To investigate an Arctic cloud case observed during ARTofMELT, we used the large-eddy simulation code MIMICA, which has been applied extensively to AMPCs in the boundary layer e.g.,. A full model description is available from , but here we provide the necessary details to contextualize this study.

Within MIMICA, a two-moment bulk microphysical scheme predicts number concentrations (NC) and mass mixing ratios (Q) for five hydrometeor types: cloud droplets, raindrops, ice crystals, snow, and graupel . While cloud droplets remain suspended, liquid drops of at least 40 µm in diameter are classified as raindrops and allowed to sediment. Snow originates from the collision and aggregation of ice crystals, whereas graupel forms upon the collection of liquid water by any frozen hydrometeor. Frozen hydrometeors are not assumed to be spherical, and in contrast to , MIMICA incorporates multiple ice habits whereby the size-mass and fall speed-mass relations can be altered, as well as process rates for deposition, melting, and sublimation. Hydrometeor mass distributions follow generalized gamma functions, with size and fall speeds that depend on mass via power-law relations. The represented microphysical processes include condensation/evaporation, autoconversion, accretion/aggregation, riming, melting, and evaporation/sublimation. Under supersaturated conditions, condensation onto droplets and deposition onto frozen hydrometeors are computed explicitly . Primary ice formation is parameterized using different schemes; here, we present simulations using both prescribed ICNC and temperature-dependent INP concentrations. SIP is represented via rime-splintering , and our model version additionally incorporates collisional breakup , implemented following .

In MIMICA, aerosol populations are defined by lognormal distributions, and characterized by their hygroscopicity parameter κ, density ρ, and molecular weight Mw. Here, we represent the aerosol size distribution via observationally derived Aitken and accumulation modes (see Sect. ). Aerosols defined in this manner act exclusively as CCN and are entirely decoupled from ice nucleation. In our configuration, cloud droplet activation is determined by κ-Köhler theory and then follows to compute the activated fraction of the aerosol distribution. Aerosol processes other than activation into cloud droplets – such as scavenging or in-cloud chemistry – are not represented, and therefore the aerosol number size distribution remains constant throughout the simulation.

The cloud is initialized by prescribing a vertical profile for the cloud droplet mixing ratio Qc. This profile is usually based on observations, as was done in this study (see Sect. ). From the first time step, droplets can freeze according to the selected ice nucleation parameterization, allowing the cloud to evolve into a mixed-phase state. As the cloud evolves, further aerosol activation occurs whenever local supersaturation conditions are met.

Subgrid-scale turbulence is based on the 0th order approach by , and surface fluxes are calculated according to Monin-Obukhov similarity theory . Radiative transfer is computed across six solar and twelve terrestrial frequency bands . While cloud ice does not affect the radiative fluxes, the cloud-driven radiative effects depend on Qc, the raindrop mixing ratio Qr, and the cloud and rain effective radii.

The ARTofMELT expedition took place during May and June 2023 in the Fram strait onboard the Swedish icebreaker Oden. describe the observations used in this study in detail, so we only briefly summarize them here.

The modular multiplatform compatible air measurement system (MoMuCAMS) tethered balloon platform operated 23 times during the campaign . The MoMuCAMS Helikite payload measured cloud droplet and aerosol number concentrations, aerosol size distributions and optical properties, and standard meteorological variables. A miniaturized scanning electrical mobility spectrometer (mSEMS model 9404, Brechtel Manufacturing Inc., USA) measured aerosol size distributions covering electrical mobility diameters between 8 and 280 nm, and a portable optical particle spectrometer (POPS, Handix Scientific, USA) covered optical diameters between 186 and 3370 nm. Mounted on the MoMuCAMS, an in-situ light optical aerosol counter (LOAC, Meteomodem, France) with a vertical inlet measured aerosol and cloud droplet size distributions from 200 nm up to 50 µm. The system also collected filter samples for offline analysis of INP concentrations and STXM/NEXAFS analysis scanning transmission X-ray microscopy coupled with near-edge absorption fine structure spectroscopy; which provides information on chemical composition, mixing state, and morphology of individual aerosol particles. The MoMuCAMS sampled aerosol particles (including for the filters) through an interstitial inlet.

Onboard the Oden, instruments on the fourth deck continuously measured complementary aerosol physical and chemical properties and INP concentrations. For this study, we used INP filter sampling and immersion freezing assays to determine INP concentrations . A CCN counter (CCNC, CCN-100, Droplet Measurement Technologies, USA) and a differential mobility particle sizer (DMPS, own development, see ), which sampled behind a whole-air inlet, were used to characterize aerosol hygroscopicity.

During the cruise, radiosondes RS41, Vaisala, Finland; were launched every 6 h to provide vertical profiles of temperature (T), relative humidity (RH), and horizontal winds (u,v). A microwave radiometer HATPRO, Radiometer Physics GmbH, Germany;, a 94 GHz Doppler cloud radar RPG-FMCW-94-SP, Radiometer Physics GmbH, Germany;, and a lidar ceilometer CL31, Vaisala, Finland; operated continuously from the ship. The radiometer retrieved LWP with a typical uncertainty of 25 g m⁻² . To calculate liquid water content LWC; profiles, we identified liquid cloud layers using cloud radar and ceilometer data processed by CloudnetPy version 1.66.10 , and subsequently distributed the LWP adiabatically across them. Full details of the specific CloudnetPy implementation and the assumptions used to generate the LWC profiles are described in and , respectively. Ice water content (IWC) profiles were calculated by applying the default CloudnetPy method, which is based on a statistical relationship between temperature and radar reflectivity . Vertical integration of the IWC profiles yielded an estimate of the ice water path (IWP). Quantifying the precise uncertainty for this derived IWP is difficult, though it likely approaches a factor of 2. Broadband radiometers CGR4 and CMP22, Kipp and Zonen, The Netherlands; deployed on the sea ice adjacent to Oden measured the upwelling and downwelling longwave and shortwave radiative fluxes with uncertainties of 3 % and 1 %, respectively.

For our case study, we selected a slightly supercooled mixed-phase stratocumulus observed west of northern Svalbard on 7 June 2023 . During this period, Oden was moored to an ice floe at approximately 2.902° E, 79.565° N. Weak pressure gradients and a surface pressure of 1015.72 hPa measured aboard Oden characterized the synoptic conditions, as a pressure trough extended from the surface into the mid-troposphere directly over the ship Fig. ;. A stratocumulus layer developed around 06:00 UTC on 7 June, persisting and gradually rising for more than 48 h (Fig. ). CloudnetPy classified the cloud as mixed-phase based on the presence of hydrometeors not meeting the criteria for classification as droplets in the ceilometer attenuated backscatter data (indicative of ice phase particles), and a positive liquid water path measured by the microwave radiometer. During three flights on 7 June 2023 (10:10–13:32, 14:31–17:35, and 19:09–22:13 UTC), the Helikite successfully conducted measurements below, within, and above the cloud layer, whereas filter sampling was restricted to within and above the cloud . Continuous observations of ice needles on the ship deck and via the Helikite camera provided further physical evidence for an active ice phase, corroborating the mixed-phase classification derived from the remote sensing instrumentation.

Using the baseline setup described in Sect. , the simulated LWP lies between the 75th and 90th percentiles of the observations towards the end of the simulation time (solid black line in Fig. a). After the 3 h spin-up period, LWP remains relatively steady before decreasing slightly toward the end of the simulation. As noted previously, we do not aim to reproduce the observed temporal evolution; we instead focus on the conditions during the final 2 h of the simulation. The simulated IWP lies slightly below the 10th percentile of the observations (Fig. b). The IWP retrieval is subject to substantial uncertainty (Sect. ); against this background, it is encouraging that the simulation produces non-zero IWP (0.19 g m⁻² averaged over the final 2 h; Table ).

By the end of the simulation, DWLW aligns closely with the lower percentiles of the observations (Fig. c and Table ). DWLW increases slightly over time because its primary contribution originates from the cloud base, where the temperature gradually rises (Fig. a). Consistent with increasing solar radiation, netSW increases throughout the simulation, exceeding the observations by 5 to more than 10 W m⁻² (Fig. d).

The simulated vertical structure aligns with the characteristic features of low-level AMPCs. Liquid water mass peaks near the cloud top (Qc in Fig. a), whereas the maximum frozen-water mass occurs near the cloud base (ice, snow, and graupel mixing ratios Qi, Qs, and Qg in Fig. g, j, m) – a distribution consistent with previous Arctic observations . Liquid dominates the total condensate mass (Figs.  and ), reflecting typical AMPC properties expected at these relatively high in-cloud temperatures. The CDNC peaks within the mid-cloud layer and decreases sharply toward the cloud top and base (Fig. b), reflecting the entrainment of drier air at the cloud boundaries. Raindrop number concentration (RDNC) is highest near the cloud top, decreasing toward the cloud base (Fig. e). Given that Qr remains relatively constant in the lower cloud (Fig. d), this vertical structure indicates that collision-coalescence produces fewer but larger raindrops at lower levels (mean raindrop diameter Dr in Fig. f). Throughout the cloud layer, ICNC remains at the prescribed value of 900 m⁻³ (Fig. h). The number concentrations of the other frozen hydrometeors (snowflake number concentration, SFNC, and graupel number concentration, GNC) remain small throughout the domain, peaking at or below the cloud base (Fig. k, n).

Figure  summarizes the process rates for Qi. Deposition represents the primary source for Qi (net positive; Fig. a), while precipitation acts as the dominant sink (net negative; Fig. h). SIP processes contribute only weakly in the baseline simulation. Rime-splintering is entirely absent (Fig. e) – although cloud temperatures fall within the Hallett-Mossop range -8 to -3 °C;, the mean cloud droplet diameters Dc remain well below the 50 µm threshold required by MIMICA (Fig. c). The other implemented SIP process, collisional breakup, is active primarily until approximately 4.5 h and again between 5.5 and 7 h of simulation time (Fig. g), but its vertically and temporally integrated Qi tendency remains orders of magnitude smaller than for other Qi processes.

The ceilometer data (Fig. a) indicate continuous cloud cover starting from approximately 07:00 UTC on 7 June. The baseline simulation successfully captured this persistence, maintaining a fully overcast domain throughout the simulation. Furthermore, cumulative surface precipitation remained below 0.1 mm in both the observations and the simulation.

Replacing the prescribed ICNC in the baseline simulation with the temperature-dependent INP(T) parameterization (Eq. ) results in an LWP 4.5 g m⁻² higher than in the baseline simulation, exceeding the 90th percentile of the observed LWP distribution (Fig. a; Table ). Simultaneously, this simulation produces zero IWP (Fig. b; Table ). Horizontally averaged profiles confirm the absence of frozen hydrometeors (Fig. g, h, j, k, m, n). Under the specific atmospheric conditions of this case, the model is unable to produce IWP > 0 based on the observed INP concentrations. To form the IWP of 0.2 g m⁻² in the baseline simulation, an ICNC of 900 m⁻³, was required. The INP(T)-based parameterization produces an ICNC at least four orders of magnitude lower than the baseline simulation (Fig. ), explaining the absence of an appreciable ice mass. The Qi process rates confirm that some freezing occurs (Fig. f), but the tendency is almost one order of magnitude lower than in the baseline simulation (Fig. f). This produces too few ice crystals to support depositional growth (Fig. a). The few ice crystals that do form remain too small and too few to trigger any SIP processes (Fig. e, g), further preventing the development of an appreciable ice mass.

Among the four analyzed parameters (ANC, Atype, ICNC, and IChab), ANC contributes the largest relative variance share (44.5 %) to LWP and acts as the dominant parameter for DWLW, whereas it contributes considerably less to the IWP variance (Fig. ; Table ). The boxplots in Fig.  illustrate this strong influence: the blue and green ANC boxes do not overlap for LWP and DWLW, with the exception of simulations ancHI_atypeD_icncHI_ichabP and ancHI_atypeS_icncHI_ichabP (Table ). These two specific simulations result in a glaciated cloud without LWP and exhibiting minimal DWLW, even when using the high ANC value (Table ). Comparing two example simulations (ancLO_atypeD_icncLO_ichabP and ancHI_atypeD_icncLO_ichabP) reveals the physical mechanisms driving these differences between low and high ANC. Using the low ANC value yields a substantially smaller CDNC, as fewer aerosols are available to act as CCN (Fig. a).

The Qc profile responds accordingly (Fig. c), although the individual cloud droplets in the low-ANC simulation (ancLO_atypeD_icncLO_ichabP) are larger than those in the high-ANC simulation (ancHI_atypeD_icncLO_ichabP). These larger droplets enhance collision-coalescence efficiency, generating a larger RDNC and higher Qr under low-ANC conditions (Fig. b, d). Nevertheless, RDNC is several orders of magnitude smaller than CDNC (Fig. a–b). Furthermore, the cloud is significantly shallower for the low ANC value because the initial cloud liquid rapidly precipitates during the early stages of the spin-up simulation (Fig. ). The combination of lower Qc and a shallower cloud depth ultimately leads to a significantly smaller LWP for the low-ANC simulation (Fig. a). This reduction in liquid water directly causes a significantly lower DWLW, because the longwave radiation emitted by the cloud depends strongly on LWP within the ranges of values occurring here (Fig. c).

In contrast, ANC exerts a much weaker influence on IWP, reflected by the narrower gap between the two ANC boxplots (Fig. b). Even for the low ANC value, specific simulations (e.g., ancLO_atypeD_icncHI_ichabC and ancLO_atypeS_icncHI_ichabC) still produce a relatively high IWP (Table ). Comparing specific simulations highlights this behavior (Fig. ): although LWP and DWLW are very similar between ancLO_atypeD_icncLO_ichabP and ancLO_atypeD_icncHI_ichabC (both with low ANC), the IWP for the latter is considerably higher. Even though ancLO_atypeD_icncHI_ichabC has a low LWP of 10.7 g m⁻², the combination of high ICNC and IChab columns generates a higher IWP than most simulations with high ANC, e.g., ancHI_atypeD_icncLO_ichabP. This illustrates the lower relevance of ANC for IWP, compared to LWP and DWLW.

ANC shows a considerable interaction only with ICNC and for LWP (Fig. ; Table ). Consequently, the sensitivity of LWP to changes in ANC depends on the ICNC value. The LWP is more sensitive to ICNC for high ANC than for low ANC (Table ). For low ANC, simulations with a high ICNC value (e.g., ancLO_atypeD_icncHI_ichabC: 10.7 g m⁻²) can still result in an LWP similar to those using the low ICNC (e.g., ancLO_atypeD_icncLO_ichabC: 12.6 g m⁻²). Conversely, for high ANC, all simulations with high ICNC produce considerably less LWP than their low-ICNC counterparts (e.g., ancHI_atypeD_icncHI_ichabC: 53.4 g m⁻² vs. ancHI_atypeD_icncLO_ichabC: 81.7 g m⁻²). Enhanced deposition rates for high ICNC drive this pronounced difference, resulting in a stronger depletion of the liquid cloud water.

The parameter ICNC contributes the largest variance share to IWP and the second-largest variance share to LWP and DWLW (Fig. ; Table ). Similar to the ANC boxplots, the ICNC boxes do not overlap for IWP and overlap only slightly for LWP and DWLW (Fig. ). Furthermore, IWP in simulations with high ICNC is not limited by low ANC; even when combining low ANC with high ICNC, IWP is at least 0.28 g m⁻² (ancLO_atypeD_icncHI_ichabP; Table ).

The simulations experiencing complete glaciation (resulting in an LWP of nearly zero and a minimum DWLW of approximately 220 W m⁻², in contrast to the observed cloud that persisted as mixed-phase) all have high ICNC (ancLO_atypeD_icncHI_ichabP, ancLO_atypeS_icncHI_ichabP, ancHI_atypeD_icncHI_ichabP, and ancHI_atypeS_icncHI_ichabP; Table ). However, whether the cloud glaciates depends additionally on IChab: only the combination of high ICNC and IChab plates leads to complete glaciation. Specifically, the IChab plates dictates depositional growth and sedimentation velocities that allow the WBF process to entirely consume the liquid cloud water. The interconnection between IChab and ICNC manifests as a strong interaction for both IWP and DWLW (Fig. ; Table ). For LWP, the interaction between ICNC and IChab is significant, although it is smaller than the interaction between ANC and ICNC described above. In simulations that glaciate, the depletion of liquid water removes the cloud-top radiative cooling (Fig. a versus c), which acts as the main source of turbulent kinetic energy (TKE) necessary to maintain the cloud. Consequently, TKE largely dissipates in these glaciated clouds (Fig. b versus d), eliminating the vertical winds required for aerosols to activate into cloud droplets.

The parameter IChab acts as the third-largest variance contributor to all three variables, with its highest relative contribution for DWLW (Fig. ; Table ). Despite ranking third, IChab controls critical phase transitions. For simulations with high ICNC, IChab determines whether the cloud glaciates or persists as mixed-phase. Furthermore, for simulations with low ICNC, IChab dictates whether the cloud contains any ice at all. Specifically, ancLO_atypeD_icncLO_ichabC, ancLO_atypeS_icncLO_ichabC, ancHI_atypeD_icncLO_ichabC, and ancHI_atypeS_icncLO_ichabC all are liquid-only clouds due to their IChab columns (Table ). This strong interdependence between IChab and ICNC manifests as high interaction terms across almost all variables (Fig. ; Table ).

Figure  visualizes that the variability of IWP is substantially larger for the IChab columns than for plates. For columns, IWP stretches from 0.01 to a maximum of 1.5 g m⁻². For plates in contrast, IWP reaches at least 0.05 g m⁻² but does not exceed 0.44 g m⁻² (Table ).

The parameter Atype contributes less than 1 % of the variance to all three variables (Fig. ; Table ). Pairwise comparisons of simulations differing only by Atype reveal some differences in LWP and DWLW, particularly for low ANC: For all non-glaciating simulation pairs with low ANC (ancLO_atypeD_icncLO_ichabP and ancLO_atypeS_icncLO_ichabP, ancLO_atypeD_icncLO_ichabC and ancLO_atypeS_icncLO_ichabC, ancLO_atypeD_icncHI_ichabC and ancLO_atypeS_icncHI_ichabC), changing Atype from dust to sea salt increases LWP by approximately 50 % (Table ). Although the blue and green boxes in Fig.  differ slightly between the two Atype values, their mean differences are so small compared to the other parameters that Atype exerts a negligible influence within our specific study setup (Table ).

Overall, these results demonstrate that for our model setup with prescribed ICNC, ANC is the dominant parameter, followed by ICNC. Although IChab contributes less to variable variance, it critically impacts the phase partitioning of the simulated cloud (Fig. ; Table ).

The ANC parameter contributed most significantly to the variance of LWP and DWLW because the lower ANC value falls within the CCN-limited regime (“tenuous cloud”), whereas the higher value is outside this regime . Similar reductions in LWP under low CCN concentrations were shown in previous modeling studies e.g.,. Ice nucleation in our model setup was independent of aerosol concentrations, restricting the impact of ANC to indirect ice creation pathways via the liquid phase, specifically through droplet number concentrations, mixing ratios, and saturation. This lack of connection between aerosol populations and ice nucleation likely explains why ANC did not emerge as the dominant, or at least a more important, parameter for IWP.

It is not surprising that ICNC exerted a large impact on IWP, given that the high and low ICNC values span two orders of magnitude and higher ice crystal concentrations directly generate more ice mass. Furthermore, aerosol availability did not constrain the prescribed ICNC value. The low ANC value exceeds the high ICNC value by more than two orders of magnitude, ensuring that an activation fraction of merely ≲ 1 % generated sufficient cloud droplets to support the prescribed ICNC. The important role of ICNC aligns with previous modeling studies that have demonstrated that AMPC water paths have high sensitivity to ice crystal concentrations e.g.,. The complete glaciation observed in simulations with high ICNC was driven by the WBF process, which rapidly enhances ice mass at the expense of liquid water. This mechanism manifested clearly in the glaciating simulation ancLO_atypeD_icncHI_ichabP, wherein following the spin-up period, substantial deposition occurred (Fig. a) while cloud droplets evaporated (Fig. c). Ultimately, the liquid cloud dissolved, as illustrated by the overlapping dotted and dashed lines marking the cloud boundaries from approximately 7 h onward.

However, glaciation additionally depended on ice habit, occurring exclusively for the combination of high ICNC and IChab plates. The glaciation in some of the sensitivity simulations diverges from the actual observations and the baseline simulation, where the cloud persisted as a mixed-phase cloud for at least 12 h after the radiosounding that initialized the MIMICA simulation. Observations on the ship deck identified ice needles, and the baseline simulation accordingly assumed columns as the ice habit. The crucial role of ice habit in the simulations arises from how the morphology affects depositional growth and the sedimentation velocity in MIMICA. For a given ice mass, plates correspond to a larger characteristic size than columns, defined in the microphysics scheme as the maximum diameter for non-spherical hydrometeors . For example, compared to the 200 µm column assumed in the baseline simulation (Sect. ), a plate of identical mass has a size of 676 µm. Deposition onto ice crystals in MIMICA scales directly with ice crystal size , driving substantially stronger simulated depositional growth for plates (e.g., ancLO_atypeD_icncHI_ichabP; Fig. a) than for columns (e.g., ancLO_atypeD_icncHI_ichabC; Fig. b). This enhanced deposition strengthened the WBF-driven transfer of water vapor toward ice, causing cloud droplets to evaporate throughout the cloud (Fig. c versus d) and driving complete cloud glaciation. Additional to the direct impact of habit-dependent size on the deposition rates, the sedimentation velocity depends on the ice habit. For the two ice crystals with the same mass, but different habits, the columnar crystals sediment at 0.47 m s⁻¹, while the plates sediment at 0.19 m s⁻¹. This means that the plates reside longer in a cloud with constant depth, accumulating water vapor for a longer time and thus driving additional cloud glaciation. Such glaciation occurred only in simulations with high ICNC and IChab plates, whereas for combinations of high ICNC with columns, and low ICNC with plates, the cloud remained mixed-phase. Conversely, for low ICNC and IChab columns, the cloud evolved into a purely liquid cloud. The importance of ice habit for modeled clouds aligns with previous findings e.g.,, including findings of stronger sensitivity to ice habit at higher ICNC conditions .

Atype impacted LWP slightly for low ANC; overall, however, Atype was irrelevant in the factorial analysis compared to the other parameters. Hygroscopicity, rather than aerosol density or molecular weight, is the main property within Atype capable of impacting the simulated cloud. Previous MIMICA simulations of a different AMPC demonstrated that using only Aitken mode aerosol combined with a low κ of 0.2 caused the cloud to dissipate, while the impact of κ was much smaller when accumulation mode CCN were assumed . Our setup used a bimodal size distribution comprising both an Aitken and an accumulation mode. Consequently, even when applying the Atype value of dust (with a very low κ=0.05) combined with low ANC, sufficient large aerosols are available to act as CCN and sustain the cloud.

Our factorial analysis results apply strictly within the bounds of the analyzed parameter space. Therefore, the rankings reported here are specific to the explored ranges and to slightly supercooled AMPC conditions comparable to our case. For example, the chosen ICNC values differ by two orders of magnitude (90 versus 9000 m⁻³), representing a deliberately large spread; a smaller separation between these ICNC values would likely reduce the variance contribution of ICNC. Furthermore, the chosen model and specific setup inherently limit our findings. Our model setup fixed the surface temperature to a constant value. If we had instead allowed the surface temperature to respond to radiative cooling in the cloud glaciation cases, it would have suppressed turbulence more drastically, further accelerating the cloud dissipation. An additional limitation regarding radiation concerns the absence of interactions with frozen hydrometeors. Liquid hydrometeors, however, modulate radiation much more strongly, and sensitivity simulations conducted with a newer version of MIMICA that includes ice-radiation interactions (not shown) demonstrated only minor differences compared to the simulations presented here. Consequently, this omission does not affect the main conclusions drawn from our factorial analysis. A further caveat is that the ICNC was estimated instead of being measured in situ, even though employing an aerosol-dependent ice nucleation parameterization would have been more physically consistent. Using such a setup, ICNC could not be prescribed directly, but would instead be controlled indirectly by aerosol concentrations and properties. Under such conditions, ANC and likely Atype would exert stronger influences on IWP and the other variables – consistent with the large sensitivity we found with respect to the prescribed ICNC. A natural next step involves using an aerosol-dependent ice nucleation scheme that explicitly couples aerosol properties to the formation of the ice phase. However, producing appreciable ice mass using an aerosol-dependent parameterization remains difficult at warm temperatures e.g.,, potentially limiting the effectiveness of such schemes for this specific AMPC case study.

To generalize the relevance of the four chosen parameters, future studies should utilize similar factorial analyses with atmospheric models that span larger and smaller scales beyond LES. Applying factorial analyses to different case studies, including colder AMPCs, offers another path forward. Alternatively, including parameters related to the large-scale environment or turbulence may reveal further controls on AMPC behavior.