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  • View in gallery

    Domains used in the study: SO (40°–60°S, 125°–145°W) and NA (45°–65°N, 13°–35°W).

  • View in gallery

    Seasonal mean thermodynamic profiles over the SO (red) and NA (blue) during winter (solid lines) and summer (dashed lines): (a) wintertime T, (b) wintertime RH, (c) summertime T, and (d) summertime RH. Thermodynamic variables are from ECMWF reanalysis. Shadings represent one standard deviation.

  • View in gallery

    A comparison of histograms showing the relative frequencies of CTTs decomposed into their constituent CTPs, using retrieval products from (a),(b),(g),(h) MODIS, (c),(d),(i),(j) CALIPSO, and (e),(f),(k),(l) the DARDAR-MASK: SO (a),(c),(e) winter and (b),(d),(f) summer and NA (g),(i),(k) winter and (h),(j),(l) summer. Note that in version 1 of the DARDAR-MASK product radar detection above the lidar (caused by the 500-m radar vertical resolution brought to a 60-m vertical resolution with the help of the lidar mask) would cause a false cloud top identified as ICE (Ceccaldi et al. 2013). Those false cloud tops have been manually removed in this study. CF is the fractional cloud cover viewed from the TOA for each region during each study period. The CALIPSO class HOI is horizontally oriented ice.

  • View in gallery

    An example from a segment of an A-Train overpass over the SO. (a) MODIS radiance imagery from a near-infrared (NIR) channel 31 (10.78–11.28 μm); (b) MODIS cloud-top phase (NIR); (c) CloudSat CPR Ze; (d) CALIPSO lidar β532; (e) CALIPSO categorization; (f) DARDAR-MASK classification. The A-Train ground track is overlaid in (a),(b). Contours of ECMWF temperatures are overlaid in (c),(e),(f). In (e),(f), the classifications include horizontally oriented ice (HOI), polluted dust (PD), clean continental (CC), polluted continental (PC), clean marine (CM), uncertain (UN), stratosphere feature (SF), supercooled liquid water (SLW), liquid warm (LW), and mixed phase (MIXED).

  • View in gallery

    The corresponding CFADs and CFTDs for the example cloud mask shown in Fig. 4. The Ze CFAD (a) without and (b) with the inclusion of the UN class and β532 CFAD (c) without and (d) with the inclusion of the UN class. (e)–(h) As in (a)–(d), but for CFTDs. Retrievals below 960 m have been removed in the Ze CFADs in (a) and (b) because of the contamination from ground clutter.

  • View in gallery

    The 4-yr climatologies of CPR Ze and CALIOP β532 CFADs for clouds over the SO and NA: Ze CFAD for (a) SO and (b) NA winter and (c) RD between (a) and (b). (d)–(f) As in (a)–(c), but for summertime. (g)–(i) As in (a)–(c) but for CALIOP β532. (j)–(l) As in (d)–(f) but for CALIOP β532. The gray lines in (g)–(l) indicate the altitude of 1.5 km. Pixels with frequencies below 1 × 10−4 are unaccounted for the RD diagrams. Retrievals below 960 m have been removed in the Ze CFADs and RD diagrams.

  • View in gallery

    As in Fig. 6, but for CFTDs. Pixels with frequencies below 3 × 10−4 are unaccounted for the RD diagrams.

  • View in gallery

    As in Fig. 7, but for ICE class.

  • View in gallery

    As in Fig. 8, but for SLW containing clouds (SLW + MIXED).

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A-Train Observations of Maritime Midlatitude Storm-Track Cloud Systems: Comparing the Southern Ocean against the North Atlantic

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  • 1 Monash Weather and Climate, Monash University, Monash, Australian Capital Territory, Australia
  • | 2 Centre for Australian Weather and Climate Research, Melbourne, Victoria, Australia
  • | 3 Monash Weather and Climate, and ARC Centre of Excellence for Climate System Science, Monash University, Monash, Australian Capital Territory, Australia
  • | 4 Monash Weather and Climate, Monash University, Monash, Australian Capital Territory, Australia
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Abstract

Cloud and precipitation properties of the midlatitude storm-track regions over the Southern Ocean (SO) and North Atlantic (NA) are explored using reanalysis datasets and A-Train observations from 2007 to 2011. In addition to the high-level retrieval products, lower-level observed variables—CloudSat radar reflectivity and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar attenuated backscatter—are directly examined using both contoured frequency by altitude diagrams (CFADs) and contoured frequency by temperature diagrams (CFTDs) to provide direct insight into thermodynamic phase properties. While the wintertime temperature profiles are similar over the two regions, the summertime environment is warmer over the NA. The NA atmosphere is generally moister than the SO, while the SO boundary layer is moister during winter. The results herein suggest that although the two regions exhibit many similarities in the prevalence of boundary layer clouds (BLCs) and frontal systems, notable differences exist. The NA environment exhibits stronger seasonality in thermodynamic structure, cloud, and precipitation properties than the SO. The regional differences of cloud properties are dominated by microphysics in winter and thermodynamics in summer. Glaciated clouds with higher reflectivities are found at warmer temperatures over the NA. BLCs (primarily below 1.5 km) are a predominant component over the SO. The wintertime boundary layer is shallower over the SO. Midlevel clouds consisting of smaller hydrometeors in higher concentration (potentially supercooled liquid water) are more frequently observed over the SO. Cirrus clouds are more prevalent over the NA. Notable differences exist in both the frequencies of thermodynamic phases of precipitation and intensity of warm rain over the two regions.

Corresponding author address: Yi Huang, School of Earth, Atmosphere and Environment, Monash University, Clayton Campus, Wellington Road, Clayton, Victoria 3800, Australia. E-mail: vivian.huang@monash.edu

Abstract

Cloud and precipitation properties of the midlatitude storm-track regions over the Southern Ocean (SO) and North Atlantic (NA) are explored using reanalysis datasets and A-Train observations from 2007 to 2011. In addition to the high-level retrieval products, lower-level observed variables—CloudSat radar reflectivity and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar attenuated backscatter—are directly examined using both contoured frequency by altitude diagrams (CFADs) and contoured frequency by temperature diagrams (CFTDs) to provide direct insight into thermodynamic phase properties. While the wintertime temperature profiles are similar over the two regions, the summertime environment is warmer over the NA. The NA atmosphere is generally moister than the SO, while the SO boundary layer is moister during winter. The results herein suggest that although the two regions exhibit many similarities in the prevalence of boundary layer clouds (BLCs) and frontal systems, notable differences exist. The NA environment exhibits stronger seasonality in thermodynamic structure, cloud, and precipitation properties than the SO. The regional differences of cloud properties are dominated by microphysics in winter and thermodynamics in summer. Glaciated clouds with higher reflectivities are found at warmer temperatures over the NA. BLCs (primarily below 1.5 km) are a predominant component over the SO. The wintertime boundary layer is shallower over the SO. Midlevel clouds consisting of smaller hydrometeors in higher concentration (potentially supercooled liquid water) are more frequently observed over the SO. Cirrus clouds are more prevalent over the NA. Notable differences exist in both the frequencies of thermodynamic phases of precipitation and intensity of warm rain over the two regions.

Corresponding author address: Yi Huang, School of Earth, Atmosphere and Environment, Monash University, Clayton Campus, Wellington Road, Clayton, Victoria 3800, Australia. E-mail: vivian.huang@monash.edu

1. Introduction

Satellite remote sensing techniques serve as a valuable tool for studying cloud properties, particularly over the remote oceans where in situ observations are scarce. The top-of-atmosphere (TOA) cloud radiative effects, derived from the passive spaceborne instruments, have been used as a critical constraint for the evaluation of global climate models. However, cloud properties can be rearranged in many ways along the vertical profile to produce the same observed TOA radiation budget (Potter and Cess 2004; Mace and Wrenn 2013). As such, this constraint per se is insufficient to examine cloud physical processes, which is a major cause of the present uncertainty in cloud feedbacks (Dufresne and Bony 2008).

CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), launched in 2006 as part of the A-Train constellation (Stephens et al. 2002), added crucial active remote sensing capabilities to cloud observations. The 94-GHz cloud profiling radar (CPR; Im et al. 2005) on CloudSat and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on CALIPSO (Winker et al. 2007) enabled the retrieval of unprecedented information on vertical profiles of microphysical and radiative properties of clouds. Using A-Train observations, research has been conducted to study the vertical distribution of cloud properties (e.g., Haynes et al. 2011), precipitation processes (e.g., Haynes and Stephens 2007; Suzuki et al. 2010), and cloud–radiation interactions (e.g., Henderson et al. 2013), as well as to evaluate climate models (e.g., Franklin et al. 2013; Bodas-Salcedo et al. 2014) and weather forecast models (e.g., Protat et al. 2014b; Huang et al. 2014).

Despite these exceptional successes, a great challenge remains in evaluating these products under different conditions. Limitations of the spaceborne instruments and their retrieval algorithms have been recognized in several studies (e.g., Marchand et al. 2008; Austin et al. 2009; Haynes et al. 2009), but the effect of potential biases is not well understood and characterized. This challenge is even greater when multisatellite observations are combined with supplementary information and integrated together.

By dividing the clouds into regimes based on cloud-top height and geometrical thickness, Mace (2010, hereafter M10) documented and compared the midlatitude storm-track clouds over the Southern Ocean (SO) and the North Atlantic (NA) with a synthesis of A-Train observations, concluding that there is a “high degree of similarity in cloud occurrence statistics, in cloud properties, and in the radiative effects of the clouds.” Studies comparing cyclones using reanalysis datasets and retrieval products from spaceborne radiometry also concluded that cyclones in the two hemispheres are broadly similar in terms of strength and moisture (Field and Wood 2007). These findings are, perhaps, surprising when reflecting upon the different nature of the storm track systems of the two hemispheres. For instance, studies by Hoskins and Hodges (2002, 2005) investigated in detail the organization and evolution of storm track activities of the two hemispheres, in relation to differences in large-scale dynamics. With A-Train observations, Naud et al. (2012) examined the differences of cloud and precipitation in the warm front for the two hemispheres and found that the Southern Hemisphere cyclones are colder and drier but more vigorous than their northern counterparts. Also, Naud et al. (2013) identified further differences in the seasonal variation of cyclones in terms of moisture, low-level wind speed, and cloud fractions. These findings, albeit largely limited to midlatitude cyclones, suggest that the nature of cloud and precipitation of the two hemispheres may be measurably different.

Another expected substantial difference between the NA and the SO environments is aerosol characteristics, which can alter cloud structure, microphysical, and radiative properties through various finescale processes (e.g., Lohmann and Feichter 2005; Rosenfeld et al. 2014). While the aerosol conditions over the NA contain anthropogenic and terrestrial emissions, clean marine conditions prevail over the SO (Minikin et al. 2003; Murphy et al. 1998). With CALIPSO observations, Hu et al. (2010) discovered the extensive presence of supercooled liquid water (SLW) cloud tops over the SO, consistent with other studies using either MODIS (Morrison et al. 2011) or limited in situ observations (Morrison et al. 2010; Chubb et al. 2013). It has been speculated that the lack of ice nuclei is largely responsible for the abundance of SLW over the pristine SO (e.g., Kanitz et al. 2011; Burrows et al. 2013). The important role of aerosols, therefore, provides a necessary reference point for a further exploration of hydrometeor behaviors under the different environments.

Unfortunately, given the large uncertainties of different merged satellite retrieval products that have been thoroughly discussed in previous studies (e.g., M10; Zhang and Mace 2006; Protat et al. 2010), it is not possible at this point in time to estimate the robustness of the retrieved cloud properties over the remote oceans where there is a lack of in situ measurements that can be used for evaluation. A complementary approach could be an intercomparison of different datasets, which may provide a reference of what the relative uncertainties might be and how it could affect advanced algorithms. In addition to these high-level retrievals, it is also worthwhile to examine the directly observed variables obtained by individual instruments. The latter method can help avoid the complication of the potential ambiguities introduced by different algorithms and their underlying assumptions, at least to some degree. It also provides a basis for understanding the retrieved cloud information.

The objectives of this study are twofold: 1) to illustrate the potential inconsistencies between different satellite retrieval products and how any differences can affect our ability to study cloud properties and 2) to investigate differences in cloud properties of two selected regions over the NA and the SO based on an analysis of both high-level retrievals and the observed variables obtained by the active remote sensors. Here, we only consider the CloudSat CPR reflectivity (Ze) and CALIOP lidar attenuated backscatter at 532-nm wavelength (β532) as our observed variables, as these are the two key variables obtained from the active instruments that underpin numerous higher-level retrieval algorithms.

It should be pointed out that the emphasis of this analysis is on seeking new evidence to revisit the important question posed by M10: whether there are intrinsic differences between the NA and SO midlatitude storm-track cloud systems. The intention is not to supplant what can be accomplished with detailed retrieval processes, but to illuminate any absolute differences and to consider the physical meaning of these observable discrepancies.

The structure of the paper is as follows. Section 2 provides a description of the datasets analyzed. Section 3 presents the intercomparison of reanalysis and retrieval products for the two study regions. Section 4 describes the methodology for examining the observed variables. Results comparing hydrometeor properties for the two study regions with the proposed method are presented in section 5. A discussion and conclusions are provided in section 6.

2. Datasets

Our study domains (Fig. 1) are identical to the SO (40°–60°S, 125°–145°W) and NA (45°–65°N, 13°–35°W) domains examined in M10. We chose the study period from June 2007 to February 2011. The austral winter (summer) is defined as June–August (December–February). Note that the datasets processed in this study are subject to the availabilities of individual products over the two regions during the study period.

Fig. 1.
Fig. 1.

Domains used in the study: SO (40°–60°S, 125°–145°W) and NA (45°–65°N, 13°–35°W).

Citation: Journal of Climate 28, 5; 10.1175/JCLI-D-14-00169.1

a. MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua measures radiances at 36 wavelengths from 0.413 to 14.235 μm. Flying in formation within the A-Train, Aqua MODIS has a repeat time of 16 days and a swath width of 2330 km. The MODIS level-2 version 5.1 cloud products (Platnick et al. 2003) are used in this study. Variables examined in this study are cloud-top temperature (CTT) and cloud-top thermodynamic phase (CTP) that range in size from 5 × 5 km2 to greater than 5 × 20 km2, depending on the viewing angle. Note that MODIS science team supports two CTP products, one based on a near-infrared (NIR) channel and the other based on a visible channel, which is available for daylight passes only. Only the CTP NIR is used here. It is shown in Holz et al. (2008) that MODIS and CALIOP exhibit good agreement in terms of cloud detection, but MODIS tends to overestimate cloud-top height in regions where a low-level temperature inversion is present. Approximately 4500 MODIS images were processed for each region for the 4-yr winter and summer, respectively.

b. CALIPSO

CALIPSO carries CALIOP, a nadir-viewing two-wavelength (532 and 1064 nm) polarization-sensitive lidar (Liu et al. 2009) and a three-channel IR radiometer. The spatial resolution of CALIOP is degraded with increasing altitude. It has the maximum resolutions of 30 m in the vertical and 333 m in the horizontal between altitudes of −0.5 and 8.2 km. Between 8.2 and 20.2 km, the vertical and horizontal resolutions decrease to 60 m and 1 km. The level 2 version 3.01 of the cloud and aerosol layer products (Powell et al. 2010) contains a map of all the layers detected by CALIPSO along with an identification of cloud/aerosol type and subtype and cloud thermodynamic phase averaged to 5-km horizontal resolution. Cloud phase is retrieved with the algorithm described in Hu et al. (2009), using lidar attenuated backscatter β and depolarization ratio. Temperature at the same resolution is derived from the Goddard Earth Observing System Model version 5 (GEOS-5) data product provided to the CALIPSO project by the Global Modeling and Assimilation Office (GMAO) data assimilation system. CALIOP is sensitive to optically tenuous layers and becomes fully attenuated in the presence of clouds with optical thickness greater than 3 (Hu et al. 2009), especially through liquid water layers. A total of 718 (851) and 778 (1077) tracks were processed for winter and summer climatology for the SO (NA), respectively.

c. CloudSat

One of the CloudSat datasets employed in this study is the level-2c precipitation column algortithm (2C-PRECIP-COLUMN) product (Haynes et al. 2009). This product provides the presence, and often intensity, of precipitation derived from estimates of the path-integrated attenuation (PIA) of the CPR signal. PIA is determined for each footprint of the radar at 1.7-km horizontal resolution based on surface wind speed, sea surface temperature, and atmospheric temperature and moisture profiles over the oceans from the European Centre for Medium-Range Weather Forecasting (ECMWF) weather model (Simmons et al. 2007). Compared to other precipitation radars, the CPR precipitation product is relatively sensitive to the presence of small water droplets including incipient precipitation (Stephens and Haynes 2007). But given that the intensity of rain is proportional to the PIA, total attenuation of the radar and multiple scattering limit estimates of the heaviest rain to about 3–5 mm h−1 (e.g., L’Ecuyer and Stephens 2002; Haynes et al. 2009). In heavy rain events when full attenuation of the CPR signal occurs, a flag will still be given to indicate the presence of precipitation, allowing event detection covering the full spectrum of precipitation intensities. A total of 799 (621) and 580 (879) tracks were processed for winter and summer climatology for the SO (NA), respectively.

d. DARDAR-MASK

DARDAR is a radar–lidar project initiated by the Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS) and the University of Reading to provide collocated CloudSat, CALIPSO, and MODIS measurements as well as a cloud classification and ice cloud retrievals on a 60-m vertical and 1-km horizontal resolution grid. Version 1 of the DARDAR-MASK product (Delanoë and Hogan 2010; Ceccaldi et al. 2013) was developed based on the CPR Ze from the CloudSat level-2b cloud mask and radar reflectivities (2B-GEOPROF) product, CALIOP β532 from the CALIPSO lidar level 1b product (Anselmo et al. 2006), and ECMWF wet-bulb temperature (Tw), using a modified variational scheme originally proposed by Delanoë and Hogan (2008). This dataset contains original A-Train measurements (i.e., CPR Ze and CALIOP β532), a set of thermodynamic variables from the CloudSat ECMWF auxiliary (ECMWF-AUX) dataset, and a radar–lidar categorization that returns a range of categories including clear, ground, stratospheric features, insects, aerosol, rain (RAIN), SLW, liquid warm (LW), mixed-phase (MIXED) and ice (ICE), and uncertain (UN) class.

In the version 1 product, a SLW layer is identified based on the β532, the vertical gradient of β532, and Tw. The RAIN category is included for completeness (as the version 1 algorithm does not distinguish between cloud ice and precipitating ice). In the lowest 1.5 km, RAIN may also contain pixels affected by ground clutter (Marchand et al. 2008). The UN class is commonly assigned underneath optically thick clouds where the radar and lidar signal have been heavily attenuated or are missing (Protat et al. 2014a). For this reason we expect the frequency of cloud occurrence to be largely underestimated in the lower troposphere, especially below 1.5 km. As the aerosol categorization is not a primary objective of this product, it is simply reproduced from the CALIPSO official datasets. The DARDAR products can be obtained from the Cloud–Aerosol–Water–RadiationInteractions (ICARE) Thematic Centre (http://www.icare.univ-lille1.fr/). A total of 744 (688) and 622 (814) tracks were processed for winter and summer climatology for the SO (NA), respectively.

3. Comparisons of high-level products for the SO and NA

a. Thermodynamic structures

A critical factor that determines the storm-track cloud properties is the large-scale meteorology, in which the dynamical variations have a vital impact on cloud structure and population. Any differences in the cloud regimes may largely be explained by the different atmospheric conditions of the two regions. Here, we examine as a starting point the vertical structures and variability of temperature (T) and relative humidity (RH), which are the key predictors for cloud formation. These thermodynamic variables are taken from ECMWF datasets included in the DARDAR-MASK product.

Focusing first on the wintertime profiles, the temperature profiles (Fig. 2a) of the two regions display a strong similarity in terms of mean values and standard deviations. The mean freezing level is at approximately 0.8 km. The RH profiles (Fig. 2b), on the other hand, show that the SO environment is moister in the lowest kilometer and slightly drier aloft, while the peaks of the profiles both occurred in the boundary layer (between 0.4 and 0.7 km). In summertime, the environment over the SO is about 4°C colder than that over the NA, and has a stronger variability (Fig. 2c). This difference persists nearly throughout the troposphere. The regional differences in the temperature profiles are consistent with the interhemispheric temperature contrast showed in Feulner et al. (2013). The summertime RH profiles (Fig. 2d), however, show that the NA environment is generally more humid, particularly in the mid and upper troposphere. In the boundary layer, the two RH profiles are fairly close, with the RH over the SO being slightly lower. The heights of the peaks also seem to be lower compared to that in wintertime. In addition to the regional differences, the seasonality of the thermodynamic structure over the SO is noticeably weaker than that over the NA.

Fig. 2.
Fig. 2.

Seasonal mean thermodynamic profiles over the SO (red) and NA (blue) during winter (solid lines) and summer (dashed lines): (a) wintertime T, (b) wintertime RH, (c) summertime T, and (d) summertime RH. Thermodynamic variables are from ECMWF reanalysis. Shadings represent one standard deviation.

Citation: Journal of Climate 28, 5; 10.1175/JCLI-D-14-00169.1

Indeed, the summertime dissimilarity in the thermodynamic structures may be an indicator of the fundamental differences in the meridional profile of the underlying sea surface temperature and the locations of the storm tracks over the two regions. These background states could affect surface fluxes (heat, moisture, momentum, etc.) and energy transports, which are the primary control of the regional cloud and precipitation properties. Also, the uncertainty in the accuracy of the reanalysis over the SO (derived exclusively from satellite observations) should be acknowledged as a potential reason for these differences.

b. Cloud properties from Mace (2010)

The work of M10 in examining a range of cloud properties through a blend of A-Train and supplemental observations was innovative and insightful. M10 describes in detail the algorithms and underlying assumptions employed to derive cloud and radiative properties. For the year 2007, clouds were divided into regimes for both the NA and SO based on cloud-top height and geometrical thickness. Frequency distributions of the annual liquid water path, ice water path, liquid, and water effective radius were derived along with the mean, median, and standard deviation of these fields. Seasonality was not considered. Similarly, the frequency distributions of the cloud radiative effect at the TOA, through the atmosphere, and at the surface for both IR and solar radiation were derived. M10 went further to highlight the dominance of boundary layer clouds (BLCs) in these regions. Based on the similarity of these frequency distributions, M10 concluded that there was an overall high degree of similarity in the cloud fields between the two regions.

c. Comparison of cloud-top thermodynamic phase

Following on from M10, a range of cloud and radiation products exist today that are derived from a combination of A-Train and complementary datasets (e.g., the DARDAR-MASK). One of the innovations of this product is to produce the cloud phase through the depth of the atmosphere, which is of great interest given the importance of the cloud phase to both radiative (e.g., Shupe and Intrieri 2004; Sedlar et al. 2012) and precipitation (e.g., Choi et al. 2010; Storelvmo et al. 2011) processes. Even more algorithms exist if the analysis of cloud phase is constrained to just cloud-top heights that passive instruments can also provide estimates of.

Huang et al. (2012b) examined three such CTP products (MODIS NIR, CALIOP, and DARDAR-MASK) over the SO to explore the seasonality of SLW and to identify the level of consistency between these products. Just as the cloud properties of M10 were used to compare the NA to the SO, so too can these CTP products (Fig. 3). As discussed in Huang et al. (2012b), these CTP statistics all reveal a bimodal structure to the CTTs. The peak at colder temperatures (from −35° to −65°C) highlights the prevalence of cirrus clouds, which are primarily glaciated. The peak at warmer temperatures (from 5° to −10°C) highlights the prevalence of BLCs, which are primarily liquid.

Fig. 3.
Fig. 3.

A comparison of histograms showing the relative frequencies of CTTs decomposed into their constituent CTPs, using retrieval products from (a),(b),(g),(h) MODIS, (c),(d),(i),(j) CALIPSO, and (e),(f),(k),(l) the DARDAR-MASK: SO (a),(c),(e) winter and (b),(d),(f) summer and NA (g),(i),(k) winter and (h),(j),(l) summer. Note that in version 1 of the DARDAR-MASK product radar detection above the lidar (caused by the 500-m radar vertical resolution brought to a 60-m vertical resolution with the help of the lidar mask) would cause a false cloud top identified as ICE (Ceccaldi et al. 2013). Those false cloud tops have been manually removed in this study. CF is the fractional cloud cover viewed from the TOA for each region during each study period. The CALIPSO class HOI is horizontally oriented ice.

Citation: Journal of Climate 28, 5; 10.1175/JCLI-D-14-00169.1

When comparing the SO to the NA, clear differences are found for both the overall distribution of the CTTs (cloud macrophysical properties) and the partitioning of the CTP at various temperature ranges. Focusing first on the winter, an immediate difference in the macrophysical properties of the clouds is that more BLCs are observed over the SO than the NA (Figs. 3a,c,e versus Figs. 3g,i,k, cumulative frequencies at temperatures greater than −10°C). This difference is most pronounced for CALIOP where the cumulative frequency at temperatures warmer than −15°C is approximately 33% over the SO but only approximately 21% over the NA. To test the significance of the differences, we use a Monte Carlo method by which 500 tracks are randomly drawn from the A-Train tracks that cover either the SO or the NA domain to produce the regional mean climatology for winter and summer, respectively. For each region, this experiment is repeated 1000 times. The 4-yr statistics for the two seasons are then compared with the 1000 regional mean climatologies. The results presented above are statistically significant at 95% confidence level.

To a lesser extent this regional difference in the wintertime BLC occurrence is also found in both the MODIS and DARDAR-MASK products. The regional difference in the summertime BLC occurrence is not nearly as obvious, although the BLCs over the NA are warmer, consistent with the regional difference in temperature (Fig. 2).

The opposite holds when looking at the cirrus clouds with CALIOP and DARDAR-MASK: more cirrus clouds are evident over the NA than the SO, particularly during summer (Figs. 3d,f versus Figs. 3j,l, cumulative frequencies at temperatures below −40°C). As these are spaceborne observations, recording more cirrus clouds will accordingly correspond to less opportunity to observe lower-altitude clouds. The differences in the wintertime BLCs between the two regions are not simply an artifact of the differences in the occurrence of the overlying cirrus (Figs. 3c,e versus Figs. 3i,k, cumulative frequencies at temperatures below −40°C).

Regional differences are also evident in the CTP between 0° and −35°C. Overall, more SLW and MIXED exist over the SO than over the NA, although this observation will be biased by the cloud macrophysical properties. As discussed in Huang et al. (2012b), there are relatively few observations of glaciated cloud tops over the SO at temperatures warmer than −30°C for both MODIS and CALIOP. The same can also be said for the NA, although glaciation is significantly more frequent. The bias arising from the cloud macrophysics can largely be avoided by focusing on individual temperature bands. Consider the temperature interval from −35° to −30°C with CALIOP for winter. Over the SO roughly 45% of the observations are of SLW + MIXED, compared to only 20% over the NA. From −30° to −25°C the wintertime percentage of SLW + MIXED increases to 70% (45%) over the SO (NA) for CALIOP. Again, these differences are statistically significant.

Huang et al. (2012b) also commented on the uncertainties arising between different instruments and algorithms. This issue is clearly illustrated in Fig. 3. It is apparent that the differences in the CTP are actually larger between instruments and algorithms than they are between regions. Table 1 provides an overview of the retrieval algorithms that helps explain the origins of the inconsistencies between different thermodynamic phase products.

Table 1.

An overview of MODIS, CloudSat, and CALIPSO observations and cloud thermodynamic phase retrieval products/algorithms.

Table 1.

d. Comparison of precipitation

Another means of comparing the SO and NA with A-Train high-level products is the CloudSat precipitation. For every footprint this product records the presence of precipitation, the phase of any precipitation (warm rain, snow, and mixed), and the intensity of warm rain (no estimate of the precipitation rate is made for snow and mixed). Further, the confidence in these observations (certain, probable, possible, and uncertain) is recorded. The 4-yr gross statistics for the two regions are shown in Table 2. The overall frequency of observing precipitation (all phases and all confidence levels) is more common during winter for both regions: the wintertime frequency over the SO (NA) is 31.7% (35.9%) compared to the summertime 26.9% (24.3%). Warm rain at all confidence levels is observed less frequently during winter at 15.3% (17.3%) than during summer at 17.2% (21.7%) over the SO (NA), which is not surprising as the colder temperatures during the winter lead to the more frequent observation of snow and mixed phase precipitation.

Table 2.

A comparison of precipitation properties for the Southern Ocean (SO) and North Atlantic (NA) using CloudSat precipitation product. The results presented are significant above 95% confidence level.

Table 2.

Focusing on the regional differences, the frequency of wintertime precipitation is greater for the NA (35.9%) than the SO (31.7%). This difference is in the rain and the mixed phase precipitation, not snow. During the summer, snow and mixed phase are only rarely recorded over the NA compared to the SO, which is again consistent with the NA being warmer than the SO during summer (Fig. 2). Rain (certain) alone is more frequently observed over the NA: the wintertime (summertime) frequency over the NA is 10.7% (11.5%) compared to 8.8% (9.5%) over the SO. Again, these results presented are statistically significant.

Ultimately it is difficult to integrate these different products into a single coherent view of the cloud properties over the SO and NA. M10 suggests that the differences in the microphysics are insignificant using their methods, yet an analysis of CTP suggests that the NA clouds are more glaciated, and the differences in CTP are even larger between instruments and algorithms than they are between regions. Also, an analysis of the precipitation suggests that the wintertime precipitation is more frequent over the NA but not in summer. Without in situ observations, it is not readily possible to truly evaluate any of these. Indeed, it is conceivable that all are essentially correct. The complexity of many of these high-level algorithms inhibits a clear understanding of the range of variability in these products. This ambiguity suggests that it may be enlightening to examine some of the directly observed variables where, at least, our appreciation of the products is more grounded. Any differences (or lack thereof) between the NA and SO clouds should readily be evident in these products.

4. Methodology for examining observed variables

To examine the observed variables, contoured frequency by altitude diagrams (CFADs; Yuter and Houze 1995) are used to characterize the statistical properties of the CPR Ze and CALIOP β532 for the SO and NA. Although we refer to these two variables as being directly observable, necessary constraints are applied to condition the analysis to the preferred targets. Similar to M10, we perform the analysis for “cloud” pixels only. A cloud pixel is defined as a pixel being classified by the DARDAR-MASK as one of the following classes: ICE, MIXED, SLW, LW, and RAIN (note that the merged product does not provide a single “cloud mask” variable). We consider this as a practical step as the level-1 measurements from the spaceborne sensors contain considerable noise that may inhibit a meaningful interpretation of the statistics. Note that statistics including the UN pixels have also been undertaken, but are not presented. We will, however, compare the statistics with the two datasets to analyze the sensitivity of the results to the UN class. As such, the statistics presented is a conservative but more robust representation of the cloud properties of the two regions. It should be noted that the cloud mask defined here is similar to that in the standard products (e.g., the 2B-GEOPROF-lidar product), except that the official datasets do not include the target categorization and are not available on fine resolution.

In addition to the CFAD tool, a modified CFAD where the frequencies of hydrometeors are contoured by temperature and the range of either Ze or β532 (hereafter CFTD) is applied to further examine cloud characteristics of the two regions. An advantage of this method is that it facilitates an appreciation of the primary observations of cloud properties in relation to temperature. For comparison of the cloud phase composition, we also perform separate statistics on individual phases classified by the DARDAR-MASK. Indeed, this practice requires using the DARDAR-MASK phase retrieval as a reference, but we will demonstrate that the cloud properties differentiate more significantly by the actual difference between the two regions, rather than by the cloud phase partitioning.

To understand the statistical properties in CFADs and CFTDs, it is necessary to explain the physical relationships between the observed Ze, β532, and the cloud properties. The 95-GHz CPR reflectivity is most sensitive to the sixth moment of the particle size distribution (Rayleigh scattering regime), hence being dominated by large particles (e.g., large ice and precipitation). The CALIOP β532 is proportional to the second moment of the particle size distribution, hence being most sensitive to small particles in higher number concentration (liquid droplets and small ice crystals, or a mix of them). The difference in nature enables the two instruments to detect different cloud microphysical characteristics covering spectra for a wide range of cloud particle sizes (e.g., Wang and Sassen 2002; Tinel et al. 2005; Delanoë and Hogan 2008).

Here, a CPR reflectivity thresholding technique is applied to interpret the radar Ze under different meteorological conditions. In a cold atmosphere (T < 0°C), strong reflectivities (e.g., Ze > 5 dBZe) indicate the presence of large ice particles (snowflakes, aggregates, graupel, etc.), whereas lower Ze indicates lower ice water content or small ice particles, and, to some extent, supercooled liquid water droplets (Bodas-Salcedo et al. 2011). In a warm atmosphere (T > 0°C), the wide range of Ze can reflect the transition from nonprecipitating clouds (Ze < −15 dBZe) to drizzle (−15 < Ze < −7.5 dBZe) and precipitation (Ze > −7.5 dBZe) (Wood et al. 2009; Matrosov et al. 2004; Franklin et al. 2013).

Complementary information can partly be gained with the CALIOP β532. Unlike CPR reflectivities, documentation on applying CFADs/CFTDs directly for lidar returns is relatively sparse. Part of the reason is that lidar signals contain too much “noise” (e.g., molecular scattering and solar background), especially during daytime (Rogers et al. 2011). For climate model evaluation, however, the lidar scattering ratio (SR = β/βmolecule) is commonly used to detect clouds as seen by CALIOP, by reducing the noise caused by molecular scattering (Chepfer et al. 2010; Bodas-Salcedo et al. 2011). In our study, given that we have chosen to examine cloud pixels only, the noise level is assumed to be minimal. However, lidar signal extinction and multiple scattering will impact the CFADs and CFTDs presented in this study, through underestimation and overestimation of the true lidar backscatter, respectively.

To understand the regional differences, a diagram presenting the ratio of difference [RD; defined by Eq. (1)] of CFADs is further analyzed.
e1
The same formula is also applied to calculate the RDs for CFTDs.

Similarly, the Monte Carlo method is used to test the significance of the RD for each individual bin. In the following sections, only the RDs that are statistically significant at the 95% significance level are presented.

An example showing the MODIS radiance imagery from a near-IR channel, MODIS cloud-top phase (NIR product), the CPR Ze, CALIOP β532, CALIOP categorization, and DARDAR-MASK classification along a segment of an A-Train transect over the SO is presented in Fig. 4. The observed cloud field includes a frontal passage (38°–48°S), midlevel clouds (36°–38°S and 51°–52°S), and BLCs (45°–48°S and 53°–55°S) residing below 1 km. Despite the ubiquitous cloud cover, the disparities in cloud types, cloud macrostructure, and thermodynamic phase as reported by the different products are clearly observed.

Fig. 4.
Fig. 4.

An example from a segment of an A-Train overpass over the SO. (a) MODIS radiance imagery from a near-infrared (NIR) channel 31 (10.78–11.28 μm); (b) MODIS cloud-top phase (NIR); (c) CloudSat CPR Ze; (d) CALIPSO lidar β532; (e) CALIPSO categorization; (f) DARDAR-MASK classification. The A-Train ground track is overlaid in (a),(b). Contours of ECMWF temperatures are overlaid in (c),(e),(f). In (e),(f), the classifications include horizontally oriented ice (HOI), polluted dust (PD), clean continental (CC), polluted continental (PC), clean marine (CM), uncertain (UN), stratosphere feature (SF), supercooled liquid water (SLW), liquid warm (LW), and mixed phase (MIXED).

Citation: Journal of Climate 28, 5; 10.1175/JCLI-D-14-00169.1

Figure 5 shows the corresponding CFADs and CFTDs for the cloud mask identified in the example. The purpose of this figure is to illustrate how the detected cloud features translate into statistical properties. Looking first at the Ze CFAD (Fig. 5a), overall the detected clouds are dominated by three regimes: higher-level ICE, nonprecipitating clouds, and RAIN, based on the physical relationships introduced in section 4. In the upper levels (above 6 km), the maximum frequency reduces with height and moves toward larger Ze so that the peak of Ze reaches approximately 10 dBZe at 6 km. This structure reflects the aggregation process with ice particles growing in size as they collect other particles (Bodas-Salcedo et al. 2011). In the lower atmosphere (below 6 km), the structure of frequency is featured by three regimes clustered around −25, −10, and 8 dBZe, representing nonprecipitating clouds, drizzle, and RAIN, respectively. At midlevels, the transition zone (−10 < Ze < −20 dBZe and T < 0°C) suggests a likelihood of either mixed- or liquid-phase clouds. This likelihood can also be inferred from the β532 CFAD (Fig. 5c), where liquid layers are characterized by notably stronger β532. The strong β532 is most pronounced in the upper reaches of the clouds, and followed by a drastic decrease through the layers as a result of strong attenuation. This feature is found to be reappearing at up to 8 km, suggesting the existence of SLW down to about −25°C (note that CALIOP is most reliable near cloud tops). The BLCs that dominate the β532 CFAD, however, can only be partly detected by the CPR because of ground clutter contamination. The ambiguity in cloud detection by CPR below the lowest kilometer limits a further exploration (Huang et al. 2012a; Protat et al. 2014a).

Fig. 5.
Fig. 5.

The corresponding CFADs and CFTDs for the example cloud mask shown in Fig. 4. The Ze CFAD (a) without and (b) with the inclusion of the UN class and β532 CFAD (c) without and (d) with the inclusion of the UN class. (e)–(h) As in (a)–(d), but for CFTDs. Retrievals below 960 m have been removed in the Ze CFADs in (a) and (b) because of the contamination from ground clutter.

Citation: Journal of Climate 28, 5; 10.1175/JCLI-D-14-00169.1

Comparing Figs. 5a,c with Figs. 5b,d, it is clear that the inclusion of the UN pixels as cloud pixels does not dramatically change the overall structure of the CFADs and CFTDs, except that it results in more occurrences of weak Ze (between −20 and −30 dBZe) below approximately 4 km. It also allows a production of a full spectrum for β532 at the same altitudes, without the truncation at the lower end of the stripes. The truncations or cutoffs mark the major difference between Figs. 5c and 5d, implying that the a priori cloud-masking threshold may be being applied too tightly in the algorithm, leading to a possible underestimate of low-lying liquid clouds.

The frequency distributions shown in the CFTDs (Figs. 5e–h) are generally similar to their counterparts in the CFADs, except for the clouds at temperatures warmer than −10°C. The frequency distributions in the CFTDs are broader, which highlights the higher variability of temperature and cloud properties in the lower troposphere.

5. Results of comparing observed variables between the SO and NA

a. Comparison of CFADs

The 4-yr CPR reflectivities and CALIOP β532 for the observed clouds over the SO and NA can be compiled into a simple climatology (Fig. 6). Focusing first on the Ze CFADs (Figs. 6a–f), for both regions, the histograms are generally characterized by a boomerang-like structure, which has been well documented in the literature (e.g., Franklin et al. 2013). Along the upper-branch, Ze increases as height decreases, corresponding to the transition from small to large precipitating ice (when T < 0°C) and RAIN (when T > 0°C) until reaching the maximum frequencies near 2 km. A transition from non-precipitating clouds to drizzle and precipitating ICE (or RAIN) accentuates the lower branch (below 2 km) of the boomerang. This transition is more pronounced over the SO with weak seasonality, whereas the opposite holds for the NA. During winter, a dipole structure is present in the lower troposphere for the NA region, dominated by precipitating ICE (or RAIN) and nonprecipitating clouds, whereas the occurrence of drizzle is more frequent in summer. These results are largely consistent with the precipitation characteristics discussed in section 3c and agree with the midlatitude cyclone seasonality discussed in Naud et al. (2013).

Fig. 6.
Fig. 6.

The 4-yr climatologies of CPR Ze and CALIOP β532 CFADs for clouds over the SO and NA: Ze CFAD for (a) SO and (b) NA winter and (c) RD between (a) and (b). (d)–(f) As in (a)–(c), but for summertime. (g)–(i) As in (a)–(c) but for CALIOP β532. (j)–(l) As in (d)–(f) but for CALIOP β532. The gray lines in (g)–(l) indicate the altitude of 1.5 km. Pixels with frequencies below 1 × 10−4 are unaccounted for the RD diagrams. Retrievals below 960 m have been removed in the Ze CFADs and RD diagrams.

Citation: Journal of Climate 28, 5; 10.1175/JCLI-D-14-00169.1

The differences in the statistical properties are more clearly reflected in the RDs of the Ze CFADs (Figs. 6c,f). During winter, the frequencies of Ze between 0 and 15 dBZe and from 2 to 4 km are higher over the NA (15%–40% higher than the regional mean), reflecting larger hydrometeors (either ICE or RAIN) being more frequently detected over the NA. During summer, the difference between the two regions is much weaker, except that more midlevel clouds (RDs of 10%–20%) between 4 and 6 km are observed over the SO in the range between −15 and −25 dBZe, while more lower clouds (RDs of 10%–20%) are detected between 1 and 4 km over the NA below −25 dBZe. High-level clouds with Ze < −25 dBZe are more prevalent over the NA year round, although the CPR is known to only have weak sensitivity to cirrus clouds.

The CALIOP β532 CFADs (Figs. 6g–l), on the other hand, exhibit an asymmetric dipole structure, characterized by the prevalence of BLCs and upper-level clouds that consist primarily of geometrically smaller hydrometeors (the discontinuity at 8.2 km is an artifact due to the change of CALOP vertical resolution). The major difference (Figs. 6i,l), however, mainly comes from the boundary layer. The wintertime SO is dominated by BLCs below 1.5 km, while the wintertime condensation level over the NA is noticeably higher. This difference suggests that the boundary layer is shallower over the SO during winter, which is likely a reflection of stronger subsidence. The frequency of wintertime BLCs is also significantly higher over the SO (26% below 1.5 km with respect to the total counts at all levels) than that over the NA (16%). This difference is related to the smaller amount of LW CTP retrieved by both MODIS and CALIOP over the NA (Fig. 3). In summer, however, while the environment over the SO appears to be colder and drier (Figs. 2b,d), the occurrences of BLCs over the SO are still more frequent, although the BLC heights between the two regions are more comparable. Interestingly, this difference does not seem to be reflected by the summertime RH profiles (Fig. 2d). Note that the maximum frequencies of β532 clustered between 10−3 and 10−2 km−1 sr−1 below 1.5 km are mainly a consequence of heavy attenuation of lidar signals through optically thick clouds (especially liquid). In addition to the BLCs, there are also more midlevel clouds being observed over the SO between 3 and 9 km, particularly during summer. At high levels (9–12 km), cirrus clouds are more often present over the NA for both seasons, especially in summer.

The inclusion of the UN class (not shown) does not change the overall distribution of the RDs for Ze and β532. It does, however, increase the frequencies of occurrence of Ze < −20 dBZe and β532 < 10−2 km−1 sr−1 by approximately a factor of 2 below 2 km for both regions, where the UN class is returned 15%–50% of the time. This difference is more prominent over the SO, implying that the significant contrast in the populations of BLCs presented above may still be the lower bound of the estimate.

b. Comparison of CFTDs

1) All hydrometeors

The statistical properties of CPR Ze and CALIOP β532 for the observed hydrometeors can also be analyzed with CFTDs (Fig. 7). Similar to the CFADs, the Ze CFTDs (Figs. 7a–f) again display a boomerang-type shape, but the bottom branches become less pronounced, which is a reflection of the larger temperature variability in the lower troposphere. When comparing the SO and NA (Figs. 7c, f), it is clear that the CFTDs differ in both winter and summer. During winter, when the temperature profiles are similar over the two regions (Fig. 2), the difference is dominated by the microphysics. The frequencies of greater Ze (larger hydrometeors) are markedly higher over the NA from −5° to −40°C, whereas the SO environment is favored by moderate to weak Ze (smaller hydrometeors) from −15° to −35°C and around freezing temperature (indicative of warm clouds or drizzle). During summer, when the atmosphere is warmer over the NA as compared to the SO, the CFTD differences can be explained by the temperature differences. Significantly more hydrometeors across nearly the full spectrum of Ze are evident over the NA (SO) above (below) the temperature of freezing. Note that for temperatures warmer than freezing, Ze > 15 dBZe may be contaminated by ground clutter and hence should be treated with caution.

Fig. 7.
Fig. 7.

As in Fig. 6, but for CFTDs. Pixels with frequencies below 3 × 10−4 are unaccounted for the RD diagrams.

Citation: Journal of Climate 28, 5; 10.1175/JCLI-D-14-00169.1

As for CALIOP β532 CFTDs (Figs. 7g–l), the immediate feature for winter is that the SO is populated with more clouds of high β532 (β532 > 10−2.5 km−1 sr−1) between −5° and 5°C (Fig. 7i). This feature corresponds well with the predominating BLCs shown in Figs. 3 and 6i, which is largely missed by CloudSat because of ground contamination. The SO region also has more clouds in the temperature range between −30° and −15°C. During summer, the hydrometeor populations over the two regions are, again, largely a function of temperature. The SO is populated with more (RDs of 30%–50%) highly reflective clouds between freezing and −10°C, while warmer clouds are more prevalent over the NA. In addition, more clouds with a narrower β532 distribution are observed between −15° and −30°C over the SO for both seasons. This feature corresponds well with the more frequent occurrences of midlevel clouds suggested by the CFADs (Fig. 6).

2) ICE clouds

Given the significant impact of cloud phase on precipitation efficiency and radiative properties, it is interesting to examine if any traceable differences between the two regions are relevant to the phase characteristics. Here, we perform the analysis by selecting the “cold cloud” categories from the DARDAR-MASK. This choice is not perfect, as the categorization in this product is also a retrieval. However, we consider this choice reasonable as it reduces the level of complexity of the analysis by offering a range of phase candidates that are selected in a physically realistic manner. We will return to a discussion later on the possible consequences and implications of potential misclassification.

Figure 8 presents the CFTD statistics for ICE identified by the DARDAR-MASK (note that the DARDAR-MASK algorithm also uses Tw from the ECMWF reanalysis for phase partitioning, so the ICE occurrence when temperature is above freezing should be taken with caution). Focusing first on the Ze, the lower half of the boomerang in Figs. 6 and 7 that consists primarily of liquid-phase hydrometeors has disappeared. In general, the ICE clouds over the NA are characterized by glaciation with stronger Ze (larger particle size) between −5° and −40°C during winter (Fig. 8c), and, to a lesser extent, summer (Fig. 8f). Particularly, heavier glaciation (8–18 dBZe) tends to occur more often (average RD of ~35%) at warmer temperatures (from freezing to −15°C) over the NA during winter. This feature can also be inferred by the dual-core structure of the NA CFTDs (Figs. 8b,e): two frequency maxima are present at both ends of the branch. In comparison, the frequency distributions in the SO CFTDs are more linear and uniform. This difference suggests that the stronger Ze values over the NA in Fig. 6 between −5° and −40°C are predominantly contributed by larger ICE particles, suggesting that ice aggregation may be more efficient, or storm intensities are relatively stronger over the NA. We believe the ICE classification within the ZeT range above is reasonably robust, as the chance of liquid-phase hydrometeors being misclassified as ICE in that context is very small.

Fig. 8.
Fig. 8.

As in Fig. 7, but for ICE class.

Citation: Journal of Climate 28, 5; 10.1175/JCLI-D-14-00169.1

The CALIOP β532 CFTDs (Figs. 8g–l), on the other hand, produce a similar structure as shown in Figs. 6 and 7, except for the absence of the BLCs that are primarily liquid. The stronger seasonal cycle over the NA is evident. Small particles in relatively high concentration (10−2.5 < β532 < 10−1.5 km−1 sr−1) are more frequently observed over the SO in a broad temperature range from freezing to −55°C during winter and −40°C in summer. In the colder temperature range from −50° to −70°C (from −40° to −60°C), more cirrus clouds are observed over the NA in winter (summer).

Note that in the DARDAR-MASK version 1 product, identification of potential SLW layers is only possible for a limited depth from cloud tops. These layers, if identified, are usually geometrically thin (generally no more than 300 m; Ceccaldi et al. 2013). Given that the CPR is not able to detect pure SLW if the lidar signals have been heavily attenuated, there has been a concern of some SLW being misidentified as small ICE in high concentration when moving beyond cloud tops (Huang et al. 2012b). This potential may even be higher over the SO where relatively large amounts of SLW are known to occur in cloud tops (Fig. 3).

3) SLW-containing clouds (SLW + MIXED)

Likewise, the statistical properties of Ze and β532 for SLW-containing clouds are examined (Fig. 9). Different from previous studies of which the primary focus is on cloud tops (e.g., Morrison et al. 2011; Hu et al. 2010), our study extends beyond cloud tops through the depth of clouds where SLW or MIXED class is categorized by the DARDAR-MASK classification.

Fig. 9.
Fig. 9.

As in Fig. 8, but for SLW containing clouds (SLW + MIXED).

Citation: Journal of Climate 28, 5; 10.1175/JCLI-D-14-00169.1

In the DARDAR algorithm, SLW exists down to −40°C, which is the homogenous nucleation temperature. For both regions, SLW-containing clouds reside primarily in the temperature range between freezing and −15°C. For both seasons, the frequencies of Ze < −20 dBZe over the SO are higher (RDs of 25%–60%) than the mean frequencies between the two regions in the temperature range between −10° and −35°C. Between −5° and −30°C (between 10° and −20°C), the frequencies of Ze > −15 dBZe over the SO are lower than the regional mean during winter (summer). Meanwhile, the summertime occurrences of β532 > 10−2.0 km−1 sr−1 over the SO are more frequent (RDs of 20%–50%) over the SO than the NA from −5° to −30°C. The combination of these features suggests that the particle sizes of the SLW-containing clouds at colder temperatures over the SO are appreciably smaller and in higher concentration, compared to their counterparts over the NA. Note that the apparent cutoff of β532 at approximately 10−1.5 km−1 sr−1, seemingly an artifact caused by the subjectively defined criteria of the DARDAR-MASK algorithm, may be indicative of possible underestimate of SLW occurrence.

As our analysis is performed through the depths of the cold clouds, it is likely that the prevalence of SLW over the SO as suggested is not only constrained to cloud tops (e.g., Rauber and Tokay 1991; Shupe et al. 2004; see also Fig. 3), but potentially through some depth of cloud layers, compared to the NA. There have been limited in situ observations of thick layers of SLW over the SO, with high quantities of SLW (up to 0.47 g m−3) being observed in clouds as cold as −22°C (e.g., Mossop et al. 1970; Chubb et al. 2013). Indeed, without comprehensive in situ measurements it is not possible to rule out the existence of small ice crystals in these SLW-containing clouds, but we consider this unlikely as the crystals would have grown rapidly into large ice crystals if they are present in significant concentration (e.g., Korolev and Isaac 2003; Hill et al. 2014).

6. Discussion and conclusions

This study employs reanalysis datasets and high-level retrieval products of the individual and multisensor measurements from the A-Train satellites, as well as lower-level observed variables—radar reflectivity (Ze) from CloudSat CPR and lidar attenuated backscatter at 532-nm wavelength (β532) from CALIPSO—to explore cloud and precipitation properties over the midlatitude storm-track regions of the Southern Ocean (SO) and the North Atlantic (NA).

The observations suggest that the two regions do display many similarities, such as the prevalence of BLCs and frontal systems (which produce cirrus clouds up to ~12 km). However, notable differences are also clearly found. The NA displays stronger seasonality in terms of thermodynamic structure and cloud and precipitation properties, whereas the SO undergoes a weaker seasonal cycle. The key aspects of the regional differences are summarized below.

a. Comparison of high-level retrieval/reanalysis

  • The wintertime temperature profiles are comparable over the two regions, while the summertime environment over the NA is notably warmer.
  • The NA atmosphere is generally moister than the SO year round, but the SO boundary layer is more humid during winter.
  • The presence of SLW is more pronounced at cloud tops over the SO, especially in summer, while glaciated cloud tops are more commonly observed over the NA.
  • The frequency (warm and mixed phase) and intensity (warm rain) of wintertime precipitation are greater over the NA, whereas the frequency (all phases) of summertime precipitation is greater over the SO. The summertime warm rain is less frequent but more intense over the SO.

b. Comparison of observed variables

  • The regional difference of overall cloud properties is dominated by microphysics in winter and thermodynamics in summer.
  • Glaciated clouds with higher radar reflectivities are more evident at warmer temperatures over the NA during winter, and to a lesser extent, summer, suggesting that ice aggregation may be more efficient, or storm intensities are relatively stronger over the NA.
  • BLCs are a predominant component over the SO year round, residing primarily below 1.5 km. The cloud-top heights of wintertime BLCs over the NA are higher than that over the SO, suggesting that the wintertime boundary layer is shallower over the SO. The frequency of wintertime BLCs is also significantly lower over the NA.
  • The cloud-top heights of BLCs over the NA are lower but less frequent than that over the SO during summer.
  • Midlevel (3–9 km) clouds consisting of geometrically smaller hydrometeors in higher concentration are more frequently observed over the SO, particularly during summer. These clouds potentially contain a large amount of SLW.
  • Cirrus clouds are more prevalent over the NA, especially in summer.
The predominance of BLCs over the SO detected by CALIOP (but largely missed by the CPR) has many implications. A quantitative study by Christensen et al. (2013) reveals that the CPR on CloudSat detects less than 5% of low clouds with tops less than 1 km. This deficiency warrants a note of great caution: the ubiquitous BLCs may have been underrepresented. Therefore, the microphysical characteristics, hydrometeor-induced heating, and radiative properties of these BLCs may have been largely biased in many of the retrieval products, as these products depend primarily upon the observations from passive remote sensing instruments and the CPR. This issue may be exacerbated over the SO where the BLCs reside primarily in the lowest kilometer and have little seasonality (Huang et al. 2012a).

In addition to the BLCs, the common presence of midlevel clouds and their geometrically smaller particle sizes (higher concentration) are another unique aspect of the SO storm-track cloud system. Although the current observations are not sufficient to confirm the phase properties of these hydrometeors, it is reasonable to expect that the optical and radiative properties of these clouds (reflectance, scattering efficiency, optical transparency, etc.) would fundamentally differ from those with similar morphologies given the same meteorological context. The smaller cloud particle size could also delay cloud life time, thus influencing precipitation efficiency.

Recent studies have illustrated that the underprediction of the low- and midlevel clouds over the SO in climate models contributes to the largest reflected shortwave radiation biases in this region (e.g., Bodas-Salcedo et al. 2014; Franklin et al. 2013). Thus, understanding the characteristics of these clouds is a vital step toward improving climate model predictions. Given that remote sensing observations continue to be a powerful tool for model evaluation over the remote oceans, there is a critical need for continuing the synergy of combined active and passive remote sensing measurements to better characterize the observed cloud properties. Advanced retrieval products, although widely used, may contain varying degrees of uncertainty and ambiguity because of the limitations of various instruments as well as the underlying assumptions used in the retrieval algorithms. These products may not always be able to reflect the true state of the observed hydrometeor properties, and may even conceal some meaningful information within this uncertainty and consequently lead to misunderstandings, or biased interpretations, if applied without due caution.

Our analysis has shed some light on addressing this underappreciated issue by underpinning the differences in the storm-track cloud properties over the SO and NA through a careful examination on the observed variables from CloudSat and CALIPSO. Indeed, uncertainties arising from limitations of the observations and the merged product still exist and may have some influence on the conclusions. For instance, differences are to be expected between the two versions of the DARDAR-MASK datasets (Ceccaldi et al. 2013). Also, this product is yet to be thoroughly validated with in situ observations under various conditions. However, within the context of this study, as the cloud detection scheme uses the same thresholds everywhere, it does not produce specific differences between regions. Therefore, any differences, if observed, must be explained by particular components of the systems that physically differ.

Having demonstrated that the hydrometeor behaviors of the NA and SO storm-track regions are different in nature, a series of questions remain open. For instance, what factors (large-scale dynamics, boundary layer processes, aerosol characteristics, etc.) determine these discrepancies? What is the implication of these differences to Earth’s water and energy budget (Trenberth and Fasullo 2010)? Dedicated field campaigns involving ground-based radar, lidar, microwave radiometer, and aircraft in situ observations are needed and would offer tantalizing possibilities to address these important questions.

Acknowledgments

This work is supported by Australian Research Council Linkage Project LP120100115. The authors thank the three anonymous reviewers for their constructive comments. Thanks also to the ICARE Data and Services Center for providing access to the satellite datasets and the computation resources.

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