Generated using version 3.2 of the official AMS LATEX template
Observed Southern Ocean Cloud Properties and Shortwave
1Reflection Part I: Calculation of SW flux from observed cloud
2properties
3Daniel T. McCoy,
∗Dennis L. Hartmann, and Daniel P. Grosvenor
Department of Atmospheric Scienes, University of Washington, Box 351640, Seattle, WA 98195-1640, USA.
4
∗Corresponding author address: Daniel T. McCoy, Department of Atmospheric Sciences, University of
Washington, Box 351640, Seattle, WA 98195-1640. E-mail: dtmccoy@atmos.uw.edu
ABSTRACT
5
The sensitivity of the reflection of shortwave radiation over the Southern Ocean to the cloud
6
properties there is estimated using observations from a suite of passive and active satellite
7
instruments in combination with radiative transfer modeling. A composite cloud property
8
observational data description is constructed that consistently incorporates mean cloud
liq-9
uid water content, ice water content, liquid and ice particle radius information, vertical
10
structure, vertical overlap, and spatial aggregation of cloud water as measured by optical
11
depth versus cloud-top-pressure histograms. This cloud database is used to compute reflected
12
shortwave radiation as a function of month and location over the ocean from 40◦S− 60◦S, 13
which compares well with observations of reflected shortwave radiation. This calculation is
14
then used to test the sensitivity of the seasonal variation of shortwave reflection to the
ob-15
served seasonal variation of cloud properties. Effective radius decreases during the summer
16
season, which results in an increase in reflected solar radiation of 4– 8 W m−2 during summer
17
compared to what would be reflected if the effective radius remained constant at its annual
18
mean value. Summertime increases in low cloud fraction similarly increase the summertime
19
reflection of solar radiation by 9–11 W m−2. Liquid water path is less in summertime, causing
20
the reflected solar radiation to be 1–4 W m−2 less.
1. Introduction
22Over the last few decades we have gained unprecedented insight into the role of clouds
23
in Earth’s radiation budget due to advances in spaceborne instrumentation. The Southern
24
Ocean region (hereafter defined 40◦S− 60◦S), while very important to the Earth’s radiation 25
budget, is less explored and of special interest because of its pristine environment and
exten-26
sive cloud cover. Of greatest importance to the albedo are the low-topped and optically thick
27
clouds that are ubiquitous in this region (Haynes et al. 2011) and that have a considerable
28
influence on the reflected solar radiation. Given that the low cloud shortwave feedback is
29
found to be the largest source of inter-model spread in climate sensitivity (Bony et al. 2006)
30
and that a strongly negative and localized optical depth feedback has been diagnosed within
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the CMIP3 and CMIP5 multi-model ensemble results over the Southern Ocean (Zelinka et al.
32
2012, 2013), it is important to fully utilize remote sensing data to better understand cloud
33
processes in this region. To this end we use the strengths of several spaceborne instruments
34
to better define the structure, phase, and particle size of clouds in this region and their
sea-35
sonal variations. These data sets are used to construct a database of Southern Ocean cloud
36
properties from which upwelling shortwave radiation may be calculated. This methodology
37
allows us to separate the contributions to the annual mean and seasonal variations of cloud
38
reflectivity in the Southern Ocean by different physical properties of the clouds.
39
We have drawn on many remote-sensing datasets in order to construct a database of the
40
cloud properties that are needed by radiative transfer models to calculate the monthly
reflec-41
tivity. The applicability of our cloud property construction is tested through comparisons of
42
our “reconstructed” reflectivity to that observed by CERES. Each remote sensing dataset
43
has its own strengths and weaknesses, and each elucidates a different aspect of the cloud
44
population over the Southern Ocean. The observed seasonal variation of cloud properties
45
sheds light on mechanisms that govern cloud processes and the relative importance of the
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annual cycle of different cloud properties to the reflection of solar radiation.
2. Remotely Sensed Cloud Properties
48a. The multi-instrument approach 49
Cloud properties show considerable variability in the midlatitudes and determining the
50
influence of these variations on the radiative budget is important. In order to examine
51
the dependence of albedo on the seasonal cycle of cloud properties, we reconstruct the
52
upwelling shortwave radiation using plane-parallel radiative transfer in combination with the
53
observations of a suite of cloud properties. These properties are: cloud fraction, the liquid
54
and ice water paths of these clouds, the effective radius of the particles which compose the
55
liquid and ice, and the vertical structure and spatial coverage of the clouds.
56
In the creation of mean-monthly clouds from observations we are presented with two
57
options: using a single instrument that measures these cloud properties simultaneously or
58
using a combination of several different instruments, which each measure individual cloud
59
properties that we may then combine in a consistent way. The Moderate Resolution Imaging
60
Spectroradiometer (MODIS) (Foot 1988; Platnick et al. 2003) produces the most reasonable
61
single set of retrievals of cloud optical properties, but several elements of the MODIS retrieval
62
methodology make using it in synergy with observations from other instruments an attractive
63
strategy.
64
The fractional coverage of cloud is of paramount importance to the SW flux calculation
65
over dark ocean surfaces. In the Southern Ocean region the clouds that impact the SW most
66
significantly are the low cloud (Haynes et al. 2011). Thus we must choose an observation of
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cloud fraction that is well suited for very accurately retrieving the coverage of cloud and the
68
distribution of cloud in height and optical thickness.
69
The International Satellite Cloud Climatology Product (ISCCP), MODIS, and
Multi-70
angle Imaging Spectroradiometer (MISR) instruments retrieve cloud fraction as well as height
71
and optical depth, but with differing fidelity. The cloud mask agrees between these
instru-72
ments, but the MODIS cloud top height optical depth (CTH-OD) histogram differs greatly
from those retrieved by ISCCP and MISR (Marchand et al. 2010). This is because optical
74
depth is not retrieved for pixels on cloud edges by MODIS, thus lowering the zonal-mean
75
cloud fraction within the CTH-OD histogram by 10%− 35% in the Southern Ocean relative
76
to ISCCP, MISR and the MODIS cloud mask and raising the average optical depth of the
77
clouds within the histogram. While it would be possible to rescale the MODIS CTH-OD
78
histogram by the cloud mask to bring the total coverage of cloud into agreement with other
79
instruments, the probability density function (PDF) of optical depth within the scaled
his-80
togram would be tilted in favor of optically thicker clouds as shown in Marchand et al. (2010).
81
Consequently, this would lead to SW↑ calculated from a rescaled CTH-OD histogram to be
82
unphysically high because the mean optical thickness would be biased high.
83
Given that MODIS is not suited to retrieval of consistent cloud fraction and optical depth
84
for reconstruction of SW flux, we are left to choose between CTH-OD histograms from ISCCP
85
and MISR. ISCCP is shown to retrieve optically thick cloud fractions similar to MISR and
86
MODIS in the tropics and subtropics but produces a lower optically thick cloud fraction
87
than either instrument in the midlatitudes. This behavior is consistent with the latitudinal
88
dependence of ISCCP cloud fractions as the ISCCP transitions from geostationary to
polar-89
orbiting platforms in the midlatitudes (Evan et al. 2007; Marchand et al. 2010). It may
90
also potentially be due to biases in the ISCCP optical depth retrieval induced by the
one-91
dimensional radiative transfer, assumptions as to cloud phase, or the horizontal resolution
92
(Marchand et al. 2010). Not only is the accurate retrieval of cloud fraction important,
93
but also the vertical structure of clouds. Cloud top retrievals for low clouds using thermal
94
channels are often confused by inversions in temperature, making the MISR multi-camera
95
instrument, which detects low cloud tops at a comparable skill to CALIPSO (Wu et al.
96
2009), an excellent choice for accurately diagnosing low cloud amount and top height. Given
97
these issues we have chosen to use MISR to retrieve cloud fraction, optical depth, and height,
98
particularly because low clouds dominate over the Southern Ocean.
99
Even if the MODIS CTH-OD histogram could be brought into agreement with the
trievals performed by instruments which do not discard cloud edges, there are still
con-101
siderable problems with using its retrieved liquid and ice water path. Besides the overall
102
omission of MODIS water path retrievals in edge pixels, the liquid and ice water path are
103
empirically determined based on the cloud optical thickness, cloud top particle radius, and
104
cloud top thermodynamic phase (Stein et al. 2011). In the case of clouds flagged by the
ther-105
modynamic phase algorithm as cold-topped, the optical thickness will potentially be some
106
combination of liquid and ice water signals which are impossible to separate (Horvath and
107
Davies 2007). This is particularly problematic in the midlatitudes where synoptic systems
108
and mixed-phase boundary-layer clouds occur frequently. In order to disentangle the liquid
109
and ice water paths we have utilized the microwave liquid water path, which is relatively
110
insensitive to ice (O’Dell et al. 2008), and the combined lidar-radar derived ice water path
111
(Mace and Deng 2011), which is highly sensitive to larger ice crystals. Use of these data
112
sets to reconstruct the upwelling shortwave, as opposed to optical depth derived liquid and
113
ice water paths, is arguably a more rigorous challenge because the technique is completely
114
independent of cloud shortwave reflectance, which is the quantity that we ultimately wish to
115
calculate from the cloud properties. While both radar-lidar and microwave techniques might
116
suffer from systematic errors in their retrievals (Chubb et al. 2013; O’Dell et al. 2008), they
117
are likely the most accurate remote sensing observations available that detail the behavior
118
of cloud liquid and ice in cold-topped and mixed phase clouds.
119
In summary, observations from a variety of different instruments that are well suited to
120
the remote-sensing challenges of the Southern Ocean have been selected. These observations
121
are combined in a consistent way to form a mean cloud scene. While they will suffer from
122
random errors and systematic biases as all observations do, it is argued that the choice of
123
data sets is more optimal for the problem of interest. While using a single instrument to
124
consistently describe the cloud properties in the Southern Ocean has some advantages, in
125
this case the problem of interest and the remote sensing challenges of the region favor a
126
multi-instrument approach. In the following sections we describe the data from which we
will calculate the upwelling SW.
128
b. Cloud Fraction 129
To measure the cloud fraction we use the MISR cloud top height and optical depth
130
(CTH-OD) histogram (Marchand et al. 2010). Because MISR is a passive instrument, it
131
only detects the height of the topmost cloud layer. Information of cloud coverage beneath
132
visible cloud is not retrieved. In order to determine the total fraction of cloud coverage
133
in each layer, we combine MISR with cloud overlap information. This may be either a
134
simple assumption such as random overlap or may be inferred from observations such as the
135
Cloudsat 2B-GEOPROF product (Mace et al. 2009). In the following section we examine the
136
application of overlap information to the MISR histogram in the creation of an approximate
137
total cloud fraction in several vertical categories.
138
Overlap probabilities are calculated using the cloud profiling radar (CPR) cloud mask
139
in the 2B-GEOPROF data. In this study we consider clouds segregated based on cloud top
140
pressure (CTP) into low (CT P > 680 hPa), middle (440 hPa < CT P < 680 hPa), and
141
high (CT P < 440 hPa) categories consistent with ISCCP definitions of these categories.
142
Throughout the rest of this study we use the same definitions of the low, middle, and high
143
cloud categories. Because of the simplicity of our three category system we may explicitly
144
write down the potential overlap cases that we consider in terms of underlying layers and
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top-most cloudy layer (denoted by a subscript T): P (L|MT) (The probability of a low cloud
146
occurring when a middle topped cloud is present), P (L¬M|HT) (the conditional probability
147
of a low cloud beneath a high topped cloud with no middle cloud), P (L ∧ M|HT) (the
148
conditional probability of a low cloud and a middle cloud when a high cloud is present), and
149
P (M|HT) (the probability of a middle cloud beneath a high cloud). The oceanic
zonally-150
averaged day-time only values of these probabilities are shown in Fig. 1. For comparative
151
purposes we also show the raw cloud fraction detected by Cloudsat (fractional detection
152
in each cloud category whether or not there is overlying or underlying cloud) written as
P (L),P (M ), and P (H) and the fractional detection of cloud tops in each category (with 154
no overlying cloud by the CPR) written as P (LT),P (MT), and P (HT). The probability
155
of detecting a low cloud may be written as P (L) = P (LT) + P (L¬M|HT)P (HT) + P (L∧
156
M|HT)P (HT) + P (L|MT)P (MT) and P (M ) = P (MT) + P (M|HT)P (HT). The total cloud
157
detected may also be compared to the MODIS cloud mask overlapping with the Cloudsat
158
swath that is contained in the 2B-GEOPROF data set as shown in Fig. 2. Uncertainty is
159
estimated for the 2B-GEOPROF cloud fraction by calculating the cloud fraction including
160
very weak echoes where the reflectivity strength is less than the single column sensitivity
161
of the radar (Mace 2007). Uncertainty in the MODIS cloud mask overlapping with the
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2B-GEOPROF data is calculated using the MODIS cloud mask confidence flag. Given that
163
2B-GEOPROF does not efficiently detect low, liquid clouds (Stephens et al. 2002) we would
164
not expect it to give a reasonable value for the low cloud overlap cases. This is borne out
165
by the relatively low values of P (LT) and P (L) as shown in Fig. 1 compared to passive
166
estimates as well as the overall lower cloud coverage compared to MODIS cloud mask shown
167
in Fig. 2. On examination of Fig. 1 it is clear that in the Southern Ocean region low and
168
middle clouds are frequently found beneath high clouds.
169
To examine the seasonal cycle of cloud fraction we use the native resolution of the MISR
170
histogram at 1◦×1◦and interpolate the climatological zonal-mean overlap probabilities from
171
2B-GEOPROF to this grid. This probability climatology is used to create an approximate
172
overlapped cloud fraction using the MISR CTH-OD histogram for the period 2000-2009 as
173
shown in Fig. 3. It should be noted that at latitudes poleward of 50◦S during winter high 174
solar zenith angles do not allow MISR to retrieve cloud fraction, and these months have been
175
left blank.
176
The 40◦S− 60◦S region has large fractional coverage of low cloud with a peak amount in 177
the summer (Fig. 3(a)). Middle cloud fraction peaks in wintertime at 40◦S and in summer 178
at 60◦S (Fig. 3(b)). MISR detects a relatively small amount of high cloud, which peaks in 179
the wintertime (Fig. 3(c)).
Upper level cloud fraction is under-estimated by MISR (Marchand et al. 2007). MISR
181
fails to see tenuous upper level cloud because height retrieval is dependent on feature contrast.
182
Thus, optically thin cloud above optically thick cloud is typically classified as the optically
183
thick cloud’s category because its feature contrast is frequently higher. This leads to optically
184
thin middle and high cloud fraction being underestimated by MISR. Given that our primary
185
interest is to estimate the impact of the radiatively dominant low cloud fraction on the
186
upwelling shortwave (Haynes et al. 2011), we accept this as a known bias.
187
1) Comparison to other cloud fraction retrievals
188
The fractional coverage of cloud plays a central role in the calculation of upward radiative
189
flux, but presents a variety of remote sensing challenges. Thus, we compare several data sets:
190
our MISR and 2B-GEOPROF derived overlapped cloud fraction; the combined
CALIPSO-191
Cloudsat climatology of cloud fraction of Kay and Gettelman (2009) (for the period
2006-192
2011); a MISR CTH-OD overlapped cloud fraction using a simple random overlap; and
193
CALIPSO-GOCCP (Chepfer et al. 2009) (for the period 2006-2009). These data sets are
194
shown in Fig. 4. The cloud fraction is split into the 40◦S − 50◦S and the 50◦S − 60◦S 195
regions. In the more poleward latitude band MISR does not provide retrievals during the
196
winter period of darkness and low sun angle, and thus we must infer the seasonal cycle during
197
this period. The cloud fraction seems to have a similar behavior to the more equatorward
198
regime shown in Fig. 4. We can see that in both regions MISR detects distinct seasonal cycles
199
in each cloud category. We find wintertime peaks for middle and high cloud as calculated by
200
the overlap from 2B-GEOPROF that are consistent with previous studies, which diagnosed
201
an upper level cloud fraction peak in winter driven by extratropical cyclone activity in the
202
Southern Ocean equatorward of 60◦S (Bromwich et al. 2012; Haynes et al. 2011). This is 203
also echoed by the seasonal cycle shown by the CALIPSO-Cloudsat synergy, which is highly
204
suited for the detection of icy upper level cloud. The season of peak high and midlevel
205
cloud amount differs between the two instruments by a few months, but this may be due
to the interplay between solar zenith angle and optical depth. Visible optical depth tends
207
to be overestimated at times of high solar zenith angle (Grosvenor and Wood 2014; Loeb
208
and Coakley 1998; Loeb and Davies 1997; Loeb et al. 1997; Loeb and Davies 1996), which
209
would tend to bias MISR tenuous upper-level cloud detections toward the time of highest
210
solar zenith angle. Finally, the mean amount of middle and high cloud detected by the active
211
instruments is higher in most cases than that diagnosed by MISR, which is consistent with the
212
known under-estimation of tenuous upper-level cloud by the MISR instrument, Cloudsat’s
213
sensitivity to strongly scattering ice particles, and CALIPSO’s sensitivity to optically thin
214
cirrus.
215
Comparison between low level cloud fraction as inferred by MISR assuming a random
216
overlap shows qualitatively the same cycle as the 2B-GEOPROF overlap combined with
217
MISR with slight under-estimation during the winter and over-estimation during the summer,
218
which is consistent with attenuation by thick cloud and under-detection of liquid cloud. The
219
overall amount peaks in summer, which is consistent with the cloud fraction being stability
220
dependent (see LTS in Fig. 4).
221
While the differences in overlap scheme may not have as great a qualitative effect on
222
low cloud, we argue that random overlap is not appropriate for diagnosing middle cloud.
223
Both the synoptic activity peak in winter in the Southern Ocean and the measured cloud
224
fraction from CALIPSO-Cloudsat support the notion of a wintertime peak in middle and
225
high cloud, which requires correlation between middle and high cloud. Thus, we utilize the
226
measured probability of overlap from 2B-GEOPROF to diagnose middle cloud beneath high
227
cloud. This seems especially apt given that both middle and high cloud are reflective to
228
radar because they are primarily ice phase. The correlation between middle and high cloud
229
occurrence is expected if they are both driven by storm track activity.
230
Another source of disagreement between the CALIPSO-Cloudsat and MISR+2B-GEOPROF
231
data is in the seasonal cycle of low cloud, which MISR shows as peaking in the warmer
232
months and at generally higher fractional coverage than CALIPSO-Cloudsat (Fig. 4), while
CALIPSO-Cloudsat shows a rough wintertime peak with less low cloud. This disagreement
234
is consistent with the nature of Cloudsat-CALIPSO and MISR, the former is highly accurate
235
at detecting upper-level clouds and the latter at optically thick clouds. Cloudsat misses a
236
large amount of optically thin liquid cloud and Cloudsat-CALIPSO excludes the bottom 720
237
m (Stephens et al. 2002; Kay and Gettelman 2009). Globally, Cloudsat is estimated to detect
238
only 70% of all liquid cloud over oceans (Stephens et al. 2002). The increase in wintertime
239
in-cloud IWP ( shown later in Fig. 8(a)) leads us to hypothesize that the wintertime peak
240
in low cloud, as diagnosed by active remote sensing, is more due to an increase in radar
241
reflectivity than an overall increase in low cloud coverage.
242
We might expect low cloud fraction to be more driven by stability as shown in Klein
243
and Hartmann (1993). The lower tropospheric stability (LTS) (Wood and Bretherton 2006)
244
calculated from ECMWF-Interim is consistent with this notion, showing peak inversion
245
strength during warmer months in step with low cloud fraction. Finally, comparison with
246
CALIPSO-GOCCP, which is able to detect a larger fraction of liquid clouds than Cloudsat
247
although it suffers heavily from attenuation, shows a similar cycle in low cloud in the 40◦S− 248
50◦S latitude band to MISR, leading us to believe that this cycle is real and not an artifact of
249
MISR’s cloud detection algorithm. It is unclear if the difference between these two low cloud
250
retrievals in the more poleward region from 50◦S − 60◦S is due to increased attenuation 251
of the lidar by thick upper level cloud, or if the MISR histogram is in error. Given the
252
consistency of the MISR histogram and the simplicity of the method by which we retrieve
253
the low cloud fraction from the observed cloud tops, we believe the MISR low cloud in this
254
region is physically reasonable.
255
c. Liquid Water Path 256
We calculate an approximate cloud liquid water path using a combination of several
in-257
struments over the period 2001-2008. In order to calculate the upwelling SW and determine
258
the contributions due to variability of liquid water content in different cloud categories we
must know not only the amount but also the vertical distribution of liquid water. Microwave
260
and optical techniques are able to retrieve liquid water path, but both retrieve integrated
261
quantities that are not vertically resolved, and the latter cannot distinguish between
sig-262
nals originating from ice and liquid (Horvath and Davies 2007). The liquid water content
263
calculated from millimeter wave radar reflectivity contained in 2B-CWC-RO (Austin and
264
Stephens 2001) retrieves a vertically or horizontally resolved profile of liquid content, but
265
is limited by the inability of radar to determine thermodynamic phase in the mixed-phase
266
cloud and thus the necessity of assuming phase attribution in the mixed-phase temperature
267
range (Huang et al. 2012). To reduce biases in the vertically resolved liquid water content
in-268
troduced by these limitations we force the integrated liquid water path to be consistent with
269
the passive microwave liquid water path in the UWISC data set, which is well attuned to the
270
detection of liquid (O’Dell et al. 2008). This is done by calculating the ratio of liquid water
271
in each pressure category to the vertically integrated liquid water path as determined by the
272
2B-CWC-RO algorithm, which uses the reflectivity measured by Cloudsat in combination
273
with the temperature profile. The zonal mean behavior of this ratio is shown in Fig. 5, and
274
uncertainty in this quantity is calculated using the uncertainty in the 2B-CWC-RO data.
275
This ratio may then be used to partition the vertically integrated daytime liquid water path
276
measured using passive microwave. The UWISC liquid water data has been chosen over
277
optical depth based methods because it produces a low systematic error in the Southern
278
Ocean and is able to retrieve liquid water effectively in clouds that contain both ice and
279
liquid (O’Dell et al. 2008). The day time liquid water path is calculated from the diurnal
280
cycle provided in the UWISC data set. This method provides a liquid water path in three
281
layers that we then divide by the overlapped MISR cloud fraction from the preceding section
282
to yield an in-cloud liquid water path in each pressure category. Creation of a vertically
283
resolved liquid water path by this method offers improvement on simply assuming a vertical
284
partitioning of liquid amount based on the temperature profile. This technique indicates
285
that most of the liquid water is in the low clouds in the midlatitudes as shown in Fig. 5.
The middle and high in-cloud liquid content generated by this technique is found to
287
change with temperature (Fig. 6(b)-(c)). Both middle and high in-cloud liquid water path
288
are maximum in summertime with a general trend toward higher in-cloud liquid content at
289
lower latitudes (warmer temperatures). While it should be noted that the in-cloud
upper-290
level liquid water path is quite large, this is an effect of MISR detecting too little high cloud
291
cover and thus biasing in-cloud liquid water in this pressure regime. When averaged over
292
the entire scene, the amount of liquid water at pressures below 440hPa is negligible, and so
293
does not significantly impact the calculation of SW↑. Both Cloudsat-derived overlap for low
294
cloud and random overlap show an increase in low cloud liquid water path in the fall and
295
wintertime (Fig. 6(a)).
296
d. Ice Water Path 297
Because of the large particle size of ice, lidar and radar data from both CALIPSO and
298
Cloudsat are sensitive to ice and combined information from these instruments is used to
299
constrain the 2C-ICE product (Mace and Deng 2011). When used in conjunction, these
in-300
struments allow the creation of a vertical profile of ice water content and ice particle effective
301
radius that contains both the tenuous ice clouds detected by lidar and the optically thicker
302
clouds to which radar is sensitive. Validation of this product in the TC4 and SPARTICUS
303
field campaigns has shown good agreement between the ice effective radius (rge) and IWC
304
retrieved by the 2C-ICE algorithm and the in-situ measurements of these quantities (Deng
305
et al. 2010; Heymsfield et al. 2008), although its retrievals of boundary-layer ice clouds may
306
tend to be biased high in the Southern Ocean due to attributing reflections from large cloud
307
droplets to ice crystals (Chubb et al. 2013). The integrated IWP in each category is shown
308
in Fig. 7.
309
We use data from the period 2007-2008 from the 2C-ICE product, divided by the cloud
310
fraction from MISR in each level, to give an in-cloud ice water path over the Southern Ocean.
311
Low altitude cloud ice water path is found to increase strongly in winter; high altitude
cloud IWP shows a summertime peak, and middle altitude in-cloud IWP shows a wintertime
313
peak at low latitudes, which transitions to a summertime peak at higher latitudes (Fig. 8).
314
We have removed points whose ice water is in excess of 4 kg m−2 from the mean, which are
315
assumed to be artifacts of under-detection by MISR of optically thin ice clouds over high
316
contrast boundary layer cloud. These data points account for less than 0.0001% of the total
317
volume of data. In addition, during the month of September 2008, insufficient swaths were
318
available in the Cloudsat data archive at the time of writing to achieve good coverage and
319
the September data from 2007 was substituted in its place.
320
The creation of an in-cloud ice water path by this method tends to bias the value toward
321
being too high in upper level cloud because tenuous ice clouds are frequently undetected by
322
MISR and may be biased slightly high by 2C-ICE (Deng et al. 2010). We use this technique
323
to be consistent with our calculation of in-cloud liquid in the preceding section. While our
324
method tends to fail when dealing with upper level cloud, we choose data and methods with
325
the intent of resolving the low cloud as accurately as possible because low cloud is the primary
326
contributor to the cloud radiative effect in this region (Haynes et al. 2011). The seasonal
327
cycle of in-cloud IWP at pressures above 680 hPa seems relatively strong and physically
328
plausible. Together, the liquid and ice water paths show an increase in low cloud total water
329
path during the winter. This may be caused by efficient long-wave cooling at cloud-top
330
during the high-latitude winter darkness leading to buoyant production of turbulence and
331
an increase in liquid, some of which transitions to ice (Curry 1986; Morrison et al. 2011;
332
Solomon et al. 2011); as well as more active dynamics during the winter season (Simmonds
333
et al. 2003; Verlinden et al. 2011).
334
The effective radius of ice diagnosed by 2C-ICE in the Southern Ocean differs between
335
cloud categories, with low, middle and high clouds having a mean effective radius and RMS
336
uncertainty of 61µm± 6, 50µm ± 5, 35µm ± 5, respectively. Middle and high clouds have
337
somewhat higher values in the summer, but the difference is smaller than the RMS
uncer-338
tainty attached to the data set.
e. Liquid Effective Radius 340
The MODIS (Moderate Resolution Imaging Spectroradiometer) instruments onboard the
341
Aqua and Terra polar orbiting satellites retrieve liquid cloud effective radius (re) based upon
342
the combination of one non-absorbing optical wavelength and one absorbing near-infrared
343
band (Foot 1988; Nakajima and King 1990; King et al. 1997; Platnick et al. 2003). The latter
344
can be either 1.6, 2.1, or 3.7 µm, although prior to the upcoming collection 6 only the 2.1 µm
345
band is available for Level-3 products. Retrieving reremotely is somewhat uncertain and yet
346
very important because the reflectivity of clouds is sensitive to re in the size range of typical
347
marine stratocumulus droplets. King et al. (2013), Breon and Doutriaux-Boucher (2005), and
348
Painemal and Zuidema (2011) have diagnosed over-estimation of re in marine stratocumulus
349
by the standard retrieval approach relative to in-situ measurements and POLDER.
350
Furthermore, large differences between re from these different bands have been observed
351
(Zhang and Platnick 2011; Zhang et al. 2012), although the magnitude of these differences
352
varies and is generally fairly small in high cloud fraction stratocumulus regions (Zhang and
353
Platnick 2011; Zhang et al. 2012; Painemal and Zuidema 2011; King et al. 2013). Such
spec-354
tral differences generally become larger in more strongly precipitating regions and it has been
355
suggested that this reflects vertical changes in re as a result of the presence of precipitation
356
hydrometeors (Chen et al. 2008) and the differing penetration depths of the three bands
357
(Platnick 2000). However, recent evidence has suggested that precipitation hydrometeors
358
would have little effect on the retrieved re (Zhang et al. 2012; Zinner et al. 2010) and that
359
the MODIS instrument information content may not be sufficient to discriminate vertical
360
variations in re (King and Vaughan 2012). Precipitating regions are also associated with
361
increased cloud heterogeneity and recent evidence suggests that this is a major cause of re
362
differences between the different bands (Zhang et al. 2012; Painemal et al. 2013).
Addition-363
ally, Zhang (2013) showed that the wide droplet size distributions present in precipitating
364
regions can lead to spectral differences. The prevalence of drizzling low cloud in the Southern
365
Ocean (Haynes et al. 2011) increases the likelihood of such retrieval problems for this region.
Based upon observations of Arctic stratocumulus Grosvenor and Wood (2014) showed
367
that the Solar Zenith Angle (θ0) at which MODIS retrievals are made will likely affect re.
368
Reductions in the mean re of up to 9 % relative to low θ0retrievals were observed for θ0>65o,
369
depending upon view zenith angle and wavelength. Solar zenith angle was also shown to
370
affect the relative differences in mean re between the different bands; at low θ0 there was
371
very little relative difference, whereas at high θ0 a spread of around 1 µm was observed.
372
As in other studies, the sign and magnitude of these relative differences was found to be
373
dependent on the cloud heterogeneity. Given all of the above, it is useful to consider re data
374
retrieved from the different bands as this may give some estimate of the degree of uncertainty
375
in MODIS re.
376
Because of the inconsistencies in the MODIS retrieval at high θ0, in order to obtain a
377
consistent re dataset it is prudent to restrict MODIS retrievals to θ0<65 o. A global dataset
378
akin to MODIS Level-3 with such a restriction has been assembled from one year of Collection
379
5.1 MODIS Level-2 data from the Terra and Aqua satellites, as described in Grosvenor and
380
Wood (2014). This dataset also contains re retrievals from the different re bands. Further,
381
only data from 1x1o gridboxes for which the liquid cloud fraction is >80% are included in
382
an attempt to avoid re biases due to cloud heterogeneity within broken cloud scenes (e.g.
383
see Wood and Hartmann 2006; Bennartz 2007). Comparison to the standard Collection 5.1
384
Level-3 product, which is based on the 2.1 µm channel (Fig. 9), shows that the new dataset
385
re values are around 1 µm lower, despite the removal of high θ0 retreivals that were likely
386
biased low. This is due to the effect of the >80% cloud fraction screening. As observed in
387
the previous studies mentioned above, large differences between the different MODIS bands
388
of up to almost 3 µm are apparent in the monthly means. Here we take these differences as
389
being representative of retrieval uncertainties
390
Each re retrieval shows a consistent decrease in summer and an increase in winter (Fig.
391
10). The collection 5.1 results show that the annual cycle of re is fairly robust from year
392
to year, leading us to believe that the single year of data from our modified dataset is a
relatively good representation of the long term climatology. However, since the collection
394
5.1 dataset contains retrievals at very high θ0 during wintertime it is possible that some of
395
this cycle is an artifact of θ0 on the retrieval of re. Such high θ0 retrievals have been removed
396
from the modified dataset, but as a result it has data missing in the wintertime when θ0 is
397
always >65o. Given that the collection 5.1 dataset shows a relatively smooth behavior in
398
winter, we use a spline interpolation to fill in the missing wintertime data in the modified
399
dataset. Grosvenor and Wood (2014) showed that retrievals of re at high θ0 (as occur in
400
winter) are likely to be biased low compared to retrievals at low θ0. Therefore, the amplitude
401
of the annual cycle from collection 5.1 is likely to be underestimated compared to the real
402
cycle and thus can be taken as a lower limit of seasonal variation. It should be noted that it
403
is unknown whether the absolute values of re in the new dataset are correct. However, the
404
variation of re through the seasonal cycle is likely to be less prone to seasonally dependent
405
biases (e.g. due to θ0) and to heterogeneity biases, which may also vary seasonally, and thus
406
the amplitude of the cycle is likely to be more accurate.
407
It is possible that the seasonal re cycle is simply due to the meteorology of the clouds in
408
this region leading to changing cloud top liquid water contents (LWCs). On the other hand,
409
for a constant LWC, it is also consistent with an increase in the cloud top Ndand therefore the
410
notion of the peaking of ocean biogenic production of CCN in summer (Korhonen et al. 2008;
411
Vallina et al. 2006). For a cloud in which the liquid water content increases adiabatically
412
and for which Nd is constant in the vertical re ∝ LW P1/6Nd−1/3. Given this, it is likely that
413
re is twice as sensitive to changes in Nd than to changes in LW P , indicating that seasonal
414
changes in Nd are likely to play a significant role in the observed seasonal variation of re.
3. The Creation of a Cloud Database and the
Calcula-416tion of SW Exitance
417In this section we will describe the process by which we create a database of cloud
418
properties that is consistent with observations and from which the upwelling SW may be
419
calculated utilizing plane-parallel radiative transfer. In the creation of this database our
420
objective is to bring the observations of the distribution of clouds in optical depth and height
421
from the MISR CTH-OD histogram into agreement with the coarsely vertically resolved
422
area-averaged cloud properties. That is to say, we wish to find a distribution of liquid and
423
ice content within the bounds of the MISR CTH-OD histogram such that it satisfies the
424
observed liquid and ice water path resolved into low, middle, and high categories and the
425
observed effective radius of liquid and ice. This database is resolved in time and space and
426
covers the Southern Ocean for the period 2007-2008. For each month and latitude-longitude
427
point within the database there is a data structure that describes the clouds that occur in
428
that month and region. Cloud scenes are categorized into several possible configurations of
429
plane parallel clouds and each data structure describes the frequencies of the different cloud
430
configurations. We will refer to these configurations as elements of the data structure.The
431
usefulness of the creation of this database is twofold: first, it allows evaluation of the extent
432
to which observed cloud properties are able to reproduce the observed upwelling shortwave;
433
second, it enables the calculation of the contributions from individual cloud properties to
434
the upwelling shortwave. In the following section we will discuss the way in which the data
435
structure for a given region and time in the database is created and the methodology by
436
which an upwelling SW is calculated from it.
437
First we shall describe the basic data structure used. The overarching descriptor of the
438
cloud population is derived from the MISR CTH-OD histogram, which bins the observations
439
for a given month and region in sixteen height and seven optical depth bins (Marchand et al.
440
2010). The MISR CTH-OD histogram is further refined with the use of the cloud overlap
probabilities to yield four histograms similar to the original MISR CTH-OD histogram each
442
of which describes a particular overlap state. The four possible overlap states are: none (the
443
cloud has no clouds in other height categories beneath it), low beneath the top-most cloud,
444
middle and low beneath the top-most cloud, and middle beneath the top-most cloud. The
445
overlap probability and overlap cases are described in detail in section 2b. The overlap
prob-446
abilities are defined in terms of the low, middle, and high categories. Due to the systematic
447
errors present in the detection of low altitude liquid cloud fraction by Cloudsat (Stephens
448
et al. 2002), we have chosen to utilize a random overlap probability in the calculation of the
449
low cloud fraction instead of the 2B-GEOPROF overlap probability, which is used for upper
450
level clouds. By combining the overlap probability with the MISR cloud fraction, a cloud
451
fraction is calculated for each element of the data structure.
452
The combined MISR CTH-OD histogram and cloud overlap yields a data structure that
453
contains 16(CT P )× 7(τ) × 4 elements that correspond to combinations of cloud top height,
454
cloud optical thickness, and cloud overlap. This data structure for a given month and
455
location is sparsely filled because some overlap cases are impossible given the cloud top
456
height detected by MISR or no clouds have been detected in that particular optical depth
457
and height range. For instance: low and middle cloud beneath a high cloud may only
458
occur in elements of the data structure that correspond to a high-topped cloud. Elements
459
of the data structure that have a non-zero cloud fraction contain the cloud-properties and
460
the atmospheric sounding data that the radiative transfer algorithm requires to calculate an
461
upwelling shortwave flux.
462
The clouds described by the data structure must be consistent with the input data from
463
observations. These input data are: the near cloud top liquid effective radius as measured
464
by MODIS; the ice effective radius and the IWP as retrieved by the 2C-ICE algorithm;
465
the liquid water path from UWISC partitioned vertically utilizing data from 2B-CWC-RO
466
(henceforth UWISC+2B-CWC-RO); the CTH-OD histogram from MISR; and the overlap
467
probability measured by 2B-GEOPROF. For instance, the observed overall liquid water path
from UWISC+2B-CWC-RO for a given time and location should be the same as the liquid
469
water path calculated using all the elements of the data structure.
470
We will now describe the calculation of liquid and ice water contents within the elements
471
of the data structure that are consistent with the observed coarsely resolved liquid and ice
472
water paths as well as the effective radii of liquid and ice. In other words, we will find a
473
distribution of liquid and ice within the granular distribution from MISR CTH-OD that
474
satisfies the coarsely vertically resolved liquid and ice water paths and the effective radii
475
of liquid and ice. This is accomplished by dividing the optical depth pertaining to each
476
element of the data structure into contributions from liquid and ice as well as contributions
477
from low, middle, and high clouds. The optical thickness that describes the cloud in a given
478
data structure element pertaining to the optical depth range(˜τ ), a given top-most CTP, 479
and one of the four potential overlap cases (η ∈ [1, 4]) is written as τCT P,˜τ ,η. The optical
480
thickness τCT P,˜τ ,η exists between the bounds of the MISR optical depth histogram for the
481
optical depth range ˜τ . We may write the optical depth for the data structure element as a 482
sum of all contributions from categories and phases where LM H refer to the low, middle,
483
and high cloud height categories that exist in the data structure element, where CTP refers
484
to the top-most cloud’s cloud top pressure and ice, liq refers to the ice and liquid phases:
485 τCT P,˜τ ,η = LM H∑ m ice,liq∑ p τmp (1)
where the sums are over the existing categories and phases within the atmospheric column
486
that describes the clouds which exist in a given element of the data structure.
487
We may write the 3× 2 matrix of contributions to the optical thickness, τmp, as the 488
product of the scalar total optical thickness τCT P,˜τ ,η for a given data structure element with
489
a 3× 2 coefficient matrix C
490
where the matrix τmp that will be used to calculate water paths is written 491 τmp = τH,ice τH,liq τM,ice τM,liq τL,ice τL,liq (3)
and where C is the matrix
492 C = aHbH,ice aHbH,liq aMbM,ice aMbM,liq aLbL,ice aLbL,liq (4)
that contains coefficients am that apportion the optical thickness vertically between low,
493
middle, and high categories and coefficients bm,p that apportion the optical thickness of a
494
given layer between liquid and ice (liq, ice). The coefficients are defined such that τCT P,˜τ ,η
495
for a given data structure element is completely accounted for by the sum of clouds in the
496
three height categories and two phases such that
497 LM H∑ m am = 1 ice,liq∑ p bm,p = 1 (5)
In order to calculate water paths for an element of the data structure we must know
498
am,bm,p, and the optical depth τCT P,˜τ ,η. The value of bm,p for the data structure at a given
499
time and location is determined by assuming that in each height category the ratio of ice
500
to liquid does not depend on the overlying clouds height or the optical depth of the clouds
501
in the column and only depends on the coarse cloud category. This means that the clouds
502
in a given height category all have the same ratio of ice to liquid. Thus, we may calculate
503
bm,p for all CT P, ˜τ , η in a given data structure in the database using the liquid and ice water
504
paths contained in UWISC+2B-CWC-RO and 2C-ICE for each category and the appropriate
505
effective radii from MODIS and 2C-ICE that correspond to the location and time of the data
506
structure.
507
This leaves τCT P,˜τ ,ηand amas unknowns. We do not know which value of τCT P,˜τ ,η between
508
the bounds of the MISR histogram optical depth intervals best describes the clouds within
that element of the data structure. The MISR histogram bins are set to be consistent with
510
ISCCP bins and are quite wide, thus the choice of a mean bin optical thickness is important.
511
The mean optical thickness within a histogram bin is observable, but the objective here is
512
to compute reflected shortwave from observed intrinsic cloud properties and to evaluate the
513
sensitivity of SW↑ to variation in these cloud properties rather than calculate SW↑ from τ .
514
In addition, there is no way to determine the values of am observationally. There are many
515
configurations of the values of am(CT P, ˜τ , η) and the optical depth τCT P,˜τ ,η constrained by
516
the bin edges of the MISR CTH-OD histogram within the data structure that satisfy the
517
observed liquid water path, ice water path, cloud fraction, and effective radius for the data
518
structure as a whole and we wish to select one of these in a way which does not assign an
519
a-priori distribution of optical thickness.
520
In order to objectively choose a configuration of τCT P,˜τ ,η and am(CT P, ˜τ , η) across the
521
data structure that satisfies the observational constraints for that location and time we utilize
522
a Monte-Carlo technique similar to Kirkpatrick et al. (1983). The Monte-Carlo is used to
523
determine values of am(CT P, ˜τ , η) and τCT P,˜τ ,η that minimize the difference ˜∆W P between
524
observed and modeled area-averaged water paths (W P and W P′, respectively) for ice and
525
liquid and each height category as given by
526 ˜ ∆W P = v u u tLM H∑ m ice,liq∑ p (W Pm,p− W Pm,p′ )2 (6)
The area-averaged water path in each category and phase (W Pm,p′ ) is calculated using the
527
liquid and ice water path and cloud fraction contained in the data structure. In the creation
528
of water paths within the data structure, the liquid and ice water path for a given τm,p are 529
calculated using the observed cloud top effective radius of liquid and the effective radius of
530
ice for that category. Coefficients from Heymsfield et al. (2003) are used in the calculation
531
of IWP from the optical depth and effective radius of ice. Liquid water path as a function
532
of re and τ is calculated according to Slingo (1989). We have chosen to use a Monte-Carlo
within the elements of the MISR CTH-OD histogram, nor the distribution of optical depth
535
between coarse vertical levels and only requires that the data structure generated by this
536
method conform to all the observed cloud properties for that region and time.
537
Using the method described above, the distribution of ice and liquid is found that is
538
determined by the Monte-Carlo algorithm to be in agreement with observations from
2C-539
ICE and UWISC+2B-CWC-RO; the observed effective radii of ice and liquid; and the bin
540
edges of MISR and the cloud fraction distribution within the MISR histogram. Any residual
541
difference between the observed liquid and ice water path in each layer and the values
542
calculated from the data structure are removed by multiplying the water path in each height
543
category, phase, and element of the data structure by a constant factor equal to the ratio of
544
observed to modeled water paths in that height category and phase for that element of the
545
database. This process is used to create a database of mean-monthly cloud populations over
546
the Southern Ocean for 1.18o× 12o (lat × lon) regions during the period 2007-2008.
547
From the data structure for a given region and time we may calculate upwelling shortwave
548
radiation utilizing the Rapid Radiative Transfer Algorithm for GCMs (RRTMG) (Mlawer
549
et al. 1997) and the surface albedo parameterization of Jin et al. (2011). Calculations were
550
performed using a modified version of RRTMG with liquid cloud optical properties from Mie
551
calculations using a modified gamma size distribution with dispersion equal to 0.12. These
552
properties are planned to be implemented in a future version of RRTMG (Mlawer 2013).
553
In order to calculate the monthly mean upwelling shortwave from our data structure,
554
orbital equations were used to calculate the distribution of solar zenith angles (θ0) for a
555
given month and location. The SW↑ for each element of the data structure was calculated
556
for six evenly spaced θ0 values within the month’s range. These values were then interpolated
557
to 150 values of θ0. Values of SW↑ at each interpolated θ0 were weighted by time and cloud
558
fraction for each data structure element to calculate a monthly mean SW↑for a given region.
559
For each cloud layer represented within each element of the data structure the RRTMG
560
was supplied with a vertically homogeneous layer consisting of liquid and ice of fixed pressure
thickness consistent with Wang et al. (2000). A single mean effective radius is provided
562
for the liquid and a single mean effective radius is provided for the ice. MODIS retrieves
563
droplet effective radius close to cloud top (Platnick 2000). In order to calculate an re that
564
describes the rethroughout the cloud rather than just near cloud-top it is assumed that LWC
565
increases linearly with height from cloud base which, at least for low clouds, is consistent
566
with observations (Painemal and Zuidema 2011). Thus, using the method described by
567
Brenguier et al. (2000), we calculate an effective radius for a vertically homogeneous layer
568
that has the same LWP and optical depth as observed. This equates to an re value that
569
is 5/6 of the retrieved cloud top re and thus results in a higher reflectance. We also show
570
comparisons to observations at the end of this section using the unmodified retrieved re to
571
give some indication of the uncertainty range introduced by these assumptions.
572
To test the realism of our cloud database and the accuracy of our radiative transfer
573
calculations we compared our calculated monthly SW exitance values to those from CERES
574
EBAF 2.6r (Loeb et al. 2009). In order to examine the implications of uncertainty in observed
575
cloud properties in the calculation of SW↑ the data used as input to the algorithm have
576
been repeated many times varying the observed liquid water path, the ice water path, and
577
the effective radius between the minimum and maximum of the uncertainty range attached
578
to each data set. A list of different cases is shown in the supplementary material online.
579
While this abridged (for computational efficiency) list of possible input cases studied does
580
not completely evaluate the range in SW↑ due to input uncertainty, it does allow a rough
581
estimation of the dependence of upwelling shortwave on uncertainty in the input data.
582
The seasonal cycle of exitance and reflectivity (defined as the ratio SW↑SW↓−1 at the
583
TOA) compared to CERES is shown in Fig. 11. Comparing observed and calculated SW↑
584
found values of R≥ 0.95 and comparing observed and calculated reflectivity, which is more
585
sensitive to small errors in wintertime shortwave flux, found values of R ≥ 0.67 (Fig. 12).
586
Grosvenor et al. (2012) and Chubb et al. (2013) reported that in situ observations of
587
Southern Hemisphere high-latitude low clouds from aircraft show high abundances of
cooled water, but relatively infrequent observations of ice particles. Chubb et al. (2013) also
589
noted the frequent occurrence of drizzle, which, because of the large size of drizzle drops,
590
may be confused with ice by the active remote sensing instruments. In consideration of this
591
possible systematic error in the 2C-ICE data we also show the case in which low cloud ice
592
in the 2C-ICE data set is set to zero (Fig. 11 and 12). Removal of the low cloud ice reduces
593
the Southern Ocean reflectivity, especially during the winter, but due to the relatively weak
594
radiative effect of ice it does not drastically this alteration in the seasonal cycle of SW↑
595
relative to the possible range of SW↑ engendered by the uncertainty in the other cloud
596
properties such as the channel of MODIS used to retrieve re or its assumed profile within
597
the cloud as can be seen on the large range of the cases without low ice shown in figures
598
11 and 12. While it is probably somewhat unphysical that no ice exists at pressures above
599
680 hPa, it is also worthwhile to consider the results from the 2C-ICE carefully given that
600
the radiative signature of the low cloud ice is negligible.
601
4. The Impact of Cloud Properties on TOA CRE
602Because the upwelling shortwave computed from our synthesis of remotely detected cloud
603
properties is in agreement with observations of upwelling shortwave, this analysis framework
604
may be used to probe the importance of variability in individual cloud properties for the
605
SW exitance over the Southern Ocean. The framework of this analysis makes it relatively
606
straight-forward to alter cloud properties and recompute the upwelling shortwave, thus
al-607
lowing examination of the contributions of annual variation in cloud properties to the annual
608
variation in albedo.
609
The effect of the annual cycle of various cloud properties on the upwelling shortwave
610
is examined because the annual cycle is the strongest signal in most cloud properties. To
611
examine the impact of the seasonal cycle of a given cloud property on shortwave flux, SW↑
612
is recomputed using the annual mean distribution of that property contained in each data
structure, while allowing the distribution of the other properties to vary. The resulting
614
distribution contained in the data structure is denoted as ξ. We then define the variation
615
in cloud radiative effect (CRE) due to variation in cloud properties as CRE(ξ)− CRE(¯ξ)
616
where ξ refers to the case where the distributions of all cloud properties are allowed to vary
617
according to their seasonal cycles. To study the effects of the seasonal cycle of the radiatively
618
dominant low cloud fraction the cloud fraction within each data structure is adjusted to keep
619
the low cloud fraction constant while allowing upper level cloud fractions to freely vary. That
620
is to say, for a given month and location both the low cloud with no overlying cloud and the
621
low cloud beneath overlying cloud will be adjusted.
622
An ensemble is created to test the uncertainty in the impact of the annual cycle of a given
623
cloud property on changes in SW↑ due to measurement uncertainty in the observations used
624
as input. Ensemble members are created by randomly selecting values for each input variable
625
from values that are normally distributed about the mean observed value and within the
626
measurement uncertainty at each point in time and space. The input variables that are
627
selected from normal distributions within the observational uncertainty are: the liquid and
628
ice water paths; the effective radius of ice and liquid (the latter is assumed to be adiabatically
629
distributed); the probability of cloud overlap from Cloudsat; and the ratio of liquid in each
630
height category from 2C-CWC-RO. For each calculation of CRE(ξ)−CRE(¯ξ), ten ensemble
631
members are created, and an estimate of the uncertainly of this value due to uncertainty in
632
input variables is calculated. We will now discuss the effect on SW↑ of each seasonal cycle
633
calculated in this way.
634
The effect of the observed annual variation of re in low cloud shows a strong contribution
635
to CRE between 6− 8 W m−2 (or around 2− 4% of the SW↑ as calculated by dividing the
636
change in CRE in each location and month by the unperturbed SW↑ for that location and
637
month and taking the mean of the fractional changes for all locations) during the summer
638
months due to the decrease in droplet size as seen in Fig. 13. There is a mean annual
639
contribution of 1.9 W m−2 (0.84%) to SW↑ and an absolute contribution (e.g. the mean of
the absolute values of the difference) of 2.5 W m−2 (1.8%). This result is intriguing as it
641
implies that some appreciable amount of summertime cloud brightness in the Southern Ocean
642
is due to the seasonal cycle of re. Providing that re is controlled by Ndmore than LW P , this
643
is consistent with Kruger and Grassl (2011) who found a high level of correlation between
644
marine CCN production and liquid cloud optical depth. It is also interesting to note that this
645
result is similar to estimated impact of dimethyl sulfide on the ECHAM5-HAMMOZ model.
646
Cloud fraction was found to be increased by 2.5%, re was found to decrease by 15-18%, and
647
upwelling shortwave in the summer increased by 9.32 W m−2 upon the inclusion of dimethyl
648
sulfide emissions in the model (Thomas et al. 2010).
649
We may also examine the effects of using the annual average in-cloud liquid water path
650
for low clouds rather than the seasonally varying one (Fig. 14). We can see that the peaking
651
of total water path at pressures above 680 hPa in wintertime contributes positively to the
652
CRE during this season (3− 6 W m−2 (5− 8%) ), while the summertime decrease in in-cloud
653
liquid water paths in the low cloud leads to a decrease in CRE (1 − 4 W m−2 (1− 3%)
654
). The seasonal cycle of LWP makes a mean contribution to SW↑ of 2.1 W m−2 (3.8%)
655
and an absolute contribution of 5.7 W m−2 (6%). The fact that the wintertime shortwave
656
exitance computed from these cloud properties is not significantly over-estimated compared
657
to CERES gives us some confidence in the observed wintertime increase in LWP diagnosed
658
by MISR and UWISC because Fig. 14 suggests that without such an increase the wintertime
659
SW↑ would be much less than observed by CERES.
660
Similarly to the examination of the impact of the seasonal cycle of in-cloud liquid water
661
path within the low clouds performed above, the effect of using the average in-cloud ice water
662
path for low clouds rather than the seasonally varying one is investigated. The seasonal cycle
663
of in-cloud ice water path has a significant radiative impact (Fig. 15), producing a decrease
664
in SW↑ ranging from 3− 5 Wm−2 (1− 2%) during the summer and an increase less than
665
1 Wm−2 (< 1%) during the winter. The seasonal cycle of in-cloud ice water path makes
666
a mean contribution to SW↑ of −0.93 W m−2 (−0.3%) and an absolute contribution of
2 W m−2 (1.7%).
668
Given that MISR effectively detects low cloud and that they exhibit a strong annual
669
cycle, we examine the impact of low cloud fraction variations on the shortwave exitance.
670
For each month we adjust the total cloud fraction in the low category to be equal to the
671
annual mean value. The distribution of clouds in optical depth and height is different in
672
every month. The distribution for each month is adjusted by a constant multiple so that
673
the total low cloud for that month is equal to the annual mean low cloud fraction. In cases
674
where no cloud fraction is retrieved, a spline fit is used to fill in missing values so that an
675
annual mean may be calculated. The fractional change in CRE due to variation in low cloud
676
coverage is shown in Fig. 16. The increase in low level cloud in the summer increases SW↑
677
by approximately 9− 11 W m−2 (2− 4%) and decreases it in the winter by approximately 3
678
W m−2 (6− 8%). The seasonal cycle of cloud fraction is relatively symmetric with a small
679
mean effect on the SW radiation of 2.1 Wm−2 (−1.5%), where the difference in sign is due to
680
the latitudinal gradient of insolation, but a large absolute contribution of 8.3 Wm−2 (7.7%).
681
Finally, we attempt to quantify the significance of the seasonal transition of ice to liquid
682
in middle and low cloud for a fixed total water path. Figure 17 and Fig. 18 show the
683
impact on SW↑ for two different microphysical assumptions in order to illustrate the likely
684
importance of considering how rechanges during phase transitions. In the first case the extra
685
liquid mass resulting from decreases in ice fraction is transferred to the existing number of
686
droplets so that re increases. In the second case we keep re the same and thus assume
687
that the extra water mass becomes additional cloud droplets. We see that the increase in
688
liquid fraction during the summer increases the upwelling shortwave in this season. This
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brightening is strengthened by holding the re fixed rather than the number of droplets as
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seen by comparing Fig. 17 to Fig. 18. Summertime increases yield increases in upwelling
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SW around 2− 4 Wm−2 (1− 2%) in the former case and around 1 − 2 Wm−2 (< 1%) in the
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latter. The difference in the strength of the phase transition dependent on microphysical
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assumptions draws attention to the importance of careful consideration of the influence of
