A Midlatitude Cirrus Cloud Climatology from the ... - AMS Journals

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A Midlatitude Cirrus Cloud Climatology from the Facility for Atmospheric Remote Sensing. Part II: Microphysical Properties Derived from Lidar Depolarization KENNETH SASSEN

AND

SALLY BENSON

Department of Meteorology, University of Utah, Salt Lake City, Utah (Manuscript received 5 July 2000, in final form 8 January 2001) ABSTRACT In Part II of this series of papers describing the results of the extended time observations of cirrus clouds from the University of Utah Facility for Atmospheric Remote Sensing (FARS), the information content of laser backscatter depolarization measurements in terms of cloud microphysical content is treated. The authors rely on scattering principles indicating that polarization lidar can be applied to identifying cloud phase, and describing ice particle shape and orientation. It is found that 0.694- mm lidar linear depolarization ratios d obtained in the zenith display a steady increase with height. With respect to temperature, a minimum of d 5 0.25 is found at 217.58C, where horizontally oriented planar ice crystals are to be expected, and the d increase up to 0.45 at 277.58C. This trend indicates a basic transition in cirrus ice crystal shape with temperature, likely reflecting not only the effects of crystal axis ratio (i.e., plate-to-column) but also internal and radial crystallographic features. Evidence for transient supercooled liquid clouds embedded in cirrus is found at temperatures generally .2208C. Off-zenith lidar data show that the effects of oriented plate crystals in lowering d are widespread in cirrus, especially at temperatures .2458C. Comparisons with other lidar studies are made, and it is concluded that the depolarization data reveal fundamental distinctions in cirrus cloud particle properties that vary with temperature and probably geographical location. It is important to understand such variations in order to improve the ability to model the effects of cirrus clouds on climate.

1. Introduction Cirrus clouds have long fascinated observers. Their shape, texture, and color set them apart from all other clouds. Although phenomonological classifications are relatively recent (Lynch 2001), their delicate structures, like mares’ tails, or shrouds of continuous stratus with accompanying halos and arcs, are firmly routed in early art and science. Yet, what makes cirrus distinct, other than their obvious frozen nature, is the fact that their structures reveal formation mechanisms that are dissimilar to the generally mixed phase clouds of the middle and lower atmosphere: they are far removed from precipitation, but they are often harbingers of precipitating cloud systems. The details of the composition of cirrus have recently become of heightened scientific interest because of their role in modulating the climate of the earth, but this should not lessen their visual impact in the heavens. In a series of papers, we are undertaking a comprehensive review of findings from a 12-yr cirrus cloud research program being conducted at the University of Utah Facility for Atmospheric Remote Sensing (FARS; see Sassen et al. 2001). These measurements have been Corresponding author address: Kenneth Sassen, 135 S. 1460 E. (819 WBB), University of Utah, Salt Lake City, UT 84112. E-mail: [email protected]

q 2001 American Meteorological Society

obtained in support of the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (Cox et al. 1987) Extended Time Observations (FIRE ETO) program, as described in Sassen and Campbell (2001, hereafter Part I). In Part I we considered the macrophysical and dynamical properties of this extensive cirrus dataset, and discussed the connection between cirrus and the prevailing synoptic weather conditions. The latter is needed to put our observations in the context of findings from other regional studies. Here in Part II, we examine what can be revealed about the basic nature of cirrus cloud particles, and how they are generated at midlatitudes, from laser backscatter depolarization research. As we will show, the data provide indications of how cirrus particle shape and orientation vary as functions of height and temperature. We rely on a ;6-yr subset of ruby (0.694 mm) lidar linear depolarization measurements from the FIRE ETO dataset to infer this information. It has been established through decades of experimental and theoretical research that the lidar linear depolarization ratio, or d value, is uniquely sensitive to particle shape and orientation, and allows for unambiguous cloud phase discrimination (Sassen 1991). The application of this remote sensing technique to the study of cirrus clouds is reviewed in section 2. In view of the significant dependence of radiative

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TABLE 1. Visible backscatter d values computed through ray tracing for randomly oriented solid and hollow ice crystals with the indicated length (L) to radius (a) ratios (in mm). The length of hollow depth is given by d, where d 5 0 is a solid crystal and a completely hollow crystal contains inserted cones of d 5 100 mm. Adapted from Takano (1987) and Takano and Liou (1995). Linear depolarization ratio (d) L/2a 8/80 (thin plate) 16/80 (plate) 32/80 (thick plate) 64/80 (short column) 200/80 (column) 400/80 (long column)

d 5 0 d 5 10 d 5 50 d 5 75 d 5 100 0.339 0.355 0.394 0.382 0.550 0.563

0.529

0.447

0.340

0.242

transfer on cirrus cloud macrophysical and microphysical properties (Stephens et al. 1990; Ou and Liou 1995; Liou et al. 1998), findings from such a comprehensive ground-based research program have a number of applications. Improving our basic knowledge of cirrus cloud properties would benefit the large-scale models used to simulate the effects of cirrus clouds on climate, and possible feedbacks to climate change. In addition, knowledge of cirrus particle shape and orientation are important for interpreting the radiances measured from satellites, which would allow for the improved characterization of cirrus clouds on a global scale. 2. The backscatter depolarization technique for cirrus research Whereas spherical particles do not cause depolarization during single backscattering, the internal skew ray paths through arbitrarily shaped crystals of ice normally produce copious amounts of backscatter depolarization using visible and near-infrared lasers (Sassen 1991). (The strong internal absorption suffered by infrared energy in ice inhibits the production of depolarization through this process using CO 2 lasers, however.) Although nonspherical particles have to be at least as large as the incident wavelength (Mischenko and Sassen 1998) in order to generate significant depolarization, ray tracing theory predicts that the amount of depolarization is controlled by the precise ice crystal shape (i.e., axis ratio), orientation, and internal structure (Takano and Jayaweera 1985; Takano 1987; Takano and Liou 1995). Table 1 summarizes the results of ray tracing theory for large (relative to a 0.55-mm wavelength), randomly oriented, hexagonal ice crystals of various axial ratios. Clearly, laser depolarization is predicted to increase with increasing axial ratio, and so is greatest for column crystals that are solid. For hollow (column) crystals, it appears that internal structures interfere with the ray paths responsible for the strongest depolarization, at least for the type of simplistic (conelike) cavitations that have been modeled so far. However, simple ice crystal habits with dimensions large enough to have fall attitudes controlled by aero-

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dynamic drag forces (.;100 mm) will tend to orient uniformly in space (Sassen 1980), which produces an anisotropic environment with respect to solar and lidar scattering geometries. In other words, the maximum dimension of a plate (a axis) or column (c axis) crystal will orient in a plane orthogonal to the direction of gravity. Under these conditions, experimental and modeling studies indicate that the fluttering motions of crystals from the horizontal plane will be distributed in a Gaussian fashion (Sassen 1987). Lidar evidence suggests that normally the extent of the crystal deviations from the horizontal plane will be on the order of a few degrees (Sassen 1991), as least as far as the rapid decay in returned parallel-polarized energy is concerned. More recent information provided by our high-resolution scanning polarization diversity lidar (Sassen and Takano 2000; Sassen et al. 2001) indicates that under conditions associated with many vivid halo/arc displays, the crystal wobble angles are noticeably narrower (;61.08), but at relatively warm temperatures in the dendritic growth regime, the wobble angles can approach 6108. The light specularly reflected off a properly oriented crystal face will produce very strong, but nondepolarized backscattering. The intense scattering layers displaying near-zero d values generated by populations of horizontally oriented plates can be identified by tilting the lidar a few degrees off the zenith direction (Platt 1978). This ensures unambiguous cloud phase identification. In practice, only planar (plate, sector, or dendritic) ice crystals will display this effect, since even though appropriately sized columnar crystals will orient with their long axis horizontally, there is normally no mechanism for a pair of prism faces to stay oriented strictly parallel to the ground. The rare exception is the peculiar crystal that produces the Parry arc in cirrus clouds, a subject that is still under investigation (Sassen and Takano 2000). 3. The FARS lidar depolarization dataset The cloud polarization lidar (CPL) is a relatively simple device built around a commercial vertically polarized ruby laser with 1.5-J output power and 0.1-Hz pulse repetition rate. The receiver consists of a 28-cm (C-11) telescope with perpendicular and parallel polarization channels employing dual photomultiplier tubes (PMTs) and a Glan-air polarizing prism (Part I; Sassen et al. 2001). Data are digitized at a range resolution of 7.5 m, and 100 pretrigger points are saved to provide a background signal voltage for each shot using a digital oscilloscope. The lidar pedestal is situated at FARS below an opening skylight in a manually steerable yoke, which permits the lidar table to be titled ;66.08 from the zenith direction to identify ice crystal backscattering anisotropy. The lidar is normally pointed away from the zenith for a few shots to distinguish between the presence of supercooled liquid water cloud and oriented plate crystal layers, but also for more extended periods

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on those occasions when middle-level oriented plates from glaciating upwind altocumulus clouds are present (see Part I). The latter data dominate the off-zenith sample, because although typically subvisual such targets produce very strong zenith signals that can saturate the detectors. Note that the laser pointing direction is monitored and recorded with each shot using a potentiometer with 0.188 precision, and aligned vertically using an electronic level to .0.58 accuracy. It was shown early on (Schotland et al. 1971) that the lidar d value obtained from the ratio of the lidar returns in two orthogonal polarization planes reduces to the backscattering coefficient ratio, or simply the ratio of the backscattered powers. In our studies, in recognition of an additional source of experimental uncertainty, we define the linear depolarization ratio as a function of height z as

d(z) 5 [P(z)⊥/P(z) \ ] Kpmt 2 x,

(1)

where P is the signal strength (or power) in the orthogonal ⊥ and parallel \ planes; Kpmt the ratio of the receiver channel gains (in our case simplified by applying the same high voltage to both PMTs); and x is a correction factor accounting for the imprecise alignment between the laser E vector and the orientation plane of the receiver polarizing prism, as well as any randomness in the transmitted laser pulse polarization properties. The polarization correction factors involve a number of potential sources of error, and so should be based on regular calibrations. Our methods for determining the constants in Eq. (1) are discussed here. A value for Kpmt is obtained from viewing an unpolarized light source positioned at the minimum telescope focus (to avoid polarizing prism acceptance angle errors), as the PMT high voltage is varied. To determine x, it is first necessary to understand the nature of this correction. From laboratory laser backscattering studies using four-channel Stokes analysis (Griffin 1983), it was determined that ice cloud depolarization was comprised of the superimposition of specularly reflected parallel-polarized light and seemingly randomly polarized contributions from a myriad of internal reflections, which depend on each crystals exact shape and orientation. Thus, to account for this error, it is sufficient to decompose the parallel-polarized component back into the true plane using the correction x 5 tanf, where f is the angular error between the transmitter and receiver polarization planes. In practice, although f is typically only ;2.08–4.08 in our setup, x is determined by examining those mid- to upper-tropospheric signals believed to contain no significant aerosol or cloud backscattering (compared to the molecular background), such that d ; 0.02 at our wavelength should be found after correcting for Kpmt . This quality check is regularly applied to our data to monitor any receiver housing misalignment problems. In view of the enormous quantity of data collected over several years at 7.5-m height and mostly 10-s time

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resolutions (totaling approximately 1800 h), a data-averaging scheme was created to both reduce the overall amount of data and improve its accuracy. After examining the time and height variabilities inherent in the lidar dataset and applying a variety of averaging schemes, we determined that 75-m (10 point) by 1-min (6 shot) d value averages effectively compressed the dataset, considerably improved the signal-to-noise ratio, and at the same time preserved the salient small-scale depolarization features associated with basic cirrus cloud structures (Benson 1999). In other words, the use of larger cloud volumes tend to average over gradients in d to yield intermediate values that do not represent the actual variations in the depolarization fields, as from pockets of oriented planer crystals, for example. Average d values are calculated from the sum of the perpendicular-to-parallel signals, rather than the average of individual ratios. Importantly, unreliable d values based on too weak signals (i.e., ,2.5 bits digitized at 256-bit resolution) were deleted from the dataset. However, before applying this procedure to the dataset, we manually screened each day’s dataset in the form of time–height color d value and relative backscattering displays, and referred to FARS field notes describing cloud conditions, to place an envelope around valid cirrus cloud regions and exclude all returns that were due to other cloud and aerosol layers (based on their visual appearance in the field). Moreover, data were not included if a local temperature sounding from the Salt Lake City National Weather Service (,6 h from the observation time) was unavailable, in order to attach a representative interpolated temperature to each data point. All zenith cirrus cloud data reported here are formulated using these averaging, signal rejection, and manual identification schemes: the total amount of 75m by 1-min data points is 1 272 440. The 53 671 offzenith data points, however, were derived from 75-m single-shot data, because many of these observations were collected singly or in limited, manual elevation angle scans. 4. Lidar depolarization results a. Mean value for cirrus The mean d value for all zenith data points is 0.33 6 0.11. However, as we show below, the variability of d with height and temperature, and its dependence of the lidar pointing angle, contain much more useful information regarding the vertical gradient in the microphysical content of cirrus clouds. b. Height dependence of depolarization Shown in Fig. 1 are the mean d values stratified by 2-km intervals of height above mean sea level for three categories of CPL elevation angle: ‘‘zenith’’ (,1.58),

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FIG. 1. Comparison of mean cirrus cloud d values in 2-km height intervals above sea level, for zenith (,1.58), and .2.58 and .4.08 off zenith lidar measurements.

.2.58, and .4.08 data. The zenith category should encompass those cases affected by horizontally oriented plate crystals, the next much less influenced, and the last perhaps only slightly influenced by such effects. As Fig. 1 combines data collected from all seasons, over which time the cirrus cloud heights can vary considerably (Part I), the cirrus dataset extends over the great depth of ;11 km. All three curves reveal the presence of a gradual increase in laser backscatter depolarization with height. Moreover, the off-zenith data clearly have higher d, sometimes as much as 0.05–0.10 greater. This finding shows the importance of the scattering anisotropy created by horizontally oriented planar crystals in cirrus. Below we provide an interpretation of these depolarization trends in more detail in terms of the underlying d temperature dependence. c. Temperature dependence of depolarization Figure 2 and Table 2 provide our results in terms of the mean d values for 5.08C temperature intervals for the three classes of zenith and off-zenith data. Also included in the table are the total number of data points that went into each average so that their statistical significance can be assessed. It is apparent that very few data points occur for temperatures .2108 and ,2708C, thus helping to define our midlatitude cirrus clouds (see also Part I). In addition, we present in Figs. 3 and 4 the

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FIG. 2. As in Fig. 1, except for the temperature dependence in cirrus cloud d. Values in 5.08C intervals.

probability density functions (PDFs) in 108C temperature intervals for the d values in both the zenith and .4.08 datasets, where each bar represents a 0.1 interval. The last panel in each case gives the total PDF over the entire temperature range. Inserted in each panel are the mean and standard deviation of d, and the total number of data points in thousands. TABLE 2. Mean d values in 58C temperature intervals and the number of points in each temperature interval for the zenith, .2.58 off zenith, and .4.08 off zenith data. Zenith Temperature interval (8C)

d

Number of points

0 to 25 25 to 210 210 to 215 215 to 220 220 to 225 225 to 230 230 to 235 235 to 240 240 to 245 245 to 250 250 to 255 255 to 260 260 to 265 265 to 270 270 to 275 275 to 280

0.382 0.333 0.260 0.249 0.262 0.278 0.298 0.314 0.323 0.335 0.352 0.369 0.395 0.430 0.384 0.451

418 2366 11 488 32 866 64 752 105 138 143 573 172 217 178 106 180 034 156 815 126 443 73 791 21 971 2230 232

.2.58 off zenith .4.08 off zenith

d

Number of points

d

Number of points

0.069 0.136 0.228 0.295 0.300 0.304 0.303 0.330 0.346 0.324 0.347 0.389 0.367 0.406

7 507 3266 8611 6285 6343 7302 6545 4980 3706 2593 2295 1064 166

0.069 0.133 0.209 0.290 0.304 0.308 0.308 0.336 0.365 0.330 0.355 0.392 0.390 0.422

7 484 2234 5085 3482 3204 4187 4105 2665 1867 1066 993 598 95

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FIG. 3. Relative frequencies of occurrence of zenith d values in 108C intervals, where each bar represents a 0.1 average, and for the total dataset at bottom right. Inserted in each panel are the average and standard deviation in d, and the sum of the data points in thousands.

It is clear that the basic trend in Fig. 2 is for the mean zenith depolarization to increase with decreasing temperature, as it had done with increasing height. However, in terms of the dependence of d on temperature, there are exceptions to this trend. Although relatively small excursions can be attributed to the inadequate size of some data samples (see Table 2), a significant, broad minimum in d centered at 217.58C is present in the zenith data. Understanding the reason behind this feature is facilitated by comparing the zenith and off-zenith data, particularly the PDFs in Figs. 3 and 4. It can be seen that although the mean off-zenith d values are typically higher than the zenith values, the

difference maximizes at 217.58C at .0.04. At warmer temperatures, on the other hand, the off-zenith data actually decrease to well below the zenith values, a behavior that is inconsistent with the effects of horizontally oriented crystals on depolarization. An examination of the cases producing this result show that the offzenith data have been biased by supercooled liquid water layers embedded in the lower cirrus cloud or just below cloud base, which is not an uncommon occurrence (Sassen 2001). Recalling that the lidar was often tipped off the zenith to unambiguously identify such liquid layers, it is not surprising that the 08 to 2108C PDF sample in Fig. 4 shows a preponderance of ,0.1

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FIG. 4. As in Fig. 3, except for off-zenith lidar collected at .4.08.

d values typical of water clouds. Significant frequencies also occur between 2108 and 2208C, reflecting the occasional occurrence of more strongly supercooled liquid clouds positioned in the lower cirrus cloud region. That the broad zenith d minimum centered at 217.58C is due mainly to horizontally oriented ice plates is confirmed by the high frequency of d , 0.2 in the 2108 to 2208C PDF in Fig. 3. Comparatively high numbers of such low values are also present in the 208 to 2108C and 2208 to 2308C zenith PDFs, showing that oriented planar crystal effects occupy a wider temperature range in cirrus than usually considered appropriate for the plate–dendrite growth regime (e.g., Pruppacher and Klett 1997). As for the off-zenith data in Fig. 4, the

10% frequency of d , 0.1 from 2108 to 2208C is probably the result of backscatter ‘‘leakage’’ of oriented planar crystals with relatively large wobble angles from the horizontal plane, in addition to embedded supercooled water cloud regions. Interestingly, some d , 0.2 values are found at all temperature intervals in Fig. 4, which is discussed below in the context of a mixedhabit cloud content model. Note that the dip in the dvalue profile at 272.58C in Fig. 2 has been traced to a few case studies in which a thin layer of horizontally oriented planar crystals was generated at the tops of these cold clouds. Finally, we conclude that the strong d increase for .2158C can be linked to a preponderance of column and complexly shaped crystal aggregates

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likely to occur at these temperatures (Pruppacher and Klett 1997). d. Discussion According to the ray tracing predictions in Table 1, no ice crystal shape so far modeled has been found to generate d , 0.3. Although the variety of crystal models simulated for backscattered depolarization is far from exhaustive, this leaves no explanation, other than possible ice crystal evaporation and size effects considered later, for the abundance of 0.1–0.3 d values in Figs. 3 and 4. After all, specular reflections off the basal faces of planar crystals have been shown theoretically (Takano and Jayaweera 1985) and experimentally (Sassen 1977) to generate essentially zero depolarization. We believe that the answer to this dilemma is that the microphysical composition of deep cirrus clouds is fundamentally inhomogeneous, as is typically seen in cirrus cloud in situ samples (e.g., Sassen et al. 1994). Cirrus cloud modeling and other evidence points to the effects of local ice crystal generation, gravitational sedimentation, turbulent mixing, and aggregation as sources for the inhomogeneity in cloud content and the spreading of the size distribution (e.g., see Khvorostyanov and Sassen 1998). To account for the likelihood of ice crystal shape–size–orientation mixtures in cirrus we offer the following simple model to prescribe the measured d value under such conditions:

d 5 d i /[1 1 (N h /N r )(b h /b r )],

(2)

where d i refers to the linear depolarization ratio characteristic of randomly oriented crystals of a certain type, and N and b are the relative concentrations and backscattering coefficients for horizontally h and randomly r oriented crystals, respectively. To illustrate the effects on depolarization of a mixture of uniformly and randomly oriented planar crystals, Fig. 5 shows the dependence of d on the concentration ratio assuming d i 5 0.4, and a backscattering ratio of 360:1 in comparison to that of an equivalent maximum-dimension sphere reported by Sassen (1977). (Actually, the b h /b r ratio for typical ice crystal shapes is likely to be higher in view of the comparatively weaker 1808 scattering of many ice particle shapes.) Clearly, relatively few oriented plate crystals are quite effective at lowering d; for example, one oriented plate out of 1000 in the scattering volume will generate d 5 0.3, a d decrease of 0.1 or 25% in this case. Thus, a few simple plates or more complex particles with the fortuitous orientation of a retroreflecting face can drastically affect cirrus cloud d values and explain our findings regarding intermediately low d. However, we should also mention two other mechanisms that could contribute to low d values in cirrus. The first is the effect of minute ice crystals on depolarization, but at the ruby wavelength the dominant particles would have to be less than a few microns in diameter (Mish-

FIG. 5. Model results revealing the dependence of d values on the ratio of randomly oriented to horizontally oriented plate ice crystals, assuming the randomly oriented d i 5 0.4 and b h /b r 5 360 [see Eq. (2)].

chenko and Sassen 1998), a condition that would not likely be found in cirrus over large cloud volumes. On the other hand, the ice crystal shape response to evaporation, which produces rounded crystal forms (Nelson 1998), is likely to generate weaker depolarization than a pristine crystal. We have previously reported cases indicating relatively low depolarization near cirrus cloud base caused by crystal sedimentation into dry subcloud air (Sassen et al. 1994). We suggest that this effect may also be more subtle in lowering d values in the subsaturated regions within cirrus, which may be spatially extensive (Part I). Finally, although multiple scattering collected by the 1 mrad field of view CPL receiver will lead to depolarization increases with pulse penetration depth in dense water clouds, the microphysical properties and azimuthal scattering patterns of typical cirrus clouds should not be conducive for this process to affect significantly our results. Note that even physically thin, high cirrus clouds are quite capable of producing strong depolarization. 5. Comparison with previous findings Although the current study represents the most climatologically representative cirrus dataset to date describing lidar depolarization, it is from a locale. Thus it is important to compare our results with available data from other locations to help assess any geographic variability, and the implications for comprehending global differences in cirrus radiative properties. Thus we provide in Fig. 6 a comparison of our findings with available mean d-value versus temperature tendencies from Platt et al. (1998) for Southern Hemisphere midlatitude, subtropical, and tropical cirrus, and the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) tropical west Pa-

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FIG. 6. Comparison of mean d value vs temperature relations from previous studies, and the current FARS work for zenith and .4.08 elevation angle CPL data. The TOGA COARE data from the tropical western Pacific are taken from Sassen et al. (2000), and the remaining Kavieng and Australian curves from Platt et al. (1998).

cific lidar data from Sassen et al. (2000). Note, however, that the Platt et al. (1998) results were derived using the midcloud temperature of the cirrus layer and the vertically integrated linear depolarization ratio, rather than the d and temperature at each 75-m height interval in our current (zenith and .4.08 off zenith) and the Sassen et al. (2000) data. We interpret this intercomparison as signifying a universal increasing d-value trend with decreasing temperature. Although the results for temperatures .2308C diverge significantly, we attribute this to the consequences of variable sample size and the basic cloud identification criteria employed: that is, the possible inclusion of midlevel mixed-phase clouds. The results in Sassen (2001) should demonstrate that care must be taken to properly and uniformly identify cirrus clouds, regardless of location. Another possible contributor to the differences at the warmer temperatures could reflect differences in the frequency of ice crystal orientation effects in the Platt et al. (1998) zenith-restricted data. Furthermore, to help account for the different curves in Fig. 6 we can also call on geographical differences in the microphysical contents of cirrus clouds that are reflected in lidar depolarization. These differences could result from dissimilarities in the dominant formation mechanism responsible for local cirrus, and in the nature and source of the cloud particle forming nuclei that

could affect the shapes of ice crystals (Sassen 1999; Sassen and Takano 2000). For example, the TOGA COARE airborne data used in Sassen et al. (2000) are unique in that they were collected over the tropical western Pacific Ocean from cirrus and anvils in the vicinity of thunderstorms. The unusually high d values thus could be a consequence of the relatively fresh and pristine cirrus particles derived from the marine (or possibly Pinotubo volcanic) aerosol, using an airborne system that consistently collected off-zenith data. Whereas the midlatitude curves dominated by synoptic cirrus are similar for ,2308C, perhaps to within experimental uncertainties, the equatorial and especially the tropical data are distinct. Whether this is due primarily to cloud microphysical differences, or is simply a result of sample size from these relatively short field campaigns, is uncertain. 6. Conclusions In this second part of a series of papers describing the uniquely comprehensive FARS Project FIRE ETO cirrus cloud observational campaign, we have presented an extended record of ruby (0.694 mm) lidar linear depolarization data as a proxy of radiatively important cirrus cloud microphysical properties. In other words, the details of cirrus composition revealed in laser light

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backscatter depolarization should provide insights into understanding the effects of particle shape and orientation on crucial broadband radiative parameters, such as the single scattering albedo, angular scattering pattern, and scattering anisotropy based on the solar angle. Further work along these lines is clearly needed. First, after employing a manually interactive approach to remove from the dataset cloud layers that were not visually identified as cirrus, we reiterate that supercooled liquid water clouds do not play an important role in the formation and maintenance of cirrus clouds (Sassen 2001). Although there are cases where cirrus formation at the warmest (;2358 to 2408C) temperatures are facilitated by the initial growth of highly supercooled droplet clouds such as along the leading edges of orographic cirrus (Sassen et al. 1989), these are uncommon in comparison to the synoptic generation of cirrus from haze particle freezing at much colder temperatures. On the other hand, transient liquid clouds embedded within cirrus, although not widely observed, have affected the d values at temperatures warmer than about 2208C. Even so, modeling the radiative effects of spherical cloud droplets as surrogates for complexly shaped ice crystals has little basis and remains an unsound approach. The effect on lidar depolarization of the tendency for planar crystals to align in the horizontal plane, which can also generate near-zero d values, plays an important role. The results in Figs. 1 and 2 demonstrate that at most cirrus heights and temperatures, d values increase significantly when the laser is tipped .2.58 off the zenith, and continues to increase somewhat between 4.08 and 6.08. This evidence confirms that the majority of crystals wobble ,2.58 from the horizontal plane (Sassen 1991). An examination of the individual height–time depolarization plots indicates the following rule of thumb. Using a d , 0.2 to signify an environment containing enough oriented plates to dominate the laser backscatter depolarization from a cloud volume, we find that about one-half of the cirrus clouds contain regions showing such low depolarizations. However, these regions are typically spatially limited and so represent only a small fraction of the total cloud volume. It should also be recalled from Fig. 5 that relatively few oriented particles can generate low d values. These occurrences are mostly found in the lower (warmer) portions of cirrus, but it is not uncommon to detect planar crystal orientation effects in thin cloud-top layers regardless of temperature. These observations must reflect the ambient conditions promoting the growth of relatively large plate crystals. It is also important to note that because it does not take many fortuitously oriented ice crystals to yield a noticeable decrease in d, a relatively few widely fluttering crystals can have hidden effects on near-zenith depolarization measurements. For example, if 1 out of 1000 plates is displaced from the horizontal by, say, 5.08, a lidar at 858 elevation angle will measure ;0.3

instead of 0.4 for the case treated in Fig. 5. (In contrast, for the case of complete random plate orientation with our 1 mrad lidar, only about 1 out of 10 million crystals will have the proper orientation to produce a specular reflection, which clearly has a negligible impact on d.) In the future we intend to utilize the scanning polarization diversity lidar to comprehensively study the angular range of backscattering anisotropy produced by various crystals with near-horizontal orientations. When comparing our dataset with previous results from various geographical locations, we conclude that the basic trend of increasing depolarization with decreasing temperature (or increasing height) in the upper troposphere reveals a fundamental dependence of cirrus ice crystal shape, internal structure, and size (i.e., ability to orient). Although it appears that at .;2308C the results from various locales have been influenced by a mixture of high (ice) and midlevel (mixed phase) cloud types, most of the curves in Fig. 6 are rather similar at colder temperatures. A number of potential theoreticaland experimental-based explanations can account for this basic trend. These include the following, although it is most probable that a combination of these factors is responsible: in moving from warmer to colder temperatures, the ice crystals change from plates to columns, simple to radial, hollow to solid, rounded (from evaporation) to pristine, and from potentially a mixedphase to pure ice cloud. We provide the following caveat, however. Ice crystals with more realistically complex internal structures, instead of the simple coneshaped inclusions so far modeled, may be effective at increasing depolarization by virtue of the overall increased shape complexity. In conclusion, to improve our comprehension of the effects of cirrus clouds on climate it is important for radiative simulations of cirrus to treat the basic vertical inhomogenuities in cloud particle size (Heysmfield and Platt 1984; Sassen et al. 1989), and shape as indicated here. As shown through simulations using explicit microphysics and radiation, failure to take into account the vertical gradient in cirrus ice crystal size can lead to significant errors in the radiative balance and cloud forcing (Khvorostyanov and Sassen 1998), and particle shape and orientation will similarly be an important factor. It is hoped that generalized cloud content models for use in large-scale models and satellite cloud property retrieval algorithms can be devised by combining cirrus cloud simulation, in situ probe, and lidar depolarization findings, a subject of future research at FARS. Acknowledgments. Continuing FARS cirrus cloud research has recently been funded by NSF Grant ATM9528287 and NASA Grant NAG-2-1106. REFERENCES Benson, S., 1999: Lidar depolarization study to infer cirrus cloud microphysics. M.S. thesis, Dept. of Meteorology, University of Utah, 136 pp.

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