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The Arctic is one of the most rapidly warming regions of the globe. Low-level clouds and fog modify the energy transfer from and to space and play a key role in the observed strong Arctic surface warming, a phenomenon commonly termed "Arctic amplification". The response of low-level clouds to changing aerosol characteristics throughout the year is therefore an important driver of Arctic change that currently lacks sufficient constraints. As such, during the NASCENT campaign (Ny-Ålesund AeroSol Cloud ExperimeNT) extending over a full year from October 2019 to October 2020, microphysical properties of aerosols and clouds were studied at the Zeppelin station (475 m a.s.l.), Ny-Ålesund, Svalbard, Norway. Particle number size distributions obtained from differential mobility particle sizers as well as chemical composition derived from filter samples and an aerosol chemical speciation monitor were analyzed together with meteorological data, in particular vertical wind velocity. The results were used as input to a state-of-the-art cloud droplet formation parameterization to investigate the particle sizes that can activate to cloud droplets, the levels of supersaturation that can develop, the droplet susceptibility to aerosol and the role of vertical velocity. We evaluate the parameterization and the droplet numbers calculated through a droplet closure with in situ measurements. A remarkable finding is that, for the clouds sampled in situ, closure is successful in mixed-phase cloud conditions regardless of the cloud glaciation fraction. This suggests that secondary ice production through ice-ice collisions or droplet-shattering may explain the high ice fraction, as opposed to rime-splintering that would significantly reduce the cloud droplet number below levels predicted by warm cloud activation theory. We also show that pristine-like conditions during fall led to clouds that formed over an aerosol-limited regime, with high levels of supersaturation (generally around 1 %, although highly variable) that activate particles smaller than 20 nm in diameter. Clouds formed in the same regime in late spring and summer, but aerosol activation diameters were much larger due to lower cloud supersaturations (c.a. 0.5 %) that develop because of higher aerosol concentrations and lower vertical velocities. The contribution of new particle formation to cloud formation was therefore strongly limited, at least until these newly formed particles started growing. However, clouds forming during the Arctic haze period (winter and early spring) can be limited by updraft velocity, although rarely, with supersaturation levels dropping below 0.1 % and generally activating larger particles (20 to 200 nm), including pollution transported over a long range. The relationship between updraft velocity and the limiting cloud droplet number agrees with previous observations of various types of clouds worldwide, which tends to confirm the universality of this relationship.

The representation of orographic clouds in numerical weather prediction models remains a great challenge, as a consequence of our incomplete understanding of the microphysical processes acting on them and the complex interactions between the large-scale and orographic flow dynamics. Mixed-phase conditions are frequently occurring in orographic clouds, highlighting the importance of correctly simulating the microphysical evolution of ice- and liquid-phase hydrometeors. In this study we employ the mesoscale Weather Research and Forecasting (WRF) model to investigate the drivers of intense snowfall events observed during the Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISHTO) campaign, that took place from Fall 2021 to Spring 2022 at Mount Helmos in Peloponnese, Greece. Vertical profiles of reflectivity, Doppler velocity, as well as full Doppler spectra measured by a vertically pointing W-band (94 GHz) Doppler cloud radar, in synergy with Doppler and aerosol depolarization lidar data, help gain insight into the snowfall microphysics involved and set the basis for evaluating the performance of the WRF model. A radar simulator coupled with WRF enables the direct comparison between the mesoscale simulations and remote sensing products, and allows us to find the optimal model set-up that minimizes deviations from the observations. Comparing the modeled ice crystal number concentrations (ICNCs) with the Ice Nucleating Particles (INPs) measured in-situ at the Helmos High Altitude Monitoring Station (2314 m, 42°N 05' 30'', 34°E 14' 25'') by the Portable Ice Nucleation Experiment (PINE) instrument, we seek to quantify the ice enhancement factors due to secondary ice production (SIP) or seeding ice particles and their potential role in enhancing orographic precipitation. The synergy between high-resolution modeling and radar observations gives us the opportunity to infer SIP signatures from remote sensing observations, which is an important outcome given the abundance of the latter.

Clouds are large contributors to uncertainty in climate projections, with aerosol-cloud interactions playing a key role. To better reproduce heterogeneous ice clouds and, ultimately, the Earth’s changing energy budget in EC-Earth3 (one of the CMIP6 Earth System Models), its heterogeneous ice nucleation scheme has been updated. Specifically, the temperature-based parameterization has been substituted by a combination of aerosol-and-temperature-sensitive ice nucleating parameterizations. In particular, the model now considers a dust-and-soot-sensitive deposition nucleation scheme for cirrus clouds and aerosol-sensitive immersion freezing schemes for mixed-phase clouds. The latter is sensitive to the mineralogical composition of dust, specifically to the content of K-feldspar and quartz, and to marine organic aerosols, which are explicitly traced in EC-Earth3. Moreover, a secondary ice production parameterization based on a random forest regressor enhances the ice formed by the primary processes.We evaluate the model against an extended observational dataset of INP concentrations and analyze the effect of modelled aerosols upon heterogeneous ice nucleation in mixed-phase clouds and cirrus clouds produced with the new parameterizations. We also investigate the sensitivity of the simulated liquid and ice water content and the atmospheric radiative fluxes to the two different soil mineralogy atlases. Additionally, we use this updated model version to study in more detail a severe dust intrusion event that produced dust-infused clouds that affected Europe in March 2022.The results with the new ice nucleation parameterizations show a clear association of the simulated ice crystal number concentrations with the aerosol sources and transported regions. We also show that replacing the temperature-dependent ice nucleation parameterization with an aerosol-sensitive parameterization in EC-Earth3 significantly impacts surface temperature at high latitudes.

Global climate models poorly represent mixed-phase clouds, which leads to uncertainties in cloud radiative forcing and precipitation. In the FORCeS ice experiment (FOR-ICE) we compare three global climate models (ECHAM-HAM, NorESM, EC-Earth) and show which processes are crucial for a realistic representation of cloud ice and supercooled water in each global climate model framework using the factorial method as a statistical approach. A specific focus of the experiments is on secondary ice production (SIP) - which apart from one mechanism (rime splintering) is typically not represented in models, even if observations of ice crystal concentrations of ice crystal number in warm mixed-phase clouds often exceed available ice nuclei by orders of magnitude. We evaluate the importance of three SIP mechanisms combined (rime splintering, ice-ice collisions, and droplet shattering) compared to all other processes that can modulate ice mass and number in mixed-phase clouds: ice nucleation, sedimentation, and transport of ice crystals, and the Wegener-Bergeron-Findeisen process. To describe SIP we adopt two approaches: an explicit microphysical representation of the processes, and a parameterization based on a random forest regression of high-resolution two-year simulations in the Arctic using the polar Weather Research and Forecast model (polar-WRF). Satellite observations are used to evaluate if including descriptions of SIP leads to a more realistic representation of mixed phase clouds.

The regulation of energy transfer by clouds and fog is a key process affecting the climate of the Arctic, a region that exhibits frequent cloud cover and suffers an extreme vulnerability to climate change. Measurements were performed over a whole year at the Zeppelin station, Ny-Ålesund, Svalbard, Norway from October 2019 to October 2020 in the framework of the NASCENT campaign (Ny-Ålesund AeroSol Cloud ExperimeNT). Aiming at a better understanding of the susceptibility of cloud droplet formation, we analyzed particle number size distributions obtained from differential mobility particle sizers and chemical composition derived from filter samples and an aerosol chemical speciation monitor. Combined with updraft velocity information from a wind lidar and an ultrasonic anemometer, the data were used as input parameters for a state-of-the-art cloud droplet formation parameterization to investigate the particle sizes that can activate to cloud droplets, the levels of supersaturation as well as potential cloud droplet formation and its susceptibility to aerosol. We showed that low aerosol levels in fall and early winter led to clouds that are formed under an aerosol-limited regime, while higher particle concentrations centered around the Arctic Haze together with a drop in cloud supersaturation could be linked to periods of updraft velocity-limited cloud formation regime. In the latter case, we observed that the maximum number of cloud droplets forming - also called the limiting droplet number - and the updraft velocity follow a relationship that is universal, as proved by similar studies previously performed in different environments and cloud types. Finally, we successfully performed a droplet closure, proving, for the first time, the ability of our cloud droplet parameterization to predict cloud droplet number not only in liquid clouds but also in mixed-phase clouds with a very high degree of glaciation. This closure suggests that rime splintering may not be significant enough to affect droplet concentrations, which is consistent with previous observations and model simulations.

Secondary ice production (SIP) has an essential role in cloud and precipitation microphysics. In recent years, substantial insights were gained into SIP by combining experimental, modeling, and observational approaches. Remote sensing instruments, and among them meteorological radars, offer the possibility to study clouds and precipitation in extended areas over long time periods, and are highly valuable to understand the spatio-temporal structure of microphysical processes. Multi-modal Doppler spectra measured by vertically-pointing radars reveal the coexistence, within a radar resolution volume, of hydrometeor populations with distinct properties; as such, they can provide decisive insight into precipitation microphysics. This paper leverages polarimetric radar Doppler spectra as a tool to study the microphysical processes that took place during a snowfall event on 27 January 2021, in the Swiss Jura Mountains, during the ICE GENESIS campaign. A multi-layered cloud system was present, with ice particles sedimenting through a supercooled liquid water (SLW) layer in a seeder-feeder configuration. Building on a Doppler peak detection algorithm, we implement a peak labeling procedure to identify the particle type(s) that may be present within a radar resolution volume. With this approach, we can visualize spatio-temporal features in the radar time series that point to the occurrence of distinct mechanisms at different stages of the event. By focusing on three 30-minute phases of the case study, and by using the detailed information contained in the Doppler spectra, together with dual-frequency radar measurements, aircraft in-situ images, and simulated profiles of atmospheric variables, we narrow down the possible processes which can be responsible for the observed signatures. Depending on the availability of SLW and the droplet sizes, on the temperature range, and on the interaction between the liquid and ice particles, various SIP processes are identified as plausible, with distinct fingerprints in the radar Doppler spectra. A simple modeling approach suggests that the ice crystal number concentrations likely exceed typical concentrations of ice nucleating particles by one to four orders of magnitude. While a robust proof of occurrence of a given SIP mechanism cannot be easily established, the multi-sensor data provides various independent elements each supporting the proposed interpretations.

The representation of Arctic mixed-phase clouds (MPCs) in global climate models (GCMs) is becoming a widely acknowledged challenge, which highlights the necessity of revisiting the microphysical parameterizations associated with this type of clouds. The relatively sparse ice-nucleating particles (INPs) in the Arctic region (Wex et al., 2019) cannot always account for the high ice crystal number concentrations (ICNCs) found in Arctic MPCs. This indicates the presence of additional ice multiplication processes, known as secondary ice production (SIP), that can rapidly enhance the few primary ice crystals (e.g., Korolev and Leisner, 2020). All GCMs include parameterizations of primary ice production (PIP), but they still lack description of some important SIP processes.In this study we propose a new approach towards parameterizing SIP in polar stratiform clouds. The new parameterization encompasses the use of the ice-enhancement factor (IEF), which is a multiplication factor applied to primary ice crystals, to consider the effect of the three most important SIP mechanisms, namely the Hallett-Mossop (HM), the ice-ice collisional break-up (BR) and the droplet-shattering (DS) process. The derivation of the IEF parameterization is based on two-year regional climate simulations over the Ny-Ålesund station performed by the mesoscale Weather Research and Forecasting model (WRF) with augmented cloud microphysics (Sotiropoulou et al., 2021; Georgakaki et al., 2021) to account for all the SIP mechanisms. The WRF simulations indicate that the mean production rates of SIP can be up to 5 orders of magnitude higher than PIP at warm subzero temperatures higher than -10 ˚C. The production of secondary ice particles in the simulated Arctic clouds is found to be dominated by the BR process, with the contribution of DS and HM being substantially smaller. Machine learning techniques are then used to automatically detect patterns in the WRF dataset and to extract a parameterized expression of the IEF as a function of key thermodynamic and microphysical parameters. The newly developed formulation can effectively be implemented in GCMs with double-moment representations of the ice hydrometeors, which is expected to improve the modeled liquid-ice phase partitioning and therefore, the representation of radiation patterns and precipitation processes. Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss, https://doi.org/10.5194/acp-2021-760, in review, 2021.Korolev, A. and Leisner, T.: Review of experimental studies of secondary ice production, Atmos. Chem. Phys., 20, 11767–11797, https://doi.org/10.5194/acp-20-11767-2020, 2020.Sotiropoulou, G., Vignon, É., Young, G., Morrison, H., O'Shea, S. J., Lachlan-Cope, T., Berne, A., and Nenes, A.: Secondary ice production in summer clouds over the Antarctic coast: an underappreciated process in atmospheric models, Atmos. Chem. Phys., 21, 755–771, https://doi.org/10.5194/acp-21-755-2021, 2021.Wex, H., Huang, L., Zhang, W., Hung, H., Traversi, R., Becagli, S., Sheesley, R. J., Moffett, C. E., Barrett, T. E., Bossi, R., Skov, H., Hünerbein, A., Lubitz, J., Löffler, M., Linke, O., Hartmann, M., Herenz, P., and Stratmann, F.: Annual variability of ice-nucleating particle concentrations at different Arctic locations, Atmos. Chem. Phys., 19, 5293–5311, https://doi.org/10.5194/acp-19-5293-2019, 2019.

This paper explores the impact of Secondary Ice Processes onboundary layer clouds during marine Cold-Air Outbreaks (CAO) . The Weather Research and Forecasting (WRF) model is used to investigate the impact of the most well-known SIP mechanisms (Hallett-Mossop, mechanical break-up upon collisions between ice particles and drop-shattering) on a CAO case observed north of the United Kingdom in 2013. While Hallett-Mossop is the only SIP process extensively implemented in atmospheric models, our results indicate that the other two SIP mechanisms are also favored in the examined conditions. Activation of drop-shattering and especially collisional break-up can result in enhanced riming, ice depositional growth and/or ice aggregation. The first two processes quicken liquid depletion in the stratocumulus cloud, while along with aggregation, they enhance precipitation. The increased precipitation results in enhanced evaporation/sublimation in the sub-cloud layer, promoting boundary-layer decoupling, which further accelerates the onset of the stratocumulus break-up. However, the strong sensitivity to the expression ofterminal veloityof the precipitating particles and the rimed fraction of cloud ice/snow suggests that the robust implementation of SIP to improve CAO predictions requires data from a large number of CAO events.

The Arctic region suffers an extreme vulnerability to climate change, with an increase in surface air temperatures that have reached twice the global rate during several decades (McBean et al., 2005). The role of clouds, and in particular low-levels clouds and fog, in this arctic amplification by regulating the energy transport from and to space has recently gained interest among the scientific community. The NASCENT 2019-2020 campaign (Ny-Ålesund AeroSol Cloud ExperimeNT) based in Ny-Ålesund, Svalbard (79º North) aimed at studying the microphysical and chemical properties of low-level clouds using measurements both at the sea level and at the Zeppelin station (475 m a.s.l.). Specifically, the susceptibility of droplet formation, which has recently been shown to be highly dependent on aerosol levels in European alpine valleys (Georgakaki et al., under review), could strongly vary between the fall to winter months, with pristine-like conditions, and the higher particle concentrations generally found in spring, known as the arctic haze. First results using a scanning mobility particle sizer (SMPS) and a cloud condensation nuclei counter (CCNC) confirmed that aerosol concentrations in the range 10 < Dpart [nm] < 500 were approximatively 4-5 times higher during the months of spring 2021 compared to those of fall 2020. In addition, we found relatively low values of the aerosol hygroscopic parameter κ, generally below 0.3, consistently with previous studies in the arctic region (Moore et al., 2011). Georgakaki, P., Bougiatioti, A., Wieder, J., Mignani, C., Kanji, Z. A., Henneberger, J., Hervo, M., Berne, A. and Nenes, A.: On the drivers of droplet variability in Alpine mixed-phase clouds, , 34, under review.McBean, G., Alekseev, G., Chen, D., Førland, E., Fyfe, Groisman, J., P. Y., King, R., Melling, H., Voseand, R., Whitfield, P. H.: Arctic climate: past and present. Arctic Climate Impacts Assessment (ACIA), C. Symon, L. Arris and B. Heal, Eds., Cambridge University Press, Cambridge, 21-60, 2005.Moore, R. H., Bahreini, R., Brock, C. A., Froyd, K. D., Cozic, J., Holloway, J. S., Middlebrook, A. M., Murphy, D. M. and Nenes, A.: Hygroscopicity and composition of Alaskan Arctic CCN during April 2008, Atmospheric Chemistry and Physics, 11(22), 11807–11825, https://doi.org/10.5194/acp-11-11807-2011, 2011.

Aerosol-cloud interactions remain the largest uncertainty in assessments of anthropogenic climate forcing, while the complexity of these interactions require methods that enable abstractions and simplifications that allow their improved treatment in climate models. Marine boundary layer clouds are an important component of the climate system as their large albedo and spatial coverage strongly affect the planetary radiative balance. High resolution simulations of clouds provide an unprecedented understanding of the structure and behavior of these clouds in the marine atmosphere, but the amount of data is often too large and complex to be useful in climate simulations. Data reduction and inference methods provide a way that to reduce the complexity and dimensionality of datasets generated from high-resolution Large Eddy Simulations.In this study we use network analysis, (the δ-Maps method) to study the complex interaction between liquid water, droplet number and vertical velocity in Large Eddy Simulations of Marine Boundary Layer clouds. δ-Maps identifies domains that are spatially contiguous and possibly overlapping and characterizes their connections and temporal interactions. The objective is to better understand microphysical properties of marine boundary layer clouds, and how they are impacted by the variability in aerosols. Here we will capture the dynamical structure of the cloud fields predicted by the MIMICA Large Eddy Simulation (LES) model. The networks inferred from the different simulation fields are compared between them (intra-comparisons) using perturbations in initial conditions and aerosol, using a set of four metrics. The networks are then evaluated for their differences, quantifying how much variability is inherent in the LES simulations versus the robust changes induced by the aerosol fields.

The representation of boundary layer clouds during marine Cold-Air Outbreaks (CAO) remains a great challenge for weather prediction models. Recent studies have shown that the representation of the transition from stratocumulus clouds to convective cumulus open cells largely depends on microphysical and precipitation processes, while Abel et al. (2017) further suggested that Secondary Ice Processes (SIP) may play a crucial role in the evolution of the cloud fields. In this study we use the Weather Research Forecasting model to investigate the impact of the most well-known SIP mechanisms (rime-splintering or Hallet-Mossop, mechanical break-up upon collisions between ice particles and drop-shattering) on a CAO case observed north of UK in 2013. While Hallet-Mossop is the only SIP process extensively implemented in atmospheric models, our results indicate that collisional break-up is also important in these conditions. Abel, S. J., Boutle, I. A., Waite, K., Fox, S., Brown, P. R. A., Cotton, R., Lloyd, G., Choularton, T. W., & Bower, K. N. (2017). The Role of Precipitation in Controlling the Transition from Stratocumulus to Cumulus Clouds in a Northern Hemisphere Cold-Air Outbreak, Journal of the Atmospheric Sciences, 74(7), 2293-2314. Retrieved Jan 9, 2021, from https://journals.ametsoc.org/view/journals/atsc/74/7/jas-d-16-0362.1.xml

The influence of aerosols serving as cloud condensation nuclei (CCN) on the production of droplets in mixed-phase cloud systems is an ongoing research problem that influences their optical and microphysical properties. During February and March 2019, the Role of Aerosols and CLouds Enhanced by Topography on Snow (RACLETS) field campaign collected unique and detailed airborne and ground-based in-situ measurements of cloud and aerosol properties over the Swiss Alps. This study presents analysis of the observed CCN activity of the aerosol, which combined with observed aerosol size distributions, can be introduced into a cloud droplet activation parameterization to investigate the drivers of droplet variability in these clouds. The implications for secondary ice production are then discussed.

In-situ observations of mixed-phase clouds (MPCs) forming over mountain tops regularly reveal that ice crystal number concentrations (ICNCs) are orders of magnitude higher than ice-nucleating particle concentrations. This discrepancy has often been attributed to the influence of surface processes such as blowing snow and airborne hoar frost. Ιn-cloud secondary ice production (SIP) processes may also explain this discrepancy, but their contribution has received less attention.Here we explore the potential role of SIP processes on orographic MPCs observed during the Cloud and Aerosol Characterization Experiment (CLACE) 2014 campaign at the mountain-top site of Jungfraujoch in the Swiss Alps using the Weather Research and Forecasting model (WRF). The Hallett-Mossop (H-M) mechanism, included in the default version of the Morrison scheme in WRF, is ruled out since the simulated clouds were outside the active temperature range for this process. This study investigates if the implementation of two additional SIP mechanisms in WRF, namely collisional break-up (BR) between ice hydrometeors and frozen droplet shattering (DS), can bridge the gap between observed and modeled ICNCs. DS is inefficient in the examined conditions due to a lack of sufficiently large raindrops to trigger this process. The BR mechanism is likely important in Alpine MPCs, but the process is activated only within seeder-feeder situations, when precipitation particles are seeding the low-level MPCs inducing their glaciation. At times when a cloud exists near the ground, blowing snow ice particles may be mixed among supercooled liquid droplets and thus contribute significantly to ice growth, but they cannot account for the observed ICNCs. Our findings indicate that outside the H-M temperature range, ice-seeding and blowing snow can initiate ice multiplication in the Alps through the BR mechanism, which is found to elevate the modeled ICNCs up to 3 orders of magnitude, providing a better agreement with in-situ measurements. This highlights the importance of considering both SIP and surface-based processes in weather-prediction and climate models.

Droplet formation provides a direct microphysical link between aerosols and clouds (liquid or mixed-phase), and its adequate description poses a major challenge for any atmospheric model. Observations are critical for evaluating and constraining the process. To this end, aerosol size distributions, cloud condensation nuclei (CCN), hygroscopicity, and lidar-derived vertical velocities were observed in alpine mixed-phase clouds during the Role of Aerosols and Clouds Enhanced by Topography on Snow (RACLETS) field campaign in the Davos, Switzerland, region during February and March 2019. Data from the mountain-top site of Weissfluhjoch (WFJ) and the valley site of Davos Wolfgang are studied. These observations are coupled with a state-of-the-art droplet activation parameterization to investigate the aerosol–cloud droplet link in mixed-phase clouds. The mean CCN-derived hygroscopicity parameter, κ, at WFJ ranges between 0.2–0.3, consistent with expectations for continental aerosols. κ tends to decrease with size, possibly from an enrichment in organic material associated with the vertical transport of fresh ultrafine particle emissions (likely from biomass burning) from the valley floor in Davos. The parameterization provides a droplet number that agrees with observations to within ∼ 25 %. We also find that the susceptibility of droplet formation to aerosol concentration and vertical velocity variations can be appropriately described as a function of the standard deviation of the distribution of updraft velocities, σw, as the droplet number never exceeds a characteristic limit, termed the “limiting droplet number”, of ∼ 150–550 cm−3, which depends solely on σw. We also show that high aerosol levels in the valley, most likely from anthropogenic activities, increase the cloud droplet number, reduce cloud supersaturation (< 0.1 %), and shift the clouds to a state that is less susceptible to changes in aerosol concentrations and very sensitive to vertical velocity variations. The transition from an aerosol to velocity-limited regime depends on the ratio of cloud droplet number to the limiting droplet number, as droplet formation becomes velocity limited when this ratio exceeds 0.65. Under such conditions, droplet size tends to be minimal, reducing the likelihood that large drops are present that would otherwise promote glaciation through rime splintering and droplet shattering. Identifying regimes where droplet number variability is dominated by dynamical – rather than aerosol – changes is key for interpreting and constraining when and which types of aerosol effects on clouds are active.