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Keywords:

  • organic CCN;
  • indirect aerosol effect

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observations
  5. 3. Simulations
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[1] Organic aerosols represent an important fraction of the fine particle aerosol, yet little is known about the role that these particles play in the indirect effect of aerosols on climate. The growth rates of organic acid particles due to the condensation of water were measured in a cloud condensation nucleus chamber. Delays in the cloud activation of organic acid particles were observed relative to ammonium sulfate, (NH4)2SO4. The inclusion of particle dissolution with time according to its water solubility in a kinetic model of condensational growth of droplets was able to reasonably reproduce the observed delays, indicating that the delays in the growth of the organic acid particles were mainly due to their lower solubilities. Applying the results in an adiabatic simulation of cloud droplet nucleation, the number of cloud droplets nucleated on particles with solubility equivalent to adipic acid were reduced relative to those nucleated on (NH4)2SO4 by up to 85%. The relative solubility of organic species must be considered when simulating the indirect effect of organic aerosol particles.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observations
  5. 3. Simulations
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[2] Indirectly, aerosols affect the radiative properties and lifetimes of clouds by acting as sites for condensation of water, i.e. acting as cloud condensation nuclei (CCN). Changes in the concentrations of CCN lead to changes in the concentration of cloud droplets. If the cloud liquid water path is invariant with changes in CCN, then the number of droplets increases and the size of the droplets decreases with increasing CCN. This results in a change in the reflectance of the cloud or albedo effect [Twomey, 1977] and can alter the development of precipitation. The latter may affect the lifetime of clouds [e.g., Rosenfeld, 2000; Albrecht, 1989; Liou and Ou, 1989]. Although the real extent of the indirect effect is highly uncertain, the resultant radiative cooling could be large enough to counter current greenhouse gas warming [Intergovernmental Panel on Climate Change (IPCC), 2001].

[3] The ability of organic compounds to act as CCN contributes to the large uncertainty in the indirect effect [IPCC, 2001]. Organic species comprise a significant fraction of the fine particle aerosol [e.g., Li et al., 1996; Novakov et al., 1997]. Several studies have indicated that certain organic particles act as CCN [e.g., Novakov and Penner, 1993; Rivera-Carpio et al., 1996; Novakov and Corrigan, 1996; Cruz and Pandis, 1997, 1998; Virkkula et al., 1999; Corrigan and Novakov, 1999]. However, studies of biomass burning particles, largely organic in composition, have not unambiguously distinguished between the activation of the organic material or the smaller inorganic component [Leaitch et al., 1996].

[4] The main questions surrounding organic compounds as CCN focus on the changes in surface tension of the droplet due to the presence of the organic and the solubility of the compound [Shulman et al., 1996], and the ability of organic species to inhibit the condensation of water. Several studies have considered the inhibiting aspects of organic materials on the surface of water droplets, including the most recently Cantrell et al. [2001] and Feingold and Chuang [2001]. Facchini et al. [1999, 2000] have focused on the surface tension issue, whereas the present work addresses the effect of the solubility of a particle on the growth rate of droplets. Effects of the organics on the surface tension and the accommodation coefficient are not explicitly considered, although it is acknowledged that such effects may be inherent in this work.

[5] The definition of the critical supersaturation needed to activate an aerosol particle into a cloud droplet is based on the assumption of complete dissolution of the solute in the solution droplet. Shulman et al. [1996] pointed out that many organic substances have lower solubilities and require more water for complete dissolution than typically available in droplets below 100% relative humidity. Thus, particles of lower solubility may not fully dissolve until above cloud base and that may mean a slower growth rate for the particles. Laaksonen et al. [1998] presented a modified equilibrium theory to take the reduced solubility into account. However, to date there has been very limited experimental indication of a delay in the growth of cloud droplets on an organic acid [e.g., Hegg et al., 2001], and the question remains as to whether a delay in dissolution can have a significant effect on the ability of a particle to activate in cloud.

[6] Here, the growth rates of water droplets on organic particles of known size and composition are measured using a CCN chamber (CCNc) and modeled using a kinetic model of droplet growth modified to account for solubility. The observations are used to verify the ability of the model to represent the kinetic effects of lower solubility on the particle activation and droplet growth. Finally, the model is used to examine the potential impact of species of lower solubility on the nucleation of cloud droplets in the atmosphere.

2. Observations

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observations
  5. 3. Simulations
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

2.1. Laboratory Experiments

[7] Particles of known size and composition were generated by atomization followed by electrostatic classification using a TSI model 3071 Electrostatic Classifier. The concentrations of the nearly monodisperse particles were measured with a TSI 3025 Ultrafine Condensation Particle Counter. The number concentrations and particle sizes were controlled to within ±15% of 950 cm−3 and 170 ± 10 nm respectively in order to reduce the influence of differences in the competition for water vapor on these measurements for reasons other than chemical composition. The growth of particles into cloud droplets was measured using a University of Wyoming Model MA100 thermal gradient static cloud diffusion chamber (CCNc) operated at a range of effective supersaturations between 0.2–0.9%. de Oliveira and Vali [1995] describe a prototype of this chamber and also discuss the possible role of different compounds in interpreting the CCN signal.

[8] The effective supersaturations of the CCNc were estimated using the critical supersaturation of ammonium sulfate ((NH4)2SO4) particles of known size. Processes within the CCNc can bias the supersaturation below the nominal value, with a few explanations as follows. Particles falling from above into the detection region are activated at a lower supersaturation. Measurement of the temperature of the plate may not be the temperature of the overall surface and the surface may have temperature variations. Mixing within the chamber (due to the creation of eddies from the flush state or falling particles pulling the air down) reduces the supersaturation as particles and gases would move faster than just simple diffusion and would not be limited to equilibrium. In addition, the effective supersaturations can be underestimated slightly because of particle losses in the chamber inlet.

[9] The compounds discussed here are (NH4)2SO4, succinic acid, glutaric acid, adipic acid, pimelic acid and suberic acid as well as 50–50 mixtures of (NH4)2SO4 and organic acids. These organics were investigated because they have been observed in ambient particulate matter [Grosjean et al., 1978] and represent a relatively large range of solubilities and carbon numbers. (NH4)2SO4 is included as a reference for the organic acids due to its prominence in the atmosphere and its relatively high solubility. Table 1 shows the calculated critical supersaturation (Sc) for particles of 170 nm diameter as well as the solubilities for the species investigated here. These values were calculated using Köhler theory, with the implicit assumption that the compound is completely dissolved.

Table 1. Summary of Properties for Compounds Investigated
Molecule (Carbon No.)Molecular FormulaMolecular Weight Ms, g/moleFirst Dissociation Constant in Aqueous Solution, moles/LNumber Ions, νDensity of Solute ρs, g/cm3Critical Supersaturation Sc,a % (as = 170 nm)Solubility, g/L
Ammonium sulfate(NH4)2SO4132.141.4331.770.06754c
Succinic acid (4)HO2C(CH2)2CO2H118.096.89e-0511.570.1188c
Glutaric acid (5)HO2C(CH2)3CO2H132.114.58e-051b1.420.121160c
Adipic acid (6)HO2C(CH2)4CO2H146.113.71e-051b1.360.1325c
Pimelic acid (7)dHO2C(CH2)5CO2H160.173.09e-0511.330.14710e
Suberic acid (8)HO2C(CH2)6CO2H174.202.99e-0511.30f0.14≈0e

[10] The CCNc uses a 670 nm laser diode to scatter light from growing droplets. Figure 1 shows the scattered light signals inside the CCNc, given as a voltage from the photodetector, from droplets growing on nearly monodisperse particles of (NH4)2SO4 and even carbon number organic acids at an effective supersaturation of 0.9%. The (NH4)2SO4 curve is the average of measurements of 170 nm diameter (NH4)2SO4 particles from five different days, and the error bars on the (NH4)2SO4 curve indicate the day-to-day variability; this curve is used as the reference for the subsequent laboratory comparisons. Over the full sampling period of the CCNc (about 20 seconds), the droplets grow and then fall out of the detection region. Only the first 5 seconds of sampling time is shown because this is the initial period of droplet growth and the influence of gravitational settling in the chamber is smallest. Relative to the (NH4)2SO4 curve, delays in the growth of the droplets are present for all of the organic acid curves shown and the delay increases with increasing carbon number. The adipic acid curve exceeds the succinic acid curve after 3 seconds, due to the higher number concentration of adipic acid particles. Suberic acid shows a relatively low response, suggesting that the activation threshold for these particles is larger than 0.9% supersaturation. This is not in agreement with the calculated values in Table 1 that assume complete dissolution.

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Figure 1. Laboratory experiments: pure organic acids, even carbon numbers. Growth curves for nearly monodisperse distributions of even carbon number organic acids, performed on 3 different days, between October–November 1999. The effective supersaturation was 0.9%. The diameter of these particles was 170 nm and number of carbons and number concentrations are shown in brackets in the legend. The “170 nm (NH4)2SO4 average” curve is the average of ammonium sulfate ((NH4)2SO4) curves at 170 nm from 5 different days. This curve shows the day-to-day variations of (NH4)2SO4 at this size. The error bars show 95% confidence value using t-distribution. Lines have been fitted to the data points.

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[11] There is a transient development of the supersaturation in the CCNc. Because of this, the critical supersaturation of particles with larger activation thresholds will be reached later than for particles with smaller thresholds. It was thought that this effect might have led to a delay in the growth rate of the particles independent of the solubility of the particle. This possibility can be examined with the aid of Figure 2. Figure 2 compares growth rate curves of (NH4)2SO4, glutaric acid, adipic acid, and a 50:50 mixture by mass of adipic acid and (NH4)2SO4. There is a delay in adipic acid relative to glutaric acid, and both are delayed relative to (NH4)2SO4, yet glutaric and adipic acids have similar activation thresholds. The lower solubility of adipic acid, compared with glutaric acid, suggests that the simple activation threshold is not the reason for the delays and points to the rate of dissolution as a more likely cause. At first glance, the delay in glutaric acid relative to (NH4)2SO4, despite the very high solubility, might be thought to be the result of its weak dissociation and higher activation threshold, however, the simulations described later do not support that possibility.

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Figure 2. Laboratory experiments: glutaric acid, adipic acid and a mixture case. As for Figure 1, but for adipic acid, glutaric acid and a 50:50 mixture of adipic acid and ammonium sulfate ((NH4)2SO4), performed on 2 days in October 1999. Lines have been fitted to the data points.

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[12] The initial growth for the adipic acid-(NH4)2SO4 mixture case (Figure 2) is very close to the (NH4)2SO4 curve, which suggests control of the growth rate in the CCNc by the compound with the higher solubility.

[13] Delays in growth rates are much less for the odd carbon numbered particles than the even carbon numbered particles. The pattern in the delays is similar to the pattern in the solubilities; the odd carbon numbered species are more soluble than the even carbon numbered species, and the solubility decreases with increasing carbon number [Saxena and Hildemann, 1996]. Pimelic acid (Table 1) exhibited a similar response to glutaric acid.

2.2. Calspan Chamber Experiments

[14] Experiments were conducted at the Calspan Environmental chamber (Springville, New York) in the fall of 1998 to study secondary processes of aerosol formation [e.g., Hoppel et al., 2001a; Caffrey et al., 2001a, 2001b]. A detailed description of this facility is given by Hoppel et al. [1999]. Two of the experiments that dealt with the aerosol formed from the oxidation of α-pinene by ozone are considered here. Details of these particular experiments, from November 9 and 12, are also described by Hoppel et al. [2001b], Gao et al. [2001], and Hegg et al. [2001]. The measurements of polydisperse (NH4)2SO4 particles from November 13 are used as a reference.

[15] Figure 3 shows the growth rates of the droplets for the Calspan experiments. The particles were all polydispersions and the measurements were made at an effective supersaturation of 0.3%. The mode diameter and number concentrations for the polydispersions are given in the legend. Despite the higher number concentrations and larger mode radius, a relatively large delay is evident for the Nov. 9th particles that were the products of the reaction of α-pinene and O3 in the chamber. On Nov. 12th, nebulized (NH4)2SO4 was used as a seed for the condensable products of the oxidation of α-pinene by O3. Even with (NH4)2SO4 in the mixture, the initial delay was similar to that of the pure organic case. The mixture result is in contrast to the laboratory mixture (Figure 2), which showed little or no delay relative to (NH4)2SO4. A possible reason for this is that the Calspan α-pinene-ozone products condensed on and coated the (NH4)2SO4 seed, and hence the water vapor encountered the organic first. In contrast, the laboratory organic and (NH4)2SO4 were mixed in solution and atomized. In that case, much of the (NH4)2SO4 may be on the external portion of the particles, and hence accessible to water vapor, as the (NH4)2SO4 will stay in solution longer as the droplets evaporate.

image

Figure 3. Calspan chamber experiments: Nov. 9, 12 and 13, 1998. Growth curves for polydisperse distributions with effective supersaturation of 0.3%. These measurements were performed on 3 separate days. The number concentrations and the mode diameter are shown in brackets in the legend. Lines have been fitted to the data points.

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[16] The growth rates of the products of α-pinene – O3 oxidation into cloud droplets are consistent with those of organic species of relatively low solubility and higher carbon number expected from α-pinene oxidation.

3. Simulations

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observations
  5. 3. Simulations
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

3.1. Simulations of the Particles in the CCN Chamber

[17] The kinetic parcel model described by Leaitch et al. [1986] was used to attempt to reproduce the observed growth rates of the particles into cloud droplets. This model uses the Köhler equilibrium equation in a kinetic framework much as described by Pruppacher and Klett [1978]. In the original model the source term for the supersaturation is the lifting and adiabatic cooling of an air parcel using a specified updraft velocity. To apply this model to the observations in the CCNc, it is necessary to change the supersaturation source term to approximate the supersaturation development in the CCNc. For this, the observed growth curves for different size particles of (NH4)2SO4 were used as a reference to enable later separation of the effects of the chamber from those due to reduced solubility. The data are shown in Figure 4a and were collected on the same day at a supersaturation of 0.4%. The four sizes shown here 64 nm, 84 nm, 130 nm and 170 nm have the critical supersaturations of 0.3%, 0.2%, 0.09% and 0.06% respectively (calculated using Köhler theory). The detector voltage has been normalized to a number concentration of 1000 #/cc; the actual number concentrations are given in the legend. The larger particles tend to grow into larger and more visible droplets earlier than the smaller particles, and there are increasing delays associated with the smaller particles.

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Figure 4. (a) Laboratory experiments: ammonium sulfate ((NH4)2SO4) of various sizes. Growth curves for nearly monodisperse distributions of (NH4)2SO4. All these experiments were performed on the same day in October 1999. The effective supersaturation in these cases was 0.4%. The legend shows the diameters for each experiment. The detector voltages were all normalized to a number concentration of 1000 #/cc. The original number concentrations can be seen in the legend for each experiment. Lines have been fitted to the data points. (b) Simulations of CCNc: ammonium sulfate ((NH4)2SO4) of various sizes. Growth curves for nearly monodisperse distributions of (NH4)2SO4. These model runs begin with a transient supersaturation that approaches a constant supersaturation of 0.4%. The total number concentration was set to 1000 #/cc. The 64 nm particles start to activate at 2 seconds but only reach a maximum of 88% activation by 3.4 seconds.

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image

Figure 4. (continued)

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[18] The model was modified to include a transient increase in the supersaturation to a constant value. The transient was represented by a linear rise in relative humidity, starting at 95%; although in the laboratory experiments the aerosol introduced to the CCNc had a relative humidity of < 20% it was assumed to rise rapidly to near saturation. The equation used for the transient supersaturation (S) is: S = 3.30831*t–5.022, where S is supersaturation in percent and t is time in seconds. The slope of the transient increase was chosen to best approximate the pattern of growth of the 170 nm (NH4)2SO4 particles laboratory case (Figure 4a). Subsequently, the other three particle sizes were simulated using the same time constants. The simulations were performed for a maximum supersaturation of 0.4% with nearly monodisperse particles. The scattering cross-sectional area of the growing particles was calculated as a surrogate for comparison with detector voltage of the CCNc. The Mie scattering efficiency of the droplet scattering was calculated but it is not included here as it introduced small oscillations in the curves. These oscillations did not affect the relative delays, and the fact that the observed curves showed no such oscillations may be explained by variability in the droplet sizes and supersaturation within the CCNc that are not accounted for in the simulations. Another plausible explanation for the absence of oscillations in the observations is the photodetector views the droplets over a range of angles, thus smoothing the curves.

[19] The simulated growth curves, corresponding to the measured values (Figure 4a), are shown in Figure 4b. The simulations reproduce the general pattern and approximate time of delay seen in the observations.

[20] The point at which all of the particles fully activate is indicated for each curve in Figure 4b, with the exception of the 64 nm case. Only a portion of the 64 nm particles activated despite the fact that their critical supersaturation is slightly lower than 0.4%. Comparing the 84 nm, 130 nm and 170 nm cases shows that the smaller particles activate before the larger ones. Thus, it is not activation of the larger particles that accounts for their faster initial growth rate.

[21] The delays for the different sizes of (NH4)2SO4 particles are smaller at higher supersaturations. Little delay in both the measured (laboratory) and simulated growth rates was found at 0.9% supersaturation.

[22] The second step in the modeling of the water growth rates in the CCN chamber was to provide for compounds of lower solubility. The model initially specifies particles that are composed of two parts: one insoluble and one soluble. The soluble part is assumed to be completely and instantaneously dissolved (i.e. has high solubility) at the starting relative humidity. This is appropriate for species like (NH4)2SO4. In order to treat species with low solubilities that will not dissolve completely until more water is present on a particle, the model was modified to make the insoluble portion of particle dissolve according to specified solubility. At each time step, part of the insoluble portion was allowed to dissolve based on water mass and compound solubility. The kinetics of the dissolution process itself is not considered, making this approach one that will tend to underestimate the delay.

[23] Simulations of the solubilities of the organic species measured in the laboratory were conducted for nearly monodisperse particles centered at 170 nm. The ionic dissociation factor was assumed to be 1, because these organic acids have small dissociation constants (Table 1). Solution properties, other than the solubility and the ionic dissociation factor, including the osmotic coefficient were assumed to be equivalent to those of (NH4)2SO4. The initial particles were assumed to be composed of 0.1% (NH4)2SO4 and 99.9% of the species with the solubility of interest. The small (NH4)2SO4 component was included to ensure that the particles retain water at relative humidities below saturation. The transient supersaturation was slightly different for the maximum supersaturation of 0.9% than for 0.4% as discussed earlier. The equation used for this transient supersaturation (S) is: S = 3.3683*t–5.0262, where S is supersaturation in percent and t is time in seconds.

[24] The results of the simulations are shown in Figure 5, with the numbers in the legend indicating the solubilities tested. The pattern of the delays is similar between the simulations and observations (Figures 1 and 2), although the observed delays for succinic and glutaric acids are larger than simulated. The comparisons with succinic and glutaric acids reflect a conservative aspect to this model, however the reason for the absence of a larger delay for these two organic acids is unknown. It is not the result of a weaker dissociation that leads to the higher critical supersaturation estimate, since the model considers this factor. It may be that differences between the osmotic and accommodation coefficients for (NH4)2SO4 and glutaric acid are responsible.

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Figure 5. Simulations of CCNc: ammonium sulfate ((NH4)2SO4) plus slightly soluble compound. Growth curves for model runs with supersaturation of 0.9%. The solubility is shown in the legend. The total number concentration was set to 1000 #/cc and the mode diameter was set to 170 nm. For these runs, the (NH4)2SO4 case employed ν = 3 (number of ions from dissociation of one solute) whereas all other cases had ν = 1 (to simulate not only the solubility but the number of ions of the organics).

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[25] Figure 5 also shows that at approximately 100 g/L solubility and greater, there is essentially complete activation and the substance can be considered completely soluble. At approximately 10 g/L and less, the particles do not activate at all and can be considered completely insoluble. This implies that the impact of particles with solubilities in the range 10–100 g/L may be more difficult to represent in models of the indirect effect of aerosols.

[26] Figure 6 shows a direct comparison between laboratory results and simulations for (NH4)2SO4 and adipic acid. The model (NH4)2SO4 curve was positioned to approximate the laboratory (NH4)2SO4 curve. The resulting comparison of the simulated and observed adipic acid curves indicates that the solubility and the dissociation factor explain much of the observed delays by the organic species in the rate of water uptake.

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Figure 6. Laboratory experiments CCNc voltages compared with simulations of CCNc: ammonium sulfate ((NH4)2SO4) and adipic acid. The laboratory average (NH4)2SO4 curve from Figures 1 and 2 and the adipic acid curve from Figure 1 is compared to the simulations for (NH4)2SO4 and the slightly soluble compound with solubility similar to adipic acid from Figure 5. The simulation for (NH4)2SO4 was fitted to the laboratory average case. The effective supersaturation was 0.9%. The diameter of these particles was 170 nm (nearly monodisperse aerosol) and number concentrations were approximately 1000 #/cc. Lines have been fitted to the data points.

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[27] According to Facchini et al. [1999], a reduction in the surface tension of the droplets might be expected. The effect of this would be to reduce the particle’s critical supersaturation and increase its growth rate. Such an effect cannot be discounted in the present results; however, its presence is not delineated.

3.2. Simulations of the Effect of Reduced Solubility on Cloud Droplet Number Concentrations

[28] Here the impact of the delays in growth rates of droplets due to the solubility of the CCN on the nucleation of cloud droplets is considered. To do this, the source term for the supersaturation in the model was reverted back to the lifting and adiabatic cooling of an air parcel using a specified updraft velocity. The initial particle distribution was represented by a lognormal distribution with standard deviation of 1.5 and a mode diameter of 160 nm. The model was run for number concentrations of 100 cm−3 and 500 cm−3 and updraft velocities of 20 cm/s and 50 cm/s. Figure 7 shows the simulated cloud profiles of relative humidity for particles of low particle solubility (solubility equal to that of adipic acid and an ionic factor of 1) and particles composed of (NH4)2SO4 (ionic factor of 3). The particle number concentration and updraft velocity are 100 cm−3 and 20 cm/s for each. The point of complete dissolution of the particles is shown on the adipic acid curve; (NH4)2SO4 dissolves completely before saturation is reached.

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Figure 7. Adiabatic cloud parcel model simulations: relative humidity (RH) for ammonium sulfate ((NH4)2SO4) and the solubility for adipic acid (AA). (NH4)2SO4 was fully dissolved soon after the simulation began, whereas adipic acid took quite a while to fully dissolve (see legend). The point of activation is indicated in the legend. The updraft velocity was set to 20 cm/sec. The distribution was lognormal, with one mode centered on diameter 160 nm, standard deviation of 1.5. Lines have been fitted to the data points.

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[29] For the droplets nucleating on the particles of lower solubility, the lower droplet growth rate results in a reduced water uptake, which in turn raises both the level and the value of the maximum supersaturation in the cloud. For the low solubility case, only the particles that have had enough water to condense on them to enable complete dissolution of the solute are activated. Despite the higher supersaturation, the delay in dissolution reduces the cloud droplet number concentrations for the low solubility case to 23 cm−3 compared with 95 cm−3 in the (NH4)2SO4 case. These droplet number concentrations are based on everything larger than and including the smallest activated particle [e.g., Nenes et al., 2001].

[30] Table 1 indicated that adipic acid would have a critical supersaturation of 0.13%, based on Köhler theory and complete solubility. However Figure 7 shows that the adipic acid does not activate until greater than 0.4% supersaturation. This implies that measurements in the CCNc of critical supersaturation may tend to give a higher value because the particle requires more water to be completely dissolved. This agrees with the tendency for less soluble species to have larger critical diameters, as suggested by Shulman et al. [1996]. It is also consistent with the observations of Corrigan and Novakov [1999], who found that organic species with higher solubilities had critical diameters approaching those of the inorganics in contrast to the less soluble adipic acid.

[31] The simulated cloud droplet concentrations for all the model run cases are listed in Table 2. In all cases, the cloud droplet number concentrations are lower for the cases of particles of low solubility compared with particles composed only of (NH4)2SO4. There is an increase in the number of cloud droplets nucleated on particles when the updraft speed is higher. This increase is relatively larger for adipic acid than for ammonium sulfate (NH4)2SO4 particles. This is due to the peak in the supersaturation being higher above cloud base resulting in higher liquid water content and more dissolution of adipic acid particles.

Table 2. Number of Cloud Droplets Activated
Updraft Velocity, Initial Particle Number ConcentrationAmmonium SulfateAdipic Acid
20 cm/sec, 100 #/cc9523
50 cm/sec, 100 #/cc9941
20 cm/sec, 500 #/cc34050
50 cm/sec, 500 #/cc46097

4. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observations
  5. 3. Simulations
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[32] Through a combination of observations and modeling, the growth of cloud droplets on organic particles of lower solubility has been shown to be delayed relative to (NH4)2SO4. Particles that are a mixture of organics of low solubility and (NH4)2SO4 may exhibit a delay if the organic coats the (NH4)2SO4. Although the delays due to solubility were found to be of the order of 1–2 seconds, the effect was found to reduce cloud droplet number concentrations relative to (NH4)2SO4 by up to 85%. Solubility must be considered when attempting to simulate the indirect effect of organic aerosols.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observations
  5. 3. Simulations
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[33] We would like to thank T. Albrechcinski, J. Ambrusko, S. Bacic, M. Couture, G. Frick, D. Hastie, D. Hegg, W. Hoppel, and M. Mozurkewich for their contributions to this work. The authors would also like to thank the NOPP program and the Calspan group for support of the Calspan Chamber experiments. This work was supported in part by a NSERC PGS-B postgraduate research scholarship.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observations
  5. 3. Simulations
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Observations
  5. 3. Simulations
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

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