataset from GFED2 (van der Werf et al., 2006).
Global
No scaling applied, but used emission factors of BC (1 g kg-1) and OC (8 g kg-1) that are 40–100 % higher than commonly used values (Andreae and Merlet, 2001).
Sakaeda et al. (2011)
Aerosol fields taken from MATCH chemical transport model
Southern Africa.
OC and BC masses were increased by a factor of 2 over 10∘ N–30∘ S and 20∘ W–50∘ E.
Johnston et al. (2012)
GFED2 (van der Werf et al., 2006)
Global
Scalar adjustments made for 14 continental-scale regions: NHSA (2.48–2.7), SHSA (1.9–3.3), NHAF (1.02–1.08), SHSA (1.68–2.01), SEAS (2.43–3.08), EQAS (2.3–2.72). Scaling factors were applied to modelled surface fire PM2.5 to match satellite observations of AOD (non-fire aerosol was also scaled).
Kaiser et al. (2012)
GFED3 and GFASv1.0
Global
Model was biased low in South America and Africa by factors of 4.1 and 3.0. Recommended a global enhancement of 3.4 for PM emissions from fires.
Ward et al. (2012)
Calculated from Kloster et al. (2010, 2012) CLM3 simulations of global fire area burned, using emission factors from Andreae and Merlet (2001) and updates from Hoelzemann et al. (2004). Compared against GFED2.
Global
Scalar adjustments were made for continental-scale regions following Johnston et al. (2012) with slight modifications: SHSA (2.0), NHAF (1.0), SHAF (3.0), SEAS (1.5), EQAS (3.0). Scaling factor directly applied to model fire emissions.
Tosca et al. (2013)
GFED3
Global
Biomass burning BC and OC emissions scaled by factor of 2 globally with additional regional scaling factors applied: South America (2.4), Africa (2.1), Southeast Asia (1.67).
Marlier et al. (2013)
GFED3
Southeast Asia
Total aerosol burden scaled by 1.02–1.96 (depending on model), with additional scaling factors of 1.36–2.26 applied to fire aerosol.
Scaling biomass burning emissions
Previous model simulations, summarised in Table 2, underestimate AOD in
regions impacted by biomass burning. To improve simulation of AOD, these
studies have scaled particulate emissions from biomass burning (or aerosol
concentrations) by a factor of 1.02 to 6. We have found that our model with
three different fire emission datasets also underestimates both PM2.5 and
AOD across tropical regions (although to a lesser extent in Southeast Asia).
In this section we explore the impact of scaling biomass burning emissions
on simulated AOD and PM2.5 concentrations. We performed two sensitivity
simulations with each emission inventory where we perturbed the biomass
burning emission fluxes of BC and POM upwards by factors of 1.5 and 3.4 (as
recommended for GFED3 and GFAS1 by Kaiser et al., 2012).
Figure 3b and c show the NMBF and correlation between simulated and observed
multi-annual monthly mean PM2.5 concentrations for the two simulations
with scaled biomass burning emissions. The outcome of scaling the emissions
by a factor of 1.5 depends on the site location. At the sites strongly
impacted by biomass burning, the model bias in PM2.5 is reduced
(FINNx1.5: -0.16 to 0.08; GFEDx1.5: -0.67 to -0.15; GFASx1.5: -0.89
to -0.22) with little change in the correlation. At the preserved forest
site near Manaus, the positive model bias is increased (FINNx1.5: 1.33;
GFASx1.5: 0.69; GFEDx1.5: 0.66). The outcome of scaling the emissions by a
factor of 3.4 depends on both the site location and the emission dataset. The
model bias is increased at all sites with FINN1 emissions (0.63–2.72), with
mixed results for GFED3 (-0.39 to 1.18) and GFAS1 (-0.16 to 1.25)
emissions. Any scaling of the emissions leads to an overestimation of
PM2.5 at Manaus with all three emission datasets.
In summary, a scaling factor of 1.5 applied to the FINN1 emissions is
adequate for the model to capture surface PM2.5 concentrations observed in
regions of high fire activity in the Amazon region. In contrast, the GFAS1
emissions require a larger scaling factor (closer to 3.4) for the model to
capture surface PM2.5 observed at these sites.
The results of scaling the GFED3 emissions are more complex. With GFED3
emissions scaled by a factor of 1.5, the model bias becomes relatively small at
Alta Floresta (-0.36) and Porto Velho (-0.15) but remains large and negative
at Santarem (-0.67). Scaling the emissions by a factor of 3.4 reduces the
model bias at Santarem (-0.39) but leads to an overestimation of PM2.5 at
the other three sites (0.33–1.18). At Santarem, scaling GFED3 emissions by a
factor of 3.4 only marginally improves agreement with the observations; the
correlation remains below 0.5 and model bias remains negative (despite a
positive model bias at the other sites). This is because GFED3 emission
fluxes in the peak biomass burning season months in the region of Santarem
(November and December) are very low or non-existent, likely due to an
omission of small fires (Sect. 4.2); thus, there are very few emissions to
scale. This result suggests that even by scaling GFED3 emissions by a large
factor it is still possible to underestimate PM from fires in regions
influenced by emissions from small fires.
Figure 6a and b show the NMBF and correlation between simulated and
observed multi-annual monthly mean AOD with scaled biomass burning
emissions. For the model with GFAS1 emissions, scaling by a factor of 3.4
reduces the model bias at all but one site in Indochina, Africa and South
America (relative to the simulations without scaling or with a scaling
factor of 1.5), resulting in the best overall match to observed AOD in these
regions. In equatorial Asia the scaling required to capture observed AOD
depends on the site location (two sites require no scaling and two sites
require a scaling factor of either 1.5 or 3.4).
For GFED3 emissions, scaling by a factor of 3.4 results in the best overall
match to observed AOD in Africa and Indochina, but leads to an increased
model bias at half the sites in South America. However, even with a scaling
factor of 3.4, the model with GFED3 emissions continues to underestimate
observed AOD in northern Brazil (Belterra; NMBF = -0.94), indicating that a
large scaling factor does not fully compensate for the likely omission of
small fire emissions in this inventory (Sect. 4.2). The overall result of
scaling GFED3 emissions in equatorial Asia is the same for GFAS1 emissions.
Scaling FINN1 emissions by a factor of 3.4 improves the agreement with
observed AOD in Africa (at all sites) but generally leads to overestimation
and increased model bias at sites in South America and Southeast Asia.
Scaling FINN1 emissions by a factor of 1.5 is adequate to capture observed
AOD at the majority of sites in South America (mean NMBF = -0.16), with no
scaling required for the majority of sites in Indochina (mean NMBF = 0.02)
and equatorial Asia (mean NMBF = 0.02).
We note that, even with a scaling factor of 3.4 applied to the biomass
burning emissions, the model underestimates observed AOD at the African
AERONET sites with all three fire emission inventories (mean NMBF = -0.31).
This may indicate that a larger scaling factor is required to capture
observations in this region. However, using too high a scaling factor is
likely to compensate for model error, e.g. too efficient removal of aerosol
or underestimation of dust emissions, and therefore overestimate the
contribution of biomass burning to AOD. The potential for compensation
errors with emission scaling is relevant for all three regions. For example,
in South America the model bias in AOD in the wet season (∼ December to May) is increased at four or more sites when the FINN1, GFED3,
and GFAS1 emissions are scaled by a factor of 3.4, which may be an
indication of compensation errors. Compensation errors are also likely to be
occurring when emissions are scaled by a factor of 3.4 at sites in urban
locations (see Table S1 for location classifications), where a global model
is unable to capture sub-grid-scale urban emissions.
Conclusions
Particulate emissions from open biomass burning (landscape fires) are very
uncertain. Numerous previous studies underestimate AOD in regions impacted
by fires and scale particulate emissions by up to a factor of 6 to match
observed AOD (see Table 2). We have used the GLOMAP global aerosol model
evaluated against surface PM2.5 observations and AERONET AOD to better
understand particulate emissions from tropical biomass burning.
Simulated AOD is sensitive to a range of variables including (i) vertical
profile of aerosol, (ii) aerosol optical properties, chemical composition,
size distribution and hygroscopic growth, (iii) relative humidity, and (iv) model spatial resolution. In particular, we found that simulated AOD is very
sensitive to the calculation of hygroscopic growth, with simulated AOD
varying by a factor of ∼ 1.6 between our upper and lower
estimates of water uptake. Here we assume an upper estimate of aerosol
hygroscopic growth resulting in an upper estimate of AOD, reducing any
emission scaling required to match observed AOD.
We compared three different satellite-derived fire emission datasets (GFED3,
GFAS1 and FINN1). Total pan-tropical particulate emission (BC + OC) varied
by less than 30 % between the different datasets. Regional differences
were much larger (often exceeding 100 %), leading to important differences
in aerosol concentrations simulated by the global model.
We found that GLOMAP underestimated both PM2.5 and AOD in regions strongly
impacted by biomass burning, with all emission datasets. The largest
underestimation of AOD occurred across Africa, which may be partly due to a
large contribution of dust. The smallest underestimation of AOD occurred
over equatorial Asia, where the contribution of fire emissions to simulated
AOD was also smallest. Overall, the smallest bias between model and both
PM2.5 and AOD observations was found using FINN1 emissions. The model with
FINN1 emissions also best simulated the seasonal variability in AOD over
Indochina, potentially because of the dominance of smaller fires in this
region that are better captured by the FINN1 dataset.
In South America, where we have coincident surface PM2.5 and AOD
observations, underestimation of AOD is greater than underestimation of
surface PM2.5, even though we assume upper estimates for aerosol water
uptake. We suggest this discrepancy could be caused by errors in the
calculation of AOD (see above). We caution against using observations of AOD
to scale emissions before these issues are fully understood.
For each emission dataset we ran two additional simulations where we scaled
emissions by factors of 1.5 and 3.4, within the range of previous studies
(Table 2). We find that the scaling that results in the best agreement with
observations is regionally variable and depends on the emission dataset
used. With FINN1 emissions, PM2.5 concentrations and AOD in South America
are well simulated when emissions are increased by 50 %, whereas AOD in
Africa is more consistent with a factor of 3.4 scaling. In Southeast Asia,
observed AOD is well simulated without any scaling applied; scaling FINN1
emissions by 50 % generally leads to overestimation in this region. With
GFAS1 emissions, PM2.5 concentrations in South America and AOD in South
America, Africa and Indochina are best simulated when emissions are scaled
by a factor of 3.4. With GFED3 emissions, observations of PM2.5 in northern Brazil
and AOD in Africa, Indochina and some regions of South America are also
better simulated with a factor of 3.4 scaling; for PM2.5 concentrations and AOD
observed in active deforestation regions of South America, a 50 % scaling
is sufficient. In equatorial Asia, the results of scaling both GFAS1 and
GFED3 emissions are mixed and depend on site location; overall observed AOD
is captured best either without scaling or with a scaling factor of 1.5.
A factor of 1.5 scaling is within the uncertainty of emission datasets and is
substantially smaller than the emission scaling applied by many other
studies (see Table 2). We note that a factor of 1.5 scaling is within the
uncertainty of assumed OM to OC ratios; we assume an OM : OC ratio of 1.4,
which is at the low end of other studies (Tsigaridis et al., 2014). We have
treated biomass burning emissions as primary and non-volatile. Formation of
semi-volatile SOA in biomass burning plumes may be important (Konovalov et
al., 2015; Shrivastava et al., 2015) and needs to be explored in future
work.
Problems with the detection of small fires are an acknowledged issue for
GFED3, which relies on detections of area burned to derive emissions
(Randerson et al., 2012). Over regions that are likely dominated by small
fires, the model with GFED3 emissions substantially underestimates both PM2.5
(northern Brazil) and AOD (northern Brazil and Thailand). The model with FINN1
emissions better simulates aerosol in these regions providing independent
evidence that this dataset better represents emissions from small fires. We
note that the most recent version of GFED emissions (GFED4) includes an
estimate of emissions from small fires (Giglio et al., 2013). Future work
should evaluate these emissions against aerosol observations to assess the
representation of small fire emissions in the specific regions highlighted
here.
An important finding of our study is the greater underestimation of AOD
compared to surface PM2.5 in many tropical locations impacted by fires. We
identified a number of potential causes for this discrepancy and note that
there is the potential for compensating errors across these different
uncertainties. AOD is sensitive to a range of variables, meaning it offers a
relatively poor constraint on the aerosol burden. A better top-down
constraint of particulate emissions from tropical fires will require
analysis of co-located aerosol optical, microphysical and chemical
measurements (Brito et al., 2014; Andreae et al., 2015).