JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D18, 8579, doi:10.1029/2002JD002566, 2003
Modeling the radiative impact of mineral dust during
the Saharan Dust Experiment (SHADE) campaign
Gunnar Myhre,1,2 Alf Grini,1 James M. Haywood,3 Frode Stordal,1,2 Bernadette Chatenet,4
Didier Tanré,5 Jostein K. Sundet,1 and Ivar S. A. Isaksen1
Received 24 May 2002; revised 18 September 2002; accepted 20 January 2003; published 30 July 2003.
[1] The Oslo chemical transport model (Oslo CTM2) is driven by meteorological data to
model mineral dust during the Saharan Dust Experiment (SHADE) campaign in September
2000. Model calculations of the optical properties and radiative transfer codes are used to
assess the direct radiative impact in the solar and terrestrial regions of the spectrum. The
model calculations are compared to a wide range of measurements (satellite, ground-based,
and aircraft) during the campaign. The model reproduces the main features during the
SHADE campaign, including a large mineral dust storm. The optical properties and the
vertical profiles are in reasonable agreement with the measurements. There is a very good
agreement between the modeled radiative impact and observations. The strongest local
solar radiative impact we model is around 115 Wm2. On a global scale the radiative effect
INDEX TERMS:
of mineral dust from Sahara exerts a significant negative net radiative effect.
0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801); 0360 Atmospheric
Composition and Structure: Transmission and scattering of radiation; 3337 Meteorology and Atmospheric
Dynamics: Numerical modeling and data assimilation; 3359 Meteorology and Atmospheric Dynamics:
Radiative processes; KEYWORDS: aerosols, single scattering albedo, transport model, aircraft measurements
Citation: Myhre, G., A. Grini, J. M. Haywood, F. Stordal, B. Chatenet, D. Tanré, J. K. Sundet, and I. S. A. Isaksen, Modeling the
radiative impact of mineral dust during the Saharan Dust Experiment (SHADE) campaign, J. Geophys. Res., 108(D18), 8579,
doi:10.1029/2002JD002566, 2003.
1. Introduction
[2] Among the major components contributing to the
direct aerosol effect mineral dust is the most uncertain.
Even the sign of the radiative forcing due to mineral dust is
unresolved [Intergovernmental Panel on Climate Change
(IPCC), 2001]. The main uncertain factors are the degree of
absorption of solar radiation (an important part is the
refractive index), the influence of mineral dust on longwave
radiation, the abundance of mineral dust in the atmosphere,
and in particular the human influence on this abundance.
[3] Mineral dust has a cooling effect over ocean in the
absence of clouds. Haywood et al. [2001] measured a strong
radiative impact (or radiative effect) during a dust event.
However, over brighter surfaces (as over desert and above
clouds) the radiative impact will be strongly altered. Haywood
and Shine [1995] and Hansen et al. [1997] showed that single
scattering albedo is a critical factor in determining the sign
of the solar radiative impact for various surface albedos.
The longwave radiative impact of mineral dust will always
1
Department of Geophysics, University of Oslo, Oslo, Norway.
Norwegian Institute for Air Research, Kjeller, Norway.
3
Met Office, Bracknell, UK.
4
Laboratoire Interuniversitaire des Systèmes Atmosphériques, Universités Paris VII et Paris XII, Centre Nationale de Recherche Scientifique,
Cretéil, France.
5
Laboratoire d’Optique Atmospheriques, Centre Nationale de Recherche Scientifique, U.S.T. de Lille, Villeneuve d’Ascq, France.
2
Copyright 2003 by the American Geophysical Union.
0148-0227/03/2002JD002566$09.00
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be positive, but the magnitude depends strongly on the size
of the particles, the refractive indices and the altitude. Therefore the net radiative impact (sum of solar and longwave)
exhibits large regional variations, and this explains why
the global mean is difficult to estimate. Several studies
have investigated the radiative impact of mineral dust [Tegen
et al., 1996; Sokolik and Toon, 1996; Myhre and Stordal,
2001; Woodward, 2001; Weaver et al., 2002] with a wide
range in the results.
[4] Tegen et al. [1996] and Sokolik and Toon [1996]
estimated that the human contribution to the mineral dust
abundance can be as large as 30– 50%. These numbers are
highly uncertain and it is an unresolved question whether
this is a direct climate forcing mechanism or a climate
feedback (see discussion by Myhre and Stordal [2001]).
[5] The Saharan Dust Experiment (SHADE) took place in
September 2000 on the west coast of Africa to improve
knowledge about the radiative impact of mineral dust using a
wide range of measurements [Tanré et al., 2003]. This study
uses meteorological data to model the production and transport of mineral dust during the campaign and compares the
results against various in-situ and remotely sensed airborne
and surface-based measurements and calculates the direct
radiative impact for a larger region surrounding the Sahara.
2. Models
2.1. Transport Model
[6] Oslo CTM2 is an off-line chemical transport/tracer
model (CTM) that uses precalculated transport and physical
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MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
fields to simulate tracer distributions in the atmosphere. The
model represents the global troposphere and is three-dimensional with the model domain reaching from the ground up
to 10 hPa for the current data sets. In the horizontal and
vertical the model has a variable resolution that is determined by the input data provided. In this study we use
forecast data from the European Center for Medium-Range
Weather forecast (ECMWF) with 1.875 1.875 degrees
horizontal resolution and with 40 levels in the vertical with
a resolution of about 600 meters in most of the troposphere.
[7] The second-order moment method is used for advection [Prather, 1986]. Convection is based on the Tiedkte
mass flux scheme [Tiedkte, 1989; Sundet, 1997], where
vertical transport of species is determined by the surplus/
deficit of mass flux in a column. Dry and wet deposition are
treated as in the work of Grini et al. [2002a].
[8] The forecast data are constructed from merged forecasts where each forecast is run for 36 hours with the first
12 hours discarded as ‘‘spin-up.’’ The data are stored at
three hours interval for August and September 2000. Most
of the data that are diagnosed from the ECMWF model are
standard model output (cloud cover, surface properties etc.)
and is thus the state of the art parameterizations of the
relevant processes. Alas, since the CTM is off-line there will
be no feedback from the CTM to the data that are diagnosed
in the ECMWF model. Data that are not part of the
ECMWF diagnostic (convective fluxes, rain fall) have been
diagnosed explicitly with the built in diagnostic system in
the ECMWF model, and the physical properties of the fields
are described by Tiedkte [1987].
2.2. Dust Production
[9] Production of dust is parameterized using the formula
production ¼ const u2* u* u*;tr ;
ð1Þ
where production is in kgm2s1 and u* is friction velocity
(see definition in the work of Stull [1988]) in m/s. The
constant used is 1.7 105 kg s2m5. The threshold friction
wind speed (u*,tr) is the minimum friction wind assumed to
give dust emission.
[10] Different landscapes have different threshold winds
for dust emissions, normally between 6 m/s and 20 m/s [see,
e.g., Chomette et al., 1999]. We have used global data sets
to describe the vegetation. An average procedure was
developed using the data set of Olson et al. [1983],
Matthews [1983], Wilson and Henderson-Sellers [1985],
Ramankutty and Foley [1999], and SARB http://wwwsurf.larc.nasa.gov/surf/ ). The lowest threshold wind of
6 m/s was used where all data sets agreed on pure sand
deserts. The highest threshold wind of 20 m/s was used in
areas where all data sets agreed on shrub. To convert
threshold wind (Utr) speeds to threshold friction wind
speeds, we used the formula u*,tr = 0.035 Utr (based on
the work of Nickling and Gillies [1987, Table 3]). The
constant used in equation (1) gives the same production as
in the work of Tegen and Fung [1994] for a wind speed of
10 m/s and a threshold wind speed of 6.5 m/s.
[11] For areas with large roughness elements, the friction
wind velocity is very high as the friction velocity increases
with roughness length. In arid areas with large roughness,
for instance over the Moroccan Atlas mountains, the
ECMWF data have a very large friction wind speed. In
such areas, we use the formula production = CU2 (U Utr)
with C equal to 0.7 mg s2m5 as proposed by Tegen and
Fung [1994]. For areas with large roughness elements, large
wind friction speeds reflect large momentum flux to the
surface because of these elements. It will not reflect the
momentum appropriate for dust production (we assume that
the dust production takes place at smooth surfaces, and it
should thus not be influenced by large scale roughness
elements). In our model, the threshold wind speeds do not
change with surface roughness, but only with vegetation
cover. For a further discussion on use of wind or friction
wind in dust modeling, see Liu and Westphal [2001].
[12] The dust flux is given a size distribution which is
lognormal with mass median diameter Dg,mass = 2.5 mm
and standard deviation s = 3.2 as given by Shettle [1984].
Note that this size distribution is given in the source
regions of the mineral aerosols and thereafter atmospheric
processes alter the size distribution. The calculations are
performed with eight size bins. Several studies [Guelle et
al., 2000; Schultz et al., 1998] have argued that s should be
closer to 2. However, we chose to keep the original,
broader size distribution and leave out the small and large
modes given by Shettle [1984], giving better agreement
with observations, particularly the spectral variation in the
aerosol optical depth. The size distribution at the source is
difficult to establish due to the fact that only limited
observations have been made. Calculating the size distributed flux of dust aerosols is a complicated task, e.g., since
soil characteristics are not available for large scale modeling and since wind speeds interact to give the size
distributed flux. Some studies have made approaches to
parameterize these processes [Lu and Shao, 1999; Shao,
2001; Alfaro and Gomes, 2001, Grini et al., 2002b], but
much work remains before the size distributed fluxes of
dust can be modeled realistically.
[13] The soil moisture makes the soil more difficult to
erode as the water increases the interparticle capillary forces
in the soil. Fecan et al. [1999] proposed relationships to
deal with the increase in threshold wind speed with increasing soil water content. The soil moisture from the ECMWF
model is not believed to be realistic enough to be used to
calculate the increase in threshold wind because in the
ECMWF model there is no evaporation from soils with
humidity lower than a prescribed, globally uniform permanent wilting point of 0.171 m3/m3. This value is much larger
than the values discussed by Fecan et al. [1999] (P. Viterbo,
personal communication, 2001). To deal with the problem,
we chose to set dust emission to zero for the next two days
whenever rainfall is higher than 2 mm in 24 hours in a grid
square. similar to the approach of Claquin [1999]. Our
simple dust production formulation is a source for uncertainty in the model, but this formulation will capture the
largest dust outbreaks.
2.3. Dust Optical Properties
[14] The specific extinction coefficient, the single scattering albedo, and the asymmetry factor are modeled using
Mie scattering theory. In Mie scattering theory spherical
particles are assumed and particle size and refractive index
need to be specified. Haywood et al. [2003] show that the
mineral dust aerosols are not spherical, but Mishchenko et al.
MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
[1997] show that this simplification introduces only a modest
uncertainty in calculation of radiative fluxes. The size distribution is explicitly modeled in the transport model and
adopted in the optical and radiative calculations. The refractive indices for mineral dust are highlighted as the single most
important factor for the large uncertainty in the radiative
impact of mineral dust [Myhre and Stordal, 2001]. Kaufman
et al. [2001] show that the single scattering albedo at solar
wavelengths is much higher and that the imaginary part of the
refractive index is substantially lower than in previous
studies. We have chosen to use refractive indices in the solar
spectrum from the Dubovik retrieval from the Sun photometer at Cape Verde. Real refractive index of 1.48, 1.47, 1.43,
at 1.42 at 440, 670, 870, and 1020 nm, respectively and
imaginary refractive index of 0.0014, 0.0011, 0.0011, and
0.0011 at 440, 670, 870, and 1020 nm, respectively are
adopted. In the thermal infrared spectrum we use refractive
indexes from Fouquart et al. [1987] as these compare most
favorably with the detailed measurements and modeling
presented by Highwood et al. [2003]. The density of the
mineral aerosols, used in the calculations of the specific
extinction coefficient, are taken to be 2600 kgm3 [Hess et
al., 1998].
2.4. Radiative Transfer Model
[15] We apply separate longwave and shortwave multistream models in the radiative transfer calculations using
the discrete-ordinate method [Stamnes et al., 1988]. In the
calculations in this study eight streams are used. The solar
radiative transfer model includes radiative effects of aerosols, clouds, Rayleigh scattering, and the exponential sum
fitting method [Wiscombe and Evans, 1977] is used to
account for absorption by gases. There are 4 bands in the
solar spectrum (see Myhre et al. [2003] for further details).
The longwave scheme takes into account absorption by
gases and clouds in addition to scattering and absorption by
aerosols. The exponential sum fitting method [Wiscombe
and Evans, 1977] is used to account for absorption by water
vapor, carbon dioxide, and ozone. Five spectral regions are
used in the longwave spectrum (see Myhre and Stordal
[2001] for further details).
[16] Our calculations are performed in T63 spatial resolution and 40 vertical layers. The meteorological data of
temperature, water vapor, clouds used in the radiative
transfer calculations during the campaign period are described in section 2.1. Within each model grid column we
perform separate radiative transfer calculations in clear sky
and cloudy regions to establish an overall radiative flux for
the column. Hogan and Illingworth [2000] use radar observations to 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 in the solar
region are calculated using the procedure described in the
work of Slingo [1989] with effective radius of 10 mm for
low clouds and 18 mm for high clouds [Stephens, 1978;
Stephens and Platt, 1987]. In the thermal infrared spectrum
nonscattering clouds are assumed with absorption coefficients dependent on the altitude of the clouds [see Myhre
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and Stordal, 2001]. There is no interaction between the
mineral dust aerosols and the cloud microphysics in the
model.
[17] The surface albedo is modeled as a function of solar
zenith angle and solar spectral region. Over ocean the surface
albedo dependence on solar zenith angle is modeled according to Glew et al. [2003], 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].
[18] We have performed radiative transfer calculations
including and excluding mineral dust. The difference between these two simulations at the top of the atmosphere is
taken as the radiative impact of the aerosols. This is similar
to the radiative forcing concept, except that we include both
anthropogenic and natural abundance of the aerosols.
3. Results
[19] The purpose of our calculations is to compare our
results with intensive observations during the SHADE period
19 to 28 September 2000. We first calculate the dust distribution using the CTM to simulate production of dust, the
transport, and deposit of the dust particles. These calculations
are started 1 August, in order to spin up the model. Thereafter
we calculate optical properties and radiative impact of the
dust during the campaign period.
3.1. Production
[20] Figure 1 shows the modeled production of mineral
dust during the SHADE campaign. A substantial diurnal
variation can be seen, with much larger production during the
day. This indicates that solar heating is an important factor in
the generation of the surface winds. Further, the model
indicates an important shift in the source regions during the
campaign. During the first part of the campaign the source
regions seem to be closer to the coast compared to the main
dust storm during the period 22– 24 September 2000. This is
due to a shift in the region with friction velocity above the
threshold value. The threshold friction velocity is somewhat
higher at the coast than in the main source region during the
campaign, affecting the magnitude of the production. Based
on the model, the mineral dust transported over the campaign
area mainly has its origin in the western part of Sahara.
3.2. Modeled Aerosol Optical Depth
[21] The modeled aerosol optical depth at 550 nm during
the campaign is shown in Figure 2a. It clearly reveals the
large dust storm starting in western Sahara the 22th and
developing over the next days and with a westward movement of the mineral dust plume. Over the main campaign
area near Sal Island and Dakar it shows that AOD is
moderate during the 2 or 3 first days. Thereafter there are
some days with low AOD before the dust starts to move
over ocean the 24th. A maximum AOD over the campaign
area can be seen the 26th. The AOD weakened as the
mineral dust move westward, but is still not insignificant as
the dust plumes cross the Atlantic ocean in accordance with
earlier measurements.
3.3. Comparison With MODIS Aerosol Optical Depth
[22] In Figure 2b the AOD from the Moderate Resolution
Imaging Spectroradiometer (MODIS) [Kaufman et al.,
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MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
Figure 1. Production of mineral dust (109 kgm2s1) during the campaign period 19– 28 September
2000 at 0 and 12 UTC.
1997; Tanré et al., 1997] aboard the Terra satellite is shown
for 25– 26 September 2000. The pattern with high AOD
along the coast 25 September and the mineral dust transported further out over the ocean is clearly evident and
rather similar to the model. Note in particular the similar
shape of the dust plume for 26 September in the satellite
retrieval and the model results. However, it seems that the
plume is slightly displaced eastward in the model compared
to the satellite retrievals. Further the maximum AOD is
somewhat higher in the satellite retrievals than in the model,
which can partly be explained by the much higher horizontal resolution in the satellite data. The AOD values from
MODIS compare well with AERONET observations over
ocean [Remer et al., 2002]
3.4. Comparison With AERONET Aerosol
Optical Depth
[23] Aerosol optical depths from the model are compared
to surface Sun photometers at Cape Verde and Dakar during
the campaign period and shown in Figure 3. These instruments are part of the AERONET which is a global groundbased network of Sun photometers (see Holben et al. [1998]
for a description). The general pattern which can be divided
in four periods is in reasonable agreement given the large
MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
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Figure 2. AOD at 550 nm. (a) Modeled during the period 19– 28 September 2000 at 0 and 12 UTC.
(b) MODIS data for 25– 26 September between 9 and 14 UTC. Note that both panels have the same color
scale. Cape Verde is located at 16.73N and 22.94W, and Dakar is at 14.39N and 16.96W.
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MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
Figure 3. Modeled AOD compared with Sun photometers
(level 2) data at (a) Cape Verde and (b) Dakar. Included are
two observations from level 1.5 data, 24 September for
Cape Verde and 23 September for Dakar. The observations
are instantaneous values. The model has a resolution of
3 hours.
fluxes. We concentrate on measurements made between
Cape Verde and Dakar, and mainly from the flight a797 on
25 September, see the C-130 flight pattern in the work of
Tanré et al. [2003].
3.5.1. Size Distribution
[26] Figure 4 shows a comparison of the modeled size
distribution and several lognormal size distributions fitted to
the observations. The modeled size distributions are shown
for 25 September (heavy dust loading) and 19 September
(moderate dust loading). The lognormal distribution based
on the observations is grouped into flights with heavy dust
loading (a796, a797, and a798) and moderate dust loading
(a778, a783, and a794) [see Haywood et al., 2003]. The
observations show higher abundance of large particles in the
cases with high AOD, whereas in the model this is vice
versa. The difference in the measured size distribution with
dust loading can arise from differences in the source of the
mineral aerosols, but also during the transport of the
aerosols. The model indicates that in the beginning of
the campaign period there was a different main source
region compared to in the period with heaviest dust loading,
which may be the reason why the observed size distribution
changes during the campaign.
[27] The modeled size distributions compare relatively
well with the observed distributions for particles below
1.0 mm, but the model clearly underestimates the fraction
of larger aerosols. We have tried replacing the size distribution we have adopted in the source region with an
alternative distribution [Schultz et al., 1998] (a three mode
lognormal distribution Dg,mass = 0.011, 2.52, and 42.3 mm,
respectively and s = 2.13, 2.00, and 1.89, respectively)
without significant change in the number of large particles.
3.5.2. Vertical Profiles
[28] Figure 5 shows the vertical profile of scattering,
single scattering albedo, and asymmetry factor. A comparison is made between the C-130 aircraft observations in the
main dust layer and the model for two locations during the
a797 flight (see Haywood et al. [2003] for a description of
the flight). Figures 5a and 5d show the vertical profiles of
aerosol scattering at 550 nm from the model compared
uncertainties in the source function of mineral dust. In the
beginning of the campaign there was moderate AODs,
thereafter much lower values, followed by large AOD with
maximum values above 1, and finally more moderate
values. The modeled AOD is lower than the maximum
observed values, in particular at Cape Verde. Further higher
modeled AOD is indicated during the period from 21st to
23st with very low observed values.
[24] Another feature is the stronger wavelength dependence of AOD in the model compared to the observations.
This is most pronounced during high AOD conditions. This
difference most likely arises from a higher number of small
particles in the model compared what was observed (see
section 3.5.1).
3.5. Comparison With C-130 Aircraft Data
[25] Haywood et al. [2003] describes the details of the
C-130 aircraft measurements. The C-130 was equipped
with instrumentation to measure aerosol size distribution,
chemical composition, optical properties, and radiative
Figure 4. A comparison of observed and modeled size
distributions. The observations are shown when the mineral
dust loading is heavy and moderate, and the model results
are shown for 1 day (25 September) with heavy and one day
with moderate (19 September) mineral dust loading.
MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
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Figure 5. Comparison of vertical profiles between the C-130 observations and the model at two
different locations: (a – c) near Cape Verde (P1 from flight a797, see Haywood et al. [2003] for a
description) and (d – f ) from P14 near Dakar. Figures 5a and 5d show profiles of scattering (km1),
Figures 5b and 5e single scattering albedo, and Figures 5c and 5f the asymmetry factor. All values are for
550 nm. The measured single scattering albedo and asymmetry factor values are 1 min averages.
against those from the nephelometer on board the C-130
aircraft. The comparisons are made near Cape Verde and near
Dakar during the a797 flight on the 25 September 2000 (see
Haywood et al. [2003] for a full description of this flight).
The C-130 derived scattering coefficient includes corrections
for variations from STP, truncation of the scattered radiation,
and for deficiencies in the illumination source [Anderson and
Ogren, 1998]. An additional correction factor of 1.5 is
applied to account for the significant contribution to the
aerosol scattering from supermicron particles that are not
sampled efficiently by the inlet system [Haywood et al.,
2003]. Figures 5b and 5e show comparisons of the single
scattering albedo at 550 nm from the model against those
derived from the C-130. In deriving the single scattering
albedo from the C-130, vertically resolved 1-min averages of
the aerosol size distribution from the PCASP and identical
refractive indices to those used in the model are assumed in
Mie scattering calculations. This method is preferred to
determining the single scattering albedo from measurements
of particle scattering by the nephelometer and particle scattering by the Particle Absorption Soot Photometer (PSAP),
because it is difficult to obtain representative values from two
vertical profiles owing to variability. The refractive index
used in this study is derived from campaign averages of
scattering and absorption. An additional benefit from using
the PCASP size distribution to determine the optical parameters is that it provides the asymmetry factor which is
compared in Figures 5c and 5f.
[29] The profiles of the scattering show that the mineral
dust is between 1 and 5 km, but with somewhat different
distribution in the model and the observations. The observations show more distinct peaks, which the model with
much coarser spatial resolution does not reproduce. The
modeled single scattering albedo and asymmetry factor both
show substantial vertical variations. The modeled single
scattering albedo increases with altitude and the asymmetry
factor decreases with altitude, which both indicate smaller
particles in the upper part of the dust plume. For the
observed single scattering albedo and asymmetry factor no
such systematic changes with altitude can be found. In
comparison with the observations the modeled single scattering albedo and asymmetry factor are higher and lower,
respectively, in both cases mainly because the modeled
particles are slightly smaller than the observed ones. The
differences between the modeled and measured values are
relatively small, and the modeled single scattering albedo
shown in this figure is within the range of averaged
measured single scattering albedo for the C-130 flights
during SHADE (range from 0.95 to 0.98 and in the transit
from Ascension Island it was 0.99). Actually, the measured
single scattering albedo during this particular flight (a797)
was the lowest of the 6 flights [Haywood et al., 2003].
[30] In Figure 6 vertical profiles of scattering for the
entire flight a797 are shown. The observed averaged profile
shows a rather homogeneous scattering from 1 to 4 – 5 km,
whereas in the model there is a more clear maximum around
3 km. The observed standard deviations are largest in the
lower part of the dust plume, in particular around 1 km.
However, in the model the largest standard deviations are in
the middle and upper part of the dust plume. Overall the
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MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
aircraft measurements of the radiative impact compare well
with satellite derived radiative impact from the CERES
instrument [Haywood et al., 2003].
Figure 6. Vertical profiles of scattering for the C-130
observation and the model for flight a797. Standard
deviations are shown as horizontal lines.
agreement in the vertical profile is good, with the vertical
position of the top and the bottom of the dust plume being
well represented as well as the absolute magnitude of the
scattering when an entire flight of scattering data is
included.
3.5.3. Radiative Measurements
[31] To allow for an easier comparison of the radiative
impact of mineral dust between the measurements and the
model we introduce a normalized radiative impact (radiative
impact divided by AOD). Clouds are excluded in the
radiative transfer calculations also for a better comparison
with the measurements which also are performed for clear
sky. The model results are shown for the region 16.9 –
22.5W and 13.1 – 16.9N.
[32] Figure 7 shows that the modeled normalized radiative impact is between around 80 to 120 Wm2 during
the day. The normalized radiative impact is stronger at 9 and
18 UTC than closer to noon (local noon deviates from UTC
noon by 1 – 2 hours in the campaign region) as the magnitude of the radiative impact of non-absorbing particles is
highest for large solar zenith angles ([see Haywood and
Shine, 1997]. This effect is almost compensated by surface
albedo of the ocean being higher for large solar zenith
angles. The day-to-day variation in the normalized radiative
impact is small at 12 and 15 UTC; however, at 9 and 18 UTC
the day-to-day variation is significant. This day-to-day
variation is mostly caused by changes in AOD with weakest
normalized radiative impact for high AOD. Furthermore, the
variation is also slightly related to the size distribution.
[33] The modeled and measured normalized radiative
impact are relatively similar during the middle of the day
and this is an important result. In the model the values are
around 80 Wm2, whereas around 90 Wm2 in the
measurements. The normalized radiative impact depends
slightly on the AOD, with decreasing normalized radiative
impact with increasing AOD. Results from the measurements are only shown for small solar zenith angles as the
uncertainties in the measurements increase significantly for
larger solar zenith angles owing to uncertainties in the
surface albedo of ocean for large solar zenith angles. The
3.6. Radiative Impact
[34] Above normalized radiative impact is discussed,
below solar and longwave radiative impact without normalization is presented.
3.6.1. Solar
[35] The solar radiative impact of mineral dust in the
period 24– 26 September is shown for clear sky and when
clouds are included in Figure 8. The radiative impact
follows mainly the pattern of AOD. Clouds reduce the
radiative impact of mineral aerosols. This is most pronounced west of 30W. The radiative impact shows strong
negative values, with some small positive values associated
with very limited cloudy regions. The maximum radiative
impact is 6 Wm2. Mineral dust influences the solar
radiation strongly not only near the source regions and its
associate regions, but we can see a significant influence
even in the western part of the Atlantic ocean. The strongest
values reach almost 115 Wm2. This is in accordance
with measurements in the work of Haywood et al. [2003],
who reported values ranging down to nearly 130 Wm2.
The strongest radiative impact is rather similar over land
(24 September) and over ocean (26 September), despite the
AOD is stronger 24 September. This is due to the fact that
the normalized radiative impact is at least a factor two
stronger over ocean than over land owing to the difference
in surface albedo.
[36] In the work of Myhre et al. [2003] it was found that
neglect of spectral and solar zenith angle variations in the
surface albedo weakened the radiative impact of aerosols
from biomass burning by 25 –30% over the region which is
Figure 7. Modeled and observed normalized clear sky
radiative impact (given as Wm2 per unit AOD) for various
times during the day. Results from the model are shown in
the period 19– 28 September 2000. The measurements are
shown from a797 (R6; see Haywood et al. [2003]) on 25
September with about 5 min averages. Measurements were
made at the top of the dust layer (about 5 km) and the model
results at the top of the atmosphere (consistent with the rest
of the model results). In the model the difference in
radiative impact of mineral dust is negligible between 5 km
and the top of the atmosphere.
MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
Figure 8. Diurnal mean solar radiative impact during the
period 19– 28 September 2000 (Wm2), excluding and
including clouds. The results are for the region shown in
Figure 9.
influenced by aerosols from biomass burning in southern
Africa. For mineral dust the influence of spectral and solar
zenith angle variation is much smaller (5 – 10%). The
reduction due to neglect of such variations is slightly larger
over land than over ocean. The weaker response to surface
albedo parameterization for mineral dust compared to for
biomass burning aerosols are due to the much stronger
spectral dependence of specific extinction coefficient and
single scattering albedo than for biomass aerosols.
[37] In the work of Myhre and Stordal [2001] a large
number of sensitivity calculations of the radiative forcing
due to mineral dust were performed, pointing to refractive
indices as a major source to the large uncertainty. In
addition to the calculations performed using the refractive
indices derived from Sun photometers, we also performed
calculations using the refractive indices of d’Almeida et al.
[1991] which is the base refractive index used in the
calculations of Myhre and Stordal [2001]. Due to the much
higher imaginary part of these data for the refractive index
compare to the one based on the Sun photometers this yields
a reduction in the single scattering albedo at solar wavelengths; see Haywood et al. [2003] for discussion. The
radiative impact over the region of interest decreases by
about 25% for clear sky and as much as 50% when clouds
are included. The strongest radiative impact is weakened
from around 115 Wm2 to around 80 Wm2. Positive
radiative impact of up to 50 Wm2 was then estimated, as
opposed to 6 Wm2 in the original calculation. The increase
in the radiative impact was much larger over land than over
ocean owing to the brighter surface.
[38] Figure 8 shows modeled diurnal mean solar radiative
impact when clouds are excluded and included in the radiative transfer calculations over the region shown in Figure 9.
The radiative impact is somewhat stronger during the end of
the campaign period than in the beginning. The figure shows
that the radiative impact for clear sky is 40– 50% stronger
than when clouds are taken into account. When clouds are
included in the simulations the solar radiative impact is
around 6 Wm2 over the region, corresponding to a
global mean radiative impact of 0.4 Wm2 (scaled accord-
SAH
6-9
ing to the fraction of the area of this region relative to
Earth’s surface). Note that these numbers take into account almost the entire radiative effect of mineral dust
particles originating from Sahara, but not from other
sources.
3.6.2. Longwave
[39] The diurnal mean longwave radiative impact is
shown in Figure 10a. The longwave radiative impact is
much smaller in magnitude than the solar part in our
simulations. The radiative impact is strongest over land
near the source regions where the AOD is highest, but in
addition the surface temperature is higher and the troposphere is less humid compared to over ocean contributing to
an even larger land/ocean contrast in the results. The
maximum diurnal mean longwave radiative impact is close
to 8 Wm2. Figure 4 indicates that the model underestimates the number of larger mineral particles. This causes
that the longwave radiative impact is underestimated in our
calculations. In a calculation using the lognormal size
distribution based on the observations shown in Figure 4
resulted in 10– 20% stronger longwave radiative impact. A
sensitivity calculation with the refractive index from
d’Almeida et al. [1991] instead of from Fouquart et al.
[1987] is performed. This resulted in a weakening of the
longwave radiative impact by a factor more than 2. The
optical properties changes substantially with the change in
refractive indices [see Highwood et al., 2003].
[40] Figure 11 shows the regional mean longwave radiative impact during the campaign (region as shown in
Figures 9 and 10. As in Figure 8 we see that the radiative
impact is stronger during the end of the campaign. Clouds
weakened the radiative impact by about 20%. The regional
mean longwave radiative impact varies from around 0.8 to
slightly more than 1.0 Wm2 and is in magnitude about a
factor 6 – 7 weaker than the solar radiative impact.
3.6.3. Net Radiative Impact
[41] Figure 10b shows the diurnal mean net radiative
impact of mineral aerosols during the SHADE campaign
period. The radiative impact is strongest on 26 September
with diurnal mean values close to 50 Wm2 off the coast
of western Africa. The small positive values of the net
radiative impact is associated with clouds. The ratio of the
magnitude of the longwave and solar radiative impact is
generally a factor of 2 higher over land than ocean. The
diurnal and regional mean net radiative impact of mineral
aerosols is slightly stronger than 5 Wm2 when clouds are
included in the simulations.
4. Summary
[42] In an attempt to reduce the large uncertainties linked
to the direct effect of the mineral dust, we model the
radiative impact of the mineral dust with a comprehensive
comparison with measurements obtained during the
SHADE campaign. We use meteorological data for the
campaign period, which enables us to reproduce the main
pattern of mineral dust during SHADE. The general agreement with observations is very good. Satellite retrievals
show that the general pattern of mineral dust during the
campaign is reproduced. MODIS data is used during the
major dust storm, and also TOMS aerosol index (not
shown) reveals a very similar feature to the model during
SAH
6 - 10
MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
Figure 9. Radiative impact of mineral dust in the period 24– 26 September for (Figures 9a – 9d and 9i –9l)
four times each day, and (Figures 9q– 9t) clouds are included in the calculations, whereas in Figures 9e – 9h,
9m – 9p, and 9u –9x, clouds are excluded.
the entire campaign, even the transport of mineral dust to
the north of Sahara. Comparison with ground-based Sun
photometers strengthens the agreement of the main features.
In addition the possibility to compare the model against in
situ aircraft data is very valuable. Most important in this
respect is the radiative effect of aerosols, which is of large
scientific value. Given the fact that the model is completely
independent of the measurements, a radiative effect in the
model and observations within about 10% is very encour-
aging, demonstrating the usefulness of larger aerosol campaigns. Both in the model and observations a weak
dependence of the normalized radiative impact on AOD is
found close to noon. The modeled size and vertical profiles
are compared with the aircraft measurements with reasonable agreement, except for the larger sized mineral particles
which are underestimated in the model. This affects most
strongly the longwave radiative impact results, and only to a
small extent the shortwave results.
MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
SAH
6 - 11
Figure 10. Diurnal mean radiative impact of mineral dust during the campaign period 19– 28
September. (a) Longwave. (b) Net.
[43] The problem with the size distribution is a result of the
currently limited knowledge of the physical processes in the
source of mineral dust. Further, few observations in these
areas are available. The physical uncertainties of the source
are not only linked to dependence on friction velocity and
surface composition, but also on data characterizing the
surface. A problem for a global model is also to represent
physical processes that occur on a small scale. A major result
of the SHADE campaign is that it confirms the recent findings
in the work of Kaufman et al. [2001], who reported a much
higher single scattering albedo than earlier measurements
indicate [Haywood et al., 2003]. This can either be due to the
fact that in earlier measurements mineral dust has been mixed
with aerosols from biomass burning or that the source regions
are located in other regions than during SHADE. Clarke et al.
[2001] find a similar high single scattering albedo for dust
over China, but on the other hand Claquin et al. [1999] show
that the mineral composition varies substantially in Sahara
and the absorbing hematite has a much higher abundance in
the Sahel region than in the rest of Sahara.
[44] We estimate a much stronger solar radiative impact
than the longwave radiative impact, and thus a strong net
SAH
6 - 12
MYHRE ET AL.: RADIATIVE IMPACT OF MINERAL DUST
Figure 11. Longwave radiative impact during the period
19– 28 September 2000 (Wm2), excluding and including
clouds. The results are for the region shown in Figures 9
and 10.
negative radiative impact or cooling effect of the mineral
dust from Sahara during the SHADE campaign. The strongest modeled net radiative impact is around 110 Wm2,
occurring around local noon on 26 September. Over a
region (latitude 0N–30N, longitude 60W – 40E) around
Sahara the diurnal mean radiative impact during the
SHADE campaign is between 5 and 6 Wm2. This
translates to a global mean net radiative impact of Saharan
dust aerosol of around 0.4 Wm2 when averaged over the
globe. The solar and longwave radiative impact results are
based on refractive indices which are from observations or
compared closely to measurements. In accordance with
earlier studies we find a strong sensitivity to the refractive
indices and the use of refractive indices from commonly
used sources resulted in solar and longwave radiative
impact a factor of 2 weaker than our best estimates.
[45] The diurnal and regional mean of the solar radiative
impact is between 8 and 10 Wm2 when clouds are
excluded. This is somewhat stronger than the global mean
due to all kinds of aerosols, as found in several studies
based on satellite data [Haywood et al., 1999; Boucher and
Tanré, 2000; Loeb and Kato, 2002]. Our results yield
stronger radiative impact than the ones derived globally
from observations, as we are focusing on a region with large
contributions from strongly reflecting dust aerosols. However, also large areas in the regions considered in our study
are only weakly influenced by dust aerosols.
[46] Acknowledgments. This work has been supported by the EU
project AIRARF funded through the LSF program CAATER and from the
Research Council of Norway, through the ChemClim project.
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