Fig. 1. Three representations of K¨ohler theory for a 50 nm dry parti- cle, comparing traditional K¨ohler theory without (grey shaded area) and with (hatched surface area) an (unrealistically large) influence of surface active material assuming the surface active molecules do not reduce the Raoult effect, and our new formulation which al- lows the co-condensation of any numberof compounds (coloured area). Above a saturation ratio of 1 the axis scale has been changed to more clearly display the critical point. Each area is constructed by changing the properties of a core (comprising 30 % of the con- densed mass at 0 % RH) from insoluble organic compound with molecular weight of 320 g mol −1 to (NH 4 ) 2 SO 4 , with the solid line through each area representing a soluble compound with molecu- lar weight of 320 g mol −1 , (in each case, moving from the insolu- ble compound to (NH 4 ) 2 SO 4 decreases the critical saturation ratio, S crit ). Circle markers represent the assumption of a slightly soluble shell using conventional K¨ohler theory. The dashed blue line rep- resents co-equilibration of Ammonia and Nitric acid for brief com- parison to the work of Kulmala et al. (1993). In this instance the equilibrium constants were taken from Seinfeld and Pandis (1998) and the core assumed to be (NH 4 ) 2 SO 4 to obtain the lowest S crit . Equilibration of multiple semi-volatile components results in by far the largest decrease in critical supersaturation and increase inclouddroplet forming behaviour, by increasing the soluble mass as the RH increases, towards activation. The actual amount of soluble mate- rial condensing per particle will depend on the size distribution andnumber concentration. As all simulations at 0 % RH correspond to a number concentration of ∼600/cc in this example, the multicolored arrows indicate the direction the coloured areas would follow on in- creasing and decreasing the concentration of particles. The shape of the size distribution, concentration, volatility profile and amount of condensable material will dictate this final result, a solution of the condensation dynamics the focus of future work. The small in- set at the top left of the figure is a simple schematic displaying the contribution of the Raoult (bottom red line) and Kelvin terms (top red line) to the resulting K¨ohler curve (black line). The Kelvin term asymptotically approaches infinity as the size tends to zero.
of the collector ice crystal and the sizeof the cloud droplets being collected, and de- creases rapidly for droplets with diameters below 10 µm (Pruppacher and Klett, 1997). Therefore, an increase in CCN, if resulting in smaller droplet sizes, can decrease rim- ing, leading to suppressed precipitation totals on the windward side of the orogra- phy and increased accumulated precipitation on the leeward slope as unrimed, slowly
Overall, the simplified low resolution model agreed fairly well with the reference model under marine and rural condi- tions. In the polluted urban case, the simplified model, which cannot resolve the kinetic effects ofclouddroplet growth, was not able to predict the clouddropletnumber accurately. For marine and rural conditions, the assumption of a distri- bution profile inside the critical size section(s) clearly im- proved the prediction of the clouddroplet concentration. In most cases simulated here, however, the shape of the profile was almost irrelevant as a flat profile (approach 3), a tilted linear profile (approach 4) and a profile which in a crude way takes into account the local shape of the particle dis- tribution (approach 5) produced nearly identical results. Ap- proach 5 still performed slightly better than its counterparts, and would be advantageous especially in situations when the aerosol size distribution shows steep local slopes inparticle concentration in the CCN size range. Although the low reso- lution scheme cannot reproduce the small details of the cloud processed particlesize distribution, it predicted the changes of total dry mass and surface area during the cloud cycles to a good accuracy. Only one of the activation approaches (approach 2) was clearly inferior to the others in this respect. The simulations show that a simplified cloud model with a size resolution typical to large scale models can describe the CCN activationandcloud processing of aerosol particles fairly accurately. Only under conditions for which the ki- netic limitations of the droplet growth are significant, i.e. es- sentially urban and other highly polluted environments, the simplified model clearly overestimates the concentration of forming cloud droplets. Since the vast majority of the Earth’s surface is covered by marine, remote or rural environment, for which the tested scheme agrees well with reference simu- lations, a simplified low-resolution cloud model is in general suited for large scale modelling purposes. It can, however, show uncertainties in areas with strong pollution from an- thropogenic sources.
Size selection in this study is performed using the DMA that classifies a particle according to its electric mobility. Electrical mobility can then be related to the physical diameter if the numberof elementary charges per particleand χ are known (together with the strength of the electric field and other operational parameters in the DMA). Often χ is assumed unity. For mineral dust, however, χ >1 which translates into a larger
Ervens et al. (2007) used in-situ data from the ICARTT field study to determine the main parameters required for achiev- ing CCN closure; they found that knowledge of the aerosol size distribution along with a simple representation of aerosol composition (soluble fraction) was adequate, especially at high supersaturations. Ervens et al. (2010) found that while freshly-emitted pollution aerosol could not be represented without knowledge of more complex composition, CCN de- rived from aged aerosols could be predicted within a factor of two with any mixing state assumption. Furthermore, a factor of two uncertainty in CCN concentration was found to yield ∼ 15 % uncertainty in CDNC. Korhoren et al. (2010), using GLOMAP, studied the sensitivity of CDNC, aerosol size dis- tribution, and natural aerosol processes to sea salt emissions. Woodhouse et al. (2010) used GLOMAP to calculate the sen- sitivity of CCN to changes in dimethyl-sulfide (DMS) emis- sion using multiple present-day and future sea-surface DMS climatologies. Liu and Wang (2010) used the NCAR Com- munity Atmosphere Model (CAM) to examine the sensitivity of global CCN and aerosol indirect forcing to the hygroscop- icity of primary and secondary organic aerosols. Barahona et al. (2011) used the NASA GMI model to calculate the rel- ative change of CDNC associated with diabatic activation. The largest relative change of CDNC was found in the trop- ics, downwind of large emission sources, andin South Amer- ica and North Africa where clouds with moderate CDNC (100–300 cm −3 ) are present. Alterskjær et al. (2012) investi-
In the ice nucleation studies described in the previous sec- tion aerosol particles with known composition, sizeand ori- gin were introduced into ice chambers. The nature of ambi- ent IN cannot be investigated as easily with chamber experi- ments due to the low, ∼10’s per liter, number density ofINin the atmosphere (DeMott et al., 2003). To analyze the chemi- cal compositionand ice nucleating ability of ambient aerosol, several field studies have applied a CFDC in combination with mass spectrometric analysis, mainly on single particle basis (DeMott et al., 2003). A CVI (counterflow virtual im- pactor) was used between the CFDC and the PALMS (Par- ticle Analysis by Laser Mass Spectrometry) instrument to evaporate condensed phase water from the IN before chemi- cal analysis (Cziczo et al., 2003). During the INSPECT (Ice Nuclei Spectroscopy) campaigns this combination was oper- ated at the Storm Peak Laboratory, CO, USA, at 3220 m a.s.l. (Cziczo et al., 2003, DeMott et al., 2003; Richardson et al., 2007). It is reported that IN which formed in the CFDC were dominated by Si and SiO. A classification of all INSPECT ice nuclei spectra (Cziczo et al., 2006) identified mineral dust and fly ash as the predominant species, but also metallic com- pounds, sulfate, organics and potassium were found.
4.3 Stochastic nature of freezing and time dependence The longstanding discussion of the stochastic theory (i.e., the freezing process is time-dependent) vs. the determinis- tic approximation (i.e., freezing occurs at specific tempera- ture and humidity conditions) of heterogeneous freezing has introduced another complication towards complete under- standing of heterogeneous ice nucleation in the atmosphere (Vali, 2014). Many studies have attempted to characterize ice nucleation based on the classical nucleation theory (CNT), which incorporates a nucleation rate (Murray et al., 2012; Kashchiev, 2000; Mullin, 2001). In this treatment, the ice nucleation process is always of a stochastic nature (i.e., time- dependent; Bigg, 1953; Vali, 1994, 2014). According to the nucleation rate approach, the heterogeneous ice nucleation rate is strongly sensitive to INP sizeand the kinetic activa- tion energy of the ice embryo on the nucleating site/surface at a specific temperature (Khvorostyanov and Curry, 2000; Fletcher, 1962). A few variants of the CNT-based approaches have been developed over the past few decades. These ap- proaches assume uniform surface characteristics and only one ice nucleation probability (i.e., a single contact angle), nominally categorized as the single component nucleation rate approach (e.g., Bigg, 1953). Several recent studies have applied a probability density function (PDF) of contact an- gles and active sites over the INP surface in CNT, or in other words described a distribution of nucleation efficien- cies, bridging the gap between the stochastic theory and the deterministic treatment (Marcolli et al., 2007; Lüönd et al., 2010; Kulkarni et al., 2012; Niedemeier et al., 2011; Wright and Petters., 2013; Broadley et al., 2012).
Acknowledgements. We thank K. O’Brien (Northern Arizona University) for providing data ofatmospheric ionization by galactic cosmic rays, N. A. Tsyganenko (Saint Petersburg State Uni- versity) for providing the GEOPACK 2005 code, and C. Timmreck (Max Planck Institute for Meteorology) for carefully reviewing the manuscript. The first author thanks K. D. Froyd (NOAA Earth System Research Laboratory) for helpful comments. This work has been partly funded
The time series of selected VOCs measured with PTR-MS at the two locations de- scribed in Sect. 2.3.1 are plotted together with the CO data from the mast in Fig. 10. The PTR-MS unit that operated in the REA cottage and took its sample from above the canopy sampled some of the clearest smoke plume passages during the flam- ing phase, but no data were available after 12:00 EET when the smoke plumes with
ing Lorenz–Mie theory). Therefore, in the retrieval, the CDR and EV are derived simultaneously by matching the satellite- measured polarized reflectance curve to pre-computed po- larized phase functions. The structures of the rainbow and supernumerary bows are dominated by the single scattering properties of the upper layer clouds; the signal tends to satu- rate for cloud optical thicknesses greater than 2–3 (Bréon and Goloub, 1998; Goloub et al., 2000). Surface albedo (surface type) can then be omitted in the retrieval algorithm (Bréon and Doutriaux-Boucher, 2005). Sensitivity studies based on simulated data sets demonstrate that the polarized technique is robust against uncertainties of 3-D radiative transfer, solar- viewing geometry and aerosol layers above clouds (Alexan- drov et al., 2012a). The polarized technique can also be ap- plied to multi-modal cloudsize distributions by means of the Rainbow Fourier transform (Alexandrov et al., 2012b, 2015). Comparison between the bi-spectral method and the polar- ized technique is important for the improvement of the two approaches. The global clouddroplet radii of POLDER in 2003 were compared with the MODIS estimates by Bréon and Doutriaux-Boucher (2005). Significant differences are found between the CDRs estimated from the two sensors. However, the retrievals of the two approaches based on homogeneous marine clouds show much better agreement (Alexandrov et al., 2015). Several studies attribute the bias to the effects of the cloud heterogeneity on the bi-spectral method (Painemal et al., 2013; Zhang and Platnick, 2011). Notably, the spatial resolution of the POLDER CDR prod- ucts (150 km × 150 km) is much larger than that of the MODIS products (5 km × 5 km). Further investigation is re- quired to better understand the effects ofcloud horizontal in- homogeneity on the polarized retrieval ofclouddroplet sizes from POLDER.
after thorough mixing of the two phases to reach the equilibrium using the shake flask method (mass-balance approach). For the IEPOX (1–4) and alkene diol (7) com- pounds, stock solutions (∼ 45 mM) were prepared in high purity analytical grade 1- octanol (Sigma Aldrich) presaturated with water. Equal volumes of stock solutions and deionized water were mixed in three separate 15 mL propylene conical tubes. Due
be moderately surface active and as discussed in recent papers (Facchini et al., 1999; Shulman et al., 1996 ) this can affect the critical supersaturation ofatmospheric aerosol particles, but experimental data and parameterizations of relevant drop surface ten- sions are rare. Therefore surface tension measurements of the mixtures at relevant concentrations were conducted, parameterized as a function of the carbon content
Fig. 3. A summary of the evolution ofcloud-droplet spectrum as calculated in the model run discussed in Sect. 3.1. The wet spectra are drawn at six selected time-steps using separate histograms for sulphate (thin blue lines) and sea-salt (thick blue lines). The vertical scaling factor is the same for all twelve histograms, and has an arbitrary value. The thick orange line denotes average drop radius, while the two surrounding thin orange lines represent the width of the spectrum ( ±σ r ). Both the average radius and the standard deviation are shown
iments were attributed to oxidation products of oceanic dimethylsulphide (DMS) acting as CCN, due to seasonal variation in the productivity of the ocean (Boers et al., 1998). At the time it was widely hypothesized that DMS-derived particulates made up the bulk of all sub-micrometer particles (Charlson et al., 1987), which linked ocean productivity to cloud albedo and thus global climate through the so-called “CLAW” hypothesis. However, a review of 2 decades of sub- sequent research suggested that the evidence for each of the stages in this mechanism was rather weak (Quinn and Bates, 2011), and that sea spray aerosol (SSA) comprises a substan- tial fraction of the marine boundary layer CCN concentra- tion.
(Zhu et al., 2002a, b; Bukowiecki et al., 2003; Gidhagen et al., 2003; Sturm et al., 2003; Kittelson et al., 2004; Burtscher, 2005; Morawska et al., 2006) and several mathematical mod- els have been developed to explain the formation and evolu- tion processes of these particles (Kim et al., 2002; Gidha- gen et al., 2003; Jacobson and Seinfeld, 2004; Zhang and Wexler et al., 2004; Zhang et al., 2004; Vouitsis et al., 2005). Commercially available ultrafine particle sizers such as Scan- ning Mobility Particle Sizer (SMPS) take as long as 30 s to yield a full 3–560 nm particlesize spectrum although individ- ual researchers have custom-improved the scanning speed of SMPS to 1–2.5 s (Shah and Cocker, 2002; Wang et al., 2002). Faster scanning speed data are deemed to be more favorable for mechanistic studies. However, none of these fast instru- ments are commercially available, and most of the literature reports are still based on 30-s SMPS spectra. In addition, other fast particle sizers such as the Electrical Aerosol Spec- trometer (Tammet et al., 2002), and the Differential Mobility Spectrometer (Biskos et al., 2005) are also developed by in- dividual researchers. Upon the introduction of the Engine Exhaust Particle Sizer (EEPS, TSI, Inc.) in 2004, size (5.6– 560 nm) distribution measurements as fast as 32-channel per second can be obtained (Johnson et al., 2004; Jacobson et al., 2005; Yao et al., 2005), and the results showed that particlenumber concentration frequently varied at least as rapidly as 1 s in concentrated particle environments such as roadside, on roads andin tunnels. This poses the question on the general- ity of using averages of long scan-time spectra for dynamic and/or mechanistic studies in rapidly and perhaps randomly varying high concentration environments. Conceivably, in slow or relatively non-varying environments, time-averaging ofparticlesize distributions for mechanistic studies would not be a problem.
are simulated in each size section (particlenumber density and mass per particle). The use of a sectional (or bin-resolved) aerosol scheme is advantageous for this study as sectional schemes (unlike modal schemes) do not make assumptions about the shape of the size distribution (Zhang et al., 2002). The model includes processes of binary homogeneous nucleation, condensation, coagulation, andsize-resolved dry and wet
Analyzing the new particle formation events and cal- culating growth rates was not straight forward. Our size- distribution measurements started from 10 nm, which means that the beginning of the nucleation was not detected. The median growth rate we determined for the class 1 events in the size range of 10 to 25 nm, as well as the frequency of event days during the summer, was similar to that measured at coastal station Aboa (Asmi et al., 2010). This indicates that there is not a very significant difference in the growth rates between these two sites, which is somewhat surprising, con- sidering that Aboa is close to the ocean that is the source of most condensable compounds in the region, whereas Dome C is high above the ocean and far away from the coast. In future, to better classify new particle formation events and calculate growth rates, it is crucial to be able to measure par- ticle properties at sizes smaller than 10 nm.
Abstract. The ability of a particle to serve as a cloud con- densation nucleus in the atmosphere is determined by its size, hygroscopicity and its solubility in water. Usually sizeand hygroscopicity alone are sufficient to predict CCN activ- ity. Single parameter representations for hygroscopicity have been shown to successfully model complex, multicomponent particles types. Under the assumption of either complete sol- ubility, or complete insolubility of a component, it is not nec- essary to explicitly include that component’s solubility into the single parameter framework. This is not the case if spar- ingly soluble materials are present. In this work we explic- itly account for solubility by modifying the single parameter equations. We demonstrate that sensitivity to the actual value of solubility emerges only in the regime of 2×10 −1 –5×10 −4 , where the solubility values are expressed as volume of so- lute per unit volume of water present in a saturated solution. Compounds that do not fall inside this sparingly soluble en- velope can be adequately modeled assuming they are either infinitely soluble in water or completely insoluble.
nitrate treated by EMAC only), but they differ in the treatment of sea spray emission and aerosol wet and dry deposition. All models used the same criteria to distinguish between the four size categories: nucleation (<5 nm), Aitken (5–50 nm), accumulation (50–500 nm) and coarse (>500 nm) dry radius (see Table 4 for a summary of the mod- els). In the figures shown in this paper, we calculate the total numberof aerosol in a