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Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE 10.1002/2014JD022385 Key Points: • Ice size, aspect ratio, crystal distortion, and asymmetry parameter are retrieved • Rate at which effective radii increase with temperature varies substantially • Ice shape and asymmetry parameter vary with convection strength

Correspondence to: B. van Diedenhoven, [email protected]

Citation: van Diedenhoven, B., A. M. Fridlind, B. Cairns, and A. S. Ackerman (2014), Variation of ice crystal size, shape, and asymmetry parameter in tops of tropical deep convective clouds, J. Geophys. Res. Atmos., 119, 11,809–11,825, doi:10.1002/2014JD022385.

Received 1 AUG 2014 Accepted 2 OCT 2014 Accepted article online 7 OCT 2014 Published online 31 OCT 2014

Variation of ice crystal size, shape, and asymmetry parameter in tops of tropical deep convective clouds Bastiaan van Diedenhoven1,2 , Ann M. Fridlind2 , Brian Cairns2 , and Andrew S. Ackerman2 1 Center for Climate System Research, Columbia University, New York, New York, USA, 2 NASA Goddard Institute for Space

Studies, New York, New York, USA

Abstract

The variation of ice crystal properties in the tops of deep convective clouds off the north coast of Australia is analyzed. Cloud optical thickness, ice effective radius, aspect ratio of ice crystal components, crystal distortion parameter and asymmetry parameter are simultaneously retrieved from combined measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Polarization and Directionality of the Earth’s Reflectances (POLDER) satellite instruments. The data are divided into periods with alternating weak and strong convection. Mostly plate-like particle components with aspect ratios closer to unity and lower asymmetry parameters characterize strongly convective periods, while weakly convective periods generally show lower aspect ratios, relatively more column-like shapes and somewhat greater asymmetry parameters. Results for strongly convective periods show that, with increasing cloud top temperature, the distortion parameter generally decreases, while the asymmetry parameter and effective radius increase. For one of the strongly convective periods, the rate at which effective radii increase with cloud top temperature is more than double that of the other periods, while the temperature dependence of the other microphysical quantities for this period is substantially weaker. Atmospheric state analysis indicates that these differences are concurrent with differences in middle-to-upper tropospheric zonal wind shear. The observed variation of microphysical properties may have significant effects on the shortwave radiative fluxes and cloud absorption associated with deep convection. Additionally, MODIS collection 5 effective radii are estimated to be biased small with an artificially narrow range. Collection 6 products are expected to have less severe biases that depend on cloud top temperature and atmospheric conditions.

1. Introduction Ice clouds associated with tropical deep convection play an important role in the climate system [e.g., Hartmann et al., 2001; Kubar et al., 2007]. The fundamental radiative properties of such ice clouds can be represented in terms of cloud optical thickness, effective ice crystal size, and the first moment of the scattering phase function, commonly referred to as the asymmetry parameter [Coakley and Chylek, 1975; Fu, 2007; van Diedenhoven et al., 2014]. Of these fundamental radiative properties, the cloud optical thickness and the effective ice crystal size in the top of clouds can be inferred from total reflectance measurements at visible and shortwave infrared wavelengths using established techniques [Nakajima and King, 1990]. However, current knowledge on the natural variation of ice crystal asymmetry parameter and its possible correlation with, for example, ice cloud type, cloud top temperature, and atmospheric state is limited [e.g., Baran, 2009]. Moreover, the retrieval of cloud optical thickness and effective ice crystal size using visible and shortwave infrared measurements is sensitive to the assumed ice crystal model, and in particular the asymmetry parameter of the ice model’s phase function [Yang et al., 2008; van Diedenhoven et al., 2012a]. The asymmetry parameter at visible wavelengths is primarily determined by the ice crystal shape [Yang et al., 2013], most importantly the aspect ratios of hexagonal components of complex aggregated ice crystals and the microscopical crystal distortion or roughening [Fu, 2007; Um and McFarquhar, 2007, 2009; van Diedenhoven et al., 2014]. Evidence is accumulating that crystal surface roughening or crystal distortion is present in most ice crystals formed in natural ice clouds [Neshyba et al., 2013; van Diedenhoven, 2014; Cole et al., 2014; Magee et al., 2014], but it is unclear how the level of roughening or distortion relates to the environmental conditions in which the clouds are found. In addition to the radiative properties, crystal shape characteristics such as aspect ratio, as well as crystal size, also largely determine microphysical properties of ice particles, such as fall speeds and capacitance [Böhm, 1989, 1992; Westbrook et al., 2004]. However, VAN DIEDENHOVEN ET AL.

©2014. American Geophysical Union. All Rights Reserved.

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although basic theory exists [Bailey and Hallett, 2009], the variation of detailed crystal shape characteristics with environmental and cloud conditions is largely unknown. Thus, in order to improve the representation of both radiative and microphysical properties of ice clouds in climate models, further information about the natural variation of ice crystal size and shape characteristics is essential. Recently, we developed a new method to retrieve the aspect ratio of ice crystal components, crystal distortion parameter, and asymmetry parameter in the tops of ice clouds using multidirectional polarized reflectance measurements [van Diedenhoven et al., 2012b] and applied this technique to measurements of the airborne Research Scanning Polarimeter (RSP) [van Diedenhoven et al., 2013]. Here we apply the method to measurements of the Polarization and Directionality of the Earth’s Reflectances (POLDER) satellite instrument on the Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) platform, which was in the A-train satellite constellation from December 2004 until December 2009 [Fougnie et al., 2007]. Moreover, for each POLDER pixel we subsequently retrieve cloud optical thickness and the effective ice crystal size using colocated measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Aqua platform. For the retrieval of cloud optical thickness and ice crystal size, an ice model with the asymmetry parameter retrieved from POLDER is used. This approach allows for a retrieval of cloud optical thickness and effective ice crystal size that is unbiased by the assumption of a preselected ice crystal model. Using this method, in this paper we study the variation of ice crystal size, shape and asymmetry parameter in the tops of optically thick ice clouds associated with tropical deep convection. The retrieval procedures are discussed in section 2, and the satellite and atmospheric state data is introduced in section 3. Then, results are presented in section 4 and further discussed in section 5 before concluding remarks summarizing our principal findings are given in section 6.

2. Retrieval Procedures 2.1. Retrieval of Ice Crystal-Component Aspect Ratio, Distortion Parameter, and Asymmetry Parameter The method to infer ice crystal-component aspect ratio, distortion parameter, and asymmetry parameter (g) from multidirectional polarization measurements is described and evaluated in detail by van Diedenhoven et al. [2012b, 2013]. Based on previous observations that scattering properties of complex crystals resemble those of their components [e.g., Fu, 2007; Um and McFarquhar, 2007, 2009], the retrieval method uses individual hexagonal ice columns and plates as radiative proxies for more complex shapes and aggregates. Here crystal-component aspect ratio is defined as the ratio of height to width of the hexagonal components of ice crystals, leading to aspect ratios greater than unity for column-like components and smaller than unity for plate-like components. The crystal distortion parameter is defined per Macke et al. [1996] and is used as a proxy of randomization of the angles between crystal facets possibly caused by stochastic processes such as large-scale crystal distortion, microscale surface roughness, or impurities within the crystals [Liu et al., 2013; Neshyba et al., 2013]. The polarized phase function (P12 elements of the phase matrices) of such hexagonal ice prisms is shown to systematically vary with varying crystal aspect ratio and distortion parameter [van Diedenhoven et al., 2012b]. Since polarized reflectances are mainly determined by low orders of light scattering, part of the single-scattering polarized phase functions can be effectively sampled by multidirectional polarized reflectance measurements. The aspect ratio and distortion parameters of proxy hexagonal ice prisms are inferred by determining a best fit to multidirectional polarization measurements at 0.86 μm within a look-up table (LUT) of simulated multidirectional polarized reflectances calculated by assuming individual hexagonal particles with varying aspect ratios (AR = 0.02–50, 51 in total) and distortion parameters (𝛿 = 0–0.7, 15 in total). This best fit is determined by the combination of ice crystal aspect ratio and distortion parameter that leads to the lowest relative root-mean-squared difference (RRMSD) between measurements and simulated values [van Diedenhoven et al., 2012b]. The aspect ratios that are retrieved with this approach are to be interpreted as a proxy for the aspect ratios of components of the more complex ice crystals that are usually found in ice clouds. Moreover, since the asymmetry parameter of hexagonal ice prisms at nonabsorbing wavelengths is mainly determined by aspect ratio and distortion parameter, the asymmetry parameter of ice crystals in the top of ice clouds are indirectly derived using this approach. In van Diedenhoven et al. [2012b], this approach was evaluated using simulated measurements based on a large collection of solid and hollow, pristine and roughened ice crystals habits [Baum et al., 2011; Yang et al., 2013], and asymmetry parameters were found to be retrieved within about 0.04 (5%) for individual simulated VAN DIEDENHOVEN ET AL.


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measurements, while for the whole collection of crystal habits, the mean bias and standard deviation of the difference between true and retrieved asymmetry parameters were found to be 0.004 and 0.018, respectively. Moreover, particles with plate-like and column-like components were found to be generally correctly identified. The aspect ratio of the components of bullet rosettes were found to be retrieved within about 20% in general, while the distortion parameter was shown to be retrieved within 0.05 in absolute terms. In the case of mixtures of crystals with plate-like and column-like components, the technique is expected to yield aspect ratios approximately equal to the area-weighted mean of AR for plates plus 1/AR for columns. The retrieval scheme was found to be largely independent of calibration errors, range and sampling density of scattering angles, and random noise in the measurements. However, scattering angles between 120◦ and 150◦ are needed for a successful retrieval. Finally, it was estimated that the retrieved asymmetry parameters correspond to the values in the top one or two units of optical depth in the cloud layer [van Diedenhoven et al., 2013]. Previous attempts to extract information about ice crystal shape from POLDER measurements generally assumed a relatively limited selection of ice crystal geometries because of computational limitations [C.-Labonnote et al., 2000; Knap et al., 2005; Baran and C.-Labonnote, 2006; Cole et al., 2013, 2014]. Although qualitative agreement with temporally and spatially averaged measurements can be obtained, such approaches generally lead to ambiguous results and are limited in their ability to characterize the temporal and spatial variation of ice crystal geometry. Using single hexagonal columns and plates as radiative proxies allows us to include a virtually continuous range of component aspect ratios and distortion values, which are recognized as the main particle characteristics determining the scattering properties [Fu, 2007; Um and McFarquhar, 2009; van Diedenhoven et al., 2012b, 2014]. The LUT for this retrieval is produced using ice optical properties calculated with the geometric optics (GO) code by Macke et al. [1996] and using a doubling-adding radiative transfer code [De Haan et al., 1987]. A rough ocean surface [Cox and Munk, 1956] is assumed using a wind speed of 4 m/s. The optical properties are integrated over a normalized gamma size distribution N of the form N(D) = N0 D𝜇p e−𝜆p D ,

(1)

where D is the maximum particle dimension, N0 is a normalization factor, and 𝜇p and 𝜆p are the shape and slope parameters of the size distribution, here taken to be 1.5 and 100 cm−1 , respectively [van Diedenhoven et al., 2012a]. Note, however, that since the phase functions do not change with particle size in the geometric optics framework applied here, the results are independent of the assumed particle size distribution. A solar zenith angle of 24◦ is assumed, which is a characteristic value for the POLDER and MODIS measurements used here. The viewing zenith angles range from 60◦ to −60◦ in 152 steps, and the relative azimuth angles are varied from 0◦ to 90◦ in 10 steps, spanning the full range of possible geometries. Within each POLDER pixel, up to 16 measurements with varying viewing geometries are available, and for each such geometry, the closest relative azimuth angle in the LUT is selected. Subsequently, the LUT is interpolated toward the scattering angles of the observed geometries. Finally, the RRMSD values are calculated using the POLDER measurements and all corresponding simulated measurements within the interpolated LUT. As shown by van Diedenhoven et al. [2013], for clouds with an optical thickness lower than about 5, the polarized reflectances are sensitive to the cloud optical thickness. In these cases, the cloud optical thickness is retrieved using the POLDER total reflectance measurements at 0.86 μm for each combination of aspect ratio and distortion parameter. This step is needed since the retrieved cloud optical thickness depends on the asymmetry parameter. The LUT includes calculations for cloud optical thicknesses ranging from 3 to 10 with steps of 1. For pixels that have a total reflectance at 0.86 μm that is higher than the maximum value in the LUT for a cloud optical thicknesses of 5, the polarized reflectance is assumed saturated and the LUT corresponding to an arbitrarily large cloud optical thicknesses of 50 is used [see van Diedenhoven et al., 2012b]. However, the cloud optical thickness is separately determined using the method described in section 2.2 for all pixels, and only pixels with a retrieved cloud optical thickness above 5 are analyzed in the study described below. 2.2. Retrieval of Ice Cloud Optical Thickness and Crystal Effective Radius Ice cloud optical thickness is retrieved from POLDER total reflectance measurements at 0.86 μm. For each pixel, the measurement with a viewing angle closest to nadir is used. At 0.86 μm, ice crystals are generally large compared to the wavelength and essentially nonabsorbing. Hence, cloud reflectance does not depend VAN DIEDENHOVEN ET AL.


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on the ice crystal size under the assumption of geometric optics [Macke et al., 1996]. Cloud reflectance at 0.86 μm does, however, depend on the ice asymmetry parameter [Zhang et al., 2009]. Therefore, for each POLDER pixel, the ice cloud optical thickness is retrieved using an asymmetry parameter that corresponds to the asymmetry parameter retrieved for that pixel with the approach described in section 2.1. For the inference of cloud optical thickness, an LUT is calculated for cloud optical thicknesses in the range 1–20 with steps of 1 and in the range 22–100 with steps of 2. These LUT calculations use the same radiative transfer model, size distribution, ocean surface properties, and scattering geometries as described in section 2.1. Furthermore, the GO calculations described in section 2.1 are used to obtain the ice optical properties. Bi et al. [2014] have shown that, at least for the ice effective radii and optical thicknesses in the range observed here, the approximations associated with GO do not lead to substantial biases in retrieved cloud optical thickness and effective radius. To include a range of asymmetry parameter assumptions in the LUT, we average all phase functions in the database described in section 2.1 that have asymmetry parameters falling in 0.02 wide bins with centers ranging between 0.7 and 0.86, spanning the range of retrieved asymmetry parameter values. Thus, we do not include all 765 combinations of aspect ratio and distortion parameters described in section 2.1 in order to limit the size of the LUT. For each analyzed POLDER pixel, the LUT that corresponds exactly to the retrieved asymmetry parameter is obtained from the calculated LUT using linear interpolation. Then, the closest match to the measured total reflectance is found within the LUT, and subsequently linear interpolation is used to estimate the cloud optical thickness for which the simulated reflectance exactly matched the measured reflectance. Finally, ice crystal effective radii are retrieved using the reflectances from the MODIS 2.13 μm band that are averaged over the POLDER pixels as described below in section 3.1. For a given cloud optical thickness and ice crystal asymmetry parameter, the reflectance of a uniform cloud layer in this band is controlled by the single-scattering albedo [Coakley and Chylek, 1975]. In turn, the single-scattering albedo at a given wavelength is mainly determined by the ice crystal effective radius, defined as three fourths of the ratio of total ice volume over the total projected area of the ice. The sensitivity of single-scattering albedo to particle shape is minimal [Key et al., 2002; van Diedenhoven et al., 2014]. However, to faithfully retrieve effective radius from reflectances at 2.13 μm, it is essential to use the correct asymmetry parameter at that wavelength [Yang et al., 2008; van Diedenhoven et al., 2012a]. In the geometric optics approximation, the asymmetry parameter at an absorbing wavelength is determined by (1) the refraction plus reflection asymmetry parameter, (2) the diffraction asymmetry parameter, and (3) the single-scattering albedo [Macke et al., 1996; van Diedenhoven et al., 2014]. For ice crystals with size parameters much larger than 100, which can be assumed to dominate ice crystal size distributions in the clouds observed here, the diffraction asymmetry parameter can generally be assumed to be unity [van Diedenhoven et al., 2014]. As is the case for the total asymmetry parameter at nonabsorbing wavelengths, the refraction and reflection asymmetry parameter of complex ice crystals at a given absorbing wavelength is mainly determined by the aspect ratio of their components and the distortion parameter. Thus, the total asymmetry parameter at 0.86 μm and the refraction plus reflection asymmetry parameter at 2.13 μm are closely related. Hence, an ice model that yields an asymmetry parameter at 0.86 μm that is consistent with the asymmetry parameter retrieved at that wavelength, as described in section 2.1, will also lead to a correct asymmetry parameter at 2.13 μm for a given effective radius [van Diedenhoven et al., 2014]. Based on this reasoning, an LUT is created for the inference of ice effective radius containing 2.13 μm reflectances for different combinations of cloud optical thickness, effective radius, and asymmetry parameter as follows. To obtain optical properties for a range of effective radii, we follow Baum et al. [2005, 2011] and use a large variety of normalized gamma size distributions as defined by equation (1) with shape and slope parameters based on more than 14,000 different in situ measurements in different cloud types [Heymsfield et al., 2013]. These size distributions are applied to optical properties for all 765 combinations of aspect ratio and distortion parameters in our database. Subsequently, the optical properties of particles are binned for effective radii that fall within 3 μm wide bins between 5 μm and 65 μm. Then, the optical properties for all 765 combinations of aspect ratio and distortion parameters are stratified according to the asymmetry parameter at a wavelength of 0.86 μm, and subsequently binned in five bins ranging from 0.7 to 0.9. Optical properties within each bin of the 100 different combinations of effective radii and asymmetry parameters are averaged, and 2.13 μm reflectances are calculated for each of the 100 bins at cloud optical thicknesses of 1, 2, 5, 10, 25, 50, 75, and 100. For any given satellite pixel, the LUT is linearly interpolated to the cloud optical thickness and asymmetry parameter at 0.86 μm retrieved using the previously described VAN DIEDENHOVEN ET AL.


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techniques. Finally, the effective radius is retrieved by first determining the closest match within the LUT to the measured 2.13 μm reflectance, after which linear interpolation is used to exactly match the simulated and measured reflectances. Generally, measured 2.13 μm reflectances fall within the ranges of simulated values in our LUT. About half of the 14,000 different in situ measurements used to produce our LUT were collected at campaigns targeting subtropical and tropical deep convection, while the rest is mostly associated with storm and cirrus clouds at midlatitudes and low latiFigure 1. Calculated (solid) and estimated (dashed) relative biases in tudes. However, the effective radius retrieved ice cloud optical thickness (red) and ice effective radius (blue) retrieval results are not expected to as a function of absolute bias in assumed asymmetry parameter g at substantially depend on the choice of 0.86 μm. Estimated biases in retrieved ice effective radius and ice cloud size distributions since, as shown by optical thickness are equal to the relative bias in (1 − g) and its inverse, Wyser and Yang [1998], the relation respectively. The reference calculations assume a cloud optical thickness of 10, effective radius of 29 μm and an asymmetry parameter of 0.79. A between size distribution-integrated solar zenith angle of 24◦ and a nadir viewing geometry is assumed. single-scattering albedo and ice effective radius does not substantially depend on the shape of the size distribution used. To further investigate this, we produce a simulated data set of 2.13 μm reflectances for clouds with various effective ice crystal radii based on in situ measurements only from campaigns targeting deep convection (CRYSTAL-FACE, ACTIVE, and TC4 ). The correct effective radii are found to be retrieved from these simulated measurements using our original LUT to within 1 μm, confirming the assumption that the shape of the size distributions used to create the LUT does not substantially affect the effective radius results. As discussed above, the retrieved ice cloud optical thickness and ice crystal effective radius are sensitive to the ice optical model assumed in the retrieval algorithm, specifically its asymmetry parameter [Rolland et al., 2000; Yang et al., 2008; Zhang et al., 2009; van Diedenhoven et al., 2012a]. Using the calculated LUTs described above, we can assess the biases in retrieved cloud optical thickness and ice crystal effective radius caused by biases in asymmetry parameter of the assumed ice model. For this, simulated measurements at 0.86 and 2.13 μm from our LUT with a combination of cloud optical thickness of 10, effective radius of 29 μm and an asymmetry parameter of 0.79 are taken as an arbitrary reference. Then, the cloud optical thickness and effective radius retrieval procedures described above are applied assuming a range of different asymmetry parameters. Figure 1 shows that the resulting relative bias in effective radius scales nearly linearly with the bias in asymmetry parameter and that effective radii are underestimated/overestimated by about 50% if the asymmetry parameter at 0.86 μm is overestimated/underestimated by 0.1. Similarly, the optical thickness is overestimated by about 65% if the asymmetry parameter is overestimated by 0.1, but the bias in optical thickness is reduced to an underestimation of about 30% if the asymmetry parameter is underestimated by 0.1. These biases are found to be not substantially sensitive to assumed wavelengths used for the retrievals, the solar and viewing geometries or choice of reference cloud optical thickness, effective radius, and asymmetry parameter. Figure 1 also shows that the relative bias in optical thickness is well approximated by the inverse of the relative bias in (1 − g), where g denotes the asymmetry parameter at 0.86 μm, which is to be expected based on the two-stream radiative transfer approximation at nonabsorbing wavelengths [Coakley and Chylek, 1975; King, 1987; van Diedenhoven et al., 2014]. We also find that the relative biases in the effective radius is approximately equal to the relative bias in (1 − g) at 0.86 μm. This latter finding is less obvious from the two-stream radiative transfer theory, although it has been shown before that biases in effective radius and optical thickness nearly cancel when using their product to estimate cirrus ice water paths [e.g., Yang et al., 2008]. Figure 1 clearly demonstrates that accurate information about the asymmetry parameter is critical to the quality of cloud optical thickness and effective radius retrievals. VAN DIEDENHOVEN ET AL.


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3. Data 3.1. Combined MODIS and POLDER Data Here we use a data set compiled for previous work [van Diedenhoven et al., 2012a], consisting of combined data from MODIS and POLDER instruments on the Aqua and PARASOL satellite platforms, respectively. Both instruments were in the A-train constellation during the investigated period with Aqua at a local equatorial crossing time around 1:38 P.M. and PARASOL lagging about 3 min behind. The swath of POLDER is about 2100 km wide and completely overlapped by that of MODIS. Combined POLDER and MODIS measurements obtained from 16 January 2006 to 20 February 2006 between −5◦ and −12◦ latitude and within 128–138◦ longitude are selected (see Figure 2). Figure 2. Map showing study region indicated by the red box Satellite overpasses over this box are (−5◦ to −12◦ latitude, 128–138◦ longitude). The marker is located at Darwin. Map produced using Google Maps. available for most days in the selected range. This time range encompasses the observational period of the Tropical Warm Pool International Cloud Experiment (TWP-ICE) campaign near Darwin, Australia, which lasted from 20 January until 13 February 2006 [May et al., 2008; Frederick and Schumacher, 2008]. POLDER multidirectional total and polarized reflectances at 0.86 μm [Fougnie et al., 2007] and MODIS collection 5 cloud top pressures, ice effective radii and reflectances in the 2.13 μm band [Platnick et al., 2003; Baum et al., 2005; Yang et al., 2007] are used in this study. The MODIS measurements have a spatial resolution of about 1 km at nadir, while POLDER measurements are provided on a ∼ 6 × 6 km grid [Bréon, 2005; Fougnie et al., 2007]. To obtain combined MODIS and POLDER data, here we average all MODIS measurements and products for which the pixel center is within the corresponding POLDER pixel boundaries. Recently, collection 6 (C6) level 1B and level 2 data were released by the MODIS team. However, the cloud top pressures and 2.13 μm band reflectances are not expected to have changed significantly between C5 and C6 for the thick ice clouds sampled here. The expected improvements for the effective radius retrievals in C6 are discussed in section 5. Ice clouds are selected by computing a liquid index as described by van Diedenhoven et al. [2012a]. This liquid index indicates to what degree the rainbow feature from spherical drops is detectable in the directional polarized reflectance. Here only pixels with liquid index values below 0.3 are selected, which selects ice-topped clouds only [van Diedenhoven et al., 2012a]. Furthermore, only pixels with ice clouds for which the algorithm retrieves an optical thickness above 5 are included in the analysis. Based on our previous analysis [van Diedenhoven et al., 2012b], asymmetry parameters are only retrieved if measurements are available for scattering angles between 120◦ and 150◦ . This requirement generally excludes the western half of the POLDER swath and conveniently also excludes geometries for which POLDER polarized reflectances are overwhelmed by sunglint. 3.2. Atmospheric State Daily accumulated precipitation derived from the 3 hourly Tropical Rainfall Measuring Mission (TRMM) data set [Kummerow et al., 1998] and averaged over the study region is shown in Figure 3. Based on accumulated rainfall, eight periods are identified. In the following, we use accumulated rainfall as a proxy for convection strength [Del Genio and Kovari, 2002]. The time series starts with strong convective conditions (period 1), leading up to a mesoscale convective event (period 2). After 24 January, weak convection occurs (period 3) until a convective event on 1–2 February (period 4) and its aftermath (period 5). Subsequently VAN DIEDENHOVEN ET AL.


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a pentad occurs with very little rainfall (period 6), followed by a pentad with slightly more rainfall (period 7). The time series ends with a strongly convective pentad (period 8) after which the daily rain rate continues to be about 35 mm/d for at least another 20 days (not shown). We note that the active, suppressed, clear, and break periods over Darwin during the Figure 3. Daily precipitation rate for study region derived from 3 h TRMM data. Filled/open dots indicate days for which satellite data is TWP-ICE campaign as identified by May available/unavailable. The eight periods identified are indicated by et al. [2008] roughly correspond to peridotted lines. ods 2, 3–4, 5, and 6–7, respectively. The mesoscale convective event around 23 January was also observed over Darwin, although the event around 1–2 February (period 4) was not [cf. Fridlind et al., 2012]. The time series of relative humidity and vertical and horizontal wind speed profiles averaged over the study region as reported by the National Center for Environmental Prediction (NCEP) reanalysis data [Kalnay et al., 1996] are shown in Figure 4. Periods 1 and 2 are characterized by a moist troposphere throughout, strong ascent, and strong middle-to-upper tropospheric zonal wind shear. For the rest of the time series, the troposphere above 500 hPa is substantially drier. Wind profiles remain similar up to the end of period 5 (5 February), after which zonal winds become weaker throughout, and middle-to-upper tropospheric wind shear substantially decreases. Furthermore, period 6 is characterized by weak large-scale vertical motions averaged over the study region. Note that westerly winds at 900 hPa are prevalent, which is considered as a characteristic of monsoon conditions in this region [Drosdowsky, 1996]. The vertical profiles of relative humidity and winds observed using radiosondes from Point Stuart, Australia, as part of the TWP-ICE campaign [May et al., 2008] show a similar variation over time. A synopsis of the atmospheric state during the eight periods is given in Table 1.

4. Retrieval Results

Figure 4. NCEP reanalysis for study region of relative humidity with respect to (top) liquid, (middle) vertical wind speed (positive upward), and (bottom) zonal wind speed. The considered periods are indicated by dotted lines.

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For the eight different periods, the joint histograms of ice cloud pixels filtered using the criteria discussed in section 3.1 are shown in Figure 5 as a function of retrieved cloud optical thickness and MODIS C5 cloud top pressure. The ambient temperatures corresponding to the pressure scale is given on the right figure axes. The total number of included satellite pixels and the number of pixels per day of filtered pixels are also given in the figure. As expected, Figure 5 shows that periods 1 and 2 with strong convective conditions are characterized by an abundance of optically thick clouds with cold tops, which are interpreted as deep convective cores. The 11,815


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Table 1. Date Ranges for the Eight Periods and Mean Values of Rain Rate, Relative Humidity (RH) at 300 hPa and 600 hPa, Large-Scale Vertical Wind Speed w at 500 hPa, and Zonal Wind Shear Between 600 and 200 hPaa Period 1 2 3 4 5 6 7 8 a Data

Dates

Rain Rate (mm/d)

300 hPa RH (%)

600 hPa RH (%)

500 hPa w (m/s)

600–200 hPa Wind Shear (m/s)

16–20 Jan 21–25 Jan 26–31 Feb 1–2 Feb 3–5 Feb 6–10 Feb 11–15 Feb 16–20 Feb

57 74 10 43 7.1 1.5 12 38

62 66 54 51 49 30 36 45

76 78 66 57 56 54 47 62

−0.081 −0.12 −0.038 −0.050 −0.043 −0.035 −0.014 −0.048

19 21 30 20 13 6.4 11 6.9

sources described in main text.

histograms resemble those obtained for deep convective regimes over the Tropical Western Pacific region by Jakob et al. [Jakob and Tselioudis, 2003; Jakob et al., 2005] using retrievals from the International Satellite Cloud Climatology Project (ISCCP) [Rossow and Schiffer, 1991]. The third period shows fewer, shallower, and optically thinner clouds. The fourth period is characterized by a strong peak in precipitation (Figure 3) and

Figure 5. Number of filtered ice cloud pixels as a function of retrieved cloud optical thickness and cloud top pressure (left axes) or temperature (right axes). Each panel is for a separate period as indicated. The total number of pixels per period and total number of pixels divided by the number of days with data availability are also given.



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Figure 6. Statistics of (a) mean retrieved ice crystal-component aspect ratio, (b) distortion parameter, (c) asymmetry parameter, and (d) effective radius near cloud top. Symbols connected by solid lines indicate mean values in 25 hPa wide bins, while the dashed lines indicate the standard deviations 𝜎 using the scales on the top axes. The aspect ratios of column-like particles (i.e., AR > 1) are inverted before averaging. Different periods are represented by different colors as indicated. Periods with strong convection are indicated by dots, while weakly convective periods are represented by squares.

correspondingly by an substantive increase in observed clouds. Although the cloud distribution of the fourth period is similar to that of period 3, more than twice as many clouds are observed per day. More thick, cold clouds are observed during period 5, which may be somewhat unexpected since this period occurs after the peak in precipitation (Figure 3). During periods 6 and 7, very few clouds are observed, consistent with the minimal rain rates observed then (Figure 3). Finally, the strongly convective period 8 shows an abundance of clouds again, with many optically thick cold tops. Next, we investigate the variation of statistics of the retrieved microphysical parameters between the different periods. For simplicity, we group periods 1 and 2 together, as well as periods 6 and 7, since they have similar cloud distributions (Figure 5) and microphysical parameters (not shown). Furthermore, periods 4 and 5 are grouped together as they can be associated with the convective event starting around 1 February, and similar microphysical parameters are observed for these periods as well (not shown). Figure 6 shows mean values and standard deviations of aspect ratio, distortion parameter, asymmetry parameter, and effective radius within 25 hPa wide cloud top pressure bins for each of the periods. The associated ambient temperature scale is also given. To ensure reasonable statistics, points are included only for bins with at least 50 pixels. The aspect ratios of columns (i.e., AR >1) are inverted to ensure that all aspect ratios are in the range 0.02–1 before averaging. Mean values of all parameters for each period are given in Table 2. The percentage of retrievals that are best matched by the simulated data corresponding to hexagonal columns, rather than plates, is given in Table 2 for each period as well. Note that the profiles of microphysical parameters shown in Figure 6 do not represent soundings within convective clouds but instead represent values near cloud top for clouds reaching different elevations. As seen in Figure 6, most parameters appear to have an approximately linear relationship with cloud top pressure (or cloud top temperature) for most periods. The slopes of linear least squares fits with respect to cloud top temperature are also shown in Table 2. Similar slopes are found with respect to cloud top pressure, instead of cloud top temperature, for all parameters (not shown). Based on the maximum values of the standard deviations shown in Figure 6 and the constraint that the minimum number of pixels for each plotted point is 50, the estimated standard errors of the VAN DIEDENHOVEN ET AL.


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Table 2. Retrieval Statistics for the Five Combined Periodsa Period 1–2 3 4–5 6–7 8

Convection

% Columns

AR

𝛿

g

Reff (𝜇 m)

Strong Weak Strong Weak Strong

21 (0.44) 28 (0.05) 21 (0.12) 36 (−0.15) 19 (0.13)

0.62 (−0.0020) 0.50 (−0.0039) 0.58 (−0.00071) 0.49 (0.00057) 0.61 (0.00013)

0.55 (−0.0027) 0.50 (−0.00057) 0.54 (−0.0030 ) 0.48 (0.00073) 0.56 (−0.0011)

0.76 (0.00071) 0.78 (0.00050) 0.77 (0.00053) 0.79 (−0.00017) 0.76 (0.00021)

28 (0.16) 29 (0.16) 30 (0.096) 28 (0.13) 36 (0.39)

a The convection characteristic is given in the first column. Other columns give mean values of the retrieved percentage of column-like crystals, aspect ratio, distortion parameter 𝛿 , asymmetry parameter g, and effective radius Reff . Slopes (in units of ◦ C−1 ) of linear fits to the retrieved parameters with respect to temperature are given in parentheses. The aspect ratios of column-like particles (i.e., AR >1) are inverted before averaging.

points included in Figure 6 are smaller than 0.042, 0.028, 0.0057, and 1.4 μm for the aspect ratio, distortion parameter, asymmetry parameter, and effective radius, respectively. Figure 6 and Table 2 show that, for strongly convective periods (1–2, 4–5, and 8), mean component aspect ratios are near 0.6 and slightly decrease with cloud top temperature, while generally lower mean component aspect ratios around 0.5 are observed for the weakly convective periods (3 and 6–7). Given the estimated standard errors, these differences in aspect ratios between strongly and weakly convective periods are statistically significant at the 95% level. Furthermore, as given in Table 2, during periods with strong convection, the polarized reflectances indicate crystals with plate-like components, rather than column-like components, for over about 80% of the pixels, while for weakly convective periods, plate-like particles are retrieved for only about 65–70% of the pixels. For the strongly convective periods, the fraction of pixels with column-like crystals increases somewhat with increasing cloud top temperature. In earlier work using part of the same data set, van Diedenhoven et al. [2012a] also estimated component aspect ratios around 0.7 for the ice crystals in coldest cloud tops of period 1–2. However, because only two aspect ratios and a limited set of distortion values were tested in that study, we had underestimated the aspect ratios in the warm cloud tops (> −38◦ C) to be near 0.15. For strongly convective periods, the distortion parameter values are about 0.55 on average and decrease significantly with increasing cloud top temperature. The weakest decrease with temperature is seen in strongly convective period 8. The mean distortion parameter values for the weakly convective periods are somewhat lower around 0.5 and show less dependence on cloud top temperature, although much variation is seen in them as also indicated by larger standard deviations. Furthermore, the differences in mean distortion values between strongly and weakly convective periods may not be statistically significant. In our previous work [van Diedenhoven et al., 2012a], distortion parameters for the coldest cloud tops of period 1–2 were correctly estimated to be near 0.6, while the decrease of distortion parameter with increasing cloud top temperature shown in Figure 6 was misinterpreted as a stronger decrease in aspect ratio. For strongly convective periods, mean asymmetry parameters are around 0.76 and are found to generally increase significantly with cloud top temperature, which is expected from the observed temperature relationships of aspect ratio and distortion parameter, since asymmetry parameters generally increase with aspect ratios increasingly deviating from unity and distortion parameter decreasing [van Diedenhoven et al., 2012a, 2014]. The weakest variation with cloud top temperature is found for strongly convective period 8. Generally, significantly greater asymmetry parameters around 0.78 are retrieved for the weakly convective periods. Note that in van Diedenhoven et al. [2012a], we overestimated the asymmetry parameter at warm cloud tops (> −38◦ C) during period 1–2 by about 0.05, resulting from the underestimation of component aspect ratio discussed above. Finally, effective radii are 28–36 μm on average and significantly increase with cloud top temperature. The positive correlation with cloud top temperature is consistent with previous remote sensing and in situ observations [e.g., Yuan and Li, 2010; Protat et al., 2010; Lawson et al., 2010]. Also, the retrieved values are roughly consistent with previous radar and in situ estimates of effective radii in deep convective clouds [e.g., Heymsfield et al., 2006, 2009; Protat et al., 2010; Lawson et al., 2010], although large uncertainties are associated with such estimates and definitions of effective particle size vary [Mace et al., 2005; Heymsfield et al., 2006; Stein et al., 2011; Zhao et al., 2012]. Interestingly, by far the strongest dependence of effective radius on VAN DIEDENHOVEN ET AL.


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cloud top temperature is observed for the last strongly convective period (8), whereas the other parameters varied the least with temperature for this period compared to the other convective periods. Table 2 shows that the slope of effective radius with respect to cloud top temperature for period 8 is more than twice as large than that for the other convective periods. Hence, the difference between effective radii at around, e.g., −26◦ C (312 hPa) for period 8 and the other periods is about 15 μm, which is at least 10 times greater than the standard error in effective radius. Note also that, although mean effective radii are generally larger for period 8 at warm temperatures, the standard deviations for that period are comparable to those of the other periods. The profiles shown in Figure 6 and Table 2 are statistics for all clouds with optical thicknesses above 5. However, no substantial difference is observed when limiting the analysis to specific optical thickness ranges above 5. This suggests that optically thick convective cores, somewhat optically thinner cold stratiform areas, and anvil outflow (cloud optical thickness >5) with similar cloud top temperatures and environmental conditions all have comparable microphysical properties at cloud tops, consistent with our earlier analysis of the data from period 1–2 [van Diedenhoven et al., 2012a].

5. Discussion Figure 6 and Table 2 show a clear distinction between periods with strong versus weak convection. Mostly plate-like particles components with aspect ratios close to unity and low asymmetry parameters characterize strongly convective periods, while weakly convective periods generally show particles with lower component aspect ratios, more column-like shapes and somewhat larger asymmetry parameters. The abundance of compact plate-like crystals in the tops of convective clouds is consistent with previous observations of the dominance of compact and aggregated ice crystals with plate-like components observed in tropical deep convection [e.g., Noel et al., 2004; Connolly et al., 2005; Um and McFarquhar, 2009; Baran, 2009]. The more column-like ice crystals with component aspect ratios further deviating from unity as indicated by the observations during the weakly convective periods may be consistent with a stronger contribution of particles grown in situ, which are more likely to form as column-like crystals, such as bullet rosettes, at the observed temperatures [Bailey and Hallett, 2009; Baran, 2009; Gallagher et al., 2012]. Interestingly, these observations appear consistent with findings by Um and McFarquhar [2009] and Protat et al. [2011] who reported that bullet rosettes and their aggregates dominated the area distributions measured in situ in aged ice clouds near Darwin on 27 and 29 January 2006 during TWP-ICE, that is, during weakly convective period 3, while plates and their aggregates were more abundant in measurements of fresh convective clouds probed near the Tiwi islands on 2 February, which is during strongly convective period 4–5. The microphysical parameters retrieved for the two periods with weak convection (3 and 6–7) are generally similar to some degree. However, somewhat more column-like crystals with lower component aspect ratios and roughness levels and somewhat greater asymmetry parameters are retrieved for period 6–7, compared to period 3, which may be somehow related to the weaker large-scale ascent and drier conditions in the upper troposphere during period 6–7. Furthermore, weaker vertical wind shears during period 6–7 may have influenced cloud properties. Comparing the results for the three strongly convective periods shows that microphysical parameters observed during period 1–2 and period 4–5 are very similar, while period 8 has different characteristics. Especially the rate of increase of effective radius with cloud top temperature is much stronger during period 8 compared to the other periods. The profiles of humidity, vertical wind, and zonal wind (Figure 4) for the different periods show that, while during period 8 the upper troposphere is much drier and weaker large-scale ascent is observed compared to period 1–2, the humidity and large-scale vertical wind profiles for periods 4–5 and 8 are similar. The meteorological quantity that possibly distinguishes period 8 from the periods 1–2 and 4–5 is the middle-to-upper tropospheric zonal wind shear, which is much weaker for period 8 (Table 1). Previous studies [e.g., Robe and Emanuel, 2001; May et al., 2009; Zeng et al., 2009; Fan et al., 2012; Kumar et al., 2013] have demonstrated potential effects of shear of horizontal winds on organization, development and microphysics of tropical convective clouds, and their susceptibility to ice nuclei. Alternatively, the fact that midtropospheric winds (400–600 hPa) are mainly easterly before period 8, while they are westerly before the other periods, may have played a role in the development of different microphysical properties. For most cases, the microphysical parameters retrieved for the strongly convective periods systematically depend on cloud top temperature. Laboratory and in situ observations have shown that crystal habits and VAN DIEDENHOVEN ET AL.


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size distributions depend on environmental conditions, such as temperature and humidity [e.g., Bailey and Hallett, 2009; Lawson et al., 2010]. Less attention has been devoted to the natural variation of aspect ratios of components of crystals and crystal distortion or surface roughness, which largely determine the asymmetry parameter as discussed above. Our results suggest that aspect ratios of components of crystals in tops of deep convective clouds decrease only slightly with temperature. However, the retrieved distortion parameter shows a substantial decrease with cloud top temperature. Recent laboratory studies using scanning electron microscope imagery indicate that surface roughness on ice crystals is prevalent over all investigated conditions and that the surface roughness structures vary with temperature and humidity, although this variation has not been quantified [Pfalzgraff et al., 2010; Pedersen et al., 2011; Neshyba et al., 2013; Magee et al., 2014]. Since the crystal distortion parameter retrieved using our approach is a proxy for crystal surface roughness, our results may indicate that crystal roughness increases with decreasing temperature. Further quantification of the dependency of the degree of crystal surface roughness on environmental conditions is needed, as crystal roughness or distortion substantially impacts the shortwave radiative properties of ice clouds [van Diedenhoven et al., 2014, also see below]. Previous investigations of satellite-retrieved effective radii [Yuan and Li, 2010; Hong et al., 2012] also yielded correlations of effective radius with cloud top temperature for convective clouds, similar to those shown in Figure 6. Using MODIS C5 retrievals of convective cores over various regions, Yuan and Li [2010] showed increases of cloud top effective radius with cloud top temperature at rates varying between about 0.15 and 0.25 μm/◦ C. However, strong vertical variations of effective radius such as observed here during period 8 with rates close to 0.4 μm/◦ C (Table 2) have not been previously reported, nor are substantial differences in the rates for different conditions as indicated by our results. The lack of strong vertical variation in previous results may have been caused by several factors. First, previous studies focused on retrievals averaged over regions, rather than distinguishing periods with varying meteorological conditions. Second, our results suggest that the MODIS C5 retrievals contain substantial biases that hamper the observation of such strong vertical variations of effective radius. These biases are mainly attributable to the fact that the asymmetry parameter of the ice model that is used in the C5 retrievals is a strong function of effective radius, varying from about 0.81 at an effective radius of 20 μm to about 0.87 at an effective radius of 50 μm [Baum et al., 2005]. Our retrievals shown in Figure 6 indicate lower values of the asymmetry parameter with a narrower range than assumed in the C5 model and suggest no consistent relation between effective radius and asymmetry parameter. These biases in the C5 asymmetry parameter assumptions lead to a low bias in retrieved effective radius (Figure 1) [van Diedenhoven et al., 2012a] and, more importantly, to an artificial narrowing of the range of observed effective radius values. For example, for a cloud containing ice crystals with asymmetry parameter of 0.77 and an effective radius of 25 μm, the MODIS C5 asymmetry parameter would be biased by about 0.04, leading to a low bias in the effective radius of about 20% and, hence, a retrieval of about 20 μm (Figure 1). For a cloud with the same asymmetry parameter of 0.77, but an effective radius of 50 μm, the MODIS C5 asymmetry parameter would be biased by about 0.1, leading to a bias in inferred effective radius of 50% and a resulting retrieval of about 25 μm. Thus, in this example the real range in effective radius of 25–50 μm would be artificially narrowed in the retrieval to just 20–25 μm. Indeed, analyzing the vertical variation of effective radii for the data presented here, but using MODIS C5 effective radii, yields rates of 0.086 and 0.12 μm/◦ C for periods 1–2 and 4–5, respectively, which are only slightly lower than rates obtained using our retrievals (Table 2), but results in a rate of 0.22 μm/◦ C for period 8, which is almost half the rate shown in Table 2. Furthermore, mean effective radii of about 24–27 μm are obtained by MODIS C5, about 4 μm smaller than retrieved here (Table 2), consistent with the earlier conclusions of van Diedenhoven et al. [2012a]. Ice cloud effective radius and cloud optical thickness retrievals are expected to be substantially improved for convective clouds in the recently released MODIS collection 6 (C6) product. This latest collection uses an ice model based on severely roughened aggregates of columns with a crystal geometry (i.e., aspect ratio of the crystal components) that is invariant with size, yielding an asymmetry parameter at visible wavelengths of about 0.76 that is essentially also invariant with size [Yang et al., 2013; Platnick et al., 2014]. The adoption of this ice model will generally result in larger retrieved effective radii compared to the C5 results and more importantly will remove the bias in the range of retrieved effective radius values attributable to the size-dependence of the assumed asymmetry parameter in the C5 retrievals that is discussed above. However, our results do suggest more subtle variations in asymmetry parameters depending on cloud top VAN DIEDENHOVEN ET AL.


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temperature and atmospheric state, which are expected to bias the C6 retrievals for convective clouds in other ways. For example, ignoring the substantial variation of asymmetry COT g Reff (μm) F↑ F↓ acloud parameter with cloud top temperature 5 0.80 30 395 562 87 found using the POLDER measure5 0.80 50 385 554 111 5 0.74 30 459 512 87 ments for periods 1–2 and 4–5 (Figure 6 5 0.74 50 449 505 111 and Table 2), would lead to a stronger 40 0.80 30 855 146 174 dependence of retrieved effective 40 0.80 50 832 144 203 radius on cloud top temperature for 40 0.74 30 895 119 167 those periods. In fact, using the results 40 0.74 50 872 117 195 shown in Figure 1 we can estimate the a Computation details described in main text. effective radii that would be retrieved by assuming an asymmetry parameter of 0.76 by scaling the effective radii retrieved for individual pixels by a factor of (1 − 0.76)∕(1 − g), where g represents the asymmetry parameter retrieved for the corresponding pixels. This estimate yields mean effective radii agreeing to the values in Table 2 to within 1 μm for all strongly convective periods. However, this scaling increases the dependence with cloud top temperature for periods 1–2 and 4–5 by about 60–68% to 0.26 and 0.16 μm /◦ C, respectively. For period 8, the rate increases by only 10% to 0.43 μm /◦ C because the retrieved temperature dependence of the asymmetry parameter is small for that period (Figure 6 and Table 2). Furthermore, our results suggest that the MODIS C6 model generally underestimates the asymmetry parameter for thick ice clouds occurring under weakly convective conditions (Figure 6), leading to a high bias in mean retrieved effective radius of about 10–20%. Table 3. Upward (F↑ ) and Downward (F↓ ) Shortwave Fluxes and Cloud Absorptance (acloud ) in Units of W/m2 for Clouds With Different Combinations of Cloud Optical Thickness (COT), Asymmetry Parameter at 0.86 μm (g), and Effective Radius (Reff )a

To estimate the implications for shortwave radiative fluxes related to the retrieved variation in the cloud microphysical properties discussed in section 4, we use the flexible parameterization for shortwave optical properties of ice crystals described by van Diedenhoven et al. [2014]. In addition, we use the two-stream radiative transfer formulas given in that paper, based on Coakley and Chylek [1975], Wiscombe and Grams [1976], and Stephens et al. [2001]. Details about the setup of this code are given by van Diedenhoven et al. [2014]. Assuming a solar zenith angle of 24◦ and a cloud top height of 11 km, Table 3 gives upward and downward fluxes and cloud absorption for a variety of clouds assuming ice crystals with asymmetry parameters (at 0.86 μm) of 0.80 and 0.74, effective radii of 30 μm and 50 μm, and optical thicknesses of 5 and 40. These asymmetry parameters and effective radii approximately bracket the retrieved values shown in Figure 6. Table 3 shows that a change of the asymmetry parameter from 0.80 to 0.74 leads to increased upward fluxes of about 16% and 5% for optical thicknesses of 5 and 40, respectively, while downward fluxes decrease by 9% and 19%, respectively. These changes are largely independent of assumed particle effective radius [cf. van Diedenhoven et al., 2014]. In turn, changing the effective radius from 30 to 50 μm leads to increased cloud absorption of about 28% and 17% for optical thicknesses of 5 and 40, respectively, largely independent of the assumed asymmetry parameter. Such changes in shortwave cloud fluxes and cloud absorption attributable to changes in ice microphysics can have profound effects on the net cloud radiative effects and heating rates associated with convective cloud systems [Kubar et al., 2007; McFarlane et al., 2007]. Thus, it is important that natural variations of ice cloud crystal shape and size with cloud top temperature and atmospheric conditions, such as observed in the data presented in this paper, are well represented in cloud and climate models, which is currently not the case. More research is needed using remote sensing and in situ observations to better establish and understand the relations between variations in ice microphysics and cloud type, cloud top temperature, and atmospheric state.

6. Conclusions This paper presents results of consistent retrievals of ice crystal-component aspect ratio, crystal distortion parameter, asymmetry parameter, cloud optical thickness, and effective radius for ice-topped deep convective clouds using combined measurements of the POLDER and MODIS satellite instruments. Crystal-component aspect ratios, crystal distortion parameters (as defined by Macke et al. [1996]) and asymmetry parameters are retrieved from multidirectional polarized reflectance measurements for each satellite pixel using a recently developed method [van Diedenhoven et al., 2012b, 2013]. Cloud optical thickness and VAN DIEDENHOVEN ET AL.


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effective radius are subsequently inferred from measurements in visible and shortwave infrared wavelength bands, respectively [Nakajima and King, 1990; King et al., 2004], using an ice optical model that is consistent with the retrieved asymmetry parameter. Here observation of deep convective clouds observed off the north coast of Australia from 16 January 2006 to 20 February 2006 are analyzed. Based on TRMM daily accumulated rainfall data, the observations are divided into different periods with alternating strong and weak convection. Periods with weakly convective conditions generally show crystal-component aspect ratios of around 0.5, distortion parameters of around 0.5, and asymmetry parameters of around 0.78. Furthermore, about 30–35% of the measurements for weakly convective conditions are consistent with crystals with column-like components as opposed to plate-like components. Effective radii during these weakly convective periods are generally about 28 μm, somewhat increasing with cloud top temperature. Strongly convective periods show component aspect ratios of around 0.6, distortion parameters around 0.55, and asymmetry parameters of about 0.76. Generally, the distortion parameters decreases and the asymmetry parameter increases with increasing cloud top temperature. Effective radii during the strongly convective periods are generally about 27 μm at the coldest cloud tops and increase with increasing cloud top temperature. Interestingly, the rate at which effective radius increases with cloud top temperature is more than double for the last strongly convective period in our data record compared to the previous two. In turn, the cloud top temperature dependencies of component aspect ratio, distortion, and asymmetry parameter for this last strongly convective period are substantially weaker compared to the previous strong periods. Vertical profiles of relative humidity, large-scale vertical motion, and zonal wind speed from NCEP reanalysis show that the middle-to-upper tropospheric zonal wind shear is much weaker for the last strongly convective period compared to the previous two. These results merit further investigation on the effect of wind shear on the microphysical properties of ice crystals at tops of deep convective clouds using more extensive measurements and cloud model simulations.

Acknowledgments We would like to thank Christian Jacob of Monash University in Melbourne, Australia, for his comments on the work presented in this paper. We thank three anonymous reviewers for their contributions. The authors are grateful to Centre National d’Etudes Spatiales (CNES) and NASA for providing the POLDER and MODIS data. POLDER data were obtained through the ICARE Data and Services Center (www.icare.univ-lille1.fr). MODIS data are available at http://ladsweb.nascom.nasa. gov. NCEP data were provided by the NOAA-ESRL Physical Sciences Division, Boulder Colorado (www.esrl.noaa.gov/psd). TRMM data were obtained from the Giovanni online data system, developed, and maintained by the NASA GES DISC (http://disc.sci.gsfc.nasa. gov/giovanni). We are grateful to Bryan Baum at the University of Wisconsin-Madison for providing the ice particle size distributions (www.ssec.wisc.edu/ice_models/ microphysical_data.html). This work is supported by the NASA ROSES program under grants NNX11AG81G and NNX14AJ28G.


Additionally, we use the results to estimate biases in the operational MODIS C5 and C6 effective radii products. Owing to the assumption of an ice model with generally too large asymmetry parameters, the C5 retrievals of effective radii are shown to be biased low by 20–50%. More importantly, the unrealistic assumption in C5 that the asymmetry parameter substantially increases with particle size leads to a substantial and artificial narrowing of the effective radius range in C5 retrievals. Our results suggest that, over tropical deep convection, the ice model adopted for the MODIS C6 product, which is based on severely roughened aggregates of columns with an asymmetry parameter of around 0.76 that is invariant with size, is more consistent with the POLDER observations and will result in more accurate retrievals of effective radius and cloud optical thickness. However, the variations in asymmetry parameters presented here indicate that there will still be biases in the C6 retrievals for convective clouds that depend on cloud top temperature and conditions such as convective strength and atmospheric state. The variations in microphysical properties of the ice in tops of deep convective clouds are estimated to have significant impacts on the shortwave radiative fluxes and absorption associated with these clouds. Furthermore, crystal shape characteristics such as crystal-component aspect ratio, as well as crystal size, also largely determine microphysical properties of ice particles, such as fall speeds and capacitance. Therefore, it is important to better establish the relations between variations in ice microphysics and cloud type, cloud top temperature and atmospheric state, such as observed in the data presented in this paper, in order to improve the representation of radiative and microphysical properties of ice clouds in cloud and climate models.

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