Giant and Ultragiant Aerosol Particle Variability over the Eastern Great Lakes Region

Sonia Lasher-Trapp Purdue University, West Lafayette, Indiana

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Justin P. Stachnik Purdue University, West Lafayette, Indiana

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Abstract

Numerous studies have indicated the potential for giant and ultragiant aerosol particles to expedite the warm-rain process as a result of their extreme sizes. The central question regarding their importance is, Are they present in large enough numbers to influence the microphysics of the clouds significantly? Thus, quantification of these particles and their variability is paramount. New observations collected during the second Alliance Icing Research Study (AIRS II) are presented as evidence of the presence and variability of giant and ultragiant aerosol particles over a continental region—in this case, within the eastern Great Lakes region and parts of the midwestern United States and Canada during one month in winter 2003. Sources and factors contributing to the amount of these particles observed in the lower atmosphere were difficult to identify separately; future studies incorporating high-resolution weather modeling are likely needed.

Corresponding author address: Sonia Lasher-Trapp, Dept. of Earth and Atmospheric Sciences, Purdue University, 550 Stadium Mall Dr., West Lafayette, IN 47907. Email: slasher@purdue.edu

Abstract

Numerous studies have indicated the potential for giant and ultragiant aerosol particles to expedite the warm-rain process as a result of their extreme sizes. The central question regarding their importance is, Are they present in large enough numbers to influence the microphysics of the clouds significantly? Thus, quantification of these particles and their variability is paramount. New observations collected during the second Alliance Icing Research Study (AIRS II) are presented as evidence of the presence and variability of giant and ultragiant aerosol particles over a continental region—in this case, within the eastern Great Lakes region and parts of the midwestern United States and Canada during one month in winter 2003. Sources and factors contributing to the amount of these particles observed in the lower atmosphere were difficult to identify separately; future studies incorporating high-resolution weather modeling are likely needed.

Corresponding author address: Sonia Lasher-Trapp, Dept. of Earth and Atmospheric Sciences, Purdue University, 550 Stadium Mall Dr., West Lafayette, IN 47907. Email: slasher@purdue.edu

1. Introduction

The classical warm-rain process includes the nucleation of water droplets upon certain aerosol particles called cloud condensation nuclei (CCN), the growth of these water droplets by diffusion of vapor toward the droplet (condensation), and the collection of smaller water droplets (coalescence) once sufficient collision trajectories and fall velocities are attained, culminating in raindrop formation. The second step in this process, condensation, is notably slow (in terms of increasing particle radius) once the droplets are larger. Classical calculations of warm-rain formation that include these processes within an air-parcel framework cannot explain the speed with which natural clouds are sometimes observed to rain.

Scientists have thus sought improvements to the simplified calculations outlined above. Beard and Ochs (1993) provide a review of possible factors that might reconcile the calculations and observations. It is likely that different mechanisms accelerate warm-rain formation in different regions and different types of clouds. The study presented in this note is pertinent to one of the older hypotheses [originally proposed by Ludlam (1951)], in which extremely large aerosol particles (present in the ambient air) can enter the cloud and initiate the growth of much larger cloud droplets more quickly than do the smaller CCN, thus speeding the onset of droplet coalescence and drizzle/precipitation formation.

Atmospheric aerosol particles have traditionally been divided into categories based on their dry diameter size D (Junge 1955, 1963): Aitken (or small; D < 0.2 μm), large (0.2 < D < 2.0 μm), giant (2.0 < D < 20.0 μm), and (more recent; Johnson 1976) ultragiant (D > 20 μm). (Cloud condensation nuclei typically included in warm-rain calculations fall in the “Aitken” and “large” categories.) Once ingested into a cloud, the giant and ultragiant aerosol particles (especially if they are composed of soluble material) require less time to grow to sizes at which droplet collection begins to become very efficient [drop sizes near 50–60 μm in diameter, based on, e.g., the collision efficiencies of Klett and Davis (1973)], and warm-rain formation then proceeds quickly. Numerous studies employing numerical modeling have indicated a potential for giant and ultragiant aerosol particles to accelerate warm-rain formation (e.g., Rosinski and Kerrigan 1969; Johnson 1982; Feingold et al. 1999; Szumowski et al. 1999; Lasher-Trapp et al. 2001, Blyth et al. 2003; Geresdi and Rasmussen 2005), based on surface observations of giant and ultragiant particles (Reitan and Braham 1954; Okita 1955; Noll and Pilat 1971; Johnson 1976; etc.) or on atmospheric sampling of these particles with aircraft (Woodcock 1952, 1953; Nelson and Gokhale 1968; Mészáros and Vissy 1974; Johnson 1976; Lasher-Trapp et al. 2002; etc.).

The central issue about the importance of giant and ultragiant aerosol particles (hereinafter referred to together simply as GA, for brevity) to warm-rain formation is, Are they present in sufficient numbers to influence precipitation formation in the cloud? These particles are always many orders of magnitude lower in number concentration than the Aitken and large CCN that are primarily responsible for determining the cloud-droplet population. Thus, quantification of their sizes and number concentration is paramount to predicting their ability to influence precipitation formation significantly in clouds.

The actual number of GA needed to accelerate precipitation formation may not depend only on their number concentration, however. Numerical modeling studies have indicated that GA are most effective in accelerating drizzle formation in clouds with high droplet number concentrations, such as those occurring over land, rather than in clouds with lower droplet number concentrations, such as those over the oceans (Ochs and Semonin 1979; Johnson 1982; Feingold et al. 1999; Geresdi and Rasmussen 2005; etc.). If the numerical modeling studies are correct, it is unfortunate that most of the observations of GA published in the literature have been collected over the oceans, rather than over land, where their consideration in calculations might lead to more accurate predictions of drizzle and precipitation formation.

The observations reported in this study are intended as a step toward documenting number concentrations of GA in such a continental region. Aircraft observations of GA over the eastern Great Lakes and parts of the midwestern United States and Canada over one month during the winter of 2003 are presented. Despite the potential importance of GA in continental clouds, few aircraft observations of GA over continental regions have been published in the formal literature,1 and none have presented vertical profiles over the same geographical region over a duration sufficient to allow analysis of their temporal variability. Given the relative high tendencies over this region for drizzle to be observed at the surface (Sears-Collins et al. 2006) and for the intercept of supercooled drizzle by aircraft (Cober et al. 2001), these observations should prove useful to numerical modeling studies that seek to understand these high tendencies, in addition to decreasing the paucity of GA observations over continental areas.

2. Giant and ultragiant aerosol particle vertical profiles

a. Data collection

The second Alliance Icing Research Study (AIRS II) consisted of a multinational team of scientists brought together to investigate mesoscale and microscale features associated with aircraft icing over the eastern Great Lakes region. The study was based in Mirabel, Quebec, Canada, with groups of scientists also located in Ottawa, Ontario, Canada, and Cleveland, Ohio, during the winter of 2003/04. The AIRS II goals included the development of new techniques to detect, diagnose, and forecast hazardous winter conditions at airports, the characterization of the aircraft-icing environment, an investigation of the conditions associated with supercooled large-drop formation (including warm-rain processes acting under supercooled conditions), the determination of conditions favorable for cloud glaciation, the documentation of the spatial distributions of ice crystals and supercooled water, and the verification of the response of remote sensors to various cloud particles, among other goals (Isaac et al. 2005). A specific goal of the team of investigators using the National Center for Atmospheric Research (NCAR) C-130 aircraft was to characterize the aerosol, CCN, and ice nuclei associated with the aircraft-icing events in this region.

The C-130 aircraft carried a suite of instruments designed for sensing atmospheric conditions and cloud characteristics during AIRS II. The instruments most pertinent to this study included a new-generation forward-scattering spectrometer probe (FSSP)2 for counting and sizing particles of 3–41-μm diameter, a one-dimensional optical array probe (260X)3 for counting and sizing particles of 50–620-μm diameter, and a Cloud Particle Imager4 (CPI; Lawson and Cormack 1995) for taking digital photographs of particles that are greater than 20 μm in diameter. These instruments were originally designed for counting and sizing cloud and precipitation droplets, and only the last one can distinguish between aerosol and cloud particles. Thus, data from these instruments used for quantifying GA were restricted to time periods during which the aircraft was flying in clear air to prevent the counting of cloud or precipitation particles as aerosol particles. The sizing of particles by the FSSP also inherently assumes the detected particles are liquid; the refractive index of liquid water is used to relate the forward scattering of the laser resulting from the passage of a particle to a size according to Mie theory.5 When more opaque particles with a greater index of refraction pass through the FSSP laser, they may be undersized. The 260X has no such dependency on the refractive index of water; instead the particles simply shadow an array of diodes illuminated by a laser as they pass over it. Both probes can also over- or underestimate nonspherical particles, depending on the particle orientation as they pass through the probe arms. No systematic biases were found when comparing the FSSP data with the 260X and CPI data in the clear air, but this comparison is admittedly limited because of little overlap in the size ranges of particles detected by the probes.

Lasher-Trapp et al. (2002) showed that the 260X probe used on the C-130 aircraft during the Small Cumulus Microphysics Study in July–August 1995 was likely influenced by electromagnetic noise. This noise caused the probe to trigger falsely on occasion, which could be confused with credible signals from ultragiant aerosol particles in clear-air measurements. Since that field program, the 260X-probe electronics have been extensively insulated against such problems (J. Jensen, Research Air Facility at NCAR, 2003, personal communication), and indeed most of the time the probe was “quiet” when flying in the clear air during AIRS II. At times when the probe detected particles in the clear air, the CPI was used to verify that ultragiant particles were indeed present. Thus, the 260X proved to be a valuable resource for measuring ultragiant particles in the clear air during AIRS II.

Data collected in clear air from 14 of the NCAR C-130 flights during AIRS II are presented here. The C-130 flights were often used to characterize air upstream of Mirabel that was forecast to arrive there on the following day. Many of the aerosol sampling periods are thus farther from the Mirabel area but provide the opportunity to investigate the GA particle variability over a larger geographic region. Table 1 lists the flights used in this study and the geographic vicinity of the clear-air sampling. The scanning aerosol backscatter lidar (SABL) on the C-130 aircraft was used to identify cloud boundaries and to eliminate time periods possibly contaminated by precipitation falling from clouds overhead.6 The CPI images were also used to eliminate time periods for which precipitation (e.g., ice crystals or aggregates, with or without supercooled drops) intercepted along the flight path might have contaminated the GA estimates. Any particles detected within approximately 6 km (1 min of sampling) from the edges of cloud were eliminated from the analysis to avoid contamination by cloud-detrainment regions. Each sample was required to be at a nearly constant altitude, within 100 m. Also, the low concentrations of the GA relative to the sample volumes of the probes required the length of the clear-air sampling times to be as long as possible to achieve a smaller sampling uncertainty:7 at an aircraft speed of 100 m s−1, the accumulated FSSP sample volume is approximately 3 L min−1 and the 260X size-dependent sample volume ranges from approximately 2 to 240 L min−1. Sample times of longer than several hundred seconds were typically required, with sample times greater than 1000 s needed to arrive at a GA number concentration with an uncertainty of 10% or less.

b. Analysis and results

For each clear-air period, a GA size distribution can be created from the FSSP (with possible size underestimation for particles with high indices of refraction) and 260X data, and some examples are shown in Fig. 1. The peak in the size distributions was typically between 2 and 4 μm in diameter, with one of the few exceptions shown in Fig. 1c with a peak between 8 and 9 μm. Particles detected by the FSSP on a given day were often at the largest sizes measurable by the probe (41 μm).8 Particles as large as several hundred micrometers were detected by the 260X but only in 15% of the clear-air sampling periods. Examples of some of the images concurrently captured by the CPI are also shown in Figs. 1b and 1c and are typical of the dataset, showing that many of the particles are filaments, with some more-irregular, opaque particles also present. Chemical analysis of some GA collected by impaction on glass slides for four of the flights showed that these particles were very variable in their composition (C. Twohy 2004, personal communication); it will be discussed in a future paper. The largest particles detected by the 260X in the clear-air periods during the AIRS II flights were 500 μm in diameter, although particle images from the CPI showed particles greater than 700 μm in diameter. The ultragiant particles, if present in large enough number to be detectable by the probes at all, were always several orders of magnitude lower in number concentration than the giant particles and thus contribute only minimally to the total GA number concentration. The small number concentrations of the ultragiant particles are evident in Figs. 1a–c, where the giant aerosol concentrations are 2, 31, and 427 L−1, respectively, but the number concentrations for ultragiant particles detected by the probes are 0, 0.33, and 0.18 L−1, respectively.

For each of the clear-air segments listed in Table 1, the size distributions from the FSSP and the 260X probe were added to produce a total number concentration of GA averaged over the sampling period and a sampling uncertainty based on the number of particles detected during that period. The number concentrations of GA vary from as many as 327 L−1 at lower altitudes to as few as 0.004 L−1 at higher altitudes, with substantial variation on different flights. The shorter sampling periods with lower particle concentrations have the greatest uncertainties in these estimates and tend to occur at the highest altitudes.

A vertical profile of GA number concentration for each flight listed in Table 1 is shown in Fig. 2. The vertical profiles shown are limited to altitudes of less than 4 km, where maximum uncertainties in total number concentrations were typically less than 10%–20%. The general trend of the profiles is a decrease in GA number concentration with height, as expected for these GA particles that have their source at the earth’s surface and are largely influenced by gravitational settling. Small deviations from a decrease with height in an individual profile (“wiggles in the lines” in Fig. 2) are often related to a smaller sampling period at a single altitude and thus likely to be a less representative sample. (See, e.g., the shorter sampling periods of the data collected at 2.0, 1.1, and 1.8 km on 5, 10, and 25 November, respectively, in Table 1.) Some of the data collected during a flight can span a wide geographical region or be under the influence of different weather conditions, resulting in occasional deviations of the trend of the vertical profiles and even occasionally resulting in an increase with altitude.

Although the analysis region spanned by this dataset covers multiple states in the United States and multiple provinces in Canada, it is still striking that the GA concentrations can vary by four orders of magnitude over a duration of 1 month. The variability in the number concentration among the flights is greater at lower levels than at the upper levels, consistent with other atmospheric quantities (such as atmospheric water vapor) that have a primary source at the earth’s surface and tend to exhibit a greater variation at lower rather than at higher altitudes. Because the importance of GA to clouds and precipitation depends upon their number, the variability shown here precludes any simple determination of their potential importance to the microphysics of the clouds and would require numerical modeling to evaluate. This dataset quantifies the GA and their variability to use as constraints for such future studies.

3. Discussion

Giant and ultragiant aerosol particle concentrations have been quantified for 14 different days during a 1-month period in winter over the eastern Great Lakes region, including parts of the midwestern United States and southern Ontario and Quebec during AIRS II. Daily vertical profiles exhibit substantial variability, with concentrations ranging from 2 to more than 300 L−1 at altitudes of less than 500 m AGL and decreasing to 0.2–2 L−1 near 4 km AGL. Concentrations above 4 km were several orders of magnitude less. Particles larger than 700 μm in diameter were observed at low altitudes on some flights, although in very low numbers.

The source of these GA observed during AIRS II is not easily identified. Multiple ensembles of backward trajectories from the sampling locations given in Table 1 were computed for the 24 h prior to sampling, using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Hess 1998). The HYSPLIT model allows the user to choose the numerical weather prediction model data as input for computing the trajectories. Sets of trajectories were generated using both the National Centers for Environmental Prediction–NCAR reanalysis gridded meteorological dataset (Kalnay et al. 1996; Kistler et al. 2001), which has a coarse resolution (2.5° horizontally, 16 vertical levels, and updated every 6 h), and the higher-resolution (80 km horizontally, 22 vertical levels, and updated every 3 h) North American Mesoscale gridded meteorological dataset [formerly known as the Eta Model (Black 1994)]. The trajectories for both sets of model data input showed some tendency for higher GA concentrations to be associated with air trajectories passing over major urban sites at altitudes of less than 1.5 km AGL. (“Major” is defined loosely as a population in the U.S. 2000 census having more than 350 000 residents.) However, the trajectories resulting from the different model data input were inconsistent in their location over particular urban sites, and so a strong conclusion regarding the source of the GA could not be made. Surface-based observations of GA presented in past studies have documented larger GA number concentrations within and downwind of large urban and industrial areas relative to upwind of these areas (e.g., Noll and Pilat 1971; Johnson 1976), supporting the likelihood that major urban areas are the source of these observed GA as well. The collection of the AIRS II GA observations during November and December over these northern latitudes would seem to limit the possibility that the source was vegetation (such as pollen, mold, and spores); this additional source might actually enhance the number concentrations of GA during the summer months.

Numerical modeling at a much higher resolution—for example, having multiple grid points within the horizontal scale covered by a major city—would be necessary to gain confidence in the placement of the air trajectories over the cities and into the sampling locations. Linking such high-resolution numerical modeling to surface monitoring sites such as the PM10 network, which samples particles up to 10 μm in diameter at 4300 ground-based stations across North America (Demerjian 2000), would help to constrain the modeling to identify the source of the GA.

Identifying the source of the variability in the GA profiles has also proven to be difficult in that many factors can modulate the GA in the atmosphere, so that air passing over a strong source region for GA may not be correlated with a higher sampled GA number concentration 24 h downstream. Surface and upper-air weather observations were scrutinized for each of the AIRS II cases to identify potential influential weather-related factors, such as ascending or descending air moving into the sample area, the scavenging effects of clouds or precipitation, and strong surface convergence or gusty winds associated with a frontal boundary at the surface (a transient source of GA). Each of these factors might have been important in one or a few cases, but correlations computed for these factors with the GA observations across the AIRS II dataset were low, likely because these factors are often inextricably linked as a result of the predominating weather pattern on a given day. Thus, judging their importance to GA number concentrations individually from the observations alone was not possible. Here too, high-resolution modeling studies of the weather patterns associated with each day during AIRS II might elucidate how combinations of these multiple influences act to modulate GA number concentrations and might aid in the development of a predictive tool for GA in the absence of field observations.

Acknowledgments

The authors acknowledge all of the participants of AIRS II who made this study possible, including other principal investigators on the C-130 aircraft (J. Hallett, C. Twohy, J. Hudson, and P. DeMott), the staff at the Research Air Facility at NCAR, George Isaac and the Meteorological Service of Canada staff, and the NASA Glenn Aircraft Icing Group. Three reviewers provided constructive comments that substantially improved the manuscript, and J. Trapp also provided suggestions on an earlier version of the manuscript. This work was funded by the National Science Foundation, under Award ATM-0312439.

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Fig. 1.
Fig. 1.

Examples of size distributions of GA detected in clear air by the FSSP (solid line) and 260X probe (dotted line) during AIRS II. Inserted within (b) and (c) are images of particles detected by the CPI during the same time periods, with scales (μm) attached; no particles were detected by the CPI during the time period of (a). Altitudes of the observations are (a) 1.4, (b) 0.7, and (c) 1.1 km AGL.

Citation: Journal of Applied Meteorology and Climatology 46, 5; 10.1175/JAM2490.1

Fig. 2.
Fig. 2.

Giant aerosol (with ultragiant included, if observed) number concentration observed in clear air below 4-km altitude AGL for 14 flights during AIRS II. Data points are represented by markers; uncertainty in each value is given in Table 1. Lines connecting markers are presented only for ease in viewing different profiles, and data points below detectable limits for probe sampling volumes are not plotted.

Citation: Journal of Applied Meteorology and Climatology 46, 5; 10.1175/JAM2490.1

Table 1.

Giant and ultragiant particle data collected in clear air during the AIRS II field campaign.

Table 1.

1

There is a lack of published studies that quantify GA number concentrations over land that encompass all GA particles; a plethora of studies that quantify particles of a single chemical composition or biological source exist, and the reader is referred to Pruppacher and Klett (2000) for a summary of these studies.

2

FSSP is manufactured by Droplet Measuring Technologies, Inc., in Boulder, Colorado.

3

260X is manufactured by Particle Measuring Systems, Inc., in Boulder, Colorado.

4

CPI is manufactured by Stratton Park Engineering Company in Boulder, Colorado.

5

A review of the probe operation principles and characteristics can be found in Baumgardner (1983).

6

Although lidar can be useful for detecting layers of smaller aerosol particles, the relative scarcity of the GA prevent their detection by SABL, limiting its use to cloud boundary detection for this study.

7

The sampling uncertainty here is calculated using Poisson statistics (justified by the rareness of these particles; e.g., Taylor 1982) as ±Nn1/2n−1, where n is the number of particles counted during the sampling time and N is the total number concentration of the particles observed during the sampling time. The sampling uncertainty is thus decreased for a longer time interval (resulting in a larger particle count) or a sample of air with higher particle concentration.

8

The uncertainty in the number concentrations within individual bins of the FSSP and 260X bins is not constant across the range of sizes; the number concentrations at the larger sizes resulted from the detection of only a few particles on any given day and thus are considerably less certain than the number concentrations at the small and intermediate sizes measured by the FSSP, which resulted from the detection of many more particles. The uncertainty for the latter number concentrations was usually less than 10%.

Save
  • Baumgardner, D., 1983: An analysis and comparison of five water droplet measuring instruments. J. Appl. Meteor., 22 , 891910.

  • Beard, K. V., and H. T. Ochs III, 1993: Warm-rain initiation: An overview of microphysical mechanisms. J. Appl. Meteor., 32 , 608625.

  • Black, T. L., 1994: The new NMC Eta Model: Description and forecast examples. Wea. Forecasting, 9 , 265278.

  • Blyth, A. M., S. G. Lasher-Trapp, W. A. Cooper, C. A. Knight, and J. Latham, 2003: The role of giant and ultragiant aerosols in the initiation of rain in warm cumulus clouds. J. Atmos. Sci., 60 , 25572572.

    • Search Google Scholar
    • Export Citation
  • Cober, S. G., G. A. Isaac, and J. W. Strapp, 2001: Characterizations of aircraft icing environments that include supercooled large drops. J. Appl. Meteor., 40 , 19842002.

    • Search Google Scholar
    • Export Citation
  • Demerjian, K. L., 2000: A review of national monitoring networks in North America. Atmos. Environ., 34 , 18611884.

  • Draxler, R. R., and G. D. Hess, 1998: An overview of the HYSPLIT_4 modeling system for trajectories, dispersion, and deposition. Aust. Meteor. Mag., 47 , 295308.

    • Search Google Scholar
    • Export Citation
  • Feingold, G., W. R. Cotton, S. M. Kreidenweis, and J. T. Davis, 1999: The impact of giant cloud condensation nuclei on drizzle formation in stratocumulus: Implications for cloud radiative properties. J. Atmos. Sci., 56 , 41004117.

    • Search Google Scholar
    • Export Citation
  • Geresdi, I., and R. Rasmussen, 2005: Freezing drizzle formation in stably stratified layer clouds. Part II: The role of giant nuclei and aerosol particle size distribution and solubility. J. Atmos. Sci., 62 , 20372057.

    • Search Google Scholar
    • Export Citation
  • Isaac, G. A., and Coauthors, 2005: First results from the Alliance Icing Research Study II. AIAA 43d Aerospace Sciences Meeting and Exhibit, Reno, NV, American Institute of Aeronautics and Astronautics, paper AIAA-2005-0252.

  • Johnson, D. B., 1976: Ultragiant urban aerosol particles. Science, 194 , 941942.

  • Johnson, D. B., 1982: The role of giant and ultragiant aerosol particles in warm rain initiation. J. Atmos. Sci., 39 , 448460.

  • Junge, C. E., 1955: The size distribution and aging of natural aerosols as determined from electrical and optical data on the atmosphere. J. Meteor., 12 , 1325.

    • Search Google Scholar
    • Export Citation
  • Junge, C. E., 1963: Air Chemistry and Radioactivity. Academic Press, 382 pp.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77 , 437471.

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  • Fig. 1.

    Examples of size distributions of GA detected in clear air by the FSSP (solid line) and 260X probe (dotted line) during AIRS II. Inserted within (b) and (c) are images of particles detected by the CPI during the same time periods, with scales (μm) attached; no particles were detected by the CPI during the time period of (a). Altitudes of the observations are (a) 1.4, (b) 0.7, and (c) 1.1 km AGL.

  • Fig. 2.

    Giant aerosol (with ultragiant included, if observed) number concentration observed in clear air below 4-km altitude AGL for 14 flights during AIRS II. Data points are represented by markers; uncertainty in each value is given in Table 1. Lines connecting markers are presented only for ease in viewing different profiles, and data points below detectable limits for probe sampling volumes are not plotted.

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