GEOPHYSICAL RESEARCH LETTERS, VOL. 38, L01804, doi:10.1029/2010GL045588, 2011
Satellite‐observed relationships between aerosol and trade‐wind
cumulus cloud properties over the Indian Ocean
Sagnik Dey,1,2 Larry Di Girolamo,1 Guangyu Zhao,1 Alexandra L. Jones,1
and Greg M. McFarquhar1
Received 24 September 2010; revised 4 November 2010; accepted 24 November 2010; published 11 January 2011.
[1] We examine the relationships between simultaneous
observations of aerosol properties from the Multiangle
Imaging SpectroRadiometer (MISR) and trade‐wind cumulus macrophysical properties including cloud fraction ( fc),
cloud size and cloud top height (hct) distribution from the
Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER) over the Indian Ocean during the
dry season (Nov–Apr) of 2006–2007. Mean fc increases
from 0.029 to 0.089 with an increase in aerosol optical depth
(ta) from 0.1 to 0.3, plateaus between 0.3 < ta < 0.45 and
decreases by >30% at ta > 0.45. The hct displays insensitive
response to increasing ta for ta < 0.4, but reduces at ta > 0.4.
Cloud size distribution shifts significantly to large cloud size
at ta > 0.45, possibly due to more efficient desiccation of
small clouds under semi‐direct effect. The observed changes
in cloud properties in relation to increasing ta suggest a very
complex interaction between trade‐wind cumuli and absorbing aerosols that cannot be explained by potential remote
sensing artifacts or synoptic meteorology. Further examination of the potential role of mesoscale meteorology on
such relationships using multi‐year data sets is warranted.
Citation: Dey, S., L. Di Girolamo, G. Zhao, A. L. Jones, and
G. M. McFarquhar (2011), Satellite‐observed relationships between
aerosol and trade‐wind cumulus cloud properties over the Indian
Oc ean, G e o p h y s . R e s . L e t t . , 3 8 , L 0 1 8 0 4 , d o i : 1 0 .1 0 2 9 /
2010GL045588.
1. Introduction
[2] Quantifying the impact of aerosols on cloud properties
remains a large source of uncertainty in estimating anthropogenic climate change [Intergovernmental Panel on
Climate Change, 2007]. Aerosols can either enhance cloud
albedo and lifetime through indirect effects [Heymsfield and
McFarquhar, 2001] or reduce cloud lifetime through the
semi‐direct effect [McFarquhar and Wang, 2006]. Koren
et al. [2008] have hypothesized a smooth transition
between these two aerosol effects through dynamic and
radiative feedback processes, supported by observations
over the Amazon. However, the combined influence of these
two competing effects of aerosols on cloud properties may
be region specific depending upon the distribution of aerosol
optical and microphysical properties, cloud types and
1
Department of Atmospheric Sciences, University of Illinois at
Urbana‐Champaign, Urbana, Illinois, USA.
2
Now at Centre for Atmospheric Sciences, Indian Institute of
Technology Delhi, New Delhi, India.
Copyright 2011 by the American Geophysical Union.
0094‐8276/11/2010GL045588
meteorology. Due to this complex and uncertain interaction
between aerosol and clouds, Stevens and Feingold [2009]
called for efforts to intensify research aimed at specific
cloud regimes to understand their responses to the changing
environment.
[3] One such cloud regime is the shallow, warm cumulus
clouds found ubiquitously over the tropical trade wind
regions. These clouds are known to play an integral part in
the maintenance of tropical circulations through their
influence on energy and moisture budgets [e.g., Betts,
1997], yet their interplay with aerosols are poorly understood. Observations from the Indian Ocean Experiment
(INDOEX) first addressed the issue of aerosol effects on
trade‐wind cumuli [Heymsfield and McFarquhar, 2001].
More recent efforts to understand the evolution of these
small clouds have included the Rain in Cumulus over the
Ocean (RICO) field campaign [Rauber et al., 2007]. In
quantifying aerosol effects on these clouds based on
relationships between satellite‐retrieved aerosol and cloud
properties, we must recognize that these relationships may
be influenced by physical factors such as the coupling of
aerosol and cloud properties to the meteorology and artificial factors caused by the varied sources of systematic
errors within our remotely sensed data products [Loeb and
Schuster, 2008].
[4] In this study, we take advantage of a unique dataset,
namely the high‐resolution (15 m) Advanced Spaceborne
Thermal Emission and Reflection Radiometer (ASTER)
data that has been tasked over the Indian Ocean as part of
the validation efforts for Multiangle Imaging SpectroRadiometer (MISR) data products [Zhao et al., 2009].
Both ASTER and MISR are on the EOS‐Terra satellite,
thus offering coincident data from both instruments.
ASTER has also been tasked over the Caribbean Sea as
part of RICO [Zhao and Di Girolamo, 2007]. However,
we focus only on the ASTER data over the Indian Ocean,
where higher variation of aerosol optical depth (ta) relative to the Caribbean Sea dataset allows for an examination of relationships between trade‐wind cumuli properties
and aerosol characteristics, and where aircraft measurements during INDOEX suggest a strong influence of
aerosols on cumulus clouds [McFarquhar et al., 2004].
Because of the low cloud fractions (fc) of trade‐wind
cumuli, the cloud macrophysical properties derived from
ASTER can be directly correlated with the simultaneous
retrievals of aerosol properties from MISR, in which the
clouds are embedded. The objective of this study is to
examine and interpret these correlations in terms of the
plausible aerosol effects on clouds, with careful consid-
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Figure 1. Normalized frequency distributions of ta, fc and
a derived from 3641 samples over the study region in the
Indian Ocean (shown by the bold rectangle with the spatial
distribution of ta from MISR on an outline map of the
Indian Subcontinent in inset, where the ‘star’ and ‘circle’
show the locations of the radiosonde and AERONET station, respectively) during Nov 2006–Apr 2007. The last
(first) bin of ta and fc (a) histogram represents the normalized frequency of ta and fc (a) >0.55 and >0.5 (<0.8). The
latitudinal distribution of the samples (#) used in the statistics is also shown in inset (‘black’ represents no data).
eration of meteorological and potential artificial remote
sensing influences.
2. Data and Methodology
[5] A detailed description of the ASTER dataset collected
over the Indian Ocean is given by Zhao et al. [2009]. In
brief, ASTER archives data collected over 60 km × 60 km
regions when tasked to do so. The ASTER instrument was
tasked to collect 1235 scenes over part of the Indian Ocean
(12°N–5°S, 68°–78°E, Figure 1) during Nov 2006–Apr
2007. Each ASTER scene was visually inspected and any
scene containing any amount of visible cirrus and stratiform
clouds, and/or impeded by sun glint or land was discarded
from the analysis. A cloud mask for each of the remaining
277 scenes (12, 41, 84, 99, 24 and 17 scenes for the consecutive six months from Nov to Apr) has been developed
using a single, scene‐dependent and manually chosen
threshold applied to the 0.67 mm channel at 15 m resolution
[Zhao et al., 2009].
[6] Version 22 of the MISR level‐2 aerosol products,
reported at 17.6 km × 17.6 km resolution [Kahn et al.,
2009], were collected over the overlapped region for each
ASTER scene. We focus only on ta at 558 nm and Angstrom Exponent (a) that is derived by MISR as the negative
of the slope of the fitted line of the natural logarithm of ta
vs. the natural logarithm of wavelength using all four MISR
wavelengths. This version of the ta product has reached a
mature stage of validation relative to a as summarized by
Kahn et al. [2009]. In total, 3641 MISR pixels, whose
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spatial distribution is shown in Figure 1, have a valid aerosol
retrieval overlapping the 277 ASTER scenes.
[7] Cloud macrophysical properties (fc, cloud size and
cloud top height (hct) distribution) were generated from the
ASTER scenes at a 17.6 km × 17.6 km domain size overlapping each of these 3641 MISR pixels. fc is derived as the
ratio of the total number of cloudy pixels to the total number
of pixels in a 17.6 km domain. The cloud size distribution is
represented by l (obtained by single least square fit to the
center of each size bin of 100 m width) following Zhao and
Di Girolamo [2007]. The hct are retrieved at 90 m spatial
resolution using the ASTER 12 mm channel (see Zhao and
Di Girolamo [2007] for a detailed methodology). The
radiance of each 90 m fully cloudy pixel is converted into a
brightness temperature, which is then converted into hct
using the temperature profiles measured by the radiosonde
at 0Z (closest to Terra’s overpass time) on the same day at
Minicoy Island (Figure 1). Mean hct were derived by averaging 90 m hct within each of the MISR aerosol pixels. The
ASTER hct retrieval may be biased due to a north–south
gradient in the vertical temperature profiles and the
boundary layer diurnal cycle [McFarquhar and Wang,
2006] because the 0Z sounding was measured at a single
site ∼5 hour prior to the Terra overpass. To address this
issue, version 17 of MISR‐hct, retrieved by stereo technique
using the “best quality winds” and independent of temperature profiles [cf. Genkova et al., 2007] was also analyzed.
Median hct were calculated from all 1.1 km MISR‐hct
overlapping each MISR aerosol pixel. The cloud properties
were grouped into narrow ta bins of 0.05 width to examine
their variability as a function of ta. The large number of
samples, even at high ta conditions, allows the observed
dependence of cloud properties on ta to be statistically
robust as demonstrated below. Because domain size can
impact cloud macrophysical properties [Dey et al., 2008],
cloud and aerosol properties were regenerated at a 60 km
domain corresponding to one ASTER scene and their
relations were examined for both domain sizes. We focus
our discussion on the 17.6 km domain unless stated otherwise, since aerosol properties from MISR are reported at the
17.6 km domain and we have ten times the sample density
for our statistics at 17.6 km domain compared to 60 km
domain.
3. Results
[8] The mean (±standard deviation, s) fc, ta and a of the
valid 3641 samples over the Indian Ocean during the study
period were 0.074 ± 0.069, 0.232 ± 0.102 and 1.12 ± 0.25,
respectively with a wide frequency distribution as shown in
Figure 1. ta varies from 0.006 to 1.09 with a strong north
(high ta) to south (low ta) gradient (Figure 1). The fc histogram shows a peak (52%) at fc < 0.05 with a gradual
decrease in the normalized frequencies at higher fc values.
a > 1 for 68% of the time implies the relative dominance of
small particles having a significant fraction transported from
the South Asian landmass with anthropogenic origins and
light absorption properties [Ramanathan et al., 2007]. This
is supported by a low mean (±s) single scattering albedo of
0.82 ± 0.1 at 440 nm retrieved from Level 1.5 cloud‐
screened AERONET data from the Hanimaadhoo site
(location shown in Figure 1).
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Figure 2. Variations of (a) mean fc and l from ASTER (in
left Y‐axis) and number of samples (in right Y‐axis), and (b)
the median and 90th percentile of the hct distribution from
MISR and ASTER with increasing ta over the Indian Ocean
at 17.6 km × 17.6 km domain. The solid vertical bars
through mean l and the dotted vertical bars through mean
fc for each ta bin are the uncertainty of the linear fits to
the corresponding cloud size distribution and ±1s, respectively. The values in the X‐axis are the upper limits of the
ta bins. The variation of mean fc (±1s shown as vertical
bars) with increasing a is displayed in inset of Figure 2a.
[9] Figure 2a shows the mean (±s) fc increasing from
0.029 ± 0.01 to 0.089 ± 0.03 with an increase in ta from 0.1
to 0.3. The increase is significant at 99% confidence level
according to two‐tailed t‐test, and the respective increases in
mean fc at each ta bin are also statistically significant at 99%
confidence level according to the Mann‐Whitney test. There
is a statistically insignificant variation of mean fc with ta in
the range 0.3 < ta < 0.45 according to two‐tailed t‐test,
beyond which the mean fc decreases by >30% in a statistically significant manner. The mean fc shows a similar
response (increasing from 0.026 ± 0.01 at ta = 0.1 to 0.107
± 0.04 at ta = 0.35 followed by a decrease) to increasing ta
for the 60 km domain (Figure S1 of the auxiliary material).1
The mean fc increases with increasing a for a < 1 (indicating relative dominance of natural aerosols) and subsequently decreases by a rate of 10% per unit a for larger a
1
Auxiliary materials are available in the HTML. doi:10.1029/
2010GL045588.
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(inset in Figure 2a). l decreases from 3.53 ± 0.1 (2.99 ± 0.1)
to 3.42 ± 0.1 (2.89 ± 0.07) in the first two ta bins and varies
within the uncertainty levels until ta reaches 0.3 (0.25) at
17.6 km (60 km) domain as shown in Figure 2a (Figure S1).
The variation of l at 0.3 < ta < 0.45 resembles the fc‐ta
variation. A decrease of l at ta > 0.45, significant at 99%
confidence level according to the Mann‐Whitney test,
indicates a decrease in the normalized frequencies of the
small clouds. However, at the 60 km domain size there is a
significant and continuous decrease in l for ta > 0.25 even
when fc increases with ta at 0.25 < ta < 0.35. These differences in the variations of fc and l to increasing ta are
attributed to a well‐understood domain size effect on the
distribution of fc and l [Dey et al., 2008]. Nevertheless, the
trends (i.e., an increase in fc along with insignificant variation in l followed by a decrease in both fc and l with
increasing ta) are similar at two different domain sizes.
[10] The changes in fc in relation to increasing ta and a (a
proxy for relative amount of anthropogenic aerosol in this
region) are similar to the trends observed over the Amazon
[Koren et al., 2008]. However, the increasing and decreasing cloud cover over Amazon were attributed to invigoration
of deeper clouds and inhibition of shallow clouds respectively. In our case, the positive correlation between fc and ta
at ta < 0.3 may be in part explained by an increase in
number of individual clouds. Since l (representing the cloud
size distribution) does not change significantly, the growth
of the individual clouds may be retarded by the evaporation‐
entrainment feedback, hypothesized by Jiang et al. [2006],
as aerosol concentration increases. Because invigoration of
shallow cumulus clouds is unlikely, the peak of the fc‐ta
curve over the Indian Ocean is much lower compared to the
Amazon. High relative humidity (RH) near cloud edges
[Loeb and Schuster, 2008] can also contribute to a positive
correlation between fc and ta. The negative correlation
between fc and ta for higher ta may be explained by aerosol
semi‐direct effect [McFarquhar et al., 2004]. Simultaneous
decrease in l at high ta suggests a more efficient desiccation
of small clouds relative to large clouds [Koren et al., 2008].
The stabilization of the lower atmosphere due to aerosol
heating further contributes to this decrease in fc. Since
drizzle did not occur frequently in either clean or polluted
clouds during INDOEX [Heymsfield and McFarquhar,
2001], the cloud‐lifetime effect may not be an important
factor here.
[11] ASTER‐hct shows insignificant variation with ta for
ta < 0.4. However, the ASTER infrared retrieval of hct may
be impacted by the use of a temperature profile from a single
site, unlike the MISR stereo retrieval of hct. The median hct
from MISR displayed in Figure 2b also shows relative
insensitivity to ta for ta < 0.4, but reduces significantly to
541 m at ta > 0.5. The 90th percentile of the MISR‐hct
distribution increases from 893 m to 1638 m with ta
between 0.1 to 0.4 and decreases significantly according to
the two‐tailed t‐test for ta > 0.4. The reduction in hct in both
ASTER and MISR datasets at large ta is consistent with
previous INDOEX observations [McFarquhar et al., 2004]
and the hypothesis of stronger downward motion at cloud
tops due to enhanced cooling by evaporation in polluted
conditions [Xue and Feingold, 2006].
[12] McFarquhar and Wang [2006] showed that fc and hct
are reduced due to the semi‐direct effect when aerosols are
present within the cloud layer; however, only fc is reduced
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when aerosols with similar absorption characteristics are
present below the cloud layer. The hct increases in the latter
case because warmer air has to rise further to reach the
lifting condensation level due to enhanced radiative heating
below clouds relative to the air above clouds. This implies a
larger variability in the response of hct compared to fc to
increasing aerosol concentration when aerosols are distributed above, within and below the cumulus cloud layers.
Although coincident aerosol vertical profiles are not available, aerosols were distributed within 0.5–3 km during this
period [Ramanathan et al., 2007] compared to hct mostly
confined to within 2 km from surface. This may explain the
insensitive response of hct at ta < 0.4 compared to fc
observed here in relation to increasing ta [McFarquhar and
Wang, 2006].
4. Discussion and Conclusions
[13] Although undetected clouds at the sub‐pixel level can
increase ta and decrease a, the observed relationships are
not influenced by cloud contamination because direct
cumulus contamination is negligible on MISR aerosol
retrievals [Zhao et al., 2009]. The entire regime of the
‘umbrella’ shaped relations of fc‐ta and hct distribution‐ta
and decreases in relative frequencies of small clouds cannot
be explained by radiative cloud adjacency effects because
such effects would result in an increase in ta and a (due to a
larger increase of ta at shorter wavelengths than longer
wavelength near the clouds) [Marshak et al., 2008] with fc.
The fc, l and hct may increase even without any influence of
aerosols, as the boundary layer deepens and the air mass
traveling over the Indian Ocean towards the equator
moistens. But, the cloud macrophysical properties do not
show any latitudinal gradient (not shown here) in contrast to
ta decreasing from north to south (Figure 1), and hence, the
variations of cloud properties with increasing ta are consistent with the hypothesized aerosol effect.
[14] However, a big challenge in quantifying aerosol
effects on clouds is to disentangle them from meteorological
influences. Here, variations of lower tropospheric potential
temperature difference (Dq = q700 − q1000), sea surface
temperature (SST), RH below700 hPa (RH>700), vertical
wind at 850 hPa (w850) and precipitable water (PW), derived
from National Centers for Environment Prediction (NCEP)‐
reanalysis data, with ta (Figure S2) were considered to test
their potential influence on the variability of observed cloud
properties. RH>700, Dq and SST show no statistical dependence on ta, while PW shows a decrease (statistically significant at ta > 0.35) and w850 shows an increase with
increasing ta for ta < 0.35. Their variability with ta suggests that the synoptic‐scale meteorology cannot explain the
changes in the cloud properties across the entire spectrum of
ta. For example, the reduction in fc at high ta may partially
be influenced by the decrease in PW, but then the three‐fold
increase in fc with an increase in ta from 0.1 to 0.3 cannot be
explained by a decrease in PW or by an increase in w850
(suggesting increasing subsidence). In the absence of synoptic scale changes in meteorological forcing, the lack of
statistical significance in the variation of RH>700 with ta is
not surprising given the expected small hygroscopic effect
on ta in our observed range of RH>700 relative to the very
large variability in ta. Moreover, although the turn‐over
point varies, the persistence of ‘umbrella’ shaped fc‐ta
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curves over a wide range of RH>700 (Figure S3) indicates the
dominance of a strong aerosol forcing over other meteorological forcings in influencing the trade‐wind cumulus
properties sampled. Nevertheless, the potential influence of
mesoscale meteorology cannot be ruled out, as data at finer
resolution than NCEP are not available to carry out the
analysis.
[15] The observed relationships between aerosol and
shallow cumulus cloud properties, consistent with a transition from increasing to decreasing fc with ta, implies that
investigations of such relationships in ‘polluted’ and ‘clean’
regimes [McFarquhar et al., 2004] may produce varying
results depending on the threshold between ‘clean’ and
‘polluted’ conditions. The mean fc from ASTER over the
Indian Ocean (0.074) and the RICO domain (0.086 [Zhao
and Di Girolamo, 2007]) are comparable. But, the Indian
Ocean has more small clouds (mean l of 3.01 ± 0.05) than
RICO (mean l of 2.85 ± 0.05) at the same 60 km × 60 km
domain, similar to the simulations of more small cumuli in
polluted conditions compared to clean conditions based on
RICO soundings [Jiang et al., 2009]. Quantitative differences in the variation of cloud properties with increasing ta
over the Indian Ocean and RICO region are expected, given
the larger ta and higher aerosol absorption over the Indian
Ocean. In fact, this contrast emphasizes the importance of
regional comparisons of aerosol‐cloud interaction under
varying environmental conditions with models that can
resolve the changes in cloud properties in response to
changes in aerosol properties, in an effort to test our current
understanding of aerosol‐cloud interactions and their role in
weather and climate.
[16] Due to the limitation in collecting ASTER data over
the oceans the observations presented here were restricted to
a six month period and do not account for inter‐annual
variability. To account for this variability, a dedicated high‐
resolution satellite imager for collecting cloud and aerosol
properties over the ocean is required. Until then, fc data from
more common sensors (e.g., MISR and MODerate resolution Imaging Spectrometer, MODIS) must be used to cover
multi‐season, multi‐year observations to improve the
understanding of aerosol impacts on trade‐wind cumuli (and
other clouds). However, the true observed relationships
between aerosol properties and fc may be difficult to assess
because MISR and MODIS fc calculated from the respective
cloud masks reported at 1.1 and 1 km resolutions over the
trade‐wind cumuli dominated regions tend to be larger than
the true fc. The amount of overestimated fc is not linearly
related to the true fc and varies with the spatial distribution
of clouds [Zhao and Di Girolamo, 2006]. The observed
changes in cloud size distribution in response to increasing
ta may also be impacted because of the shifting of cloud
size distribution to larger cloud size due to amalgamation of
many small clouds into fewer large clouds at these coarse
resolutions [Dey et al., 2008]. Hence, a strategy to correct
the biases in MISR and MODIS cloud masks is needed
before using those data to examine such complex relations.
[17] Acknowledgments. This research is partially supported by Jet
Propulsion Laboratory of the California Institute of Technology under contract 1260125, the National Oceanic and Atmospheric Administration under
grant NA06OAR4310003 and the National Aeronautic and Space Administration under NNG06GB92G. The MISR, ASTER, meteorological and
sounding data were obtained from the Atmospheric Sciences Data Center
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at the NASA Langley Research Center, the Land Process Distributed Active
Data Center, NCEP‐reanalysis data set and University of Wyoming website,
respectively. We acknowledge two anonymous reviewers whose comments
helped to improve the manuscript.
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S. Dey, Centre for Atmospheric Sciences, Indian Institute of Technology
Delhi, Hauz Khas, New Delhi 110016, India. ([email protected])
L. Di Girolamo, A. L. Jones, G. M. McFarquhar, and G. Zhao,
Department of Atmospheric Sciences, University of Illinois at Urbana‐
Champaign, 105 S. Gregory St., Urbana, IL 61801, USA.
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