Cloud Condensation Nuclei Predictions Research Articles

Prediction of CCN spectra parameters in the North China Plain using a random forest model

Cloud condensation nuclei (CCN) spectra, that indicate the dependence of CCN number concentrations (NCCN) on supersaturation, are important factors in aerosol–cloud interactions. Owing to the lack of direct measurements of NCCN, calculating the CCN spectra based on conventional observational data of aerosols is important to obtain NCCN; however, this is challenging owing to the complex relationship between aerosol properties and CCN activity. Machine learning techniques have recently been applied to estimate NCCN and found to be a promising method for CCN prediction. In this study, the random forest (RF) model was applied to predict CCN spectral parameters using the observation data measured in four campaigns in the North China Plain, and the effects of chemical and optical properties of aerosol on the estimation of CCN spectra were investigated. The results show that the RF model trained with the data of one campaign can be used to estimate CCN spectral parameters in another campaign, with the coefficient of determination between the estimated and measured CCN spectral parameters being approximately 0.5. The deviations of the estimation by the RF model may result from the difference in both the aerosol properties and measurement uncertainties among different campaigns, whose influence on the deviations can be further magnified by overfitting the RF model. Further analysis revealed that the major aerosol properties among the input variables of the RF model were the mass concentration of black carbon and aerosol hemispheric backscattering fraction at a wavelength of 450 nm. In addition, the roles of chemical compositions of aerosol in estimating CCN spectra parameters are different among different campaigns because the poor correlation between aerosol chemical compositions and CCN spectra parameter can affect the performance of the RF model. For estimating CCN spectra, using aerosol optical properties, including the hemispheric backscattering fraction and absorption coefficient, as model inputs is more recommended than using aerosol chemical properties.

Characterization of Aerosol Hygroscopicity Over the Northeast Pacific Ocean: Impacts on Prediction of CCN and Stratocumulus Cloud Droplet Number Concentrations.

During the Marine Aerosol Cloud and Wildfire Study (MACAWS) in June and July of 2018, aerosol composition and cloud condensation nuclei (CCN) properties were measured over the N.E. Pacific to characterize the influence of aerosol hygroscopicity on predictions of ambient CCN and stratocumulus cloud droplet number concentrations (CDNC). Three vertical regions were characterized, corresponding to the marine boundary layer (MBL), an above-cloud organic aerosol layer (AC-OAL), and the free troposphere (FT) above the AC-OAL. The aerosol hygroscopicity parameter (κ) was calculated from CCN measurements (κCCN) and bulk aerosol mass spectrometer (AMS) measurements (κAMS). Within the MBL, measured hygroscopicities varied between values typical of both continental environments (~0.2) and remote marine locations (~0.7). For most flights, CCN closure was achieved within 20% in the MBL. For five of the seven flights, assuming a constant aerosol size distribution produced similar or better CCN closure than assuming a constant “marine” hygroscopicity (κ = 0.72). An aerosol-cloud parcel model was used to characterize the sensitivity of predicted stratocumulus CDNC to aerosol hygroscopicity, size distribution properties, and updraft velocity. Average CDNC sensitivity to accumulation mode aerosol hygroscopicity is 39% as large as the sensitivity to the geometric median diameter in this environment. Simulations suggest CDNC sensitivity to hygroscopicity is largest in marine stratocumulus with low updraft velocities (<0.2 m s−1), where accumulation mode particles are most relevant to CDNC, and in marine stratocumulus or cumulus with large updraft velocities (>0.6 m s−1), where hygroscopic properties of the Aitken mode dominate hygroscopicity sensitivity.

Impact of urban aerosol properties on cloud condensation nuclei (CCN) activity during the KORUS-AQ field campaign

Ground measurements of physical, chemical and hygroscopic properties of aerosols were made at the Olympic Park, Seoul, as part of the KORea-United States Air Quality study (KORUS-AQ) campaign in May–June 2016. The average number concentrations of aerosols larger than 10 nm in diameter and cloud condensation nuclei (CCN) at 0.6% supersaturation (S) were 10800 cm−3 and 3400 cm−3, respectively. The average geometric mean diameter (Dg) was 44 nm, and size-resolved aerosol hygroscopicity (κ) from HTDMA ranged from 0.11 to 0.24 for particle diameters in the range of 30–150 nm. Aerosols were classified into three types based on mixing state and hygroscopic growth factor (GF): Type 1 (externally mixed aerosol), Type 2 (Internally mixed and growth aerosol) and Type 3 (internally mixed and non-growth aerosol). These three aerosol types showed distinct diurnal patterns. The difference in physical and chemical properties of aerosols for different air mass sources crucially impacted aerosol hygroscopicity. Using the external mixture assumption with measured hygroscopicity data improved the results of CCN prediction compared to those from the simple internal mixture assumption because externally mixed aerosols comprised a significant portion of aerosols in this urban area. Moreover, the simple assumption of aerosol size distribution with a fixed chemical composition sufficiently explained more than 50 percent of the variation of CCN number concentrations, although the information of chemical composition was still meaningful. Overall, the measured data showed consistency with Megacity Air Pollution Studies (MAPS-Seoul) campaign held during May–June 2015, implying that these results may represent urban aerosols in spring/summer in the Korean Peninsula.

Metrics to quantify the importance of mixing state for CCN activity

Abstract. It is commonly assumed that models are more prone to errors in predicted cloud condensation nuclei (CCN) concentrations when the aerosol populations are externally mixed. In this work we investigate this assumption by using the mixing state index (χ) proposed by Riemer and West (2013) to quantify the degree of external and internal mixing of aerosol populations. We combine this metric with particle-resolved model simulations to quantify error in CCN predictions when mixing state information is neglected, exploring a range of scenarios that cover different conditions of aerosol aging. We show that mixing state information does indeed become unimportant for more internally mixed populations, more precisely for populations with χ larger than 75 %. For more externally mixed populations (χ below 20 %) the relationship of χ and the error in CCN predictions is not unique and ranges from lower than −40 % to about 150 %, depending on the underlying aerosol population and the environmental supersaturation. We explain the reasons for this behavior with detailed process analyses.

Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 1: Aerosol size distribution, hygroscopicity, and new model parametrizations for CCN prediction

Abstract. Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a 1-year period and full seasonal cycle (March 2014–February 2015). The measurements provide a climatology of CCN properties characteristic of a remote central Amazonian rain forest site.The CCN measurements were continuously cycled through 10 levels of supersaturation (S = 0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The mean critical diameters of CCN activation range from 43 nm at S = 1.10 % to 172 nm at S = 0.11 %. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (κAit = 0.14 ± 0.03), higher values for the accumulation mode (κAcc = 0.22 ± 0.05), and an overall mean value of κmean = 0.17 ± 0.06, consistent with high fractions of organic aerosol.The hygroscopicity parameter, κ, exhibits remarkably little temporal variability: no pronounced diurnal cycles, only weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. We find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes.For modeling purposes, we compare different approaches of predicting CCN number concentration and present a novel parametrization, which allows accurate CCN predictions based on a small set of input data.

Characterization and parameterization of aerosol cloud condensation nuclei activation under different pollution conditions.

To better understand the cloud condensation nuclei (CCN) activation capacity of aerosol particles in different pollution conditions, a long-term field experiment was carried out at a regional GAW (Global Atmosphere Watch) station in the Yangtze River Delta area of China. The homogeneity of aerosol particles was the highest in clean weather, with the highest active fraction of all the weather types. For pollution with the same visibility, the residual aerosol particles in higher relative humidity weather conditions were more externally mixed and heterogeneous, with a lower hygroscopic capacity. The hygroscopic capacity (κ) of organic aerosols can be classified into 0.1 and 0.2 in different weather types. The particles at ~150 nm were easily activated in haze weather conditions. For CCN predictions, the bulk chemical composition method was closer to observations at low supersaturations (≤0.1%), whereas when the supersaturation was ≥0.2%, the size-resolved chemical composition method was more accurate. As for the mixing state of the aerosol particles, in haze, heavy haze, and severe haze weather conditions CCN predictions based on the internal mixing assumption were robust, whereas for other weather conditions, predictions based on the external mixing assumption were more accurate.

CCN closure study: Effects of aerosol chemical composition and mixing state

AbstractThis study presents a detailed cloud condensation nuclei (CCN) closure study that investigates the effects of chemical composition (bulk and size resolved) and mixing state (internal and external) on CCN activity of aerosols. Measurements of the chemical composition, aerosol size distribution, total number concentration, and CCN concentration at supersaturation (SS = 0.2–1.0%) were performed during the winter season in Kanpur, India. Among the two cases considered here, better closure results are obtained for case 1 (low total aerosol loading, 49.54 ± 26.42 μg m−3, and high O:C ratio, 0.61 ±0.07) compared to case 2 (high total aerosol loading, 101.05 ± 18.73 μg m−3, and low O:C ratio, 0.42 ±0.06), with a maximum reduction of 3–81% in CCN overprediction for all depleted SS values (0.18–0.60%). Including the assumption that less volatile oxidized organic aerosols represent the soluble organic fraction reduced the overprediction to at most 40% and 129% in the internal and external mixing scenarios, respectively. At higher depleted SS values (0.34–0.60%), size‐resolved chemical composition with an internal mixing state performed well in CCN closure among all organic solubility scenarios. However, at a lower depleted SS value (0.18%), closure is found to be more sensitive to both the chemical composition and mixing state of aerosols. At higher SS values, information on the solubility of organics and size‐resolved chemical composition is required for accurate CCN predictions, whereas at lower SS values, information on the mixing state in addition to the solubility of organics and size‐resolved chemical composition is required. Overall, κtotal values are observed to be independent of the O:C ratio [κtotal= (0.36 ±0.01) × O:C − (0.03 ± 0.01)] in the range of 0.2&lt;O:C&lt;0.81, which indicates that the variation in the chemical composition of aerosols is not well represented by the changes in the O:C ratio alone.

Cloud condensation nuclei (CCN) activity of aliphatic amine secondary aerosol

Abstract. Aliphatic amines can form secondary aerosol via oxidation with atmospheric radicals (e.g., hydroxyl radical and nitrate radical). The particle can contain both secondary organic aerosol (SOA) and inorganic salts. The ratio of organic to inorganic materials in the particulate phase influences aerosol hygroscopicity and cloud condensation nuclei (CCN) activity. SOA formed from trimethylamine (TMA) and butylamine (BA) reactions with hydroxyl radical (OH) is composed of organic material of low hygroscopicity (single hygroscopicity parameter, κ, ≤ 0.25). Secondary aerosol formed from the tertiary aliphatic amine (TMA) with N2O5 (source of nitrate radical, NO3) contains less volatile compounds than the primary aliphatic amine (BA) aerosol. As relative humidity (RH) increases, inorganic amine salts are formed as a result of acid–base reactions. The CCN activity of the humid TMA–N2O5 aerosol obeys Zdanovskii, Stokes, and Robinson (ZSR) ideal mixing rules. The humid BA + N2O5 aerosol products were found to be very sensitive to the temperature at which the measurements were made within the streamwise continuous-flow thermal gradient CCN counter; κ ranges from 0.4 to 0.7 dependent on the instrument supersaturation (ss) settings. The variance of the measured aerosol κ values indicates that simple ZSR rules cannot be applied to the CCN results from the primary aliphatic amine system. Overall, aliphatic amine aerosol systems' κ ranges within 0.2 &lt; κ &lt; 0.7. This work indicates that aerosols formed via nighttime reactions with amines are likely to produce hygroscopic and volatile aerosol, whereas photochemical reactions with OH produce secondary organic aerosol of lower CCN activity. The contributions of semivolatile secondary organic and inorganic material from aliphatic amines must be considered for accurate hygroscopicity and CCN predictions from aliphatic amine systems.

Weak global sensitivity of cloud condensation nuclei and the aerosol indirect effect to Criegee + SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; chemistry

Abstract. H2SO4 vapor is important for the nucleation of atmospheric aerosols and the growth of ultrafine particles to cloud condensation nuclei (CCN) sizes with important roles in the global aerosol budget and hence planetary radiative forcing. Recent studies have found that reactions of stabilized Criegee intermediates (CIs, formed from the ozonolysis of alkenes) with SO2 may be an important source of H2SO4 that has been missing from atmospheric aerosol models. For the first time in a global model, we investigate the impact of this new source of H2SO4 in the atmosphere. We use the chemical transport model, GEOS-Chem, with the online aerosol microphysics module, TOMAS, to estimate the possible impact of CIs on present-day H2SO4, CCN, and the cloud-albedo aerosol indirect effect (AIE). We extend the standard GEOS-Chem chemistry with CI-forming reactions (ozonolysis of isoprene, methyl vinyl ketone, methacrolein, propene, and monoterpenes) from the Master Chemical Mechanism. Using a fast rate constant for CI+SO2, we find that the addition of this chemistry increases the global production of H2SO4 by 4%. H2SO4 concentrations increase by over 100% in forested tropical boundary layers and by over 10–25% in forested NH boundary layers (up to 100% in July) due to CI+SO2 chemistry, but the change is generally negligible elsewhere. The predicted changes in CCN were strongly dampened to the CI+SO2 changes in H2SO4 in some regions: less than 15% in tropical forests and less than 2% in most mid-latitude locations. The global-mean CCN change was less than 1% both in the boundary layer and the free troposphere. The associated cloud-albedo AIE change was less than 0.03 W m−2. The model global sensitivity of CCN and the AIE to CI+SO2 chemistry is significantly (approximately one order-of-magnitude) smaller than the sensitivity of CCN and AIE to other uncertain model inputs, such as nucleation mechanisms, primary emissions, SOA (secondary organic aerosol) and deposition. Similarly, comparisons to size-distribution measurements show that uncertainties in other model parameters dominate model biases in the model-predicted size distributions. We conclude that improvement in the modeled CI+SO2 chemistry would not likely lead to significant improvements in present-day CCN and AIE predictions.

Analysis of CCN activity of Arctic aerosol and Canadian biomass burning during summer 2008

Abstract. The NASA DC-8 aircraft characterized the aerosol properties, chemical composition, and cloud condensation nuclei (CCN) concentrations of the summertime Arctic during the 2008 NASA Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) campaign. Air masses characteristic of fresh and aged biomass burning, boreal forest, Arctic background, and anthropogenic industrial pollution were sampled. Observations were spatially extensive (50–85° N and 40–130° W) and exhibit significant variability in aerosol and CCN concentrations. The chemical composition was dominated by highly oxidized organics (66–94% by volume), with a water-soluble mass fraction of more than 50%. The aerosol hygroscopicity parameter, κ, ranged between κ = 0.08–0.32 for all air mass types. Industrial pollution had the lowest κ of 0.08 ± 0.01, while the Arctic background had the highest and most variable κ of 0.32 ± 0.21, resulting from a lower and more variable organic fraction. Both fresh and aged (long-range transported) biomass burning air masses exhibited remarkably similar κ (0.18 ± 0.13), consistent with observed rapid chemical and physical aging of smoke emissions in the atmosphere, even in the vicinity of fresh fires. The organic hygroscopicity (κorg) was parameterized by the volume fraction of water-soluble organic matter (εWSOM), with a κ = 0.12, such that κorg = 0.12εWSOM. Assuming bulk (size-independent) composition and including the κorg parameterization enabled CCN predictions to within 30% accuracy for nearly all environments sampled. The only exception was for industrial pollution from Canadian oil sands exploration, where an external mixture and size-dependent composition was required. Aerosol mixing state assumptions (internal vs. external) in all other environments did not significantly affect CCN predictions; however, the external mixing assumption provided the best results, even though the available observations could not determine the true degree of external mixing and therefore may not always be representative of the environments sampled. No correlation was observed between κorg and O : C. A novel correction of the CCN instrument supersaturation for water vapor depletion, resulting from high concentrations of CCN, was also employed. This correction was especially important for fresh biomass burning plumes where concentrations exceeded 1.5×104 cm−3 and introduced supersaturation depletions of &amp;amp;geq;25%. Not accounting for supersaturation depletion in these high concentration environments would therefore bias CCN closure up to 25% and inferred κ by up to 50%.

Hygroscopicity and composition of Alaskan Arctic CCN during April 2008

Abstract. We present a comprehensive characterization of cloud condensation nuclei (CCN) sampled in the Alaskan Arctic during the 2008 Aerosol, Radiation, and Cloud Processes affecting Arctic Climate (ARCPAC) project, a component of the POLARCAT and International Polar Year (IPY) initiatives. Four distinct air mass types were sampled including a cleaner Arctic background and a relatively pristine sea ice boundary layer as well as biomass burning and anthropogenic pollution plumes. Despite differences in chemical composition, inferred aerosol hygroscopicities were fairly invariant and ranged from κ = 0.1–0.3 over the atmospherically-relevant range of water vapor supersaturations studied. Organic aerosols sampled were found to be well-oxygenated, consistent with long-range transport and aerosol aging processes. However, inferred hygroscopicities are less than would be predicted based on previous parameterizations of biogenic oxygenated organic aerosol, suggesting an upper limit on organic aerosol hygroscopicity above which κ is less sensitive to the O:C ratio. Most Arctic aerosols act as CCN above 0.1 % supersaturation, although the data suggest the presence of an externally-mixed, non-CCN-active mode comprising approximately 0–20% of the aerosol number. CCN closure was assessed using measured size distributions, bulk chemical composition, and assumed aerosol mixing states; CCN predictions tended toward overprediction, with the best agreement (±0–20 %) obtained by assuming the aerosol to be externally-mixed with soluble organics. Closure also varied with CCN concentration, and the best agreement was found for CCN concentrations above 100 cm−3 with a 1.5- to 3-fold overprediction at lower concentrations.

Size-resolved and bulk activation properties of aerosols in the North China Plain

Abstract. Size-resolved and bulk activation properties of aerosols were measured at a regional/suburban site in the North China Plain (NCP), which is occasionally heavily polluted by anthropogenic aerosol particles and gases. A Cloud Condensation Nuclei (CCN) closure study is conducted with bulk CCN number concentration (NCCN) and calculated CCN number concentration based on the aerosol number size distribution and size-resolved activation properties. The observed CCN number concentration (NCCN-obs) are higher than those observed in other locations than China, with average NCCN-obs of roughly 2000, 3000, 6000, 10 000 and 13 000 cm−3 at supersaturations of 0.056, 0.083, 0.17, 0.35 and 0.70%, respectively. An inferred critical dry diameter (Dm) is calculated based on the NCCN-obs and aerosol number size distribution assuming homogeneous chemical composition. The inferred cut-off diameters are in the ranges of 190–280, 160–260, 95–180, 65–120 and 50–100 nm at supersaturations of 0.056, 0.083, 0.17, 0.35 and 0.7%, with their mean values 230.1, 198.4, 128.4, 86.4 and 69.2 nm, respectively. Size-resolved activation measurements show that most of the 300 nm particles are activated at the investigated supersaturations, while almost no particles of 30 nm are activated even at the highest supersaturation of 0.72%. The activation ratio increases with increasing supersaturation and particle size. The slopes of the activation curves for ambient aerosols are not as steep as those observed in calibrations with ammonium sulfate suggesting that the observed aerosols is an external mixture of more hygroscopic and hydrophobic particles. The calculated CCN number concentrations (NCCN-calc) based on the size-resolved activation ratio and aerosol number size distribution correlate well with the NCCN-obs, and show an average overestimation of 19%. Sensitivity studies of the CCN closure show that the NCCN at each supersaturation is well predicted with the campaign average of size-resolved activation curves. These results indicate that the aerosol number size distribution is critical in the prediction of possible CCN. The CCN number concentration can be reliably estimated using time-averaged, size-resolved activation efficiencies without accounting for the temporal variations.

Sensitivity of global cloud condensation nuclei concentrations to primary sulfate emission parameterizations

Abstract. The impact of primary sulfate emissions on cloud condensation nuclei (CCN) concentrations, one of the major uncertainties in global CCN predictions, depends on the fraction of sulfur mass emitted as primary sulfate particles (fsulfate), the fraction of primary sulfate mass distributed into the nucleation mode particles (fnucl), and the nucleation and growth processes in the ambient atmosphere. Here, we use a global size-resolved aerosol microphysics model recently developed to study how the different parameterizations of primary sulfate emission affect particle properties and CCN abundance. Different from previous studies, we use the ion-mediated nucleation scheme to simulate tropospheric particle formation. The kinetic condensation of low volatile secondary organic gas (SOG) (in addition to H2SO4 gas) on nucleated particles is calculated based on our new scheme that considers the SOG volatility changes arising from the oxidation aging. Our simulations show a compensation effect of nucleation to primary sulfate emission. We find that the change of fnucl from 5% to 15% has a more significant impact on the simulated particle number budget than that of fsulfate within the range of 2.5–5%. Based on our model configurations, an increase of fsulfate from 0% to 2.5% (with fnucl = 5%) does not improve the agreement between simulated and observed annual mean number concentrations of particles &gt;10 nm at 21 stations but further increase of either fsulfate from 2.5% to 5% (with fnucl = 5%) or fnucl from 5% to 15% (with fsulfate = 2.5%) substantially deteriorates the agreement. For fsulfate of 2.5%–5% and fnucl of 5%, our simulations indicate that the global CCN at supersaturation of 0.2% increases by 8–11% in the boundary layer and 3–5% in the whole troposphere (compared to the case with fsulfate=0).

Measured and modelled cloud condensation nuclei number concentration at the high alpine site Jungfraujoch

Abstract. Atmospheric aerosol particles are able to act as cloud condensation nuclei (CCN) and are therefore important for the climate and the hydrological cycle, but their properties are not fully understood. Total CCN number concentrations at 10 different supersaturations in the range of SS=0.12–1.18% were measured in May 2008 at the remote high alpine research station, Jungfraujoch, Switzerland (3580 m a.s.l.). In this paper, we present a closure study between measured and predicted CCN number concentrations. CCN predictions were done using dry number size distribution (scanning particle mobility sizer, SMPS) and bulk chemical composition data (aerosol mass spectrometer, AMS, and multi-angle absorption photometer, MAAP) in a simplified Köhler theory. The predicted and the measured CCN number concentrations agree very well and are highly correlated. A sensitivity study showed that the temporal variability of the chemical composition at the Jungfraujoch can be neglected for a reliable CCN prediction, whereas it is important to know the mean chemical composition. The exact bias introduced by using a too low or too high hygroscopicity parameter for CCN prediction was further quantified and shown to be substantial for the lowest supersaturation. Despite the high average organic mass fraction (~45%) in the fine mode, there was no indication that the surface tension was substantially reduced at the point of CCN activation. A comparison between hygroscopicity tandem differential mobility analyzer (HTDMA), AMS/MAAP, and CCN derived κ values showed that HTDMA measurements can be used to determine particle hygroscopicity required for CCN predictions if no suitable chemical composition data are available.

CCN predictions using simplified assumptions of organic aerosol composition and mixing state: a synthesis from six different locations

Abstract. An accurate but simple quantification of the fraction of aerosol particles that can act as cloud condensation nuclei (CCN) is needed for implementation in large-scale models. Data on aerosol size distribution, chemical composition, and CCN concentration from six different locations have been analyzed to explore the extent to which simple assumptions of composition and mixing state of the organic fraction can reproduce measured CCN number concentrations. Fresher pollution aerosol as encountered in Riverside, CA, and the ship channel in Houston, TX, cannot be represented without knowledge of more complex (size-resolved) composition. For aerosol that has experienced processing (Mexico City, Holme Moss (UK), Point Reyes (CA), and Chebogue Point (Canada)), CCN can be predicted within a factor of two assuming either externally or internally mixed soluble organics although these simplified compositions/mixing states might not represent the actual properties of ambient aerosol populations, in agreement with many previous CCN studies in the literature. Under typical conditions, a factor of two uncertainty in CCN concentration due to composition assumptions translates to an uncertainty of ~15% in cloud drop concentration, which might be adequate for large-scale models given the much larger uncertainty in cloudiness.

Investigation of cloud condensation nuclei properties and droplet growth kinetics of the water‐soluble aerosol fraction in Mexico City

We present hygroscopic and cloud condensation nuclei (CCN) relevant properties of the water‐soluble fraction of Mexico City aerosol collected upon filters during the 2006 Megacity Initiative: Local and Global Research Observations (MILAGRO) campaign. Application of κ‐Köhler theory to the observed CCN activity gave a fairly constant hygroscopicity parameter (κ = 0.28 ± 0.06) regardless of location and organic fraction. Köhler theory analysis was used to understand this invariance by separating the molar volume and surfactant contributions to the CCN activity. Organics were found to depress surface tension (10–15%) from that of pure water. Daytime samples exhibited lower molar mass (∼200 amu) and surface tension depression than nighttime samples (∼400 amu); this is consistent with fresh hygroscopic secondary organic aerosol (SOA) condensing onto particles during peak photochemical hours, subsequently aging during nighttime periods of high relative humidity. Changes in surface tension partially compensate for shifts in average molar volume to give the constant hygroscopicity observed, which implies the amount (volume fraction) of soluble material in the parent aerosol is the key composition parameter required for CCN predictions. This finding, if applicable elsewhere, may explain why CCN predictions are often found to be insensitive to assumptions of chemical composition and provides a very simple way to parameterize organic hygroscopicity in atmospheric models (i.e., κorg = 0.28ɛWSOC). Special care should be given, however, to surface tension depression from organic surfactants, as its nonlinear dependence with organic fraction may introduce biases in observed (and predicted) hygroscopicity. Finally, threshold droplet growth analysis suggests the water‐soluble organics do not affect activation kinetics.

Subarctic atmospheric aerosol composition: 3. Measured and modeled properties of cloud condensation nuclei

Aerosol particles can modify cloud properties by acting as cloud condensation nuclei (CCN). Predicting CCN properties is still a challenge and not properly incorporated in current climate models. Atmospheric particle number size distributions, hygroscopic growth factors, and polydisperse CCN number concentrations were measured at the remote subarctic Stordalen mire, 200 km north of the Arctic Circle in northern Sweden. The CCN number concentration was highly variable, largely driven by variations in the total number of sufficiently large particles, though the variability of chemical composition was increasingly important for decreasing supersaturation. The hygroscopicity of particles measured by a hygroscopicity tandem differential mobility analyzer (HTDMA) was in agreement with large critical diameters observed for CCN activation (κ ≈ 0.07–0.21 for D = 50–200 nm). Size distribution and time‐ and size‐resolved HTDMA data were used to predict CCN number concentrations. Agreement of predictions with measured CCN within ±11% was achieved using parameterized Köhler theory and assuming a surface tension of pure water. The sensitivity of CCN predictions to various simplifying assumptions was further explored: We found that (1) ignoring particle mixing state did not affect CCN predictions, (2) averaging the HTDMA data in time with retaining the size dependence did not introduce a substantial bias, while individual predictions became more uncertain, and (3) predictions involving the hygroscopicity parameter recommended in literature for continental sites (κ ≈ 0.3 ± 0.1) resulted in a significant prediction bias. Future modeling studies should therefore at least aim at using averaged, size‐resolved, site‐specific hygroscopicity or chemical composition data for predictions of CCN number concentrations.

Uncertainty in global CCN concentrations from uncertain aerosol nucleation and primary emission rates

Abstract. The indirect effect of aerosols on climate is highly uncertain and limits our ability to assess anthropogenic climate change. The foundation of this uncertainty is uncertainty in the number of cloud condensation nuclei (CCN), which itself stems from uncertainty in aerosol nucleation, primary emission and growth rates. In this paper, we use a global general circulation model with aerosol microphysics to assess how the uncertainties in aerosol nucleation, emission and growth rates affect our prediction of CCN(0.2%) concentrations. Using several nucleation rate parameterizations that span six orders of magnitude of globally averaged nucleation rates, the tropospheric average CCN(0.2%) concentrations vary by 17% and the boundary layer average vary by 12%. This sensitivity of tropospheric average CCN(0.2%) to the nucleation parameterizations increases to 33% and 20% when the total primary emissions are reduced by a factor of 3 and the SOA condensation rates are increased by a factor of 3.5, respectively. These results show that it is necessary to better understand global nucleation rates when determining CCN concentrations. When primary emissions rates are varied by a factor of 3 while using a binary nucleation parameterization, tropospheric average CCN(0.2%) concentrations also vary by 17%, but boundary layer average vary by 40%. Using the fastest nucleation rate parameterization, these changes drop to 3% and 22%, respectively. These results show the importance of reducing uncertainties in primary emissions, which appear from these results to be somewhat more important for CCN than the much larger uncertainties in nucleation. These results also show that uncertainties in nucleation and primary emissions are more important when sufficient condensable material is available to grow them to CCN sizes. The percent change in CCN(0.2%) concentration between pre-industrial times and present day does not depend greatly on the nucleation rate parameterization used for our base case scenarios; however, because other factors, such as primary emissions and SOA, are uncertain in both time periods, this may be a coincidence.

Cloud condensation nuclei closure during the International Consortium for Atmospheric Research on Transport and Transformation 2004 campaign: Effects of size‐resolved composition

Measurements of cloud condensation nuclei (CCN), aerosol size distribution and chemical composition were obtained at the UNH‐AIRMAP Thompson Farms site, during the ICARTT 2004 campaign. This work focuses on the analysis of a week of measurements, during which semiurban and continental air were sampled. Predictions of CCN concentrations were carried out using “simple” Köhler theory; the predictions are subsequently compared with CCN measurements at 0.2%, 0.3%, 0.37%, 0.5% and 0.6% supersaturation. Using size‐averaged chemical composition, CCN are substantially overpredicted (by 35.8 ± 28.5%). Introducing size‐dependent chemical composition substantially improved closure (average error 17.4 ± 27.0%). CCN closure is worse during periods of changing wind direction, suggesting that the introduction of aerosol mixing state into CCN predictions may sometimes be required. Finally, knowledge of the soluble salt fraction is sufficient for description of CCN activity.

CCN predictions: Is theory sufficient for assessments of the indirect effect?

This study quantitatively assesses the sensitivity of cloud droplet number (CDNC) to errors in cloud condensation nuclei (CCN) predictions that arise from application of Köhler theory. The CDNC uncertainty is assessed by forcing a droplet activation parameterization with a comprehensive dataset of CCN activity and aerosol size and chemical composition obtained during the ICARTT field campaign in August 2004. Our analysis suggests that, for a diverse range of updraft velocity, droplet growth kinetics and airmass origin, the error in predicted CDNC is (at most) half of the CCN prediction error. This means that the typical 20–50% error in ambient CCN closure studies would result in a 10–25% error in CDNC. For the first time, a quantitative link between aerosol and CDNC prediction errors is available, and can be the basis of a robust uncertainty analysis of the first aerosol indirect effect.