Dataset for "Drivers of Droplet Formation in East Mediterranean Orographic Clouds" by Foskinis et al. (2024)

Atmos. Chem. Phys., 24, 9827–9842, 2024 https://doi.org/10.5194/acp-24-9827-2024 © Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License. Research article Drivers of droplet formation in east Mediterranean orographic clouds Romanos Foskinis1,2,3,4, Ghislain Motos3, Maria I. Gini4, Olga Zografou4, Kunfeng Gao3, Stergios Vratolis4, Konstantinos Granakis4,5, Ville Vakkari6,7, Kalliopi Violaki3, Andreas Aktypis2, Christos Kaltsonoudis2, Zongbo Shi8,9, Mika Komppula10, Spyros N. Pandis2,11, Konstantinos Eleftheriadis4, Alexandros Papayannis1,3, and Athanasios Nenes2,3 1Laser Remote Sensing Unit (LRSU), Physics Department, National Technical University of Athens, 15780 Zografou, Greece 2Center for Studies of Air Quality and Climate Change, Institute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas, 26504 Patras, Greece 3Laboratory of Atmospheric Processes and their Impacts, School of Architecture, Civil & Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland 4Environmental Radioactivity & Aerosol technology for atmospheric & Climate impacT Lab (ENRACT), Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety, National Centre of Scientific Research “Demokritos”, 15310 Athens, Greece 5Climate and Climatic Change Group, Section of Environmental Physics and Meteorology, Department of Physics, National and Kapodistrian University of Athens, Athens, Greece 6Finnish Meteorological Institute, Helsinki, 00101, Finland 7Atmospheric Chemistry Research Group, Chemical Resource Beneficiation, North-West University, Potchefstroom, South Africa 8School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK 9Key Laboratory of Environmental Optics and Technology, Anhui Institutes of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China 10Finnish Meteorological Institute, Kuopio, 70211, Finland 11Department of Chemical Engineering, University of Patras, Patras, Greece Correspondence: Alexandros Papayannis ([email protected]) and Athanasios Nenes ([email protected]) Received: 20 February 2024 – Discussion started: 25 March 2024 Revised: 25 May 2024 – Accepted: 15 June 2024 – Published: 5 September 2024 Abstract. The purpose of this study is to understand the drivers of cloud droplet formation in orographic clouds. We used a combination of modeling, in situ, and remote sensing measurements at the high-altitude Helmos Hellenic Atmospheric Aerosol and Climate Change ((HAC)2) station, which is located at the top of Mt. Helmos (1314m above sea level), Greece, during the Cloud–AerosoL InteractionS in the Helmos Background TropOsphere (CALISHTO) campaign in fall 2021 (https://calishto.panacea-ri.gr/, last access: 1 August 2024) to examine the origins of the aerosols (i.e., local aerosol from the planetary boundary layer (PBL) or long-rangetransported aerosol from the free-tropospheric layer (FTL) contributing to the cloud condensation nuclei (CCN)), their characteristics (hygroscopicity, size distribution, and mixing state), and the vertical velocity distributions and resulting supersaturations. We found that the characteristics of the PBL aerosol were considerably different from FTL aerosol and use the aerosol particle number and equivalent mass concentration of the black carbon (eBC) in order to determine when (HAC)2was within the FTL or PBL based on time series of the height of the PBL. During the (HAC)2 Published by Copernicus Publications on behalf of the European Geosciences Union. 9828 R. Foskinis et al.: Drivers of droplet formation in east Mediterranean orographic clouds cloud events we sample a mixture of interstitial aerosol and droplet residues, which we characterize using a new approach that utilizes the in situ droplet measurements to determine time periods when the aerosol sample is purely interstitial. From the dataset we determine the properties (size distribution and hygroscopicity) of the precloud, activated, and interstitial aerosol. The hygroscopicity of activated aerosol is found to be higher than that of the interstitial or pre-cloud aerosol. A series of closure studies with the droplet parameterization shows that cloud droplet concentration (Nd) and supersaturation can be predicted to within 25% of observations when the aerosol size distributions correspond to pre-cloud conditions. The analysis of the characteristic supersaturation of each aerosol population indicates that droplet formation in clouds is aerosol-limited when formed in FTL air masses – hence droplet formation is driven by aerosol variations, while clouds formed in the PBL tend to be velocitylimited and droplet variations are driven by fluctuations in vertical velocity. Given that the cloud dynamics do not vary significantly between air masses, the variation in aerosol concentration and type is mostly responsible for these shifts in cloud microphysical state and sensitivity to aerosol. With these insights, the remote sensing of cloud droplets in such clouds can be used to infer either CCN spectra (when in the FTL) or vertical velocity (when in the PBL). In conclusion, we show that a coordinated measurement of aerosol and cloud properties, together with the novel analysis approaches presented here, allows for the determination of the drivers of droplet formation in orographic clouds and their sensitivity to aerosol and vertical velocity variations. 1 Introduction Aerosol–cloud interactions hold the largest source of uncertainty in predictions of anthropogenic climate change (IPCC, 2023). A large fraction of this uncertainty arises from impacts of aerosols on cloud droplet formation in liquidand mixed-phase clouds (IPCC, 2013; Lohmann, 2017). High aerosol levels generally lead to increased cloud droplet number and cloud albedo (Twomey, 1974, 1991), but the exact relationship depends on many factors, including cloud dynamics (cloud-scale vertical velocity distributions), aerosol size distribution, and hygroscopicity, while the description of these dependencies in a realistic way in models poses a challenge that the development of large observation datasets can help resolve. Not all clouds are equally sensitive to changes in the cloud condensation nuclei (CCN), i.e., the subset of aerosol that activates into cloud droplets. For clouds to be sensitive to aerosol variations, there needs to be sufficient supersaturation during the stages of cloud droplet formation (Nenes et al., 2001) so that droplet formation can take place. At a relatively low concentration of aerosols, water vapor availability (i.e., supersaturation) is large, so variations in precloud aerosol readily translate to droplet variations. These conditions correspond to aerosol-limited clouds, and the cloud droplet concentration (Nd) is very sensitive to aerosol changes. When CCN concentrations become large, the competition for water vapor required to activate them to cloud droplets becomes so significant that supersaturation is low and Ndbecomes insensitive to aerosol load changes. Under such conditions, clouds are said to be “velocity-limited” (Reutter et al., 2009; Georgakaki et al., 2021) because vertical velocity is the driver of the expansion cooling that generates supersaturation. In cases of extreme competition for water vapor, the droplet number tends to reach a “limiting” value that is solely a function of vertical velocity (e.g., Georgakaki et al., 2021). The differences between the aerosols that are involved in cloud droplet formation (i.e., the CCN) and those that are not, called “interstitial”, are important to understand. Studies focusing on both the activated and the interstitial particles have been carried out on airborne platforms (e.g., Ditas et al., 2012; Kleinman et al., 2012) and at high-altitude stations (Collaud Coen et al., 2018) that can reside in clouds formed on mountain tops, such as Puy de Dôme (Venzac et al., 2009; Asmi et al., 2012), Jungfraujoch (Hammer et al., 2014; Bukowiecki et al., 2016), Storm Peak (Obrist et al., 2008), Mont Sonnblick (Schauer et al., 2016), Monte Cimone (Marinoni et al., 2008; Cristofanelli et al., 2016), and Zeppelin Station (Tunved et al., 2013). The established way to separate interstitial aerosols from cloud droplets is to use a “twin inlet system”, one of which is used for sampling the interstitial aerosols (“interstitial inlet”) and the other for sampling the interstitial and the evaporated cloud droplets (“total” or “whole air inlet”). This sampling strategy is based on the large size difference between droplets and interstitial aerosol, so an appropriate selection of inlet cut-off size for the interstitial inlet allows for the separate collection of interstitial aerosols. The challenge is therefore to correctly select the cut-off diameter/size to avoid mixing droplets with interstitial aerosols in the interstitial inlet, given that droplet size varies considerably between clouds. Hammer et al. (2014) and Krüger et al. (2014) used an interstitial inlet consisting of a cyclone with a 2.5 µm cut-off diameter (PM2.5) to remove droplets, while Portin et al. (2014) and Väisänen et al. (2016) used a PM1(1µm cut-off diameter) impactor nozzle plate to prevent the cloud droplets from entering the sample line. Other studies, such as that of Mertes et al. (2005), Drewnick et al. (2006), and Asmi et al. (2012), have used a 5µm cut-off diameter inlet system. Given the Atmos. Chem. Phys., 24, 9827–9842, 2024 https://doi.org/10.5194/acp-24-9827-2024 R. Foskinis et al.: Drivers of droplet formation in east Mediterranean orographic clouds 9829 large variability in droplet and aerosol sizes, the use of a fixed cut-off size in the interstitial inlet may not always sufficiently separate interstitial aerosols from some evaporated cloud droplets. This can be a problem in velocity-limited clouds with low supersaturation, where interstitial aerosol may have a comparable size to the activated droplets (Charlson et al., 2001). It may also be possible to separate interstitial aerosols from evaporated cloud droplets with a single-inlet (total) system if there are concurrent in situ measurements of droplet size that allow for the application of a temporal filter on the time series (e.g., consider only parts of the time series for which droplets do not pass through the inlet). We explore this technique at the high-altitude Helmos Hellenic Atmospheric Aerosol and Climate Change (HAC)2station at Mt. Helmos, Greece, during the CALISHTO campaign and study the factors driving cloud formation at an orographic site. We specifically examine the origins and sources of aerosols contributing to the CCN (e.g., local aerosol in the PBL or longrange-transported aerosol from the FTL), their characteristics (hygroscopicity, size distribution, and mixing state), and the vertical velocity distributions and resulting supersaturations. Several closure studies are carried out to test the ability to predict the droplet number and supersaturation. 2 Methodology 2.1 Measurement sites Mt. Helmos is the second highest mountain in the Peloponnese (southern Greece), peaking at 2355m above sea level (a.s.l.), while the (HAC)2station is located at the mountain top (coordinates 37.984076°N, 22.196115°E), generally isolated from local human activities and surrounded, at lower altitudes, by lush forests and pristine alpine landscapes (Fig. 1). A second temporary site during CALISHTO, called “Vathia Lakka” (VL), was located at the lee side of the mountain approximately 1.7km away (coordinates 37.999473°N, 22.193391°E) and 500m below (HAC)2. In situ measurements are available at both (HAC)2and VL, the latter being used as a preor post-cloud proxy. Given that (HAC)2can be either within the FTL or within the PBL (Foskinis et al., 2024a), a major parameter controlling the aerosol sampled at any given time is the height of the PBL (PBLH); here we used the PBLH data that were derived according to Foskinis et al. (2024a) based on the turbulence threshold technique applied to the wind Doppler lidar measurements. When the PBLH exceeds the (HAC)2altitude, the site resides within the PBL, which may be rich in biogenic particles originating from the nearby forest and anthropogenic emissions originating from the greater region. When the PBLH is below (HAC)2, the station is in the FTL and receives air masses and aerosol from long-range transport: continental aerosols originating from Europe and the Balkans, marine aerosols from the Mediterranean Sea, and dust from the Sahara (Papayannis et al., 2005, 2008; Kallos et al., 2007; Kaskaoutis et al., 2012; Soupiona et al., 2018). (HAC)2frequently resides in the clouds, about ∼25 % in September to ∼65% in October, and in November and December the (HAC)2cloud coverage is 45 % (Fig. S5 in the Supplement). During the whole period (HAC)2resides about half of the time within the FTL (Foskinis et al., 2024a). 2.2 Dataset and study period The dataset analyzed was collected during the CALISHTO campaign, which was designed to study the cloud microphysical properties using in situ and remote sensing techniques. We focus on October and November 2021, dividing the study period into PBL-influenced and FTL-influenced regimes based on the PBLH time series by Foskinis et al. (2024a). Furthermore, we divide each regime into “cloudy” when the cloud liquid water content (LWC) exceeded 0.02 g m−3 (Prabhakar et al., 2014; Braun et al., 2018; Dadashazar et al., 2018) and “cloud free” otherwise. 2.3 Instrumentation 2.3.1 Inlets The aerosol inlets at (HAC)2are designed to maintain laminar flow and minimize particle losses (vertical orientation, stainless steel tubing) over a wide range of aerosol sizes (<10µm), in line with Aerosol, Clouds and Trace gases Research Infrastructure (ACTRIS) recommendations for aerosol inlets and sampling tubes. For this setup, the diffusion losses are negligible (<1% for particle sizes greater than 100nm) for scanning mobility particle sizer (MPSS), Aethalometer (AE31), and time-of-flight aerosol chemical speciation monitor (ToF-ACSM) measurements. 2.3.2 TROPOS scanning mobility particle sizer (MPSS) A TROPOS (Leibniz Institute for Tropospheric Research) scanning mobility particle sizer (MPSS) was used to measure submicron aerosol size distributions at (HAC)2every 5min. The MPSS employs a Vienna-type differential mobility analyzer (DMA; electrode length 28cm) with a condensation particle counter (CPC model 3772, TSI Inc.) to measure particles ranging from 10 to 800nm. The MPSS operated at a sheath flow rate of 5Lmin−1and an aerosol flow rate of 1Lmin−1. Before detection, the ambient aerosol enters the DMA and passes through an Kr-85 neutralizer to achieve an equilibrium charge distribution. Both the aerosol sample flow and the sheath airflow were dried below 40% relative humidity (RH) using Nafion dryers. The temperature, relative humidity, and pressure inside the instrument are continuously monitored during the sampling process. Additionally, there was a second MPSS (DMA model 3081 and CPC model 3775, TSI Inc.) located at VL, and it was operated at a sheath flow rate of 3Lmin−1and an aerosol flow rate of https://doi.org/10.5194/acp-24-9827-2024 Atmos. Chem. Phys., 24, 9827–9842, 2024 9830 R. Foskinis et al.: Drivers of droplet formation in east Mediterranean orographic clouds Figure 1. The study area (left) and the sub-domain over Greece (middle) and the regional area around HELMOS (right) shown in the red color on the altimeter map given by the NASA Digital Elevation Model (NASA JPL, 2020). The symbols represent the location of the two sites, (HAC)2and VL, while Kalavrita is the closest village to the site. 0.3Lmin−1, and it was equipped with a Nafion dryer to dry the sheath airflow below 40 % relative humidity. The second MPSS was used to provide the pre-cloud proxy in the cases where (HAC)2was in clouds. In both cases, the number size distribution data are corrected for diffusion losses within the aerosol inlet and the instrument’s internal tubing (TROPOS MPSS inversion software) using the method of “equivalent length” (Wiedensohler et al., 2012, 2018). 2.3.3 Time-of-flight aerosol chemical speciation monitor (ToF-ACSM) The ToF-ACSM (Aerodyne Research Inc., Billerica, MA, USA) measures the non-refractory submicron aerosol mass and chemical composition (ammonium, sulfate, nitrate, chloride, and organics) every 10min (Fröhlich et al., 2013). The sampling air enters through a PM2.5virtual impactor, which is followed by a Nafion drier. A 120 µm orifice (for highaltitude measurements) was used for sampling the PM1fraction. An aerodynamic particle focusing lens converts the sample into an air beam under high-vacuum conditions. The non-refractory material is then flash vaporized on a tungsten plate surface at 600°C and is subsequently ionized by electron impact ionization at 70eV. The resulting ions are detected by a TOFWERK time-of-flight mass analyzer. The instrument allows the detection of aerosols of ∼40–1000nm vacuum aerodynamic diameters. A collection efficiency (CE) of 0.3 (Zografou et al., 2024) was chosen based on a comparison of the PM1mass as the sum of the ACSM and eBC concentrations versus the total PM1mass by the MPSS (Fröhlich et al., 2015) using densities of 1.8 g cm−3for sulfates and 1.3gcm−3for organics. The CE accounts for the fraction of the non-refractory particles that bounce off the vaporizer and are not detected. The relative ionization efficiencies for sulfate and ammonium were determined after the calibration of the instrument with ammonium sulfate and ammonium nitrate to be 1.19 and 3.11, respectively. 2.3.4 Aethalometer (AE31) A seven-wavelength (370, 470, 520, 590, 660, 880, and 950nm) Aethalometer (AE31 model, Magee Scientific) measures absorption by light-transmission measurements through a filter tape and was used to determine the eBC concentration following Hansen et al. (1982) and Petzold et al. (2013). We used the 880 nm channel to constrain the equivalent black carbon concentration. eBC is used in this study as an FTL/PBL proxy, since black carbon mostly originates from anthropogenic activities and is expected to be much higher in the PBL than in the FTL; thus when eBC is high, (HAC)2is expected to be within the PBL, while as eBC reduces, (HAC)2is influenced less from the PBL and tends to be within the FTL (Lund et al., 2018; Motos et al., 2020). 2.3.5 Particulate volume monitor (PVM-100) A particulate volume monitor (Gerber Scientific Inc, PVM100) was used to obtain the liquid water content (LWC), the particle surface area (PSA), and the effective droplet radius (Reff) and diameter (Deff) of clouds by measuring the forward scatter of droplets encountered by a diode laser beam along a 40cm path (Gerber, 1984) in an open-path geometry. The signals are then converted to droplet size and number concentration as follows: Nd=1.07 LWC ρwR3 eff (Rezacova et al., 2007). 2.3.6 Cloud condensation nuclei counter (DMT CCN-100) CCN concentrations as a function of supersaturation (“CCN spectra”) were measured with a Droplet Measurement Technologies (DMT) CCN-100 counter, which is based on the continuous-flow streamwise thermal-gradient chamber design of Roberts and Nenes (2005). The instrument generAtmos. Chem. Phys., 24, 9827–9842, 2024 https://doi.org/10.5194/acp-24-9827-2024 R. Foskinis et al.: Drivers of droplet formation in east Mediterranean orographic clouds 9831 ates supersaturation through the principle of relative diffusion of heat and water vapor. An aerosol sample, surrounded by filtered sheath air, flows through a cylindrical, metallic tube in the axial direction with wetted inner walls and a positive streamwise temperature gradient. The relative diffusion of water and heat from the tube walls towards the centerline generates a supersaturation (s) that peaks at the centerline. The value of this maximum supersaturation depends on the flow rate, streamwise temperature gradient, and pressure (Roberts and Nenes, 2005; Lance et al., 2006). Part of the aerosol sample, which is mostly located around the centerline, becomes activated and grows to sizes (0.75–10µm diameter) large enough to be detected at the exit of the flow tube by an optical particle counter (OPC). The CCN concentration then at the centerline sis equal to the number concentration of droplets measured in the OPC. By changing the streamwise temperature gradient every 6min (and ignoring data collected during the instrument transients), we cycle through 0.1% up to 1% with a supersaturation step of 0.1% to obtain a CCN spectrum every hour. 2.3.7 Wind Doppler lidar The vertical velocity of the air was derived by using a wind Doppler lidar (StreamLine XR, HALO Photonics) operating in stare mode (Pearson et al., 2009). It was installed at the VL site in order to obtain the updraft currents towards (HAC)2and the surrounding area. Excluding precipitation, the HALO lidar provides vertical velocity (w) at 30m range resolution. The range of the HALO StreamLine XR lidar is 12km, but during the campaign the maximum range of useful signal varies from 2 to 3km depending on the atmospheric aerosol load. Vertical stare was configured at 5s integration time, alternating between coand cross-polar receiver. In addition to the vertical stare, velocity azimuth display (VAD) scans were included but are not utilized here. The HALO lidar is a pulsed Doppler lidar that operates at 1.5 µm wavelength (Pearson et al., 2009). The backscattered frequency of each pulse shifts due to the “Doppler effect”, which depends on the relative motion of the scatterer and HALO lidar (Newsom and Krishnamurthy, 2020). The backscattered fraction of the initial pulse is collected back from the HALO lidar and analyzed as a timeand frequency-resolved signal. The time delay between each outgoing and backscattered pulse indicates the distance of the scatterers, while the Doppler shift reveals the radial velocity of the scatterer, which corresponds to the aerosol velocity at the given height. Following Barlow et al. (2011) and Newsom and Krishnamurthy (2020), we excluded the data with a signal-tonoise ratio (SNR) lower than −20dB, which limits instrumental uncertainty in wto 0.1ms−1at maximum (Pearson et al., 2009). Then, we generated segments containing datasets with a 30min moving window (Schween et al., 2014) of the noise-filtered dataset for every 5min (Lenschow et al., 2012), and we calculated the standard deviation of w(σw) for every height. Considering that a convective plume within the PBL has on average an ascent speed of 1ms−1and that the typical mixing layer height at our site is about 1 km, then the average interval is about twice the lifetime of the plume. This is typical of the derivation of turbulent fluxes from eddy covariance stations (Schween et al., 2014). The 30min window is comparable to the average mixing time in the boundary layer. 2.4 Modeling 2.4.1 Aerosol hygroscopicity and critical supersaturation Two approaches are used to constrain the aerosol hygroscopicity. First, we determine the bulk hygroscopicity parameter (κ) of the submicron aerosol (Petters and Kreidenweis, 2007) using the measured chemical composition and the approach outlined in Padró et al. (2010). This involves applying the ISORROPIA II thermodynamic equilibrium model (Fountoukis and Nenes, 2007) using as inputs the observed inorganic components measured by ToF-ACSM to calculate the composition (e.g., NH4HSO4, NH4Cl, NH4NO3, (NH4)2SO4, H2SO4) of the inorganic aerosol fraction. The Zdanovskii, Stokes, and Robinson (ZSR) mixing rule was then applied to the volume fraction of the inorganic salts, including the volume fraction of eBC and hygroscopicity parameters from Table 2 of Padró et al. (2010), while the hygroscopicity value of eBC was considered equal to 0.2 based on Ding et al. (2021). Second, we determined the characteristic hygroscopicity parameter (κ∗) which was explicitly obtained from the combination of the CCN-100 and MPSS data based on the CCN spectrum cycles. For each supersaturation cycle we calculated the characteristic critical supersaturation (s∗) (Cerully et al., 2011), which is defined as the supersaturation at which half of the CCN population is activated to droplets and is determined using the procedure of Cerully et al. (2011), and the characteristic size (D∗ cr), which is defined as the corresponding MPSS distribution integrated from the largest resolved size (800nm) until the D∗ cr to give an aerosol number equal to the observed CCN concentration. Then, the κ∗of each CCN cycle was determined from κ-Köhler theory (Petters and Kreidenweis, 2007) as κ∗=4A3 27D∗ cr3s∗2, where A=4Mwσ R T ρwis the Kelvin parameter, Mw(kg mol−1) is the molar mass of water, σ(J m−2) is the surface tension of the activated droplets (here assumed to be equal to pure water), R(Jmol−1K−1) is the universal gas constant, T(K) is the ambient temperature, ρwis the density of liquid water, and D∗ cr is the characteristic dry size of the particle that activates at supersaturation s∗. Both approaches give similar results (not shown here) but occasionally diverge. For instance, the bulk hygroscopicity assumes that particles are internally mixed, which for periods of sampling in the FTL is an excellent assumption (e.g., Bougiatioti et al., 2016); for PBL-dominated periods it is also a reasonable assumption due to the remote location of the https://doi.org/10.5194/acp-24-9827-2024 Atmos. Chem. Phys., 24, 9827–9842, 2024 9832 R. Foskinis et al.: Drivers of droplet formation in east Mediterranean orographic clouds station, while the characteristic hygroscopicity strictly corresponded to the average hygroscopicity for particles of size D∗ cr and was used to characterize the hygroscopicity of the resolved CCN spectrum. During periods when particles at the station will originate from both PBL and FTL, it is expected that they have an intermediate mixing state that will introduce some uncertainty in subsequent calculations. 2.4.2 Droplet activation parameterization We use a physically based aerosol activation parameterization developed by Nenes and Seinfeld (2003) and further expanded by Fountoukis and Nenes (2005), Barahona et al. (2010), and Morales Betancourt and Nenes (2014) to calculate the droplet number formed in clouds using the wind vertical velocity and the aerosol characteristics. The droplet activation parameterization solves the equations of motion of an ascending air parcel which contains aerosols and water vapor and calculates the point where the supersaturation is maximum (smax) as well as the corresponding droplet number, Nd. The parameterization inputs include pressure, temperature, aerosol size distribution measured by the MPSS, the bulk hygroscopicity parameter κ, and the updraft velocity obtained by the HALO lidar. Given that the latter varies considerably over time and within each cloud event, we consider a probabilistic approach by first computing the hourly probability density function (PDF) of vertical velocity. We then apply the parameterization to calculate the PDF-integrated Nd, which is assumed to describe the average droplet number in clouds that form in the vicinity of the sampling site. This PDF-averaging approach has been shown to successfully reproduce cloud-scale values of Ndin numerous field studies in the case of cumulus and stratocumulus clouds (Conant et al., 2004; Meskhidze et al., 2005; Fountoukis and Nenes, 2007; Kacarab et al., 2020; Georgakaki et al., 2021; Foskinis et al., 2022). The PDF-integrated Ndis computed using the characteristic velocity (w∗) approach of Morales and Nenes (2010), in which the parameterization is applied once using w∗in its input but provides directly the PDF-averaged droplet number. w∗is given by (Foskinis et al., 2022) w∗=ε˜ λσw,(1) where σwis the standard deviation of the vertical velocity PDF (assumed to be a Gaussian with zero mean), εis the entrainment parameter, and ˜ λis the characteristic nondimensional velocity. The entrainment parameter (ε) accounts for lateral diabatic mixing of entrained air in the updraft zones – which effectively reduces smax, hence w∗.ε=1 corresponds to adiabatic updrafts, but the parameter can be lower in the case of cumulus and convective clouds, affecting the vertical distribution of liquid water and the number of droplets (Morales et al., 2011). Based on numerous in situ sampling campaigns of boundary layer clouds, εhas been found to be on average 0.68. According to Morales and Nenes (2010), ˜ λis affected by the total aerosol concentration (NTotal), and it is assigned values equal to 0.70, 0.79, 0.84, and 0.98, when NTotal ranges between 0–340, 340–500, 500–6400, and 6400–106000cm−3, respectively. 3 Experimental results 3.1 Dataset overview Figure 2a shows when (HAC)2is in the FTL and when it is in the PBL based on the relative position of the PBLH (given by Foskinis et al., 2024a) against the altitude of the (HAC)2station (shown with the horizontal black line). When the station is in the FTL, the PBLH is below the (HAC)2 line and vice versa when it is in the PBL. The air mass typing is consistent with the observed moisture content, since under cloud-free conditions, the FTL air masses are markedly dryer (RH=34±26%) than the PBL air masses (RH=65±16%) (Fig. 2c). Also, when (HAC)2is in the FTL, we observed two dominant wind directions, one at 30°N and one at 80°N, where for both NTotal approaches the lowest values are observed (∼45cm−3) (Fig. S2d), and eBC levels approach its detection limit (∼0.01 µg m−3). These wind directions are directly related to the long-rangetransported air mass; when arriving from the north, it usually originates from eastern Europe and the Balkans and is rich in sulfur (Stavroulas et al., 2021). When the air mass arrives from the east or southeast, it often carries dust aerosols (Gao et al., 2024). Additionally, we identified three prevailing wind directions that correspond to the local transport patterns (Fig. S2f) from 90, 180, and 320°N. We also found that the NTotal tends to maximize (>1000 cm−3) when the wind blew from 225–270°N and air masses originated from the PBL. For the remaining wind directions, NTotal varied between 300–1000cm−3(Fig. S2f), and the eBC values increase up to ∼0.4µgm−3when the wind speed exceeds 6ms−1and becomes maximum when the wind blows from 160–220°N (Fig. S2c). Additionally, we found a dependence of the PBLH on the wind direction, since when the wind passes over mountain tops before reaching the site, the PBLH tends to be higher (Fig. S2a) and the σwtends to be lower (Fig. S2b). We observed that the increase in NTotal (from ∼250 to ∼750cm−3) (Fig. S3a) leads to an increase in Nd(from ∼100 to ∼300cm−3) (Fig. S3b) and decrease in the cloud droplet size (Deff) (from ∼17.5 to ∼10µm) (Fig. S3c), consistent with the Twomey effect (Twomey, 1977) of aerosols on clouds (IPCC, 2023). 3.2 Separating interstitial aerosol from cloud droplet residuals at (HAC)2 Aerosol particles that act as CCN have a dry diameter on the order of 100nm and grow at least 10-fold when they acAtmos. Chem. Phys., 24, 9827–9842, 2024 https://doi.org/10.5194/acp-24-9827-2024 R. Foskinis et al.: Drivers of droplet formation in east Mediterranean orographic clouds 9833 Figure 2. Time series of characteristic parameters for the CALISHTO campaign. (a) PBLH (circles) and filled colors correspond to the eBC. If PBLH is below (HAC)2(horizontal black line), (HAC)2is within the FTL (eBC <0.01µg m−3– blue color), and vice versa if in the PBL (eBC>0.01µgm−3, red color). On the right-hand-side axis, the σwdrives the PBLH. (b) The moments when (HAC)2is within FTL (blue color) and within PBL (green color). (c) The ambient air temperature trace is colored by the RH (in the FTL when <40 %, and vice versa when inside the PBL). The horizontal wind speed is given by the circles colored by the horizontal wind direction in gray scale, and the ambient pressure is presented by the magenta line. (d) NTotal measured at (HAC)2(colored by the eBC amount) and the NTotal measured at VL (black line). NTotal measured at (HAC)2comparable to the one of VL implies both sites reside in the same atmospheric layer – also indicated by a high concentration of eBC. SO2− 4concentration is presented by the green symbols and is also used as a proxy for (HAC)2 being in the PBL. (e) Ndcolored by the Deff, while on the right axis is the LWC. (f) The CCN time series at different supersaturation values (s=0.1, 0.3, and 0.7). https://doi.org/10.5194/acp-24-9827-2024 Atmos. Chem. Phys., 24, 9827–9842, 2024 9834 R. Foskinis et al.: Drivers of droplet formation in east Mediterranean orographic clouds tivate into cloud droplets (Fig. 3) (Rogers and Yau, 1996; Pierce et al., 2015). Indeed, when the (HAC)2station was in clouds, the droplet effective diameter, Deff, varied between 2 and 15µm (Fig. 2). When the clouds are formed with FTL aerosol, the Deff was on average 17.0±2.7 and 10.3±1.9 µm when formed with PBL aerosol. The average size differences between the two types of air masses can be explained by the different CCN concentrations in them. The FTL has fewer CCN, and hence droplets are generally larger than in PBL air masses (Fig. 2f). In both cases, the aerosol inlet (which is a PM10 inlet – 10µm cut-off diameter) would sample inactivated (known as “interstitial”) aerosol together with some of the droplets. These droplets subsequently evaporate in the heated inlet and contribute to the size distribution and other aerosol characteristics measured by the online in situ aerosol instrumentation. This means that when the station is in-cloud, the aerosol sampled from the PM10 inlet corresponds to a mixture of interstitial aerosol and evaporated cloud droplet residuals. However, Deff varies considerably during a cloud event (Fig. 2e) and often exceeds 10µm. This implies that considering subsets of the in-cloud time series when the Deff is large enough can ensure that during these periods the PM10 inlet samples only interstitial aerosol, as droplets would be too large to pass through the inlet. We therefore consider the above concept and develop a “virtual filter” technique to define the Deff threshold (measured in situ and continuously by the PVM-100) that ensures that the aerosol sampled by the PM10 inlet does not contain aerosol from evaporated droplets but only interstitial aerosols. In applying this filter, we ignore periods of the respective measurements during which the in situ Deff of the droplets is less than the threshold. We select the periods during which we were sampling at least 30min continuously in cloud-free conditions followed by (or proceeded by) at least 30min of cloudy conditions to allow multiple size distribution measurements during the pre-/post-cloud and the in-cloud phases. Additionally, the 30min sampling time was found to be a “best choice” to obtain a large number of samples that at the same time are subject to the least statistical noise from the natural variability occurring in each cloud event. Hence, the distributions under cloud-free conditions are then averaged to give the “total aerosol distribution”. The in-cloud distributions are averaged for periods when the droplet Deff exceeds a predefined threshold (starting from 0µm). The in-cloud distributions are averaged for different values of the Deff threshold until 16 µm. We select as the optimum Deff the minimum value above which the measured aerosol size distribution becomes insensitive to the chosen threshold value. Figure 3 displays an example of this process applied to a segment of data from CALISHTO. We find in this case that a Deff threshold of 13.5µm is the minimum for which the observed size distribution stopped to be sensitive to further increases in the cutoff size (Fig. 3a). Additionally, we compare the difference between the aerosol size distribution pre-cloud and the interstitial aerosol distribution (i.e., with the application of the 13.5µm threshold) with the observed droplet number, and we found that indeed the integrated difference between these distributions (from ∼70 nm to the largest sizes measured by the MPSS) matches with the droplet number obtained in situ with the PVM-100 to within ±25% (Fig. 3b). Thus, we confirm that this threshold is consistent with allowing only the interstitial aerosol to pass through the inlet. Given that in situ closure studies often involve this degree of uncertainty (e.g., see relevant discussion by Foskinis et al., 2024a, and relevant references cited therein) in addition to any other uncertainties that may exist at this particular site (e.g., variations in aerosol entering the cloud, sampling efficiency of the inlet, and uncertainties in the droplet number determination with the PVM-100), we conclude that the latter distribution is indeed representative of the interstitial aerosol. Additional support for this conclusion is provided later by the ability to predict cloud droplet number (Sect. 3.4) as it requires the correct parameters of hygroscopicity, size distribution, and vertical velocity. 3.3 Differences among the properties of total, activated, and interstitial aerosol We identified more than 20 periods of cloud-free and cloudy transitions during the CALISHTO campaign. We applied the methodology of Sect. 3.2 to estimate the corresponding cloud-free, interstitial-only, and mixed-aerosol (cloud residues and interstitial combined) size distributions. We then determined s∗and κ∗(Sect. 2.4.1) of the cloud-free (κcf), interstitial (κi), and interstitial and droplet residue aerosol mixture (Fig. 4a). Assuming that the pre-/post-cloud hygroscopicity is the volume-average hygroscopicity of the interstitial and activated aerosol, we estimate the hygroscopicity of the activated aerosol, κa, using the mixing rules of Petters and Kreidenweis (2007) as κa=κcf Vcf−κiVi Vcf−Vi, where Vcf is the total volume of pre-/post-cloud aerosols and Viis the total volume of interstitial aerosols. The estimation of κa assumes that all populations are internally mixed, and the activated cloud aerosol plus the interstitial aerosol is equal to the pre-/post-cloud aerosol volume. This is a reasonable assumption given that Brownian losses affect the smallest particle sizes, which have a minor contribution to the aerosol volume. Figure 4 presents the results of our analysis. Figure 4a shows the characteristic supersaturation, s∗, for each aerosol population. Typically, s∗is higher for interstitial aerosol and lower for the mixed aerosol. This is consistent with the expectation that particles that activate to form droplets tend to be more hygroscopic than the interstitial aerosol (e.g., Cerully et al., 2011). Indeed, during periods when cloud formation is influenced by FTL air masses, the average κ∗was 0.34±0.09 % for pre-/post-cloud, 0.31 ±0.15 % for interstitial, and 0.45±0.20 % for activated aerosol. During periods that clouds were forming on PBL aerosol, the average κ∗ Atmos. Chem. Phys., 24, 9827–9842, 2024 https://doi.org/10.5194/acp-24-9827-2024 R. Foskinis et al.: Drivers of droplet formation in east Mediterranean orographic clouds 9835 Figure 3. (a) The averaged dN/dlogDpsize distribution of the captured cloud transition moments when using different cut-off threshold values, the so-called “sensitivity analysis”, and the cut-off threshold (Deff as measured in situ with the PVM-100) that was applied is represented by the color scale. (b) The integrated difference between pre-cloud and in-cloud aerosol size distributions from ∼70nm to the largest sizes when compared to the droplet number measured in situ concurrently by the PVM-100. Each symbol corresponds to the application of a different Deff threshold (as indicated by the symbol color, using the same scheme as in a), while the error bars correspond to the standard deviation. Figure 4. Daily averaged (a) s∗and (b) κ∗of the aerosol in cloud-free regimes, of the mixture of interstitial aerosols and some droplets residues, and of the activated aerosols; (c, d) the distributions of κ∗and s∗when the air mass originates from the FTL or the PBL, respectively. was 0.43±0.12 % for pre-cloud, 0.29 ±0.19 % for interstitial, and 0.44±0.18 % for activated aerosol (Fig. 4b). The average s∗(Fig. 4c) during periods where cloud formation is influenced by FTL air masses was equal to 0.56±0.21 % and 0.59 ±0.22 %, while during PBLinfluenced periods, it was found to be 0.27±0.18 % and 0.28±0.16 % for the pre-/post-cloud and interstitial aerosols, respectively. These results showed little sensitivity to air mass origins, i.e., FTL or PBL. Clearly, the interstitial aerosol is less hygroscopic on average, and the activated aerosol can be up to twice as hygroscopic. This is important for understanding the role of cloud processing in the aging of particles and transferring hygroscopic material to evaporated cloud residuals. Analysis of s∗of the activated aerosol can provide important insights about cloud formation when FTL or PBL air masses are involved. The critical supersaturation of most of the activated cloud droplet residuals should be close to the maximum supersaturation in the cloud. Indeed, when cloud smax is high, droplet formation is aerosol-limited and vice versa when droplet formation is velocity-limited. According to Georgakaki et al. (2021) and Motos et al. (2023), clouds are velocity-limited when the smax is ∼0.15% or lower and aerosol-limited otherwise. Indeed, using s∗of the activated aerosol population as a proxy for smax (which is supported by the analysis of Sect. 3.4), we see that clouds formed from FTL air masses have s∗>0.5%; hence the corresponding clouds are highly aerosol-sensitive. 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