1
2
3
Supplementary Information:
4
Over 1600 ship tracks are carefully identified and hand-logged from MODIS near-
5
infrared imagery for the period June 2006 to December 2009 (fig. S1). Part of this dataset
6
was used in an earlier ship track study [Christensen and Stephens, 2012]. However, that
7
study provided too-few cases to analyze mixed-phase clouds. Therefore, we expanded the
8
dataset by including ship tracks in the North Atlantic and Arctic Oceans during the cold
9
season (October to April). A ship track is cataloged into the database if the feature is
1. Ship Track Database
10
connected to a clearly visible point source. The point source location is where the exhaust
11
first meets the clouds. This criterion is used so that gravity waves, and other phenomena,
12
are not mistakenly identified as ship tracks. It’s noteworthy that several hundred ship
13
tracks were identified over lakes in the interiors of the continental landmasses. However,
14
these cases were removed to limit biases due to differences in the aerosol-cloud responses
15
between oceanic and continental clouds.
16
The locations of polluted and unpolluted clouds are identified in MODIS 1-km pixels
17
using the same method outlined in a previous study [Christensen and Stephens, 2011].
18
This approach uses a semi-automated scheme to identify the polluted clouds along the
19
skeleton of hand-logged ship tracks. The scheme uses a threshold-based approach of
20
near-infrared pixels to determine which clouds are polluted. Near-infrared reflectivity is
21
used because it is sensitive to the size of cloud droplets. Ship tracks with smaller cloud
22
droplets often stand out in the near-infrared compared to the ambient clouds [Coakley et
23
al., 1987]. Pixels are grouped into segments, 20 km long (along the track) by 100 km
24
wide (perpendicular to the track), centered at the location where CALIOP and CloudSat
1
25
intersect the ship track. First, a linear least squares fit of the pixels at near-infrared
26
reflectances perpendicular to the ship track are used to determine the mean background
27
cloud reflectivity. Values greater than the fit-line plus three standard deviations are
28
considered polluted. Two controls (one for each side of the ship track) are obtained by
29
projecting the spatial distribution of the polluted pixels on both sides of the ship track.
30
For this study, pixels from both controls are combined to calculate the mean properties of
31
the clouds. Once the polluted and unpolluted cloud portions of ship tracks have been
32
identified, observations from CALIOP and CloudSat are collocated to the nearest pixels
33
in MODIS imagery. This method combines multiple state-of-the-art satellite sensors
34
needed quantify the aerosol indirect effects in liquid and mixed-phase clouds.
35
36
2. Screening Procedure
37
Two levels of screening are applied to the ship track database to limit biases relating to
38
statistical sampling and those due to known uncertainties in the retrieved cloud properties
39
from MODIS 1-km pixels. Uncertainty related to sampling is reduced by requiring a
40
minimum number of observations for the polluted and unpolluted cloud portions of each
41
ship track. We refer to the results of a previous ship track study [Christensen and
42
Stephens, 2012] to set the minimum number of samples needed to construct
43
representative averages for each segment. To be included ship tracks are required to have
44
at least: five cloud retrievals using 1-km MODIS pixels, CALIOP, and reflectivity and
45
precipitation data from CloudSat. The final criterion removes a significant number of
46
ship track cases because the CPR is unable to measure the reflectivity in low-clouds at
47
altitudes less than 720 m above the surface [Christensen et al., 2013a]. To preserve as
2
48
many cases as possible, the analysis is performed on two datasets. The first dataset
49
contains 1250 ship tracks that are synergistically observed by MODIS and CALIOP but
50
excluding CloudSat observations. This dataset is primarily used to quantify and test the
51
uncertainty of the thermodynamic phase and its affects on retrieved cloud properties (see
52
figures Fig. 3a, Fig S1, and Fig S3). In the second, and more predominant set of data
53
analyzed, the CloudSat radar has detectable reflectivities and measures precipitation
54
amounts in both the ship tracks and nearby unpolluted control clouds in 297 cases. While
55
differences between these datasets exist (e.g., clouds are optically thicker by 11% in the
56
second dataset) the effects of pollutants from the ships have approximately the same
57
relative change on the properties of the clouds for both datasets. A list of averages for the
58
cloud properties of warm and cold-topped clouds is provided in Table S1 along with the
59
distributions of mean effective radius and optical depths shown in Figure S2.
60
An additional level of screening is employed to reduce prominent biases in the retrieved
61
cloud properties from MODIS pixels. Pixels are carefully selected to avoid retrievals that
62
are greatly influenced by weak signal-to-noise (between the cloud and the surface) and 3-
63
D effects [Marshak et al., 2006] caused by low optical thickness [Zhang and Platnick,
64
2011] and spatial heterogeneity of the clouds [Zhang et al., 2012]. When the clouds are
65
optically thin ( < ~5), the signal from the cloud can be smaller than the noise from
66
uncertainties in instrument error, ancillary surface albedo data, and solutions used in
67
look-up tables. In these optically thin-cloud conditions, the uncertainty in the effective
68
radius retrieval is large. Furthermore, if an ice-layer within the cloud is not sufficiently
69
thick, the signal-to-noise ratio of the cloud layer can confound the inference of the
70
MODIS thermodynamic phase. Therefore, the retrieved properties from MODIS 1-km
3
71
pixels are selected using the newly released MODIS collection 6 data that solves these
72
underlying issues through better quality assessment of the cloud mask and retrieval
73
algorithms [Baum et al., 2012].
74
Of particular interest, we find that the screening method has a substantial effect on the
75
results. The cloud albedo comparison (ship minus controls) is more than two times larger
76
when using MODIS collection 5.1 data. However, if adequate screening measures are
77
applied (by selecting optically thick clouds with high cloud coverage) to the retrieved
78
cloud properties in collection 5.1 data, uncertainties can be reduced. Overall, this study
79
provides a lower-bound (conservative) estimate of the indirect effect and it is possible
80
that the cloud albedo effect could be much larger for warm clouds.
81
Furthermore, multilayer clouds, such as a cirrus layer over a stratocumulus cloud, can
82
cause erroneous retrievals in the properties of the stratocumulus analyzed here
83
[Christensen et al., 2013b]. Overlying clouds decrease the outgoing infrared emission
84
(and brightness temperature) causing a negative bias in the retrieved cloud top
85
temperature from low-level clouds. While the MODIS multilayer cloud flag is utilized to
86
screen high-over-low clouds, it often fails to detect thin cirrus that are identifiable by the
87
sensitive CALIOP instrument [Hayes et al., 2010]. Therefore, to increase the accuracy of
88
the MODIS low-cloud property retrievals, CALIOP is also used to help screen multilayer
89
clouds from the analysis.
90
91
3. Cloud Phase Retrieval Algorithms
92
Cloud phase is determined using two independent datasets: the first utilizes the attenuated
93
backscatter at 532 nm of the depolarization ratio (i.e., the ratio of the cross-polarization
4
94
and copolarization components) measured by the lidar on CALIOP and the second uses
95
reflected sunlight from multiple visible and near-infrared bands on MODIS.
96
The cloud phase algorithm for the level-2 product derived from CALIOP is based on a
97
combination of depolarization and attenuation backscattering thresholds [Hu et al., 2009].
98
The polarization lidar possesses the unique ability to infer the orientation and shape of
99
particles layer-by-layer over the depth of the atmosphere. However, CALIOP can obtain
100
these retrievals only for the upper layers of thick clouds. Once the 532 nm optical depth
101
exceeds ~3 for a spatially uniform cloud, the extinction is too strong to observe further
102
returns at deeper levels in the atmosphere. Therefore, profiles containing thin-upper layer
103
clouds were screened from the analysis and lidar retrievals were only made in
104
stratocumulus clouds below 2.5 km.
105
Cloud phase retrievals are classified as liquid, horizontally oriented ice, vertically
106
oriented ice, and undetermined phase in CALIOP data. The results are sampled at 333
107
meter resolution and averaged over extensive transects (i.e., the width of each ship track
108
or a minimum width of 5 km) to reduce the depolarization noise and achieve reliable
109
estimates of the cloud properties. Vertically oriented ice particles are rarely observed in
110
the clouds studied here, therefore, both ice categories are combined.
111
Cloud phase is also obtained from the standard MODIS level-2 (MYD06) collection 6
112
product and is provided at 1-km spatial resolution. In comparison to its predecessor
113
(collection 5.1), collection 6 data improves on the accuracy of the ice cloud optical
114
property retrieval. It utilizes an ice cloud radiative model with roughened particles and a
115
specified habit to provide closure with CALIOP lidar ratios and retrievals of cloud optical
116
depth [Baum et al., 2011]. The algorithm relies on a combination of visible and near-
5
117
infrared channels to determine cloud phase. At visible wavelengths the single-scattering
118
albedo (or absorption of shortwave radiation) is approximately the same for both water
119
and ice. In the near-infrared, ice absorbs more effectively than water. Therefore, a
120
threshold-based approach of the reflectance ratio (defined as  = 1.6 m/0.64 m ) is used
121
to determine cloud phase. The reflectance ratio tends to decrease when more ice is
122
present because the near-infrared reflectance decreases due to stronger absorption. The
123
algorithm also retrieves particle effective radius, liquid water path, and ice water path at
124
3.7 µm, and cloud optical depth at visible wavelengths for plane-parallel clouds with a
125
log-normal drop size distribution [King et al., 1998]. These assumptions lead to ~30%
126
error in optical retrievals for liquid phase clouds at the pixel scale resolution [Bennarts,
127
2007]. The shape (e.g., columns, plates, ect) and orientation of ice particles adds more
128
complexity and uncertainty to the MODIS retrievals of ice cloud properties.
129
Nevertheless, comparisons between MODIS and CALIOP level-2 products show robust
130
agreement in ice water path retrievals for sufficiently thin clouds that do not strongly
131
attenuate the lidar [Wang et al., 2011]. In this study, total water path is derived from
132
MODIS observations and is defined as the average over all liquid and ice water path
133
retrievals.
134
The primary difference between CALIOP and MODIS cloud phase retrieval is that
135
CALIOP provides information about the particle shapes in clouds while MODIS
136
measures the bulk absorption of cloud particles. Nasiri and Kahn, [2008] and Cho et al.,
137
[2009] outlined the limitations inherent to strictly using near-infrared channels in the
138
MODIS algorithm when inferring cloud ice under conditions with prevalent supercooled
139
water. These studies suggest that MODIS observations may not be adequate to resolve
6
140
high concentrations of ice at relatively warm cloud top temperatures using bulk
141
absorption differences at near-infrared wavelengths. CALIOP data is likely more useful
142
here because the algorithms uses two independent measurements (depolarization and total
143
attenuated backscatter) to infer the shape of particles in the upper portions of the cloud.
144
However, the relatively large noise in the backscattered signal can confound the ice phase
145
retrieval unless special care is taken to average the signal over relatively long transects
146
(~5 km) as was performed here. Results from Baum et al. [2012] also show significant
147
disagreement in the frequency of the retrieved ice phase between land-bearing and
148
oceanic clouds between MODIS and CALIOP observations. Over land, CALIOP
149
observes more ice in clouds at the same temperature than MODIS. Higher concentrations
150
of ice are expected in clouds over land due to more abundant ice nuclei concentrations.
151
The outstanding disagreement in ice phase retrievals between sensors clearly demands
152
further investigation.
153
While both products are used to examine cloud phase, CALIOP observations are likely
154
more trustworthy in this study. Cloud phase retrieved using MODIS data may be
155
inherently biased due to the relatively large near-infrared reflectance values needed to
156
detect ship track pixels in the semi-automated algorithm. For example, the near-infrared
157
reflectance ratio is ~10% larger in ship track clouds compared to the surrounding
158
unpolluted clouds even when the cloud tops are warmer than 0C. This bias is inevitably
159
carried over into the cold cloud top composite. As a consequence, the near-infrared
160
reflectance ratios are artificially biased to large values for polluted clouds using MODIS
161
data. Therefore, utilizing MODIS data to infer cloud ice changes as a function of aerosol
162
may be flawed for studies of this kind since an increase in aerosol typically decreases
7
163
cloud droplet size and increases the near-infrared reflectivity and hence the reflectance
164
ratio which would cause less ice to be retrieved in the polluted clouds. Finally, MODIS
165
retrieves significantly more pixels for which the cloud-phase retrieval is uncertain than
166
CALIOP. Therefore, ice detection by CALIOP probably provides a better indicator of
167
thermodynamic phase than MODIS.
168
169
4. Cloud Albedo and Ice Phase Retrieval Bias
170
Lookup tables for cloud albedo, provided by the BUGSrad radiative transfer scheme
171
[Stephens et al., 2001], are based on MODIS observations of droplet effective radius (at
172
3.7 m), liquid water path (at 3.7 m), and solar zenith angle. The BUGSrad radiative
173
transfer scheme assumes that clouds are plane-parallel and composed entirely of liquid
174
droplets. However, cloud albedo calculations from this study include a combination of
175
liquid and ice phase clouds. Therefore, data was composited by selecting liquid-only
176
clouds to determine if it is appropriate to combine cloud phase retrievals in the
177
calculations of cloud albedo and total water path (i.e., combining liquid and ice
178
retrievals). The difference in cloud albedo between polluted and unpolluted clouds is
179
plotted as a function of the total water path for the composite of ship tracks containing
180
liquid-only clouds (fig. S3a) and the composite containing the combination of liquid and
181
ice clouds (fig. S3b). Overall, the behavior of the cloud albedo response is similar
182
between composites. The bias between composites can be assessed by bias = (LOShip –
183
LOControl) – ( ALLShip – ALLControl), where, LOShip is the liquid-only average over ship track
184
pixels, LOControl is the liquid-only average over control (unpolluted clouds) pixels, ALLShip
185
is the all cloud average over ship track pixels, and ALLControl is the all cloud average over
8
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control (unpolluted) pixels. Biases for cloud albedo and total water path in cold-topped
187
clouds are 0.009 and 8 g/m2, respectively. Biases in the cloud property retrievals due to
188
combining cloud phase data are considerably smaller (by more than three times) than the
189
cloud response to changes in aerosol concentration by the ship. Given the small bias, we
190
do not expect the combination of different cloud phase retrievals to be a significant
191
source of error in the calculation of cloud albedo or total water path in cold-topped
192
clouds.
193
194
5. Ship Track Longevity
195
The lifetime of a ship track is defined here as the amount of elapsed time the polluted
196
clouds remain discernable (using near-infrared imagery) compared to the ambient clean
197
clouds. Because MODIS takes an instantaneous snap shot, the lifetime has to be inferred
198
from several factors: (i) the speed and bearing of each moving ship, (ii) the wind velocity
199
through the cloud layer, and (iii) the length of the ship track. The length of each ship
200
track is calculated from the skeleton of hand-logged positions starting from the location
201
nearest the point source region (head) to the end (tail) where the polluted clouds fade into
202
the background. While it is rare, some ship tracks are not used if part of the track falls
203
outside of the MODIS granule. Both the velocity of the moving ship and wind can cause
204
significant stretching or shrinking of the ship track depending on the orientation of these
205
vectors. For example, ship tracks tend to be longer if the ship is moving against the wind.
206
To correct for this effect, the length is normalized by the difference in magnitude
207
between the wind velocity vector, provided by NCEP (National Centers for
208
Environmental Prediction) re-analysis at 925 hPa, and ship velocity vector. To determine
9
209
the ship velocity vector we assume that the bearing of the ship is in the same direction as
210
the ship track steaming at typical speed of 12 m/s [McNicholas, 2008]. The basis for this
211
approach has been described in a previous ship track study [Christensen et al., 2009].
212
Mean ship track lifetimes in warm clouds agree remarkably well with the ensemble mean
213
ship tracks identified of a past field campaign [Durkee et al., 2000]. On average, the
214
lengths and lifetimes of ship tracks in cold-topped clouds (233 km; 4.9 hr, respectively) is
215
significantly smaller than in warm-topped clouds (334 km; 7.1 hr, respectively).
216
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6. Conversion Rate Efficiency of Cloud Water to Precipitation
218
The rate at which all of the cloud water is removed by precipitation, defined in this study
219
as the “conversion rate efficiency by precipitation,” can be estimated from the ratio of
220
rain rate to total water path (i.e., R/TWP, where R is rain rate and TWP is total water
221
path). Because aerosol can affect precipitation and total water path via multiple pathways
222
the conversion rate efficiency is a useful parameter to diagnose mechanisms that
223
contribute to the aggregate indirect effect. Rain rate is determined using a Z-R
224
relationship which uses the functional form of Z = aRb, where, Z is the radar reflectivity
225
(units of dBZe) measured using 2B-GEOPROF CloudSat data, R is the rain rate
226
expressed in mm/hr, and a and b are constant coefficients with values 75 and 2,
227
respectively. Values for a and b can vary based on the geography, season, and rain type.
228
These were chosen to match the recommendations used in the National Oceanic and
229
Atmospheric Administration (NOAA) Radar Operations Center (ROC) for winter
230
stratiform precipitation west of the continental divide.
10
231
Figure S4 shows the conversion rate efficiency for ship track and unpolluted clouds as a
232
function of cloud top temperature. In warm-topped clouds the conversion rate efficiency
233
decreases as aerosol concentrations increase because aerosols tend to strongly suppress
234
precipitation rates thereby allowing total water path to grow. In cold-topped clouds,
235
below approximately –8ºC, the conversion rate efficiency switches sign and is enhanced
236
by the aerosol emitted by ships. At this temperature, precipitation is likely enhanced by
237
the glaciation indirect effect thereby increasing the conversion rate efficiency of
238
precipitation in polluted mixed-phase clouds.
239
240
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Table S1: Mean cloud properties for warm and cold cloud-top ship tracks.
dataset two: 297 ship tracks
warm cloud top
cold cloud top
(T > 0C)
(T < 5C)
ship
control
ship
control
effective radius (µm)
12.9
16.2
10.2
12.9
optical depth
15.0
12.5
26.2
24.6
total water path (g/m2)
126
128
175
209
cloud top height (km)
1.0
0.99
1.39
1.39
radar reflectivity (dBZ)
-21.3
-19.2
-17.5
-16.7
cloud albedo
0.55
0.50
0.68
0.67
331
332
333
334
Figure S1: Location of ship tracks with mean cloud top temperature greater than 0C
(red ) and less than 0C (blue ) calculated from the standard MODIS cloud product
13
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336
337
over the combined polluted and unpolluted cloudy pixels. The observation period spans
June 2006 – December 2009.
338
339
340
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342
343
Figure S2: Distribution of mean cloud effective radius retrieved at 3.7 µm (a) and cloud
optical thickness (b) for the unpolluted clouds in each ship track domain composited by
mean cloud top temperature greater than 0C (red line) and less than –5C (blue dashed
line) calculated from the standard MODIS cloud product.
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349
350
351
352
353
354
355
Figure S3: Binned change in cloud albedo (∆A) as a function of the (a) total water path
(TWP) and (b) liquid water path (LWP) based on 832 warm and 65 cold cloud top
temperature based ship tracks observed over June 2006 – December 2009 using only the
MODIS screening criteria. Total water path is the average of liquid and ice water path
retrievals. Liquid only water path is based on only those pixels that are determined to be
in the liquid phase by MODIS. Cases are binned by 50 g/m2 wide bins in total water path
(average of liquid and ice water paths from 1-km pixels). Composites by temperature are
shown for ship tracks having warm (T > 0C, red) and cold (T < –5C, blue) cloud tops.
Error bars are determined by the standard error of the mean (e.g., standard deviation
divided by the square root of the number of samples) cloud albedo taken from the
population of ship tracks where a minimum of five is required for each bin.
14
356
357
358
359
360
361
362
363
364
365
366
Figure S4: Binned change in conversion rate efficiency by precipitation (R/TWP) as a
function of the cloud top temperature based on ship tracks observed over June 2006 –
December 2009 using all screening criteria. Cases are binned into 5 K wide bins in cloud
top temperature using the standard MODIS cloud product averaged over polluted (red
line) and unpolluted (blue line) clouds. Error bars are determined by the standard error of
the mean conversion rate efficiency by precipitation taken from the population of ship
tracks where a minimum of five is required for each bin.
15
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368
369
370
371
372
373
374
Figure S5: Average ship track length binned as a function of (a) lower troposphere
stability (LTS; potential temperature difference between the atmosphere 700 hPa and the
surface), (b) free troposphere humidity (FTH; mean relative humidity between 850 – 700
hPa), (c) 700-hPa subsidence rate (), and 10-m surface wind speed (W-10m) taken from
NCEP reanalysis data. Observations are based on 297 ship tracks using all screening
criteria. Error bars denote the standard error of the mean taken from the population of
ship tracks where a minimum of five is required for each bin.
16