The in situ measurements were performed with the MoMuCAMS described in Pohorsky et al. (2024). It was deployed from a container platform located on the aft deck of the ship (see Fig. 2c) and equipped with instruments to characterize aerosol and cloud droplet properties. The maximum altitude reached was 645 m above sea level (m a.s.l.) and the average vertical velocity of the balloon during operations was ≈15 mmin-1.

A miniaturized scanning electrical mobility spectrometer (mSEMS model 9404, Brechtel Manufacturing Inc., USA) provided particle number size distribution (PNSD) measurements for particles with electrical mobility diameters between 8 and 280 nm with 40 log-spaced size bins and a bin time of 4 s, yielding a time resolution of 2 min 40 s per scan, which corresponds to ≈40 m on average during ascents and descents of the Helikite. A portable optical particle spectrometer (POPS, Handix Scientific, USA) provided PNSD measurements for optical diameters between 186 and 3370 nm. The POPS was operated with 16 log-spaced size bins and a 1 Hz acquisition frequency.

A light optical aerosols counter (LOAC, Meteomodem, France) was installed on top of MoMuCAMS to measure the size distribution of aerosols and cloud droplets from 200 nm up to 50 µm with a time resolution of 30 s (Renard et al., 2016). In the LOAC, two detectors measure the scattering, at 12 and 60°, produced by the sampled particles traveling through a laser beam. The intensity of the forward scattering signal is used to measure the size of the particles and the difference between the two angles provides additional information to classify particle types (e.g., carbonaceous, minerals, liquid droplets).

In addition to aerosol measurements, meteorological variables were measured during the flights. A wind probe (SmartTether, Anasphere, USA) attached directly to the tether, ≈1.5 m below the MoMuCAMS collected wind speed and direction data at 0.5 Hz. Two sensors (SHT85, Sensirion, CH), installed in an insulated and actively ventilated radiation shield casing attached to the side of MoMuCAMS, measured temperature and relative humidity (RH). Pressure was recorded in triplicate by sensors located on the MoMuCAMS's board computer (MPL115A2, Adafruit, USA), the SmartTether and the POPS. A summary of deployed instruments for each flight during the campaign is listed in Table S1.

Data processing followed the procedure described in Schmale et al. (2026) based on the EBAS guidelines (https://ebas-submit.nilu.no/, last access: 20 December 2025). Raw mSEMS data files were inverted using a software package provided by the manufacturer. At level 0, one data file per flight that includes the data from the different instruments, was created. At level 1, the meteorological variables were cleaned for invalid data. Because the ship was warmed by incoming solar radiation, an influence on the temperature profiles was observed in the first tens of meters. This influence was identified by a steeper temperature gradient relative to that observed higher up. The altitude of influence on the temperature measurements was manually identified for each profile and data below this altitude were flagged. Additionally, a corrected temperature profile was produced. The temperature profile was reconstructed between the take-off altitude and the highest point of temperature influence by extrapolating the temperature data above and assuming the same temperature gradient as the observed mean gradient. An example of the temperature influence of the ship, data flagging and reconstructed temperature profile is shown in Fig. S1 in the Supplement.

The altitude above sea level was calculated using the barometric formula, 1hb=T0L01-pbp0L0Rg, where T0 is the temperature at the surface, L0=6.5 Kkm-1 is the mean environmental lapse rate, p0 and pb are the pressure at the surface and balloon height, respectively, R=287 Jkg-1K-1 is the gas constant for dry air and g is the Earth's gravitational constant.

Aerosol data were manually inspected for outliers and anomalous data, which were flagged and removed. An intercomparison of the MoMuCAMS with the ship-based aerosol instruments (see Sect. 2.3) indicated an undercounting of aerosol number concentration by the POPS (about 30 %). A size-dependent correction for aerosol counting was applied based on a comparison with a differential mobility particle sizer (DMPS) during a ground-based intercomparison. Aerosol concentrations were then converted to standard temperature and pressure using the temperature measured in the sampling line. Results of the comparison and corrected size distribution are shown in Fig. S2 and correction factors are listed in Table S2.

To derive the total aerosol number concentration (N8–3370), the mSEMS and POPS measurements were merged. For each mSEMS measurement, the higher temporal resolution POPS data were averaged over the corresponding mSEMS sampling interval. The merged size distribution consisted of the mSEMS measurements between 8 nm and the lower detection limit of the POPS (186 nm), combined with the POPS size distribution above this diameter, resulting in a total size range from 8–3370 nm.

For the cloud droplet size distribution and concentration, we selected “particles” larger than 3 µm, that were classified as droplets by the LOAC. Concentrations for different bin sizes are normalized by the respective bin width (dNdlog⁡(Dp)-1). A comparison of the data from the LOAC and a fog monitor (FM120, Droplet Measurement Technologies, USA) shows that the LOAC droplet number concentration agrees within 20 %–30 % with the reference instrument. Details of the analysis are shown in the Supplement (Fig. S3).

Pollution from the ship exhaust was identified visually for each profile. Ship exhaust plumes were identified by strong and rapid increases in the aerosol number concentration (typically between 30 and 50 m altitude). Polluted data points were flagged and removed from the analysis. An example of a profile with flagged ship pollution is shown in Fig. S1.

Finally, a 10 s arithmetic averaging was applied to the data (except for the mSEMS and LOAC data with initial coarser resolutions). For the analysis of vertical profiles presented in this study, the data were spatially averaged into 10 m altitude bins and plotted at the midpoint altitude.

To complement the analysis of the MoMuCAMS vertical profiles, ship-based remote sensing and in situ observations were used. At the front of the ship's superstructure, on the fourth deck, the mobile aerosol-cloud laboratory container from Stockholm University was equipped with a suite of aerosol instruments (see location in Fig. 2c). A differential mobility particle spectrometer (DMPS) sampled from a total inlet (i.e., sampling all aerosol sizes), providing aerosol number size distribution measurements for particles with an electrical mobility between 15 and 840 nm at a time resolution of 12 min (as in Karlsson et al., 2022). A cloud condensation nuclei counter (CCNC, Droplet Measurement Technologies, USA) (Lance et al., 2006; Roberts and Nenes, 2005) operated in parallel to measure the CCN concentration. The CCNC operated at supersaturations of 0.1 %, 0.2 %, 0.3 %, 0.5 % and 1.0 % with a duration of 15 min per supersaturation level.

A suite of remote sensing instrumentation made continuous measurements throughout the cruise. A 94 GHz W-band Doppler Cloud Radar (RPG-FMCW-94-SP, Radiometer Physics GmbH, Germany) was installed on top of a container on the 4th deck, this provided measurements of radar backscatter and Doppler velocities with a vertical resolution between 7.45 and 28.6 m (depending on range) and time resolution of approximately 5.4 s. A laser ceilometer (CL31, Vaisala, Finland) installed on the 7th deck, provided measurements of cloud base every 30 s with a range resolution of 10 m. Liquid water path was estimated by a microwave radiometer (HATPRO, Radiometer Physics GmbH, Germany). Here cloud boundaries are derived from the cloud radar and laser ceilometer. Profiles of liquid water content are derived via the Cloudnet algorithm (Illingworth et al., 2007) using data from the radar, ceilometer, HATPRO, and radiosondes. A HALO Photonics Streamline Doppler Lidar (HALO Photonics, UK), installed above a container on the 2nd deck, was used to measure the vertical velocities at cloud base and in the lower part of the cloud.

Radiosondes (RS41, Vaisala, Finland) were launched every 6 h during the campaign (nominally at 05:30, 11:30, 17:30 and 23:30 UTC). Data from the radiosondes were used to complete meteorological data from the Helikite flights, which were more sporadic in time and limited in altitude. The data used from the radiosoundings included pressure, temperature, and relative and specific humidity.

Measurements of near surface meteorology (temperature, pressure, relative humidity, wind speed and wind direction, upwelling and downwelling longwave and shortwave radiation) were obtained from a weather station composed of a meteorological mast and a radiometer stand installed on the sea ice and from the ship's weather station (at 25 m altitude).

Finally, synoptic conditions were inspected with ERA5 data (Soci et al., 2024), and backward trajectory analysis data from the Lagrangian analysis tool (LAGRANTO; Sprenger and Wernli, 2015) were used to determine the origin and influences of air masses arriving at the ship location during Helikite flights. For each backward trajectory simulation, an ensemble of ≈37 5 d trajectories was calculated. The arrival point of each trajectory was located in a 100 km circle around the ship's location at 50 hPa above sea level. Shapes and tracks of surface cyclones were calculated on ERA5 dataset based on the MOAAP tracking algorithm (Prein et al., 2023); continuous areas in the sea level pressure (SLP) anomaly field below -8 hPa and persisting for more than 12 h are identified as cyclones.

Temperature profiles from radiosoundings and Helikite flights were analyzed to identify the heights of temperature inversions. Inversions were identified by layers with a positive vertical temperature gradient (dTdz-1>0) for at least 25 m (Jozef et al., 2022; Kahl, 1990). The temperature inversion height was used to characterize thermodynamic coupling between observed LLCs and the surface. Following Pilz et al. (2024), a cloud is considered to be decoupled (thermodynamically) if a surface-based inversion was detected or if the base of an elevated inversion was located below the cloud base.

An inherent challenge in analyzing tethered balloon profiles that extend through clouds is to accurately determine cloud base and cloud top boundaries. In this study, we estimated these boundaries using a combination of relative humidity (RH≈100 %), in-flight video recordings and observed aerosol concentrations. Within clouds the MoMuCAMS' low sampling flow rate means that primarily interstitial aerosol (i.e., non-activated particles) is sampled, because cloud droplets are too heavy to be drawn into instruments. Consequently, observed aerosol concentrations decreased within cloud layers.

To validate cloud boundary estimates, and monitor changes over time, we compared them with ceilometer-derived cloud base heights and radar reflectivity data. An example of the cloud boundary determination is shown in Fig. S4. It should be noted that cloud boundaries, particularly cloud base, are inherently diffuse, and their exact definition is subject to interpretation, introducing an inherent uncertainty of approximately ±30 m.

We use an aerosol activation parametrization developed by Nenes and Seinfeld (2003) and further improved by Fountoukis and Nenes (2005), Barahona et al. (2010) and Morales Betancourt and Nenes (2014) to calculate the number of cloud droplets (Nd) formed in observed clouds and compare those values with the in situ LOAC measurements. The parametrization requires aerosol PNSD, hygroscopicity, updraft velocity and environmental conditions at cloud height (pressure and temperature) as inputs.

In the parametrization, we used the approach introduced by Morales and Nenes (2010) to calculate the probability density function (PDF)-averaged Nd and supersaturation (smax) in the cloud (which is representative of the measured droplet number concentration) by applying the parametrization using a single characteristic velocity, w∗=0.79σw (Conant et al., 2004; Fountoukis et al., 2007; Georgakaki et al., 2021; Motos et al., 2023). The σw is the standard deviation of a gaussian fit of the vertical velocity data at cloud base obtained from the Doppler Lidar. To be representative of the variability of the vertical velocity, the PDF is generated from data over one hour. The pressure and temperature data at cloud base are obtained from Helikite and radiosonde measurements. For parametrizations with data collected between profiles, the Helikite and radiosonde pressure and temperature measurements are interpolated.

The aerosol number size distribution data measured directly below cloud base (i.e. 50 m layer below cloud base) and in the entrainment zone were used as inputs for different simulation scenarios. The PNSDs were fitted with lognormal distributions using a multipeak fitting package in IGOR Pro v9.02 and reconstructed to fit the required number of bins assumed by the parametrization scheme.

The hygroscopicity parameter (κ) was calculated from ship-based measurements of aerosol PNSD and CCN concentrations following the method of Petters and Kreidenweis (2007), 2κ=4A327Dd3(ln⁡Sc)2, where Sc is the critical saturation ratio (Sc=ssc100+1), with ssc = critical supersaturation in %. The critical dry diameter (Dd) at each supersaturation was calculated by integrating the aerosol size distribution from the larger particle sizes to the lowest until the integrated concentration matched the CCN concentration.

The A is defined as follows: 3A=4σawMwRTρw where the surface tension of water in air σaw=0.072 Jm-2 and the temperature of T=298.15 K is used. ρw=997 kgm-3 is the density of water, Mw=18.015×10-3 kgmol-1 is the molecular weight of water and R=8.3145 Jmol-1K-1 is the ideal gas constant.

To assess clouds' influence on downwelling longwave radiation in response to the number and size of cloud droplets, radiative transfer modeling (RTM) was performed. We used a plane-parallel radiative transfer model with the DISORT solver (Stamnes et al., 1988) implemented in the libRadtran 2.0.6 software package (Emde et al., 2016; Mayer and Kylling, 2005). Simulations were performed with the LOWTRAN parametrization for gaseous absorption (Ricchiazzi et al., 1998). For aerosols, we used standard maritime profiles based on the model of Shettle (1990). The season was set to “summer”. Temperature and relative humidity profiles were based on radiosoundings and complemented with Helikite measurements in the lower layers as in Lonardi et al. (2024).

A profile of liquid water content (LWC) and droplet effective radius (reff) is included to account for cloud effects on radiation. The reff is obtained as in Lonardi et al. (2024), 4reff=34π⋅ρw⋅Nd⋅k⋅LWC(z)13, where ρw is the density of liquid water and the parameter k converts the effective droplet radius into the volumetric droplet radius and was set to 0.8, which is representative for stratocumulus clouds (Brenguier et al., 2011). The LWC was retrieved from remote sensing data and reff was calculated by assuming a constant Nd profile throughout the cloud column.

The remote-sensing retrievals were used because they provide continuous cloud microphysical information throughout the day, whereas the LOAC measurements were only available during balloon profiles and occasionally contained data gaps caused by detector saturation at high droplet concentrations. To evaluate the consistency between both approaches, LWC derived from the LOAC droplet size distributions was compared with the remote-sensing retrievals and showed good agreement (Fig. S5). Details on LWC and reff calculation from the LOAC measurements are provided in the Supplement.

Here we focus on the five cloud cases where the Helikite flew above the cloud top, allowing aerosol measurements to be collected aloft. Average temperature and aerosol profiles for each case study are shown in Fig. 4. To facilitate comparison across cases, the altitude of each profile was normalized such that the distances between the lowest altitude and cloud base, cloud base and cloud top, and cloud top and the maximum altitude are each mapped onto one third of the profile. The flight on 3 June was conducted in fog; therefore, the profile begins at the cloud base, which coincides with the start altitude.

The flights conducted on 23 May, 4 June, and 7 June exhibited a relatively well mixed boundary layer with no significant aerosol concentration gradients between the surface and cloud base. Measured aerosol concentrations progressively decreased with altitude as the Helikite approached cloud base and reached a minimum inside the cloud, suggesting they were progressively activated with height into cloud droplets over a distance of a few tens of meters and were therefore not sampled by the inlet (as illustrated in Fig. S4). These concentration gradients reflect the diffuse cloud boundaries discussed in Sect. 2.5, where the clouds' optical depth gradually increases along with the number of cloud droplets but is initially below the remote sensing instruments detection limit. The well-mixed boundary layer below the clouds was consistent with the temperature profiles (Fig. 4a), which showed no inversion below the cloud base on any of these days. In contrast, the 10 June flight displayed a pronounced negative aerosol gradient from the surface to the cloud base. On this day, a surface-based temperature inversion due to the advection of a warm air mass was observed, indicating reduced vertical mixing between the surface and the cloud base. This indicates that surface-based measurements were not representative of cloud-level conditions for CCN and INP concentrations at that time.

For the 10 June flight, the concentration of interstitial aerosols (≈150 cm-3) was greater than on other days consistent with increased concentrations of Aitken- and nucleation-mode particles, which likely remained below the activation size under the cloud conditions encountered during this flight. Except for 10 June higher aerosol concentrations were observed above the cloud compared to below with enhancement factors between 1.3 and 3.9. These findings are consistent with previous studies (e.g., Kupiszewski et al., 2013; Pilz et al., 2024) underlining the likely relevance of free tropospheric aerosols as an important source of CCN and INPs for Arctic LLCs, which is not captured by surface-based measurements. To further investigate the contribution of particles below and above the cloud to the cloud droplet number concentration and longwave radiative properties, a detailed case study is presented in the following section.

In Fig. 5 the sea-level pressure (SLP) anomalies north of 60° N are visualized for 7 June and complemented by five-day back trajectories arriving at 50 hPa above sea level (≈ cloud height) at the ship location. The SLP anomalies show a low-pressure trough extending along the East coast of Greenland and high-pressure ridge over the Greenland Sea that extends over the sea ice. The Oden was located at the tip of the ridge. Not seen in Fig. 5 is a cyclone located north of Svalbard between 2 June and 5 June. The back trajectories indicate that the air arriving at Oden originated from the central Arctic (north of Greenland) and followed the trough along the Greenlandic East coast before turning and traveling north towards the ship, along the marginal ice zone between the low-pressure trough and high-pressure ridge. The arriving air (at the surface) initially travelled in the free troposphere (see vertical cross-sections of trajectories in Fig. S6) and began descending approximately 72 h before reaching Oden, entering the boundary layer ≈36 h before arrival and following the surface for the last 24 h. The air arriving in the lower troposphere mainly remained in the free troposphere but trajectories show more spread compared to the trajectories arriving at the surface. About half of the trajectories exhibited subsidence over the last 36 h before reaching Oden, which is consistent with the presence of the high-pressure system.

Figure 6 shows how the cloud developed during the day on 7 June. The cloud base was initially very low (<100 m, which is below the first range gate of the cloud radar). Around 09:00 UTC, the cloud base began to rise to approximately 150 m a.s.l. and remained roughly stationary throughout the day. The radar reflectivity indicates that the cloud top increased in altitude throughout the day from ≈200 m at 06:00 UTC to ≈500 m at the end of the day. Note that a cirrus cloud was detected by the cloud radar around 7 km a.s.l. for the majority of the morning and intermittently during the afternoon (Fig. S7). However, it was not obvious from the photo records of the Helikite camera, indicating that the cirrus was likely optically thin.

Light snowfall was observed on the ground throughout the day, indicating that the cloud was a mixed-phase cloud. However, no direct in situ measurements with the Helikite could provide information on the ice water content.

Three Helikite flights traversed the cloud between 10:07 and 22:16 UTC. In Fig. 6, the altitude of the Helikite is represented by the colored path, indicative of the measured concentration of aerosols (N186–3370). In addition to the apparent depletion within the cloud due to cloud droplet formation, we observe a decrease in N186–3370 at the surface throughout the day, possibly due to scavenging by snowfall. At the same time, concentrations above the cloud increase, although the cause of this increase remains unclear.

Figure 7 shows a representative snapshot of the atmospheric column during the third ascent of the day (from 19:09 to 20:06 UTC). This profile is presented to illustrate the vertical structure of the atmosphere, cloud, and aerosol layers. Unless otherwise stated, the subsequent aerosol size distribution and cloud microphysical analyses are based on data from all six profiles obtained on 7 June.

In Fig. 7a, we observe a temperature profile with a dry adiabatic lapse rate below the cloud and wet-adiabatic lapse rate inside the cloud. Starting just below cloud top (≈25 m), a temperature inversion marks the transition to the free troposphere and shows the presence of a warm air mass aloft. The temperature below the cloud ranged from -4.8 to -3.3 °C, within the cloud from -5.8 to -4.8 °C and above cloud reached as high as +0.7 °C. The aerosol profile (N8–3370) indicates an average concentration of ≈100 cm-3 between the surface and cloud base (Fig. 7b). From about 40 m below cloud, at 125 m, the concentration decreases to reach its minimum at the defined cloud base of 165 m. Inside the cloud, the 10 s averaged N186–3370 does not exceed 6 cm-3. At the top of the cloud, the aerosol concentration increases gradually between 400 and 440 m, while the cloud top was identified at 425 m. This 40 m thick layer corresponds to the entrainment zone (the thin, turbulent layer where dry free tropospheric air mixes into the cloud), located between the boundary layer and the free troposphere. In the free troposphere, the aerosol concentration exceeds the boundary layer concentration with a maximum concentration (N8–3370) of up to about 350 cm-3. The free tropospheric aerosol profile presents, in addition, a layered structure with concentration differences between the layer in the direct vicinity of the cloud and a layer located above 500 m. Examining the temperature and relative humidity profiles, we do not identify any major features or differences indicating a separation of air masses that could explain these observations, besides a small fluctuation in the temperature profile (i.e. slightly stronger temperature gradient) around 500 m. The profile of cloud droplets (Fig. 7c) shows an increasing number concentration with altitude for all droplets equal to or larger than 3 µm. In the upper part of the cloud, Nd is around 100 cm-3 with a maximum ≈130 cm-3. Throughout the day, the median Nd was 88 cm-3. Of the measured Nd, 25 % was equal to or higher than 107 cm-3 and 10 % was equal to or higher than 125 cm-3. Such values are typically on the upper end of cloud droplet number concentrations observed in Arctic LLCs, but are realistic. For instance, from two years of continuous observations of aerosols and clouds at the Mount Zeppelin Observatory, Koike et al. (2019) reported a median Nd of 65 cm-3 between May and July, with a 75th quantile around 110 cm-3. In the present context, the observed cloud droplet concentrations are relatively high compared to the aerosol concentrations measured below cloud. This suggests that additional aerosol sources or vertical transport processes may contribute to the in-cloud CCN population, a hypothesis that is explored in the following part through analysis of the aerosol size distributions.

Based on the observed vertical profile of aerosols, we identified five different layers with distinct aerosol characteristics: the boundary layer, the cloud layer, the cloud-top entrainment zone, the free troposphere in the direct vicinity of the cloud, and above 500 m. The latter was only reached on the last of the three flights and therefore has fewer observations.

We extracted the PNSD in each layer for all three Helikite flights from 10:10 to 22:15 UTC. The measured PNSDs are shown in Fig. 8b, while Fig. 8a provides an illustration to help the reader identify which vertical layer is associated with each PNSD. Starting from the surface, the boundary layer PNSD (blue) exhibited a bimodal distribution with an Aitken mode peaking at 37 nm and a dominant accumulation mode at 125 nm. A distinct Hoppel minimum was located at 52 nm (exact value obtained from a distribution fit), indicating that the boundary layer aerosols have been cloud processed. This distribution was observed from the surface to the cloud base. In the cloud (green), the Aitken mode was identical to that in the boundary layer, but accumulation mode particles were activated into droplets and not sampled. The difference between boundary layer and in-cloud distributions appeared at the Hoppel minimum, indicating activation of particles as small as ≈52 nm. The similarity of these size distributions confirms that the cloud was coupled to the surface and that aerosols were well mixed within these two layers. In the entrainment zone, we obtained only five mSEMS scans. These scans were obtained at the cloud top interface, at the base of the temperature inversion, and were identified because of their distinct shape that differed from the cloud and free tropospheric PNSDs. Although quite noisy, we observed an Aitken mode somewhere between 30 and 50 nm. The PNSD extended into an accumulation mode but with no distinct separation between the two modes. In the free troposphere, the PNSD exhibited a different shape compared to the boundary layer. In the lower part (<500 m), the PNSD was dominated by an Aitken mode with a peak at 47 nm with an accumulation mode shoulder at 130 nm. We also observed a burst of nucleation mode particles during the first flight between 10:50 and 12:50 UTC when the Helikite remained within 50 m of the cloud top. This burst is indicative of new particles being formed in the direct vicinity of cloud top, which could be explained by increased UV irradiance due to the cloud's reflection (Wehner et al., 2015), mixing of dry and wet air in the entrainment zone and potentially a supersaturation of precursor gases that could have been transported in updrafts (Kerminen et al., 2018; Wu et al., 2021). Despite not observing these particles, nor a growth during the subsequent flights, these observations underline the potential of cloud-mediated NPF events in the free troposphere as a source of CCN in the Arctic. Without more detailed information on the composition of the particle precursors and direct observations of a growth event, we cannot speculate on the origin of this event or its impact on the aerosol population. Above 500 m, the PNSD exhibits a different shape from the one below 500 m. Because of limited sampling time, the PNSD looks noisier, but we can identify a similar accumulation mode as seen below 500 m. These particles seem to constitute an older background of the free tropospheric aerosol population. However, we see a smaller Aitken mode and no distinct Hoppel minimum. The reason for the difference between these two free tropospheric layers is not clear. As indicated earlier, we do not observe a distinct indication of a separation of air masses and back trajectory analysis suggests the same origins (Figs. 5 and S6). We can only hypothesize that the layer up to 500 m is more directly influenced by the cloud and the Aitken mode is the result of more recently formed particles directly above the cloud which then grew further.

Observations of the PNSD in the boundary layer, cloud, and lower free troposphere tend to indicate that aerosols within the boundary layer – and consequently at the surface – are representative of the population governing cloud microphysical properties. However, the number of observed cloud droplets cannot be explained solely by boundary layer CCN. The integrated aerosol number concentration above the Hoppel minimum (below the cloud) is ≈80 cm-3, which represents a gap of ≈50 cm-3 (±25, taking into account uncertainties of Nd measurements) compared to the observed droplet concentration.

These results indicate that the aerosol population within the boundary layer alone is insufficient to explain the observed cloud droplet number concentrations. Therefore, an additional source of CCN is required. The observations in the entrainment zone and lower free troposphere suggest that entrainment of aerosol-rich air from above the cloud provides a plausible source of these additional CCN.

To understand how significant the contribution from the free troposphere is, we investigate more deeply the interface between the very top of the cloud and the free troposphere, i.e. the entrainment zone. There, the concentration of Aitken mode particles is greater than in the boundary layer but smaller than in the free troposphere, suggesting a mix of free tropospheric and boundary layer air, with some accumulation mode particles that are activated, and hence not sampled. To verify this hypothesis, we computed the mean of the low free troposphere PNSD (yellow) and in-cloud PNSD (green), assuming a one-to-one mixing ratio, and compared it to the entrainment zone PNSD. The resulting PNSD shows a good agreement with the observed PNSD in the entrainment zone (Fig. 8b, red dashed line) strongly supporting the idea that the air from the free troposphere is mixed downwards, contributing to the cloud's CCN population. To further assess the contribution of the entrained air at the top of the cloud, we use a cloud droplet parametrization based on the PNSD measured in the boundary layer and an average of the boundary layer and free tropospheric PNSDs and compare the results to the in situ observations of cloud droplets.

Here we used the methodology introduced in Sect. 2.6 to parametrize the number of droplets in the cloud using two different PNSD inputs. First, we used the boundary layer PNSD, which is in this case representative of the surface measurements. In a second set of parametrizations, we used the average distribution of the boundary layer and lower free tropospheric PNSDs (referred hereafter as EZ PNSD for simplicity), which is a proxy for the total aerosol PNSD in the entrainment zone and represents the cloud-top particle entrainment scenario. We applied the parametrization using the hourly updraft velocity fits from the Doppler Lidar between 10:00 and 23:00 UTC. While this approach is appropriate for a parametrization based on boundary-layer aerosol properties, it represents a simplification for the case including entrainment of aerosols at cloud top. In particular, the vertical velocity and supersaturation conditions near cloud top may differ substantially from those at cloud base, and cloud-top supersaturation can be influenced by different processes than the expansion of air in updrafts, notably by longwave radiative cooling. These processes are not explicitly represented in the present framework. However, due to the lack of direct constraints on cloud-top updrafts and radiatively driven supersaturation, a consistent characteristic velocity based on cloud base measurements is retained for both parametrizations. This limitation should be considered when interpreting the results and highlights the need for future studies to better account for such mechanisms when parametrizing cloud droplet formation in Arctic LLCs.

The κ value was calculated as described in Sect. 2.6. Since the value obtained with this method depends on the considered supersaturation, the Dd (i.e. minimum dry diameter of activated particles) in the cloud was estimated from the difference between the in-cloud and boundary layer size distributions. The supersaturation of the cloud was then estimated by interpolating that from the CCNC for the two nearest Dd. The κ value was then calculated using Eq. (2) with the best estimates of Dd and supersaturation (details are shown in Fig. S9).

The κ (based on ship-based measurements) was calculated as 0.58 with an uncertainty range from 0.57 to 0.66 corresponding to a supersaturation range between 0.35 % and 0.49 %.

To account for the uncertainty range in the κ-value and the unknown κ-value of free tropospheric aerosols, we performed a sensitivity analysis with an extended range of values between 0.5 and 0.9. Results indicate no significant difference for each end of the κ-value range for parametrizations with the boundary layer PNSD input. The median Nd ranges from 67 to 70 cm-3 (see Fig. S9a). For the parametrizations with the EZ aerosols, results of the sensitivity analysis range between 117 and 130 cm-3.

Another sensitivity analysis was performed on the characteristic velocity (w∗) where different coefficients of σ were used to account for the uncertainties related to processes controlling the supersaturation in our comparison (see Fig. S9b). We observe a similar behavior as for the sensitivity test on κ-values, where Nd is almost not sensitive to different coefficients of σ for parametrizations with the boundary layer PNSD. For parametrizations with the EZ aerosols, the median ranges from 116 to 133 cm-3. Overall, parametrizations with the EZ aerosols show slightly higher sensitivity to κ and w∗ but the absolute Nd difference between the two scenarios remains within the same order of magnitude (i.e., a difference ranging between ≈50–60 cm-3). The higher sensitivity to κ and w∗ suggests that with the contribution of free tropospheric CCN, the cloud was in an updraft-limited regime but if we only consider the boundary layer aerosols, the number of cloud droplets would be more limited by the availability of CCN within the tested range of κ and w∗ as an increase in the updraft velocity or κ did not lead to an increase in Nd (or is only limited).

In Fig. 9, we see results of (a) Nd and (b) maximum supersaturation (ssmax) for parametrizations with a κ of 0.58 and w∗=0.79σ (center estimates). The median Nd for boundary layer and EZ aerosols is 68 and 122 cm-3, respectively, which represents an increase of 80 % for the EZ scenario. The ssmax is lower for the parametrization with EZ aerosols, because of the higher number of CCN but the values remain similar to the estimated ssmax range from the κ-value calculations (see Fig. S8).

The Nd results from the parametrization show a very good agreement between the EZ scenario and the in situ observations with concentrations around 100 cm-3 and occasionally over 130 cm-3 (Fig. 7). This confirms the significant influence of entrained free tropospheric aerosols on the cloud's microphysics.

The longwave radiative absorption, re-emission and transmission through a cloud are a function of the cloud's optical depth, cloud phase, droplet size and emitting temperature (Stephens, 1978). The longwave flux follows the Stefan–Bolzmann relationship: 6F=εσT4, where F is the radiation flux, ε is the apparent emissivity, σ (5.67×10-8 Wm-2K-4) is the Stefan–Bolzmann constant and T is the emitting temperature in Kelvin. The emissivity is a function of the longwave optical depth and is often specified as a function of the LWP, 7ε=1-e(-a0LWP), wherein a0 (m2g-1) is the mass-absorption coefficient (Stephens, 1978). For a cloud with a LWP between roughly 30 to 50 gm-2, the emissivity approaches unity (Shupe and Intrieri, 2004). Although it is not explicitly included in Eq. (7), the effective radius of cloud droplets can alter emissivity for non-opaque clouds (Garrett et al., 2002; Garrett and Zhao, 2006). This effect has been found to be negligible at the global scale (Rotstayn and Penner, 2001) but observations suggest that the effect can be significant for Arctic clouds where the longwave warming effect is dominant and where CCN concentrations are typically orders of magnitude lower than in the midlatitudes (e.g., Curry and Herman, 1985; Garrett et al., 2002; Garrett and Zhao, 2006).

Here, we use idealized RTM to examine how additional CCN originating from the free troposphere modify the cloud's emissivity and surface longwave radiative forcing through their impact on cloud droplet number concentrations (cf. Sect. 2.7). We focus on the longwave part of the radiation spectrum to specifically evaluate the difference in emissivity for different Nd. Simulations were performed omitting the ice phase since we do not have direct measurements of the ice water content and because the longwave emissions are largely dominated by the liquid droplets (Curry et al., 1993; Hofer et al., 2024).

The RTM simulation was run using data at 11:30 and 17:30 UTC (radiosonde launches) with LWPs for the first and second profiles of 26.2 and 37.9 gm-2, respectively. Figure 10a shows results of longwave radiative forcing for simulations with different cloud droplet number concentration profiles. The longwave forcing was determined by computing the difference in downwelling longwave radiation (LW_↓) between cloudy conditions and a cloud-free (cloud was removed) scenario. The cloud's microphysical properties were based on the Nd parametrization outputs from Sect. 4.3 (located on each side of the grey stripe in Fig. 10), representing a scenario with and without free tropospheric CCN contribution. We also extended the simulation to lower and higher Nd to create broader context for the interpretation of the results. The reff profiles typically increase with height inside the cloud because of the increase in LWC near cloud top.

The reff values at cloud base are around 1 µm and range between ≈5 and 18 µm at cloud top. for profiles with the lowest and highest Nd, respectively. An example of the corresponding reff profiles for the data at 11:30 UTC is shown in Fig. S10. Results from the RTM remain within 10 Wm-2 from surface-based observations on the ice, an uncertainty which is to be expected for such simulations (Lonardi et al., 2024; Mauritsen et al., 2011). Because of these uncertainties, our analysis focuses mainly on the absolute difference between the various simulations, and we do not use the observations to directly validate the simulated values.

The average difference for simulations with Nd=68 and 122 is equal to 1.3 Wm-2. This difference accounts for ≈2 % of the longwave cloud forcing in this range of Nd (and measured LWP). By rearranging Eq. (6), we can calculate the cloud's apparent emissivity from the longwave radiation results. Apparent emissivity values are generally high (between 0.89 and 0.95 for Nd∈[68,122]) and Fig. 10b shows that the emissivity is slightly higher for the later profile (dark blue points), which is consistent with the increase in LWP between 11:30 and 17:30 UTC.

To put these results in perspective, we compare them to the study of Garrett and Zhao (2006) who performed a similar analysis but compared clouds affected by local anthropogenic pollution to clouds formed in clean background conditions over four years of observations at Utqiagvik (Alaska). They reported increased longwave radiative forcing between 3.3 and 5.2 Wm-2 due to the increased cloud droplet concentrations when clouds were affected by pollution. They reported an average Nd of 53 cm-3 and a LWP of 31.1 gm-2 for clean conditions and 153 cm-3 and 33.5 gm-2 for polluted conditions. While the LWP reported by Garrett and Zhao (2006) is similar to the LWP of our case study, the increase in Nd between their clean and polluted cases was larger than in our observations, with Nd increasing from 53 to 153 cm-3 (a factor of approximately 2.9). We performed additional simulations with Nd=53 and 153 cm-3 to compare our case study with their results and observe an increase in the cloud's longwave radiative forcing between 2.5 and 3.4 Wm-2. The similarity of our results indicates that the influence of free tropospheric CCN sources on Arctic cloud radiative properties may be as important as the perturbations from local anthropogenic sources.

Further examining the simulations with even fewer or more droplets, i.e. beyond the grey bar in Fig. 10a, we find that the cloud's emissivity and radiative forcing exhibit the greatest sensitivity to Nd for values at low concentrations (<≈70 cm-3). As the concentration increases, the emissivity tends to approach a maximum for the given LWP and cloud's temperature. Conditions with low aerosol concentration (<100 or even <10 cm-3) are commonly observed in the central Arctic year-round (e.g., Bigg et al., 1996; Boyer et al., 2023; Lannefors et al., 1983). Hence, under those conditions, the impact on the radiative properties of a cloud from the presence of enhanced aerosol layers located above becomes important and model representations of clouds based on surface-based measurements will lead to a negative longwave forcing bias. This is especially true for clouds with low LWP, e.g., during foggy conditions in the marginal ice zone and in summer and fall over the sea ice. For reference, during ARTofMELT, 50 % of the measurements indicated a LWP below 27.9 gm-2 and 25 % below 12.3 gm-2. It is important to note, however, that while higher aerosol concentrations above the boundary layer/cloud-top are likely common, they are not universal and concentrations might also be lower.