Clouds are surrounded by a transition zone of rapidly changing aerosol properties. Characterizing this zone is important for better understanding aerosol-cloud interactions and aerosol radiative effects as well as for improving satellite measurements of aerosol properties. We present a statistical analysis of a global dataset of CALIPSO (Cloud-Aerosol Lidar and infrared Pathfinder Satellite Observation) Lidar observations over oceans. The results show that the transition zone extends as far as 15 km away from clouds and it is ubiquitous over all oceans. The use of only high confidence level cloud-aerosol discrimination (CAD) data confirms the findings. However, the results underline the need for caution to avoid biases in studies of satellite aerosol products, aerosol-cloud interactions, and aerosol direct radiative effects

Marshak, Alexander

Varnai, Tamas

Yang, Weidong

NASA Technical Reports Server

CALIPSO OBSERVATIONS OF AEROSOL PROPERTIES NEAR CLOUDS
Alexander Marshak', Tails V'arnai'`, Weidong Yang'
' NASA Goddard Space Flight Center, Greenbelt A1D USA, .Rle ander.Marslrak(,k<nasa.gov
` University gI'Marvland Baltimore Countt r, Baltimore .111) USA, Tamas. I'arnai(a,nasa.gov
3 Universittll of Maryland Baltimore Count y, Baltimore ML7 USA, Weidong.Yang^nasa.gov
ABSTRACT
Clouds are surrounded by a transition zone of rapidly
changing aerosol properties. Characterizing this zone is
important for better understanding aerosol-cloud
interactions and aerosol radiative effects as well as for
improving satellite measurements of aerosol properties.
We present a statistical analysis of a global dataset of
CALIPSO (Cloud-Aerosol Lidar and infrared
Pathfinder Satellite Observation) Lidar observations
over oceans. The results show that the transition zone
extends as far as 15 km away frorn clouds and it is
ubiquitous over all oceans. The use of only high
confidence level cloud-aerosol discrimination (CAD)
data confirms the findings. However, the results
underline the need for caution to avoid biases in studies
of satellite aerosol products, aerosol-cloud interactions,
and aerosol direct radiative effects.
INTRODUCTION
Several recent studies have indicated that clouds are
surrounded by a transition zone of rapidly changing
aerosol properties [1-4]. Characterizing this transition
zone is important for better understanding two critical
yet poorly understood aspects of anthropogenie climate
change—aerosol-cloud interactions and aerosol
radiative effects— and also for devising effective
sampling strategies for satellite measurements of
aerosol properties.
Satellites offer excellent opportunities to study the
transition zone near clouds, though the separation
between clouds and cloud-free areas is often uncertain
and ambiguities can occur at all scales [5]. As was
summed up in [6], "Multiple lines of evidence exist that
call into question the degree to which clear and cloudy
sky can be separated...... Consequently, improving our
knowledge of the transition zone requires
improvements in understanding and separating remote
sensing uncertainties and physical phenomena such as
aerosol swelling in humid air.
One reason why such improvements are important is
that aerosol changes and remote sensing uncertainties
near clouds create a dilemma: excluding the transition
zone in order to avoid its remote sensing uncertainties
can bias a study toward low aerosol loads and radiative
effects, but including the transition zone despite the
remote sensing uncertainties can bias the study toward
too high aerosol loads and radiative effects.
Lidar instruments offer excellent opportunities for
studying the transition zone, because their
measurements are not affected by 3D cloud radiative
effects [7-8]. Recently, paper [9] used the spaceborne
CALIPSO (Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observation) lidar for studying the
transition zone. Following their results this study
analyzes a large global dataset of CALIPSO
measurements, which can reveal even the subtle
changes that occur at the more distant portions of the
transition zone.
2. DATA AND METHODOLOGY
We analyze a monthlong (Sept 15-Oct.14, 2008) global
dataset of CALIPSO lidar data over all oceans free of
sea ice. Our statistical analysis uses the operational I
km-resolution CALIPSO cloud mask and 333 in-
resolution attenuated backscatter profiles at both 532
nm and 1064 nm wavelengths. The results shown are
for nighttime data simply because the weaker
background .illumination creates smaller noise and
allows more reliable cloud detection.
Following [9], we characterize the transition zone by
examining how rnedian attenuated backscatter values
vary with distance from the nearest cloud. Median
values are well-suited for this purpose because they are
less atTectcd by uncertainties in cloud detection or by
unobserved clouds right near the satellite track.
This study focuses on clear areas closest to low-level
clouds (below 3 km), but we note that results for clear
areas near any clouds were very similar to those
presented here. One reason for the similarity is that
50% of all clear profiles analyzed here are closer than
4.4 km to a cloud below 3 km, and 75% is closer than
20 km. The remaining 25% of clear profiles includes
both those clear profiles that are closest to a cloud
higher than 3 km, and those clear profiles that are more
than 20 km away from any cloud. We note that paper
141 found 66% of all clear areas over the Indian Ocean
to be closer than 4 km to clouds.
Our results indicate that much more clear profiles lie
close to clouds below 3 km than to higher clouds, even
though the highest cloud top is more often above than
https://ntrs.nasa.gov/search.jsp?R=20100026398 2019-08-30T10:33:37+00:00Z
10	 15	 20
Distance to nearest cloud (km)
Color ratio
5	 10	 15	 20
below 3 kin. This occurs because high clouds often
form large continuous layers, and so only a few clear
profiles can be close to high Clouds - whereas low
cloud layers often consist of numerous small clouds
separated by clear gaps, which allows many clear
profiles to be close to them. The CALIPSO cloud
detection algorithm [10] may further emphasize this
tendency, when it reduces the effects of observational
noise by detecting faint clouds after averaging observed
backscatter values over larger scales, which fills in any
small gaps in thin high clouds,
532 nm attenuated backscatter
5.0
4.7
4.3
ro
-0
4.0
0
0.55
0.50
0.45
0 0.40
ai
75
0.35
Distance to nearest cloud (km)
Figure 1. (a) Median vertically integrated attenuated
backscatter for all nighttime CALIPSO observations over
ice-free oceans. (b) Median color ratio.
Finally, we, note that results shown in Fig. I are the
attenuated backscatter values integrated up to 3 kin
altitude, but the results of integrating to 15 km altitude
behaved very similarl y . We chose to present the results
for low-level backscatter because this is where the bulk
of near-cloud aerosol changes Occur.
3. RESULTS ON PARTICLES NEAR CLOUD
Figure ]a confirms the overall tendency in earlier
papers	 that	 attenuated	 backscatter	 increases
systematically toward clouds. This tendency is
sufficiently ubiquitous to appear in global statistics, and
the transition zone extends well beyond the —4 kin
range of earlier studies, typically to about 15 kin.
Figure I b shows that the spectral behavior of attenuated
backscatter (fl) also changes near clouds: the median
"color ratio" P10641"A32 increases systematically near
clouds. Because backscatter decreases with wavelength
faster for smaller particles than for larger particles (i.e.,
smaller particles have larger Angstrom exponents), the
color ratio increase in Fig. lb indicates that particle size
increases near Clouds. This increase is consistent with
the effects of both aerosol humidification and cloud
detrainment, but establishing the relative importance of
these two processes as in [9] will require a more
detailed study. The increase near Clouds in Fig. I is a
global phenomenon and the overall trend is neither
caused by a single dominant region nor is it an artifact
of combining observations from vastly different regions
of the Earth. While undoubtedly there are regional
variations, much of the variations likely come from
sampling issues,
4. RESULTS ON REMOTE SENSING
EFFECTS NEAR CLOUDS
While aerosol particles are responsible for a large part
of the near-cloud enhancements discussed above,
misidentified or undetected cloud particles are also
likely to contribute. Undetected cloud particles can
contribute to clear-sky statistics because the separation
of cloudy and cloud-free areas is sometimes ambiguous
due to uncertainties both in detecting layers of
scattering particles and in distinguishing clouds from
aerosols [10-11]. Identifying cloud particles is
especially difficult when small clouds cover only a
portion of the lidar field-of-view--a situation that often
occurs in sparse cumulus fields, where paper [5] found
very small clouds to be fairly abundant.
As an example of remote sensing issues, Fig, 2 shows
that lidar backscatter increases near clouds even in
pristine atmospheric columns where the operational
CALIPSO product did not detect any aerosol lavers.
The increase is most pronounced within 4-5 km from
the clouds identified by CALIPSO. These results
indicate that aerosol or cloud particles are often present
even in these pristine profiles,
4^5
4 0
E	 5-5,
	 confidence data. They confirm that the observed
C\j
	
All columr`s
	 increase in attenuated backscatter is statistically robust.
— Aa,,
W.
a;
1^ 	35
3o ----- - - ---
5	 10	 15	 20
Distance to nearest cloud (krn)
-- -- 
------- -T
 -
11 7'.
F_
	
0,1
4.6
O	 4.4
tU
C
fU
Ca	 4.2
E
Figure 2. Median vertically integrated 532 nin attenuated
backscatter for nighttime CALIPSO observations over ice-
free oceans. This panel considers separately the atmospheric
columns that are free of both cloud and aerosol layers and the
columns that contain an aerosol layer, as specified by the
operational CALIPSO.color ratio.
CALIPSO uses the lidar cloud and aerosol
discrimination (CAD) algorithm [11] to identify aerosol
and cloud features. Figure 3 illustrates the median of
vertically integrated 532 nrn backscatter as a function of'
the distance to nearest clouds for all aerosol data and
only for data with the best 5% CAD score (between -95
and -100). This corresponds to the highest confidence
level of aerosol cloud discrimination. As expected, for
the highest confidence data, the increase in backscatter
towards clouds is smaller but still statistically
significant. Also as expected, the fraction of* high
confidence level observations decreases towards clouds
from 70% to 35% (Fig. 3c). Therefore, while we can
confidently state that particle backscatter and size
certainly increase near the clouds detected by
CAIAPSO, both the nature of these particles (cloud vs.
aerosol) and the processes creating them (e.g., aerosol
swelling in humid air) need to be clarified waiting them
with the appropriate CAD score.
5. CONCLUSIVE THOUGHTS
This paper presents a statistical analysis of a monthlong
global dataset of CALIPSO lidar observations over
oceans. The results show that clouds are surrounded by
a transition zone of enhanced particle backscatter and
size that is ubiquitous over all oceans. The transition
zone is much wider (-t5 km) than the ranges examined
in earlier studies. In addition, the result s also provide
information on uncertainties in aerosol remote sensing
near clouds comparing all aerosol data with the highest
10	 15	 20
0 	 (D
distance to nearest cloud (km)
-E 65
2 N552
0—
o 45
(C)35
distance to nearest cloud (km)
Figure 3. (a) Median vertically integrated 532 nin attenuated
backscatter for nighttime CALIPSO observations over ice-
free oceans. The upper curve corresponds to all aerosol data
while the lower one to only data with the CAR score between
-95 and -100. (b) The difference between the two curves
from panel (a). (c) Fraction of data with the CAD score
between -95 and -100.
The width and ubiquity of the transition zone near
clouds imply that studies of aerosol-cloud interactions
and aerosol direct radiative effects need to account for
aerosol changes near clouds. For example, these
changes can cause systematic biases toward smaller
aerosol radiative forcing if areas near clouds are not
included in order to avoid aerosol retrievals ambiguities
that arise near clouds (e.g., 31) radiative effects,
instrument blurring. cloud contamination). On the
other hand, including aerosol products near clouds
despite these ambiguities may overestimate aerosol
radiative forcing. As a result. there is an urgent need
for developing methods that can assess and account for
remote sensing challenges [12-13] and thus allow for
including the transition zone into the study.
Alternatively, one may remove biases by seeking a
balanced sampling strategy that may compensate for
excluding both near-cloud and far-from-cloud areas to a
similar degree [14]. Finally, statistical adjustments of
results limited to areas far from clouds may also be
possible if the statistical behavior of the transition zones
becomes well understood. More detailed future
analysis of transition zones is expected to provide
further insights into their origins and influence on the
atmosphere, radiative processes, and satellite data
products.
REFERENCES
[1] Koren, L, L. A. Remer, Y. J. Kaufman, Y. Rudich,
and J. V. Martins, 2007: On the twilight zone between
clouds and aerosols. Geot)hYs. Res. Lett., 34, 1_08805,
doi: 10. 1029/2007GL029253.
[2] Su, W., G. L, Schuster, N. G. Loeb, R. R. Rogers,
R. A. Ferrare, C. A. Hostetler, J. W. Hair, and M. D.
Obland, 2008: Aerosol and cloud interaction observed
fi-orn high spectral resolution lidar data, J. Geopkvs,
Res., 113,D24202, doi: 10. 1029/2008JD010588.
[3] Redemarm, J., Q. Zhang, P. B. Russell, J. M.
Livingston, L. A. Remer, 2009: Case studies of aerosol
remote sensing in the vicinity of clouds. J. Geopkys.
Res- 114, D06209, doi: 10.1029/2008JD010774
[4] Twohy, C. H., J. A. Coakley Jr., and W. R. Tahnk,
2009: Effect of changes in relative humidity on aerosol
scattering near clouds. J. Geol)kvs. Res., 114, D05205,
doi: 10. 1029/12008JDO 1099 1.
[5] Koren, L, L. Oreopoulos, G. Feingold, L. A.
Remer, and O. Altaratz, 2008: How small is a small
cloud?. Almos. Cheat, Phys., 8, 3855-3864.
[6] Charlson, R. J„ A. S. Ackerman, F. A.-M. Bender,
T. L. Anderson, and Z. Liu, 2007: On the climate
forcing consequences of the albedo continuum between
cloudy and clear air. Tellits, 59, 715-727.
[7] Wen, G., A. Marshak, R, F. Cahalan, L. A. Remer,
and R. G. Kleidman, 2007. 3-D aerosol-cloud radiative
interaction observed in collocated MODIS and ASTER
images of cumulus cloud fields, .1. Geopkl s. Res., 1 12,
D13204, doi: 10. 1029/-1006JI)008267.
[8] Virnai, T., and A. Marshak, 2009: MODIS
observations of enhanced clear sky reflectance near
clouds.	 Geoljhys.	 Res.	 Lett.,	 36,	 1,06807,
doi: 10. 1029/2008GI,,037089.
[9] Tackett, J. L., and L. Di Girolarno, 2009: Enhanced
aerosol backscatter adjacent to tropical trade wind
clouds revealed by satellite-based lidar. Geopins. Res,.
Lett., 36, L 14904, doi: 10. 1029!2009GLO39264.
[10]Liu, Z., M. A. Vaughan, D. M. Winker, C. A.
Hostetler, L. R. Poole, D. Iflavka, W. Hart, and M.
McGill, 2004: Use of probability distributions for
discriminating between cloud and aerosol in lidar
backscatter data, I Geophrs. Res., 109, 1315202, doi:
10.1029; 2004JD0047 32.
[I I ] Liu, Z., M. A. Vaughan, D. M. Winker, C. Kittaka,
B. Getzewich, R. Kuehn, A. Omar, K. Powell, C.
Trepte, and C. Hostetler, 2009: The CALAPSO lidar
cloud and aerosol discrimination: Version 2 algorithm
and initial assessment of performance. J Atmos. Ocean.
Tech., 26, D15202, 1198-1213.
[12]Kassianov, E. L, and M. 0vtchinnikov, 2008: On
ratios and 'I p roso l op Ca! depth retrieal in
11 1c presellkle Cloud', Gcoph 
* 
vs. Res. Lett, 35,
L06311, doi: 10. 1029/2007GLO329-') 1.
[13]Marshak, A., G. Wen. J. A. Coakley, L. A. Remer,
N. G. Loeb, and R. F. Cahalan, 2008: A simple model
of the cloud adjacency effect and its impact on retrieved
aerosol properties in the vicinity of clouds, J Geophys.
Res., 113, D14S17, doi:10,1029/2007JD009196,
[141Remer, L. A., Y. J. Kaufman, D. Tanre, S. Mattoo,
D. A. Chu, J, V. Martins, R. R. Li, C. Ichoku, R. C.
Levy, R. G. Klcidman, T. F. Eck, E. Vermote, and B.
N. 11olben, 2005: The MODIS aerosol algorithm,
products and validation. I Atmos. Sci., 62, 947-973.


CALIPSO Observations of Aerosol Properties Near Clouds

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