On the drivers of ice nucleating particle diurnal variability in Eastern Mediterranean clouds

1 On the drivers of ice nucleating particle variability for Eastern Mediterranean orographic clouds Kunfeng Gao1, Franziska Vogel2,a, Romanos Foskinis1,3, 4, Stergios Vratolis5, Maria I. Gini5, Konstantinos Granakis5, Olga Zografou5, Prodromos Fetfatzis5, Alexandros Papayannis1,3, Ottmar Möhler2, Konstantinos Eleftheriadis5, Athanasios Nenes1,4 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 3Laser Remote Sensing Unit (LRSU), Physics Department, National Technical University of Athens, Zografou, Greece 4Centre for the Study of Air Quality and Climate Change, Institute of Chemical Engineering Sciences, Foundation for Research 10 and Technology Hellas, Patras, Greece 5ENvironmental Radioactivity & Aerosol Technology for atmospheric & Climate ImpacT Lab, INRASTES, NCSR Demokritos, 15310 Ag. Paraskevi, Attica, Greece aNow at: Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Bologna, Italy 15 Correspondence to: Athanasios Nenes (athan[email protected]h) and Kunfeng Gao ([email protected]) Abstract We report the drivers of spatiotemporal variability of ice nucleating particles (INPs) for mixed-phase orographic clouds (~ −25°C) in the Eastern Mediterranean. In the planetary boundary layer, pronounced INP diurnal periodicity is observed, which is mainly driven by biological and dust particles but not biomass burning aerosol particles. The comparison of size-resolved 20 and fluorescence-discriminated aerosol particle properties with INPs reveal the primary role of fluorescent biological aerosol particles. The presence of Saharan dust increases INPs during nighttime more than daytime, because of stronger dust layer intrusions and less availability of boundary layer aerosols with lower boundary layer height. However, INP diurnal periodicity is absent in the free troposphere, despite any observed dependence of INPs on bioaerosol and dust particle abundance. Given the effective ice nucleation ability of bioaerosols at warm temperatures and subsequent effects from ice multiplication, the lack 25 of such cycles in models point to important, overlooked cloud formation cycles in mountainous regions. 2 INTRODUCTION 30 Atmospheric ice nucleation plays a vital role in cloud formation and cloud microphysical properties, which considerably influences regional and global precipitation, hydrological cycle1, atmospheric radiative forcing2 and the Earth’s energy balance3-4. For temperature (T) lower than −38°C, atmospheric ice formation can occur spontaneously via homogeneous freezing5. However, for warmer temperatures, the initiation of cloud ice formation in most clouds necessitates ice nucleating particles (INPs) that heterogeneously freeze6. Considering the strong impacts INPs can have on cloud properties, through a 35 minor fraction of total particles7, INPs can bear a large impact on the hydrological cycle and the climate8. The ice formation ability and the abundance of INPs depend on temperature, particle types and their degree of atmospheric aging9. It is well known that dust particles from desert and agricultural lands7, biological particles and soil dust may contain biological material9 and constitute major sources of INPs for warmer mixed-phase clouds (MPCs), together with regionally black carbon and organic particles from biomass burning emissions for colder cirrus clouds4,6. Moreover, airmass 40 transport10-11 and atmospheric aging processes12-13 can modulate INP concentrations and characteristics14. The large uncertainty of the spatiotemporal variability of INPs7,15 (both abundance and distribution), together with the uncertainty of subsequent cloud processes such as ice multiplication16-18, leads to large uncertainty in the effects of INPs in regional and global weather, climate, and Earth system models4,9. Therefore, there is a significant need to improve the predictability of INPs in models8. Driven by periodic (seasonal and diurnal) solar radiation and anthropogenic activities and their effects on aerosol 45 sources19, INPs may also exhibit a following periodicity20-21. Previous studies reported a seasonal periodicity of INPs in different regions22-24, however, studies about INP variabilities within a day are still scarce, owing to the insufficient time resolution of offline INP spectrometers, short duration of observations and incomplete attribution of INP sources. Diurnal variability of INPs can be an especially important driver for ice formation in orographic cloud systems, given their especially dynamic nature that requires high time resolution of INPs for accurate predictions. Rosinski et al.25 found that INPs (at −15 50 and −20°C) show concentration maxima at 06:00 and 18:00 local time of the day. Despite the short duration and time resolution of the data (4 hours), the results from Rosinski et al.25 suggested an INP diurnal cycle that subsequent studies supported11,23,2526, albeit with limited insights on the implications. For example, Isono et al.26 reported no appreciable diurnal variations for INPs (at −15°C) observed at the Manuna Loa observatory at ~3400 m above sea level (a.s.l.) (Hawaii, US) using an offline static cloud chamber to measure INPs every ~6 hours. Wieder et al.11 observed INPs at the Weissfluhjoch mountaintop (2693 55 m a.sl., Alps) at Davos, Switzerland every 2 hours using an offline droplet freezing assay and found a diurnal cycle of INPs showing a minimum in the morning and a peak after sunset. Night time observations, however, were missing11. Recently, the availability of automated online INP spectrometers27-28 with high temporal resolution ability facilitates investigations on the INP diurnal periodicity. Using an automated continuous flow diffusion chamber, Brunner et al.23 performed a year-long continuous INP observations at a high altitude observatory at Jungfraujoch (~3580 m a.s.l., Alps, Switzerland) and reported 60 the diurnal periodicity of INPs for days only with planetary boundary layer (PBL) air mass intrusions without Saharan dust events. However, specific aerosol source that drives the diurnal cycles was not determined23. Temporal changes of INPs also 3 depend on the airmass origin at the site, given that the diurnal cycle of aerosols shows differences if it originates from the PBL or free troposphere (FT)29-30. Contrast to these studies, Wieder et al.11 found no diurnal cycles of INPs at Wolfgangpass (1632 m a.s.l., Alps, at Davos) which tends to reside in the PBL during the observation period, but different from another in parallel 65 observation site near Weissfluhjoch ( ~1.0 km higher) at the mountaintop. Therefore, determining the atmospheric condition of the observation site is necessary for investigating the INP abundance and its diurnal periodicity. In mountainous regions, local or regional biological particle emission20 sources in the PBL exhibiting a diurnal cycle may regulate INP variabilities; thus the diurnal changes of PBLH may also regulate the availability of aerosol particles from sources in the PBL31. Of particular interest is the importance of bioaerosols, since most often forested areas are in mountainous 70 regions. If not forested, arid regions could also be sources of dust, given that mountain flows in general can generate high velocities that are capable of lifting large amounts of dust. Such processes are poorly described or lacking in models, thus it is critical to assess their importance for INP diurnal variability. This is because when combined with the effects of ice multiplication they can generate heavy snowfall and extreme precipitation close to the ground during winter storms, which is demonstrated in a modelling study18 in parallel with this study during the same field campaign. 75 In this study, we present the diurnal variability of INPs and the drivers thereof for orographic MPCs in the Eastern Mediterranean. The data were collected during a field campaign, the Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISHTO, https://calishto.panacea-ri.gr/) between October and November, 2021. CALISHTO was conducted at the Helmos Hellenic Atmospheric Aerosol and Climate Change (in short as (HAC)2 hereafter) station (37.9843° N, 22.1963° E, 2314 m a.s.l.) close to the summit of Mt. Helmos in the Pelloponnese. The diurnal cycle of INPs was determined for a 80 variety of atmospheric states, including the relative position of the (HAC)2 with respect to the PBL, airmass origin and the effect of Saharan dust events. The PBL property and detailed INP source apportionment results achieved in parallel works for CALISHTO32-33 aid the determination of the drivers for INP cycles. We examine the distinct contributions of bioaerosol and dust to the diurnal cycle and express the INP distribution in relation to the PBL height (PBLH). RESULTS 85 Determination of atmospheric conditions at (HAC)2 (HAC)2 contributes data to the Global Atmospheric Watch and ACTRIS programs since 2016 and is located near the summit of Mt. Helmos at the heart of the Peloponnese in Greece. (HAC)2 is frequently situated in the FT or at the FT/PBL interface31 at a cross-road of different airmasses, each of which is characterized as different INP sources32. The observational setup of CALISHTO is presented in the Supplementary Fig. S1, and also elsewhere in detail32, including high resolution measurements 90 of in-situ INPs (~6‒7 min), microphysical and chemical aerosol properties at (HAC)2, remote sensing measurements conducted at Vathia Lakka (VL) lower than (HAC)2 by ~0.5 km and back trajectory analysis for calculating the origin of airmasses sampled at (HAC)2. Timeseries of measurement results are presented and introduced in Fig. S2 and Text S2 in the Supplementary. The PBLH measured by the wind lidar at VL, i.e. the distance between VL and the FT/PBL interface, is used 4 to determine whether (HAC)2 is in the PBL31 by comparing PBLH with the distance between (HAC)2 and VL, i.e. ~0.5 km 95 (illustrated in Supplementary Fig. S1). We also compare the concentration of particles between 95 nm and 800 nm (SMPS>95nm, <800nm) measured by a scanning mobility particle sizer (SMPS, model 3938, TSI Inc., US) with a threshold value (100 std cm−3) to determine if the site is in the PBL32,34 when PBLH from lidar remote sensing measurements is not available. A thorough evaluation against other metrics and the PBLH31,35 demonstrated that the site is in the PBL when SMPS>95nm, <800nm exceeds 100 std cm−3, given a general fact that aerosols in the FT will be diluted after transporting from far-away PBL sources31. 100 Throughout the whole campaign, our results show that SMPS>95nm, <800nm is generally (for 82.5% of the period) larger than 100 std cm−3 when (HAC)2 is in the PBL (PBLH>0.5 km), while SMPS>95nm, <800nm is generally (for 85.0% of the period) smaller than 100 std cm−3 when (HAC)2 is outside of the PBL (PBLH<0.5 km). Fig. 1 Diurnal cycles of PBLH measured by wind lidar31 at the VL site (on the left axis) and corresponding diurnal cycles of the 105 number concentration of particles with diameter between 95 nm and 800 nm (SMPS>95nm, <800nm) measured at (HAC)2 (on the right axis). Solid lines indicate the median value and the shading area around the median line shows the range between 25th and 75th quartiles. The horizontal dashed red line indicates both the altitude difference between (HAC)2 and the lidar (~0.5 km) and the threshold particle number value of 100 std cm−3 31,35. Different (HAC)2 atmospheric conditions are classified in different panels. (a) All observations during the campaign. (b) (HAC)2 in the FT throughout the day. (c) (HAC)2 in the PBL throughout the day. (d) 110 (HAC)2 in the PBL throughout the day without dust event influence. (e) (HAC)2 in the PBL throughout the day with dust event 5 influence. (f) (HAC)2 for days with both PBL and FT influences. The data points of each (HAC)2 position scenario are resampled for every 20 min and each panel shows a period cycle of 24 h starting at 00:00 UTC+2 (local time) of the day. Depending on the (HAC)2 position with respect to the FT/PBL interface (indicated by PBLH), we classify its atmospheric scenario. The scenarios include (HAC)2 in FT throughout the day (Fig. 1b for PBLH<0.5 km, 5 days), (HAC)2 in PBL 115 throughout the day (Fig. 1c for PBLH>0.5 km, 9 days), and (HAC)2 partially in the PBL throughout the day (Fig. 1f, 30 days), which is termed (HAC)2~PBL means (HAC)2 fluctuates around PBL/FT interface. Continuous dust events were recorded between November 5 and 9 (Supplementary Fig. S2). Thus, we further divide the scenario of (HAC)2 in the PBL throughout the day into two cases, i.e. without (Fig. 1d, 4 days) and with (Fig. 1e, 5 days) dust events – to investigate the effect of Saharan dust events on INP variability. The presence of dust event is determined by significant increase in coarse-sized particles (>2.5 120 μm), low Ångstrӧm exponent (<1)32 from optical scattering measurement and the spatiotemporal dust distribution predicted by modelling experiments (Supplementary Fig. S2c, d and f respectively). For the scenario of (HAC)2 in FT throughout the day (Fig. 1b), no appreciable diurnal cycle is seen for both PBLH and SMPS>95nm, <800nm, while a clear diurnal cycle is observed for days where the (HAC)2 is in the PBL (Fig. 1c), showing the maximum in the afternoon. The drivers of PBLH diurnal cycles are discussed in Supplementary Text S3 based on results in Figs. S3 to S6. We note that larger SMPS>95nm, <800nm concentrations 125 of non-dust days (Fig. 1d) compared with those of dust days (Fig. 1e) are because of difference in aerosol sources. On nondust days, PBL is influenced by continental aerosols enriched in fine mode particles (generally <500 nm), whereas the presence of dust events on dust days tends to shift the size distribution of aerosols to larger particle with a depletion in fine mode particles according to parallel studies on the aerosol sources32 and PBLH36 at (HAC)2 during CALISHTO campaign. This is also supported by results presented in Supplementary S4 showing the diurnal cycles of aerosol particles in different size ranges 130 (in particular for particles <500 nm, see Figs. S7 and S8). 6 INP diurnal cycles under different atmospheric conditions Fig. 2 Diurnal cycles of INP (tested at T=−25.2±1.4°C, on the left axis) and APS>0.5μm, total (the total number concentration of particles between 0.5 and 20 μm measured by an Aerodynamic Particle Sizer, on the right axis) measured at (HAC)2 under different 135 atmospheric conditions. Solid lines indicate the median value and the shading area around the median line shows the range between 25th and 75th quartiles. Different (HAC)2 atmospheric conditions are classified in different panels. (a) All observations during the campaign. (b) For days only in the FT. (c) For days only in the PBL. (d) Days in the PBL without dust events. (e) Days in the PBL with dust events. (f) Observations for days not exclusively in the PBL or FT. The data points of each scenario are resampled for every 20 min and each panel shows a cycle period of 24 h starting at 00:00 UTC+2 (local time) of the day. The Pearson correlation 140 coefficient (R) and Spearman’s rank coefficient (ρ), as well as corresponding p values, are provided to evaluate the correlation between INP concentration and APS>0.5μm, total. The P value is the probability of obtaining an R (ρ) value no smaller than the true R (ρ) value if there is no liner correlation between INP and APS>0.5μm, total. The number of data points (n) for each case of above statistical analysis is 72. Analysis of the INPs over 24h periods (e.g. Fig. 2) reveals that INPs at (HAC)2 follow diurnal periodicities depending on the 145 PBL condition. The INP number concentration was tested by a portable ice nucleation experiment (PINE) at T=−25.2±1.4°C in the mixed phase cloud regime (Fig. S2b). PINE samples aerosols from an omnidirectional total inlet and tests INPs in all freezing mode by addressing supersaturated conditions with respect to water32. Median INP concentration overall increases from 3.0 std L−1 during daybreak to a maximum of 12.0 std L−1 in the early afternoon (12:00‒15:00) and subsequently decreases to approximately 3.0 std L-1 (Fig. 2a). Then it remains a fairly constant level of 2.0‒3.0 std L−1 throughout the evening until 150 the early morning (at 3:00), after which the concentration occasionally spikes up to ~10.0 std L−1 (Fig. 2a). Fig. 2 shows that 7 the INP diurnal patterns for days when (HAC)2 is influenced by PBL airmasses (Fig. 2c, d ,e and f) are generally analogous to the overall INP diurnal periodicity presented in Fig. 2a but show different maximum and minimum values, however, INPs for days only in the FT (Fig. 2b) do not exhibit such a diurnal cycle but generally show median concentration values less than 3.0 std L−1 throughout the day. The above results suggest that the source of INPs observed at (HAC)2 originates primarily from 155 the PBL, and that the diurnal cycle of INPs is driven by the influx of aerosol particles from the PBL to the site. This is consistent with Brunner et al.23, that found for Jungfraujoch, an absence of a diurnal cycle was observed when the site is in the FT but a clear diurnal cycle is observed when influenced by PBL airmasses. Fig. 3 Diurnal cycles of INP (tested at T=−25.2±1.4°C, on the left axis) and the concentration of aerosol particles between 2.5 and 20 160 μm (APS>2.5μm, on the right axis) measured at (HAC)2 under different atmospheric conditions. Solid lines indicate the median value and the shading area around the median line shows the range between 25th and 75th quartiles. Different (HAC)2 atmospheric conditions are classified in different panels. (a) All observations during the campaign. (b) For days only in the FT. (c) For days only in the PBL. (d) Days in the PBL without dust events. (e) Days in the PBL with dust events. (f) Observations for days not exclusively in the PBL or FT. The data points of each scenario are resampled for every 20 min and each panel shows a cycle period of 24 h 165 starting at 00:00 UTC+2 (local time) of the day. The Pearson correlation coefficient (R) and Spearman’s rank coefficient (ρ), as well as corresponding p values, are provided to evaluate the correlation between INP concentration and APS>2.5μm. The p value is the probability of obtaining an R (ρ) value no smaller than the true R (ρ) value if there is no liner correlation between INPs and APS>2.5μm. The number of data points (n) for each case of above statistical analysis is 72. 8 170 Table 1. The Pearson correlation coefficient (R) and Spearman’s rank coefficient (ρ) for the relationship evaluation between diurnal INP median number concentration and the median number concentration of aerosol particles with different sizes under different atmospheric conditions. A critical p value of 0.05 from F-test for R and ρ is used to assess the significance level of the relationship. A p value smaller than 0.05 suggests that the probability of obtaining an R (ρ) value no smaller than the true R (ρ) value is less than 5% if there is actually no liner correlation between INPs and the given parameter, thus the calculated R (ρ) is of statistical 175 significance. Evaluated significant relationship is indicated in bold. The correlation coefficients are also provided in Figs. 2 and 3 and Figs. S7 to S12 in Supplement S4. Scenarios All observations (HAC)2 in FT (above PBL) (HAC)2 in PBL (HAC)2 in PBL without dust events (HAC)2 in PBL with dust events (HAC)2 ~ PBL top R (p) ρ (p) R (p) ρ (p) R (p) ρ (p) R (p) ρ (p) R (p) ρ (p) R (p) ρ (p) APS>0.5μm, total a 0.69 0.52 0.40 0.45 0.35 0.39 0.46 0.53 0.73 0.65 0.65 0.54 <0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 SMPS+APStotal a 0.45 0.29 0.13 0.03 0.51 0.46 0.05 0.11 0.15 0.23 0.33 0.25 <0.01 0.01 0.27 0.81 <0.01 <0.01 0.70 0.37 0.20 0.06 0.05 0.04 SMPS<500nm b 0.40 0.23 0.12 0.01 0.51 0.47 0.04 0.10 0.12 0.18 0.27 0.20 <0.01 0.05 0.31 0.90 <0.01 <0.01 0.73 0.40 0.33 0.13 0.02 0.09 APS>0.5μm, <1.0μm c 0.59 0.43 0.33 0.43 0.58 0.55 0.47 0.53 0.44 0.40 0.62 0.53 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 APS>1.0μm, <1.5μm d 0.71 0.62 0.43 0.42 0.09 0.08 0.37 0.34 0.77 0.77 0.63 0.55 <0.01 <0.01 <0.01 <0.01 0.47 0.48 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 APS>1.5μm, <2.0μm e 0.60 0.53 0.44 0.38 0.08 0.09 0.28 0.32 0.76 0.77 0.57 0.54 <0.01 <0.01 <0.01 <0.01 0.49 0.46 0.02 0.01 <0.01 <0.01 <0.01 <0.01 APS>2.0μm, <2.5μm f 0.58 0.53 0.43 0.35 0.10 0.09 0.24 0.31 0.75 0.75 0.51 0.46 <0.01 <0.01 <0.01 <0.01 0.41 0.44 0.05 0.01 <0.01 <0.01 <0.01 <0.01 APS>2.5μm g 0.64 0.54 0.46 0.40 0.15 0.14 0.30 0.34 0.70 0.65 0.57 0.49 <0.01 <0.01 <0.01 <0.01 0.19 0.23 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 a Total particle (0.5 ‒ 20 μm) number concentration measured APS; b Total particle (0.01 ‒ 20 μm) number concentration measured by both SMPS and APS; c The number concentration of particles smaller than 500 nm measured by SMPS; d The number concentration of particles between 0.5 and 1.0 μm measured by APS; e The number concentration of particles between 1.0 and 1.5 μm measured by APS; f The number 180 concentration of particles between 1.5 and 2.0 μm measured by APS; g The number concentration of particles between 2.0 and 2.5 μm measured by APS; h The number concentration of particles larger than 2.5 μm measured by APS. The dependence of INP diurnal cycles on aerosol particle size To study the correlations between the diurnal variabilities of INPs and aerosol particle sizes, we compare the INP diurnal cycles with diurnal changes of the number concentration of total aerosol particles in different size ranges measured by a SMPS 185 (10‒800 nm, electrical mobility diameter) and an Aerodynamic Particle Sizer (0.5‒20 μm, aerodynamic diameter). Particles in different size ranges used to compare with INPs include SMPS+APStotal (0.01‒20 μm, Fig. S7), APS>0.5μm, total (0.5‒20μm, Fig. 2), SMPS<500nm (Fig. S8), APS>0.5μm, <1.0μm (Fig. S9), APS>1.0μm, <1.5μm (Fig. S10), APS>1.5μm, <2.0μm (Fig. S11), APS>2.0μm, <2.5μm (Fig. S12) and APS>2.5μm (Fig. 3). The definition of particles in each size range is provided in the footnote of Table 1. SMPS+APStotal is superimposed by both SMPS and APS results32,37. Additionally, scatter plots comparing INP number 190 concentrations with meteorological parameters (ambient T, horizontal wind velocity and direction) and different aerosol properties (fluorescent and optical properties, as well as eBC mass concentration) under different PBL conditions are provided 9 in Fig. S13 in Supplementary S5. Also, the correlation between INP diurnal cycles and eBC mass concentration and aerosol optical properties are also examined respectively (Figs. S14 to S16 in Supplementary S6). Given the size dependence of INPs6,38, APS>0.5μm, total particles are assumed to be major INP contributors in the 195 literature whereas smaller-sized particles (<0.5 μm) are assumed to be insignificant INPs, which are even not included in some INP parameterizations7,39-40. Coarse mode particles (e.g. APS>2.5μm) are relevant for dust and bioaerosols and considered with a higher probability of serving as INPs6-7. Here, we evaluate the contribution of APS>0.5μm, total and APS>2.5μm particles to the observed INPs under different atmospheric conditions (Fig. 2 and Fig. 3) and also discuss the importance of particles in other size ranges (Table 1). The APS>0.5μm, total (Fig. 2a) and APS>2.5μm (Fig. 3a) concentration cycles from all observations show an 200 overall diurnal cycle similar to that of INPs, respectively. Overall diurnal cycles and significant correlations with INPs are also observed for particles in other size ranges presented in Table 1 and Figs. S7a to S12a. Analogous to the INP data for days only in FT, none of size-resolved particles presents a diurnal cycle (e.g. Fig. 2b and Fig. 3b), but all shows the lowest median value at the same hours as compared to the other scenarios influenced by PBL air masses. Notably, Fig. 2b shows an overall constant APS>0.5μm, total to INP concentration ratio (for median values) of 205 approximately 250, while Fig. 3b presents that the difference between APS>2.5μm and INP medians is generally within a factor of 10. Table 1 shows that particles having a size range larger than 0.5 μm have significant (p<0.05) and positive correlations with INPs throughout the day in FT whereas particles smaller than 0.5 μm (SMPS<500nm) show an insignificant role, which highlights the importance of particles larger than 0.5 μm and is consistent with the literature7,39-40. The case of SMPS+APStotal in FT (Table 1 and Fig. S7b) presents similar results to that of SMPS<500nm, given that SMPS<500nm takes a major fraction of 210 SMPS+APStotal32. For days when (HAC)2 is only in the PBL, Table 1 shows that both APS>0.5μm, total and SMPS+APStotal present significant contributions to the observed INP diurnal cycles (also Fig. 2c and Fig. S7c). It also shows that only particles within a size range smaller than 1.0 μm significantly contribute to INP diurnal cycles whereas particles with a larger size range (>1.0 μm) are weakly linked to INP variabilities. This may be because fine mode particles have much higher number concentrations 215 than coarse mode particles32 and also because particles from various sources that span different size ranges are responsible for the observed INPs in the PBL32. Thus, a higher particle number concentration (despite being associated with smaller-sized particles) is associated with INP variability when aerosol sources affecting the aerosol population are many. Only when INP sources are further determined, can the importance of larger-sized particles for INP variability be determined, which is demonstrated by the results in Table 1 for the case of (HAC)2 in PBL with and without dust events. It shows that only particles 220 in a size range larger than 0.5 μm have significant correlations to INP diurnal medians while smaller-sized particles (SMPS<500nm and SMPS+APStotal) are insignificant. This is consistent with the case of (HAC)2 in FT and the literature7,39-40. In addition, there are different size dependences for INP variability observed between cases of (HAC)2 in PBL with and without dust events. Table 1 shows that with an increasing size range for particles larger than 0.5 μm (from a range of 0.5‒1.0 μm to a range of larger than 2.5 μm), the significance of larger-sized particle become less and less pronounced for non-dust days. 225 Again, this highlights a more important role of number concentration for particles to contribution to INPs in the PBL with 16 days (e.g. ABCWIBS with R=0.78 in Fig. 5d) but weaker on dust days (e.g. ABCWIBS with R=0.40 in Fig. 5e). The generally much stronger correlations between the diurnal cycles of INPs and all different types of FBAPs for non-dust days in PBL compared with those of dust days suggest the overall enrichment of different types of biological particles for non-dust days. Also, it indicates that continental and local aerosols are the primary sources of biological particles but not the transported dust plume, which is consistent with the aerosol source apportionment conducted in a parallel study32 and also results in Table 2. 370 The above demonstrate FBAPs are primary drivers of the INP diurnal cycles observed at (HAC)2 in PBL. Fig. 6. Diurnal cycles of different types of fluorescent biological aerosol particles on days without (left axis) and with (right axis) dust events. Solid lines indicate the median value and the shading area around the median line shows the range between 25th and 75th quartiles. (a) FluoWIBS, (b) ABCWIBS, (c) AWIBS, (d) BWIBS, (e) CWIBS, (f) ABWIBS, (g) ACWIBS and (h) BCWIBS. 375 17 The influence of dust events on aerosol property and INP diurnal variability Remotely-transported dust plumes modulate aerosols and INPs at (HAC)2 in the PBL. We present the influences of dust events on aerosol property observed at (HAC)2 by comparing the diurnal cycles of size-resolved total aerosol particle (Fig. S28) and FBAP number concentration (Fig. S29), as well as different types of FBAP (Fig. 6), observed on non-dust days with dust days. Further discussions on the correlations between aerosol property changes and INP variabilities (see Fig. 2 to 5) for both cases 380 will be conducted. The results in Fig. S28 show that total aerosol particles with size smaller than 0.5 μm during non-dust days are more than those on dust days by at least a factor of 10 (panel c). Dust events enrich particles in size ranges larger than 1.0 μm (panel e to h) for dust days by a factor of 5~10 compared with those of non-dust days. Similarly, comparing with dust days, Fig. S29 shows that non-dust days contain more FBAPs of size below 2.0 μm (panel b, c and d) but less FBAPs larger than 2.5 μm (panel f). These results suggest continental aerosols enriched with fine mode aerosols dominate the INPs during non385 dust days and exhibit substantially different size and contribution of INPs in comparison with dust episode days, and is consistent with Gao et al.32 for air mass characteristics at (HAC)2. Fig. 6 generally show that dust days have much higher number concentration of total FBAPs indicated by FluoWIBS in the morning from 00:00 to 08:00 hrs. The enriched FABPs are primarily attributed to CWIBS and BCWIBS particles (Fig. 6e and h) often reported to be associated with dust events and biological particles47. The enriched CWIBS and BCWIBS may be large390 sized (3−10 μm as seen in Fig. 6e from Gao et al.32) dust-containing particles that are more effective INP (Fig. S29f ) than seen for smaller particles, thus can explain the higher INP concentrations on dust days (Fig. 2e to 5e) compared with those on nondust days (Fig. 2d to 5d). From 08:00 to 16:00 hrs, FluoWIBS particles on non-dust days majorly consist of ABCWIBS, BWIBS and BCWIBS particles, while FluoWIBS particles on dust days mostly include ABCWIBS, AWIBS, CWIBS and BCWIBS particles (Fig. 6). The higher 395 concentration of ABCWIBS (by a factor of ~2) and BWIBS particles (by a factor of ~4) on non-dust days may explain their high number concentration of INP (by ~50%) compared with those on dust-days, given that ABCWIBS are of highest probability of being biological particles32,47 thus IN-active at warm temperatures. BWIBS particles on non-dust days are likely from continental aerosols smaller than 2.0 μm (see Fig. 6c in Gao et al.32), and may be related to bacteria that may be IN active53. Differently, the enriched AWIBS and CWIBS particles on dust days are probably of sizes between 3 and 10 μm (see Fig. 6a and d in Gao et 400 al.32), which can be attributed to fungal spores and/or fragmented pollen grains of similar sizes47,50 or small bacteria combined with large-sized dust particles45. Additionally, the higher concentrations of FABPs for both non-dust and dust days between 08:00 and 16:00 hrs than the other periods of the day coincide with the noon peaks of INPs for both cases (Fig. 2 to 5). Notably, the peak of INPs on non-dust days at 12:00 hrs overall shows an overlap with that of ABCWIBS (Fig. 5d and Fig. 6b) but not for APS>2.5μm particles (Fig. 3d), supporting that the contribution of FBAPs to the observed INPs is more important than the 405 total coarse-sized aerosol particles. In contrast, the coincided overlaps of INP and BCWIBS (APS>2.5μm) particles on dust days as shown in Fig. 6h (Fig. 3e) may confirm the significant role of dust particles as INP contributors. 18 From 16:00 hrs to the end of the day, FluoWIBS particles on non-dust days are up to twofold higher than for dust days (Fig. 6a). Those enriched FABPs on non-dust days are generally contributed by ABCWIBS, BWIBS and BCWIBS particles (panel b, d and h). In particular, the elevated concentration levels of FABPs (ABCWIBS, BWIBS and BCWIBS) coincide with the spark of 410 INP evening peak (by 50%, from 20:00 to 22:00 in Fig. 2d to 5d) on non-dust days, suggesting the contribution of FBAPs to the increase in INPs. In contrast, a relatively constant FABP median concentration (e.g. FluoWIBS and the other types) corresponds to a stable INP abundance during the same period (Fig. 2e to 5e). Therefore, FBAP and dust particles are the key drivers for the INP diurnal cycles observed at (HAC)2. ABCWIBS and BWIBS FBAPs are the most important particles regulating INPs in the PBL without dust events. Dust events can substantially enrich larger-sized dust containing FABPs (AWIBS and 415 CWIBS) during noon time when PBL is high (suggesting that airmasses in PBL may also be of the sources of AWIBS and CWIBS FBAPs), while they supply more CWIBS and BCWIBS FABPs when PBLH is below the (HAC)2, which contribute to the INP when the site is outside of the PBL (nighttime). 19 Vertical INP distributions with respect to PBL/FT interface for each atmospheric classification 420 Fig. 7 INP concentration distribution as a function PBLH. The color scale shows the ice nucleation experiment temperature. Solid lines indicate the median value and the shading area around the median line shows the range between 25th and 75th quartiles. INP data was filtered within a small temperature range as indicated in each panel, to reduce the temperature dependence of INPs. Different (HAC)2 atmospheric conditions are classified in different panels. (a) All observations during the campaign. (b) For days only in the FT. (c) For days only in the PBL. (d) Days in the PBL without dust events. (e) Days in the PBL with dust events. (f) 425 Observations for days not exclusively in the PBL or FT. Data points were resampled for every 20 min. The Pearson correlation coefficient (R) and the Spearman rank coefficient (ρ), as well as corresponding p values, are provided to evaluate the correlation between INP concentration and PBLH. The p value is the probability of obtaining an R (ρ) value no smaller than the true R (ρ) value if there is no liner correlation between INP concentration and PBLH. The n value is the number of data points for the statistical analysis. 430 Fig. 7 illustrates INP concentration distributions with PBLH under different atmospheric conditions. In general, INPs show a positive and significant linear correlation with increasing PBLH (Fig. 7a). For days exclusively in the FT (Fig. 7b), INPs 20 overall decrease with decreasing PBLH, showing a statistically significant (p=5.37×10-3) but weak correlation level (R=0.25). This means that INPs become rarer deeper inside the FT, as the total aerosol particles do (Supplementary Figs. S31b to S36b). For days only in PBL without dust events (Fig. 7d), INPs show small variations with changing PBLH. This is because airmasses 435 in the PBL are intensively mixed54 and most aerosol particles in the PBL generally show insignificant dependence on the PBLH (Figs. S31d to S33d), except FBAPs, i.e., FluoWIBS and ABCWIBS (Figs. S34d and S35d). Supplementary Figs. S34d and S35d present that both FluoWIBS and ABCWIBS for non-dust days increase with increasing PBLH, showing strong correlations (R=0.72 and 0.64 respectively). The reason for the different dependence of INPs and FBAPs on the PBLH for the case of days in the PBL without dust may be twofold. First, considering the active ice nucleation ability of bioaerosols that activate as ice 440 at warm temperatures (>−20°C)39, FBAPs may only partly contribute to INPs tested at colder temperatures (~−27°C) in PBL case. In addition, FBAPs, such as ABCWIBS and FluoWIBS, may not include the total number of particles with biological material that contribute to the observed INPs. For days when (HAC)2 was only in PBL with dust events (Fig. 7e), the INP concentration does not vary significantly with changing PBLH and it shows an INP peak close to PBLH=1.0 km. The peak can be explained by the peak (also for PBLH=1.0 km) of APS>2.5μm as a function of PBLH (Supplementary Fig. S33e), given that coarse-sized 445 particles are more effective INPs. For days not exclusively in the PBL or FT (Fig. 7f), the positive correlation between INP and PBLH is moderate and significant, similar to the overall observations for the campaign. DISCUSSION AND CONCLUSIONS We demonstrate the existence of diurnal INPs cycles for moderately-cool mixed-phase orographic clouds (~ −25°C) in the E. Mediterranean and determine their drivers. The diurnal cycles of aerosols sourced from planetary boundary layer (PBL; e.g., 450 bioaerosol) and remotely transported particles (e.g., Saharan dust) regulate the INP periodicity and levels. A strong diurnal INP cycle is seen when the observation site resides in the PBL and practically vanishes when in the free troposphere (FT). In particular, we reveal the relative importance of different INP sources, including bioaerosols, dust and eBC-containing particles, on the observed INP diurnal cycles. We show that INPs in FT come from total aerosol particles in different size ranges larger 0.5 μm (Table 1) but are not correlated with any types of fluorescent biological aerosol particles (FBAPs, Table 3). This 455 highlights the INP population observed in the FT is different from that in the PBL. Likely, this is because INPs in the FT are far away transported and aged particles, where as INPs in the PBL are much closer to their sources, e.g. biological particles. This is also indicative of the necessities of further studies on the source apportionment of INPs in the FT, which is also suggested by Gao et al.32 and will be investigated in our next field campaign at (HAC)2. The diurnal cycle of FBAPs in the PBL significantly contribute to the INP cycles at warm MPC temperatures (>−20°C). Abrupt 460 INP source changes from dust plume intrusions, can perturb the diurnal variability and vertical distribution of INP. In the presence of Saharan dust drives INP variability by enriched larger-sized particles (>1.0 μm; Table 1), while in its absence INPs are overall more linked to particles of size between 0.5 and 1.0 μm (Table 1). The elevated concentration of coarse-sized dust particles during Saharan dust events does not significantly increase INPs during daytime because the uplifting of PBL 21 airmasses with increased PBLH may bring more continental aerosol that impedes the dust plume intrusions or mixes with 465 dusty aerosols inducing aging and INP activity suppression. The competition between uplifting PBL airmasses and dust plume intrusions may also reduce the supply of bioaerosols from the lower PBL. During nighttime with Saharan dust events, coarsesized dust particles can increase INPs when the availability of PBL airmasses is low because of PBL contraction, which diminishes the variance of INPs throughout the day. Thus, the different contribution of dust particles during daytime and nighttime provides a control test and reveals the important role of bioaerosols for INPs in the MPC regime during daytime. It 470 is also notable that FluoWIBS concentrations explain more than 90% of INP concentrations during the dust event. Additionally, we demonstrate that eBC-containing particles (originating from anthropogenic activities or wildfires) play a negligible role in the diurnal periodicity of INPs in the MPC regime. In conclusion, we show that the diurnal cycles of INPs for orographic clouds in the E. Mediterranean is driven by the PBL airmasses, and that bioaerosols are a major driver of this variability in the absence of dust. The presence of dust influences the 475 variabilities of both FBAPs and INPs, showing enrichment in INPs particularly in the morning when PBL aerosol sources is less available with low PBLHs. Considering that bioaerosols are present at forested mountainous regions, they are one of the key regulators that primarily modulates the ice formation in orographic clouds and may also influence secondary ice production18. Such INP sources and diurnal forcing of clouds are rarely considered in models but are expected, based on the generality and ubiquity of the sources determined here. Given this and the importance of bioaerosol and the correct vertical 480 distribution of INPs for the formation of extreme precipitation in mountainous environments18 and potentially for cloud system development17, it becomes clear that such sources and corresponding cycles may be underappreciated drivers of cloud formation, aerosol-cloud interactions and extreme events. METHODS CALISHTO field campaign 485 To investigate aerosol-cloud interactions (including INP abundance and variability) in the eastern Mediterranean region, a field campaign, called the Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISTHO, https://calishto.panacea-ri.gr/)18,31-33 was carried out on the basis of the Hellenic Atmospheric Aerosol and Climate Change station (termed (HAC)2, ~2.3 km a.s.l., 37.984033° N and 22.196060° E) at Mount Helmos (Greece) between October and November 2021. (HAC)2 is an observation site that allows the study of INPs both under the PBL and FT conditions55 because 490 of its high altitude. The instrumentation set-up and timeseries results of different measurements are presented in Supplementary Figs. S1 and S2, respectively. An online INP spectrometer called Portable Ice Nucleation Experiment (PINE)28 was used to measure INP concentrations at (HAC)2 from an omnidirectional total inlet with a temporal resolution of 6~7 minutes. The PINE inlet has a 80% sampling efficiency for particles between 3 and 5 μm, and it decreases to approximately 50% for particles between 5 and 10 μm. In this study, the PINE was operated in a T range from ~−24 to −27 °C and Sw (saturation ratio with 495 respect to water) >1.0 to measure INPs activating as ice in all freezing mode32. A wind Doppler Lidar (HALO, StreamLine 22 Wind Pro model, HALO Photonics), deployed at a lower site than (HAC)2 (by ~0.5 km), was used to measure the PBLH and to determine the (HAC)2 position with respect to the PBL. Aerosol properties and meteorological parameters at (HAC)2 were monitored simultaneously to understand the variations of observed INPs. The characterized aerosol properties include the number concentration of aerosol particles larger than 95 nm recorded by a scanning mobility particle sizer (SMPS>95nm and 500 <800nm, SMPS model 3938, TSI Inc., US, measuring particle size distributions in the 10 to 800 nm diameter size range), the total concentration of aerosol particles (0.5‒20 μm, aerodynamic diameter) recorder by an aerodynamic particle sizer (TotalAPS, APS model 3321, TSI Inc., US), the concentration of particles (0.5‒30 μm, optical diameter) showing fluorescence from three fluorescent channels of a wideband integrated bioaerosol sensor (WIBS-5/NEO, Droplet Measurement Technologies, LLC. US), the scattering coefficient at 450 nm (Scatt450nm) and Ångstrӧm exponent at the 450-700 nm wavelength pair measured 505 by a nephelometer (Model 3563, TSI Inc., US), as well as the mass concentration of elemental black carbon (eBC) measured by an aethalometer (AE31, Magee Scientific, US). The recorded meteorological parameters include wind velocity and direction data, relative humidity wrt. water (RHw) and ambient temperature (Tambient). In addition, dust particle mass concentrations at different altitudes are calculated by the SKIRON model56-57 to diagnose the presence of Sahara dust events. The field campaign lasts for more than 6 weeks from October 12 to November 24, 2021. 510 Planetary boundary layer (PBL) condition determination The HALO wind lidar measures the vertical velocity of air masses carrying micron-sized aerosol particles at a stare mode emitting pulsed laser beams at 1.5 μm. Using the Doppler effect, the radial wind velocity along the direction of the laser beam can be retrieved and the distance between scatters (aerosols) in the beam path can be calculated58-59. The maximum detection range varies from 2.0 to 3.0 km depending on the micron-sized aerosol load in the atmosphere60. More detailed information 515 about the measurements and calculation for PBLH can be found in our parallel work31-32 for the CALISHITO campaign. To determine the PBL condition at (HAC)2 for time periods when PBLH is missing due to insufficient micron-sized aerosol load, a SMPS N>95nm threshold value of 100 std cm-3 was used to diagnose the position of (HAC)2 relative to the PBL27,34. For SMPS>95nm and <800nm > 100 std cm-3, it means (HAC)2 is within the PBL. Otherwise, it may be above PBL and more in the FT. The determination of dust events 520 To determine the presence of dust events around (HAC)2, the dust mass concentration calculated by the SKIRON model56-57 and the Ångstrӧm exponent32 at the 450-700 nm wavelength pair (see Supplementary S1) measured by nephelometer are used61. From Supplementary Fig. S2f, dust mass concentration decreases with decreasing altitude below (HAC)2. Thus, the dust mass concentration at (HAC)2 may be higher than the concentration calculated by the model at the highest altitude (2.17 km). Also, the Ångstrӧm exponent is compared to a threshold value of 1.0 and a smaller Ångstrӧm exponent value indicates 525 the presence of dust events at (HAC)2. Additionally, the footprints of air masses from Sahara are provided by air mass dynamic simulations in our parallel work to confirm the dust plumes originate from the Saharan desert. 23 Daily based data classification based on (HAC)2 condition with respect to the PBL An observation day will be classified as the scenario of (HAC)2 only in the PBL (or (HAC)2 only in the FT) if the PBLH is larger (or lower) than 0.5 km for more than 23 h throughout day, which otherwise will be attributed to a day neither exclusively 530 in the FT nor in the PBL ((HAC)2~PBL top). When PBLH data is missing, SMPS>95nm and <800nm results will be used to diagnose the atmospheric condition. Based on the above criteria and considering the influence of dust events, we classify the daily results into four cases as introduced in the main text, to calculate the diurnal cycles of INPs and aerosol properties using 20 min averaged data. The median values and 25th to 75th percentiles (Q25%–Q75%) are used to describe the INP and aerosol particle concentration levels in the diurnal cycles. This is because, away from sources, atmospheric aerosol particles tend to be log535 normally distributed62. Assuming the log-normally distributed data without any skewness, the median value equals to the lognormal mean value. Therefore, we report the median results as Brunner et al27. DATA AVAILABILITY The data presented in this publication will be made available at https://www.envidat.ch. The DOI link will be activated for 540 public access upon acceptance of publication. ACKNOWLEDGEMENTS This work was supported by PyroTRACH (ERC-2016-COG) funded from H2020-EU.1.1. (ERC), project ID 726165), the Swiss National Science Foundation project 192292, Atmospheric Acidity Interactions with Dust and its Impacts (AAIDI), the 545 European Union’s Horizon Europe project “CleanCloud” (Grant agreement No. 101137639), the “PANhellnfrastructure for Atmospheric Composition and climatE change (PANACEA)” (MIS 5021516) and the Laboratory of Atmospheric Sciences and their Impacts (LAPI) of the École Polytechnique Fédérale de Lausanne, Switzerland. AP and RF acknowledge funding by the Basic Research Program PEVE (NTUA) under contract PEVE0011/2021. We are also grateful to Biomedical Research Foundation of the Academy of Athens (BRFAA) for providing its mobile platform to host the NTUA AIAS lidar system. Dr. 550 Ghislain Motos is thanked for his help with WIBS data processing. AUTHOR CONTRIBUTIONS AN, AP and KE lead and coordinated the CALISHTO campaign. KuG and AN conceived and led this study. KuG led the analysis, wrote the original manuscript together with AN. KuG prepared all the figures with contributions from RF, AMB and 555 SV. RF setup and operated the radar and wind lidar instrumentation. FV and OM provided the PINE timeseries. All authors discussed the manuscript and provided feedback. COMPETING INTERESTS The authors declare that they have no conflict of interests. 560 24 ADDITIONAL INFORMATION Supplement information is provided to support the results and discussions in the main text. Correspondence and requests for materials should be addressed to Athanasios Nenes (athanasios.nen[email protected]) and 565 Kunfeng Gao ([email protected]h). REFERENCES 1. Mülmenstädt, J., Sourdeval, O., Delanoë, J., and Quaas, J. Frequency of occurrence of rain from liquid-, mixed-, and icephase clouds derived from A-Train satellite retrievals, Geophys. Res. Lett., 42, 6502-6509, (2015). 2. Lohmann, U., Friebel, F., Kanji, Z. A., Mahrt, F., Mensah, A. A., et al. Future Warming Exacerbated by Aged-Soot Effect 570 on Cloud Formation, Nat. 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