Dataset for "Biological and Dust Aerosols as Sources of Ice-nucleating Particles in the Eastern Mediterranean" by Gao et al. (2024)

Atmos. Chem. Phys., 24, 9939–9974, 2024 https://doi.org/10.5194/acp-24-9939-2024 © Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License. Research article Biological and dust aerosols as sources of ice-nucleating particles in the eastern Mediterranean: source apportionment, atmospheric processing and parameterization Kunfeng Gao1, Franziska Vogel2,a, Romanos Foskinis1,3,4,5, Stergios Vratolis4, Maria I. Gini4, Konstantinos Granakis4, Anne-Claire Billault-Roux6, Paraskevi Georgakaki1, Olga Zografou4, Prodromos Fetfatzis4, Alexis Berne6, Alexandros Papayannis1,3, Konstantinos Eleftheridadis4, Ottmar Möhler2, and Athanasios Nenes1,5 1Laboratory of Atmospheric Processes and Their Impacts, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland 2Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany 3Physics Department, Laser Remote Sensing Unit (LRSU), National Technical University of Athens, Zografou, Greece 4ENvironmental Radioactivity&Aerosol Technology for atmospheric and Climate ImpacT Lab, INRASTES, NCSR Demokritos, 15310 Agia Paraskevi, Attica, Greece 5Centre for Studies of Air Quality and Climate Change, Institute of Chemical Engineering Sciences, Foundation for Research and Technology – Hellas, Patras, Greece 6Environmental Remote Sensing Laboratory (LTE), School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland anow at: Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Bologna, Italy Correspondence: Kunfeng Gao (kunfeng.g[email protected]) and Athanasios Nenes ([email protected]) Received: 23 February 2024 – Discussion started: 27 February 2024 Revised: 30 June 2024 – Accepted: 8 July 2024 – Published: 9 September 2024 Abstract. Aerosol–cloud interactions in mixed-phase clouds (MPCs) are one of the most uncertain drivers of the hydrological cycle and climate change. A synergy of in situ, remote-sensing and modelling experiments were used to determine the source of ice-nucleating particles (INPs) for MPCs at Mount Helmos in the eastern Mediterranean. The influences of boundary layer turbulence, vertical aerosol distributions and meteorological conditions were also examined. When the observation site is in the free troposphere (FT), approximately 1 in ×106aerosol particles serve as INPs around −25°C. The INP abundance spans 3 orders of magnitude and increases in the following order: marine aerosols; continental aerosols; and, finally, dust plumes. Biological particles are important INPs observed in continental and marine aerosols, whereas they play a secondary, although important, role during Saharan dust events. Air masses in the planetary boundary layer (PBL) show both enriched INP concentrations and a higher proportion of INPs to total aerosol particles, compared with cases in the FT. The presence of precipitation/clouds enriches INPs in the FT but decreases INPs in the PBL. Additionally, new INP parameterizations are developed that incorporate the ratio of fluorescent-to-nonfluorescent or coarse-to-fine particles and predict >90% of the observed INPs within an uncertainty range of a factor of 10; these new parameterizations exhibit better performance than current widely used parameterizations and allow ice formation in models to respond to variations in dust and biological particles. The improved parameterizations can help MPC formation simulations in regions with various INP sources or different regions with prevailing INP sources. Published by Copernicus Publications on behalf of the European Geosciences Union. 9940 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean 1 Introduction Clouds in the atmosphere can be composed solely of liquidwater droplets, ice crystals or a mixture of both (mixed-phase clouds; MPCs). The cloud phase regulates the optical properties and microphysical characteristics of clouds, further influencing their impacts on the hydrological cycle and climate (Tan et al., 2016; Lohmann and Neubauer, 2018; Zhou et al., 2022). Modulation of cloud properties by anthropogenic particles is one of the leading sources of uncertainty in anthropogenic climate change (e.g. Seinfeld et al., 2016). MPCs are ubiquitous (D’Alessandro et al., 2019) but have a much more uncertain impact on climate compared with single-phase clouds (Bjordal et al., 2020). This uncertainty stems from the large number of interactions that can take place among liquid droplets, ice crystals and water vapour – each of which can cool or warm the climate. Furthermore, MPCs exhibit considerable dynamic variability over time and space, as they are thermodynamically unstable. Under supercooled conditions, the saturation vapour pressure with respect to ice (Si) is higher than that with respect to water (Sw), which favours the mass transfer – through deposition from the vapour phase – of liquid water from particles onto ice existing in MPCs, i.e. the growth of the latter at the expense of the former. This process is known as the Wegener–Bergeron–Findeisen (WBF) process (Wegener, 1912; Bergeron, 1935; Findeisen, 1938; Findeisen et al., 2015). The number of ice crystals in MPCs is also regulated by the abundance of aerosol particles capable of initiating ice formation, i.e. ice-nucleating particles (INPs). INPs can trigger primary ice formation in the MPC regime (Kanji et al., 2017; Burrows et al., 2022; Knopf and Alpert, 2023) with the absence of spontaneous ice formation via the homogeneous freezing of solution droplets, which requires temperatures (T) lower than the homogeneous nucleation temperature (Barahona and Nenes, 2009; Lohmann et al., 2016). Thus, the heterogeneous ice nucleation of INPs can lead to MPC glaciation. An added complexity is ice multiplication (or secondary ice processes, SIPs) occurring in warmer MPCs, which can multiply ice crystal numbers by orders of magnitude above the primary ice levels generated by INPs (Field et al., 2017; Sullivan et al., 2018; Georgakaki et al., 2022; Pasquier et al., 2022). Therefore, constraining the abundance and origin of INPs is critical for understanding MPC formation and the effects of MPCs on the hydrological cycle and climate. MPCs persistently exist in mountainous terrain where local and remote air masses may be present (PousseNottelmann et al., 2015; Lohmann et al., 2016; Henneberg et al., 2017). Different air masses (e.g. continental pollution, dust plumes and sea spray aerosols from remote marine areas) may contain distinct INP populations with a characteristic abundance and ice formation ability (DeMott et al., 2010; Tobo et al., 2013; McCluskey et al., 2018; Brunner et al., 2021). The INP type, which depends on its air mass source, is crucial for determining MPC formation. Different types of INPs nucleate ice in different Tregimes. Biological particles, as effective INPs, are active at Tvalues warmer than −15°C, whereas dust particles generally form ice at Tvalues lower than −15°C (Murray et al., 2012). In addition, the formation and evolution of MPCs over orographic terrain are influenced by the planetary boundary layer height (PBLH) (Miltenberger et al., 2020), which regulates the aerosol sources depending on whether the observation site is inside or outside of the planetary boundary layer (PBL) (Conen et al., 2015; Wieder et al., 2022). In situ observations at high altitudes in mountainous terrain provide the possibility to specifically investigate INP populations relevant for MPCs under different atmospheric conditions, given that the relative position of a mountaintop in the atmosphere can vary with a changing PBLH (Foskinis et al., 2024). We study the source of INPs and the characteristics of different INP sources relevant for orographic MPCs in the eastern Mediterranean region. A field campaign, the CloudAerosoL InteractionS in the Helmos background TropOsphere (CALISHTO), was conducted at the Helmos Hellenic Atmospheric Aerosol and Climate Change (“(HAC)2” hereafter) station (37.9843°N, 22.1963°E; 2314 m a.s.l., metres above sea level) close to the summit of Mount Helmos. It is reported that (HAC)2is a station among 12 in Europe with the lowest impacts from the PBL (Collaud Coen et al., 2018), suggesting that it is an appropriate station to study INPs from remote sources and to evaluate the characteristics of INPs under background conditions, e.g. in the free troposphere (FT). This study presents the observations of INPs and aerosol properties at (HAC)2during the CALISHTO campaign from a period between 12 October and 27 November 2021. The objectives of this study are twofold. First, we aimed to identify different INP sources at Mount Helmos and evaluate the characteristics of these INP sources. To this end, a synergy of in situ aerosol property measurements, remote-sensing measurements and model simulations of air mass trajectories were used to identify different INP sources. In addition, the influence of precipitation and/or clouds on the INP characteristics was also investigated, considering that precipitation or clouds may serve as sinks or sources of aerosol particles (Isokääntä et al., 2022; Khadir et al., 2023), thereby impacting the INP abundance of the source. Second, we aimed to use the data and analysis carried out to evaluate existing INP parameterizations and propose new ones that more successfully capture the INP number concentration (NINP) for the wide diversity of particle types encountered at Mount Helmos during CALISHTO. Compared with different parameterizations reported in the literature (DeMott et al., 2010, 2015; Niemand et al., 2012; Tobo et al., 2013; Ullrich et al., 2017; McCluskey et al., 2018), the improvement provided by new INP parameterizations is based on the advantage proAtmos. Chem. Phys., 24, 9939–9974, 2024 https://doi.org/10.5194/acp-24-9939-2024 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean 9941 vided by the inclusion of source characteristics, i.e. the partitioning of fluorescent and non-fluorescent (or coarse and fine) particles, yielding important implications for modelling studies aimed at quantifying climate effects of cloud–aerosol interactions in MPCs. 2 Methods 2.1 Overview of field campaign (HAC)2is an atmospheric monitoring station that has been contributing data to the Global Atmosphere Watch (GAW) and Aerosols, Clouds and Trace gases Research Infrastructure (ACTRIS) since 2016 (Laj et al., 2020); it is located near the summit of Mount Helmos, at the heart of the Peloponnese, in Greece. (HAC)2is frequently situated in the FT or at the FT–PBL interface (Foskinis et al., 2024), and it is also frequently covered by clouds in the fall and springtime. These conditions allow for the in situ study of aerosol–cloud interactions for warm and MPC clouds. (HAC)2is also located at a crossroads of different air masses, including continental pollution, Saharan dust events, long-range-transported biomass burning and marine sea spray aerosols. This allows one to explore the effects of different aerosol types on cloud formation, as is done here. The experimental set-up of the CALISHTO observations is presented in Fig. 1, including in situ ice nucleation (IN) experiments; observations of aerosol properties (size distribution, chemical composition, fluorescent and optical properties) at (HAC)2; remotesensing measurements conducted at Vathia Lakka (VL), a site 500m lower than (HAC)2; and back-trajectory analysis to calculate the origin of air masses sampled at (HAC)2. Furthermore, meteorological standard parameters recorded at (HAC)2were used to correct the measured INP and aerosol particle number concentrations to values under the equivalent atmospheric standard conditions (i.e. per standard volume of sampled air, hereafter denoted using “std”). 2.2 INP observations 2.2.1 Offline INP observation The Ice Nucleation Spectrometer of the Karlsruhe Institute of Technology (INSEKT) freezing assay (Schiebel, 2017; Schneider et al., 2021) was used to measure the immersion freezing of INPs from 0 to −25°C (Fig. 1). INSEKT measures the freezing Tof small water volumes (50 µL) that contain aerosol suspended in them. To prepare the freezing aliquots, aerosol particles were first sampled onto filters (0.2µm Whatman Nuclepore track-etched polycarbonate membranes, 47mm, with a flow rate of 9Lmin−1) from an omnidirectional total inlet at (HAC)2. They were then extracted in Nanopure water that had been filtered beforehand through a 0.1µm Whatman syringe filter. The aerosol suspension was diluted using two different ratios, 15 :1 and 225 :1, and pipetted into two 96-well polymerase chain reaction (PCR) plates. In addition to the original solution and the dilutions, some of the wells (∼32) were filled with Nanopure water for freezing background tests. The PCR plates were then placed in an aluminium block cooled by an ethanol chiller to perform freezing experiments, and the frozen fraction of the prepared aliquots was recorded as a function of T. Following the analysis protocol reported in Vali (1971) and Vali (2019), the INP concentration of aerosol samples (in particlesL−1) as a function of Tcan be calculated using the tested frozen fraction, the sampled aerosol volume, the suspended liquid volume and the dilution ratio. The sampling time of each filter sample was approximately 24h, although a few filter samples had a longer sampling time. Detailed information on each INSEKT filter will be provided in a follow-up overview paper for the CALISHTO campaign. 2.2.2 Online INP observations A portable ice nucleation experiment (PINE) (Möhler et al., 2021) chamber was used for automated real-time observations of INPs at (HAC)2(Fig. 1). The PINE chamber is designed on the basis of the expansion cooling of air parcels (Möhler et al., 2003), where ambient air (10 L) is sampled into a pre-cooled cloud chamber after passing through Nafion dryers to remove excess moisture and avoid chamber frosting. The air is then expanded and cools down until supersaturation is reached, causing the aerosol particles in the sample to form supercooled droplets and/or ice crystals. Both the number concentration and phase of aerosol particles are monitored, so INPs that activate to ice crystals can be differentiated from droplets and counted. The PINE instrument can be operated in repeated cycles by refilling the cloud chamber with fresh aerosol at the end of an expansion. In this study, the PINE chamber was operated in a Trange from −23 to −28°C and at a saturation ratio with respect to water (Sw) >1.0 to measure INPs activating as ice in all freezing mechanisms. A single PINE expansion cycle has a time resolution of 6min, and the INP number concentration detection limit for a single experiment is approximately 0.5 particlesL−1; averaging over an hour of samples reduces the detection limit to approximately 0.05 particlesL−1(Möhler et al., 2021). The same sampling inlet was used for both the PINE and INSEKT instruments. 2.3 Aerosol property measurements 2.3.1 In situ aerosol property monitoring Ambient air was sampled through a total inlet for in situ aerosol property measurements at (HAC)2(Fig. 1). A downstream inlet with an impaction stage supplies particulate matter (aerosol) with an aerodynamic diameter of less than 10µm (PM10) to a scanning mobility particle sizer (SMPS; Vienna-type differential mobility analyser, DMA, and condensation particle counter, CPC; model 3772, TSI Inc., USA) https://doi.org/10.5194/acp-24-9939-2024 Atmos. Chem. Phys., 24, 9939–9974, 2024 9942 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean Figure 1. Overview of the instrumentation set-up for the CALISHTO campaign. 1: (HAC)2represents the mountaintop station where in situ measurements were performed, including ice nucleation measurements and aerosol property measurements using a portable ice nucleation experiment (PINE) instrument, a scanning mobility particle sizer (SMPS), an aerodynamic particle sizer (APS), a wideband integrated bioaerosol sensor (WIBS), a nephelometer, an Aethalometer, and a time-of-flight aerosol chemical speciation monitor (ToFACSM). Filters were collected for offline analysis using the Ice Nucleation Spectrometer of the Karlsruhe Institute of Technology (INSEKT). 2: VL represents the Vathia Lakka site at the base of Mount Helmos, at an altitude of ∼1.8km, at which a HALO wind lidar and a frequencymodulated continuous wave (FMCW) Doppler radar (working at 94GHz) were placed for remote sensing of wind fields, aerosols and clouds. 3: Modelling products include the FLEXible PARTicle dispersion model (FLEXPART) to determine the source regions of aerosol particles reaching the site, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to acquire air mass atmospheric trajectories and the SKIRON model to obtain dust forecasts. PBL is the planetary boundary layer, FT is the free troposphere and PBLH stands for PBL height relative to VL. and an aerodynamic particle sizer (APS; model 3321, TSI Inc., USA) to measure the aerosol particle size distribution range from 10 to 800nm (electrical mobility diameter) and from 0.5 to 20µm (aerodynamic diameter), respectively. The number concentration of aerosol particles larger than 95nm (SMPS N>95nm) was calculated from the SMPS data and used with a threshold value (100stdcm−3) to determine if (HAC)2is inside or outside of the PBL (Herrmann et al., 2015; Brunner et al., 2021), given that large aerosol particles are much rarer outside of the PBL. This threshold is confirmed by other methods determining the PBLH using wind lidar and other data (Foskinis et al., 2024). The number concentrations of total, coarse (>1.0µm, aerodynamic diameter) and fine (<1.0µm) particles were also calculated based on APS data, termed TotalAPS, CoarseAPS and FineAPS, respectively. In addition, the combined aerosol particle distribution observed by both SMPS and APS was calculated using the method reported in Khlystov et al. (2004). Accordingly, the number concentrations of total particles (TotalSMPS+APS) and total fine particles (FineSMPS+APS < 1.0µm, aerodynamic diameter) observed by both SMPS and APS were also calculated. A wideband integrated bioaerosol sensor–new electronics option (WIBS-5–NEO, Droplet Measurement Technologies, LLC., USA) was used at (HAC)2, sampling aerosols with a PM10 impactor, to measure the size distribution of aerosol particles with optical sizes larger than 0.5 µm. The WIBS also measures the fluorescence of aerosols on a singleparticle basis using ultraviolet light to trigger the excitation of the particle and then detecting the fluorescence at three fluorescent channels: FL1 (excitation wavelength at 280nm and emission detection at the 310–400nm waveband), FL2 and FL3 (emission detection waveband of 420– 650nm probing particles excited at 280 and 370nm, respectively). These three channels target different biologic fluorophores: tryptophan-containing proteins, nicotinamide adenine dinucleotide phosphate (NAD(P)H) co-enzymes and riboflavin, respectively (Kaye et al., 2005; Savage et al., 2017), which are ubiquitous in microbes (Pöhlker et al., 2012). Particles showing fluorescence exclusively in any one of the three channels are attributed to a type of AWIBS (FL1 only), Atmos. Chem. Phys., 24, 9939–9974, 2024 https://doi.org/10.5194/acp-24-9939-2024 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean 9943 BWIBS (FL2 only) or CWIBS (FL3 only), respectively. Particles carrying two types of fluorophores and simultaneously detected by two channels are termed ABWIBS (FL1 and FL2), ACWIBS (FL1 and FL3) or BCWIBS (FL2 and FL3). Particles showing fluorescence in any one of the three channels are termed FluoWIBS. Particles detected in all three channels are attributed to a type of ABCWIBS, which are much more likely to be of biological origin compared with the other types (Hernandez et al., 2016; Savage et al., 2017). Note that non-biological particles may also present in BWIBS, CWIBS and BCWIBS channels, behaving as interfering particles, such as some black carbon and dust particles associated with fluorescent materials (Toprak and Schnaiter, 2013; Savage et al., 2017). The fluorescence detection limit was determined by subtracting the mean background signal plus 9 times the standard deviation measured from routinely forced trigger tests. The WIBS data can be resampled to a customized time span because of their 15µs high time resolution for single-particle detection. The measurement rates of WIBS-5–NEO are up to 9500cm−3for all particles irrespective of fluorescence and up to 466cm−3for fluorescent particles. The hourly mean light-scattering coefficient of dry PM10 aerosols at (HAC)2was measured using an integrating nephelometer (model 3563, TSI Inc., USA) at three wavelengths (450, 550 and 700nm, termed Scatt450nm, Scatt550nm and Scatt700nm, respectively) (Laj et al., 2020). Using lightscattering coefficients measured at wavelengths of 450 and 700nm, the Ångström exponent (α) can be calculated as follows: α= −ln[Scatt700nm/Scatt450nm] ln(700/450) .(1) The wavelength pair of 450 and 700nm was used because the larger difference in the measured scattering coefficients gives more accurate αvalues (Mordas et al., 2015). A lower α value suggests the dominance of coarse particles in the sampled aerosols, whereas a larger αvalue indicates the dominance of fine particles (Pereira et al., 2008); this helps to differentiate continental aerosols mainly containing fine particles from dust plumes dominated by coarse particles. In addition, the mass concentration of refractory and carbonaceous aerosol particles, i.e. elemental black carbon (eBC), sampled through a PM10 cut-off inlet was monitored by an Aethalometer (AE31, Magee Scientific, USA) at 880nm with a minimum time base of 2min. The chemical composition of non-refractory species of submicron ambient aerosols, including organics (Org), sulfate (SO42+), nitrate (NO3−), ammonium (NH4+) and chloride (Cl−), was monitored by a time-of-flight aerosol chemical speciation monitor (ToF-ACSM, Aerodyne Research Inc., USA) with a time resolution of 10min (Zografou et al., 2024). 2.3.2 Air mass remote-sensing measurements Figure 1 shows that remote-sensing measurements were performed at VL at an altitude 500m lower than (HAC)2to measure the PBLH, radar equivalent reflectivity factor (Ze) and mean Doppler velocity (MDV). The PBLH was calculated using the Doppler velocity of aerosols measured by a pulsed Doppler scanning lidar system (StreamLine Wind Pro model, HALO Photonics, UK). The lidar was operated in the vertical stare azimuth display mode at a wavelength of 1.5µm with a time resolution of 10min and a vertical spatial resolution of 30m. The vertical wind speed distribution at a certain distance from the lidar was calculated (Barlow et al., 2011; Schween et al., 2014). The PBL top boundary is defined at a position where the standard deviation of the wind vertical velocity, σw, drops below 0.1 m2s−2(Foskinis et al., 2024). The vertical distance between VL and the PBL top boundary is then the PBLH. Note that the PBLH may be undetermined during cloudy periods, as the lidar signal is quickly attenuated in clouds; moreover, when too few particles are present, an insufficient backscattering signal will inhibit PBLH determination. When PBLH results were unavailable, SMPS N>95nm results were compared to the threshold value (100stdcm−3) to define the (HAC)2position with respect to the PBL (Herrmann et al., 2015; Brunner et al., 2021). In addition, a frequency-modulated continuous wave (FMCW) wideband Doppler spectral zenith profiler (WProf) was deployed to measure the radar reflectivity at a wavelength of 3.2mm (corresponding to 94 GHz) up to 10kma.g.l. (above ground level; Küchler et al., 2017; Billault-Roux et al., 2023; Ferrone and Berne, 2023). The Doppler radar results at the (HAC)2level (500m above VL) were used to evaluate the presence of precipitation and clouds. 2.3.3 Air mass modelling As shown in Fig. 1, the footprint and trajectory of air masses arriving at (HAC)2were calculated by the FLEXible PARTicle dispersion Model (FLEXPART) (Stohl et al., 2005; Pisso et al., 2019; Vratolis et al., 2023) and the Hybrid SingleParticle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Hess, 1998; Stein et al., 2015). FLEXPART was used to calculate the residence time of aerosol particles with a geometric mean diameter of 400nm (10 nm–10 µm; Fig. S1 in the Supplement) and a standard deviation of 3.3 in defined locations in the prior 10d. The spatial resolution of the model corresponds to a grid cell size of 1°×1°. Note that more than 90% of the aerosol particles used in FLEXPART simulations have diameters larger than 100 nm, and particles of this size range (>100nm) are mainly responsible for the observed INPs. The 24h aerosol footprint simulation for each calendar day was run every 3h (from 00:00 to 24:00LT, local time, UTC+2) by releasing 40000 air parcels from (HAC)2. Local wind, local turbulence and mesoscale wind fluctuations were https://doi.org/10.5194/acp-24-9939-2024 Atmos. Chem. Phys., 24, 9939–9974, 2024 9944 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean considered in the dispersion and transport calculations. Dry and wet aerosol deposition processes were also included in the model. The residence time of aerosol particles in each location was integrated from a height of 0 to 500ma.g.l. The HYSPLIT model was run to calculate the 7d back trajectories of air masses arriving at (HAC)2. Input meteorological data from the Global Data Assimilation System (GDAS, 1°×1° resolution) (Stein et al., 2015; Kostrykin et al., 2021) were used for the back-trajectory calculations. The source height was set at three height levels of 100, 1000 and 2000ma.g.l. The model was launched every 6h backward from the start time. The start time of back trajectories on a day was decided depending on the need to specify if there was an aerosol source change during the day. Finally, the SKIRON model (Kallos et al., 2006; Spyrou et al., 2010) was used to calculate the time series of dust mass concentration at (HAC)2at different height levels of 1250, 1614, 1881 and 2170ma.s.l. Along with the with the CoarseAPS particle concentration and the aerosol footprints from FLEXPART, dust mass concentrations below (HAC)2 predicted by SKIRON help to determine the occurrence and intensity of dust plumes and their source region. 2.4 Aerosol source apportionment and type classification In general, transported aerosol particles from remote regions showed trajectories from the north in early October, at the beginning of the campaign, but changed in a counter-clockwise direction during the campaign. Figure 2 presents exemplary FLEXPART results throughout the campaign to show major aerosol sources from remote regions. Aerosol particles arriving at (HAC)2on 12 October (Fig. 2a) were seen to come from the north and the northeast, which can be attributed to continental aerosols, whereas later, on 3 November, marine aerosols from the Atlantic Ocean and the Mediterranean Sea might have made up a large fraction of aerosols transported to (HAC)2(Fig. 2b). On 4 November and later, dust from the Sahara, possibly mixed with marine aerosols, reaches the station (Fig. 2c and d). Notably, the Saharan dust event increases the aerosol content (Fig. 2c) by more than 1 order of magnitude compared with the marine aerosols (Fig. 2b). At the beginning of the Saharan dust event, the case in Fig. 2c spanned ∼3h and was possibly a transition period with a lower mixture of local aerosols. After that, Saharan dust (Fig. 2d) persisted for about 1 week. Later on, dust mixed with continental particles reached the site (Fig. 2e), followed by primarily continental aerosols from the north of (HAC)2, e.g. the Balkans (Fig. 2f). Note that FLEXPART results provide an overview of aerosol sources on a daily basis, but the further identification of particle sources at (HAC)2relies on both in situ and remote-sensing results with a time resolution of 1h. For example, the synergy of in situ and remote-sensing results enables one to differentiate between the distinct characters of the sources in Fig. 2b and c. In addition to aerosol footprints, we consider following criteria to specifically classify the sources of air masses at (HAC)2: –comparing the PBLH (SMPS N>95nm) with a threshold value of 0.5km (100stdcm−3) to examine the relative position of (HAC)2with respect to the PBL; –comparing CoarseAPS with a threshold value of 20 particlesstdL−1to judge the presence of remotely transported air masses in the FT; –comparing Ze (MDV) with a threshold value of 10dBZ (−0.5ms−1) to evaluate the presence of precipitation/clouds; –comparing αwith a threshold value of 1.0 to diagnose the occurrence of Saharan dust events. Figure 3 summarizes the classified aerosol sources and presents their characteristics to demonstrate their distinct nature. Hourly averaged data are presented in Fig. 3; however, the presented data for the source of “South dust in the PBL after marine aerosols” (in Fig. 3 and following figures) are resampled every 15min due to the short period of observation (<3h). First, a PBLH of less than 0.5km or an SMPS N>95nm of less than 100stdcm−3(if no PBLH results are available) means that (HAC)2is above the PBL (Fig. 3a or b). Moreover, if CoarseAPS is less than 20stdL−1, the period will be attributed to (HAC)2in the FT (Fig. 3c). Furthermore, periods of Ze values larger than 10 dBZ (Hagen and Yuter, 2006) and MDV values less than −0.5ms−1are classified as periods influenced by precipitation. For periods with slightly lower Ze values, (HAC)2is likely in cloud or fog. During the campaign, periods of precipitation frequently alternated with cloudy periods. Therefore, we consider such periods jointly. If there is no influence from remotely transported air masses (CoarseAPS <20 particlesstdL−1), depending on the presence of precipitation/clouds, the condition is classified as “(HAC)2in the FT under background conditions” and “(HAC)2in the FT with precipitation/clouds”, respectively. For periods of CoarseAPS >20 particlesstdL−1, the influence of remotely transported aerosols was considered. Continental aerosols have αvalues larger than 2.0 (Fig. 3d) when (HAC)2is in the PBL. The distinct particle properties of continental aerosols also include high SMPS N>95nm values (median value of >200stdcm−3in Fig. 3b), moderate CoarseAPS values (median value of ∼200 particles stdL−1 in Fig. 3c) and very low dust particle abundance values (< 0.1µgstdm−3in Fig. 3e). Such an aerosol source is termed “North continental aerosols in the PBL”. When continental aerosols are sources of particles at (HAC)2but the site is above the PBL (Fig. 3a and b), the 75th quartile for the CoarseAPS of the aerosols decreases to 91 particlesstdL−1 (Fig. 3c) and becomes lower than the 9th percentile for the CoarseAPS of North continental aerosols in the PBL. Moreover, the αvalue of the source termed “North continental Atmos. Chem. Phys., 24, 9939–9974, 2024 https://doi.org/10.5194/acp-24-9939-2024 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean 9945 Figure 2. Overview of aerosol sources as seen from exemplary FLEXPART 10d backward residence time maps: (a) Northeast continental air masses on 12 October 2021; (b) Marine air masses on 3 November 2021; (c) South dust after marine air masses on 4 November 2021; (d) South dust on 7 November 2021; (e) South dust and North continental air masses on 16 November 2021; (f) Northwest continental air masses on 22 November 2021. The colour map scales the residence time of the particles from a height between 0 and 500 ma.g.l. in the 10 d backward trajectories. aerosols above the PBL” decreases (25th quartile >1.67), but the median value is still higher than 2.0 (Fig. 3d). Therefore, North continental aerosols above the PBL are distinctly different from the aerosols in the PBL. In addition, the distinction of marine aerosols above the PBL is indicated by the highest Cl−fraction range compared with the other scenarios not under FT conditions (Fig. 3f), given that a high Cl−concentration is reported to be a character of marine aerosols (Xiao et al., 2018). Khan et al. (2015) reported that the particle number concentration of coarse-mode sea spray aerosols is approximately 100 times less than that of dust aerosols, which is consistent with the observations in this study (Fig. 3c). Hence, a “Marine aerosols above the PBL” scenario can be classified. Followed by marine aerosols, a distinct period of several hours (on 4 November) at the beginning of a dust event (from 4 to 10 November) was observed for (HAC)2in the PBL. It shows high CoarseAPS particle concentrations (>450 particlesstdL−1in Fig. 3c), low αvalues (close to 1.0 in Fig. 3d) and the presence of dust particles (Fig. 3e). Thus, such a source is classified as South dust in the PBL after marine aerosols. Afterwards, the dust event period is termed “South dust in the PBL” which shows the highest CoarseAPS particle concentration (9th percentile >1000 particlesstdL−1in Fig. 3c), low αvalues (close to 1.0, Fig. 3d) and the largest dust mass concentration (25th quartile >10µgstdm−3in Fig. 3e). Lastly, periods of aerosol footprints similar to Fig. 2e and showing aerosol properties in Fig. 3 between those of South dust and North continental aerosols are classified as “South dust with North continental aerosols”, i.e. a mixture of both aerosols. Hereafter, we use the remotely transported aerosols identified in Fig. 3 to name the aerosol sources at (HAC)2. However, we note that particles from local sources may also be relevant for aerosol particles reaching (HAC)2, depending on the (HAC)2 position with respect to the PBL. 2.5 INP parameterization methods INP parameterization is critically important for climate models to express cloud–aerosol interactions in both ice clouds and MPCs. As a minor subset of total aerosol particles, NINP exponentially increases with decreasing T, as supported by both theory (Kampe and Weickmann, 1951) and observations (reviewed in Kanji et al., 2017). NINP also shows dependence on the concentration of available aerosol particles (Burrows et al., 2022) and their surface characteristics (e.g. IN active site density), to trigger the activation (Vali et al., 2015; Knopf and Alpert, 2023). DeMott et al. (2010) developed a parameterization (termed “DeMott2010” in Table 1) to predict NINP using Tand the number concentration of aerosol particles larger than 0.5 µm (TotalAPS), based on a suite of https://doi.org/10.5194/acp-24-9939-2024 Atmos. Chem. Phys., 24, 9939–9974, 2024 9946 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean Figure 3. Box plots for the characteristics of identified aerosol sources: (a) PBLH with respect to VL; (b) number concentration of particles larger than 95nm measured by SMPS (SMPS N>95nm) at (HAC)2;(c) coarse-particle (>1.0µm) number concentration measured by APS (CoarseAPS) at (HAC)2;(d) Ångström exponent (α) at the wavelength pair of 450 and 700 nm calculated using nephelometer data recorded at (HAC)2;(e) dust mass concentration at 2170 m a.s.l. (∼140m below (HAC)2) calculated by the SKIRON model; (f) the mass ratio of Cl−to other species measured by ToF-ACSM at (HAC)2. The box shows the median line and the range between 25th and 75th quartiles; the lower and upper caps of the box indicate the 9th and 91th percentiles, respectively. INP measurements at various locations globally. Tobo et al. (2013) augmented the DeMott2010 formulation and developed a new parameterization (termed “Tobo2013FBAP” in Table 1) to calculate NINP using the number concentration of fluorescent aerosol particles monitored by a UV-APS (ultraviolet APS, nFBAPs), to consider the explicit contributions from biological and dust particles to the coarse-mode population. Compared with DeMott2010, Tobo2013FBAP shows increased predictability with respect to calculating NINP from aerosol sources containing biological particles (Tobo et al., 2013). DeMott et al. (2015) used TotalAPS and augmented Tobo2013FBAP by introducing a calibration factor (cf); they then calculated a new suite of parameters for the formulation by fitting it to integrated laboratory and field data (termed “DeMott2015” in Table 1). However, the NINP of different aerosol sources may not scale to the total aerosol particle number concentration (e.g. TotalAPS or nFBAPs) following the same rule as used in the above-mentioned parameterizations. This is because the IN ability of potential INPs from different sources varies, and different types of INPs dominate the NINP in different Tregimes (Murray et al., 2012; Kanji et al., 2017). Particle-surface-area-based approaches have also been reported in the literature, such as the approaches termed “Niemand2012” (Niemand et al., 2012), “Ullrich2017” (Ullrich et al., 2017) and “McCluskey2018” (McCluskey et al., 2018) in Table 1. Given that different types of INPs originate from different sources and may have different IN active site densities over the particle surface, the INP concentrations calculated from different particle-surface-area-based approaches developed from different aerosol sources can vary by more than 3 orders of magnitude (Niemand et al., 2012; McCluskey et al., 2018). Further improvements to INP prediction may require parameterizations to explicitly consider additional characteristics of resolved aerosol properties to point to their sources. Mignani et al. (2021) reported that the ratio of large aerosol particles (>2.0µm) recorded by APS can be used as an identity to characterize INPs from Saharan dust and that the implementation of the ratio into the INP parameterization improves its predictability. We expand upon this and use the detailed INP source apportionment and type classification (Sect. 2.4) to incorporate INP source characteristics into the INP parameterizations developed here. The new parameterizations proposed for INPs at Mount Helmos are expressed as a function of T, aerosol particle concentration recorded by APS or WIBS, and the ratio of the particle number concentration recorded in different channels of the APS or WIBS. We demonstrate (1) the feasibility of predicting INPs from different sources using one suite of parameters for the proposed parameterizations (see Sect. 3.4) and (2) the superior Atmos. Chem. Phys., 24, 9939–9974, 2024 https://doi.org/10.5194/acp-24-9939-2024 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean 9947 Table 1. INP parameterizations from the literature. INP parameterization Region Included major aerosol types Trange of INP observations Formulation DeMott2010 Global observation covering Colorado, Wyoming and Alaska in the USA; Eastern Canada and Ottawa; the Pacific region; and the Amazon Basin Dust and biological particles −35 to −9°C (Sw>100%) NINP =a(−T)b·TotalAPS(−cT +d) (a=0.0000594, b=3.33, c=0.0264, d=0.0033) (TotalAPS in stdcm−3;Tin °C) Tobo2013FBAP Rocky Mountain region Biological particles −35 to −9°C (Sw=103%–106 %) NINP =FluoWIBS(−aT +b)·exp(−cT +d) (a= −0.108, b=3.8, c=0, d=4.605) (FluoWIBSain stdcm−3;Tin °C) DeMott2015 Pacific Ocean basin and the Virgin Islands Laboratory dust samples and dust particles in the atmosphere −35 to −20°C (Sw=105%) NINP =cf·TotalAPS(−aT +b)·exp(−cT +d) (cf =3, a=0, b=1.25, c=0.46, d= −11.6) (TotalAPS in stdcm−3;Tin °C) Niemand2012 Laboratory experiments Dust −36 to −12°C (Sw>100%) NINP =1000·SSMPS+APS ·exp(aT +b) (a= −0.517, b=8.934) (SSMPS+APSbin stdm2cm−3;Tin °C) Ullrich2017 Laboratory experiments Dust −30 to −14°C (Sw>100%) NINP =1000·SSMPS+APS ·exp(a(273.15+T)+b) (a= −0.517, b=150.577) (SSMPS+APS in stdm2cm−3;Tin °C) McCluskey2018 West coast of Ireland Sea spray aerosols, marine organics and offshore biological particles −27 to −10°C (Sw>100%) NINP =1000·SSMPS+APS ·exp(aT +b) (a= −0.545, b=1.0125) (SSMPS+APS in stdm2cm−3;Tin °C) aFluoWIBS represents the number concentration of fluorescent particles larger than 0.5µmmeasured by WIBS. Note that Tobo et al. (2013) used UV-APS to measure fluorescent particles showing fluorescence signals in the wavelength range of 400–575nm after being excited at 355nm. bSSMPS+APS total particle surface area was measured by SMPS and APS. performance of the new parameterizations compared with the approaches reported in the literature (Table 1). 3 Results and discussions In this section, we first provide evidence of the distinct characteristics of individual sources (Sect. 3.1) classified in Sect. 2.4. An overview of INPs observed at Mount Helmos is presented as a function of T, and the results are contrasted against literature observations, for both the global area and (specifically) the Mediterranean region (Sect. 3.2). The INP abundance and its correlation with the aerosol properties of each INP source are then examined. We also focus on a case study in which precipitation effects on INPs in the PBL are explicitly studied (Sect. 3.3). Finally, the ability of published parameterizations to reproduce observed INPs at Mount Helmos is examined, followed by the introduction of new parameterizations that we develop which explicitly consider the characteristics of different INP sources and display superior performance (Sect. 3.4). 3.1 Properties of identified aerosol sources 3.1.1 The particle size distribution for different aerosol types and air mass classifications Figure 4a shows the combined particle size distribution of different INP sources measured by both SMPS and APS. Scatter plots in Fig. 4b and c present the apportionment of fine (<1.0µm, FineSMPS+APS in Fig. 4b and FineAPS in Fig. 4c) and coarse (>1.0µm, CoarseAPS) particles for different aerosol sources. The results of the concentration of aerosol particles in different size ranges are provided in Fig. S2. Based on all observations, we summarize that aerosols at (HAC)2during CALISHTO generally exhibit four size modes: an ultrafine mode (Da<0.04µm), an Aitken mode (0.04 < Da<0.1µm), an accumulation mode (0.1< Da<1.0µm) and a coarse mode (Da>1.0µm). When (HAC)2is in the FT, aerosol particles in the size range Da> 0.1µm (Fig. 4a), with and without the influence of precipitation/clouds, exhibit a similar size distribution, with a characteristic decrease in number with increasing size, resembling a power law (Kim et al., 1992). Particles larger than 4.0 µm in the FT show negligible abundance. Precipitation/clouds decrease the number of particles smaller than 0.4µm, while the absence of a distinct accumulation mode may suggest inaphttps://doi.org/10.5194/acp-24-9939-2024 Atmos. Chem. Phys., 24, 9939–9974, 2024 9954 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean Figure 9. The PINE INP concentration for different aerosol sources and the relationship between INP and aerosol particle concentrations. (a) Box plots for the INP concentration from different aerosol sources; the average Tfor PINE IN experiments for each source is indicated on the bottom axis, and the uncertainty is 1 standard deviation. (b) Scatter plots of INP and TotalSMPS+APS concentrations. (c) Scatter plots of INP and TotalAPS concentrations. (d) Scatter plots of INP and CoarseAPS concentrations. (e) Scatter plots of INP and FluoWIBS concentrations. (f) Scatter plots of INP and ABCWIBS concentrations. The Pearson correlation coefficient (R), the corresponding pvalue calculated from an Ftest and the Spearman rank coefficient (ρ) are provided to evaluate the correlation between the INP concentration and different aerosol particle concentrations. The pvalue is the probability of obtaining an Rvalue no smaller than the true Rvalue if there is no linear correlation between INPs and the given parameter. tion is substantially higher than the ABCWIBS concentration (<10 particlesstdL−1in Fig. 9f), suggesting that dust particles – but not any associated biological particles – make the primary contribution to the observed INPs in the dust plume. The median INP concentration decreases to 19.8 particlesstdL−1(T= −24.5°C) when the dust plume is more extensively mixed with local aerosols in the PBL, i.e. the source of South dust in the PBL. Furthermore, the median INP concentration decreases further to 7.3 particlesstdL−1 (T= −26.0°C) when the dust plume is also mixed with continental aerosols, i.e. the source of South dust with North continental aerosols. It is likely that the source of South dust in the PBL after marine aerosols may contain more fresh dust particles than the following sources, which comprise more aged and deactivated dust particles (Boose et al., 2019). Among all of the sources presented in Fig. 9, the INP concentration in the PBL is considerably larger than that in the FT, by approximately more than 1 order of magnitude (median value), although it depends on INP sources. Both continental aerosols from the North and dust from the South are major sources of INPs at Mount Helmos. Fresh dust plume contains a larger number of INPs than the other sources mixed with local emissions and/or continental aerosols. Such a decrease in the INP abundance in the mixed dust-containing sources may result from the dilution of air masses or aerosolageing-induced INP deactivation. Furthermore, we note that the INP concentration range in North continental aerosols (T= −26.7°C) is analogous to that of South dust (T= −24.5°C) when both are mixed with local emissions in the PBL. Figure 9 and Table 2 also provide the correlation between the INP concentration and different concentrations of aerosol particles, including TotalSMPS+APS, TotalAPS, CoarseAPS, FluoWIBS and ABCWIBS. The concentration of PINE INPs is approximately higher than 1 per 1×106of TotalSMPS+APS, 1 per 1×103of TotalAPS and 1 per 500 of CoarseAPS, respectively, consistent with the established view that INPs in the atmosphere show a size dependence and that larger aerosol particles have a higher probability of behaving as INPs. Overall, a significant and positive correlation between the Atmos. Chem. Phys., 24, 9939–9974, 2024 https://doi.org/10.5194/acp-24-9939-2024 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean 9955 INP concentration and aerosol particle concentrations can be found in Fig. 9. A ρvalue larger than 0.80 for the relationship of INPs with TotalAPS, CoarseAPS or FluoWIBS (>0.5µm, optical size) means that the INP concentration increases with those three particle concentrations following a strong monotonic trend. In comparison with TotalAPS, CoarseAPS, FluoWIBS (Fig. 9) or TotalWIBS (Fig. S7), the smaller Rand ρ values for the relationship between TotalSMPS+APS and INPs also indicate that small aerosol particles in the SMPS size range may play a minor role with respect to serving as INPs compared with larger particles measured in the APS and WIBS size range. The IN dependence on the size of aerosol particles is more pronounced for the sources of North continental aerosols and South dust when both sources supply potential INPs for (HAC)2in the PBL, as shown by the fact that the Rvalue for the relationship of INPs with TotalAPS or CoarseAPS for South dust in the PBL does not show a significant difference compared with the Rvalue for the relationship of INPs with TotalSMPS+APS (Table 2). This is because the source of South dust in the PBL contains a much smaller proportion of fine particles with respect to the total than for the source of North continental aerosols in the PBL (Fig. 4). Furthermore, the ρvalue of the relationship between INPs and CoarseAPS particles for North continental aerosols is larger than that of South dust in the PBL, suggesting that INPs in North continental aerosols may be more dependent on coarse-mode particles. In addition to size dependence, we note that the observed INP concentration is close to the concentration of fluorescent particles, as it is shown that more than 80% of INP data points are within a factor of 5 compared to FluoWIBS data (Fig. 9e). Moreover, INPs from most sources show a significant correlation with FluoWIBS particles (except for the source of South dust in the PBL after marine aerosols, owing to limited number of observations). This suggests that particles showing fluorescence are of significant relevance for INPs observed at Mount Helmos. The results are also in agreement with Mason et al. (2015), who reported that INPs observed between −15 and −25°C at a coastal site are strongly correlated with fluorescent particles. Furthermore, the results are consistent with Pereira Freitas et al. (2023), who found fluorescent biological aerosol particles to be dominant sources of INPs activating at Tvalues of around −15°C. ABCWIBS would constitute a subset of the observed total INPs, as the latter is approximately 5 times larger (Fig. 9f) and shows a significant correlation with ABCWIBS particles in the source (Table 2). In particular, more than 90% of the observed INP data from sources of aerosols in the FT influenced by precipitation/clouds, marine aerosols and continental aerosols above the PBL are within a range of less than a factor of 5 compared to ABCWIBS particles in the source (Fig. 9f). Such a close correlation highlights the importance of biological particles in those INP sources when dust particles are absent. Notably, the correlation between INPs and ABCWIBS particles for aerosols in the FT influenced by precipitation/clouds becomes significant compared to the case without precipitation/cloud effects (Table 2), suggesting that precipitation/clouds may lead to an increase in ABCWIBS (Fig. 7) and contribute to observed INPs. Lastly, Table 2 shows that, of all the sources, ABCWIBS particles from marine aerosols show the strongest correlation with INPs. This is consistent with the important role of marine biogenic aerosols in serving as INPs in the MPC regime (Wilson et al., 2015). 3.2.3 The ice nucleation ability of particles in different aerosol sources Figure 10a uses the ratio of the INP concentration to the TotalSMPS+APS concentration to estimate the INP proportion in total aerosol particles for different sources, and it uses the ratio as a measure to evaluate the average IN ability of aerosol particles from different sources. To clarify, we note that the ratio statistically refers to the overall ice formation ability of the particle population in the source. However, the ratio is not relevant to the IN ability of single particles, as the IN ability specifically relies on the physiochemical properties of the particle, given that sources containing a low concentration of INPs may have effective INPs activating at warm temperatures. When (HAC)2is in the FT under background conditions without remotely transported air masses and without precipitation/clouds, the observed INP ratio is approximately 1 per 1×106aerosol particles and the median ratio value is less than that presented in Rogers et al. (1998), who reported ∼30 INPs out of 1×106particles at an altitude of 10.6km in the upper troposphere and at a Trange of between −15 and −40°C. Influenced by precipitation/clouds, the INP ratio in the FT generally increases because of the decrease in total aerosol particles and the increase in INPs (Sects. 3.1 and 3.2.2). Figure 10a also suggests that the (HAC)2position with respect to the PBL regulates the average IN ability of particles from the North continental aerosol source, as shown by the larger INP ratio when the source is in the PBL than above the PBL. This is because active INPs from the source in the PBL may primarily come from CoarseAPS particles that otherwise take a smaller proportion when the source is above the PBL (Fig. 10b). The INP ratio of marine aerosols above the PBL is analogous to that of North continental aerosols above the PBL (Fig. 10a), suggesting a similar IN ability of particle populations in both aerosol sources. In addition, the INP ratio of aerosol sources containing dust particles decreases if the source is more influenced by the PBL or if it is mixed with North continental aerosols. The results in Fig. 10 also evaluate the dependence of the INP ratio on the particle size and fluorescent/nonfluorescent particle partitioning of the source, including the ratio of CoarseAPS to TotalSMPS+APS, CoarseAPS to FineAPS, FluoWIBS to TotalSMPS+APS and FluoWIBS to NonFluoWIBS (the difference between TotalWIBS and FluoWIBS) particles. In general, the average INP ratio of a source increases with https://doi.org/10.5194/acp-24-9939-2024 Atmos. Chem. Phys., 24, 9939–9974, 2024 9956 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean Table 2. The Pearson correlation coefficient (R) and the Spearman rank coefficient (ρ) for the relationship evaluation between the INP and aerosol particle concentrations from different sources. A critical pvalue of 0.05 from an Ftest for Ris used to assess the significance level of the relationship. A pvalue smaller than 0.05 suggests that the probability of obtaining an Rvalue no smaller than the true Rvalue is less than 5% if there is actually no liner correlation between the INPs and the given parameter; thus, the calculated Ris of statistical significance. Evaluated significant relationships are indicated using bold font. Note that the correlation between INPs and TotalWIBS particles is not included in Fig. 9, but it is provided in Fig. S7. INP sources TotalSMPS+APSaTotalAPSbCoarseAPScTotalWIBSdFluoWIBSeABCWIBSf R ρ R ρ R ρ R ρ R ρ R ρ (p) (p) (p) (p) (p) (p) (HAC)2in the FT under 0.41 0.57 0.76 0.66 0.13 0.19 0.41 0.31 0.53 0.34 −0.50 −0.43 background conditions 0.01 <0.001 0.46 0.07 0.03 0.22 (HAC)2in the FT with 0.28 0.06 0.09 0.11 0.04 0.03 0.17 0.27 0.52 0.56 0.56 0.33 precipitation/clouds 0.10 0.62 0.81 0.45 0.01 0.01 North continental 0.33 0.35 0.71 0.78 0.73 0.72 NAgNA NA NA NA NA aerosols in the PBL 0.003 <0.001 <0.001 North continental 0.53 0.34 0.53 0.48 0.59 0.54 0.53 0.45 0.62 0.58 0.32 0.30 aerosols above the PBL <0.001 <0.001 <0.001 <0.001 <0.001 0.008 Marine aerosols 0.61 0.48 0.53 0.69 0.48 0.69 0.60 0.67 0.55 0.47 0.71 0.71 above the PBL 0.007 0.02 0.04 0.009 0.02 <0.001 South dust in the PBL 0.59 0.71 0.12 0.26 0.01 0.03 −0.17 −0.26 −0.38 −0.43 −0.59 0.36 after marine aerosols 0.21 0.82 0.99 0.75 0.46 0.29 South dust in the PBL 0.05 0.01 0.80 0.80 0.84 0.85 0.84 0.85 0.56 0.52 0.59 0.50 0.56 <0.001 <0.001 <0.001 <0.001 <0.001 South dust with North 0.29 0.09 0.24 0.21 0.13 0.34 0.21 0.29 0.59 0.38 0.65 0.51 continental aerosols <0.001 0.001 0.07 0.01 <0.001 <0.001 All observations 0.32 0.37 0.69 0.84 0.62 0.82 0.73 0.89 0.69 0.84 0.50 0.54 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 aTotal particle (0.01–20.0µm) number concentration measured by both SMPS and APS. bTotal particle (0.5–20.0µm) number concentration measured by APS. cCoarse-particle (>1.0µm) number concentration measured by APS. dTotal particle (0.5–30.0µmin optical size) number concentration measured by WIBS. eNumber concentration of particles showing fluoresce in any one of the WIBS fluorescent channels. fNumber concentration of particles showing fluoresce in all three WIBS fluorescent channels. gData not available. an increasing proportion of CoarseAPS (>1.0µm; Fig. 10b and c) and FluoWIBS (Fig. 10d and e) particles in the source, but such a correlation varies with respect to its strength among individual sources. Figure 10b shows that the INP ratio of North continental aerosols (for both above and in the PBL) has a weaker correlation with CoarseAPS particles (see the Rand ρvalues in Table S1 in the Supplement) compared with that of South dust in the PBL. Again, this is because North continental aerosols contain more fine-mode particles, which are less effective INPs, than aerosols from South dust in the PBL. A larger slope for North continental aerosols in Fig. 10b compared with South dust in the PBL suggests that INPs in North continental aerosols are more dependent and sensitive to CoarseAPS particles. This may be because individual CoarseAPS particles in continental aerosols of biological origin are generally more effective INPs than those in dusty aerosols. This can be true if those coarse-mode particles in continental aerosols are of biologic origin. Moreover, the insignificant correlation between the INP ratio of sources containing North continental aerosols and the ratio of CoarseAPS to FineAPS particles in the source (Fig. 10c and Table S1) suggests that some particles smaller than the APS size detection range (∼0.5µm) may contribute to the observed INPs at ∼ −26°C. Figure 10c shows that the correlation between the INP ratio and the CoarseAPS-to-FineAPS ratio is less significant (see Table S1) for the source of South dust in the PBL compared with the correlation between the INP ratio and the CoarseAPS-to-TotalSMPS+APS ratio, also suggesting a contribution of some small particles (<0.5µm) to observed INPs. This is consistent with a field study in Israel (in the eastern Mediterranean region) that focused on the IN ability of size-resolved Saharan dust particles; the aforementioned study reported that 0.3µm (aerodynamic diameter) dust particles are effective INPs at the Trange from −20 to −30°C (Reicher et al., 2019). In addition, an increasing FluoWIBS-to-TotalSMPS+APS ratio can generally predict an increasing INP ratio of an aerosol source (Fig. 10d; no data available for the source of North continental aerosols). However, Fig. 10e shows that the INP ratio for different aerosol sources generally decreases with an increasing FluoWIBS-toNonFluoWIBS ratio. The results on the xaxis of Fig. 10e show that the FluoWIBS-to-NonFluoWIBS ratios in different sources Atmos. Chem. Phys., 24, 9939–9974, 2024 https://doi.org/10.5194/acp-24-9939-2024 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean 9957 Figure 10. (a) Box plots of the ratio of the INP concentration to the TotalSMPS+APS concentration; the average Tfor the PINE IN experiments for each INP source is indicated on the bottom axis, and the uncertainty is 1 standard deviation. (b) Scatter plots of the ratio of INPs to TotalSMPS+APS particles and the ratio of CoarseAPS to TotalSMPS+APS particles. (c) Scatter plots of the ratio of INPs to TotalSMPS+APS particles and the ratio of CoarseAPS to FineAPS particles. (d) Scatter plots of the ratio of INPs to TotalSMPS+APS particles and the ratio of FluoWIBS to TotalSMPS+APS particles. (e) Scatter plots of the ratio of INPs to TotalSMPS+APS particles and the ratio of FluoWIBS to NonFluoWIBS (the difference between TotalWIBS and FluoWIBS) particles. The Pearson correlation coefficient (R), the corresponding p value calculated from an Ftest and the Spearman rank coefficient (ρ) are provided to evaluate the correlation between INP abundance and particle partitioning. The pvalue is the probability of obtaining an Rvalue no smaller than the true Rvalue if there is no liner correlation between INPs and the given parameter. The grey dashed lines in the panel confine a range of 2 orders of magnitude on both the xaxis and yaxis. are in reverse order compared with the other ratios presented in Fig. 10b–d. Such a difference suggests that the INPs of biological origin become less important when the overall IN ability and INP abundance of the source is higher, such as dust plumes, indicating a less pronounced role of biological particles in dust-containing sources with respect to serving as INPs. Overall, the scatter patterns of the INP ratio versus different aerosol partitioning indices (presented in Fig. 10) spread over 2 orders of magnitude (confined by grey dashed lines in Fig. 10b–e). Both the CoarseAPS-to-FineAPS ratio and FluoWIBS-to-NonFluoWIBS ratio show significant correlations (p < 0.05 in Table S1) with the ratio of INPs to TotalSMPS+APS particles (Fig. 10c and e), although the strengths of these two correlations are weaker compared with the results using TotalSMPS+APS data in Fig. 10b and d. This suggests the proportion of particles with different sizes and fluorescent properties conveys the IN ability of particles from different aerosol sources, which may benefit the prediction of INPs in parameterizations. In Sect. 3.4, these ratios will be incorporated into INP parameterizations to improve their prediction skill. 3.3 The influence of precipitation/clouds on INPs in the PBL 3.3.1 Different scenarios classified during the case study In addition to the effects of precipitation/clouds on INPs for periods during which (HAC)2was in the FT, precipitation/cloudy periods were also observed when the site resided in the PBL; however, these periods had different aerosol sources, so we treat them separately. For this, we focus on a case study (23 November) during which (HAC)2 was in the PBL and influenced by continental aerosols. The FLEXPART footprints and the HYSPLIT back trajectories (Figs. S9 and S10, respectively) suggest that a North continental air mass dominated the aerosol source at (HAC)2during this time (with a possible minor contribution of South dust). The dominance of North continental aerosols is also https://doi.org/10.5194/acp-24-9939-2024 Atmos. Chem. Phys., 24, 9939–9974, 2024 9958 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean supported by nephelometer results, as α∼2.0 (Mordas et al., 2015). Following the methodology introduced in Sect. 2.4, the observations during the day are classified based on the presence of precipitation/clouds and the position of (HAC)2 with respect to the PBL, by using Ze and MDV values from radar measurements presented in Fig. 11a and b, respectively. The position of (HAC)2with respect to the PBL is evaluated using SMPS N>95 nm time series in Fig. 11c, as the PBLH results from lidar are not always available during the day. Thus, observations on 23 November are classified into five periods (“cases”), including (HAC)2in the FT with precipitation (case 1) from 00:00 to 04:00 LT, (HAC)2 in the FT with precipitation (case 2) from 05:00 to 09:00LT, (HAC)2around the PBL and close to the cloud top (case 3) from 10:00 to 15:00LT, (HAC)2in the PBL (case 4) from 15:30 to 18:00LT, and (HAC)2in the PBL with precipitation/clouds (basically precipitation below (HAC)2, case 5) from 19:00 to 24:00LT. The box plots of INP abundance observed by the PINE chamber and the correlation between INPs and aerosol particles for different cases are presented in Fig. 12. Additionally, the aerosol property results, including the particle size distribution for different cases (Fig. S11); TotalSMPS+APS, CoarseAPS, SMPS N<95nm, FluoWIBS and ABCWIBS particle concentrations; and the eBC mass concentration (Fig. S12) are provided in the Supplement and used to understand the changing INP abundance of the different scenarios presented in this section. 3.3.2 Particle properties The aerosol particle properties for each case are shown in Figs. 11c–e and S11–S13. Figure 11c shows that the PBL boundary generally evolves from a position below (HAC)2 to a position above (HAC)2throughout the day, as the SMPS N>95nm increases. The increasing PBLH is also indicated by size distribution shifts to coarse particles in Fig. S11 during the day. When (HAC)2is in the FT with precipitation (case 1), CoarseAPS particles at (HAC)2show a median concentration of ∼1.5 particlesstdL−1(Fig. S12b). The median value is well below the critical value of 20 particlesstdL−1(see Sect. 2.4), suggesting that the site is exposed to clean background conditions at this time with a low probability of influence from remotely transported aerosols above the PBL. This is also supported by the low eBC mass concentration (∼0.01µgstdm−3) shown in Fig. S12f. When (HAC)2is in the FT with precipitation (case 2), CoarseAPS particle concentrations at (HAC)2are occasionally higher than 20 particlesstdL−1and show a median of ∼16.7 particlesstdL−1(Fig. S12b). This likely suggests that remotely transported continental aerosols exert an influence on aerosols at (HAC)2, although (HAC)2is still above the PBL with an SMPS N>95nm smaller than 100 stdcm−3. Furthermore, it is possible that precipitation from higher-altitude clouds compared with case 1 results in downdraughts that drive the mass entrainment of remotely transported aerosols. Moreover, compared with case 1, the decrease in ABCWIBS particles in case 2 suggests negligible biological particles in downdraughts from high altitudes (Fig. S12e), whereas the increase in the eBC mass concentration is a result of transportation (Fig. S12f). Additionally, the comparison between case 1 and 2 suggests that a CoarseAPS particle concentration of less than 20 particlesstdL−1is a more conservative evaluation standard to diagnose (HAC)2inside the FT compared with the criterion of an SMPS N>95nm value of less than 100stdcm−3(also see Sect. 2.4). In addition to the differences between the vertical particle sources for case 1 and case 2, we note that the average wind speed decreases from ∼13ms−1(case 1) to 6ms−1(case 2) (not shown), which would decrease the emission rate of ABCWIBS particles from near-ground sources, such as soils and trees. When (HAC)2is around the PBL and close to the cloud top (case 3), the CoarseAPS particle concentration increases to a level larger than 20 particles stdL−1(Fig. S12b) due to the increased influence from PBL aerosols. The adoption of aerosols from the PBL is supported by occasional updraughts (as shown in Fig. 11b) and by the increasing SMPS N>95nm (close to 100 stdcm−3). For the two scenarios in which (HAC)2is in the PBL with and without precipitation/clouds (case 4 and 5 respectively), the CoarseAPS particle concentration is well above 20 particles stdL−1. The presence of precipitation/clouds leads to a decrease in the CoarseAPS particle concentration, showing a decreased median value from 414.1 to 330.2 particlesstdL−1(Fig. S12b). This suggests the wet removal effects of precipitation on coarse-mode particles in the PBL. However, the presence of precipitation/clouds causes an increase in SMPS N<95nm particles (Figs. S11, 11c and S12c), suggesting that the effect of precipitation/clouds in the PBL may also include new particle formation (Khadir et al., 2023). In general, it can be summarized that the CoarseAPS particle concentration increases when (HAC)2is deeper inside the PBL (indicated by a larger SMPS N>95nm; Fig. S12b), suggesting that the PBL is the major source of aerosol particles at (HAC)2on 23 November. With increased influences from the PBL, the increases in both ABCWIBS and eBC particles (Fig. S12e and f) suggest that ABCWIBS particles are related to biological particles and that eBC emissions are mainly from the PBL. However, the occurrence of precipitation/clouds in the PBL leads to a decrease in ABCWIBS particles, which is the inverse of the effect on aerosols in the FT (see Sect. 3.1.2). This may result from the wet removal effects of precipitation/clouds on ABCWIBS particles that may dominate over any production of ABCWIBS from precipitation splash (Khadir et al., 2023). The occurrence of precipitation/clouds in the PBL results in a small decrease in eBC mass (Fig. S12f), from 0.07 to 0.05µgstdm−3 (median), suggesting the slight wet deposition of eBC particles during precipitation/cloudy periods. Atmos. Chem. Phys., 24, 9939–9974, 2024 https://doi.org/10.5194/acp-24-9939-2024 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean 9959 Figure 11. Time series of precipitation condition, aerosol particle and INP concentration on 23 November 2021. (a, b) Ze and MDV measured by the radar at VL, respectively. The (HAC)2level is indicated by the black line in the panels (a) and (b).(c) Particle size distribution measured by SMPS at (HAC)2for particles smaller than 800 nm (mobility diameter) and the number concentration of SMPS N>95nm particles used to evaluate the (HAC)2position with respect to the PBL. The left axis shows the size for the particle size distribution colour map, while the right axis scales SMPS N>95nm values. (d) Particle size distribution measured by APS at (HAC)2for particles with a size between 0.5 and 20µm (aerodynamic diameter) and the number concentration of CoarseAPS particles (>1.0µm). The left axis shows the size for the particle size distribution colour map, while the right axis scales CoarseAPS values. (e) TotalWIBS, FluoWIBS and ABCWIBS particle number concentrations recorded by WIBS at (HAC)2.(f) The PINE INP concentration measured at (HAC)2with time resolution of ∼6min. The temperature Tfor the PINE IN experiments is indicated by the marker colour scale shown by the colour bar. https://doi.org/10.5194/acp-24-9939-2024 Atmos. Chem. Phys., 24, 9939–9974, 2024 9960 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean Figure 12. The INP concentration and the relationship between INPs and aerosol particles under different scenarios. (a) Box plots of the PINE INP abundance for different scenarios classified on 23 November. (b) Scatter plots of the PINE INP and CoarseAPS particle concentration. (c) Scatter plots of the PINE INP and FluoWIBS particle concentration. (d) Scatter plots of the PINE INP and ABCWIBS particle concentration. The Pearson correlation coefficient (R), the corresponding pvalue calculated from an Ftest and the Spearman rank coefficient (ρ) are provided to evaluate the correlation between INP abundance and particle partitioning. The pvalue is the probability of obtaining an Rvalue no smaller than the true Rvalue if there is no liner correlation between INPs and the given parameter. The value of f represents the percentage of data points within the range confined by the dashed lines in the panel. 3.3.3 INPs observed under different scenarios The INP concentration for (HAC)2in the FT with precipitation (case 1) in Fig. 12a shows a median value of 1.0particlestdL−1, consistent with Fig. 9a for the case of (HAC)2in the FT with precipitation/clouds sampled from the other similar periods during CALISHTO. Compared with case 1 (Fig. 12a), case 2 shows a slightly lower INP concentration (median value of 0.6 particlesstdL−1), which is attributed to the decreased availability of FluoWIBS and ABCWIBS particles (Figs. 11e, S12d and S12e). The results also show that FluoWIBS and ABCWIBS particles are more important sources of potential INPs than CoarseAPS, given that a 10-fold increase in CoarseAPS particles does not lead to an increase in INPs for case 2. When (HAC)2is around the PBL and close to the cloud top (case 3), it shows an increase in INPs compared with both case 1 and case 2 when (HAC)2is in the FT. The increase in INPs may be attributed to the increased availability of FluoWIBS and ABCWIBS particles (and probably CoarseAPS particles as well). When (HAC)2is in the PBL (case 4), the INP concentration reaches a peak during the day, with a median of 7.2 particlesstdL−1. A short period of cloudiness around (HAC)2after 18:00LT and the presence of precipitation/clouds at (HAC)2(in the PBL) around 19:00 LT lead to a decrease in INPs (Fig. 11), likely because of the wet removal effects on aerosols particles. This is different from the results presented in Fig. 9a that show enriched INPs after a period of precipitation/clouds. The different INP changes caused by precipitation in the FT and in the PBL can be attributed to the corresponding increase and decrease in ABCWIBS particles in the FT (Fig. 9f) and in the PBL (Fig. S12e), respectively, highlighting the importance of ABCWIBS particles with respect to regulating the INP abundance under different atmospheric conditions. When (HAC)2is in the FT (Fig. 9a), where background INPs are rare, ABCWIBS particles produced by precipitation may easily play a pronounced role Atmos. Chem. Phys., 24, 9939–9974, 2024 https://doi.org/10.5194/acp-24-9939-2024 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean 9961 in influencing INP concentrations. In this case, ABCWIBS particles may come from cloud-processed particles released from hydrometeors in the precipitation/clouds (Prenni et al., 2013), biological particles or soil dust containing biological components produced by precipitation upon impact with the vegetation or soil surface (Conen et al., 2011, 2017). In contrast, precipitationor cloud-enriched INPs comprise a small proportion of the total INPs when (HAC)2is in the PBL (Fig. 12). Instead, the wet removal effect of precipitation/clouds for (HAC)2in the PBL may play the major role and remove some ABCWIBS particles in the coarse mode that might be active INPs. Therefore, the overall effect of precipitation/clouds on INPs observed at (HAC)2for temperatures around −24.2°C (Fig. 11f) shows a decrease when (HAC)2 lies within the PBL. Figure 12 also shows that the INP concentration observed at (HAC)2on 23 November generally has a significantly positive correlation with the CoarseAPS, FluoWIBS and ABCWIBS particle concentration. The concentration of CoarseAPS particles is generally >20 times (>65% in Fig. 12b) higher than that of INPs. Moreover, 66 % of INP concentration values are lower than those of FluoWIBS (Fig. 12c), whereas 68% of INP concentration values are higher than those of ABCWIBS (Fig. 12d). These results mean that INPs are from coarse particles (>1.0µm) showing fluorescence, and ABCWIBS particles are not the only source of INPs. 3.4 INP parameterization 3.4.1 Predicting INPs observed at Mount Helmos using published parameterizations A variety of parameterizations, as summarized in Table 1, have been proposed to estimate NINP using aerosol properties, such as particle number concentration and particle surface area. We evaluate their ability to reproduce the observed NINP at Mount Helmos. Here, we note that FluoWIBS is used to substitute the nFBAPs (the number concentration of fluorescent aerosol particles monitored by a UV-APS) used in Tobo et al. (2013). The predictability of those parameterizations is evaluated by comparing NINP observations to the calculated NINP results. The evaluation of the predictability of each INP parameterization (in Table 1) for INPs from different INP sources (discussed in Sect. 2.4) is presented in Figs. S14– S19. In addition, we note that INSEKT INPs evaluated as lower estimations beyond a factor of 5 via comparison with PINE INPs (see Sect. S3) are excluded from the parameterization dataset. The overall consistent trend between INSEKT and PINE data clusters in Figs. 13 and 14 for all INP parameterizations suggests that the filtered INSEKT dataset does not show a discrepancy compared to the PINE dataset and does not influence the INP parameterization development. DeMott2015 can predict 80% of data points within a factor of 10 compared to observations (Fig. 13b), which is better than DeMott2010 (66%; Fig. 13a). Generally, more INP data points are overestimated by DeMott2015 than are underestimated (Fig. 13b). For Tobo2013FBAP, we first note that the FluoWIBS particle concentration used in this study is larger than the fluorescent particle concentration measured by UV-APS, as used in Tobo et al. (2013). Such an input difference would have led to an overprediction of INPs if data from Mount Helmos were similar to those observed in Tobo et al. (2013). We find, however, that Tobo2013FBAP generally underestimates INPs at Mount Helmos, especially for temperatures lower than −20°C when dust particles dominate the INP sources (Fig. S16h and i). This discrepancy may be explained when considering that Tobo2013FBAP was developed based on an INP population of biological particles that activate as ice at warm temperatures (>−15°C). Thus, given that the abundance of biological particles is lower than that of dust particles, Tobo2013FBAP underestimates INPs originating from dust particles activating ice at lower temperatures. Nevertheless, Tobo2013FBAP is able to agree with 63% of INP observations in Mount Helmos within 2 orders of magnitude (Fig. 13c). The results in Fig. 13d–f are based on the subset of data for which both SMPS and APS data (hereafter INPSMPS+APS data) are available. Both Niemand2012 and Ullrich2017 systematically overestimate INPSMPS+APS (Fig. 13d and e); this may be attributed to the database used for the development of both parameterizations (dust samples tested in laboratory studies), which may have exhibited a larger active site density compared with the atmospheric particles investigated in this study. On the contrary, McCluskey2018 systematically underestimates the observed INPSMPS+APS data (Fig. 13f), likely because it is based on sea spray aerosols, which may have a lower active site density compared with the INPs observed in this study. Of all of the literature parameterizations tested, DeMott2015 is the best with respect to predicting INPs at Mount Helmos. Its comparatively good performance can be attributed to its larger and more inclusive database from both laboratory and field experiments. Therefore, DeMott2015 will be adapted in Sect. 3.4.2 to optimize its applicability for Mount Helmos by proposing new parameters. 3.4.2 Parameterizations for INPs using the CALISHTO data Nine parameterizations using different aerosol properties are developed (Table 3), including parameterizations adapted from the literature and proposed parameterizations depending on the observed relations between INPs and aerosol properties. We first adapt the DeMott2015 parameterization with a new set of parameters calculated by fitting the formula to the relevant data collected at Mount Helmos (hereafter “Helmos DeMott2015”). Considering that the IN ability of aerosol sources shows a significant correlation with the CoarseAPS-to-FineAPS ratio (Fig. 10c), the ratio, termed “APSratio” hereafter, is included in a new https://doi.org/10.5194/acp-24-9939-2024 Atmos. Chem. Phys., 24, 9939–9974, 2024 9962 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean Figure 13. Scatter plots of the observed INP concentration and the concentration calculated by parameterizations (in Table 1) from the literature. Panels (a)–(c) show the following parameterizations: (a) DeMott2010, (b) DeMott2015 and (c) Tobo2013FBAP. Here, FluoWIBS is used to substitute nFABPs measured by UV-APS, as used in Tobo et al. (2013). Panels (d)–(f) show the following parameterizations: (d) Niemand2012, (e) Ullrich2017 and (f) McCluskey2018. The temperature condition for INP data is scaled as shown in the colour bar. Parameterizations using the same aerosol properties use the same colour bar. The dashed lines confine the range for observed and predicted data points within a factor of 3. The fractions of observed and predicted data points within a factor of 3 (f3) and 10 (f10) are provided in each panel, respectively. MAPE stands for mean absolute percentage error. RMSE is the root-mean-square error used as a measure of the difference between observed and predicted data. INP parameterization termed “Helmos TotalAPS”. We also adapt Tobo2013FBAP with new parameters, and it will be compared to a new parameterization that we develop (termed “Helmos FluoWIBS”) that predicts INPs as a function of FluoWIBS, WIBSratio (FluoWIBS to NonFluoWIBS) and T. Compared with Tobo2013FBAP, the new factor “(WIBSratio)(eT +f)” in Helmos FluoWIBS is used to capture the contribution of fluorescent particles to the observed INPs at different temperatures. Given that FluoWIBS may not include all potential INPs (especially for T < −20°C, where nonbiological particles dominate), we propose two parameterizations (“Helmos TotalWIBS_1” and “Helmos TotalWIBS_2”) using TotalWIBS to represent aerosol particles that may serve as sources of INPs. Both parameterizations depend on TotalWIBS, WIBSratio and Tbut have different formula forms. Compared with DeMott2015 and DeMott2010, Helmos TotalWIBS_1 and Helmos TotalWIBS_2 also consider the effect of fluorescent particle portioning in different INP sources by using the corresponding factor including WIBSratio. Moreover, TotalSMPS+APS is used as the input for INP source particles to calculate NINP to include potentials in a larger size range, given that particles smaller than 0.5µm may also be relevant for INPs (Kanji et al., 2017). With and without including the ratio of CoarseAPS to FineSMPS+APS particles (SMPS_APSratio), two parameterizations (“Helmos TotalSMPS+APS_1” and “Helmos TotalSMPS+APS_2”, using a similar formula to that of DeMott2015 and DeMott2010, respectively) are proposed to calculate NINP based on TotalSMPS+APS and T. We also provide parameters for a surface-area-based parameterization (“Helmos SSMPS+APS”) using the concept of surfaceactive sites. In the following, we discuss the performance of the INP parameterizations introduced above (Fig. 14) and evaluate the predictability of each INP parameterization (Figs. S20–S35) for INPs from different INP sources discussed in Sect. 2.4. Atmos. Chem. Phys., 24, 9939–9974, 2024 https://doi.org/10.5194/acp-24-9939-2024 K. Gao et al.: Biological and dust aerosols as sources of INPs in the eastern Mediterranean 9963 Figure 14. Different parameterizations for predicting INPs at (HAC)2. Parameterizations in panels (a)–(f) are based on the dataset for which both APS and WIBS are available. Parameterizations in panels (g)–(i) are based on the overlapping APS and WIBS dataset. The parameterization shown in the figure as follows: (a) Helmos DeMott2015, (b) Helmos TotalAPS,(c) Helmos Tobo2013FBAP, (d) Helmos FluoWIBS,(e) Helmos TotalWIBS_1, (f) Helmos TotalWIBS_2, (g) Helmos TotalSMPS+APS_1, (h) Helmos TotalSMPS+APS_2 and (i) Helmos SSMPS+APS. The temperature condition for INP data is scaled as shown in the colour bar. Note that parameterizations using the same aerosol properties use the same colour bar. The dashed lines confine the range for observed and predicted data points within a factor of 3. The fraction of observed and predicted data points within a factor of 3 (f3) and 10 (f10) is provided in each panel, respectively. MAPE stands for mean absolute percentage error. RMSE is the root-mean-square error used as a measure of the difference between observed and predicted data. BIC is a value calculated by applying the Bayesian information criteria to evaluate the goodness of fit of parameterizations based on the same dataset (Schwarz, 1978). 3.4.3 INP parameterizations using the TotalAPS particle concentration The results in Fig. 14a and b compare the predictability of Helmos DeMott2015 and Helmos TotalAPS. After adaption, the percentage of NINP data points within 2 orders of magnitude compared to observations increases by 16% for Helmos DeMott2015 (96%; Fig. 14a) in comparison to DeMott2015 (80%; Fig. 13a). Furthermore, ∼11% of the 16% comes from the predictions of NINP values within a factor of 3 compared to the observations. 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