Introduction
Mineral dust is the most abundant atmospheric aerosol component in terms of
dry mass (Choobari et al., 2014; Textor et al., 2006). The Sahara is the largest source of atmospheric
dust aerosols, with an estimated emission of 670 Mt yr-1 (Rajot et al.,
2008; Washington et al., 2003). African dust from the Sahara is regularly lifted by strong near-surface
winds and transported westwards within the Saharan air layer (SAL) over to
the tropical North Atlantic during northern summer (Cuesta et al., 2009; Karyampudi et al., 1999). During
the transport, dust aerosols can scatter and absorb both shortwave solar
(referred to as “SW”) and longwave thermal infrared (referred to as
“LW”) radiation, and thereby influence Earth's energy budget
(McCormick and Ludwig, 1967; Tegen and Lacis, 1996; Yu et al., 2006). This is known as the direct radiative effect (DRE)
of dust, which can have a significant influence on the global energy balance
(Boucher et al., 2013), as well as regional weather
and climate (e.g., Miller and Tegen, 1998; Evan et al., 2006; Lau and Kim, 2007). Therefore, it is important to
quantify dust DREs as accurately as possible. Moreover, mineral dusts can also
influence the life cycle and properties of clouds, by altering the thermal
structure of the atmosphere (known as semi-direct effects) (Ackerman et al., 2000; Hansen et al.,
1997;
Koren et al., 2004) and by acting as cloud condensation nuclei and ice nuclei (known as
indirect effects) (Albrecht, 1989; Rosenfeld et al., 1998; Twomey, 1977) . In addition, when African dust
aerosols are deposited into the Atlantic Ocean and the Amazon basin, they supply
essential nutrients for marine and rainforest ecosystems (Yu et al., 2015), which has
important implications for biogeochemical cycles (Jickells et al., 2005). In this
study, we focus on the quantification of the dust direct radiative effect on
both SW and LW radiation.
Substantial effort has been made to understand and quantify the DRE of
mineral dust since the 1980s (Carlson and Benjamin, 1980; Cess, 1985; Liao and Seinfeld,
1998; Ramaswamy and Kiehl, 1985). Most studies have
focused on the SW DRE (DRESW) of mineral dust under clear-sky (cloud-free) conditions (Myhre et al., 2003; Tegen et al., 1996; Yu et al., 2006) . Through scattering and absorption,
dust aerosols reduce the amount of solar radiation reaching the surface,
inducing a negative (cooling) effect at the surface. The DRESW of dust
at the top of the atmosphere (TOA) also depends strongly on the albedo of
the underlying surface (Keil and Haywood, 2003; Yu et al., 2006). Over a dark surface, the scattering
effect of dust dominates; it leads to a negative DRE at TOA that cools the
climate system (Myhre et al., 2003; Tegen et al., 1996). In contrast, high reflectance of a bright
surface enhances the absorption by dust aerosols and could yield a positive
dust DRESW (warming effect on the climate system) at TOA when the
surface albedo exceeds a critical value (Zhang et al., 2016; Xu et al., 2017). Different from other aerosol
types (e.g., smoke and sulfate aerosols), dust aerosols are large enough to
have a significant LW direct radiative effect (DRELW) (Sokolik and Toon, 1999; Sokolik et al., 1998).
Lofted dust aerosols absorb the LW radiation from the warm surface and
re-emit the LW radiation usually at lower temperatures, thereby reducing the
outgoing LW radiation and leading to a positive DRE at TOA that tends to
warm the climate system. At the same time, they emit the LW radiation
downward that generates a warming effect at the surface. The dust LW effect
depends strongly on surface emissivity (Yang et al., 2009) and the vertical
profile of atmosphere temperature. The net radiative effect (DREnet) of
dust is the summation of its DRESW and DRELW. Note that DRESW
only acts during daytime, whereas DRELW operates during both day and
night.
Quantification of the DRESW and DRELW of dust remains challenging
and there is a large range of estimates in the literature. Take the Tropical
Atlantic for example. Yu et al. (2006) found that the seasonal (JJA) average
clear-sky aerosol DRESW at TOA in this region varies from -5.7 to -12.8 W m-2 based on observations and from
-3.7
to -10.4 W m-2 based on model simulations. An important reason is that
dust DRE depends on many factors, including both the microphysical (e.g.,
dust particle size and shape) and optical (e.g., refractive index)
properties, as well as the surface and atmospheric properties (e.g., surface
reflectance and temperature, atmospheric absorption). Sokolik et al. (1998)
showed that for the submicron dust particles, the DRESW is dominant
and DRELW is negligible, whereas for supermicron dust particles,
DRELW is more important (Sokolik and Toon, 1996; Sokolik et al., 1999). Therefore, an accurate
measurement of the particle size distribution (PSD) is highly important for
estimating the DRE of dust. However, dust PSDs are highly variable and
difficult to measure or retrieve, and, as a result, the observations of dust
PSD are usually subjected to large uncertainties (see Mahowald et al., 2014,
and references therein). PSD inferred from AERONET observations (Dubovik
et al., 2006) relies on observations at shortwave channels, which could bias
the dust size low. In fact, more and more observations are emerging to
suggest that the dust PSD, even in regions far from source regions, contains
a substantial fraction of coarse particles. Based on the airborne in situ
measurement of the dust PSD in the Caribbean Basin from the Puerto Rico Dust
Experiment (PRIDE) campaign, Maring et al. (2003) noted that dust
particles appear to settle more slowly than expected from the widely used
Stokes gravitational settling model. Similarly, recent measurements from the
latest Fennec project (Ryder et al., 2013b) and the Saharan Aerosol Long-Range
Transport and Aerosol–Cloud Interaction Experiment (SALTRACE) (Weinzierl et al., 2017) all
suggested that transported dust aerosols in the SAL are significantly coarser
than expected based on the Stokes gravitational deposition. Such unexpected
existence of coarse particles has important implications for understanding
the DRE of dust. In a case of significant fraction of coarse particles, the
warming effect on LW radiation (positive) DRELW would partly cancel the
DRESW, leading to a less negative or even a positive DREnet. Most
recently, Kok et al. (2017) argued that most of the current global climate
models tend to underestimate the size of dust particles and therefore
overestimate the cooling effects of dust. Their estimate of the global mean
dust DREnet is between -0.48 and +0.20 W m-2, which includes
the possibility that dust causes a net warming of the planet.
In addition to the dust particle size, the particle shape and refractive index also
have a significant influence on the dust DRE. Dust particles are generally
nonspherical in shape, which make their single-scattering properties (i.e.,
extinction efficiency, single-scattering albedo, and scattering phase matrix)
fundamentally different from those based on spherical models. A few dust
particle shape models have been developed (Dubovik et al., 2006; Kandler et al., 2009), which have been
increasingly used in aerosol remote sensing and modeling (Levy et al., 2007).
Räisänen et al. (2013) found that replacing the spherical dust
models in a general circulation model (GCM) with a nonspherical model leads to negligible changes in the
DRE of dust at TOA. However, a recent GCM-based study by Colarco et al. (2014) suggests that the influence of nonsphericity on dust DRE can be
significant at the surface and within the atmosphere, depending on the
refractive index of dust. Similarly, Kok et al. (2017) argue that a
spherical model significantly underestimates the extinction of dust, leading
to errors in estimates of dust DRE.
Over the past few decades, substantial efforts have been made to measure the
spectral refractive index of dust, mostly limited to the SW spectral range
(Balkanski et al., 2007; Dubovik et al., 2002, 2006; Formenti et al., 2011; Hess et al., 1998; Levoni et al., 1997). The current widely used LW
refractive index of dust was measured using rather old techniques in the
1970s and 1980s (e.g., Volz, 1972, 1973; Fouquart et al., 1987). Recently,
Di Biagio et al. (2014, 2017) compiled a comprehensive dust aerosol
refractive index database in the LW spectrum ranging from 3 to 15 µm,
based on 19 natural samples from 8 dust regions around the globe. This
database is the first one as far as we know to document the regional
differences in dust LW refractive index due to the regional characteristics
of dust chemical composition. We also call special attention to a newly
developed database of Saharan and Asian dust (Stegmann and Yang, 2017).
Satellite observations have long become indispensable for studying dust
aerosols. In particular, the combination of passive (e.g., MODIS and CERES)
and active (e.g., CALIPSO) sensors on board NASA's A-Train satellite
constellation provides unprecedented data to study dust aerosols, from long-range transport (e.g., Liu et al., 2008; Yu et al., 2015) to dust DREs (e.g., Yu et al., 2006; Zhang et al., 2016). As A-Train observations
become mature, substantial efforts have been made to collocate and fuse the
observations from different sensors to make the use of A-Train observations
easier for the users. A prominent example is the CERES–CALIPSO–CloudSat–MODIS (CCCM) product developed by Kato et al. (2011), which has become a
popular dataset for studying the radiative effects of clouds and aerosols
and for evaluating GCMs.
The present study is inspired and motivated by the latest measurements of
the microphysical and optical properties of dust, namely the in situ dust
PSD from the Fennec field campaign (Ryder et al., 2013a, b) and the
dust LW refractive index from Di Biagio (2014, 2017), as well as recent
studies (e.g., Kok et al., 2017) suggesting that cooling effects of dust are
overestimated in most climate models due to the underestimation of dust
size. The study is carried out in three steps, each with a distinct
objective. First, we attempt to derive a set of observation-based
instantaneous dust DRESW and DRELW for the tropical North Atlantic based on the
A-Train satellite observations reported in the CCCM product, without
imposing any assumptions on dust size, shape, or refractive index. Here, the
instantaneous dust DRE represents dust DRE derived under the conditions
(e.g., solar position, atmospheric condition) at the measured or computed
time to distinguish from the diurnally averaged DRE in Sect. 4. Then, we
perform multiple sets of radiative transfer computations of the
instantaneous dust DRE in the North Atlantic region based on the same dust extinction
profiles from CCCM in combination with different dust physical and optical
properties. The objective is to understand the sensitivity of the dust
DRESW and DRELW to the PSD, nonsphericity, and refractive index of
dust and to obtain a set of dust properties that yield the best agreement
with satellite flux observations (e.g., CERES). In the third step, we use
the derived dust properties and extend the radiative transfer computations
to the diurnal mean and to the DRE at the surface. The rest of this paper is organized as follows:
Sect. 2 describes the data and model used. Section 3 presents the
sensitivity of the dust DRE to dust size, shape, and refractive index. Section 4
discusses diurnally averaged net DRE of dust aerosols and uncertainty
analysis. Section 5 concludes the article.
Data and models
The CERES–CALIPSO–CloudSat–MODIS (CCCM)
product
To estimate instantaneous dust DRE, we use aerosol and radiation remote
sensing products from the A-Train satellite sensors, namely, the integrated
CERES, CALIPSO, CloudSat, and MODIS merged product (CCCM) developed by Kato et al. (2011).
In the CCCM product, high-resolution CALIOP, CloudSat, and MODIS retrievals
are collocated with 20 km CERES footprints. For each CERES footprint, the
CCCM product provides the TOA flux observations (both SW and LW) from CERES,
aerosol (MOD04 “dark target” product; Remer et al., 2005) and cloud
(MOD06;
Platnick et al., 2003) properties retrieved from MODIS, aerosol optical thickness for
each aerosol layer from CALIOP (Winker et al., 2010), and cloud vertical profile from the
combination of CALIOP and CloudSat (Kato et al., 2010). Up to 16 aerosol layers
identified by CALIOP are kept within a CERES footprint.
Figure 1 shows the JJA mean aerosol optical depth (AOD) from the CALIOP observations reported in the CCCM product. Clearly,
the transported dust aerosols lead to enhanced AOD in the tropical North Atlantic region.
CALIPSO-derived seasonal mean (JJA) dust aerosol optical depth
(AOD) at 0.5 µm averaged over five summers (2007–2010)
in cloud-free sky conditions from the integrated CALIPSO, CloudSat, CERES, and
MODIS merged product (CCCM).
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In addition to the raw retrievals, the CCCM product also provides
post-processed flux computations for each CERES pixel based on derived
aerosol and/or cloud extinction profiles, which is done in the following
steps. First, the CALIOP aerosol retrievals within each CERES pixel are
averaged to obtain the aerosol extinction profile at the 0.5 µm
reference wavelength. Then, the aerosol type and associated spectral optical
properties, e.g., extinction coefficient, single-scattering albedo, and
asymmetry factor, are specified, mostly based on the aerosol type simulations
from the Model of Atmospheric Transport and Chemistry (MATCH) (Collins et al., 2001), with
the exception of dust aerosols. If CALIOP observes dust aerosols (dust and
polluted dust), the aerosol type is set to dust. This is based on the
consideration that the depolarization observation capability of CALIOP is
ideal for dust detection because the nonsphericity of dust can cause
significant depolarization in contrast to most other types of aerosols.
Finally, the aerosol extinction profiles and the aerosol spectral optical
properties are used to compute the broadband fluxes at both TOA and the surface
and for both SW and LW under two conditions: (1) with aerosol and (2) without
aerosol, so that the aerosol DRE can be derived from the difference of the
two conditions. Temperature and humidity profiles used in flux computations
are from the Goddard Earth Observing System (GEOS-5) Data Assimilation
System reanalysis (Rienecker et al., 2008).
Dust physical and optical models
To investigate the sensitivity of the dust DRE to microphysical and optical
properties of particles, we use several sets of widely used or newly
obtained dust size distribution, dust shape distribution, and dust refractive
index.
Two dust particle size distributions (PSD), shown in
Fig. 2, are considered in this study. One PSD is inferred based on AERONET ground-based retrievals at the Cape Verde site
(16∘ N, 22∘ W) from Dubovik et al. (2002) (referred to as “AERONET PSD”). The other
dust PSD is obtained from the recent airborne measurements of transported
Saharan dust from the Fennec 2011 field campaign over both the Sahara
(Mauritania and Mali) and the eastern Atlantic Ocean, between the African
coast and Fuerteventura. Ryder et al. (2013a) separated the PSD measurements
from this campaign into three broad categories: fresh, aged, SAL. The fresh category over the Sahara represents
dust uplifted no more than 12 h prior to measurement; the aged category
over the Sahara represents dust aerosols mobilized 12 to 70 h prior to
measurement; the SAL category represents dust aerosols transported over the
adjacent east Atlantic, mostly from flights over Fuerteventura, Canary
Islands (28∘ N, 13∘ W). All these categories come from
the mean of vertical profile observations (excluding the marine boundary
layer for SAL categories). The Fennec airborne PSD dataset is particularly
novel, in that larger particle sizes were measured than had
previously been done in dust layers, with the exception of Weinzierl et al. (2011),
and that errors due to sizing uncertainties have been specifically
quantified (see Ryder et al., 2013b, 2015 for full
details). Because this paper focuses on the tropical Atlantic Ocean region,
we use dust size distribution in the SAL category (referred to as the
“Fennec-SAL PSD”). Evidently from Fig. 2, the Fennec-SAL PSD, which peaks around 5–6 µm and has a
significant fraction of particles with r > 10 µm, is much coarser than the AERONET PSD, which peaks around
1–2 µm and has almost no particles r > 10 µm.
Size distributions of mineral dust used in this study. Fennec-SAL
curve is from a new in situ measurement of Saharan dust taken during the
Fennec 2011 aircraft campaign (Ryder et al., 2013b). The solid curve
represents desert dust size distribution retrieved from AERONET observations
at Cape Verde site reported in Dubovik et al. (2002).
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The dust refractive indices are taken from the following three sources.
The Optical Properties for Aerosols and Clouds (OPAC) database
(Hess et al., 1998) was used, which has been widely used in climate models and
satellite remote sensing algorithms.
A merger of remote-sensing-based estimates of dust refractive indices in
the shortwave from 0.5 to 2.5 µm (Colarco et al., 2014), drawn from
Kim et al. (2011) in the visible and Colarco et al. (2002) in the UV
(referred to as “Colarco-SW”), was used. Kim et al. (2011) collected the AERONET (Version 2)
retrievals from 14 sites over North Africa and the Arabian Peninsula. Then
the dust refractive index is derived from the dust-dominant cases for these
sites selected based on the combination of large aerosol optical depth (AOD ≥ 0.4 at 440 nm) and small Ångström
exponents (Åext≤ 0.2) to select the dust cases. Colarco et al. (2002) derived the dust
refractive index in the UV by matching the simulated dust radiative
signature in the UV with the satellite observations from the Total Ozone
Mapping Spectrometer.
The refractive indices in the LW from 3 to 15 µm
from Di Biagio et al. (2017) (referred to as “Di Biagio-LW”) is used. This
database is based on the laboratory measurements of 19 natural soil sample
from 8 regions: northern Africa, the Sahel, eastern Africa and the Middle
East, eastern Asia, North and South America, southern Africa, and Australia.
The refractive index from the Mauritania site is selected for this study
because it is geographically close to the Fennec field campaign.
(a) Real and (b) imaginary part of the SW dust refractive index from
OPAC (Hess et al., 1998) and Colarco et al. (2014). (c) Real and (d) imaginary
part of the LW dust refractive index from OPAC (Hess et al., 1998) and Di
Biagio et al. (2017).
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Figure 3 compares the real and imaginary parts of the refractive index for each of these datasets. In the SW, the
imaginary part of the OPAC refractive index is much greater than that of Colarco-SW, which implies that dust aerosols
based on the OPAC refractive index are more absorptive. In the LW, the Di Biagio-LW refractive index is smaller than
the OPAC values in terms of both the real and imaginary parts.
Two spheroidal dust shape distributions models: panel (a) shows aspect
ratio distributions from Dubovik et al. (2006). The lnϵ interval is 0.09. Panel (b) shows aspect ratio distributions as a function of
the particle radius interval discretized from the measurements of Kandler et al. (2009). The first point of each line covers the measurement data from
ϵ= 1.0 to 1.3, the last point of each line covers
ϵ> 2.9 and the other points cover ϵ intervals of 0.2 (Koepke et al. 2015).
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Dust aerosols are generally nonspherical in shape. Spheroids have proven to
be a reasonable first-order approximation of the shape of nonspherical dust
(Dubovik et al., 2006; Mishchenko et al., 1997). The shape of a spheroid particle is determined by the
so-called aspect ratio, i.e., the ratio of the polar to equatorial lengths of
the spheroid. In our study, two spheroidal shape distributions are used for
computing the optical properties of nonspherical dust: (1) a
size-independent aspect ratio distribution from Dubovik et al. (2006) (see
Fig. 4a) and (2) a size-dependent aspect ratio
distribution extracted from Table 2 in Koepke et al. (2015), which is
discretized from measurement data of Kandler et al. (2009)
(Fig. 4b). The Dubovik et al. (2006) shape
distribution employs both oblate (aspect ratio < 1) and prolate
(aspect ratio > 1) spheroids, while the Kandler et al. (2009)
shape distribution only considers prolate spheroids. For comparison purposes,
we also include spherical dust in our sensitivity studies. We use the
Lorenz–Mie theory code of Wiscombe (1980) to compute the optical
properties of spherical dust particles. The optical properties of spheroidal
dust particles are derived from the database of Meng et al. (2010). Note
that we assume the volume-equivalent radius for the AERONET-PSD to be consistent
with Dubovik et al. (2006) and the maximum dimension for Fennec-SAL PSD to
be consistent with Ryder et al. (2013b).
Radiative transfer modeling
The Rapid Radiative Transfer Model (RRTM) (Mlawer et al., 1997) is used to
compute both SW and LW radiative fluxes for both clear and dusty
atmospheres. RRTM retains reasonable accuracy in comparison with
line-by-line results for single column calculations. It divides the solar
spectrum into 14 continuous bands ranging from 0.2 to 12.2 µm and the thermal infrared (3.08–1000 µm) into 16 bands.
We explicitly specify the spectral AOD, ω, and g of dust aerosols
for every band in the radiative transfer simulations.
Case selection and observation-based estimate of instantaneous
dust DRE
Selection of cloud-free and dust-dominant cases in the CCCM
product
In this study, we focus on the Saharan dust outflow region in the North Atlantic
marked by the box in Fig. 1 (10–30∘ N, 45–20∘ W). This selection is based on several considerations.
Firstly, during the summer months (JJA) this region is dominated by
transported dust aerosols from Sahara. Secondly, because the ocean surface
is dark, dust aerosols have a strong negative DRESW in this region.
Thirdly, the above-mentioned AERONET Cape Verde and Fennec-SAL PSD
measurements are made in the vicinity of this region. Finally, the dust DREs
in this region have been extensively studied in the literature, making it
easier for us to compare our results with previous work.
We first select cloud-free and dust-dominant CERES pixels in the region from
four summer seasons (2007–2010) of the available CCCM
product. Within each CERES pixel, the CCCM product reports two cloud masks,
one from CALIOP and the other from MODIS. The former is more sensitive to
optically thin clouds but has a very narrow spatial sampling rate,
only available along the CALIOP ground track. The latter provides the cloud mask for
the entire CERES pixel but may miss thin clouds. Because of the relatively large footprint size (∼ 20 km), the cloud-free condition
actually poses a very strong constraint on the CERES product. Out of the
total 36 165 of CERES pixels in this region from 4 seasons of data, we found
1663 (only 5 %) cloud-free pixels according to the CALIOP cloud mask.
The sampling is further reduced to 464 (only 1.3 %) if the MODIS cloud
mask is used to ensure the entire CERES footprint is cloud-free. This result
is not surprising because the MODIS cloud mask is more “clear-sky-conservative”; i.e., it tends to label a pixel as cloudy if there is any
ambiguity in its cloud mask test (Ackerman et al., 1998). A comparison of collocated
CALIOP and MODIS cloud mask along the CALIOP track by Holz et al. (2008) reveals that MODIS masks more pixels as clear-sky
than CALIOP does in the tropical Atlantic dust outflow region (see their
Fig. 3a), which is consistent with our result.
After selecting the cloud-free cases, we use the aerosol type information in
the CCCM product to further select dust-dominant cases (i.e., more than
90 % of the aerosols within a given CERES pixel are attributed to dust, in
terms of area coverage). As mentioned before, the CCCM product relies on
CALIOP observations for detecting dust aerosols. After imposing the
dust-dominant condition, we are left with a total of 607 and 245 cloud-free
and dust-dominant CERES pixels if CALIPSO and MODIS cloud mask are used,
respectively. Furthermore, we found that within these selected pixels, 153
out of 607 cases and 87 out of 245 cases have both CALIOP and MODIS aerosol
optical depth (AOD) retrievals in the CCCM product, and the rest (454 out of
607 cases and 158 out of 245 cases) only have AOD retrievals from CALIOP, but no AOD retrieval from MODIS. The reason for this is unclear and
beyond the scope of this study, but perhaps it is due to the more rigorous quality
control used in the passive aerosol retrieval from MODIS (Remer et al., 2005).
Linear regressions of CERES-measured upward SW flux at TOA with
satellite-retrieved AOD for the selected cloud-free and dust-dominant cases.
(Black points represent the cases selected using the CALIPSO cloud mask along
the ground track and red points represent the cases selected using MODIS cloud
mask over the entire CERES footprint.) Panel (a) shows the regression results based
on MODIS AOD for cases (153 black points and 87 red points) with MODIS AOD
retrievals. Panel (b) is for all cases (607 black points and 245 red points) with
CALIPSO AOD retrievals.
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Observation-based estimate of instantaneous dust DRE
Many previous studies have shown that the aerosol DRESW over the dark
ocean surface is approximately linear with the AOD. The increasing rate of
the magnitude of DRESW with AOD is called the DRESW
efficiency, which is an important and useful quantity in many applications such as
aerosol model evaluation (Zhou et al., 2005). We note that DRESW depends on the solar
zenith angle (SZA). However, because the selected region is relatively
small, the SZA at the A-Train overpass time in the domain only varies
slightly among our selected cases, from 20 to
28∘. Considering the limited sample size and the small SZA
variation, we therefore estimate DRE efficiency based on the combination of
all selected cases without breaking them into smaller SZA intervals. Because
of the nearly linear relationship between DRESW and AOD, the CERES TOA
flux observations and the collocated AOD retrievals from either CALIOP or
MODIS can be combined to derive an observation-based estimate of the
instantaneous dust DRE. Figure 5 shows linear regressions of CERES-measured upward SW flux at TOA with satellite-retrieved AOD
for the selected cloud-free and dust-dominant cases. Black dots and lines are for selected cases using the CALIOP cloud mask. For
the 153 cases with both CALIOP and MODIS AOD retrievals, the combination of CERES and MODIS (Fig. 5a) leads to a DRESW
efficiency of dust -49.7±7.1 W m-2 AOD-1 (AOD is at 0.5 µm), with a linear regression R2 value of 0.69. The
uncertainty, i.e., ±7.1 W m-2 AOD-1, associated with the
regression line coefficients is estimated based on the 1σ
(one standard deviation) errors following Hsu et al. (2000) The
combination of CERES flux and CALIOP AOD (Fig. 5b) leads to a DRESW efficiency of -36.5±4.8 W m-2 AOD-1
based on 1σ error, with a R2
value of 0.5. To investigate the impact of different cloud masks, we also
show the regression results derived from the cases selected based on the MODIS
cloud mask in Fig. 5 (red dots and lines). We notice that the results are
very similar to those based on the CALIOP cloud mask. Therefore, we conclude
that the selection of cloud mask has a negligible impact on our estimation of
the DRE and the main uncertainty is associated with the AOD retrieval.
Considering that the MODIS and CALIOP aerosol retrievals are based on
completely different methods, some differences between the two are not
surprising. The tighter correlation between MODIS AOD and the TOA upward SW flux
is expected because the MODIS retrieval is based on the reflected spectral solar
radiation, whereas the CALIOP AOD retrievals are based on the inversion of
backward scattering lidar signals. The potential reasons for the differences
between CALIOP and MODIS AOD retrievals are beyond the scope of this study.
Interested readers are referred to a couple of recent comparison studies by
Kim et al. (2013) and Ma et al. (2013).
Observation-based instantaneous (at A-Train overpassing time) DRE
and DRESW efficiency at the top of the atmosphere (TOA). The values in the
parentheses for DRELW are based on the assumption of 0.7 W m-2 bias
in our clear-sky OLR computation. See text for details.
TOA DRESW Efficiency
TOA DRESW
TOA DRELW
(W m-2 AOD-1)
(W m-2)
(W m-2)
CERES–MODIS AOD
-49.7±7.1
-14.2±2.0
3.1±0.60
(2.4±0.60)
CERES-CALIPSO AOD
-36.5±4.8
-10.4±1.4
3.4±0.32
(2.7±0.32)
In summary, the instantaneous dust DRESW efficiency in the
selected region during summer season is -49.7±7.1 W m-2 AOD-1 based on CERES–MODIS observations
and -36.5±4.8 W m-2 AOD-1 based on CERES–CALIOP observations. With the DRESW
efficiency the DRESW can be easily derived from the AOD observations.
The instantaneous DRESW estimated from the CERES–MODIS and CERES–CALIOP data is
-14.2±2.0 and -10.4±1.4 W m-2,
respectively (see Table 1).
Probability density function (PDF) of observed OLR from CERES (i.e., with dust) and computed
dust-free OLR based on the atmospheric profiles and surface temperature
reported in CCCM.
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In addition to the SW flux measurement, the CCCM product also provides the
CERES measurement of LW flux at TOA. Figure 6 shows
the histograms of the broadband outgoing longwave radiation (OLR) measured
by CERES for the selected cases. Note that besides dust AOD, OLR also
strongly depends on other factors such as surface temperature, atmospheric
profiles, and dust altitude. As a result, there is a high variability in
those above-mentioned factors among the selected 607 cases. Therefore, it is
not possible to derive the DRELW efficiency and DRELW in the same
way as we did for the SW. Here we use a different method. To estimate the
DRELW, we first computed the dust-free OLR based on ancillary data of
surface temperature and atmospheric profiles reported in the CCCM which is
from the GEOS Model of NASA's Global Modeling and Assimilation Office (GMAO)
(Kato et al., 2011). Then, the DRELW can be estimated from the
difference between CERES observed OLR (i.e., blue solid line in
Fig. 6) and the computed dust-free OLR (i.e.,
black dashed line in Fig. 6). We refer to this
method as “semi-observation-based” as it is based on the combination of observed dust-laden OLR
and computed dust-free OLR. To test if our computed dust-free OLR has any
potential bias due to, for example, errors in the ancillary data (i.e.,
atmospheric gas and temperature), we selected 75 cloud-free cases in the
same region and season with no dust detected by CALIPSO. Note that because
of the small dust loading in these cases the computed OLR at TOA mainly
depends on the accuracy of ancillary data of surface temperature and
atmospheric profiles. Therefore, the comparison between the computed OLR and
CERES measurements of those cases can inform us if there is any potential
bias in our computation of dust-free OLR. It turns out that the difference
between RRTM and CERES OLR has a mean value around 0.7 W m-2, with
standard deviation around 3.8 W m-2 (not shown). Therefore, in the
following analysis all our dust-free OLR values are reduced by 0.7 W m-2 to
account for this positive bias, which leads to a semi-observation-based
instantaneous DRELW of dust at 2.7±0.32 W m-2 with the 95 %
confidence level.
Sensitivity of dust DRE to microphysical and optical properties
of particles
The cloud-free and dust-laden cases from the CCCM product facilitate an
ideal test bed for investigating the sensitivity of dust DREs to the
microphysical (i.e., PSD and shape) and optical (i.e., refractive index)
properties of dust. We use the aerosol extinction profiles at the 0.5 µm from the CCCM product (which is based on CALIOP/CALIPSO
observations) and different combinations of the dust properties to drive
multiple sets of radiative transfer simulations of dust DREs. Through
comparisons of the radiative transfer simulations with CERES observations, we
study how the physical and optical properties influence both the DRESW
and DRELW of dust. It should be mentioned here that the CCCM product
also uses the same methodology to generate the aforementioned post-processed
flux profile. In the analysis, we will also compare our dust DRE simulations
with the results provided in the CCCM product.
(a) Extinction efficiency (Qe), (b) single scattering albedo (SSA),
(c) asymmetry factor (g), (d) normalized AOD with respect to AOD @ 0.5 µm, and
(e) AOD × SSA ×(1-g) of dust aerosols based on different combinations of
PSD and refractive index. PSD type and refractive index type are indicated
in legends.
![]()
Sensitivity to dust size and refractive index
In the first sensitivity study, we study the influences of dust size and
refractive index on dust scattering properties and consequently dust
DREs. Based on different combinations of the PSDs (AERONET vs. Fennec-SAL)
and SW refractive index (OPAC vs. Colarco-SW), we simulate four sets of dust
spectral scattering properties (Fig. 7), and
correspondingly four sets of dust DRESW efficiency (Fig. 8). In the simulations, dust particles
are assumed to be spheroidal, and the aspect ratio distribution from Dubovik
et al. (2006) (see Fig. 4a) is used. The OPAC-LW
refractive index is also used. The impacts of dust shape distribution and LW
refractive index on dust DRE will be discussed later.
Figure 7 shows the scattering properties for the
four different combinations of dust PSD and refractive index. The extinction
efficiency (Qe) based on the Fennec-SAL PSD is significantly larger than
that based on the AERONET PSD (Fig. 7a). The
spectral shape is also different. The Qe based on the Fennec-SAL PSD is
rather flat in the SW region due to its large size, whereas the Qe based
on the AERONET PSD decreases with wavelength. The Qe shows no sensitivity
to refractive index in Fig. 7a. It is because the
Colarco-SW and OPAC-SW are only different in the imaginary part (see
Fig. 3), which has minimal influence on Qe. In contrast, the single scattering albedo
(SSA) in Fig. 7b shows more sensitivity to
refractive index. As expected, the Fennec-SAL PSD and OPAC-SW combination
(i.e., larger size and more absorptive refractive index) has the smallest SW
SSA, while the AERONET PSD and Colarco-SW (i.e., smaller size and less
absorptive refractive index) has the largest SW SSA. The other two
combinations yield similar SW SSA values that are in between the above-mentioned two
extremes. The asymmetry factor (g) in Fig. 7c
shows a primary sensitivity to size and a secondary sensitivity to
refractive index.
Figure 7d shows spectral variation of dust AOD
normalized with respect to AOD at 0.5 µm. The peak wavelength of solar
radiation (0.5 µm) and peak wavelength of terrestrial thermal radiation
(10 µm) are highlighted with dashed lines. The 0.5 µm AOD is used
as the reference for normalization because as mentioned before, we use the 0.5 µm aerosol extinction profile in the CCCM derived from CALIOP to
drive our radiative transfer simulations. After spectral normalization, one
can see that given the same 0.5 µm AOD, the 10 µm AOD based on
the Fennec-PSD is much larger than that based on the AERONET PSD by around
80 %. This is an important feature that has important implications for the
DRELW of dust. The SW reflection of dust depends not only on AOD, but
also on SSA and g. Figure 7e shows spectral variation
of AOD*SSA*(1-g), where AOD indicates dust load, is multiplied by SSA to
take the scattered fraction, and is then multiplied by (1-g) to take the
backscattered portion. It is a quantity more relevant for understanding dust
SW reflection. Evidently, this index suggests that the combination of
smaller size (AERONET PSD) and less absorptive refractive index (Colarco-SW)
leads to the most reflective dust among the four sets of simulations, whereas
the larger size (Fennec PSD) and more absorptive refractive index (OPAC)
combination generates the least reflective dust. The other two combinations are
in between and somewhat similar.
Instantaneous DRESW and DRESW efficiency at TOA and
the surface based on different dust models (e.g., PSD, refractive index, and
shape).
PSD
Refractive index
Shape
TOA DRESW efficiency
TOA DRESW
Surface DRESW efficiency
Surface DRESW
(W m-2 AOD-1)
(W m-2)
(W m-2 AOD-1)
(W m-2)
Fennec-SAL
Colarco-SW
Dubovik
-47.6
-13.5
-179.4
-51.5
AERONET
OPAC-SW
Dubovik
-53.3
-15.5
-190.1
-55.0
Fennec-SAL
Colarco-SW
Sphere
-39.8
-11.4
-200.4
-58.2
The four sets of simulated TOA upward SW fluxes as a function of
the input AOD at 0.5 µm. For comparison purposes, the DRESW
efficiency regression results based on observations in Fig. 5, as well as
the results reported in the CCCM product, are also plotted.
![]()
Figure 8 shows the four sets of simulated TOA
upward SW fluxes as a function of the input AOD at 0.5 µm. For
comparison purposes, the DRESW efficiency regression results based on
observations in Fig. 5, as well as the results reported in the CCCM product, are also plotted. Focusing on our computations first,
we note that as expected the most reflective dust based on the combination of AERONET PSD and Colarco-SW refractive index leads to the
largest DRESW efficiency (-70.5 W m-2 AOD-1), while the least reflective dust based on the combination of Fennec-SAL PSD and
OPAC refractive index yields the smallest DRESW efficiency (–30.6 W m-2 AOD-1). Clearly, these results are outside of the range based on
observations (i.e., -36.5±4.8–49.7±7.1 W m-2 AOD-1), suggesting they are too extreme.
The other two combinations, i.e. AERONET PSD–OPAC-SW and Fennec-SAL PSD–Colarco-SW, generate a similar DRESW efficiency at
-47.6 and -53.3 W m-2 AOD-1, respectively, both comparable to the CERES–MODIS-based value. Interestingly, the DRESW
efficiency based on the flux computations reported in the CCCM product is -81 W m-2 AOD-1, even larger than that based on
AERONET PSD–Colarco refractive index, suggesting that the dust model used in the CCCM flux computations is too reflective
in the SW. The instantaneous DRESW and DRESW
efficiencies at the surface for the two combinations that agree with the CERES
observations, i.e., AERONET PSD–OPAC-SW and Fennec-SAL PSD–Colarco-SW,
are given in Table 2.
One additional point to note in Fig. 8 is that, the TOA flux vs. AOD
relations based on the radiative transfer computations are much less
scattered than those based on observations. The R2 value for the
computation-based regressions all exceed 0.95, much higher than the
observation-based results in Fig. 5. This is because, in reality, the TOA
flux is influenced not only by AOD, but also by many other factors, such as
surface reflectance variation, boundary layer aerosols that might be
undetected by satellites, and uncertainty in the satellite retrieval algorithm. Most
of these factors are not accounted for in the radiative transfer
computations, leading to a near-perfect correlation between TOA flux and
input AOD. This should not be interpreted as a lack of variability, rather
than a smaller uncertainty.
On the one hand, the results in Fig. 8 are
encouraging, as they suggest that a relatively simple combination of dust
size and refractive index can enable us to simulate the dust DRESW that
is comparable with observations. On the other hand, the fact that two
different dust models lead to similar DRESW efficiency simulations, both
comparable with observations, points to a long-lasting problem in aerosol
remote sensing. That is, different combinations of aerosol microphysical and
optical properties can lead to similar radiative signatures. The combination
of a smaller dust size with a more absorptive refractive index is as good as the
combination of a larger size with a less absorptive refractive index, as far as
DRESW is concerned.
But are the two combinations also equal in terms of closing the LW
radiation? This is an important question because ideally, an appropriate
dust model should close both SW and LW radiation. To address this question,
we extend our radiative transfer simulations to the LW. It is important to
point out that the LW and SW dust radiative properties are not independent
but are related through the physical properties of dust. For example, the AOD at
a given wavelength λ in LW is related to the visible AOD through
AODλ=AOD0.5µmQeλQe0.5µm,
where Qe is the extinction efficiency that is determined by dust size,
shape, and refractive index. The dust size and shape are obviously
independent of wavelength and therefore connect the SW and LW. Even the
refractive index in the SW and LW regions should be physically
self-consistent because the refractive index is determined by the chemical
composition of dust. Unfortunately, because the refractive index
measurements are often made either for SW only or LW only, there is a lack
of measurement of dust refractive index measurement from the visible all the way
to the thermal infrared spectrum.
Instantaneous DRELW based on different dust models. Note that
the diurnal mean values are almost identical to the instantaneous results
due to small diurnal variation in the LW.
PSD
Refractive index
Shape
TOA DRELW efficiency
TOA DRELW
Surface DRELW efficiency
Surface DRELW
(W m-2 AOD-1)
(W m-2)
(W m-2 AOD-1)
(W m-2)
Fennec-SAL
OPAC-LW
Dubovik
10.5
3.0
26.9
7.7
AERONET
OPAC-LW
Dubovik
6.3
1.8
16.4
4.7
Fennec-SAL
Di Biagio-LW
Dubovik
8.4
2.4
18.9
5.4
Fennec-SAL
OPAC-LW
Sphere
12.6
3.6
32.9
9.4
In our computations, we first use the LW dust refractive index from OPAC to
compute the dust LW scattering properties and the corresponding OLR. Based
on the same OPAC-LW refractive index, the Fennec-SAL PSD yields an
instantaneous DRELW of +3.0 W m-2 at TOA and +7.7 W m-2 at the surface (see Table 3). The results based on the AERONET PSD are significantly smaller, +1.8 W m-2 at TOA and +4.7 W m-2 at
surface. This difference between the two PSDs can be easily understood with Fig. 7b. Given the same visible AOD, the coarser Fennec
PSD has a larger infrared AOD than the AERONET PSD, and therefore stronger warming effects in the LW.
The more important question is as follows: which one, Fennec or AERONET PSD, leads to OLR
simulations that agree better with the CERES observations? The differences
between the computed OLR values and the CERES measurements of OLR for the selected
dust cases are shown in Table 4, together with the significance test results,
i.e., t score and p value from the Student's t test. Interestingly, the
OLR values based on the combination of AERONET PSD–OPAC-LW refractive index are
systematically
warmer (larger) than CERES measurements by an average of 0.9 W m-2. The high t score of 2.36 and low p value of 0.02 indicate
this warm bias to be statistically significant. In contrast, the OLR values based on the combination of Fennec PSD–OPAC-LW refractive
index have a bias only at -0.5 W m-2 and a p value (0.55) significantly larger than the commonly used 0.05 threshold,
which means that OLR of this dust model is statistically indistinguishable from the CERES measurements. Then, to investigate
the sensitivity of the computation to LW dust refractive index, we performed the computations again based on the Di Biagio et al.
LW refractive index. As shown in Table 4, the OLR based on Fennec PSD is still better than that based on the AERONET PSD, even
though both sets deteriorate slightly in comparison with the results based on the OPAC LW refractive index. Overall, the size
difference is the primary reason for the fact that the OLR based on Fennec PSD is systematically smaller than that based on the
AERONET PSD. As shown in Fig. 7, due to size difference, the Qe based on the Fennec-SAL PSD (coarser) decreases at a
slower rate than that based on the AERONET PSD (finer). As a result, according to Eq. (1) given the same SW AOD, the Fennec-SAL
has a larger LW AOD and therefore less OLR than the AERONET PSD. In comparison with our results, the OLR values reported in the CCCM
product (not shown here) are on average 3.1 W m-2 larger than CERES measurements. This warm OLR bias of the CCCM product in
the LW is consistent with its bias in the SW that is “too reflective”, shown in Fig. 8.
The difference in OLR between our computations and the CERES
measurements for the selected dust cases. The values in the table are based
on the assumption of 0.7 W m-2 bias in our clear-sky OLR computation.
PSD
Refractive index
Shape
Mean difference
Standard deviation
T score
P value
Fennec-SAL
OPAC-LW
Dubovik
-0.2
3.8
-0.62
0.55
Fennec-SAL
Di Biagio-LW
Dubovik
0.3
3.7
0.83
0.41
Fennec-SAL
OPAC-LW
Kandler
-0.4
3.9
-0.9
0.54
Fennec-SAL
OPAC-LW
Sphere
-0.8
4.0
-2.1
0.033
AERONET
OPAC-LW
Dubovik
0.9
3.7
2.36
0.02
AERONET
Di Biagio-LW
Dubovik
1.5
3.7
3.94
8.5e-5
The LW result in Table 4 is interesting and important. First of all, it suggests that the LW spectral region provides useful
information content on dust properties that is complementary to SW. As we see from Fig. 8, the Fennec-SAL PSD–Colarco-SW
refractive index and AERONET PSD–OPAC-SW SW refractive combinations yield very similar SW radiation simulations. However,
only Fennec PSD can lead to reasonable LW radiation simulation. Secondly, although the main point here is more about the usefulness
of the information content in LW, the fact that the coarser Fennec PSD leads to better OLR simulation than AERONET PSD and the CCCM
product (based on MATCH) aligns with the recent studies (e.g., Kok et al., 2017) that argue that dust size tends to be underestimated
in the aerosol simulation models.
Finally, as expected, the combination of Fennec PSD–OPAC-LW also yields
the best simulation of the dust DRELW, at 3.0 W m-2, in comparison
with the result derived from the CERES OLR observations and RRTM dust-free
OLR computation with ancillary data provided by the CCCM product (i.e.,
+3.4±0.32 W m-2 based on the CERES-CALIPSO combination).
Sensitivity to dust shape
In this section, we investigate the sensitivity of the dust DRE to the shape (or
shape distribution) of dust. For all the computations in the last section,
we have used the spheroidal dust model with the aspect ratio distribution
from Dubovik et al. (2006) (See Fig. 4a). Now, we
replace this model with another spheroidal dust model by Kandler et al. (2009), shown in Fig. 4b. For comparison purposes,
we also carry out another set of computation assuming spherical dust. For
dust size and refractive index, we use the Fennec-SAL and Colarco-SW/OPAC-LW
refractive index since dust DREs based on this combination have shown the
best agreement with the observations.
(a) Extinction efficiency (Qe), (b) single scattering albedo (SSA), (c) asymmetry
factor (g), (d) normalized AOD with respect to AOD @ 0.5 µm, and
(e) AOD × SSA ×(1-g) of dust aerosols based on different combinations of PSD and
refractive index. PSD type and refractive index type are indicated in
the legends.
![]()
In Fig. 9, we compare the scattering properties of dust based on three
different shape models. Overall, the two spheroidal models are very similar
and both significantly different from the spherical model. More
specifically, in the SW, the Qe based on spheroidal models is
significantly larger than that based on the spherical dust model. In the LW
the opposite is true. The ω in Fig. 9b suggests that spherical dust
is more absorptive than spheroidal dust in the SW region, when other aspects
are equal. Fig. 9d and e show the normalized AOD with respect to
AOD(0.5 µm) and the spectral variation of the scattering index
AOD*SSA*(1-g). From Fig. 9d we can see that given the same SW AOD, the
spherical model has larger LW AOD than the two spheroidal models. The
comparison in Fig. 9e reveals that the spherical dust model is less
reflective than the spheroidal model in the SW.
Figure 10 shows the radiative transfer simulations for the selected cases
based on the three dust shape models. The DRESW efficiency based on the
Kandler et al. (2009) model is -48.3 W m-2 AOD-1, which is almost identical to
-47.6 W m-2 AOD-1 based on the Dubovik et al. (2006) model. In contrast,
the DRESW efficiency based on the spherical dust model is much
smaller, -39.8 W m-2 AOD-1, which can be expected from the results in Fig. 9e
(i.e., spherical dust is less reflective). Because the DRESW
efficiencies based on all three shape models are within the
observation-based values, we cannot tell if the spherical dust model is
better or worse than the spheroidal models.
The radiative transfer simulations are shown for the selected cases
based on the three dust shape models.
![]()
As mentioned above, the two spheroidal dust models yield very similar OLR
simulations and are both statistically indistinguishable from the CERES
observations. In contrast, the OLR simulations based on the spheroidal dust
models have a statistically significant -0.8 W m-2 cold bias, with a
p value of 0.03 (see Table 4). Overall, the results in Fig. 10 and Table 4 indicate that the two spheroidal models provide a slightly better,
especially in LW, agreement with the observations. Note that different shape
models may have a different angular and/or spectral signature in terms of
radiance, which is more important for satellite remote sensing. But this is
beyond the scope of this study and will be investigated in future work.
Diurnally mean dust DRE in North Atlantic
The DRE computations in the last section (i.e.,
Tables 1– 3) are instantaneous values
corresponding to the overpassing time of Aqua around 13:30 local time. The
strong solar insolation makes the instantaneous DRESW much larger than
DRELW in terms of magnitude, leading to a strong negative DREnet
(cooling) of dust. However, the DRESW only operates during the daytime,
while the DRELW operates both day and night. In addition, because of
the availability of satellite observations only at TOA, we have only focused
on the DRE at TOA in the analyses above. To appreciate the relative
magnitude of DRELW with respect to DRESW we extend our DRE
simulations and analysis from the instantaneous to the diurnal mean, and also from TOA to the surface. Over tropical
ocean, the OLR is most sensitive to sea surface temperature (SST). Our
sensitivity study based on the 3 h MERRA (Modern-Era Retrospective
analysis for Research and Applications) data suggests that the diurnal SST
variation in the tropical North Atlantic region is so small that the diurnal
mean OLR is close to the instantaneous value. Similarly, we also found that the diurnal
variation of the atmospheric profile (e.g., water vapor) has a negligible impact
on the diurnal DRESW computation. Therefore, we only compute the
diurnal variation of DRESW due to the change of solar zenith angle and
ignore the small diurnal variation of DRELW as well as the impacts of
atmospheric profile change on DRESW.
Diurnally mean DRESW and DRESW efficiency at TOA and
the surface.
PSD
Refractive index
Shape
TOA DRESW efficiency
TOA DRESW
Surface DRESW efficiency
Surface DRESW
(W m-2 AOD-1)
(W m-2)
(W m-2 AOD-1)
(W m-2)
Fennec-SAL
Colarco-SW
Dubovik
-28
-9.9
-82.1
-26.0
AERONET
OPAC-SW
Dubovik
-29.4
-10.3
-85.7
-27.2
Fennec-SAL
Colarco-SW
Sphere
-22.8
-8.2
-89.6
-28.5
Histograms of (a) dust AOD, (b) surface temperature, (c) dust
temperature, and (d) total column water vapor of dust-dominant CERES pixels
with different cloud fractions.
![]()
Table 5 summarizes the key results of the diurnal mean DRESW and
DRESW efficiency at TOA, as well as at the surface. In the SW, the two most
reasonable combinations of PSD and refractive index, Fennec-SAL PSD–Colarco-SW and AERONET-PSD–OPAC-SW, lead to similar TOA DRESW
efficiencies around -29 W m-2 AOD-1, which is at the center of the -16–41 W m-2 AOD-1 range reported in Yu et al. (2006). At
the surface, the DRESW efficiency based on these two combinations
is around -83 W m-2 AOD-1, which is significantly stronger than the -27 to -68 W m-2 AOD-1 range reported in Yu et al. (2006). It
should be noted that we have limited this study to dust-dominant cases,
whereas the values in Yu et al. (2006) are based on a simple domain average
and include other types of aerosol.
By combining the information in Tables 3 and 5, we can easily
derive the net DREnet of dust in the North Atlantic during summer. The
TOA DREnet based on the combination of Fennec-SAL PSD–Colarco-SW–OPAC-LW refractive indices gives a regional mean DREnet of
-6.9 and -18.3 W m-2 at TOA and the surface, respectively. In
comparison, the corresponding values based on the combination of AERONET
PSD–OPAC-SW–OPAC-LW refractive indices are -8.5 and -22.5 W m-2, respectively. It is interesting and important to point out that
the DRELW is significant, about 17–36 %
(depending on the choice of PSD and refractive index) in terms of magnitude
with respect to the DRESW, and therefore not negligible in the
DREnet regardless whether for TOA or the surface.
Summary and discussions
In this study, we use A-Train satellite observations reported in the CCCM
product and recent in situ measurements of dust properties to investigate
the DREs of the dust aerosols in the North Atlantic African dust outflow
region during summer months. First, we select about 600 cloud-free and
dust-dominant CERES pixels from 5 seasons of the CCCM product. Based on these
cases, we first derive a set of observation-based instantaneous
(corresponding to Aqua overpass time) DRESW efficiency and
DRESW using the combination of CERES-measured TOA flux and MODIS- or
CALIPSO-retrieved dust AOD. The DRESW efficiency and DRESW based
on CERES–MODIS observation are -49.7±7.1 W m-2 AOD-1 and
-14.2±2 W m-2, respectively. The values based on the
CERES–CALIOP combination are -36.5±4.8 W m-2 AOD-1 and
-10.4±1.4 W m-2, respectively. Using the combination of
CERES-measured OLR (i.e., with dust) and computed dust-free OLR based on
ancillary data, we also derive a set of semi-observation-based TOA
DRELW between 2.38 and 3.72 W m-2.
In the follow-up sensitivity study, we use the RRTM radiative transfer model
to compute the DRE of dust using the observed 0.5 µm dust extinction
profiles from CALIPSO under various different assumptions of dust PSD,
refractive index, and shape distributions. We find that two dust models, one
based on Fennec-SAL PSD and Colarco-SW refractive index and the other on
AERONET PSD and OPAC-SW refractive index, provide the best fit to the
observation-based DRESW efficiency and DRESW. However, only the
one based on the Fennec-SAL PSD, which is much coarser than the AERONET-PSD,
can also provide a reasonable fit to the observation-based DRELW. We also
find that the DREs based on the two spheroidal dust models are quite similar
to each other, but more different from those based on spherical dust,
suggesting that the detailed shape distribution is less important in the
calculation of dust DRE. Based on the dust model that provides the best fit
to the observation-based DRE, we estimate the diurnal mean dust DRESW
efficiency in the North Atlantic region during summer months (JJA) from 2007
to 2010 to be around -28 and -82 W m-2 AOD-1 at TOA and the surface,
respectively. The corresponding DRESW is -9.9 and -26 W m-2 at TOA and the surface, respectively. The diurnal mean DRELW is
about 3 W m-2 at TOA and 7.7 W m-2 at the surface. As dust aerosol
properties vary temporally and spatially, DREs of dust aerosols also have
high spatiotemporal variation. Therefore, it is worth extending the
analysis to other regions and years in future studies.
Our estimation of the instantaneous TOA DRESW efficiency is in
reasonable agreement with the values reported in a recent study by
Mishra et al. (2017). Their observations are from a satellite
instrument similar to CERES, called Megha-Tropiques ScaRaB (MT-ScaRaB).
Flying in a low-inclination orbit, this instrument is able to observe the
TOA radiation in the tropical region at various local times. Using 4 years of
MT-ScaRaB radiation and MODIS AOD observations, Mishra et al. (2017)
estimate that the instantaneous TOA DRESW corresponding to a solar
zenith angle of ∼ 40∘ in the North Atlantic region
is about -40±3 W m-2 AOD-1, which is in between our range of
-49.7±7.1 W m-2 AOD-1 and -36.5±4.8 W m-2 AOD-1. Our estimation of the diurnal mean TOA DRESW efficiency
(-28 W m-2 AOD-1) is in between -18 W m-2 AOD-1 reported in Mishra
et al. (2017) and -35 W m-2 AOD-1 reported in Li et al. (2004).
The difference may result from the different selection of cases and domain. Note
that our analysis is limited to cloud-free and dust-dominant cases that are
selected based on MODIS and CALIOP observations.
Due to the lack of studies on dust DRELW in this region, it is difficult
to find a comparable result in the literature to validate our estimate of
DRELW. Nevertheless, our result that the positive DRELW cancels
about 30 % of the negative DRESW in the computation of the diurnal
mean net dust DRE is in agreement with many previous studies attesting to the
importance of dust DRELW (e.g., Zhang and Christopher, 2003; Haywood et
al., 2005). Note that over land, e.g., the Sahara Desert, the brighter surface
reflectance will reduce the cooling effect of DRESW or even leads to
warming (positive) DRESW. At the same time, the hot surface temperature
during the daytime may result in DRELW significantly larger than that over
ocean. Therefore, the DRELW is expected to be even more significant in
comparison with DRESW over land than over ocean, which is an
interesting topic for future studies.
Another interesting result from this study is that given the same visible
AOD, dust particle size and dust absorption in the SW can counteract each
other in determining dust DRESW. As a result, it is difficult to
specify both variables using the SW radiation alone. In such case, the LW
radiation could provide complementary and important information on dust
properties, especially dust particle size. Most of the current aerosol
property retrieval algorithms use only SW radiation observations. There are
also a few algorithms to retrieve dust properties using only LW radiation
observations (e.g., Pierangelo et al., 2004; DeSouza-Machado et al., 2006;
Peyridieu et al., 2010). It is worth exploring in future studies the
possibility and benefit of retrieving dust properties utilizing both SW
and LW observations.
Finally, as discussed in Sect. 3.1, because the selected region is quite
cloudy, and the footprint of CERES is relatively large, the sampling rate of
cloud-free and dust-dominant cases is very low. An important question that arises
from the low sampling is whether our results are representative. More
specifically, one may wonder if our cloud-free cases are also representative
of the clear-sky part of cloudy CERES pixels. To address this
question, we investigated if dust properties (e.g., AOD and dust
temperature) and meteorological conditions (e.g., surface temperature and
precipitable water) have any correlation with the cloud fraction. If the
statistics of the dust properties and meteorological conditions from our
clear-sky cases are similar to those from the cloudy cases, then our results
are arguably representative of not only the clear-sky dust-dominant CERES
pixels, but also the clear-sky part of cloudy dust-dominant CERES pixels. To
this end, we first check the AOD. This time we selected all the
dust-dominant cases based on CALIOP observations regardless of the cloud
fraction. Then, we divided all the cases into five groups according to the
cloud fraction within the CERES pixel, i.e., 0–20 %,
20–40 %, 40–60 %, 60–80 %, and > 80 %. As shown in Fig. 11a, the dust AOD from the
cloudy groups is statistically larger than that from our cloud-free cases,
which also means a larger DRESW if the DRE efficiency remains the same.
We do not know whether other dust properties, such as size, shape, and
refractive index, are correlated with cloud fraction. Investigating this is
extremely challenging, if not impossible, using satellite observations. We
have to leave this for future studies using other types of measurements
(e.g., in situ). In addition to dust AOD, we also checked the surface
temperature, the dust layer temperature (weighted by the dust extinction
coefficient from CALIOP), and the total amount of water vapor in the column.
These quantities are potentially important for the DRELW. As shown in
the Fig. 11, in terms of the surface temperature (Fig. 11b) and dust
layer temperature (Fig. 11c), the cloudy dust-dominant cases are almost
identical to our cloud-free dust-dominant cases. However, not surprisingly,
we found that the cloud-free cases are drier than the cloudy cases (Fig. 11c). Note that, given the same dust properties, an increasing of water
vapor increases the atmospheric opacity in the LW, which tends to reduce the
dust DRELW. In summary, if the dust particles' properties (i.e., dust
size, shape, and refractive index) remain the same, then the DRESW of
dust in the clear-sky part of cloudy CERES pixels would be slightly larger
than that based on our results because of the larger AOD. In the LW, the
larger AOD of the clear-sky part of cloudy CERES pixels would lead to a
larger DRELW, but on the other hand, the increased humidity under
cloudy conditions counteracts the effect of larger AOD. The net result is
dependent on the relative importance of these two competing factors.