Characterization of aerosol particles at Cape Verde close to sea and cloud level heights-Part 2 : ice nucleating particles in air , cloud and seawater

Ice nucleating particles (INPs) in the troposphere can form ice in clouds via heterogeneous ice nucleation. Yet, atmospheric number concentrations of INPs (NINP) are not well characterized and although there is some understanding of their sources, it is still unclear to what extend different sources contribute, nor if all sources are known. In this work, we examined properties of INPs at Cape Verde from different sources, the oceanic sea surface microlayer (SML) and underlying water (ULW), the atmosphere close to both sea and cloud level as well as cloud water. 5 Both enrichment and depletion of NINP in SML compared to ULW were observed. The enrichment factor (EF) varied from roughly 0.4 to 11, and there was no clear trend in EF with temperature. NINP in PM10 sampled at Cape Verde Atmospheric Observatory (CVAO) at any particular temperature spanned around 1 order of magnitude below −15 ◦C, and about 2 orders of magnitude at warmer temperatures (>−12 ◦C). NINP in PM1 were generally lower than those in PM10 at CVAO. About 83±22%, 67±18% and 77±14% (median±standard deviation) of INPs 10 had a diameter >1 μm at ice activation temperatures of −12, −15, and −18 ◦C, respectively. Among the 17 PM10 samples at CVAO, three PM10 filters showed elevated NINP at warm temperatures, e.g., above 0.01 std L−1 at −10 ◦C. However, for NINP in PM1 at CVAO, this is not the case. At these higher temperatures, often biological particles have been found to be ice active. Consequently, the difference in NINP between PM1 and PM10 at CVAO, suggests that biological ice active particles were present in the super-micron size range. 15 NINP in PM10 at CVAO was found to be similar to that on Monte Verde (MV, at 744 m a.s.l) during non-cloud events. During cloud events, most INPs on MV were activated to cloud droplets. When highly ice active particles were present in PM10 filters at CVAO, they were not observed in PM10 filters on MV, but in cloud water samples, instead. This is direct evidence that these INPs which are likely biological are activated to cloud droplets during cloud events. In general, Cape Verde was often affected by dust from the Saharan desert during our measurement. For the observed air 20 masses, atmospheric NINP in air fit well to the concentrations observed in cloud water. When comparing concentrations of both sea salt and INPs in both seawater and PM10 filters, it can be concluded that sea spray aerosol (SSA) only contributed a minor 1 https://doi.org/10.5194/acp-2019-729 Preprint. Discussion started: 18 September 2019 c © Author(s) 2019. CC BY 4.0 License.


Introduction
Ice particle formation in tropospheric clouds can affect cloud properties such as cloud lifetime, their radiative effects on the atmosphere, and the formation of precipitation (Hoose and Möhler, 2012;Murray et al., 2012).Ice crystals in the atmosphere can be formed either via homogeneous nucleation below −38 • C or heterogeneous nucleation aided by aerosol particles known as ice nucleating particles (INPs) at warmer temperatures (>−38 • C).Immersion freezing refers to the process when an INP becomes immersed in an aqueous solution e.g., through the process of cloud droplet activation (Vali et al., 2015).Immersion freezing is suggested to be the most important freezing process for mixed phase clouds (Ansmann et al., 2008;Westbrook and Illingworth, 2013), and is the process we will focus on in this study.
Dust particles are recognized as effective INPs below −20 • C (Augustin- Bauditz et al., 2014) or even below −15 • C (Hoose and Möhler, 2012;Murray et al., 2012).Laboratory studies on natural mineral dusts from different regions have been conducted to quantify the particle's ability to nucleate ice (Niemand et al., 2012;DeMott et al., 2015).Mineral dust particles from deserts are composed of a variety of minerals, and K-feldspar is supposed to be more effective for ice nucleation than other minerals in the mixed-phase cloud temperature regime (Atkinson et al., 2013;Augustin-Bauditz et al., 2014;Niedermeier et al., 2015).
However, overall, desert dust particles from diverse sources show comparable ice nucleating efficiency (Boose et al., 2016).
Different parameterizations have been employed to summarize the mineral dust particle's ice nucleating ability (Niemand et al., 2012;Ullrich et al., 2017).
A few field measurements have been carried out to quantify the ice nucleation properties of desert dust.Based on airborne measurements, DeMott et al. (2003) found that ice nucleating aerosol particles in air masses over Florida had sources from the North African dust.Chou et al. (2011) observed a good correlation between the number concentration of larger particles and INP number concentration (N INP ) during a Saharan dust event at the Jungfraujoch in the Swiss Alps.Collecting airborne dust over the Saharan desert, Price et al. (2018) observed two orders of magnitude variability in N INP at any particular temperature from ∼ −13 to ∼ −25 • C, which was related to the variability in atmospheric dust loading, while desert dust's ice-nucleating activity was only weakly dependent on the differences in desert sources.Schrod et al. (2017) found that mineral dust or a constituent related to dust was a major contributor to the ice nucleating properties of the aerosol on Cyprus, and N INP in elevated dust plumes was on average a factor of 10 higher than at ground level.
Ocean water can be a potential source of INPs (Brier and Kline, 1959) which would originate from the biosphere.The source of INPs in ocean water might be associated with phytoplankton blooms (Schnell and Vali, 1976).Recently, Wilson et al. (2015) and Irish et al. (2017) found that organic material, with a diameter <0.2 µm, is the major ice nucleator in the sea surface microlayer (SML).Based on a long-term measurement of INPs in the marine boundary layer in the south of and around Australia, Bigg (1973) suggested that INPs in ambient air were contributed by marine aerosol particles.DeMott et al. (2016) https://doi.org/10.5194/acp-2019-729Preprint.Discussion started: 18 September 2019 c Author(s) 2019.CC BY 4.0 License.found that INPs from laboratory generated sea spray aerosol (SSA) aligned well with measurements from diverse regions over the oceans.Further evidence of a connection between marine biological activity and N INP was uncovered in their laboratory study (DeMott et al., 2016).In pristine marine conditions, such as the Southern Ocean, SSA was the main source of the INP population, but N INP was relatively low in the Southern Ocean as well as in the clean marine Northeast Atlantic (McCluskey et al., 2018a, b).These field measurements are consistent with the model work by Burrows et al. (2013), which emphasizes the importance of SSA contribution to INPs in remote marine regions.
It is currently still uncertain whether the coarse mode particles or smaller particles are the major source of atmospheric INPs.Vali (1966) found that the diameters of INPs were mostly between 0.1 and 1 µm.On the high alpine research station Jungfraujoch, Mertes et al. (2007) found that ice residuals were as small as 300 nm and they were mostly present in the submicron particle size range.Modeling studies also suggest that INP are a temperature dependent fraction of all particles with sizes above 500 nm (DeMott et al., 2010(DeMott et al., , 2015)).Conen et al. (2017) found INPs at −8 • C were equally distributed amongst the particles with sizes up to 2.5 µm and with sizes between 2.5 and 10 µm.Other field measurements reported that coarse mode particles were more efficient INP, e.g., INPs (mainly bacterial aggregates and fungal spores) occurred in the size range of 2 -6 µm (Huffman et al., 2013).Mason et al. (2016) found for Arctic aerosol that 91±9%, 79±17%, and 63±21% of INPs had an aerodynamic diameter of >1 µm at ice activation temperatures of −15, −20, and −25 • C, respectively.Creamean et al. (2018) also found that super-micron or coarse mode particles are the most proficient INPs at warmer temperatures in the Arctic and they might be biological INPs.Concerning biological INP, it should be mentioned that it is well understood by now that these feature macromolecules of only some ten nanometers in size at the most (Pummer et al., 2015).Some of them are easily separated from their carrier (e.g., from pollen and fungal spores), while others are embedded in the cell membrane (e.g., for bacteria), but based on these above cited literature results, it seems that most of the biological INPs still occur together with their original carrier in the atmosphere.
Direct measurement of N INP in the cloud water can be used to estimate concentrations of INPs in the air.Joly et al. (2014) measured total and biological (i.e., heat-sensitive) INPs between −5 to −14 • C in samples from the summit of Puy de Dôme (1465 m a.s.l., France).Petters and Wright (2015) summarized many INP spectra obtained from rain water, melted sleet, snow and hail samples at different sampling locations and reported a range of N INP for these precipitation samples.Based on a shipborne measurement of the east coast of Nova Scotia, Canada, Schnell (1977) directly compared N INP in the seawater to that in the fog water and found that N INP in fog water and seawater appeared to vary quite independently of each other.As one part of the here presented study, these field measurement values will be compared with values obtained from our measurement campaign in the framework of the MarParCloud (Marine biological production, organic aerosol particles and marine clouds: a Process Chain) project.
During the MarParCloud project, samples collected for INPs analysis include: SML and underlying water (ULW) from the ocean upwind of the island; quartz fiber filter samples of atmospheric aerosol, collected on a tower installed at the island shore (inlet height: 42 m a.s.l) and on a mountaintop (inlet height: 746 m a.s.l); and cloud water collected during cloud events on the mountaintop.In this study, we will first discuss N INP in the SML and ULW.We will then discuss N INP in the air, in the cloud water will be discussed.In addition, we will provide a feasible way to link N INP in ambient air, ocean water and cloud water.This connection can be drawn only during times when there were cloud events on the mountaintop, together with data on number concentrations on cloud condensation nuclei (N CCN ).Respective information was derived and discussed in an accompanying paper (Gong et al., 2019b).For more information about the campaign itself, we refer to an upcoming overview paper by van Pinxteren et al.

Sampling site
The measurements were carried out on São Vicente island at Cape Verde from 13 September to 13 October, 2017.We set up three measurement stations at Cape Verde, at the Cape Verde Atmospheric Observatory (CVAO), on Monte Verde (MV) and an Ocean Station (OS).CVAO (16 • 51 49 N, 24 • 52 02 W) is located in the northwestern shore of the island of São Vicente, 70 m from the coastline about 10 m a.s.l.Filter samplers were installed on top of a 32 m tower.MV (16 • 52 11 N, 24 • 56 02 W) is located on a mountaintop (744 m a.s.l), ∼7 km away to the west of CVAO.Filter samplers were situated on the ground, upwind of any installations on the mountaintop.The OS covered an area at ∼16 • 53 30 N, ∼24 • 54 00 W, with a distance of at least 5 km from the island.Details on the measurement site and the meteorological conditions can be found in the accompanying paper (Gong et al., 2019b).In short, the conditions at Cape Verde were quite stable, with temperature of on average 26.6 • C at CVAO and 21.2 • C at MV and wind speeds between 0.6 and 9.7 m s −1 with directions from the northeast.
In the following, the different samples collected during the campaign are described in detail.All of these samples were stored at −20 • C right after sampling.After the campaign the long-term storage and transport of the collected samples from Cape Verde to the Leibniz Institute for Tropospheric Research (TROPOS), Germany was carried out in a cooled container at −20 • C. At TROPOS, all samples were again stored frozen at −20 • C until analysis was done.Measurement sites, locations, sample types and additional information are summarized in Tab. 1.
Following the description of the sampling, we will briefly introduce the measurement methods related to INPs, including freezing devices, N INP calculation and measurement uncertainties.Note that all the times presented here are in UTC (corresponding to local time +1).For better comparison, all ambient particle number concentrations in this study are given for standard temperature and pressure (STP, 0 • C and 1013.25 hPa).

Seawater sampling
Seawater samples were taken at the OS by using a fishing boat at a distance of at least 5 km from the coast (off-shore samples).
The SML samples were collected using a glass plate sampler (Harvey and Burzell, 1972;Irish et al., 2017;van Pinxteren et al., 2017).The glass plate had a surface area of 2000 cm −2 and was immersed vertically into the ocean and then withdrawn at a slow rate (between 5 to 10 cm s −1 ) and allowed to drain for less than 5 s.The surface film adhering to the surface of the https://doi.org/10.5194/acp-2019-729Preprint.Discussion started: 18 September 2019 c Author(s) 2019.CC BY 4.0 License.glass was scraped off from both sides of the glass plate with a framed Teflon wiper into a 1 liter glass bottle.For each SML sample, several liters were collected and 1 liter was required ∼55 dips.Based on the amount of material collected, the number of dips and the area of the plate, the averaged thickness of the layer collected was calculated as ∼91.0 µm.ULW samples were collected at the same time and location as the SML samples.ULW was collected from a depth of 1 m by a specially designed device, which consists of a glass bottle mounted on a telescopic rod in order to monitor sampling depth.The bottle was opened underwater at the intended sampling depth with a specifically designed seal-opener.After collection, the glass bottles containing both the SML and ULW samples were kept in a freezer at −20 • C up to the analysis.During the campaign, 9 SML and 9 ULW samples were collected for INP analysis.Details of SML and ULW samples, including the sampling time, location, salinity and additional information are provided in the supplement, Tab.S1.

Aerosol particle sampling
Particle sampling was done using high-volume samplers with either a PM 10 -inlet and or a PM 1 -inlet (Digitel filter sampler DHA-80, Walter Riemer Messtechnik, Germany) operating with an average flow rate of ∼500 L min −1 for 24 hours sampling periods.The high-volume samples were collected on 150 mm quartz fiber (Munktell, MK 360) filters.The filters were preheated in our laboratory at 110 • C for 24 hours to remove the organic carbon background.After sampling, the filters were transported to a freezer where they were kept at −20 µm.The 50% lower size cut for the CASCC2 was approximately 3.5 µm diameter.The flow rate through the CASCC2 was approximately 5.8 m 3 min −1 .The CASCC2 is described in more details in Demoz et al. (1996).Between cloud events, the cloud water sampler was cleaned with a large amount (∼5 L) of ultrapure water.Once the collector was cleaned, a blank was taken by spraying about 200 mL of ultrapure water into the collection strands in the collector and subsequent sampling of this water.After collection, the cloud water samples were kept in a freezer at −20 • C.During the campaign, 13 cloud samples were collected for INP analysis.The details of cloud samples, including sampling time, duration, volume and additional information are provided in the supplement, Tab.S5.

Freezing devices
Two droplet freezing devices called LINA (Leipzig Ice Nucleation Array) and INDA (Ice Nucleation Droplet Array) have been set up at TROPOS in Germany.The design of LINA was inspired by Budke and Koop (2015).Briefly, 90 droplets with the volume of 1 µL were pipetted from the samples onto a thin hydrophobic glass slide, with each droplet being placed separately into its own compartment.After pipetting, the compartments were sealed at the top with another glass slide, to prevent the droplets from evaporation and to prevent ice seeding from neighboring droplets.The droplets were cooled on a Peltier element with a cooling rate of 1 K min −1 down to −35 • C, while the setup was illuminated by a circular light source from above.Once the cooling started, pictures were taken every 6 s by a camera.The number of frozen versus unfrozen droplets was derived automatically by an image identification program in Python.LINA was employed to measure SML, ULW and cloud water samples in this study.More detailed parameters and the temperature calibration of LINA and its application can be found in previous studies (Chen et al., 2018;Gong et al., 2019a).
The design of INDA was inspired by Conen et al. (2012), but deploying PCR-trays instead of separate tubes.For quartz fiber filters, circular pieces with a diameter of 1 mm were punched out.Each of the 96 wells of a PCR-tray were filled with the filter piece together with 50 µL of ultrapure water.For SML, ULW and cloud water samples, 50 µL of the water samples was filled into each PCR-tray.After sealing by a transparent foil, the PCR-tray was placed on a sample holder and immersed into a bath thermostat, where it was illuminated from below with a LED light source.The bath thermostat then decreased the temperature with a cooling rate of approximately 1 K min −1 .Real-time images of the PCR-tray were recorded every 6 s by a CCD (charge-coupled device) camera.Frozen droplets can be identified based on the brightness change during the freezing process.A program recorded the actual temperature of the cooling bath and related it to the real-time images from the CCD camera.The temperature in the PCR-trays had been calibrated.More detailed parameters and temperature calibration of INDA and its application can be found in previous studies (Chen et al., 2018;Hartmann et al., 2019). with where N total is the number of droplets and N(θ) is the number of frozen droplets at temperature θ.Eq. 1 accounts for the

Uncertainty and background
Because the number of INPs present in the washing water is usually small (some single up to a few tens of INPs per examined droplet/well), and the number of droplets/wells considered in our measurements is limited, statistical errors need to be considered in the data evaluation.Therefore, confidence intervals for f ice were determined using the method suggested by Agresti and Coull (1998).Previous studies (McCluskey et al., 2018a;Suski et al., 2018;Gong et al., 2019a) used the same method to calculate the freezing devices' measurement uncertainties.
For the quartz fiber filters, a background freezing signal resulting from the field blind filters was determined by doing a regular INDA measurement with these filters.Measured N INP from the sampled filters was corrected by subtracting the averaged background concentrations determined for the blind filters.A detailed explanation of background subtraction can be found in the supplement of Wex et al. (2019).All values for airborne N INP presented in the following are background-corrected.

Salinity correction of SML and ULW
SML and ULW samples were adjusted to account for the freezing depression caused by dissolved salts in sea water.First, based on Kreidenweis et al. (2005), the water activity can be calculated by: where the n solute and n water are the number of moles of solute and water in solution, respectively.i is the van't Hoff factor (Pruppacher and Klett, 2010).We assumed sea salt to be mainly sodium chloride, for which the van't Hoff factor is 2. The freezing depression temperature as a function of a w was taken from Koop and Zobrist (2009).In our study, this was roughly a correction by 2.2 • C.

Active surface site density
A thorough analysis of particle number size distributions (PNSDs) has been presented in Gong et al. (2019b), and based on these PNSDs we derived the particle surface area size distributions (PASDs) for use in this study (to be seen in the supplement, Fig. S10).This provides an opportunity to determine the temperature-dependent cumulative active surface site density (n s ) for aerosol particles.The n s is a measure of how well an aerosol acts as a seed surface for ice nucleation.The n s can be calculated as: Where A total is the concentration of the total particle surface area.et al., 2019).This could be the reason why N INP in SML in this study is lower than those reported in Wilson et al. (2015).These differences in EF between studies might partially due to differences in the techniques deployed in our and the other studies.SML samples were about ∼91.0 µm thick in this study, while for Wilson et al. (2015) those were between 6 to 83 µm.

N INP in air
Three different sets of filter samples were collected at CVAO and MV, i.e., CVAO PM 10 , CVAO PM 1 and MV PM 10 .In the    As for the first feature, we found that 83±22%, 67±18% and 77±14% (median±standard deviation) of INPs had a diameter of >1 µm at ice activation temperatures of −12, −15, and −18 • C, respectively.On average, over all temperatures, this INP number fraction for super-micron particles is roughly 70% (shown for a higher temperature resolution in Fig. 5), almost independent of temperature.Mason et al. (2016) and Creamean et al. (2018) also found that the majority of INPs is in the super-micron size range.However, they see even increasing fractions towards higher temperatures.For the present study, as 10 said above, only three of the examined 17 filters showed clearly elevated N INP at high temperatures, so overall such an increase was not observed.
As for the second feature, looking at Fig. 3

N INP at cloud level
In the companion paper (Gong et al., 2019b), we discussed PNSD and CCN number concentration (N CCN ) at CVAO and MV.
We found that particles are mainly well mixed in the marine boundary layer and derived the periods with cloud events, with a time resolution of ∼30 minutes, at MV.In the present study, N INP in PM 10 at CVAO and MV are compared.The fraction of time during which there was a cloud event to the total sampling time (cloud time fraction) for each filter is summarized in the supplement, Tab.S4.All of the filters were more or less affected by cloud events, with two filters being affected only little (cloud time fraction <10%), i.e., MV 1602 and MV 1603.When comparing results from these two filters to those from filters sampled at the same time at CVAO (see Fig. 6(a)), we found that N INP are quite similar close to sea level (CVAO) and cloud level (MV).This is in line with what was discussed in the companion paper (Gong et al., 2019b), i.e., the marine boundary is often well mixed at Cape Verde.Concerning the super-micron particles of likely biological origin that activated ice already at −10 • C and above (Fig. 4), it is observed that the related corresponding bump is not seen in the corresponding data from MV (MV 1610, MV 1614 and MV 1616 -to be seen in the supplement, Fig. S6).This indicates that these INPs were all activated to cloud droplets during the cloud events, and we will come back to this below.When highly ice active particles were present for CVAO PM 10 filters (CVAO 1596, CVAO 1641 and CVAO 1643), they were not observed for MV PM 10 (MV 1610, MV1614 and MV 1616, which had cloud time fractions of 52, 87 and 100%, respectively), but instead were found in cloud water samples (Cloud 19,Cloud 20 and Cloud 24).This is in line with what was outlined in section 3.2.2 that these highly ice active particles were activated to cloud droplets during cloud events.Periods during which clouds were present at MV, together with the sampling periods of all cloud water samples and selected CVAO PM 10 filters (those that had higher N INP at warm temperatures, CVAO 1596, CVAO 1641 and CVAO 1643) can be checked in the supplement, Fig. S7.

Connecting INPs in the cloud water with these in the air
In the following, N INP in the cloud water will be compared to that in the air.To be able to do this, we used measured values of N CCN to calculate cloud droplet number concentrations.These, together with an assumption on cloud droplet size (d drop ) yields the volume of cloud water per volume of air, given as F cloud_air in Eq. 6: For the calculation, we used N CCN measured at CVAO at a supersaturation of 0.30% (Gong et al., 2019b).N CCN was averaged for the different periods when each cloud water sample was collected.The chosen supersaturation corresponds to a critical diameter of roughly 80 nm, which is at the Hoppel minimum of the respective particle number size distributions (Gong et al., 2019b), indicating that this is indeed the relevant supersaturation occurring in the prevailing clouds.Based on previous studies (Miles et al., 2000;Bréon et al., 2002;Igel and Heever, 2017), we assumed that d drop varies between 7 and 20 µm and did separate estimates for these two values and additionally for 15 µm.
Following this approach, F cloud_air varied from 4.2*10 −7 to 1.1*10 −6 , with a median of 8.5*10 −7 .Previous studies used the liquid water content (LWC), which is a measure of the mass of the water in a cloud in a specified amount of dry air.
Typical ranges for LWC in thicker clouds are between 0.2 and 0.  There is general agreement between measured and derived N INP in air, however, with some variation where the values derived from cloud water samples are somewhat lower.This might be connected to a less than optimal sampling efficiency of the cloud water sampler, which has a 50% collection efficiency at 3.5 µm.Also the spread in the derived values, originating from the different assumed d drop , is rather large.Nevertheless, it is striking that at least within an order of magnitude, based on our comparably simple assumptions, an agreement between concentrations of INP in the air and in cloud water is found.found an enrichment of organic carbon in submicron sea spray particles of about 10 4 to 10 5 (Keene et al., 2007;van Pinxteren et al., 2017), and this value decreased to 10 2 for super-micron particles (Keene et al., 2007;Quinn et al., 2015).It is not Above we derived that N INP contributed from SSA only accounted for a minor fraction of total N INP in the air, as well as in the cloud water at Cape Verde.This still holds even when considering a possible enrichment of INPs in SSA up to 10 2 , which is an enrichment as given in literature for super-micron organic particles (Keene et al., 2007;Quinn et al., 2015).It can similarly be seen when considering that it has been described in literature that mineral dust is associated with a factor of 1000 higher ice surface site density (a measure to describe the ice activity per particle surface area), compared to SSA at temperatures from −12 to −35 • C (Niemand et al., 2012;DeMott et al., 2016;McCluskey et al., 2018a).In our study, the super-micron particles that make up a large fraction of the INPs we observed were mainly mineral dust, as described in the accompanying study (Gong et al., 2019b).The comparably high ice activity of mineral dust and the presence of mainly dust particles in the super-micron size range in our study again supports that indeed most INPs observed in this study were not from sea spray.This is in line  This raises the question if and how n s should be used to parameterize atmospheric INP measurements, which, however, is a question far too prominent to be answered in this study.

Summary and conclusions
The MarParCloud campaign took place in September and October 2018 on the island of Cape Verde to investigate aerosols prevailing in the Atlantic Ocean.In addition to a thorough analysis of the atmospheric aerosol particles and CCN in a companion paper (Gong et al., 2019b), samples collected for INPs analysis in this study include: sea surface microlayer (SML) and underlying water (ULW) from the ocean upwind of the island; quartz fiber filter samples of atmospheric aerosol, collected on a tower installed at the island shore and on a 744 m high mountaintop, as well as cloud water collected during cloud events on the mountaintop.N INP were measured offline with two types of freezing devices, yielding results in the temperature range including a comparison of N INP in PM 10 and PM 1 and a comparison of N INP close to both sea and cloud level.Lastly, N INP https://doi.org/10.5194/acp-2019-729Preprint.Discussion started: 18 September 2019 c Author(s) 2019.CC BY 4.0 License.
https://doi.org/10.5194/acp-2019-729Preprint.Discussion started: 18 September 2019 c Author(s) 2019.CC BY 4.0 License.2.3 Deriving N INP 2.3.1 Basic calculation Based on Vali (1971), the cumulative concentration of INP (N INP ) as a function of temperature per air or water volume can be calculated by: possibility of the presence of multiple INPs in one vial by assuming that INPs are Poisson distributed.As for the quartz fiber filters, V is the volume of air collected onto one circular 1 mm filter piece placed in each well, resulting in airborne N INP .The information of the air volume can be found in the supplement, Tab.S2, Tab.S3 and Tab.S4.As for the SML, ULW and cloud water, V is the volume of droplet/well (V LINA =1 µL, V INDA =50 µL), resulting in N INP per volume of water.Compared to the droplets examined in a LINA measurement, INDA measurements have a larger volume of water in each well.The larger volume of water corresponds to a higher probability of the presence of INPs in each well, therefore INDA can detect INPs at warmer temperatures, where INP are more scarce.In this study, the derived N INP from LINA and INDA measurements were combined when both instruments were deployed.
in SML and ULW Based on Eq. 1, the derived N INP in seawater as a function of temperature is shown in Fig. 1, for both SML and ULW.Error bars show the 95% confidence interval.For completeness, f ice of all seawater samples is shown in the supplement, Fig. S1 (measured by LINA) and Fig. S2 (measured by INDA).The variation of N INP at any particular temperature is within one order of magnitude.Included in Fig. 1 are previous studies of N INP measured east of Greenland in the Arctic (shown as red box) and east of America in the North Atlantic Ocean (shown as black box) from Wilson et al. (2015).The concentration range detected for ULW inWilson et al. (2015)  (both in the Arctic and the North Atlantic Ocean) roughly agrees with our data.InWilson et al. (2015), N INP in the SML in the North Atlantic Ocean is at the lower end of that found in the Arctic.A possible reason for this difference could be the biological activity of the ocean water.Wilson et al. (2015)  found that organic material was correlated to N INP in SML, and that N INP per gram of total organic carbon in the Arctic and the North Atlantic Ocean were comparable.A recent study found that the SML at Cape Verde was oligotrophic, which is supported by the low Chlorophyll a and transparent exopolymer particles concentrations found during the MarParCloud campaign(Robinson

Figure 1 .
Figure 1.NINP as a function of temperature in SML (a) and ULW (b).Error bars show the 95% confidence interval.Previous field measurements of NINP in seawater by Wilson et al. (2015) are compared, as shown by red and black boxes.

Figure 2 .
Figure 2. EF as function of temperature.The EF=1 is shown by dashed line.
3.2.1 N INP close to sea level CVAO PM 10 N INP as a function of temperature from CVAO PM 10 filters and CVAO PM 1 filters are shown in Fig. 3(a) and (b).Error bars show the 95% confidence interval.The respective vales of f ice are shown in the supplement, Fig. S3 (CVAO PM 10 ) and Fig. S4 (CVAO PM 1 ), together with the results from the blind filters.The CVAO PM 10 filter samples were all active at −11.3 • C and the highest freezing temperature was found to be −5.0 • C. Filter samples collected in Cape Verde over the period 2009-2013 for INP measurement were reported by Welti et al. (2018), and they are shown as gray background in Fig. 3(a).The measured N INP in this study is within the N INP range presented by Welti et al. (2018).N INP at any particular temperature span around 1 order of magnitude below −15 • C, and about 2 orders of magnitude at warmer temperatures.This is consistent with the previous studies from O'Sullivan et al. (2018) and Gong et al. (2019a), who carried out field measurement in northwestern Europe and the eastern Mediterranean, respectively.A few samples (CVAO 1596, CVAO 1641 and CVAO 1643) showed elevated concentrations above 0.01 std L −1 at −10 • C. Biological particles usually contribute to INPs at this moderate supercooling temperature (Kanji et al., 2017; O'Sullivan et al., 2018).The correlation of N INP at two different temperatures was calculated.For temperature steps of 0.1 • C, N INP at every temperature was correlated to that at every other temperature in the measurement range.With increasing difference in temperatures, the variation in N INP at two temperatures become less correlated.As long as the examined temperature difference was less than 2 • C, N INP were correlated.But when looking at this in a broader picture, in the temperature region down to ∼ −16.8 • C, N INP at all temperatures correlated well with that at all other temperatures, with coefficient of determination (R 2 ) > 0.8 and p < 0.01.The same was true for N INP in the temperatures region < −18.4 • C. In between these two temperature regimes (between https://doi.org/10.5194/acp-2019-729Preprint.Discussion started: 18 September 2019 c Author(s) 2019.CC BY 4.0 License.>−16.8 • C and <−18.4 • C), the correlation of N INP was clearly lower.Therefore, it might be expected that INPs that are active in these two temperature regimes originated from different sources.

Figure 3 .Figure 4 .
Figure 3. NINP as a function of temperature from CVAO PM10 filters (a) and CVAO PM1 filters (b).The field measurement of NINP in PM10 by Welti et al. (2018) is shown by gray shadow in Fig. (a).Error bars show the 95% confidence interval.

Fig. 6
Fig. 6(b)  compares N INP at CVAO and MV when MV filters were mostly collected during cloud events with cloud time fractions >90%.During the cloud events, the filters did not collect droplets larger than 10 µm because of the inlet cutoff.It is obvious that for these cases, N INP at MV is much lower than that at CVAO, implying that particularly INPs that were ice

Figure 6 .
Figure 6.NINP as a function of temperature from CVAO PM10 filters and MV PM10 filters during less (cloud time fraction <10%) cloud effected periods (a) and highly (cloud time fraction >90%) cloud effected periods (b).

Figure 7 .
Figure 7. NINP as a function of temperature in cloud water.Error bars show the 95% confidence interval.Previous field measurements of NINP in cloud water by Joly et al. (2014) are compared, as shown by the red box.Based on Eq. 1, the derived N INP as a function of temperature is shown in Fig. 7. Error bars represent the 95% confidence interval.For completeness, f ice for cloud water is shown in the supplement, Fig. S8 (measured by LINA) and Fig. S9 (measured 10 8 g m 3 (Rangno and Hobbs, 2005; Petters and Wright, 2015), corresponding to F cloud_air between 2*10 −7 to 8*10 −7 , which agreed well with this study.With this F cloud_air , N INP in the respective volume of air can be compared to N INP in this volume of cloud water when assuming that all INPs are CCN, which, based on the super-micron size of most of the INPs alone, is likely.To do so, N INP obtained for cloud water was multiplied by F cloud_air (for the three different assumptions on d drop ) to yield N INP in the air (N INP,air ).

Fig. 8
Fig. 8 shows the measured N INP in the air as a function of temperature by squares.Derived N INP,air from cloud water (calculated with a d drop of 15µm) are shown by triangles.The samples with comparatively high numbers of INPs active at warm temperatures, are shown in different colors.CVAO 1596, CVAO 1641 and CVAO 1643 are shown by green squares (the rest shown by blue squares) and derived N INP,air from samples collected for Cloud 19, Cloud 20 and Cloud 24 are shown by

Figure 8 .
Figure 8.The measured NINP as a function of temperature in the air by squares.The derived NINP,air based on INP concentrations measured for cloud water are shown by triangles.The samples with highly ice active INPs at warm temperatures, are shown in a different color than the others: CVAO 1596, CVAO 1641 and CVAO 1643 are shown by green squares and derived NINP,air based on Cloud 19, Cloud 20 and Cloud 24 are shown by brown triangles.The uncertainty range indicated for the derived NINP,air originate from calculations with 7 and 20 µm cloud droplet size.

3. 4
INPs originating from sea sprayIn the following section, it will briefly be discussed whether SSA contributed noticeably to INPs in the air.Assuming sea salt and INPs to be similarly distributed in both, seawater and air (i.e., assuming that INPs would not be enriched during the https://doi.org/10.5194/acp-2019-729Preprint.Discussion started: 18 September 2019 c Author(s) 2019.CC BY 4.0 License.production of sea spray), N INP in the air originating from sea spray (N sea spray,air INP ) can be calculated based on Eq. 7: mass,air and NaCl mass,seawater are sodium chloride mass concentrations in air and seawater, respectively.N seawater INP is the INP number concentration in the seawater (this calculation can be done similarly for both SML and ULW).
Figure 9. NINP as a function of temperature from PM10 filters (black triangles), and error bars show the 95% confidence interval.NINP as a function of temperature from McCluskey et al. (2018a, b) are shown by red and light blue dots, respectively.Error bars show the 95% confidence interval.N sea spray,air INP clear if INPs are included in the organic carbon for which the enrichment was observed.Also, the INPs we detected in this study were mostly in the super-micron size range.If we increased N sea spray,air INP by about 2 orders of magnitude in agreement to the enrichment observed for super-micron organic carbon, the resulting N sea spray,air INP becomes comparable to sea spray INPs measured in the Southern Ocean (McCluskey et al., 2018a) and the Northeast Atlantic (McCluskey et al., 2018b).But even when considering such an enrichment of INPs, INPs originating from sea spray would only explain a small fraction of all INPs contributing to the measured airborne N INP in the air at Cape Verde. 4 Discussion N INP close to sea and cloud level height were compared.One major point of interest is to know whether ground-based measurements can be used to infer aerosol properties at the cloud level.In this study, we found that N INP are quite similar close to sea level (CVAO) and cloud level (MV) during non-cloud events.But it should still be noted that we only have a small number of filter samples representing non-cloud events in this study.During the observed cloud events, most INPs at MV are activated to cloud droplets.The above findings are in line with what was discussed in the companion paper (Gong et al., 2019b), i.e., (1) the marine boundary is often well mixed at Cape Verde and PNSDs and N CCN are similar close to both sea and cloud level; (2) during cloud events, larger particles are activated to cloud droplets.Most INPs are in the super-micron size range at Cape Verde.We found that about 70% of INPs had a diameter of >1 µm at ice activation temperatures between −10 and −20 • C. Mason et al. (2016) and Creamean et al. (2018) also found that the majority of INPs is in the super-micron size range in the Arctic, in agreement with the results we obtained here.
https://doi.org/10.5194/acp-2019-729Preprint.Discussion started: 18 September 2019 c Author(s) 2019.CC BY 4.0 License.with results from Si et al. (2018) and Irish et al. (2019a), both done in the Arctic, where it was also concluded that SSA only contributed little to the INP population.While the above arguments suggest that INPs in our study were mostly mineral dust particles, there were also some measurements with comparably high INP concentrations at temperatures of −10 • C and above.It is an open question where these highly ice active INPs originated.Such high ice activity is typically associated with biological particles.The times during which these highly ice active INPs were observed were times when air masses came from Southern Europe, travelling along the African coast and meanwhile crossing over the region of the Canary Islands.Therefore, for these specific samples, a contribution of INPs from these land sources might be assumed.

Figure 10 .
Figure10.ns as a function of temperature in this study is shown by black boxes.The boxes represent the interquartile range.Whiskers represent 10th to 90th percentile.Data not included between the whiskers are plotted as an outlier with a star.Two ns parameterizations(Niemand et al., 2012;Ullrich et al., 2017) for pure desert dust are shown in dashed and solid red lines, respectively.ns parameterizations fromMcCluskey et al. (2018b) for pristine SSA over the Northeast Atlantic are shown as a solid blue line.We also compare to recent data from airborne measurement in a dust layer byPrice et al. (2018)  in brown shadow and from nascent laboratory generated and ambient SSA byDeMott et al. (2016) in yellow shadow, respectively.
from roughly −5 to −25 • C. Both enrichment and depletion of N INP in SML to ULW were observed.The enrichment factors (EF) varied from 0.36 to 11.40 and from 0.36 to 7.11 at −15 and −20 • C, respectively, and were generally independent of the freezing temperature.The measured N INP in this study is consistent with the previous study of Welti et al. (2018), who characterized INPs sampled at CVAO over a time period of 4 years.A few CVAO PM 10 filter samples (CVAO 1596, CVAO 1641 and CVAO 1643) showed elevated N INP at high temperatures, e.g., above 0.01 std L −1 at −10 • C. Biological particles usually contributed the INPs at this moderate supercooling temperatures(Kanji et al., 2017;O'Sullivan et al., 2018).About 83±22%, 67±18% and 77±14% (median±standard deviation) of INPs had a diameter >1 µm at ice activation temperatures of −12, −15, and −18 • C, respectively, and over the whole examined temperature range, on average roughly 70% of all INPs were super-micron, independent of the temperature.The highly ice active INPs were not found on the CVAO PM 1 filters, which suggests that most of these likely biological INPs are in the super-micron size range.N INP were quite similar at CVAO and MV during non-cloud events.During cloud events, most INPs at MV were activated into cloud droplets.These findings aligned very well with the companion paper, i.e., during non-cloud events, PNSDs and N CCN are similar at CVAO and MV, while during cloud events, larger particles at MV are activated to clouds.When highly ice active particles were present on CVAO PM 10 filters, they were not observed on MV PM 10 filters, but were instead observed in the respective cloud water samples.This shows that these INPs are activated into cloud droplets during cloud events.By comparing N INP derived for the different examined samples, it was found that values in air and in cloud water agreed well.We also compared atmospheric N INP to those in SML and ULW, based on the ratio of sodium chloride concentrations measured for the atmosphere and for SML and ULW.From that we concluded that marine INPs from sea spray can only explain a small fraction of all atmospheric INPs at Cape Verde, unless there would be an enrichment of INPs from SML to the https://doi.org/10.5194/acp-2019-729Preprint.Discussion started: 18 September 2019 c Author(s) 2019.CC BY 4.0 License.atmosphere by at least a factor of 10 4 .Such an enrichment, however, is higher than anything observed for organic compounds in super-micron particles so far.Generally low INP concentrations are found over remote oceanic regions, compared to locations closer to land masses, implying that the ocean is a weak source of INPs, compared to land.Summarizing, it can be assumed that most atmospheric INPs detected in the present study were mainly contributed by the dust particles at cold temperatures possibly with few contributions from biological particles at warmer temperatures.https://doi.org/10.5194/acp-2019-729Preprint.Discussion started: 18 September 2019 c Author(s) 2019.CC BY 4.0 License.https://doi.org/10.5194/acp-2019-729Preprint.Discussion started: 18 September 2019 c Author(s) 2019.CC BY 4.0 License.

Table 1 .
Measurement sites, locations, sample types and measurement instruments.
Gong et al. (2019b), MV was in clouds roughly 58% of the time (a detailed analysis on this can be found inGong et al. (2019b)).Cloud water was collected with CASCC2 (Caltech Active Strand Cloud Collector Version 2) at MV.All cloud drop sizes were collected in one bulk sample.Drops were collected by inertial impaction on Teflon strands with a diameter of 508