MPhil Atmospheric Science in the Faculty of Science & Engineering WILLIAM T. HESSON

Transcription

1 A CHAMBER AND MODELLING INVESTIGATION OF THE POTENTIAL IMPACTS OF SEMI-VOLATILE MATERIAL ON CLOUD DROPLETS A thesis submitted to The University of Manchester for the degree of MPhil Atmospheric Science in the Faculty of Science & Engineering 2016 WILLIAM T. HESSON SCHOOL OF EARTH AND ENVIRONMENTAL SCIENCES

2 Contents Title Page Pg. 1 Contents Pg. 2 Abstract Pg. 3 Declaration Pg. 4 Copyright Statement Pg 4. Acknowledgement Pg Introduction Pg Overview of Thesis Pg Motivation Pg Literature Review Pg Aerosol Overview Pg Aerosol Effects Pg Humidity and Droplet Activation Pg Number Concentration and Size Distribution Pg Aerosol Composition Pg Summary Pg Attributing Credit Pg A chamber and modelling investigation of the potential impacts of semi-volatile material on cloud droplets Pg Supplementary Material for A chamber and modelling investigation of the potential impacts of semi-volatile material on cloud droplets Pg Conclusion Pg References Pg Word Count Word Count 20,404 2

3 Abstract Clouds play an important role in the Earth s radiative budget (i.e. the amount of energy lost to and gained from space by the Earth). The concentration of droplets present in clouds is a critical factor in determining their albedo so any factor which influences the formation of droplets in clouds will affect the Earth s radiative budget. Cloud droplets are formed by some aerosol particles known as cloud condensation nuclei (CCN) given appropriate ambient conditions. Secondary organic aerosols (SOAs) are one such component which is abundant in the atmosphere. Globally, SOAs have a large semi-volatile component (i.e. material which partitions between the gas and aerosol phases) and have been found in modelling work to co-condense with water, enhancing their CCN activity. In this thesis, the first chamber based evidence for CCN activity enhancement of SOA via co-condensation is presented. Experiments have been conducted in a controlled chamber environment to generate SOA from 1,3,5-trimethylbenzene, limonene, β- caryophyllene and α-pinene. These aerosols were then transferred to a cloud chamber where evacuations were conducted on the samples in order to produce clouds. The activation observed in these clouds has been compared to modelled data (which does not include co-condensation) and a discrepancy has been observed with SOA samples generated from β-caryophyllene and α- pinene which suggests enhancement from co-condensation. This conclusion is further supported by additional modelling tests which rule out the possibility of uncertainties in the volatility bin distribution or in the hygroscopicity parameter κ being responsible for the discrepancy between chamber and model data. Agreement can be reached however, by including plausible concentrations of co-condensing material. These findings are placed within the broader context of SOA properties and may explain some of the discrepancies observed concerning the value of the hygroscopicity parameter κ. 3

4 Declaration No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning. Copyright Statement i. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the Copyright ) and s/he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. iii.the ownership of certain Copyright, patents, designs, trade marks and other intellectual property (the Intellectual Property ) and any reproductions of copyright works in the thesis, for example graphs and tables ( Reproductions ), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property University IP Policy (see in any relevant Thesis restriction declarations deposited in the University Library, The University Library s regulations (see and in The University s policy on Presentation of Theses 4

5 Acknowledgement I d like to thank my supervisors Gordon McFiggans and Paul Connolly for the opportunity to undertake this project and their support to complete it and for NERC for providing funding for this project. Thanks are also due to Angela Buchholz particularly for her assistance with those long days in the laboratory. I d also like to thank all those who assisted me in learning about and fixing instrumentation and operation of the chamber facilities, in particular, Rami Alfarra, Mike Flynn, James Dorsey, Chris Emersic and Lee Paul. Finally, I d like to thank Dawn Hesson and Pam Bennett for keeping me sane. 5

6 1.Introduction 1.1 Overview of Thesis Secondary Organic Aerosol (SOA) have been shown to act as cloud condensation nuclei (CCN) but uncertainties remain with respect to their hygroscopicity and thereby their ability to activate into droplets (Whitehead et al. 2014). In this thesis, we will first discuss the background surrounding the activation of SOA particles into droplets (Chapter 1). A paper format piece of work is then presented (Chapter 2 and 3) in which new data is presented from experiments conducted using the combined Manchester Aerosol Chamber (MAC) and Manchester Ice Cloud Chamber (MICC) facility to generate SOA and to conduct cloud evacuations in a controlled chamber environment in order to probe their CCN activity. This will be compared to results from modelling this process. Finally, in Chapter 4, the thesis will be concluded looking at the significance of this work with respect to the wider field. 1.2 Motivation The radiative budget of the Earth plays a key role in determining its surface temperature and clouds are known to be a phenomenon which affects Earth s radiative budget (Fung et al. 1984; Chen et al. 2000; Corti & Peter 2009) with the influence of aerosol on warm liquid water clouds being the largest source of attributable uncertainty in global radiative forcing (Boucher et al. 2013). Adjustments in cloud properties attributable to atmospheric aerosols result from their influences on both warm (McFiggans et al. 2006)and cold (Hoose & Möhler 2012) clouds, meaning that aerosols affect the Earth s radiative budget (Bauer & Menon 2012). As such they play an important role in Earth s climate system and a good understanding of aerosols is necessary in order to understand climate change which is anticipated to have an impact on human health (Markandya & Chiabai 2009), wealth generation (Trærup et al. 2011) and food security (Vrieling et al. 2011). A variety of weather phenomena are impacted by the presence and formation of clouds, including rainfall which, for instance, can be suppressed or postponed by the presence of high concentrations of cloud condensation nuclei (CCN) due to the particles competing for a limited supply of water vapour (Chen et al. 2011); this, in turn, can reduce the water supply downwind of substantial anthropogenic emission sites (Rosenfeld et al. 2007; Andreae & D Rosenfeld 2008). An important source of CCN in the atmosphere are secondary organic aerosols which are abundant with a total mass loading of 115 Tg yr -1 (Hallquist et al. 2009). However, questions remain 6

7 concerning their hygroscopicity and hence their CCN activity particularly at low supersaturations due to disagreement between instruments (Whitehead et al. 2014); additionally, SOAs CCN activity has been predicted to be enhanced by the co-condensation of semi-volatile material and water vapour during droplet formation (Topping et al. 2013). This thesis will address the question of whether co-condensation can be observed in a controlled chamber environment and what this might mean for the observed discrepancies between different measurements of SOA hygroscopicity. 1.3 Literature Review Aerosol Overview An aerosol mass is defined as a suspension of liquid or solid particles in a gas; each particle is an aerosol particle. In the atmosphere, aerosol particles come from a number of sources, vary in size, chemical composition and hence hygroscopicity, phase and other properties which affect their behaviour and radiative forcing Aerosol Effects The impact of aerosol effects on Earth s energy budget depends both upon the position in the vertical profile of the atmosphere and the aerosol s properties (Wang et al. 2009; Haywood & Boucher 2000). These effects remain the greatest source of uncertainty in the Earth s radiative budget with an overall effect of -0.45±0.5 W m -2 (Boucher et al. 2013), the most important being the direct, semi-direct and indirect effects. The direct effect is concerned with the scattering and absorption properties of clouds. In general this has a net cooling effect on the Earth s surface by scattering incident solar radiation (Haywood & Boucher 2000), however where strongly absorbing aerosol, such as black carbon (Bond et al. 2013), are present the result can either have a net warming or cooling effect depending upon the surface albedo beneath (Haywood & Boucher 2000). The overall direct effect has been estimated at -0.5 W m -2. The semi-direct effect refers to the warming of clouds due to the presence of absorbing aerosol which causes evaporation of the cloud (Fischer & Grassl 1975; Bauer & Menon 2012); depending on where this occurs this can either have a negative or positive radiative 7

8 forcing. The net semi-direct effect has been estimated as -0.1 W m -2 (Bauer & Menon 2012). There are several indirect effects. The Twomey effect (Twomey 1977) has a negative radiative forcing, the presence of aerosol particles causes increased cloud droplet number concentration thus increasing the optical depth of the cloud making it more reflective and increasing the amount of incident solar radiation reflected back to space. High concentrations of aerosol mean more competition for water vapour making it more difficult for rain-size drops to form; this increases the liquid water content of clouds and this, in turn, increases their reflectivity so more solar radiation is reflected (Rosenfeld 2000). This suppression of rainfall means that clouds persist for a longer period of time in the atmosphere, so clouds with a greater concentration of aerosol reflect solar radiation for longer, providing an additional cooling effect (Albrecht 1989). For the same reasons, cloud thickness is increased resulting in further cooling (Pincus & Baker 1994). The total indirect effect has been estimated at -0.7±0.5 W m 2 (Quaas et al. 2009) making it a substantial source of uncertainty in Earth s radiative budget Humidity and Droplet Activation Köhler theory describes an idealised aerosol particle response to changes in relative humidity including its activation into a droplet. Two terms determine the saturation vapour pressure over a droplet: the Raoult (or solute) term which is associated with the presence of soluble material, and the Kelvin (or curvature) term associated with the curvature of a droplet (Köhler 1936). Both the Kelvin and Raoult terms have a dependency upon the size of the particle. The Kelvin term decreases proportionally to the inverse cube of the diameter of the particle (see Equation 1) meaning that it is most important at small sizes. The saturation vapour pressure over a curved surface like that of a particle is greater than the saturation vapour pressure over a flat surface. As the curvature of a particle reduces with increasing size, the surface tension of water over the curved surface of the particle tends towards the surface tension over a flat surface and so the Kelvin term reduces in size. The Raoult term is proportional to the inverse of the diameter of the particle, as such it also falls as the diameter of the particle increases, however, as the dependency of the Raoult term is lower by a factor of the square of the particle s diameter relative to the Kelvin term, the importance of the Raoult term increases as the diameter of the particle increases. The Köhler equation can be written as shown in Equation 1 (Seinfeld & Pandis 2006), where p w is the saturation vapour pressure, D p refers to aerosol particle diameter, p 0 is the vapour pressure of water over pure water, M w is the molecular mass of water, σ w is the surface 8

9 tension, R is the universal gas constant, T is the temperature and ρ w is the density of water over the droplet. pw( D ln p p 0 ) 4M w w RT D w p 6ms M D w w 3 p Equation 1(Seinfeld & Pandis 2006) From Equation 1, a critical size can be calculated for any given particle for a given supersaturation: as soon as a particle passes this size it will experience runaway growth, turning into a droplet. The hygroscopicity parameter, κ (Petters & Kreidenweis 2007), is the main approach used to parameterise water activity, a w, the ability of any type of aerosol to take up water relating it to water activity using the relationship shown in Equation 2 where V s is the volume of solute and V w is the volume of water. A smaller value of κ indicates that the aerosol particle is less hygroscopic and therefore will activate at a larger size and at a greater supersaturation and vice versa. 1 a w = 1 + κ V s V w Equation 2(Petters & Kreidenweis 2007) There are a number of approaches to making measurements of κ including, via measuring water uptake using a Hygroscopicity Tandem Differential Mobility Analyser (HTDMA) (Switlicki et al. 2008), using an electrodynamic balance (EDB) method or measuring activation by exposing the aerosol to controlled supersaturated conditions using a Cloud Condensation Nuclei Counter (CCNc). The EDB method is a single particle approach in which a particle is electrically charged and levitated. In modern studies, a double ring electrode design is typically used (Davis et al. 1990; Pope 2010; Pope et al. 2010; Gallimore et al. 2011). An electrostatic DC field and an electrodynamic AC field are used to create a trap to hold the particle. The strength of the DC field used to balance the gravitational attraction of the particle to the Earth can be used to calculate its mass so by exposing the suspended particle to different relative humidities, the growth factor of the particles can be probed and thus a value of κ derived. This approach is difficult to use when studying chamber derived SOA particles as only a single particle can be studied at one time, the chemical composition changes between particles and throughout the ageing of the aerosol mass. 9

10 This approach will not be further considered in this thesis. HTDMAs measure the growth factor of aerosol particles by size selecting particles at an initial relative humidity (e.g. a dried sample with 10 % RH) to get a sample of aerosol particles of the same size; these particles are then exposed to conditions at a different relative humidity (e.g. 90 %) and size-selected again using the second DMA to determine how many particles from the initially selected size grow by a set amount. These particles are then counted with a Condensation Particle Counter. By measuring at a range of sizes and relative humidities, a picture can be developed of the growth factor of these particles. This can be used to calculate a value of κ. CCNc s (Roberts & Nenes 2005; Alofs et al. 1995) instead measure the number of particles activated when particles are exposed to a controlled supersaturation. In general, continuous flow thermal gradient CCNc s (Roberts & Nenes 2005) are used in modern studies. These operate by exposing the aerosol sample to a column of evenly increasing temperature which draws water vapour and heat from the walls but since water vapour diffuses faster than thermal energy, a steady supersaturation can be established which depends upon the thermal gradient and total airflow (Roberts & Nenes 2005). The number of particles activated for a selected initial size at a given supersaturation can then be used to determine a value of κ. There are a number of limitations to Köhler theory. Firstly, it assumes that there is a solute and that it will all be dissolved which is not the case for all aerosol: hydrophilic aerosol particles can also be activated as droplets as described using FHH activation theory (Sorjamaa & Laaksonen 2007). Many aerosols are only partially soluble, especially some/many organic aerosols and semivolatile aerosols such as some SOA and ammonium nitrate. These can condense onto a particle as it takes up water, effectively increasing what would have been considered its dry size which can significantly enhance droplet activation (Topping et al. 2013; Topping & McFiggans 2012). Finally, there is the problem of deliquescence and efflorescence which are described by modified Köhler theory (Chen 1994). Aerosol particles grow and shrink depending on their hygroscopicity and the temperature and relative humidity of the surrounding environment along with their deliquescence and efflorescence points and the particle s history. Under subsaturated conditions, hygroscopic aerosol particles such as salts take up water as relative humidity increases. However, at a threshold relative humidity below saturation, a phase transition occurs and the particle loses its crystallinity and swells with water producing a step change in its diameter as a function of RH. Further increases in relative humidity then see the particle grow further but no further step change occurs. This threshold is known as the deliquescence point. When the relative humidity of the particle is decreased below its deliquesence point, however, the particle does not shrink back 10

11 to its original size. Instead it will remain wetted though it will continue to shrink until a lower threshold relative humidity, known as the efflorescence point, is reached when a step change in particle diameter and change of phase state will occur. For instance, ammonium sulphate has an efflorescence point of approximately 35 % RH and a deliquescence point of approximately 84.2 % at 298 K (Seinfeld & Pandis 2006) Number Concentration and Size Distribution Aerosol particles vary in size between a few nanometres to tens of microns. These sizes are split between four modes: the nucleation mode which comprises newly formed particles (sizes typically between 1 and 10 nanometres), the Aitken mode (10 to 100 nanometres) from which particles are usually lost by coagulation to the accumulation mode (sizes typically between 100 nanometres and 1 micron) which in turn are typically lost to washout, and finally coarse mode particles (sizes typically greater than 1 micron) which are usually formed by mechanical processes with sources such as sea spray and wind-blown dust and are generally lost by sedimentation (Seinfeld & Pandis 2006). Loss of atmospheric aerosol particles is most efficient for larger and smaller sizes with residence times longest for accumulation mode aerosol particles (Seinfeld & Pandis 2006). The smallest particles are generally lost by coagulation which occurs when particles collide and become a single larger particle. This is dependent upon the mean free path and sticking efficiency of the particles concerned. The other major form of aerosol growth is condensation whereby material from the gas phase condenses onto existing particles causing them to grow. This occurs when gas phase compounds are above their saturation vapour pressure, encouraging this material to enter the aerosol phase. The distribution of the condensation is proportional to the surface area of the particles present (Seinfeld & Pandis 2006). Aitken mode particles tend to dominate the number concentration of aerosol in the atmosphere except during a nucleation event (Asmi et al. 2011). Accumulation mode particles with their relative abundance and size tend to provide the largest amount of surface area amongst atmospheric nuclei meaning that they tend to be the main site for condensation of secondary aerosol. The mass of aerosol particles in the atmosphere is generally dominated by the coarse mode due to their large size (Seinfeld & Pandis 2006). 11

12 1.3.5 Aerosol Composition There are many different aerosol particles found in the atmosphere including primary and secondary organic aerosol (Sun & Ariya 2006), sea spray (Grythe et al. 2014), black carbon (Boucher et al. 2013), mineral dust (Boucher et al. 2013), volcanic ash (Rodríguez et al. 2012) and sulfate (Langmann 2014), cellular material (Jaenicke 2005), and even pollen (Pope 2010). As a result of their source, sea spray particles are primarily found in marine and marine-influenced environments where they make up % of aerosol mass (Boucher et al. 2013). In marine environments, where there are generally few sources of aerosol particles, sea spray particles are extremely important in terms of the CCN concentrations, scattering etc. and are often found to be mixed with other aerosol components, such as sulphate, from both natural and anthropogenic sources (Grythe et al. 2014). The total mass of sea salt aerosol emitted annually is not well known, however, it has been reported to fall in the range 3000 to Tg (Grythe et al. 2014). Sea spray particles occur across a wide size spectrum from approximately 20 nm (Mårtensson et al. 2010) to the micron scale (Seinfeld & Pandis 2006). Three difference mechanisms produce sea spray particles: the bursting of film droplets, the jet of water which fills in the gap left by the burst bubble, and the wind directly ripping water off the surface (Grythe et al. 2014). As such, strong winds are associated with high concentrations of sea spray aerosol (Kaufman 2005). Anthropogenic activities, particularly biomass burning and controlled combustion of fossil fuels such as diesel, are the major source of black carbon in the atmosphere (Ni et al. 2014). Black carbon is produced by incomplete combustion of fuels. These emissions typically consist of partially oxidised organics including alkanes, aromatics (Zhang et al. 2011) and poly-aromatic hydrocarbons (Cheruyiot et al. 2015). Radiative forcing due to black carbon is estimated at +0.4 W m -2 (Boucher et al. 2013). As a result of its colour, black carbon is a significant absorber of radiation and modelling work has indicated that when precipitated from the atmosphere it has a substantial effect on surface conditions, for instance being associated with ice melt in the Himalayas (Menon et al. 2010). It is also an important source of ice nuclei (DeMott et al. 1999; Levin et al. 2016). Anthropogenic activity, such as coal burning, and volcanism are the main sources of sulphate aerosol in the atmosphere. Radiative forcing due to sulphate aerosol has been estimated to be W m -2 (Boucher et al. 2013). This material is formed by the reaction of sulphur dioxide with water to form sulfuric acid which may then go on to react with any bases present in the 12

13 environment such as ammonium to form a sulphate salt. Radiative forcing due to nitrate aerosol has been estimated at W m -2 (Boucher et al. 2013). These are formed in the atmosphere. NO x is emitted from combustion sites and lightning and can be oxidised by OH radicals to form nitric acid which in turn can react with any base materials present to form salts, most commonly ammonium nitrate which is much more volatile than ammonium sulphate (Seinfeld & Pandis 2006), and the equilibrium between the aerosol and gaseous phase can be further shifted to the gaseous phase by the presence of sulphate (Kajino et al. 2008). Radiative forcing due to mineral dust has been estimated at -0.1 W m -2 (Boucher et al. 2013). Mineral dusts are generated as primary particles by the wind. Mineral dusts are another important source of ice nuclei in the atmosphere (Demott et al. 2015; Koehler et al. 2010). Radiative forcing due to primary and secondary organic aerosol has been estimated at W m -2 (Boucher et al. 2013). Organic components are ubiquitous in atmospheric aerosols (Jimenez et al. 2009) and have been variously found to account for % of total sub-micron aerosol mass (Jimenez et al. 2009; McFiggans et al. 2005). Primary organic aerosol (POA) are emitted as particles from their source whereas secondary organic aerosol (SOA) are produced in situ in the atmosphere by the condensation of organic compounds from the gas phase and have been estimated to comprise 70 % of the organic aerosol mass in the atmosphere with a global sources estimated at around 115 Tg yr -1 (Hallquist et al. 2009). Primary organic aerosols make a smaller total contribution to aerosol mass which has been estimated as 55 Tg yr -1 (Trivitayanurak & Adams 2014). Primary organic aerosol particles generally appear in the accumulation mode (Hildemann et al. 1991) i.e. the mode that tends to dominate the surface area in the atmosphere making POA particles ideal sites for the condensation of SOA, creating difficulties in separating POA and SOA in atmospheric conditions. SOA precursors come from both biogenic and anthropogenic sources with biogenic material dominating (Cahill et al. 2006). These materials mostly originate from plants, with components including acetone, methanol, cis-3-hexan-1ol and terpenes (isoprene, monoterpenes such as α-pinene and limonene, and sesquiterpenes such as β- caryophyllene) (Hewitt et al. 2011) which are known to produce SOA via ozonolysis and OH reactions (Salo et al. 2011; Tritscher et al. 2011). Anthropogenic sources of SOA precursor material are also significant sources of SOA with estimates of around 10 % of total SOA originating from urban and industrial sources though this figure is highly uncertain (Spracklen et al. 2011). It has also been suggested that the presence of anthropogenic material may encourage biogenic 13

14 aerosol formation (Hoyle et al. 2011). The oxidation of organic material in the atmosphere, which in general reduces the volatility of organic material and thereby encourages the formation of aerosol particles, is highly complex with thousands of compounds generated from any given precursor material. Under daylight conditions, oxidation of organic material in the atmosphere is dominated by OH radicals which are the primary oxidiser in the atmosphere (Lelieveld et al. 2016). OH is produced by the photodissociation of ozone by light of wavelengths shorter than 324 nm and by recycling via radical reaction chains (e.g. the formation of peroxy radicals which reproduce OH ). Recent modelling work has found that recycling of OH is the dominant process in the free troposphere (Lelieveld et al. 2016). An important set of reactions, with respect to the ageing of organic material in the atmosphere, involve sulphur dioxide which reacts with water to form sulfuric acid which can react with organic material following dissociation. Organosulfates have been found in organic aerosol both in situ and in laboratory studies (Hallquist et al. 2009) and are thought to be - produced by the reaction of acidic sulphate, ozone, and terpenes. SO 3 for instance, can open CO bonds by nucleophilic attack; these processes are still poorly understood in terms of their kinetics and the yields produced (Herrmann et al. 2015). Another significant radical in the atmosphere is NO 3 which is important under night time conditions but not in the daytime as its lifetime is too short under daylight conditions to allow chemical interactions. At night it provides an important path for the production of organonitrates which account for approximately 10 % of the organic material in urban environments (Day et al. 2010). The other major path for the production of organonitrates is via reactions with NO in daylight chemistry. NO and NO 2 are found in highest concentrations in the atmosphere near anthropogenic sources with fossil fuel combustion the dominant source (Seinfeld & Pandis 2006), so areas downwind from pollution sites tend to have the highest concentrations of organonitrates. Further oxidation of organic material occurs in the aerosol phase (Herrmann et al. 2015) and it has been suggested that SOA particle matter may even form by oxidation of volatile material in the aqueous phase (Ervens et al. 2011). Aqueous phase formation of SOA is thought to be a significant source of low volatility SOA (which are important in the formation of SOA particles), which are more water soluble than those formed in the gas phase and hence are important for the solute effect with acids, alcohols and glyoxal-like compounds acting as precursors (Ervens et al. 2011). SOA particles are known to act as cloud condensation nuclei, however measuring their CCN activity has proved to be challenging. In addition to their highly complex chemical composition, SOA particles can exist in an amorphous solid state depending upon their composition and 14

15 relative humidity which can affect their ability to accommodate water (Virtanen et al. 2010; E. Saukko et al. 2012; E Saukko et al. 2012). Furthermore, the approaches used to measure their hygroscopicity have shown inconsistency. When SOA samples are measured with HTDMA and CCNc the resulting values of κ have proved difficult to reconcile and although progress has been made in this area, further work is required to get agreement between these methods, particularly at low supersaturations (Whitehead et al. 2014) Summary Aerosols have an impact upon the Earth s radiative budget via the direct, semi-direct and indirect effects (Bauer & Menon 2012). These depend on the ambient conditions, the position of the aerosol within the atmosphere and their properties such as CCN activation ability, size, and ability to absorb and scatter radiation as a function of wavelength. Secondary organic aerosols pose particular challenges as their compositions are complex, their phase varies depending upon the ambient conditions and their hygroscopic properties are not well understood with questions remaining concerning their ability to activate as droplets at low supersaturations (Whitehead et al. 2014). Modelling work has suggested that co-condensation of secondary organic aerosol will have a substantial effect on their ability to act as CCN (Topping et al. 2013) however, evidence for this is still required either from laboratory or in situ conditions. This thesis will attempt to address these gaps in knowledge by conducting cloud evacuations of secondary organic aerosol under chamber conditions and comparing them to modelled results in order to look for evidence of increased CCN activity as expected by co-condensation and in order that these results might be compared to other work investigating the ability of secondary organic aerosol to act as CCN. 1.4 Attributing Credit In the paper presented in Chapter 2, the first author was responsible for planning and leading the experiments, data analysis of MICC data, conducting the modelling runs and analysing the results, writing the paper and creating the figures. The contribution of Angela Buchholz was to assist in the experiments by operating the CCN counter, development of software for data analysis of CCN counter and DMPS data and conducting the analysis. Paul Connolly was responsible for the development of ACPIM and developed some of the in-house software to extract data from the 15

16 MICC instrumentation. Gordon McFiggans assisted in editing the paper and acted in a lead supervisory role for the project. 16

17 2. Title: A chamber and modelling investigation of the potential impacts of semi-volatile material on cloud droplets Prepared for publication in Atmospheric Chemistry and Physics Discussions but not submitted. Page 17 17

18 5 A chamber and modelling investigation of the potential impacts of semi-volatile material on cloud droplets William Hesson 1, Angela Buchholz 1a, Paul Connolly 1, Gordon McFiggans 1 1 School of Earth and Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK a currently at: Department of Applied Physics, University of Eastern Finland, Kuopio Campus, P.O. Box 1627, 70211, Kuopio, Finland Correspondence to: William Hesson (william.hesson@googl .com) Abstract. Under appropriate conditions, droplets form on a subset of atmospheric particles that act as cloud condensation nuclei (CCN). From previous measurements in the ambient atmosphere, it is difficult to unambiguously establish the degree to which the activation of CCN into droplets is quantitatively understood. A cloud chamber environment provides greater control over the aerosol sample used and the conditions it experiences, providing the opportunity to better constrain CCN activation. For the first time, liquid cloud activation using chamber-derived secondary organic aerosol (SOA) produced in individual experiments from 1,3,5-trimethylbenzene, limonene, β-caryophyllene or α-pinene as well as a separate experiment using nebulised ammonium sulfate, have been compared to results from Monte Carlo cloud parcel model simulations across expected parametric uncertainties. The formation and evolution of cloud droplets during a pseudo-adiabatic evacuation of a cloud chamber was modeled using the Aerosol Cloud Precipitation Interaction Model (ACPIM) using each set of generated parameters. Cloud formation in the model and chamber are consistent within anticipated variability for ammonium sulfate aerosol, which have no semi-volatile material, and are thought to be well understood and represented in ACPIM. However, the model under-predicts the number concentration of cloud droplets generated in an evacuation for certain SOA, most notably in the -caryophyllene system. The under-prediction is most marked in the first chamber evacuation of the experiment when the concentration of semi-volatile organic vapor may be expected to be highest. Semi-volatile material is thought to aid the formation of droplets by condensing onto CCN alongside water (co-condensation) and thus making the particle larger and reducing the supersaturation required for a droplet to form. The Monte Carlo simulation did not include treatment of semi-volatile material and it is suggested that this observation is an indication of co-condensation of water and semi-volatile organic material. Further simulations using ACPIM demonstrated that plausible concentrations of semi-volatile material were able to bring the measured and modeled droplet number concentrations into agreement and that this was not plausible by varying the hygroscopicity parameter κ. In the limonene experiment the model over-predicts droplet concentration. This is thought to result from an overestimation of the hygroscopicity of the CCN input into the model. 1 Introduction 35 Cloud and aerosol effects remain the single largest uncertainty in Earth s radiative budget (Boucher et al., 2013; McFiggans et al., 2006). The radiative forcing caused by clouds is dependent upon the size distribution and number concentration of droplets as they have been shown to be a determining factor in cloud lifetime, cloud depth, cloud liquid water content and cloud reflectivity which in turn determine the amount of energy absorbed and reflected by clouds and thus affect the Earth s radiative budget (Albrecht, 1989; Lohmann and 1

19 Feichter, 2005; Stevens and Feingold, 2009; Twomey, 1977). Changing the Earth s radiative budget warms or cools the Earth s surface and hence alters the climate; understanding droplet formation is, therefore, a crucial part of understanding the global climate. In the atmosphere, water droplets form on cloud condensation nuclei (CCN): particles suspended in the atmosphere capable of activating as water droplets under suitably supersaturated conditions with respect to water. At atmospherically relevant temperatures, the saturation ratios required for homogeneous nucleation of water droplets (i.e. without a CCN) are several hundred percent; these conditions do not occur in the atmosphere (Rogers and Yau, 1989) and therefore can be ignored. It is, therefore, necessary to have a good understanding of the characteristics of all significant cloud condensation nuclei in order to understand droplet formation. An aerosol particle will form a water drop if it passes its critical size in a supersaturated environment with respect to water. The Köhler equation(köhler, 1936), represented by Eq. (1), describes the equilibrium size of particles in the atmosphere by combining Raoult s Law and the Kelvin Equation for a droplet. Supersaturation, S, is defined as the vapor pressure of water, e, divided by the saturation vapor pressure, e s. This is equal to the water activity, a w, which is the term dealing with the effects of a solute according to Raoult s law, multiplied by the Kelvin term shown where σ w is the air/water surface tension, M w is the molecular weight of water, R is the universal gas constant, T is temperature, ρ w is the density of water and D p is the particle s diameter. S = e e s = a w exp ( 4σ wm w RTρ w D p ) (1) There is no general analytical solution to the Köhler equation. The Raoult s law term, the first of the two terms of the right-hand side of Eq. (1), i.e. the water activity, a w, is associated with the reduction in equilibrium vapor pressure over a droplet resulting from the presence of a solute. This effect is proportional to the mixing ratio of the solute in the drop (Rogers and Yau, 1989; Seinfeld and Pandis, 2006). The Kelvin term, the second term on the right hand side of Eq. (1), i.e. the exponential term, is associated with the air/water surface tension of the droplet. This is proportional to the exponent of the inverse of diameter of the droplet; as a droplet shrinks, the curvature of the droplet s surface increases so that each molecule of water on the surface experiences a smaller attractive force from its neighbouring water molecules as they are averagely fewer in number, meaning that the energy barrier for a water molecule to leave the surface of the droplet becomes smaller with decreasing droplet size (Rogers and Yau, 1989; Seinfeld and Pandis, 2006). A widely used parameterization for water activity is the κ parameterization where water activity is defined as shown in Eq. (2): 1 a w = 1 + κ V s V w, (2) where V s is the volume of the dry solute and V w is the volume of water in the particle (Petters and Kreidenweis, 2007). A higher value of κ indicates greater hygroscopicity and therefore activation at a smaller critical size and lower critical supersaturation. Experimental studies conducted to investigate the hygroscopicity of aerosol particles with respect to the activation of CCN into droplets have concentrated either on measuring their water uptake onto particles at subsaturated conditions with respect to water using Hygroscopicity Tandem Differential Mobility Analysers 2

20 (HTDMAs) (Switlicki et al., 2008) or at supersaturated conditions using CCN counters (Nenes et al., 2001; Roberts and Nenes, 2005) although single particle electrodynamic balance methods are also employed(davis et al., 1990; Pope, 2010; Pope et al., 2010). The HTDMA provides data to measure κ for a given aerosol sample by probing the shape of the Köhler curve at subsaturated conditions while data on the maximum of the Köhler curve is provided by the CCN counter also providing a value for κ through measurements of activated fraction of particles of known size. However, studies employing both methods have found significant disagreement between HTDMA-derived and CCN counter-derived κ values, particularly at low supersaturations in the CCN counter. Although sometimes agreement is found, questions concerning the processes used in CCN measurements place a limit on our understanding of CCN activity (Whitehead et al., 2014). Causes of the discrepancy between CCN counter and HTDMA measurements broadly fall into three categories: problems with the instrumentation, problems with modeling the activation, and unaccounted-for properties of aerosol particles. Instrumentation problems include: underestimation of particle concentration with the DMPS (Differential Mobility Particle Sizer) prior to measurement with a CCN counter leading to underestimates of CCN concentration, size selection limitations of DMPSs, and concerns over whether or not particles reach equilibrium with respect to water between DMAs in an HTDMA (Fors et al., 2011). Attempts to create models based on Köhler theory have struggled to find agreement with HTDMA data; an analysis of 5 different modeling methods found that discrepancies of 15% and 6 10% were typical for critical supersaturation and difference in CCN concentrations respectively (Rissler et al., 2010). Finally, there is the possibility that the properties of the aerosol particles themselves could be the cause of the discrepancy. It has been suggested that humic-like substances may have surface effects, reducing the surface tension term of the Köhler equation and thus increasing droplet activation; alternatively, they may inhibit droplet activation by acting like a surface film, slowing water diffusion and therefore particle growth (Graber and Rudich, 2005). However, studies exploring the possibility of this effect have either found no improvement in agreement (Jurányi et al., 2010) or that implausible changes to surface tension would be required (Good et al., 2010a). The degree to which aerosol are internally mixed and the consistency of their size and variability with time may also be important in making atmospheric CCN behavior predictable (Jurányi et al., 2010). It has been suggested that another cause of the discrepancy between CCN counter and HTDMA measurements may be semi-volatile material, such as those associated with secondary organic aerosols (SOAs), facilitating increased apparent hygroscopicity and droplet production via co-condensation (Topping et al., 2013). It is clear that more work is required in this area to improve our understanding of the cause of the discrepancies between CCN counter and HTDMA data in order to accurately quantify CCN behavior in clouds (Whitehead et al., 2014). In various studies, a gap has been found to exist between the CCN counter measurements and the predicted number of CCN to be activated based upon the conditions to which the aerosol sample was exposed in the CCN counter. Calculating supersaturation based on the temperature profiles measured, CCN data is 50 80% of the anticipated value (McFiggans et al., 2006). Usually, this difference between the measured and predicted concentrations is within the margin of error, but that margin of error is very large when considering applying the values derived to a global model where a difference of 10% in CCN data is very significant (McFiggans et al., 2006). Studies conducted in locations with a significant organic component to the aerosol 3

21 have shown particularly poor agreement (Dusek et al., 2003; McFiggans et al., 2006; Roberts et al., 2002) suggesting that organics may play a role in this discrepancy. It has been suggested that humic-like organic substances may delay the formation of droplets (Bigg, 1986; Graber and Rudich, 2005). Kinetic effects may also play a role in this discrepancy. The time profile of the supersaturation produced in a CCN counter differs from clouds as in clouds there is competition for water vapor due to the growth of particles which is not necessarily matched by increased water availability from quasi-adiabatic evacuation whereas in a CCN counter supersaturation is held constant throughout a measurement. The residence time inside an instrument can bias the results; ammonium nitrate has been shown to evaporate with a long residence time in a HTDMA (Gysel et al., 2007) reducing the Raoult term and therefore leading to lower than anticipated growth factors, while delays in activation potentially caused by slower water uptake as a result of less hydrophilic surface layers may cause a bias if the residence time is too short for the particle to equilibrate (Bigg, 1986; Graber and Rudich, 2005). Another issue is that atmospheric measurements with a CCN counter require there to be no meaningful mixing with other air masses or loss of water through precipitative scavenging, making unbiased measurements of cloud droplet concentration difficult to obtain (McFiggans et al., 2006). One approach to studying the activation of aerosol particles into droplets has been inverse modeling to find parameter values which minimise the discrepancy between modeled and simulated atmospheric data from a range of environments (Partridge et al., 2011, 2012) however this falls short of using real atmospheric data (Partridge et al., 2012) and hence it is difficult to suggest that it can be truly representative of in situ droplet activation in the presence of semi-volatile material. Several studies have attempted to compare experimental results to modeling data in order to predict behavior with varying degrees of detailed microphysical calculations (Fountoukis and Nenes, 2005; Fountoukis et al., 2007; Hsieh et al., 2009; Lance et al., 2009; Nenes and Seinfeld, 2003). A significant approach is based upon the work of Nenes & Seinfeld (Fountoukis and Nenes, 2005; Fountoukis et al., 2007; Nenes and Seinfeld, 2003). Nenes & Seinfeld s approach (Nenes and Seinfeld, 2003) involves using the aerosol size distribution for each aerosol chemical composition considered to calculate a CCN spectrum i.e. the number of aerosol particles which will be activated based on the maximum supersaturation achieved for a given updraft velocity or cooling rate. The aerosol population is then split between particles which experience more or less growth between the time they reach critical saturation and the maximum supersaturation the droplet experiences than their diameter at their point of critical saturation. This allows treatment of the particles appropriate to their growth around activation and identifies particles which have critical diameters closest to the critical size for which kinetic effects may be important in determining activation. This approach was later extended in order to use lognormal aerosol size distributions (Fountoukis and Nenes, 2005), inclusion of a size dependent mass transfer coefficient (Fountoukis and Nenes, 2005) and modifications of this to correct for the presence of large CCN (Barahona et al., 2010; Morales Betancourt and Nenes, 2014) as well as constraining the parameterization by comparing the output to data from in situ cloud studies (Fountoukis et al., 2007; Hsieh et al., 2009). Accurately describing the effect of organic material on CCN activation and sensitivity issues relating to particles from different size modes remain challenging for such parameterizations and a lack of comparison to cloud data from a controlled laboratory environment may have introduced confounding factors into comparisons with atmospheric data. 4

22 Discrepancies remain between CCN counter measurements and theoretical predictions and between HTDMA and CCN counter data. Neither the data from CCN counter and HTDMA studies nor the theoretical work conducted on CCN activation provides a comprehensive understanding of droplet activation, particularly in the presence of semi-volatile material therefore there is a real gap in our understanding of the CCN activity. In situ studies of droplet activation are difficult to conduct as activation is difficult to monitor: in cloud CCN have activated into droplets, outside cloud they are aerosol. One possibility to study the formation of droplets is to artificially generate aerosol and in particular aerosol with semi-volatile material like SOA and conduct experiments that simulate cloud formation. There is little information from controlled cloud evacuation experiments looking at the CCN activity of SOAs. Such conditions provide the opportunity to test whether co-condensation could be observed on CCN. In this study we present the first cloud chamber evacuations on chamber-generated SOA and compare the experimental data to model results, not including a cocondensation treatment, but varied across all parametric and experimental uncertainties, to test the degree of agreement between experimental and modeled data and to look for evidence of co-condensation in a chamber environment. The parameters varied in this investigation were the number concentration of aerosol particles, N p, the size distribution width parameter, σ, the modal size, D m, initial temperature, T i, and the hygroscopicity parameter, κ. Under-prediction of CCN activation by the model could be indicative of co-condensation as this process is not being modeled. We then use a representation of semi-volatile co-condensation within the model to demonstrate that it is able to explain the observed droplet formation in the chamber. This study aims to test our understanding of CCN activation of SOA and whether co-condensation is required for chamber evacuations on SOA samples and model data to be consistent or whether agreement can be found by only considering the variability of parameters from experimental data. Experimental data from SOA evacuations i.e. activation data from the MICC on various SOA samples and additionally on a well understood aerosol system (ammonium sulfate) will be compared to the results of Monte Carlo simulations of each evacuation using the chamber version of ACPIM (Aerosol Cloud Precipitation Interaction Model) without the inclusion of semi-volatile material. It is then anticipated that any discrepancy between the results of the Monte Carlo simulation and the number concentration data from the chamber evacuations will be due to the presence of semi-volatile materials. The inclusion of the ammonium sulfate system allows comparison between SOA systems and a system where semi-volatiles are not relevant. Additionally, a series of tests using the model and including semi-volatiles will be conducted varying the volatility distribution and total concentration of the semi-volatile organic material to determine their effect upon droplet activation. A further series of tests using the model will be shown in which the value of the hygroscopicity parameter κ (Petters and Kreidenweis, 2007) will be varied outside the range of the Monte Carlo simulation in order to determine whether changing the hygroscopicity of the particles can explain any discrepancy between the Monte Carlo simulation results and the number concentration data from the chamber evacuations. 2 Methods 195 Material from the photo-oxidation of precursors of secondary organic aerosol were created in the Manchester Aerosol Chamber (MAC) facility or the MAC was used as a holding container for nebulised ammonium sulfate aerosol. Their properties including size, number concentration, hygroscopicity and chemical composition were measured, the instrumentation employed is shown in Table 1. These particles were 5

23 200 transferred from the MAC to the Manchester Ice Cloud Chamber (MICC). Cloud formation was induced in a series of quasi-adiabatic evacuations at an initial temperature of ~286 K from approximately atmospheric pressure to approximately 700 mbars. Following each evacuation, the MICC was refilled to approximately atmospheric pressure using clean air meaning that each evacuation took place at successively lower concentrations of aerosol particles and semi-volatile material. A schematic diagram of the combined MAC and MICC facility is shown in Fig Aerosol chamber and instrumentation The aerosol chamber and its operation The Manchester Aerosol Chamber has a maximum volume of 18m 3, consisting of two Teflon sheets at the top and bottom and two Teflon tubes, these are sealed between three aluminum sections effectively creating a Teflon bag. The central section of the bag s frame remains stationary and is the location for ports into and out of the chamber for the purposes of cleaning, introduction of gases to set experimental conditions (e.g. salt aerosol, Volatile Organic Compounds, ozone, water vapor and NOx), sampling aerosol from the chamber by instruments and transferring aerosol to the Manchester Ice Cloud Chamber (MICC). The Teflon bag can be inflated and deflated using a 3-phase blower (See Sect. S1.1.1 for further information). Volatile Organic Compounds (VOCs) are added to the Teflon bag as precursors to the formation of SOA; in these experiments -pinene, -caryophyllene, 1,3,5 trimethylbenzene and limonene were used, each in a separate experiment (for more details see Sect. S1.1.2). NO 2 is introduced into the chamber from a compressed gas cylinder (see Sect. S1.1.3). Ozone was also required for the photo-oxidation experiments and was introduced to the chamber from the ozonizer (see Sect. S1.1.4). For the ammonium sulfate experiment, seed aerosol particles were nebulised into the chamber (see Sect. S1.1.5). The aerosol chamber has seven rows of 16 halogen bulbs and a 6kW Xenon arc lamp. Photochemical nucleation and growth experiments generally use three rows of 16 halogen lamps and the arc lamp to approximate to tropospheric illumination. As it has been found to be infeasible to produce SOA in the MAC using 1,3,5-trimethylbenzene as a precursor under the usual UV conditions used, the UV filter for the arc lamp was removed for this experiment. This substantially increases the intensity of UV radiation of shorter wavelengths than ~350 nm above the tropospheric approximation used in the other experiments. In all the photochemical oxidation experiments, as the aging period was relatively brief (between 1.5 and 2.5 hours see Table 2) and the method of production was nucleation with high mixing ratios of precursor material, the concentration of particles produced was often much higher than required for cloud chamber evacuations, necessitating dilution of the aerosol prior to transferring it so that the number concentration of aerosol particles in the MICC for the first cloud chamber evacuation was less than 12000/cc. After production and aging of aerosol in MAC and preparation of the MICC (see Sect ) the aerosol sample was transferred to the MICC (see Sect. 2.4). Ongoing sampling with the instrumentation employed in the SOA experiments provides size distribution and concentration information (see Table 1). A series of inflation and deflation cycles of the chamber bag following experiments were used to remove aerosol material followed by leaving the bag with a mixing ratio of 1-2 ppm of ozone overnight. At least five cycles of inflating and deflating the MAC s Teflon bag were used to remove impurities. 1,3,5-6

24 trimethylbenzene is known to have low reactivity to ozone and so extra cleaning was completed by exposing the bag to the unfiltered arc lamp source with mixing ratios of ppm of ozone for minutes following backgrounds and experiments involving 1,3,5-trimethylbenzene. Further details of general MAC operation and the facility can be found in the supplementary material, another publication (Alfarra et al., 2012) and references therein Aerosol chamber instrumentation A variety of instrumentation is employed during the generation of aerosol particles in the chamber to monitor the conditions in the chamber bag and the aerosol mass being produced (see Table 1). Properties monitored include humidity, temperature, ozone mixing ratio, NOx mixing ratio, aerosol particle concentration, size, composition, and ability to act as CCN. Humidity and temperature in the chamber bag were monitored by an EdgeTech DewMaster dew point hygrometer with the sensor measuring near the chamber wall and an in-house Sensirion sensor measuring conditions in the center of the chamber. Ozone mixing ratio was monitored by sampling with an ozone analyzer while NO x, NO, and NO 2 concentrations were monitored by an NO x analyzer, these two instruments shared a sample line, the inlet of this line was positioned in the center of the chamber. A TSI 3776 butanol CPC, located in the laboratory directly above the chamber, was also used to monitor aerosol particle concentration in the aerosol chamber. CPCs work by exposing the aerosol sample they are measuring to supersaturated conditions with respect to either butanol or water in order to grow any particles present, these particles are then counted using an optical scattering method. A DMPS (sizing particles between approximately nm) was used to measure the number and size distribution of the particles during the growth and aging of the SOA particles in MAC and to monitor the number concentration and size distribution of ammonium sulfate particles in MAC. The DMPS sizes particles using a DMA (Differential Mobility Analyser) which sizes according to electrical mobility size by ionizing the particles being sampled and exposing them to a potential difference between two cylindrical plates to accelerate the charged particles towards the central cylinder. A small exit slit in the central cylinder allows size-selected particles to be extracted. The size of these particles depends upon the potential difference applied across the two cylinders. The particles can then be counted using a CPC, in this case, a 3786 TSI Water CPC was employed. In the Differential Mobility Particle Size, the potential difference is changed in discrete steps providing sizing information across a range of sizes at a time resolution of ~10 minutes. A Vienna style DMA (Williams et al., 2007; Winklmayr et al., 1991) was used to size particles which were then measured by Condensation Particle Counter (CPC) which creates a large supersaturation meant to activate all particles into drops to be counted combining with the DMA to act as a DMPS and a continuous flow Cloud Condensation Nuclei Counter (CCNc) (Good et al., 2010b; Lance et al., 2006; Roberts and Nenes, 2005) which exposes aerosol to a controlled supersaturation to measure their activity as cloud condensation nuclei (CCN). This experimental setup and calibration procedure have been described in the literature (Good et al., 2010a) and further information on the calibration can be found in the supplementary material (see Sect. S1.2.1) The DMA operated in the size range of approximately nm with a scan time of approximately 7.5 minutes run at 10 minute intervals. After the differential mobility analyzer (similar to the DMA used in the DMPS described above), the flow was diluted and split between a butanol CPC and 7

25 either one or two CCN counters depending upon the experiment. The flow was diluted after the DMA and the experimental set up was such that dilution was held constant in all experiments. After the DMPS scan was completed, the supersaturation in the CCN counter was set to a new value. The 2.5 minute interval between DMPS scans enabled the CCN counter to stabilize at its new supersaturation. By comparing the number of particles above 2 microns after being exposed to supersaturated conditions detected by the CCN counter to the total count provided by the CPC running parallel to it, the activated fraction of aerosol for that size and supersaturation is obtained. The raw data from both counters was inverted to correct for charging efficiency and multiple charged particles. This yielded size spectra of all particles (from the CPC) and those of particles activated under a controlled supersaturation (from the CCNc). The ratio of these spectra is the activated fraction as a function of the dry particle size which was fitted to a sigmoidal function. The turning point (i.e. the point at which there is 50% activation) provides the activation size (D50) and the set supersaturation is the critical supersaturation (SS crit). The fitting error for the turning point of the sigmoidal function was used as the uncertainty of the D50 value. A look-up table was created to determine the hygroscopicity parameter κ using Eq. 6 from Petters and Kreidenweis work (Petters and Kreidenweis, 2007) for each SS crit and D50 pair. In the same way, the minimum and maximum values for κ, κ min, and κ max were derived from the corresponding SS crit/d50 max and SS crit/d50 min pairs respectively. This was used as uncertainty in κ as a direct propagation of uncertainties is not possible. An Aerodyne High Resolution Aerosol Mass Spectrometer (AMS) (Canagaratna et al., 2007; Decarlo et al., 2006) was employed in these experiments in order to gain information regarding the chemical composition of + the SOA material. The fraction of material with m/z = 44 (indicative of the CO 2 ion) is known to be a proxy for the degree of oxygenation of material in organic aerosol particles thus AMS data can be used as a proxy to measure the degree of oxygenation of the secondary organic material which is thought to increase their hygroscopicity and therefore their CCN activity (Jimenez et al., 2009). Data was analyzed using the in-house software as found in previous studies (Alfarra et al., 2012). 2.2 Cloud chamber and instrumentation Cloud chamber and basic instrumentation The Manchester Ice Cloud Chamber (MICC) is a 10m tall, approximately cylindrical stainless steel chamber with a diameter of 1m. Ports are positioned throughout the chamber allowing access for measurements to be made. The ports at the base of the chamber were used for the pressure, cloud and aerosol probes in these experiments (see Fig. 1). Two Varian rotary vacuum pumps positioned at the top of the MICC allow the pressure in the chamber to be reduced by removing material from the chamber; this can be refilled with clean air using the MAC blower system (see Sect ). Pressure is measured using an LEX-1 piezoresistive manometer located at one of the lower ports of the chamber. Temperature is monitored using eight K-type thermocouples located at different points along the height of the chamber, a temperature gradient across the chamber of ~1.5 K (coldest at the bottom, warmest at the top) is generally observed making it necessary to measure temperature throughout the chamber s height in order to establish conditions throughout the chamber. Both pressure and temperature measurements were recorded throughout each experimental evacuation and refill (see Sect ). 8

26 315 A CR4 Dew Point Hygrometer was used to measure the relative humidity of the chamber. However, as this instrument is sensitive to pressure changes it was only employed between evacuations when the chamber was at approximately atmospheric pressure. Further details can be found in the supplementary material (see Sect. S1.2.2) Cloud chamber preparation The MICC was prepared for cloud evacuations by sealing all the ports and reducing the pressure of the chamber to 200 mbars using Varian rotary vacuum pumps before refilling it to atmospheric pressure with clean air from the MAC s blower system via the connecting stainless steel tube approximately 20m in length which leads from the inflation/deflation line in the MAC system to a port near the top of the cloud chamber. This process was repeated three times, each time diluting the initial concentration of particles in the chamber and thus reducing the background particle count which was recorded after the MICC was refilled using a CPC (see Sect and Table 3) Transfer of aerosol from the aerosol chamber to the cloud chamber Once the aerosol sample had been generated in the MAC and the MICC had been prepared, the MICC underwent a further evacuation to 200 mbars with measurements taken by the WELAS (see Sect and Table 3) between atmospheric pressure and 700 mbars at which point the WELAS was isolated from the chamber (this was taken as a background measurement). Once the pressure reached 200 mbars, the pumps were isolated from the chamber then the valves in the MAC chamber were configured so as to expose the MAC to the MICC and a part of the aerosol mass inside the MAC was drawn through the connecting stainless steel tube into the MICC until the MAC had shrunk sufficiently for the MICC to have returned to atmospheric pressure Cloud generation in MICC In these experiments, partial activation of the aerosol particles into cloud droplets was required in order to gain information regarding their CCN activation properties; as such it was necessary to have some control over the rate of reduction in pressure during evacuations. This was achieved using either one or two Varian rotary vacuum pumps and through the use of critical orifices of varying diameter (4 mm, 5.7 mm and 7 mm) installed in the line between the chamber and the vacuum pump. During fitting and removal of these critical orifices, the blower system was used to create a small overpressure inside the MICC to prevent contamination of the aerosol sample with lab air. Upon reaching atmospheric pressure, measurements were taken with the chilled mirror hygrometer, CPC, and SMPS (see Sect and Table 3) to measure the MICC s dew point and the number and size of the aerosol particles. Once the pump orifices had been set, the MAC and MICC were isolated from one another and the aerosol sample in MICC underwent a quasi-adiabatic pressure and the growth of droplets into a cloud was observed with the WELAS (see Sect and Table 3) to approximately 700 mbars and then refilled to atmospheric pressure with clean air from the MAC lab blower system. In the case of the ammonium sulfate experiment, air was drawn from the MAC instead of the blower system, allowing control of the total 9

27 350 concentration of ammonium sulfate aerosol. MICC s temperature and pressure were measured throughout these experiments using the probes mentioned in Sect Aerosol particles size and number The number concentration and size distribution of particles in the MICC were measured at atmospheric pressure using a TSI 3010 condensation particle counter (CPC), similar in mode of operation to the butanol CPC used in the MAC (see Sect ). The size distribution was measured with a TSI Scanning Mobility Particle Sizer (SMPS). This employs a DMA in a similar way to that used for a DMPS (see Sect ), however, in an SMPS the voltage is not held constant to measure a single size before undergoing a step change to measure the next size, instead the voltage is continuously varied during a scan from the smallest to the largest size. This improves the time resolution of the data, requiring approximately 3 minutes to complete a scan. In general, two scans were completed between each cloud evacuation. The CPC and SMPS used can only operate near atmospheric pressure so measurements of aerosol size and number were only made after the MICC had been refilled and thus was at approximately atmospheric pressure. To further facilitate this, the blower system s clean air supply remained on during these measurements to ensure that sampling from the MICC did not reduce the pressure. Concentration data measured using the CPC was taken to be representative of the concentration of aerosol particles throughout the chamber. SMPS measurements underwent diffusion and multi-charge corrections using the Aerosol Instrument Management, TSI software and were used to measure total concentration and size distribution Cloud droplets size and number 370 The WELAS (WhitE Light Aerosol Spectrometer) measures the size distribution and number concentration of droplets present. Details on the WELAS can be found in the literature (see Table 3). The WELAS uses a forward scattering white light technique to measure particles with diameters between 0.8 and 84 µm; its precision is determined by Poisson counting statistics that depend upon the concentration being measured Sampling and measurement strategy and techniques The WELAS sampled for 1 second followed by a 5 second gap in measurements. This was due to the limitations of the software being used to record the WELAS data. The WELAS recorded throughout each experimental evacuation as did the pressure and temperature probes. As SMPS, CPC and the hygrometer were pressure sensitive, they were only used at approximately atmospheric pressure, i.e. when the chamber had been refilled following an evacuation either from the MAC s Teflon bag (in the case of the initial transfer) or its blower system (in the case of subsequent evacuations). 2.3 Model The Aerosol Cloud Precipitation Interaction Model, ACPIM, (Connolly, 2009; Connolly et al., 2012; Dearden, 2009; Dearden et al., 2011; Topping et al., 2013) was employed to create simulations of the conditions in the cloud chamber. These can then be compared to the results from the cloud chamber to test 10

28 385 whether or not the model and therefore our theoretical understanding of cloud formation on SOA agrees with chamber data Model structure ACPIM can model the formation, growth and dissipation of cloud from aerosol by solving a series of coupled ordinary differential equations solving for conservation of water vapor mass, conservation of mass of semivolatile organic material (when the presence of semi-volatile organic material was being modeled), the hydrostatic equation, conservation of energy in the air parcel, change of mass in each size bin, and rate of change of parcel height (Topping et al., 2013) in the case of a chamber study this physically refers to the effective updraft velocity caused by the reduction in pressure during an evacuation. These equations are solved for each time step with outputs every 10 seconds enabling a description of the cloud s life to be created. In cases where semi-volatile organic material was modeled, as the size of the particles is of a similar magnitude to the mean free path of the organics, diffusivity was treated in accordance with transition regime condensation theory Model methods, configuration, and inputs ACPIM was run using a full moving bin method with 60 size sections. This is the least numerically diffusive but is unable to simulate collision-coalescence effects, though these are negligible in the MICC. In model simulations considering organic material, 10 volatility bins were used. SMPS data was fitted to a lognormal distribution. This enabled calculations of parameters pertaining to the distribution i.e. the distribution width parameter, total concentration and modal size for use in ACPIM to be obtained. These were assigned random values within the variability limits of the measurement. κ was likewise varied within the variability limits (see Table 1) of the value obtained from CCNc data except for experiments where the aerosol particles were ammonium sulfate which has a well-known value for κ. Initial temperature was varied between a maximum of the warmest temperature recorded by any of the temperature probes at the time of the beginning of the evacuation plus 1σ of the temperature probe measurement to a minimum of the coldest temperature recorded by any of the temperature probes at the time of the beginning of the evacuation minus 1σ of the temperature probe measurement of the range of temperatures measured at the start of the evacuation by the MICC s eight temperature sensors plus one error bar in the temperature probe measurement in both the cooler and warmer end of the variability range. The pressure profile used in the model was fitted to the temperature profile of the chamber experiment fitted to an exponential decay curve. The measured value was used for the initial pressure; as the range is very small, this was not thought to have any impact upon the cloud. Initial RH was calculated based on the assumption that it passes 100 % during the time interval at which the WELAS begins to observe droplets and that water vapor is conserved until this point. In this case, RH is a function of temperature and hence the initial RH can be calculated from the initial temperature and the temperature at the point of first droplet activation. The range of variables used for the Monte Carlo simulation is shown in Table 4. 11

29 Model simulations Base case simulations were generated by conducting model runs using the midpoints of the range of values for all the parameters being varied (i.e. simulations using the expectation value for each parameter) for each experimental evacuation. A Monte Carlo simulation of each experimental evacuation was conducted. For each experimental evacuation 5000 model runs were conducted across the parameter space (see Table 4). Further tests were conducted based on the results from the Monte Carlo simulation: a series of tests were conducted varying the value of the κ parameter outside the expected variability limits and a series of model simulations with the inclusion of semi-volatile components were conducted. In order to explore the potential impact of the co-condensation of semi-volatile material on activating droplets, it is necessary to define the volatility distribution of components. The distribution of oxidation products of the precursors injected into MAC at the point of transfer to MICC is highly uncertain. A coupled model of photochemical oxidation and microphysics of the SOA formation including treatment of the partitioning to the Teflon walls would be necessary to determine the mixing ratios of all partitioning species. The uncertainties in such processes are too large to make a prediction of the volatility distribution transferred to MICC. Instead, an uncertainty analysis has been conducted, initially using a volatility distribution shape following that determined in the field measurements in Mexico City of Cappa & Jimenez (Cappa and Jimenez, 2010) but including four other distributions. For each distribution shape the total concentration of semi-volatile material was varied between and g m -3 ; altering the total concentration found in Cappa & Jimenez s work by orders of magnitude. The molecular weight used as an estimate in all model simulations for SOA was g mol -1 ; leading to a concentration of between pptv and pptv. These simulations were then repeated using different volatility bin distributions to test the sensitivity of droplet activation to volatility. 2.4 Data analysis Particles measured by the WELAS were assumed to have been activated as CCN. Over the period used as a measurement of activation, the WELAS distributions are closed distributions, measuring 0 counts in the lowest size bins. Droplet number concentration data obtained using the WELAS was corrected for temperature and pressure to conditions at standard ambient temperature and pressure. Activation measured was determined by finding the initial peak in the data set, this is taken as the point before the first reduction in the concentration after the onset of cloud during an evacuation, and the nine subsequent points; meaning that the average is based on sampling 10 times for 1 second over a period of 1 minute. Simulated number concentrations from ACPIM were very stable after cloud activation until dissipation, suggesting that it should be possible to average droplet number concentration without having a substantial impact on the ratio of chamber number concentration to model number concentration obtained. The modeled maximum droplet number concentration was taken as the peak concentration from the model (based on data with a 10 second time resolution). As number concentration was very stable in the model this reflects the droplet number concentration through much of the lifetime of the cloud. Ratios of number concentration obtained from the WELAS, corrected to standard ambient temperature and pressure, to model outputs for the base case simulations (see Table 4) and the range of model outputs from Monte Carlo simulation were calculated. Ratios were also calculated between corrected WELAS data and the model simulations conducted using the base case but with additional variation in the κ parameter in order to 12

30 460 consider the sensitivity of the number concentration to κ and the change in κ required to get agreement between the corrected WELAS number concentration data and the total droplet concentration from the model simulation. Likewise, model simulations were conducted using the base case values with a semi-volatile component included to investigate the quantity of semi-volatile material necessary to get agreement between the corrected WELAS number concentration data and number concentration from modeled data. This was calculated as a ratio Results 3.1 MAC data Secondary organic aerosol, including particles, were produced by photo-oxidation in the MAC from α- pinene, β-caryophyllene, limonene and 1,3,5-trimethylbenzene and ammonium sulfate particles were nebulised into the MAC. Hygroscopicity of the CCN is treated in the model using the single parameter κ (Petters and Kreidenweis, 2007). Data on the hygroscopicity of the SOA particles was collected using the CCN counter. Figure 2 shows the size diameter and the κ value calculated from this, at which half of the particles activated. The values used in the ACPIM simulations for the hygroscopicity variable κ were taken as an interpolation between the data point before and the data point after the start of the transfer of aerosol between the chambers. 3.2 MICC data The material generated was successfully transferred to the MICC. After transfer, evacuations were conducted on the aerosol sample, an example is shown in Fig. 3. Data was collected regarding the temperature, pressure and droplet number concentration throughout the evacuation. The total number concentration shown in Fig. 3(c) is from measurements made by the WELAS corrected for temperature and pressure to standard ambient temperature and pressure. This data has been compared to model outputs in order to test our understanding of cloud activation on SOA (see Fig. 7 10). Two evacuations were conducted on the 07/11/2013 α-pinene precursor SOA after which the experiment was curtailed by contamination of the sample. Three evacuations were conducted on the limonene precursor SOA and 14/11/2013 α-pinene precursor SOA. Four evacuations were conducted on the 1,3,5-trimethylbenzene precursor SOA and β-caryophyllene precursor SOA. Six evacuations were conducted on the ammonium sulfate sample, however, in three of these evacuations, the first, third and fourth, the WELAS recording failed and so these results are not presented here. 3.3 Model inputs 490 In addition to requiring information regarding the hygroscopicity of aerosol particles (as described in Sect. 3.1), it is necessary to describe the temperature, pressure and aerosol population s size distribution and number concentration inside the chamber. An SMPS was employed in the MICC in providing data on the size distribution of the aerosol particles as shown in Fig. 4. Temperature and pressure data were recorded during each cloud evacuation (e.g. see Fig. 3), fits to the temperature are shown in Fig. 5 and were used to fit the pressure data. A full table of the model inputs is shown in the methods section (see Table 4) and a comparison of κ value ranges (the single parameter used as a measure of hygroscopicity and therefore the key 13

31 activation parameter) used in this study with values found in the literature can be found in the supplementary material (see Table S1). The SMPS distributions shown (Fig. 4) differ between cloud evacuations using the same SOA sample; a reduction in the concentration of aerosol particles is observed as the initial sample is diluted by the air from the MAC filtration system (e.g. the 1,3,5-trimethylbenzene sample see Fig. 4 (i) (l)). No significant differences are observed in the size distribution between cloud evacuations on the same sample, implying that material which partitions to the aerosol phase during expansions, both water and organic matter, evaporates. Differences in size distribution can also be observed between the different samples. The ammonium sulfate sample (Fig. 4 (f) (h)) was prepared by nebulising aerosol particles into the MAC, this resulted in a broader, less smooth aerosol size distribution than for the SOA particles which were generated by photochemical nucleation. In the example of a photonucleation experiment, in addition to loss of particles by coagulation and wall loss, the particles generated grow continuously as precursor compounds are oxidised into less volatile ones with the amount of material condensing related to the volume of the existing particles, in the case of nebulised material however the smoothing effect of condensing material is absent which leads to a more uneven distribution as shown in Fig. 4(f) which shows the distribution after 1 cloud expansion has been performed on the ammonium sulfate sample. Fig. (g)-(h) show the ammonium sulfate sample after 4 and 5 evacuations have been performed on the sample including one to 200 mbar in order to reduce the concentration of the sample. These measurements show a less well-defined distribution of material. One contributing factor to this is likely to be the reduction in concentration (approximately 1 order of magnitude) between 4(f) and 4(g)-(h). At lower concentrations, the total number of aerosol particles sampled is reduced and so sampling errors are increased. Other differences in the sample may be due to cloud processing and further coagulation of particles (4(g) and 4(h) were recorded more than 2 hours after 4(f)). The total concentration of aerosol particles present and their size varied considerably between samples. Preliminary attempts to photonucleate particles using 1,3,5-trimethylbenzene precursor were unsuccessful under the usual conditions of illumination employed in the MAC so the UV filter for the arc lamp was removed in order to expose the precursor material to greater amounts of short wavelength UV in order to generate aerosol particles. Nevertheless, this sample produced less material in the aerosol phase. The modal size of particles generated was smaller in the 1,3,5-trimethylbenzene experiment (~75 nm) than in other photonucleation experiments except for the β-caryophyllene experiment (which produced a higher concentration of particles) and the number concentration generated in the MAC was also lower than that observed in other experiments. The α-pinene experiment on 14/11/13 (Fig. 4 (i) (l)) has a much larger modal size than observed in the other photonucleation experiments. In this experiment, the number of particles produced in the nucleation event was much lower than observed in the α-pinene experiment on 07/11/13 (Fig. 4 (a) (b)). The reason for this remains unclear. Ensuring good agreement between the temperature profile observed during cloud expansions and those modeled is necessary in order to accurately calculate the RH and hence the activation in the MICC. In all cases, a good match was achieved between the measured average temperature in the MICC and the temperature in the model (see Fig. 5). It should be noted that this fit is to the average temperature in the MICC. A temperature gradient was present across the MICC with the top of the chamber approximately 1.5 K warmer than the bottom. In all cases, experimental data is only shown for the period over which the 14

32 535 evacuation took place. The pressure fits shown in Fig. 5 are based on a quasi-adiabatic expansion as calculated in the model. The good quality fit achieved indicates that temperature and pressure inputs into the model are a good match for the chamber experiment. 3.4 Comparing model and experimental data Data from the chamber experiments was compared to a variety of model simulations. The base case model simulation (i.e. model simulation using the expectation value for all variables) for each evacuation, shown alongside the total concentration from the WELAS (Fig. 6), was compared to an averaged period of 60 seconds of number concentration data from the WELAS starting from the peak concentration (Fig. 7) as discussed in Sect Results from a Monte Carlo simulation using random values in the intervals indicated for the variables being altered (see Table 4) were also compared to the average WELAS number concentration (Fig. 8). In an effort to explore the changes required to get agreement between model and experimental data, the effect of altering the value of κ (Fig. 9) and of the inclusion of semi-volatiles (Fig. 10) to the base case scenario were investigated. The droplet number concentrations calculated by the model were steady after the onset of cloud up until the cloud started to evaporate whereas the WELAS measurements from the MICC indicate that after an initial peak, the droplet concentration decreases throughout the cloud s lifetime suggesting that loss of water vapor to the MICC s walls is not well captured by ACPIM. Due to the large amount of variability between data points obtained by the WELAS (see Fig. 6), an average was taken of 10 measurements starting from the initial peak concentration (i.e. the measurement directly before the first reduction in droplet number concentration was observed in the WELAS). These averages were taken as the peak concentration observed by the WELAS and were used in the ratios of WELAS and model data (see Fig. 7 10). The ratios between the WELAS peak number concentration and the peak number concentrations from the base case ACPIM results (see Fig. 7) are always in agreement with and broadly near the center of the range of ratios from the Monte Carlo simulation (see Fig. 8). The results from the Monte Carlo simulation show agreement between the model and the WELAS (assuming ±10 % variability in the peak WELAS concentration) for all experiments except in the cases of the first evacuation with the α-pinene (7/11/13) and all three β-caryophyllene evacuations where the model under-predicts the droplet concentration and the second limonene evacuation where the model over-predicts the droplet concentration. The four expansions where the model under-predicts the droplet number concentration also show the greatest underestimation by the model when using the base case simulations but varying κ (see Fig. 9) up to 250 % of κ s value calculated from measurements using the CCN counter and not reaching agreement between model and WELAS results at any value of κ in some cases. The limonene experiment shows the greatest overestimation by the model in the case of the κ varying simulations with a κ of approximately 55 % the original value required for agreement in the most extreme case. Results from simulations using varying quantities of semi-volatile organic material proportioned into volatility bins according to the findings of Cappa and Jimenez (Cappa and Jimenez, 2010) show a large range of WELAS/Model droplet number concentration ratios encompassing agreement between model and WELAS for the evacuations where the model under-predicts the number concentration in the base case and Monte Carlo simulations (see Fig. 10). But simulations using the range of volatility bin distributions (see Fig. 15

33 575 11) produce very similar results (see supplementary material Fig. S1 S4). The evacuations using the two different α-pinene experiments, show very different responses to the addition of semi-volatile material: both evacuations conducted with the α-pinene sample from 07/11/13 show substantial changes in number concentration with the addition of semi-volatile material, however, the difference is much smaller for the evacuations conducted with the α-pinene from 14/11/13 (see Fig. 10). 4 Discussion As the WELAS measures aerosol particles, this raises the concern of whether or not the particles measured by the WELAS are in fact activated droplets or merely swollen aerosol. However, for aerosol particles of the sizes being considered here, the lower bound of the size detection limit for the WELAS is thought to be above the critical size for the particles and hence they can be considered droplets. The distribution of material in the WELAS is a closed distribution indicating that we are well capturing the activated droplet mode. No substantial differences were observed between the volatility bin distributions used in this study (see Fig. 10 and Fig. S1 S4) with the number concentrations obtained from the model almost solely dependent upon the total semi-volatile concentration rather than the volatility bin denoted by differing values of log 10C* from -6 to 3 (i.e. a ratio of vapor: aerosol under dry conditions of between 1: and 1000:1). This suggests that as relative humidity is increased, the amount of semi-volatile material in the condensed phase also increases such that by the time the aerosol particle enters a supersaturated regime with respect to water, the semi-volatile material, whatever its initial volatility, is dominated by the condensed phase meaning that the increase in size of the distribution of aerosol particles is dependent upon the amount of semi-volatile material and not its degree of semi-volatility. From this it can be surmised that the exact volatility distribution is not critical to determining activation and therefore the use of the Cappa and Jimenez (Cappa and Jimenez, 2010) distribution is a sufficient approximation for the purposes of this study. In itself insensitivity to volatility distribution is an important finding supporting the work of Topping et al. (Topping et al., 2013) which modeled the growth of aerosol particles as material condensed onto them, the proportion of semi-volatile material in the condensed aerosol phase increases with increasing RH and decreases as volatility, expressed as logc*, increases. Even for material modeled as logc*=3 (i.e. the most volatile material included), the condensed phase dominates the partition between the aerosol and gas phases at RH= %. As in these experiments droplets are activated, the RH must exceed 100 % inside the chamber. The droplets are therefore subjected to higher relative humidity than in this previous work where the condensed phase dominated, it is reasonable to expect therefore that the partition between the condensed aerosol and vapor phase would be dominated by the condensed aerosol phase in all evacuations. In most cases, the range of values for the ratio between number concentration in MICC measured by the WELAS and the modeled data is in agreement (see Fig. 8). The ammonium sulfate system has been extensively studied, the value of κ for ammonium sulfate is well known and it is not anticipated that there would be any semi-volatile material present in this sample. The evacuations completed with the ammonium sulfate aerosol sample show consistency in number concentration between WELAS data, with an estimated uncertainty of ±10 %, and model data, indicating that the model is capturing non-semi-volatile behavior well. It should be noted that ammonium sulfate particles, unlike the other aerosol particles used in these experiments were not photo-nucleated but nebulised. One effect of this is that the size distribution was much 16

34 less similar to a lognormal distribution than the photo-nucleated populations. This means that the fits to the ammonium sulfate experiment were not as close as in other experiments and therefore were less well treated by the model. This may have introduced an extra degree of error in the result from the model for each evacuation with this sample. However, this is not required to explain the observations. The largest discrepancy between the model and chamber data is the first evacuation of the β-caryophyllene experiment where the model under-predicts the droplet number concentration observed by the WELAS in the chamber. In the subsequent evacuations with this aerosol sample, the range of the chamber/ model number concentrations draws closer to agreement (Fig. 10) but in no evacuation are the results from the β- caryophyllene MICC experiment consistent with the model results. This discrepancy between experimental evacuations and model results could be explained by the presence of semi-volatile material. As the concentration of semi-volatile material will be highest in the first evacuation, because it has not been diluted by evacuations and refills and the least amount of semi-volatile material has been lost to the chamber walls, the impact of the semi-volatile material will be greatest in the first evacuation and reduced in each subsequent evacuation. Semi-volatile material is not included in the Monte Carlo simulation so the discrepancy between the Monte Carlo simulation and the WELAS data in the β-caryophyllene experiment is explicable as an effect of the presence of semi-volatiles. Figure 10 shows base case simulations with the inclusion of varying amounts of semi-volatile material for various volatility distributions (as shown in Fig. 11). In the β-caryophyllene experiment, consistency between the model and WELAS number concentration data is achieved with between g m -3 and g m -3 for the first evacuation and g m -3 and g m -3 for the subsequent two evacuations. Another possible explanation for the discrepancy observed in the β-caryophyllene experiment was that the values assigned to κ for the Monte Carlo simulation were incorrect as measurements of κ have been shown to differ between instruments and our understanding of the formation of droplets in clouds is incomplete (Whitehead et al., 2014). A set of base case simulations but using different values for κ were compared to the concentrations measured by the WELAS (Fig. 9). However, these show that a substantial difference in the value of κ would be required (560%, 400% and 270% of the measurement value for the first, second and third β-caryophyllene evacuation respectively) in order to achieve the change in activation required for the model to match the WELAS data, assuming an error of 10 % in WELAS number concentration. The model and WELAS droplet concentration ratios from evacuations conducted on the sample from the 7 th November on the α-pinene sample appear to show a similar pattern to the β-caryophyllene experiment although only for the first cloud evacuation is the range of ratios between the WELAS measured chamber number concentration and the model number concentrations from the Monte Carlo simulation is inconsistent. This effect again could be explained by the presence of semi-volatile material. The α-pinene experiment on the 14 th November exhibits different behavior with the range of chamber data/model suggesting a consistency (Fig. 7 and 8). The difference between these two values is thought to be due to the difference in modal diameter of the aerosol particles (~90 nm on the 7 th November and ~140 nm on the 13 th of November). At larger sizes, it is thought that co-condensation would have a smaller impact on droplet activation as fewer particles will cross the critical size threshold for activation when the semi-volatile organic material condenses: both because more particles will already be past the critical size threshold and because the size difference induced by the condensation of a similar quantity of semi-volatile material will have a smaller 17

35 effect on the radius of the aerosol particles as volume is proportional the cube of the radius. The idea of a reduced semi-volatile effect at larger sizes is supported by the base case simulations with the addition of varying quantities of semi-volatile material (Fig. 10) where it is clear that the increase in the number of particles activated due to the presence of semi-volatile material is much smaller for the α-pinene experiment on the 14 th than on the 7 th. This size dependency can also be observed in the 1,3,5-trimethylbenzene experiment where semi-volatile material has little impact on activation and the range of values from the Monte Carlo simulation are broadly consistent with the WELAS data from the MICC. In the limonene experiment, activation in the chamber evacuations is lower than in the model (Fig. 7 and 8). An explanation for this finding could be that there is a systematic overestimation of κ from the CCN counter measurements as a result of the reduced particle concentration following the DMA sizing stage meaning that more semi-volatile material and fewer particles are present in the CCN counter leading to an instrument artefact whereby the particles being measured have artificially increased sizes when entering the CCN counter and hence are nearer their critical size and therefore more easily activated into droplets. In the case of the limonene experiment, this explanation would require a significant change to the value of κ (to between 55 % and 90 % of the original value assuming a WELAS number concentration uncertainty of 10 %, see Fig. 9) but a much smaller difference than that required to explain the observations made in the β-caryophyllene experiment (560 % in the case of the first β-caryophyllene evacuation). This systematic over-estimation of κ could be occurring in all SOA experiments in this study in which case co-condensation may be balancing this effect in some experiments (e.g. 1,3,5-trimethylbenzene). Another possibility is that it may be the case that limonene-derived semi-volatiles are less hydrophilic than expected because this material is volatilized when the chamber is refilled. In the 1,3,5-trimethylbenzene experiment, we see that there is agreement in all cases between the range of number concentrations Monte Carlo simulation which does not include semi-volatile material. The competing factors which can be used to explain the number concentration discrepancies observed in the limonene and β-caryophyllene may also be occurring in evacuations conducted with the 1,3,5- trimethylbenzene sample in such a way that the results show agreement. The strength of the conclusions from this study could have been enhanced by the use of additional cloud droplet measuring instrumentation, the WELAS primarily being intended for the measurement of aerosol. It had been intended that a Cloud Droplet Probe, DMT (Crosier et al., 2011; Rosenfeld et al., 2008) should have been included in this study, however, as a result of a mass flow control configuration problem during these experiments, CDP concentrations are thought to be potentially quantitatively erroneous for all experiments so only WELAS data has been used. The inclusion of this or other droplet measuring instrumentation should be pursued in future chamber activation on SOA. Additionally, ACPIM should have been run in a manner which produces size data rather than showing the bin sizes from these experiments and providing an activated droplet number as this would further confirm that the WELAS and model were measuring the activated droplets. 5. Conclusion 690 Transfers of SOA (derived from α-pinene, β-caryophyllene, limonene and 1,3,5-trimethylbenzene) and ammonium sulfate photo-oxidised and nebulised into the MAC respectively, to the MICC were conducted 18

36 and a series of evacuations to generate clouds were conducted on each sample. These cloud evacuations were modeled using ACPIM. A Monte Carlo simulation of each evacuation was conducted and additional simulations using the expectation values for each of the variables, simulations using the expectation values of the variables but varying the hygroscopicity parameter κ outside the expected range and simulations using the expectation values for the variables but introducing semi-volatile material with various distributions and varying the total concentration of semi-volatile material between g m -3 and g m -3 (equating to between pptv and pptv). The ammonium sulfate evacuation chamber number concentration data and number concentrations from the Monte Carlo simulation were consistent. In the evacuations using β-caryophyllene derived SOA showed the Monte Carlo simulation under-predicted the droplet number concentration measured in the chamber. The first evacuation on the sample had the greatest under-prediction which reduced with each subsequent evacuation. Varying the value of κ was only able to render the chamber droplet concentration measurements and model droplet concentrations consistent by changing the value of κ by a factor of 2.7 to 5.6. This is thought to be unrealistic. However, concentrations of semi-volatile material between g m -3 and g m -3 were able to produce agreement, suggesting that co-condensation of semi-volatile material is being observed. A similar pattern to the β- caryophyllene experiment, but less pronounced, was observed in the α-pinene experiment conducted on 07/11/13. This is not observed in the α-pinene experiment conducted on 14/11/13. The difference between these is thought to be due to the insensitivity of the droplet activation of large aerosol particles to the presence of semi-volatile material. The Monte Carlo simulation overestimates the total number concentration for the limonene experiment, this is thought to be due to problems in measuring κ and requires smaller changes to the value of κ in order for the model and chamber measured number concentrations to achieve agreement. This work indicates that co-condensation is occurring under controlled chamber conditions, the first time such an observation has been made and demonstrating the potential importance of co-condensation to understanding and modeling cloud activation. Author contribution 720 William Hesson was responsible for planning and leading the experiments completed, data analysis of MICC data, conducting the modeling runs and analyzing the results, completing the manuscript and creating the figures. The contribution of Angela Buchholz was to assist in the experiments by operating the CCN counter, development of software for data analysis of CCN counter and DMPS data and conducting the analysis. Paul Connolly was responsible for the development of ACPIM and developed some of the in-house software to extract data from the MICC instrumentation. Gordon McFiggans assisted in editing the paper and acted in a lead supervisory role for the project. 725 Competing interests The authors declare that they have no conflict of interest. 19

37 Acknowledgements This research was funded by NERC as part of the ACID-PRUF project. References Albrecht, B. A.: Aerosols, Cloud Microphysics and Fractional Cloudiness, Science (80-. )., 245(4923), , Alfarra, M. R., Hamilton, J. F., Wyche, K. P., Good, N., Ward, M. W., Carr, T., Barley, M. H., Monks, P. S., Jenkin, M. E., Lewis, a. C. and McFiggans, G. B.: The effect of photochemical ageing and initial precursor concentration on the composition and hygroscopic properties of β-caryophyllene secondary organic aerosol, Atmos. Chem. Phys., 12(14), , doi: /acp , Alofs, D. J., Lutrus, C. K., Hagen, D. E., Sem, G. J. and Blesener, J. L.: Intercomparison Between Commercial Condensation Nucleus Counters and an Alternating Temperature Gradient Cloud Chamber, Aerosol Sci. Technol., 23(2), , doi: / , Barahona, D., West, R. E. L., Stier, P., Romakkaniemi, S., Kokkola, H. and Nenes, A.: Comprehensively accounting for the effect of giant CCN in cloud activation parameterizations, Atmos. Chem. Phys., 10(5), , doi: /acp , Benz, S., Megahed, K., Möhler, O., Saathoff, H., Wagner, R. and Schurath, U.: T-dependent rate measurements of homogeneous ice nucleation in cloud droplets using a large atmospheric simulation chamber, J. Photochem. Photobiol. A Chem., 176(1-3), , doi: /j.jphotochem , Bigg, E. K.: Discrepancy between observation and prediction of concentrations of cloud condensation nuclei, Atmos. Res., 20(1), 81 86, doi: / (86) , Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B. and Zhang, X. Y.: 2013: Clouds and Aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Canagaratna, M. R., Jayne, J. T., Jimenez, J. L., Allan, J. D., Alfarra, M. R., Zhang, Q., Onasch, T. B., Drewnick, F., Coe, H., Middlebrook, A., Delia, A., Williams, L. R., Trimborn, A. M., Northway, M. J., Decarlo, P. F., Kolb, C. E., Davidovits, P. and Worsnop, D. R.: CHEMICAL AND MICROPHYSICAL CHARACTERIZATION OF AMBIENT AEROSOLS WITH THE AERODYNE AEROSOL MASS SPECTROMETER,, , doi: /mas, Cappa, C. D. and Jimenez, J. L.: Quantitative estimates of the volatility of ambient organic aerosol, Atmos. Chem. Phys., 10(12), , doi: /acp , Connolly, P. J.: Studies of heterogeneous freezing by three different desert dust samples, Atmos. Chem. Phys., 9(9), , Connolly, P. J., Emersic, C. and Field, P. R.: A laboratory investigation into the aggregation efficiency of small ice crystals, Atmos. Chem. Phys., , doi: /acp , Crosier, J., Bower, K. N., Choularton, T. W., Westbrook, C. D., Connolly, P. J., Cui, Z. Q., Crawford, I. P., Capes, G. L., Coe, H., Dorsey, J. R., Williams, P. I., Illingworth, a. J., Gallagher, M. W. and Blyth, a. M.: 20

38 Observations of ice multiplication in a weakly convective cell embedded in supercooled mid-level stratus, Atmos. Chem. Phys., 11(1), , doi: /acp , Davis, E. J., Buehler, M. F. and Ward, T. L.: The double-ring electrodynamic balance for microparticle characterization, Rev. Sci. Instrum., 61(4), , doi: / , Dearden, C.: Investigating the simulation of cloud microphysical processes in numerical models using a onedimensional dynamical framework, Atmos. Sci. Lett., 10(3), , doi: /asl.239, Dearden, C., Connolly, P. J., Choularton, T. W. and Field, P. R.: Evaluating the effects of microphysical complexity in idealised simulations of trade wind cumulus using the Factorial Method, Atmos. Chem. Phys., 11(6), , doi: /acp , Decarlo, P. F., Kimmel, J. R., Trimborn, A., Northway, M. J., Jayne, J. T., Aiken, A. C., Gonin, M., Fuhrer, K., Horvath, T., Docherty, K. S., Worsnop, D. R. and Jimenez, J. L.: Field-Deployable, High-Resolution, Time-of-Flight Aerosol Mass Spectrometer, Anal. Chem., 78(24), , doi: /2001jd analytical, Dusek, U., Covert, D. S., Wiedensohler, A., Neusüss, C., Weise, D. and Cantrell, W.: Cloud condensation nuclei spectra derived from size distributions and hygroscopic properties of the aerosol in coastal south-west Portugal during ACE-2, Tellus, Ser. B Chem. Phys. Meteorol., 55(1), 35 53, doi: /j x, Fors, E. O., Swietlicki, E., Svenningsson, B., Kristensson, a., Frank, G. P. and Sporre, M.: Hygroscopic properties of the ambient aerosol in southern Sweden-a two year study, Atmos. Chem. Phys., 11(16), , doi: /acp , Fountoukis, C. and Nenes, A.: Continued development of a cloud droplet formation parameterization for global climate models, J. Geophys. Res. D Atmos., 110(11), 1 10, doi: /2004jd005591, Fountoukis, C., Nenes, A., Meskhidze, N., Bahreini, R., Conant, W. C., Jonsson, H., Murphy, S., Sorooshian, A., Varutbangkul, V., Brechtel, F., Flagan, R. C. and Seinfeld, J. H.: Aerosol-cloud drop concentration closure for clouds sampled during the International Consortium for Atmospheric Research on Transport and Transformation 2004 campaign, J. Geophys. Res. Atmos., 112(10), doi: /2006jd007272, Good, N., Topping, D. O., Allan, J. D., Flynn, M., Fuentes, E., Irwin, M., Williams, P. I. and Coe, H.: Consistency between parameterizations of aerosol hygroscopicity and CCN activity during the RHaMBLe discovery cruise, Atmos. Chem. Phys., 10, , 2010a. Good, N., Topping, D. O., Duplissy, J., Gysel, M., Meyer, N. K., Metzger, a., Turner, S. F., Baltensperger, U., Ristovski, Z., Weingartner, E., Coe, H. and McFiggans, G.: Widening the gap between measurement and modeling of secondary organic aerosol properties?, Atmos. Chem. Phys., 10, , doi: /acp , 2010b. Graber, E. R. and Rudich, Y.: Atmospheric HULIS: how humic-like are they? A comprehensive and critical review, Atmos. Chem. Phys. Discuss., 5(5), , doi: /acpd , Gysel, M., Crosier, J., Topping, D. O., Whitehead, J. D., Bower, K. N., Cubison, M. J., Williams, P. I., Flynn, M. J., McFiggans, G. B. and Coe, H.: Closure study between chemical composition and hygroscopic growth of aerosol particles during TORCH2, Atmos. Chem. Phys., 7(24), , doi: /acp , Hsieh, W. C., Nenes, A., Flagan, R. C., Seinfeld, J. H., Buzorius, G. and Jonsson, H.: Parameterization of 21

39 cloud droplet size distributions: Comparison with parcel models and observations, J. Geophys. Res. Atmos., 114(11), 1 11, doi: /2008jd011387, Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, a S. H., Zhang, Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng, N. L., Aiken, a C., Docherty, K. S., Ulbrich, I. M., Grieshop, a P., Robinson, a L., Duplissy, J., Smith, J. D., Wilson, K. R., Lanz, V. a, Hueglin, C., Sun, Y. L., Tian, J., Laaksonen, a, Raatikainen, T., Rautiainen, J., Vaattovaara, P., Ehn, M., Kulmala, M., Tomlinson, J. M., Collins, D. R., Cubison, M. J., Dunlea, E. J., Huffman, J. a, Onasch, T. B., Alfarra, M. R., Williams, P. I., Bower, K., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, a, Miyoshi, T., Hatakeyama, S., Shimono, a, Sun, J. Y., Zhang, Y. M., Dzepina, K., Kimmel, J. R., Sueper, D., Jayne, J. T., Herndon, S. C., Trimborn, a M., Williams, L. R., Wood, E. C., Middlebrook, a M., Kolb, C. E., Baltensperger, U. and Worsnop, D. R.: Evolution of organic aerosols in the atmosphere., Science, 326(5959), , doi: /science , Jurányi, Z., Gysel, M., Weingartner, E., DeCarlo, P. F., Kammermann, L. and Baltensperger, U.: Measured and modeled cloud condensation nuclei number concentration at the high alpine site Jungfraujoch, Atmos. Chem. Phys., 10(16), , doi: /acp , Köhler, H.: THE NUCLEUS IN AND THE GROWTH OF HYDROSCOPIC DROPLETS, Faraday Discuss., (1152), , Lance, S., Nenes, a., Medina, J. and Smith, J. N.: Mapping the Operation of the DMT Continuous Flow CCN Counter, Aerosol Sci. Technol., 40(4), , doi: / , Lance, S., Nenes, A., Mazzoleni, C., Dubey, M. K., Gates, H., Varutbangkul, V., Rissman, T. a., Murphy, S. M., Sorooshian, A., Flagan, R. C., Seinfeld, J. H., Feingold, G. and Jonsson, H. H.: Cloud condensation nuclei activity, closure, and droplet growth kinetics of Houston aerosol during the Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS), J. Geophys. Res., 114, 1 21, doi: /2008jd011699, Liu, P. S. K. and Deshler, T.: Causes of Concentration Differences Between a Scanning Mobility Particle Sizer and a Condensation Particle Counter, Aerosol Sci. Technol., 37(11), , doi: / , Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review, Atmos. Chem. Phys., 5(3), , doi: /acpd , McFiggans, G., Artaxo, P., Baltensperger, U., Coe, H., Facchini, M. C., Feingold, G., Fuzzi, S., Gysel, M., Fisica, D., Paulo, U. D. S., Matao, R., Travessa, R. and Paulo, C. E. P. S.: The effect of physical and chemical aerosol properties on warm cloud droplet activation, Atmos. Chem. Phys., 6(6), , Möhler, O., Benz, S., Saathoff, H., Schnaiter, M., Wagner, R., Schneider, J., Walter, S., Ebert, V. and Wagner, S.: The effect of organic coating on the heterogeneous ice nucleation efficiency of mineral dust aerosols, Environ. Res. Lett., 3(2), , doi: / /3/2/025007, Morales Betancourt, R. and Nenes, A.: Droplet activation parameterization: The population-splitting concept revisited, Geosci. Model Dev., 7(5), , doi: /gmd , Nenes, A. and Seinfeld, J. H.: Parameterization of cloud droplet formation in global climate models, J. Geophys. Res., 108(D14), 4415, doi: /2002jd002911, Nenes, A., Chuang, P. Y., Flagan, R. C. and Seinfeld, J. H.: A theoretical analysis of cloud condensation 22

40 nucleus (CCN) instruments, J. Geophys. Res., 106(D4), , doi: /2000jd900614, Partridge, D. G., Vrugt, J. A., Tunved, P., Ekman, A. M. L., Gorea, D. and Sorooshian, A.: Inverse modeling of cloud-aerosol interactions Part 1: Detailed response surface analysis, Atmos. Chem. Phys., 11(2), , doi: /acpd , Partridge, D. G., Vrugt, J. A., Tunved, P., Ekman, A. M. L., Struthers, H. and Sorooshian, A.: Inverse modeling of cloud-aerosol interactions Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach, Atmos. Chem. Phys., 12(6), , doi: /acp , Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7(8), , doi: /acp , Pope, F. D.: Pollen Grains are Efficient Cloud Condensation Nuclei, Environ. Res. Lett., 5(4), Pope, F. D., Dennis-smither, B. J., Griffiths, P. T., Clegg, S. L. and Cox, R. A.: Mixtures with Sodium Chloride. I. Hygroscopic Growth, Growth Factors, , doi: /jp100059k, Quant, F. R., Flagan, R. C. and Horton, K. D.: Implementation of a Scanning Mobility Particle Sizer, J. Aerosol Sci., 24, 83 84, Rissler, J., Svenningsson, B., Fors, E. O., Bilde, M. and Swietlicki, E.: An evaluation and comparison of cloud condensation nucleus activity models: Predicting particle critical saturation from growth at subsaturation, J. Geophys. Res., 115(D22), D22208, doi: /2010jd014391, Roberts, G. C. and Nenes, A.: A Continuous-Flow Streamwise Thermal-Gradient CCN Chamber for Atmospheric Measurements, Aerosol Sci. Technol., 39(3), , doi: / , Roberts, G. C., Artaxo, P., Zhou, J., Swietlicki, E. and Andreae, M. O.: Sensitivity of CCN spectra on chemical and physical properties of aerosol: A case study from the Amazon Basin, J. Geophys. Res. D Atmos., 107(20), 1 18, doi: /2001jd000583, Rogers, R. R. and Yau, M. K.: A Short Course in Cloud Physics, Third Edit., Pergamon Press, Toronto., Rosenfeld, D., Woodley, W. L., Axisa, D., Freud, E., Hudson, J. G. and Givati, A.: Aircraft measurements of the impacts of pollution aerosols on clouds and precipitation over the Sierra Nevada, J. Geophys. Res., 113(D15), D15203, doi: /2007jd009544, Seinfeld, J. H. and Pandis, S. N.: Atmoshperic Chemistry and Physics: From Air Pollution to Climate Change, 2nd ed., John Wiley & Sons, Hoboken, NJ, USA., Shi, Q., Han, H., Kerrigan, S. W. and Johnson, E. M.: Characterization of Two New Butanol - based Condensation Particle Counters ( TSI Model 3776 UCPC and 3775 CPC ), Am. Assoc. Aerosol Res. Conf. Poster, 24(1), 1, Stevens, B. and Feingold, G.: Untangling aerosol effects on clouds and precipitation in a buffered system., Nature, 461(7264), , doi: /nature08281, Switlicki, E., Hansson, H.-C., Hameri, K., Svenningsson, B., Massling, A., McFiggans, G., McMurry, P. H., Petäjä, T., TUNVED, P., GYSEL, M., TOPPING, D., WEINGARTNER, E., BALTENSPERGER, U., RISSLER, J., WIEDENSOHLER, A. and KULMALA, M.: Hygroscopic properties of submicrometer atmospheric aerosol particles measured with H-TDMA instruments in various environments a review, Tellus B, 0(0), ???, doi: /j x,

41 Topping, D., Connolly, P. and McFiggans, G.: Cloud droplet number enhanced by co-condensation of organic vapors, Nat. Geosci., 6(6), , doi: /ngeo1809, Twomey, S.: The Influence of Pollution on the Short wave Albedo of Clouds, J. Atmos. Sci., 34, , Whitehead, J. D., Irwin, M., Allan, J. D., Good, N. and McFiggans, G.: A meta-analysis of particle water uptake reconciliation studies, Atmos. Chem. Phys., 14(21), , doi: /acp , Williams, P. I., McFiggans, G. and Gallagher, M. W.: Latitudinal aerosol size distribution variation in the Eastern Atlantic Ocean measured aboard the FS-Polarstern, Atmos. Chem. Phys., 7(10), , doi: /acp , Winklmayr, W., Reischl, G. P., Lindner, A. O. and Berner, A.: A new electromobility spectrometer for the measurement of aerosol size distributions in the size range from 1 to 1000 nm, J. Aerosol Sci., 22(3), , doi: /s (05) ,

42 905 Table 1: Instrumentation used for measurements in the aerosol chamber. The DMPS used in these experiments (Alfarra et al., 2012; Williams, McFiggans, & Gallagher, 2007) follows general principles for a differential mobility particle sizer (Kready, Quant, & Sem, 1983), employing a differential mobility analyser (Brechtel Manufacturing) and a 3786 water CPC (TSI) and uses sheath air at relative humidities (RHs) similar to that of the MAC. Target measurement Aerosol mass and composition Particle number concentration and size distribution (mass inferred) Particle number concentration CCN Droplet Activity Instrument Technique Uncertainty Reference High Resolution Aerosol Mass Spectrometer (AMS), Aerodyne Differential Mobility Particle Sizer (DMPS), University of Manchester (further description in the caption) 3776 Butanol Condensation Particle Counter (BCPC), TSI Aerosol particle sizing using an inhouse DMPS, droplet activity measured with Cloud Condensation Nuclei Counter (CCNc), Droplet Measurement Technologies Mass Spectrometry ±20 25% (Mass) (Canagaratna et al., 2007; Decarlo et al., 2006) Electrical Mobility Sizing, optical counting ±10% (Number concentration) CPC error 5 10% (diameter sizing) (see caption) Optical counting ±10% (Shi et al., 2005) Initial sizing using electrical mobility sizing, supersaturation induced by continuous flow thermal gradient, optical counting of particles Calculated based on uncertainty in the sigmoidal fit to the values of the diameter at which 50% of particles activate (D50). See section for further details. For CCN counter: (Alofs et al., 1995; Lance et al., 2006; Roberts and Nenes, 2005) For DMPS:(Alfarra et al., 2012; Williams et al., 2007) 25

43 910 Table 2: Experimental conditions in MAC for photochemical secondary organic aerosol experiments. Precursor, ozone and NOx mixing ratios shown are for the initial conditions when starting exposure to light. The NOx/precursor ratio was held approximately constant. The ozone mixing ratio for the β-caryophyllene experiment is difficult to measure as β-caryophyllene reacts more quickly with ozone than the ozone concentration can equilibrate throughout the MAC, the same procedure was used as in the α-pinene and 1,3,5-trimethylbenzene experiments so approximately the same mixing ratio will have been introduced. SOA precursor Precursor mixing ratio Ozone mixing ratio NOx mixing ratio Light exposure time UV filter in use 1,3, ppb 55.4 ppb 20.9 ppb 2 h 26 min No trimethylbenzene α-pinene 250 ppb 44.2 ppb 84.9 ppb 1 h 55 min Yes (07/11/2013) α-pinene 250 ppb 32.4 ppb 78.8 ppb 1 h 31 min Yes (14/11/2013) β-caryophyllene 39 ppb See caption 81.2 ppb 1 h 26 min Yes Limonene 80 ppb 14.6 ppb 75.3 ppb 1 h 40 min Yes

44 Table 3: Instrumentation used at the Manchester Ice Cloud Chamber. Target measurement Particle number and size distribution (mass inferred) Particle number in MICC Water droplets (number and size Dew point Temperature Pressure Instrument Technique Uncertainty Reference SMPS 3080/1 TSI Electrical Classifier with (low flow mode) 3776 Butanol CPC, TSI 3010 Butanol Condensation Particle Counter (BCPC), TSI White Light Aerosol Spectrometer (WELAS), Palas CR-4 Hygrometer, Buck Research Instruments Type K Thermocouples and TC-08 control box, PicoTechnologies LEX-1 Manometer, Keller Electrical mobility sizing, optical counting Optical counting ±12 % Xenon lamp scattered 90 ± 12 flux measurement Chilled mirror hygrometer Thermocouples Piezoresistive strain gauge ±20 % (number conc.) ±3.5 % (diameter) (Liu and Deshler, 2003; Quant et al., 1993) ±10 % (Benz et al., 2005; Möhler et al., 2008) ±0.1K ±0.5K ±0.05 % 27

45 920 Table 4: Variability limits for the Monte Carlo simulation of evacuations in the MICC: Dm shows the modal diameter in nm, lnσ refers to the natural logarithm of the distribution width parameter σ, N refers to the number concentration of aerosol particles per cm 3, Ti to the initial temperature in K, and initial values for relative humidity (Ti). Experimental Evacuation α-pinene 07/11/13 evacuation 1 Dm Lnσ N κ Ti RHi α-pinene 07/11/13 evacuation Limonene evacuation Limonene evacuation Limonene evacuation Ammonium sulfate seed evacuation 2 Ammonium sulfate seed evacuation 3 Ammonium sulfate seed evacuation 5 1,3,5 trimethylbenzene evacuation 1 1,3,5 trimethylbenzene evacuation 2 1,3,5 trimethylbenzene evacuation 3 1,3,5 trimethylbenzene evacuation 4 α-pinene 14/11/13 evacuation α-pinene 14/11/13 evacuation α-pinene 14/11/13 evacuation α-pinene 14/11/13 evacuation β-caryophyllene evacuation β-caryophyllene evacuation

46 β-caryophyllene evacuation

47 Figure 1: Schematic diagram of the combined MAC/MICC facility. The blower and air filtration system can be used to inflate and deflate the MAC and to transfer filtered air to the MICC. A seed drum can be used to introduce nebulised aerosol into the MAC but in these experiments, ammonium sulfate was nebulized directly into the chamber. Ports for sampling from the chambers, a line for introducing NO2, and the ozonizer and humidifier for introducing ozone and water vapor are shown. 30

48 935 Figure 2: D50 (the diameter for which 50% of the particles activate) and κ values are shown for all experiments. Variability bars are shown only for the α-pinene experiment conducted on the 7 th Nov in order to retain clarity. The labeled vertical black lines indicate the time at which photochemical nucleation was stopped for transfer. 31

49 Figure 3: Example MICC data relating to the first evacuation on the α-pinene experiment on 07/11/13. (a) Formation and growth of SOA particles in the MAC: total particle number concentration (solid black line), total mass concentration (open black circles) overlaid on a color plot of particle number distribution with diameter shown as dn/dlogdp (color scale x10 3 ). A dilution was carried out prior to transfer resulting in the reduction in concentration observed at approximately 1.4 hours. (b) WELAS size distribution data at standard ambient temperature and pressure. (c) Total WELAS concentration at standard ambient temperature and pressure and (d) average temperature (blue) and pressure (green). The vertical pink line indicates the end time for the evacuation. 32

50 Figure 4: Fitting of SMPS data to a lognormal function. Aerosol size distributions measured by the SMPS in the MICC (black) fitted to a lognormal function (red) as inputted into ACPIM for each experimental evacuation are shown. Data is shown for α-pinene (07/11/13) evacuation 1 2 (a) (b), limonene evacuation 1 3 (c) (e), ammonium sulfate evacuation 2, 4 and 6 (f) (h), 1,3,5-trimethylbenzene evacuation 1 4 (i) (l), α-pinene (14/11/13) evacuation 1 (m) (p), β-caryophyllene evacuation 1 3 (q) (s). The other evacuations from the ammonium sulfate experiments have been excluded from this study because of data collection problems. The ammonium sulfate seed distributions are much broader (i.e. have a much larger value for σ) than the SOA samples. 33

51 Figure 5: Temperature and pressure fits for input into the model (temperature is shown in orange and pressure in green) to MICC data (temperature shown in red and pressure in blue). An exponential decay function was fitted to the temperature data from the MICC, this was then used to calculate the initial RH in the model using the initial temperature and the temperature at the time of initial cloud formation and the cloud onset time. The pressure was fitted based on the initial pressure and the reduction in temperature during the cloud evacuation. Good agreement was achieved in all cases between modeled and actual temperature and pressure. The difference is most pronounced in evacuations (m), (n), (o) and (p). Data is shown for α-pinene (07/11/13) evacuation 1 2 (a) (b), limonene evacuation 1 3 (c) (e), ammonium sulfate evacuation 2,4 and 6 (f) (h), 1,3,5-trimethylbenzene evacuation 1 4 (i) (l), α-pinene (14/11/13) evacuation 1 4 (m) (p), β-caryophyllene evacuation 1 3 (q) (s). 34

52 Figure 6: Comparison of base case simulations (i.e. simulations using the expectation values for all variables) results from ACPIM (blue) with WELAS data corrected to standard ambient temperature and pressure (red). The RH used for the ACPIM simulations was set such that for the base case in all the fields varied, the onset of droplets is found to be at the same for the chamber experiment and ACPIM simulation to the nearest output time from ACPIM (i.e. 10 second interval). The model output gives an extended maximum of number concentration, this is taken as the activation from the model. WELAS data is much more variable and therefore is averaged over 10 data points (see Sect. 2). Data is shown for α-pinene (07/11/13) evacuation 1 2 (a) (b), limonene evacuation 1 3 (c) (e), ammonium sulfate evacuation 2,4 and 6 (f) (h), 1,3,5-trimethylbenzene evacuation 1 4 (i) (l), α-pinene (14/11/13) evacuation 1 4 (m) (p), β-caryophyllene evacuation 1 3 (q) (s). 35

53 Figure 7: Ratio of WELAS concentration at standard ambient temperature and pressure to model concentration for base case simulations (i.e. simulations using the expectation value for all variable inputs). The base cases from the α-pinene experiment on 07/11/13 and β-caryophyllene show substantial underestimation of the number concentration of aerosol by the model in comparison to the WELAS data while the second limonene evacuation shows the number concentration predicted by the model to be much higher than that from the WELAS data. The variability bars shown relate to an assumed ±10 % uncertainty in the WELAS total number concentration. 36

54 990 Figure 8: Colour plot of the number of Monte Carlo simulation tests as a function of WELAS (at standard ambient temperature and pressure) /model number concentration. The first evacuation on the α-pinene 07/11/13 and all three β-caryophyllene evacuations show the model underestimating the total number concentration compared to the WELAS data. The second limonene evacuation shows the model overestimating the total number concentration compared to the WELAS data. The dark gray lines indicate the maximum and minimum value of the WELAS/model number concentration ratio from the Monte Carlo distribution with an additional variability of ±10 % to account for uncertainty in the WELAS measurements

55 1000 Figure 9: Model/WELAS (at standard ambient temperature and pressure) number concentration ratio is shown for evacuations using the base case variables except for the hygroscopicity variable κ. The red line indicates a ratio of 1. For some evacuations, no value of κ is sufficient to get a ratio less than 1. Each line is labeled with the experiment and the evacuation number, P. refers to α-pinene with the two experiments with α-pinene differentiated by the date (either 7 th or 14 th ), Lim refers to limonene, A.S. refers to ammonium sulfate, TMB to 1,3,5-trimethylbenzene and Caryo. to β-caryophyllene. 38

56 Figure 10: Ratio of maximum concentration in the model using the base case simulation values for all variables to the WELAS concentration observed for all evacuations with varying amounts of semi-volatile material. For each chamber evacuation five bars are shown, from left to right these are with a total semi-volatile concentration of g m -3 (purple), g m -3 (blue) g m -3 (green), g m -3 (orange) and no semivolatiles (red). Each set of five bars is labeled with the experiment and the evacuation number. A range of volatility distributions (see Fig. 11) were used to investigate the effect on activation. Here we show the results for the distribution employed by Cappa and Jimenez (Cappa and Jimenez, 2010). The results from other distributions can be found in the supplementary material (see Fig. S1 S4). The error bars shown assume a ±10 % error in the WELAS concentration. 39