JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D13, 8501, doi:10.1029/2002JD002313, 2003
Modeling the solar radiative impact of aerosols from biomass burning
during the Southern African Regional Science Initiative
(SAFARI-2000) experiment
Gunnar Myhre,1,2 Terje K. Berntsen,1,3 James M. Haywood,4 Jostein K. Sundet,1
Brent N. Holben,5 Mona Johnsrud,2 and Frode Stordal1,2
Received 15 March 2002; revised 23 August 2002; accepted 30 August 2002; published 29 April 2003.
[1] In this study, we model the radiative impact of biomass burning aerosols with
meteorological data for the Southern African Regional Science Initiative (SAFARI-2000)
experiment campaign period. Satellite, ground-based, and aircraft observations are used in
the validation of the modeled aerosol optical depth (AOD), vertical profiles, and radiative
impact of the aerosols. The modeled pattern and magnitude of the AOD is generally in
good agreement with the observations. The meteorological conditions are found to be
important in determining the distribution of the aerosols. The modeled radiative impact of
the biomass aerosols compares well to measurements. During September 2000, the
INDEX
modeled radiative impact of biomass aerosols reaches 50 W m 2 locally.
TERMS: 0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801); 0360
Atmospheric Composition and Structure: Transmission and scattering of radiation; 3359 Meteorology and
Atmospheric Dynamics: Radiative processes; KEYWORDS: biomass burning, transport model, single scattering
albedo, aerosol optical depth, aircraft measurements, radiative impact
Citation: Myhre, G., T. K. Berntsen, J. M. Haywood, J. K. Sundet, B. N. Holben, M. Johnsrud, and F. Stordal, Modeling the solar
radiative impact of aerosols from biomass burning during the Southern African Regional Science Initiative (SAFARI-2000)
experiment, J. Geophys. Res., 108(D13), 8501, doi:10.1029/2002JD002313, 2003.
1. Introduction
[2] The radiative forcing due to the direct effect of
aerosols from biomass burning is very uncertain [Intergovernmental Panel on Climate Change (IPCC), 2001].
This is because the abundance of aerosols from biomass
burning, the radiative effects of the biomass aerosols, and
the human contribution to the abundance of the aerosols
from biomass burning are all highly uncertain.
[3] Global studies of the radiative impact of the direct
aerosol effect of aerosols from biomass burning have been
performed by Penner et al. [1998] and Iacobellis et al.
[1999] with substantially different results, mainly due to
different burden of the aerosols. Hobbs et al. [1997], Penner
et al. [1992], and Ross et al. [1998] have performed
calculations based on observations of biomass burning in
south America which they extrapolate to global conditions.
IPCC [2001] assesses a best global mean estimate to be
0.2 W m 2 with an uncertainty as large as a factor of three
and a very low level of scientific understanding.
1
Department of Geophysics, University of Oslo, Oslo, Norway.
Norwegian Institute for Air Research, Kjeller, Norway.
Center for International Climate and Environmental Research-Oslo,
Oslo, Norway.
4
Met Office, Bracknell, UK.
5
Biospheric Sciences Branch, NASA Goddard Space Flight Center,
Greenbelt, Maryland, USA.
2
3
Copyright 2003 by the American Geophysical Union.
0148-0227/03/2002JD002313$09.00
SAF
[4] Recent satellite retrievals show a strong influence of
aerosols from biomass burning during the dry season over
land in southern Africa [Diner et al., 2001; Tanré et al.,
2001] and earlier satellite retrievals have shown the same
over ocean [Husar et al., 1997].
[5] The Southern African Regional Science Initiative
(SAFARI-2000) experiment was a large campaign in southern Africa during August and September 2000 in the main
biomass burning season [Swap et al., 2003]. The UK Met
Office C-130 conducted several flights in the period 5 – 19
September. Haywood et al. [2003] present an overview of
the measurements with C-130 and the main results obtained.
Perhaps the most significant finding regards the single
scattering albedo, which is crucial for the radiative effect
of aerosols. Haywood et al. [2003] find a single scattering
albedo of the regional haze of 0.90 (ranged from 0.86 to
0.93 for the various flights) at 0.55 mm derived both from
direct optical measurements and from observed size distribution and Mie theory.
[6] The aim of the study is to estimate the direct
radiative impact of aerosols from biomass burning (BB)
in southern Africa based on modeling, with focus on the
period of the C-130 flights. A chemistry-transport model
with meteorological data for the actual period is adopted to
simulate the distribution of the biomass aerosols. Mie
theory and observed particle size distributions are used
for calculations of the optical properties of the aerosols.
This information along with a radiative transfer model are
then used to estimate the radiative impact of the biomass
aerosols.
37 - 1
SAF
37 - 2
MYHRE ET AL.: MODELING THE SOLAR RADIATIVE IMPACT OF AEROSOLS
[7] We use the term radiative impact of the aerosols as
the difference in the net solar radiative flux at the top of the
atmosphere between the simulation with aerosols and the
simulation without aerosols. This is similar to the radiative
forcing concept, except that we include both anthropogenic
and natural abundance of the aerosols.
2. Models
2.1. Oslo CTM-2 Model (OCTM)
[8] The OSLO-CTM2 is an off-line chemical transport
model that uses precalculated meteorological fields to drive
the chemical turnover and distribution of tracers in the
troposphere. The horizontal resolution of the model is
determined by the input data provided, and currently a data
set based on European Centre for Medium-Range Weather
Forecasts (ECMWF) forecast data with a T63 (1.87 1.87) horizontal resolution is used. In the vertical the
model has 40 levels from the surface up to 10 hPa. To
generate meteorological input data for August and September 2000 we have been running the Integrated Forecast
system (IFS) model at ECMWF in a series of forecasts
starting from the analyzed fields every 24 hour. Each
forecast is run for 36 hours, allowing for 12 hours of
spin-up and the last 24 hours are diagnosed every third
hour, generating a continuous record of input data. This
procedure has at least two advantages compared to using the
standard archived data from ECMWF. We get a consistent
data set with all the relevant data (boundary layer height,
convection, and 3-dimensional rainfall) for running the
CTM, and we double the temporal resolution (only every
6 hours from the archives).
[9] The advection of chemical species is calculated by the
second order moment method, which is able to maintain
large gradients in the distribution of species [Prather, 1986].
Vertical mixing by convection is based on the surplus and
deficit of mass flux in a column. Turbulent mixing in the
boundary layer is treated according to the Holtslag K-profile
scheme [Holtslag et al., 1990].
[10] The module used to model the carbonaceous aerosols
(i.e., the hydrophobic fraction in the emissions, the transfer
rate from hydrophobic to hydrophilic aerosols (aging), and
the dry deposition velocities) are taken from Cooke et al.
[1999]. Both black carbon (BC) and organic carbon (OC)
are separated in a hydrophobic fraction and a hydrophilic
fraction. In the atmosphere hydrophobic aerosols can be
oxidized or coated with hydrophilic species to form hydrophilic aerosols [Cooke and Wilson, 1996; Wilson et al.,
2001]. In the model a constant aging rate with an exponential lifetime of 1.15 days is used. This rate is quite uncertain
(the degree of coating really needed to make the aerosol
truly hydrophilic) and probably quite variable as it depends
on the amount of hydrophilic species (e.g., sulphate) available for coating, and the local oxidizing capacity of the
atmosphere. Wilson et al. [2001] calculate in a model that
includes sulphate aerosols, a global average aging rate of
3%/hour (lifetime of 1.39 days) for BC from fossil fuels
(FF), and assume an aging rate of 2.5%/hour (lifetime of
1.65 days) for BC from biomass burning. Emissions of
BC are assumed to be 80% hydrophobic [Smith et al., 1989;
Cashier, 1998], while for OC 50% of the emissions
are assumed to be hydrophobic. These fractions are quite
uncertain, e.g., Wilson et al. [2001] assume that 50% of the
BC from BB sources are in the mixed form, i.e., hydrophilic, and that 100% of the OC from BB is condensed on
coemitted BC in a hydrophilic form. Decreasing the fraction
emitted in the hydrophilic form increases the removal rate
through wet deposition and increases dry deposition on wet
surfaces. Dry deposition of hydrophilic carbonaceous aerosols is calculated with a deposition velocity of 0.025 cm/s
over dry surfaces (land) and 0.2 cm/s over oceans. For
hydrophobic aerosols a deposition velocity of 0.025 cm/s is
applied for all surfaces. The hydrophilic aerosols are also
removed by wet deposition. These aerosols are assumed to
be 100% absorbed in the cloud droplets, and are removed
according to the fraction of the liquid water content (LWC)
of a cloud that is removed by precipitation.
[11] Emissions of BC from FF sources are taken from
Cooke et al. [1999], and BC from BB sources are taken
from the Global Emissions Inventory Activity (GEIA),
which again is based on the method by Cooke and Wilson
[1996]. Total emissions of BC from FF sources are 5.12
Tg(C)/yr. For August the emission of BC from BB is 0.64
Tg(C)/month, while for September it is 0.32 Tg(C)/month.
Total (FF + BB) monthly emissions for OC are taken from
Liousse et al. [1996]. For August and September the total
emissions of OC are 8.9 and 6.9 Tg(C)/month, respectively.
[12] Emissions from BB are assumed to be associated
with a lift due to the release of thermal heat in the fires
[Stocks et al., 1996]. In our reference simulation the a priori
assumption is that all emissions from BB are emitted at the
two model levels around the local boundary layer height
(BLH), as given by the ECMWF data. In the work of Eck et
al. [2003] it is suggested that most of the BB occurs during
the day. Therefore, in the model, the BB emissions are
evenly distributed over 12 hours period of daylight with no
emissions during the night. Over south Africa the BLH can
reach a maximum of 3 –4 km and higher than 2 km over
large regions at noon and in the early afternoon. The altitude
of the emissions are in agreement with the altitude measured
by Stocks et al. [1996]. In the morning and afternoon the
BLH is generally below 1 km.
[13] For the simulations in this study, the CTM has been
run with a 20 day spin-up, and results have been sampled
every third hour for August and September of 2000.
2.2. Radiative Transfer Model
[14] In this work impact of BB on solar radiation is
studied in detail. In the radiative transfer calculations we
apply a multistream model using the discrete-ordinate
method [Stamnes et al., 1988]. In this study eight streams
are used. The radiative transfer model includes Rayleigh
scattering and clouds, and the exponential sum fitting
method [Wiscombe and Evans, 1977] is used to account
for absorption by gases. The spectral resolution in the solar
region is four bands (see Myhre et al. [2002] for further
details).
[15] The meteorological data for temperature, water
vapor, and clouds from ECMWF with T63 spatial resolution
as described in section 2.1 are used in the radiative transfer
calculations. Within each model grid column we perform
separate radiative transfer calculations in clear and cloudy
regions to establish an overall radiative flux for the column.
Hogan and Illingworth [2000] use radar observations to
MYHRE ET AL.: MODELING THE SOLAR RADIATIVE IMPACT OF AEROSOLS
show that the random cloud overlap is a satisfactory
assumption to estimate the total cloud cover. In this model
version with 40 vertical layers we combine the random
cloud overlap (to describe effect of clouds in different
height regions) and the maximum cloud overlap assumption
(for nearby levels) to estimate a realistic total cloud cover.
The optical properties of clouds are calculated using the
procedure described in Slingo [1989] with an effective
radius of 10 mm for low clouds [Stephens and Platt,
1987], which is in reasonable agreement with observations
made by the C-130 in stratocumulus clouds off the coast of
Namibia [Keil and Haywood, 2003]. For high clouds an
effective radius of 18 mm is used.
[16] The surface albedo is modeled as function of solar
zenith angle and spectral region. Over ocean the surface
albedo dependence on solar zenith angle is modeled according to Glew et al. [2002], whereas over land as in the work
of Briegleb et al. [1986]. Surface albedo over land is based
on vegetation data from Ramankutty and Foley [1999] with
spectral albedo values from Briegleb et al. [1986].
2.3. Optical Properties
[17] The size distribution and refractive index of the
particles in the biomass burning plume are adopted from
Haywood et al. [2003] to model the optical properties
(specific extinction coefficient, single scattering albedo,
and asymmetry factor) using Mie theory. Haywood et al.
[2003] show that the observed size distribution can be fitted
with three lognormal size distributions. The third mode
represents larger particles including mineral dust which
are not included in our emission inventories. Inclusion of
the largest mode would thus lead to an underestimation of
the mass of the two smaller modes and thereby in the
extinction coefficient. Therefore the two mode size distribution is adopted in the calculations of the specific extinction coefficient. For estimation of the single scattering
albedo and the asymmetry factor the three mode size
distribution is used to better represent the observed optical
properties. The latter assumption will be discussed later in
section 3.3.
[18] Rather than using the modeled BC/OC ratio we
adopt the observed ratio from Haywood et al. [2003] of
0.12. In the model OC is derived, but organic matter (OM)
accounts for additional mass with OC. Formenti et al.
[2003] found an OM/OC ratio of 2.6 which is adopted in
our simulations. Further the same studies describe that the
biomass aerosol chemical composition measurements (particles smaller than 0.65 mm) indicate a mass fraction of
inorganic components of 17% of the total mass. To take this
into account we have increased the modeled aerosol mass
by 20%. Uncertainties in the values used for the chemical
composition of the BB aerosols are discussed by Formenti
et al. [2003] and Haywood et al. [2003]. Haywood et al.
[2003] calculate a refractive index of 1.54 –0.018i at 0.55
mm using the Maxwell-Garnet mixing rule for the internal
mixing, and we use the wavelength dependent refractive
index in our simulations. Hygroscopic growth is not taken
into account, as Kotchenruther and Hobbs [1998] have
shown that this effect influences the optical properties for
aerosol from BB only modestly in the low relative humidities found within the aerosol layer [e.g., Haywood et al.,
2003].
SAF
37 - 3
[19] We reproduce the single scattering albedo at 0.55 mm
of 0.90, which was estimated by Haywood et al. [2003].
Further, the decrease with wavelength in specific extinction
and single scattering [Haywood et al., 2003; Eck et al.,
2001], which is important for the radiative transfer calculations, is also well reproduced.
3. Results
3.1. Model Results
[20] Figure 1a shows the aerosol optical depth (AOD) at
0.55 mm for the period 5 – 19 September. The results are
given for 0900 UTC, to allow comparison with satellite data
(see section 3.2.1), which are retrieved at this time of the
day. The figure shows a maximum estimated AOD higher
than 1.0. There is a rather inhomogeneous pattern in AOD,
with higher values over land than over ocean. However, the
characteristic transport over ocean between Equator and
20S by the trade winds, as seen from satellite retrievals, is
also evident in the model calculations. To a much smaller
extent there is transport over ocean by the westerly winds
south of 30S. The day-to-day variation in the model is
substantial given the fact that the emission has been
assumed to be the same during the whole period.
[21] The monthly mean AOD (0.55 mm) is shown in
Figure 1b. The maximum monthly mean is in the western
part of Africa around 10S with a clear pattern revealing
transport toward northwest and southeast.
3.2. Comparison With Remotely Sensed Data
3.2.1. MODIS
[22] The AOD from MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the Terra satellite is shown
for a comparison to the modeled AOD, both on a daily and a
monthly basis (at 550 nm). Some is missing due to limited
coverage, clouds screening, and bright underlying surfaces.
The two first causes are especially important for the coverage of daily data, whereas the latter is most important for the
monthly mean data.
[23] The daily MODIS data in Figure 1c show that
although the AOD is very inhomogeneous, the AOD shows
a reasonably consistent transition between land and ocean
surfaces. In general the AOD is higher in the beginning of
the two weeks period than in the latter part of the period.
These features compare well with the model, but local
maximum values are higher in the satellite retrievals than
the modeled AOD. It is encouraging that the pattern of
AOD from the model has many similarities with the
MODIS data. E.g. to the south east of southern Africa the
‘‘river of smoke’’ phenomenon is reasonably well represented (H. Annegarn et al., ‘‘The River of Smoke’’: Characteristics of the southern African springtime biomass
burning haze, submitted to Journal of Geophysical
Research, 2003).
[24] In Figure 1d the monthly mean AOD from MODIS is
shown. The monthly pattern is naturally much more smooth
than the daily ones, but it is still patchy compared to the
model results. There are large regions with AOD above 0.5.
The maximum AOD (about 1.0) is near the equator in the
western part of Africa (about 5S, 15E). There is a
secondary maximum (AOD about 0.6) further east (8S,
25E). The model results yield only one maximum (AOD
SAF
37 - 4
MYHRE ET AL.: MODELING THE SOLAR RADIATIVE IMPACT OF AEROSOLS
Figure 1. Aerosols optical depth (AOD) at 0.55 mm a) modeled during the period 5– 19 September
2000 given at 0900 UTC; b) Monthly mean during September 2000; c) Satellite retrieval from MODIS
during the period 5 – 19 September 2000 between 0620 and 1125 UTC; d) Monthly mean for September
2000 from MODIS.
1.0 at 5 – 10S, 15E). However, the maximum area is quite
broad, with an excursion toward east, so that in the region of
the secondary maximum of the MODIS data, the modeled
and the retrieved AOD are very similar in magnitude (about
0.6). Further, off the coast of Angola the MODIS AOD is
somewhat higher than the modeled one. Note that the
monthly mean AOD from MODIS is based on measurements once a day with somewhat limited spatial coverage
and is cloud screened, whereas the modeled AOD is a
monthly mean based on distributions 8 times each day,
without any sort of cloud screening. The general pattern of
the satellite retrieved AOD and modeled AOD is in good
accordance with somewhat higher AOD in the satellite
retrieval.
3.2.2. AERONET
[25] AERONET is a global ground-based network of Sun
photometers (see Holben et al. [1998] for a detailed
description). In the region investigated in this study data
for 10 stations are available during large parts of September
2000. In Figure 2 a comparison between the modeled AOD
MYHRE ET AL.: MODELING THE SOLAR RADIATIVE IMPACT OF AEROSOLS
SAF
37 - 5
Figure 2. A comparison between AOD (500 nm) from ten AERONET stations (*) and modeled AOD
(solid line) during September 2000.
and observations from AERONET is shown. The comparison is performed at 500 nm.
[26] At many of the AERONET stations the agreement
between the observed AOD and the model results is very
good, but at some of the stations the model underestimates
the AOD. However, the model captures much of the day-today variations indicating that variability in the meteorological conditions is the major reason for this variation rather
than in fire activity. The model results of AOD compares
best to the measurements at Bethlehem, Inhaca, Skukuza,
Senanga, and Ascension Island. The AOD is generally
lower by the end of the month both in the model and the
AERONET observations.
3.3. Comparison With Aircraft Data
[27] In this section a comparison with measurements from
the C-130 aircraft is presented. The C-130 was equipped to
measure aerosol size distribution, chemical composition,
optical properties, and radiative fluxes (see Haywood et
al. [2003] for further details). The C-130 flight pattern is
shown by Haywood et al. [2003]. The flights took place
mostly over the ocean off the coast of Namibia and Angola,
but with some flights also over land in Namibia.
3.3.1. Vertical Profiles
[28] Vertical profiles of temperature and scattering is
shown in Figure 3 for one location over land and one over
ocean. The temperature and dew point temperature are in
very good agreement over land, but differing slightly in the
lowest few hundred meters over the ocean. In addition the
layer originating from the boundary layer over land is more
humid in the model.
[29] Over land the observed vertical profile of the scattering is rather homogeneous, and the model shows a similar
pattern with slightly higher scattering in the upper part of
the aerosol plume and close to the ground compared to the
measurements. The vertical profile over ocean is also in
reasonable agreement. The maximum in the observed scattering is around 600 hPa, whereas in the model the
maximum is at lower altitudes. In both the model and the
observations the plume with aerosols from biomass burning
is within the layer from about 900 hPa to 550 hPa. The
observed scattering below 900 hPa is presumably due to sea
salt which is not included in our simulations.
[30] In Figure 4 the averaged vertical profile of scattering
for three entire C-130 flights are compared to corresponding
model results. Two of the flights are over land (A786 and
A790) and one over ocean (A789). Note that the altitude is
given above the sea level (asl), and the land surface is more
than 1 km asl. For the two observations over land the
observed profile indicates a rather constant scattering below
about 5km, whereas the model has strong gradient in the
scattering within the lowest 1 km, and underestimates the
SAF
37 - 6
MYHRE ET AL.: MODELING THE SOLAR RADIATIVE IMPACT OF AEROSOLS
Figure 3. Observed and modeled vertical profiles of temperature (T), dew point temperature (Td), and
scattering (550 nm) over land (upper panel) and over ocean (lower panel). The modeled T and Td are
from ECMWF. See Haywood et al. [2003] for a description of the observed vertical profiles.
scattering in most of the rest of the aerosol layer. Over
ocean the vertical profiles in the model and the observation
are in better agreement. However, the scattering is lower in
the model compared to the C-130 measurements. Note that
the altitude of the maximum observed scattering in A786
and A789 is as high as 4 km.
3.3.2. Radiative Measurements
[31] In the region from 7.5– 13.1E and 20.6– 24.4S a
normalized clear sky radiative impact (radiative impact
divided by AOD) is calculated based on the modeling. In
these simulations clouds are excluded from the radiative
transfer calculations to allow comparison with observations
from the C-130, which were made under cloud free conditions. We have used the same aerosol distribution as
shown in Figure 1a. During the day the normalized radiative
impact reaches 90 W m 2 and it is mainly between 50
and 90 W m 2, see Figure 5. The normalized impact is
stronger at 0900 and 1500 UTC than at noon (local noon
deviates from UTC noon by less than 1 hr in the region in
question) as the radiative impact of particles is largest for
high solar zenith angles [see Haywood and Shine, 1997;
Myhre and Stordal, 2001]. However, this effect is almost
compensated by diurnal variation in the surface albedo of
the ocean, which is higher for large solar zenith angles. The
day-to-day variation in the normalized radiative impact is
small, about 10 W m 2 or slightly more than 10%.
[32] Measurements from the C-130 are available for one
day. The normalized radiative impact is weaker in the model
than in the observations (17%). It is encouraging that
modeled radiative impact of aerosols is in such good
agreement with measurements, given the uncertainties in
the model and observations. Two factors that influence the
modeled normalized radiative impact are the AOD and the
aerosol optical properties. The modeled AOD is substan-
MYHRE ET AL.: MODELING THE SOLAR RADIATIVE IMPACT OF AEROSOLS
Figure 4. Vertical profiles of scattering (550 nm) as a
function of altitude (km asl) for three C-130 flights and
comparison with modeled scattering over the same area. See
Haywood et al. [2003] for a description of the flights.
tially lower than the observed, which tends to give a too
strong normalized radiative impact. The observed AOD for
11 September (the day of observed normalized radiative
impact) was 0.31, and only 0.04 in the model. The aerosol
plume in the model is slightly further north (see Figure 1a)
(see also the flight pattern by Haywood et al. [2003]). On
the other hand if the two mode size distribution had been
used in the calculation of single scattering albedo and
asymmetry factor instead of the three mode size distribution
a stronger normalized radiative impact had been calculated.
Both of these two factors influence the normalized radiative
impact by 10– 20%.
3.4. Radiative Impact
3.4.1. Daily Variation During the Campaign
[33] The radiative impact of the aerosols from biomass
burning is shown in Figure 6 for the same region as in
SAF
37 - 7
Figure 1a. Clouds have been included in the simulations.
Over land the radiative impact is generally negative whereas
over ocean the sign varies. The clear sky radiative impact
(not shown) is negative over the whole region. The positive
values are therefore due to the presence of aerosols above
the clouds. The weakly absorbing biomass aerosols give a
positive radiative impact only for very bright underlying
surfaces such as clouds (see also the case study by Keil and
Haywood [2003]). However, this is not the case over arid
regions, as will be discussed below. During the period
investigated the radiative impact varies between around
50 W m 2 and 65 W m 2 with the strongest negative
impact corresponding to high AODs in clear sky cases, and
the strongest positive impact corresponding to high AODs
over highly reflectant clouds.
[34] Figure 7 shows the regional radiative impact of the
aerosols from biomass burning both for clear sky conditions
and when clouds are included in the calculations, over the
period from 5 to 19 September. The region considered is the
same as shown in Figure 6. The day-to-day variation is
relatively small. The radiative impact of the aerosols from
biomass burning decreases during the period. The clouds
reduce the radiative impact by a factor of 2 to 3. Hence, clouds
reduce the radiative impact of biomass aerosols to a greater
extent compared to purely scattering aerosols (as sulfate).
This is explained by the fact that locally clouds not only
reduce the radiative impact in magnitude but even change the
sign from negative to positive, as shown in Figure 6.
[35] The wavelength dependence of the specific extinction coefficient and single scattering albedo is particularly
strong for biomass aerosols. The decrease in these quantities
with wavelength causes the surface albedo to play an
important role for the radiative impact of the aerosols.
Vegetated surfaces have an albedo with strong wavelength
dependence [Dickinson, 1983]. Further, the surface albedo
depends on the solar zenith angle. A calculation is performed with a broadband surface albedo assuming no solar
zenith angle dependence on the surface albedo. This experiment resulted in a slightly more than 25% weaker radiative
Figure 5. Modeled and one observed (one case) normalized clear sky radiative impact (given as W m 2
per unit AOD) for various times during the day. Results are shown in the period 5 –19 September 2000.
SAF
37 - 8
MYHRE ET AL.: MODELING THE SOLAR RADIATIVE IMPACT OF AEROSOLS
Figure 6. Radiative impact of biomass aerosols during the period 5 – 19 September 2000 at 0900 UTC.
Contours every 10 W m 2.
impact, with larger effect over land than ocean. This effect
is particularly important over arid regions. The difference in
the results of these two simulations is dominated by the
spectral dependence of the surface albedo rather than the
solar zenith angle dependence.
3.4.2. Monthly Averages
[36] In Figure 8 the September 2000 monthly mean
radiative impact of biomass aerosols is shown, for cloudy
as well as for clear sky conditions. It is evident that clouds
reduce the cooling effect of biomass aerosols substantially.
The pattern of the clear sky radiative impact is very similar
to the pattern of the monthly mean AOD. Clouds change the
sign of the radiative impact over ocean areas with large
stratocumulus cover and over land areas close to the
Intertropical Convergence Zone (ITCZ) with large cumulus
clouds. The positive radiative impact of the aerosols over
the south Atlantic ocean is associated with monthly mean
cloud cover above 60– 70%.
[37] The monthly mean radiative impact due to the
aerosols from the southern Africa biomass burning, aver-
aged over the region shown in Figure 8, is 1.7 W m 2
when clouds are included and 4.3 W m 2 for clear sky. As
the area of the selected region cover about 8% of the Earth’s
surface this corresponds to global mean numbers of 0.14
W m 2 and 0.34 W m 2, respectively. The single scattering albedo has a key role in determining the magnitude of
the radiative impact of aerosols. In a sensitivity test we have
assumed purely scattering (single scattering albedo equals
1.0) biomass aerosols. The radiative impact then strengthens
from 1.7 W m 2 to about 5 W m 2.
4. Summary
[38] Based on modeling linked to measurements we
estimate the radiative impact of aerosols from biomass
burning during the SAFARI-2000 campaign, with special
focus on the period 5 – 19 September. We use a combination
of theory and observations [Haywood et al., 2003] made
during the campaign to derive a consistent set of optical
properties. A comparison between our model results and
MYHRE ET AL.: MODELING THE SOLAR RADIATIVE IMPACT OF AEROSOLS
Figure 7. Radiative impact during the period 5 – 19
September 2000 (W m 2), excluding and including clouds.
The results are for the region shown in Figure 6.
available observations are made, with regard to AOD, the
vertical profile, and the radiative impact of the BB aerosols.
Observations include in situ data from the C-130 aircraft,
ground-based, and satellite data. Using the ECMWF meteorological data for the campaign period the model manages
to reproduce some of the main patterns of AOD during the
period, found both in satellite retrievals and ground-based
AERONET measurements. The agreement between the
model data and the satellite retrieval can be seen by
comparing Figures 1a and 1c. In particular the day-to-day
variation in the transport to the south east of southern Africa
is well captured in the model. The day-to-day variation in
the AOD is also in reasonable agreement with the AOD
from AERONET, especially taking into account the fact that
we use monthly mean emission data. This indicates that
variability in the meteorological conditions is more important than in the fire activity. However, daily fire activity
information would probably have improved the day-to-day
variation further. ATSR data show a significant temporal
SAF
37 - 9
variability in countings of fires in the studied area, with
variability also on a smaller regional scale.
[39] One problem in our model simulations is the vertical
profile over land, decreasing strongly with altitude in the
lowest 1km, as opposed to the measurements which show a
rather homogeneous boundary layer. This is not so evident
in the vertical profiles close to noon shown in Figure 3, but
clearly in the profiles for the entire flights in Figure 4
covering a longer time period. In a control simulation with
no diurnal variation in the BB emission, larger differences
between the model results and the measurements were
found, including much larger differences between the
observed and the modeled vertical profile over land in
Figure 3. The vertical profile problem seems therefore
linked to the problem with the BB emission occurring at
too low altitude. This could be due to a BLH that is too low
(ECMWF data), fire activity that is more concentrated
during the day than estimated in our calculations, or that
the thermal heat associated with the fires is strong enough
for BB emission to be injected above the BLH during the
morning and late afternoon. The inclusion of a diurnal
variation in the BB emission improved the vertical profile,
but had a relatively small influence on the AOD over the
southern Africa.
[40] Haywood et al. [2003] present some evidence that
the single scattering albedo increases with distance from the
source region (or equivalently time from emission), but it is
unclear whether this is due to increase in the organic carbon
content of the aerosol or due to more complex mixing of the
particles, which is not included in our study. Eck et al.
[2003] show that for some AERONET stations the AOD
during September 2000 was high compared to some of the
previous years. This can either be caused by high emissions
of biomass aerosols during 2000 or by a meteorological
situation favorable of high AOD. The latter aspect is taken
into account in our study, whereas the former one, which is
omitted in our study, can perhaps explain some of the
underestimation in our model results.
[41] The modeled radiative impact of the aerosols from
biomass burning is compared to measurements under clear
Figure 8. Monthly mean radiative impact of biomass aerosols during September 2000 a) clouds
included, b) clear sky.
SAF
37 - 10
MYHRE ET AL.: MODELING THE SOLAR RADIATIVE IMPACT OF AEROSOLS
sky conditions. The radiative impact of the biomass aerosols
depends on optical properties, the vertical profile, the
abundance of the aerosols, and solar zenith angle. In the
single case compared in our study, the difference between
the model and observations is almost 20%.
[42] Over the region where aerosols from BB is the
dominating source of aerosols local radiative cooling and
warming stronger than 50 W m 2 magnitude is modeled.
The clouds strongly influence the radiative impact of the
aerosols. The spectral dependence in the optical properties
implies that the spectral dependence in the surface albedo is
important for the radiative impact of the aerosols from BB.
The aerosols from biomass burning in southern Africa result
in a global radiative impact of 0.14 W m 2.
[43] Acknowledgments. This work has been supported by the EUproject AIRARF funded through the LSF program CAATER and from the
Research Council of Norway, through ChemClim. We thank NASA for
making the MODIS data available.
References
Briegleb, B. P., P. Minnis, V. Ramanathan, and E. Harrison, Comparison of
regional clear-sky albedos inferred from satellite observations and models
comparisons, J. Clim. Appl. Meteorol., 25, 214 – 226, 1986.
Cashier, H., Carbonaceous combustion particles, in Atmospheric Particles,
edited by R. M. Harrison and R.e. Van Grieken, pp. 295 – 348, John
Wiley, New York, 1998.
Cooke, W. F., and J. J. N. Wilson, A global black carbon model, J. Geophys. Res., 101, 19,395 – 19,409, 1996.
Cooke, W. F., C. Liousse, H. Cachier, and J. Feichter, Construction of a
1 1 fossil-fuel emission dataset for carbonaceous aerosols and implementation and radiative impact in the ECHAM-4 model, J. Geophys.
Res., 104, 22,137 – 22,162, 1999.
Dickinson, R. E., Land surface processes and climate-surface albedos and
energy balance, Adv. Geophys., 25, 305 – 353, 1983.
Diner, D. J., W. A. Abdou, C. J. Bruegge, J. E. Conel, K. A. Crean, B. J.
Gaitley, M. C. Helmlinger, R. A. Kahn, J. V. Martonchik, and S. H.
Pilorz, Geophys. Res. Lett., 28, 3127 – 3130, 2001.
Eck, T. F., B. N. Holben, D. E. Ward, O. Dubovik, J. S. Reid, A. Smirnov,
M. M. Mukelabai, N. C. Hsu, N. T. O’Neill, and I. Slutsker, Characterisation of the optical properties of biomass burning aerosols in Zambia
during the 1997 ZIBBEE field campaign, J. Geophys. Res., 106, 3425 –
3448, 2001.
Eck, T. F., et al., Variability of biomass burning aerosol optical characteristics in southern Africa during the SAFARI 2000 dry season
campaign and a comparison of single scattering albedo estimates from
radiometric measurements, J. Geophys. Res., 108(D13), 8477,
doi:10.1029/2002JD002321, 2003.
Formenti, P., Inorganic and carbonaceous aerosols during SAFARI 2000:
Chemical characteristics, physical properties, and emission data for
smoke from African biomass burning, J. Geophys. Res., 108(D13),
8488, doi:10.1029/2002JD002408, 2003.
Glew, M. D., P. Hignett, and J. P. Taylor, Aircraft measurements of sea
surface albedo, J. Atmos. Sci., in press, 2002.
Haywood, J., S. Osborne, P. Francis, P. Formenti, and M. O. Andreae, The
mean physical and optical properties of biomass burning aerosol measured by the C-130 aircraft during SAFARI-2000, J. Geophys. Res.,
108(D13), 8473, doi:10.1029/2002JD002226, 2003.
Haywood, J. M., and K. P. Shine, Multi-spectral calculations of the direct
radiative forcing of tropospheric sulphate and soot aerosols using a
column model, Q. J. R. Meteorol. Soc., 123, 1907 – 1930, 1997.
Hobbs, P. V., J. S. Reid, R. A. Kotchenruther, R. J. Ferek, and R. Weiss,
Direct radiative forcing by smoke from biomass burning, Science, 275,
1776 – 1778, 1997.
Hogan, R. J., and A. J. Illingworth, Deriving cloud overlap statistics from
radar, Q. J. R. Meteorol. Soc., 126, 2903 – 2909, 2000.
Holben, B. N., et al., AERONET: A federated instrument network and data
archive for aerosol characterization, Remote Sens. Environ., 66, 1 – 16,
1998.
Holtslag, A. A. M., E. I. F. DrBruijn, and H.-L. Pan, A high resolution air
mass transformation model for short-range weather forecasting, Mon.
Weather Rev., 118, 1561 – 1575, 1990.
Husar, R. B., J. M. Prospero, and L. L. Stowe, Characterization of tropospheric aerosols over the oceans with the NOAA advanced very high
resolution radiometer optical thickness operational product, J. Geophys.
Res., 102, 16,889 – 16,909, 1997.
Iacobellis, S. F., R. Frouin, and R. C. J. Somerville, Direct climate forcing
by biomass-burning aerosols: Impact of correlations between controlling
variables, J. Geophys. Res., 104, 12,031 – 12,045, 1999.
Intergovernmental Panel on Climate Change (IPCC), Climate Change
2001: The Scientific Basis, edited by J. T. Houghton et al., Cambridge
Univ. Press, New York, 2001.
Keil, A., and J. M. Haywood, Solar radiative forcing by biomass burning
aerosol particles during SAFARI-2000: A case study based on measured
aerosol and cloud properties, J. Geophys. Res., 108(D13), 8437,
doi:10.1029/2002JD002315, 2003.
Kotchenruther, R. A., and P. V. Hobbs, Humidification factors of aerosols
from biomass burning in Brazil, J. Geophys. Res., 103, 32,081 – 32,089,
1998.
Liousse, C., J. E. Penner, C. Chuang, J. J. Walton, H. Eddleman, and H.
Cachier, A global three-dimensional model study of carbonaceous aerosols, J. Geophys. Res., 101, 19,411 – 19,432, 1996.
Myhre, G., and F. Stordal, On the tradeoff of the solar and thermal infrared
radiative impact of contrails, Geophys. Res. Lett., 28, 3119 – 3122, 2001.
Myhre, G., J. E. Jonson, J. Bartnicki, F. Stordal, and K. P. Shine, Role of
spatial and temporal variations in the computation of radiative forcing
due to sulphate aerosols: A regional study, Q. J. R. Meteorol. Soc., 128,
973 – 989, 2002.
Penner, J. E., R. E. Dickinson, and C. A. O’Neill, Effects of aerosol from
biomass burning on the global radiation budget, Science, 256, 1432 –
1434, 1992.
Penner, J. E., C. C. Chuang, and K. Grant, Climate forcing by carbonaceous
and sulfate aerosols, Clim. Dyn., 14, 839 – 851, 1998.
Prather, M. J., Numerical advection by conservation of second-order moments, J. Geophys. Res., 91, 6671 – 6681, 1986.
Ramankutty, N., and J. Foley, Estimating historical changes in global land
cover: Croplands from 1700 to 1992, Global Biogeochem. Cycles, 13,
997 – 1027, 1999.
Ross, J. L., P. V. Hobbs, and B. Holben, Radiative characteristics of regional hazes dominated by smoke from biomass burning in Brazil: Closure
tests and direct radiative forcing, J. Geophys. Res., 103, 31,925 – 31,941,
1998.
Slingo, A., A GCM parameterization for the shortwave radiative properties
of water clouds, J. Atmos. Sci., 46, 1419 – 1427, 1989.
Smith, D. M., M. S. Akthre, J. A. Jassim, C. A. Sergides, W. F. Welch, and
A. R. Chugtai, Studies of the structure and reactivity of soot, Aerosol Sci.
Technol., 10, 311 – 325, 1989.
Stamnes, K., S.-C. Tsay, W. Wiscombe, and K. Jayaweera, A numerically
stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Opt., 27, 2502 – 2509,
1988.
Stephens, G. L., and C. M. R. Platt, Aircraft observations of the radiative
and microphysical properties of stratocumulus and cumulus cloud fields,
J. Clim. Appl. Meteorol., 26, 1243 – 1269, 1987.
Stocks, B. J., B. W. van Wilgen, W. S. W. Trollope, D. J. McRae, J. A.
Mason, F. Weirich, and A. L. F. Potgieter, Fuels and fire behavior dynamics on large-scale savanna fires in Kruger National Park, South Africa, J. Geophys. Res., 101, 23,541 – 23,550, 1996.
Swap, R. J., et al., SAFARI 2000 overview paper, J. Geophys. Res., 2003.
Tanré, D., F. M. Bréon, J. L. Deuze, M. Herman, P. Goloub, F. Nadal, and
A. Marchand, Global observation of anthropogenic aerosols from satellite, Geophys. Res. Lett., 28, 4555 – 4558, 2001.
Wilson, J., C. Cuvelier, and F. Raes, A modeling study of global mixed
aerosol fields, J. Geophys. Res., 106, 34,081 – 34,108, 2001.
Wiscombe, W., and J. Evans, Exponential-sum fitting of radiative transmission functions, J. Comput. Phys., 24, 416 – 444, 1977.
T. K. Berntsen, Center for International Climate and Environmental
Research, Oslo, Norway.
J. M. Haywood, Met Office, Bracknell, UK.
B. N. Holben, Biospheric Sciences Branch, NASA Goddard Space Flight
Center, Greenbelt, MD, USA.
M. Johnsrudn and F. Stordal, Norwegian Institute for Air Research
(NILU), Kjeller, Norway.
G. Myhre and J. K. Sundet, Department of Geophysics, University of
Oslo, P.O. Box 1022 Blindern, 0315 Oslo, Norway. (gunnar.myhre@
geofysikk.uio.no)