We employed the Lagrangian particle dispersion model FLEXible PARTicle (FLEXPARTv10.4) to assess the emission sensitivities or “footprints” of air masses arriving at Zeppelin during the simulation period. FLEXPART is a stochastic model used to compute dispersion of hypothetical particles, based on mean, turbulent, and diffusive flows which can be run backwards in time to estimate air mass history at a site (Pisso et al., 2019). European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis meteorology with 137 height levels 1 h temporal and 0.5∘ × 0.5∘ spatial resolution was used as an input to FLEXPART (Hersbach et al., 2018a, b). The air mass history was simulated 7 d backwards in time and arriving at Zeppelin (474 m a.s.l.) every 3 h (at 00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 15:00, and 21:00 UTC) for the entire simulation period (1–25 May 2018).

FLEXPART-calculated normalized emission sensitivity fields were combined with oceanic emissions (DMS, dibromomethane, tribromomethane, iodomethane), NH3 from seabird colonies, and anthropogenic emissions (NH3, SO2, CO, NOx) derived from global inventories (see Sect. 2.2). This was done to obtain representative emissions that consider the complete emission source regions along the trajectories, upwind of the measurement station. Additional meteorological parameters such as temperature, pressure, sea surface temperature, specific humidity, and cloud liquid water content from the ERA5 reanalysis dataset were extracted along the trajectories and provided as inputs to ADCHEM.

Emissions of gas-phase biogenic volatile organic compounds (VOCs) α-pinene, β-pinene Δ3-carene, limonene, isoprene, and β-caryophyllene were modeled with a one-dimensional version of MEGAN v2.04 (Model of Emissions of Gases and Aerosols from Nature 2.04) (Guenther et al., 2006). Gas-phase emissions of marine halogens such as tribromomethane (CHBr3), dibromomethane (CH2Br2), and iodomethane (CH3I) were retrieved from CAMS-OCE global oceanic emissions (CAMS-GLOB-OCE), which are available as daily means with a spatial resolution of 0.5∘ × 0.5∘ (Granier et al., 2019; Ziska et al., 2013). CAMS-GLOB-OCE also provides gas-phase DMS emissions with the same temporal and spatial resolution (Granier et al., 2019) calculated with the air–sea flux parameterization and emission fluxes described in Lana et al. (2011) and Nightingale et al. (2000). NH3 emissions from seabird colonies were acquired from a global emission inventory (Riddick et al., 2012). To account for additional NH3 fluxes from the open ocean, we used an estimated sea surface equilibrium NH3(g) saturation concentration of 0.5 nmol m-3 (12.2 ppt at standard temperature and pressure (STP)), which approximately corresponds to a surface ocean ammonium concentration of 0.125 mmol m-3 (or ∼3 ppb, calculated based on Eqs. 3 and 4 from Wentworth et al., 2016) at a sea surface temperature of +2 ∘C. The sea surface temperature for the study period varied between -2–23 ∘C along the trajectories. The estimated surface ocean ammonium concentrations are in close agreement with the concentration estimated by the global ocean biogeochemical model COBALT (Stock et al., 2014) in the North Atlantic Ocean, but up to a factor of ∼5 higher than the concentrations simulated with other ocean biogeochemical models and/or model setups (Paulot et al., 2015). Therefore, we performed model sensitivity runs with a sea surface equilibrium NH3(g) concentration of 0.1 and 1 nmol m-3. The NH3(g) equilibrium saturation concentrations represent the ambient surface gas-phase concentration at which the air–sea flux changes direction, with a net downward flux from air to sea if the ambient NH3(g) exceeds the equilibrium gas concentrations and vice versa (Wentworth et al., 2016). For the anthropogenic trace gas and primary particle emissions, we used the CAMS-GLOB-ANT v2.1 inventory, with a spatial resolution of 0.1∘ × 0.1∘ (Granier et al., 2019).

In this work, we used the sea surface temperature (SST) and wind-speed-dependent sea spray aerosol (SSA) emission parameterization by Sofiev et al. (2011) (further referred to as Sofiev11). Sofiev11 used a modified source function based on the parameterization of Monahan et al. (1986), experiments by Mårtensson et al. (2003), and SEAS campaign by Clarke et al. (2006). The modified source function in Sofiev11 provides extrapolated SSA emissions between size ranges of 10 nm–10 µm, with appropriate correction functions employed for SST deviating from 298.15 K (Sofiev et al., 2011). Sofiev11 SSA parameterization shows that with increasing temperatures, emission flux for larger particles increases while the emission fluxes for smaller particles decreases (Barthel et al., 2019; Sofiev et al., 2011). We performed sensitivity tests using the temperature- and wind-speed-dependent SSA parameterization by Salter et al. (2015) (further referred to as Salter15). Both Salter15 and Sofiev11 are valid between 10 nm–10 µm. Model simulation comparisons between Sofiev11 and Salter15 have shown that the SSA parameterization from Sofiev11 has a stronger temperature dependence and higher particle number concentration emissions in the Aitken mode but results in lower PM10 emissions at temperatures below 25 ∘C (Barthel et al., 2019).

For this study, ADCHEM was employed as a one-dimensional column model with 40 logarithmically vertical layers, extending up to ∼2600 m. The model time step used for simulations was 30 s. The vertical atmospheric turbulent diffusion was solved using a modified Grisogono turbulent diffusivity scheme (Jeričević et al., 2010; Öström et al., 2017; Roldin et al., 2019). The ADCHEM aerosol module includes new particle formation, Brownian coagulation, condensation and evaporation of particles, and finally the dry and wet deposition of both particles and gases. The particle number size distributions were represented using 100 size bins ranging from 1.07 nm to 10 µm dry diameter. Clouds were assumed to be present in the model grid cells when the bulk liquid water content (LWC, extracted along the trajectory from ERA5 datasets) was greater than 0.01 g m-3. As a default, we used a constant cloud supersaturation (S) of 0.25 %, and the particles were activated into cloud droplets if the calculated water vapor supersaturation above the particle surface (Sc, calculated using Köhler theory) was smaller than S. For sulfate-dominated aerosol particles this corresponds to a minimum dry particle activation diameter of ∼80 nm (see Fig. S10). During the cloud processing, each activated cloud droplet was assumed to take up an equal amount of liquid water corresponding to the total bulk LWC divided by the calculated number concentration of activated cloud droplets. The gas–liquid droplet mass transfer and dissolution of 50 species in total, including HCl, HNO3, H2SO4, NH3, HIO3, H2O2, O3, OH, BrO, NO3, DMSO, MSIA, MSA, and HPMTF, and their irreversible reactions in the interstitial and activated cloud droplets are treated by the multi-phase chemistry mechanism (see Wollesen de Jonge et al. (2021) for details). The kinetic pre-processor (KPP) (Damian et al., 2002) was used to generate the multi-phase chemistry mechanism used in this study.

Recent observations of NPF at Ny-Ålesund have confirmed the importance of ion-mediated H2SO4–NH3 nucleation in spring with MSA and H2SO4 condensation contributing to the subsequent growth of particles (Beck et al., 2021; Lee et al., 2020). In this work, the Atmosphere Cluster Dynamics Code (ACDC) (McGrath et al., 2012; Olenius et al., 2013) was coupled with ADCHEM (Roldin et al., 2019). ACDC was used to model NPF, which involved H2SO4 clustering with NH3 via both neutral and ion-induced pathways with an ionization rate of 1.7 cm-3 s-1. ACDC was used to solve the evolution of molecular H2SO4–NH3 clusters by considering the loss of clusters by collisions, evaporation, or coagulation scavenging onto larger aerosol particles. At each time step, the flux of clusters (up to ∼5 H2SO4 and 5 NH3 each) growing out of the ACDC molecule-cluster domain represents the NPF rate. These newly formed clusters are assigned to the corresponding smallest particle size bin at 1.07 nm in diameter in ADCHEM, which then simulates the condensational growth of particles and losses due to evaporation, coagulation, and wet and dry deposition.

For all simulations, we used model output from the closest height levels that can represent Gruvebadet (model height of 73.5 m a.s.l.) and Zeppelin (model height of 486.0 m a.s.l.).

We utilized comprehensive measurements from the Ny-Ålesund research station sites, Zeppelin observatory (Platt et al., 2022), and Gruvebadet during the period of 1–25 May 2018. Since 2017, the atmospheric observatory at Gruvebadet, which is located about 700 m southwest of Ny-Ålesund village at almost sea level (67 m.s.l.), hosted the Neutral cluster and Air Ion Spectrometer (NAIS, Manninen et al., 2010; Mirme and Mirme, 2013) for semi-permanent measurements. Here we use NAIS-measured number size distribution of naturally charged (ions) in diameter size ranges between 0.8–40 nm and neutral particles in the size range of 2.5–42 nm, with a temporal resolution of 2 s.

During the measurement period, a scanning mobility particle sizer (SMPS) was operated to measure particle number size distribution in the diameter size range of 10–470 nm at Zeppelin. Concurrent SMPS data (TSI 3034, 54 channels) with diameter size ranging from 10 to 470 nm from Gruvebadet were also available (Dall'Osto et al., 2019; Moroni et al., 2020), thus enabling us to compare the modeled particle number size distribution with the measured size distributions at both measurement stations. Daily-resolution continuous aerosol samples with PM10 cutoff were collected at Gruvebadet using a Tecore Skypost low-volume sampler (Amore et al., 2022). The detection limit for Na+ was 0.0001 and 0.0002 µgm-3 for Cl-, NH4+, and SO42-. Since the field blank medians at Gruvebadet were less than 1 % of sampled values, the field blanks were not subtracted from the sampled values (Amore et al., 2022).

Vertical particle number concentration profiles were obtained using UAS ALADINA (Bärfuss et al., 2018; Lampert et al., 2020), which was operated during the simulation period. ALADINA was operated up to a height of 850 m a.s.l. and thus can be used for a potential closure between the two different research sites of Gruvebadet and Zeppelin. ALADINA is equipped with two condensation particle counters (CPCs model 3007, TSI Inc., St. Paul, MN, USA), measuring in the size ranges of 3 nm–2 µm (CPC1) and ∼12 nm–2 µm (CPC2) (Lampert et al., 2020; Petäjä et al., 2020). The difference between CPC1 and CPC2 provides an estimate of particle number concentrations in the size of 3–12 nm (N3-12), which was used as an indicator of NPF. Alongside the CPCs, a host of other instruments measuring meteorological parameters were operated in unison, the description of which can be found in Bärfuss et al. (2018) and Lampert et al. (2020).

Figure 1a and b show the observed and predicted particle number size distributions at Gruvebadet for the BaseCase simulation. Figure 1a includes SMPS observations starting from 10 to 470 nm and NAIS observations for neutral particles in the range 2.5–10 nm (boundary marked by the black line) since NAIS data below 2.5 nm cannot be relied upon, owing to the presence of corona-generated ions (Jayaratne et al., 2017; Manninen et al., 2011, 2016).

In the BaseCase simulations the model captures particle formation on 2 May followed by an increasing number of Aitken and accumulation mode particles during the days of 3–4 May, which is the result of more polluted air masses arriving at Ny-Ålesund from the European continent (Fig. S1 in the Supplement). Similarly, the model reproduces the particle formation on 20 May, specifically in the size range 2–8 nm, but overestimates the Aitken mode and accumulation mode particle concentration on 21 May. However, the model tends to underestimate the nucleation mode particle number concentrations between 10–25 nm (N10-25nm) around noon and overestimate the concentrations during the morning and evening (Fig. S2a). The model and measurements show an apparent time delay in the formation of new particles larger than 10 nm. While the measurements show a peak at 11:00 CEST (for all times throughout) the simulated N10-25nm shows a maximum at 03:00 and 18:00. The modeled N10-25nm maximum around 18:00 is likely a result of the formation of new particles around noon, which grow to >10 nm in diameter during the afternoon and evening by condensation of H2SO4. The predicted Aitken (N25-100nm) and accumulation mode particle concentrations (N>100nm) which form a few days upwind of the station are overall in good agreement with the measurements, which show a minor diurnal trend (Fig. 2b and c). The measurements indicate that at Gruvebadet N10-25nm contributes the most significant fraction of measured total number concentrations with 45.3 %, while N25-100nm and N100-470nm contribute 30.5 % and 23.94 %, respectively. However, the simulations predict a greater contribution of Aitken mode (∼53.85 %) to total number concentration, with N10-25nm and N100-470nm accounting for ∼36.58 % and 9.57 %, respectively (Fig. S2).

Figure 2 shows the measured size distribution in panel (a) and simulated size distribution in panel (b) for Zeppelin. At Zeppelin, the model overestimates the number concentration in nucleation and Aitken modes (also see Fig. S3). The particle number size distribution measurements at Zeppelin indicate that the relative contribution of the three modes (nucleation, Aitken, and accumulation) varies to some extent when compared to Gruvebadet. Measurements show that at Zeppelin N10-25nm contributes ∼33.46 %, N25-100nm 46.43 %, and N>100nm 20.11 % to the total particle number concentrations. The model predicts a lower relative contribution of N10-25nm (26.94 %) and a greater contribution of N25-100nm (63.44 %) to the total simulated particle number concentrations. The diurnal trends at Zeppelin agree well with earlier measurements conducted at Zeppelin in spring by Ström et al. (2009). Additionally, the measured diurnal pattern at Zeppelin varies in comparison to Gruvebadet. At Zeppelin, the N10-25nm concentrations peak in the afternoon and evening. The modeled N10-25nm shows only a weak diurnal trend. It should be noted that the measurements show a time delay of around 3 h in the peak N10-25nm concentrations at the two sites (Figs. S2 and S3). This is possibly a result of vertical mixing and dilution effects modulating the observed particle number concentrations at sites situated at different altitudes, similar to observations made at Zeppelin and Corbel by Ström et al. (2009).

ADCHEM considers the formation of new particles via both the ion-mediated and neutral H2SO4–NH3 clustering pathways. Beck et al. (2021) observed a dominant contribution of negatively charged H2SO4–NH3 clusters to secondary particle formation in May 2017 at Ny-Ålesund, with HIO3 playing a small role in the initial particle formation. However, the discrepancy in the modeled and observed diurnal trends of N10-25nm could indicate that there are other sources or vapors that might potentially contribute to the particle formation. Other possible NPF mechanisms may involve amines (Olenius et al., 2013) and pure biogenic highly oxidized molecules (HOMs) (neutral and ion induced) nucleation (Kirkby et al., 2016). We speculate that the exclusion of these other mechanisms (HIO3, H2SO4 amines, and HOM-driven particle formation) might result in the discrepancies in the modeled and observed diurnal particle number concentration trends. HIO3-induced particle formation could, for example, play an important role if the air masses upwind of Ny-Ålesund traverse the sea-ice-covered regions (Baccarini et al., 2020; Beck et al., 2021).

Figure 3 presents the median particle number size distribution for the BaseCase simulation at both Zeppelin and Gruvebadet, with the respective 25th and 75th percentiles, for the entire selected period. At Gruvebadet, the modeled and measured median particle number size distributions are in reasonable agreement for both Aitken and accumulation modes. However, the model overpredicts the median Aitken mode concentrations at Zeppelin by a factor of ∼5.5. The modeled Aitken mode peak at both measurement sites is ∼ 50 nm, while the measured Aitken mode peak is ∼30 nm. Though the modeled accumulation mode peak is at a larger size (∼150 nm), compared to the measured accumulation mode peak (∼110 nm), the predicted value is slightly lower than the monthly averaged accumulation mode peak location measured at Zeppelin in earlier studies (∼160–170 nm, Dall'Osto et al., 2019).

The discrepancy between the modeled and measured particle concentrations at Zeppelin can be caused by the underlying complexity of modeling the boundary layer dynamics at an elevated site, such as Zeppelin. The vertical mixing of aerosols along the up-slope or down-slope of a mountain site is difficult, if not impossible, for a one-dimensional column model, since it is unable to capture the topographical influence on locally varying wind speeds or latent and sensible heat fluxes (Mikkola, 2020; Wainwright et al., 2012).

Another detectable feature in the median particle number size distribution is the diameter of the Hoppel minimum (Hoppel et al., 1985, 1986) and the role of in-cloud processing in forming this minimum. A Hoppel minimum is often observed in marine air masses (Fossum et al., 2018; Tunved et al., 2013; Zheng et al., 2018) and is attributed to in-cloud processing of aerosols, with chemical processing (e.g., sulfate production via oxidation of dissolved SO2) (Feingold and Kreidenweis, 2000; Hoppel et al., 1986) and coalescence of droplets playing a key role (Flossmann and Wobrock, 2019; Hoppel et al., 1986; Hoppel and Frick, 1990; Noble and Hudson, 2013). It has been estimated that, on average, aerosols take part in about 10 non-precipitating cloud cycles before they are removed from the atmosphere by wet scavenging (Hoose et al., 2008; Hoppel et al., 1986; Rosenfeld et al., 2014). These non-precipitating cloud cycles facilitate the formation of hygroscopic accumulation mode particles, with low critical supersaturation (Sc) that readily activates to cloud droplets during subsequent cloud cycles, thus growing to larger sizes. This is because the activated particles undergo chemical processing, gas-to-particle conversions, coalescence, and coagulation with other interstitial particles. Upon evaporation of water, the emerging dry particles have a larger size and lower Sc, leading to a minimum being formed between the un-activated and activated cloud droplets (Herenz et al., 2018; Hudson et al., 2015; Noble and Hudson, 2013). The diameter at which the Hoppel minimum is observed varies depending on the cloud supersaturation and particle composition (Hoppel et al., 1986; Hudson et al., 2015), with Hoppel minimum sizes observed in ranges from 60 nm at Zeppelin Ny-Ålesund to around 90 nm at Tuktoyaktuk, Canada (Herenz et al., 2018; Tunved et al., 2013).

The median particle number size distribution in Fig. 3 shows that at both stations the measured Hoppel minima is around ∼60 nm, while the simulated Hoppel minima are around the size of ∼100 nm at both sites. This difference in location of Hoppel minima can be attributed to the assumed value of S=0.25 % in the model. The value of S used in the model lies in the range of typical marine stratocumulus clouds, which can vary between 0.1 %–1 % (Fossum et al., 2018; Quinn et al., 2017). With S=0.25 % the sulfate-dominating particles in the Arctic marine boundary layer will be activated into cloud droplets if their diameter is greater than ∼85 nm in diameter (Fig. S10).

Figure 4 shows the range of simulated gas-phase concentrations of DMS oxidation products H2SO4, MSA, and HIO3 for the entire period at height levels representing Gruvebadet. The mean measurement values (red dots) represent gas-phase concentrations for the same species from an earlier 2017 May campaign performed at Gruvebadet by Beck et al. (2021). Measurements of H2SO4 at Gruvebadet from May 2017 indicate monthly mean concentrations around ∼106 molec. cm-3 (Beck et al., 2021). The modeled H2SO4 concentrations at Gruvebadet are 3×106 molec. cm-3, implying a reasonably good model performance in predicting gaseous precursor concentrations. The simulated gas concentrations of MSA (105–108 molec. cm-3) also agree well with the measurements made at Gruvebadet in May 2017 by Beck et al. (2021), wherein they measured daily averages of MSA gas concentrations on the order of 107 molec. cm-3. The low modeled values of MSA and DMSO gas-phase concentrations at the height representing Zeppelin (see Fig. S4, e.g., between 15–17 May) coincide with the period where the planetary boundary layer height (PBLH) is below the altitude of Zeppelin station (see Fig. S5). Overall, we can conclude that the modeled precursor gas concentrations at the two measurement sites are, in general, in good agreement with earlier measurements at the two sites.

Figure 5a shows the simulated median mass size distribution of compounds Cl-, Na+, MSA, SO42-, NH4+, and NO3- for the BaseCase runs in the lowest model layer. Figure 5a indicates that the nucleation mode particles are composed mainly of SO42- and NH4+, while MSA, Cl-, and Na+ dominate PM for larger particles. The observed and modeled high MSA(g) concentrations in comparison to H2SO4(g) at Ny-Ålesund is not reflected in the respective vapor contribution to the nano-particle growth. This is because, in contrast to H2SO4, MSA is not a non-volatile condensable compound. The gas-to-particle partitioning of MSA requires co-condensation and dissolution of (NH3) (Hodshire et al., 2019) or the existence of cations such as Na+, which decreases the particle acidity ([H+]). Figure 5b shows the relative mass fraction of the abovementioned compounds to PM at different sizes. SO42- and NH4+ dominate the mass for particles in the nucleation and Aitken modes. SO42- contributes ∼74 % and ∼71 % to nucleation and Aitken mode PM, with its contribution decreasing for accumulation (100 nm–1 µm) and coarse (>1 µm) mode PM (∼6 % and 3.36 %, respectively) (Table S1). NH4+ contribution follows a similar trend, as SO42-, with 12.34 % and 6.95 % contribution to nucleation and Aitken mode PM, but is insignificant for accumulation and coarse mode PM (Table S1). The loss of primary sea spray aerosols due to wet scavenging promoted the growth of secondary aerosol particles in the nucleation and Aitken modes by NH4+ and SO42- as seen in Fig. 5b. Na+ (∼32.9 %), Cl- (∼39.5 %), and MSA (20.45 %) are the dominant contributors to accumulation and coarse mode PM. In the BaseCase simulations, gas-phase SO2 dissolves in the cloud droplets and is oxidized by H2O2 into SO42- (Wollesen de Jonge et al., 2021). Previous modeling studies have shown that a very small fraction of MSA is formed in the gas phase. Instead, most MSA is formed via ozonolysis of MSIA in the aqueous phase (Hoffmann et al., 2016; Wollesen de Jonge et al., 2021). It should be noted that HIO3 and NO3- have an insignificant contribution to total PM10, amounting to ∼0.05 % and 0.17 %, respectively.

Figure 6 compares the daily PM10 filter measurements to the modeled values at both measurement stations. The model prediction of PM10 Cl-, Na+, SO42-, and NH4+ was evaluated using statistical metrics such as NMB, FAC2, correlation coefficient (r), and RMSE (Table 3). Though the model does well in simulating the trends of PM10 SO42-, Na+, and Cl- at Zeppelin (r values of 0.35, 0.51, and 0.6, respectively), it is unable to predict the NH4+ trends accurately (r=-0.08).

Pearson correlation (r values) at Gruvebadet is in the range of 0.29–0.34 for PM10 NH4+, SO42-, Na+, and Cl-, implying that the model trends are reasonably consistent with the measured trends. However, at Gruvebadet the NMB values for PM10 NH4+ and SO42- are underpredicted (NMB =-0.88 and -0.28, respectively), while PM10 Na+ and Cl- show a large overprediction (1.81 and 1.05) in the modeled values. In contrast, at Zeppelin, the modeled PM SO42- is overestimated (NMB = 1.96). Likewise, large RMSE and negligible FAC2 values, for PM10 Na+ and Cl-, imply discrepancies between the predicted and measured values, indicating that the model is overestimating PM10 SO42-, Na+, and Cl- at Gruvebadet and PM10 SO42- at Zeppelin. In summary, the model tends to overpredict PM10 Na+, Cl-, and SO42- concentrations but, on the other hand, does reasonably well in predicting the daily measured trends. Additionally, the modeled PM10 Cl-/Na+ molar ratio at Gruvebadet and Zeppelin is ∼0.79 and ∼0.95, respectively. This is much higher than the observed PM10 Cl-/Na+ molar ratio at both sites (∼0.39). One likely reason for this is the overestimated sea spray aerosol emissions. The PM10 Cl-/Na+ molar ratios give a measure of the acidic nature of aerosol, since increased condensation of strong acid MSA and H2SO4 increases acidity of aerosols, thereby causing loss of Cl- (dechlorination) as HCl (Ayers et al., 1999; Frey et al., 2020). Thus, increased availability of H2SO4 and MSA in the particle phase in Aitken mode particles results in acid-induced Cl- loss from sea spray particles.

Figure 7a shows the measured vertical N3-12nm concentrations from CPC on board the UAS for four measurement periods overlaid onto simulated vertical profiles. Figure 7b and c show the mean vertical profiles for N3-12nm and N>12nm for both the BaseCase simulation and UAS measurements for the entire selected period. The model underestimates the measured N3-12nm and N>12nm vertical particle number concentrations below 200 m a.s.l. The NMB for N3-12nm and N>12nm is -0.28 and -0.14, respectively, implying that the model underestimates the particle number concentrations. Both the modeled and measured mean particle number concentrations for N3-12nm and N>12nm are in good agreement between the heights of 200–600 m a.s.l. The lower calculated concentrations of modeled mean particle number concentrations above 600 m a.s.l. are most likely affected by higher turbulence in the transition zone from the boundary layer to the free troposphere, which might cause a large mixing of aerosol particles. It should be noted that, at Gruvebadet, the mean SMPS particle number concentrations are in good agreement with the modeled particle number concentrations. However, at the altitude of the Zeppelin station, both the model and UAS measurements of N>12nm are substantially higher (factor of 4) than the mean particle number concentrations measured with the SMPS at Zeppelin. This finding further strengthens the conclusion that the complex orography at Ny-Ålesund highly affects the variability in the vertical scale, which may cause this discrepancy in the observed and modeled particle number concentrations at Zeppelin (see Sect. 3.1). The UAS measurements were carried out at the airport on Ny-Ålesund (and the UAS was flown around Ny-Ålesund) where the boundary layer measurements, like the model, most likely resemble the general Arctic marine boundary layer conditions. Figure S9 shows the influence of different sensitivity simulations on the modeled vertical particle number concentrations. The large spread in the modeled vertical particle number concentrations in Fig. S9 highlights the importance of constraining uncertain parameters such as cloud supersaturation and NH3 gas emissions, to better simulate secondary aerosol formation in marine polar regions.

In this section, we will discuss the results from the sensitivity tests that we performed to complement the main BaseCase simulations. The settings of different sensitivity tests are described in Table 1.