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In many industrial processes, it may not be practical to obtain sample sizes larger than one due to various reasons. As a result, practitioners must rely on control charts based on individual observations for statistical process monitoring and control. The sequential probability ratio test (SPRT) chart is an appropriate solution for such situations. Existing literature predominantly delves into the statistical aspects of designing or optimising an SPRT chart, neglecting the economic and associated design aspects. However, past experience suggests that statistical design may not always be optimal from an economic perspective. This paper aims to bridge this research gap by proposing a two-stage Markov chain approach to develop an economic model for the optimal design of the SPRT chart with individual observations. Following the principles of economic-statistical designs, optimal parameters are determined by solving a mixed-integer non-linear programming problem that minimises the long-run cost per item while ensuring compliance with practical constraints. Through an extensive study, we find that the economic performance of the SPRT chart surpasses that of competing individual control charts in many cases. This indicates that the economic-statistical design of the SPRT chart can yield significant economic benefits while maintaining desirable statistical properties. Moreover, enhancements have been introduced to refine the optimal design of the SPRT chart, retaining excellent overall performance across a wide range of shifts. Finally, we provide two industrial examples to illustrate the effectiveness of the proposed SPRT chart. [ABSTRACT FROM AUTHOR]

We describe a novel biosafety aerosol chamber equipped with state-of-theart instrumentation for bubble-bursting aerosol generation, size distribution measurement, and condensation-growth collection to minimize sampling artifacts when measuring virus infectivity in aerosol particles. Using this facility, we investigated the effect of relative humidity (RH) in very clean air without trace gases (except ~400 ppm CO2) on the preservation of influenza A virus (IAV) infectivity in saline aerosol particles. We characterized infectivity in terms of 99%-inactivation time, t99, a metric we consider most relevant to airborne virus transmission. The viruses remained infectious for a long time, namely t99 > 5 h, if RH < 30% and the particles effloresced. Under intermediate conditions of humidity (40% < RH < 70%), the loss of infectivity was the most rapid (t99 ∾ 15-20 min, and up to t99 ∾ 35 min at 95% RH). This is more than an order of magnitude faster than suggested by many previous studies of aerosol-borne IAV, possibly due to the use of matrices containing organic molecules, such as proteins, with protective effects for the virus. We tested this hypothesis by adding sucrose to our aerosolization medium and, indeed, observed protection of IAV at intermediate RH (55%). Interestingly, the t99 of our measurements are also systematically lower than those in 1-mL droplet measurements of organic-free saline solutions, which cannot be explained by particle size effects alone. [ABSTRACT FROM AUTHOR]

Given the importance of aerosols and clouds and their interactions in the climate system, it is imperative that the global Earth system models accurately represent processes associated with them. This is an important prerequisite if we are to narrow the uncertainties in future climate projections. In practice, this means that continuous model evaluations and improvements grounded in observations are necessary. Numerous studies in the past few decades have shown both the usability and the limitations of utilizing satellite-based observations in understanding and evaluating aerosol–cloud interactions, particularly under varying meteorological and satellite sensor sensitivity paradigms. Furthermore, the vast range of spatio-temporal scales at which aerosol and cloud processes occur adds another dimension to the challenges faced when evaluating climate models. In this context, the aim of this study is two-fold. (1) We evaluate the most recent, significant changes in the representation of aerosol and cloud processes implemented in the EC-Earth3-AerChem model in the framework of the EU project FORCeS compared with its previous CMIP6 version (Coupled Model Intercomparison Project Phase 6; https://pcmdi.llnl.gov/CMIP6/ , last access: 13 February 2019). We focus particularly on evaluating cloud physical properties and radiative effects, wherever possible, using a satellite simulator. We report on the overall improvements in the EC-Earth3-AerChem model. In particular, the strong warm bias chronically seen over the Southern Ocean is reduced significantly. (2) A statistical, maximum covariance analysis is carried out between aerosol optical depth (AOD) and cloud droplet (CD) effective radius based on the recent EC-Earth3-AerChem/FORCeS simulation to understand to what extent the Twomey effect can manifest itself in the larger spatio-temporal scales. We focus on the three oceanic low-level cloud regimes that are important due to their strong net cooling effect and where pollution outflow from the nearby continent is simultaneously pervasive. We report that the statistical covariability between AOD and CD effective radius is indeed dominantly visible even at the climate scale when the aerosol amount and composition are favourably preconditioned to allow for aerosol–cloud interactions. Despite this strong covariability, our analysis shows a strong cooling/warming in shortwave cloud radiative effects at the top of the atmosphere in our study regions associated with an increase/decrease in CD effective radius. This cooling/warming can be attributed to the increase/decrease in low cloud fraction, in line with previous observational studies. [ABSTRACT FROM AUTHOR]

China has long-term high PM2.5 levels, and its oxidative potential (OP) is worth studying as it may unravel the impacts of aerosol pollution on public health better than PM2.5 alone. OP refers to the ability of PM2.5 to induce oxidative stress (OS). OP and PM2.5 are influenced by meteorological factors, anthropogenic emission sources, and atmospheric aging. Although their impact on PM2.5 has been studied, OP measurements only recently became available and on a limited scale, as they require considerable technical expertise and resources. For this, the joint relationship between PM2.5 and OP for a wide range of meteorological conditions and emission profiles remain elusive. Towards this, we estimated PM2.5 and OP over China using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from the Weather Research and Forecasting (WRF) model. It was found that higher values of PM2.5 and OP were primarily concentrated in urban agglomerations in the central and eastern regions of China, while lower values were found in the western and northeastern regions. Furthermore, the probability density function revealed that about 40 % of areas in China had annual average PM2.5 concentrations exceeding the Chinese concentration limit. For OP, 36 % of the regions have OP below 1 nmolmin-1m-3 , 41 % have OP between 1 and 2 nmolmin-1m-3 , and 23 % have OP above 2 nmolmin-1m-3 , which are in line with previous measurement studies. Analysis of the simulations indicates that meteorological conditions contributed 46 % and 65 % to PM2.5 concentrations and OP variability, respectively, while anthropogenic emissions contributed 54 % and 35 % to PM2.5 concentrations and OP variability, respectively. The emission sensitivity analysis also highlighted the fact that PM2.5 and OP levels are mostly determined by secondary aerosol formation and biomass burning. [ABSTRACT FROM AUTHOR]

Aerosol–cloud interactions in mixed-phase clouds (MPCs) are one of the most uncertain drivers of the hydrological cycle and climate change. A synergy of in situ, remote-sensing and modelling experiments were used to determine the source of ice-nucleating particles (INPs) for MPCs at Mount Helmos in the eastern Mediterranean. The influences of boundary layer turbulence, vertical aerosol distributions and meteorological conditions were also examined. When the observation site is in the free troposphere (FT), approximately 1 in ×106 aerosol particles serve as INPs around -25 °C. The INP abundance spans 3 orders of magnitude and increases in the following order: marine aerosols; continental aerosols; and, finally, dust plumes. Biological particles are important INPs observed in continental and marine aerosols, whereas they play a secondary, although important, role during Saharan dust events. Air masses in the planetary boundary layer (PBL) show both enriched INP concentrations and a higher proportion of INPs to total aerosol particles, compared with cases in the FT. The presence of precipitation/clouds enriches INPs in the FT but decreases INPs in the PBL. Additionally, new INP parameterizations are developed that incorporate the ratio of fluorescent-to-nonfluorescent or coarse-to-fine particles and predict >90 % of the observed INPs within an uncertainty range of a factor of 10; these new parameterizations exhibit better performance than current widely used parameterizations and allow ice formation in models to respond to variations in dust and biological particles. The improved parameterizations can help MPC formation simulations in regions with various INP sources or different regions with prevailing INP sources. [ABSTRACT FROM AUTHOR]

The purpose of this study is to understand the drivers of cloud droplet formation in orographic clouds. We used a combination of modeling, in situ, and remote sensing measurements at the high-altitude Helmos Hellenic Atmospheric Aerosol and Climate Change ((HAC) 2) station, which is located at the top of Mt. Helmos (1314 m above sea level), Greece, during the Cloud–AerosoL InteractionS in the Helmos Background TropOsphere (CALISHTO) campaign in fall 2021 (https://calishto.panacea-ri.gr/ , last access: 1 August 2024) to examine the origins of the aerosols (i.e., local aerosol from the planetary boundary layer (PBL) or long-range-transported aerosol from the free-tropospheric layer (FTL) contributing to the cloud condensation nuclei (CCN)), their characteristics (hygroscopicity, size distribution, and mixing state), and the vertical velocity distributions and resulting supersaturations. We found that the characteristics of the PBL aerosol were considerably different from FTL aerosol and use the aerosol particle number and equivalent mass concentration of the black carbon (eBC) in order to determine when (HAC) 2 was within the FTL or PBL based on time series of the height of the PBL. During the (HAC) 2 cloud events we sample a mixture of interstitial aerosol and droplet residues, which we characterize using a new approach that utilizes the in situ droplet measurements to determine time periods when the aerosol sample is purely interstitial. From the dataset we determine the properties (size distribution and hygroscopicity) of the pre-cloud, activated, and interstitial aerosol. The hygroscopicity of activated aerosol is found to be higher than that of the interstitial or pre-cloud aerosol. A series of closure studies with the droplet parameterization shows that cloud droplet concentration (Nd) and supersaturation can be predicted to within 25 % of observations when the aerosol size distributions correspond to pre-cloud conditions. The analysis of the characteristic supersaturation of each aerosol population indicates that droplet formation in clouds is aerosol-limited when formed in FTL air masses – hence droplet formation is driven by aerosol variations, while clouds formed in the PBL tend to be velocity-limited and droplet variations are driven by fluctuations in vertical velocity. Given that the cloud dynamics do not vary significantly between air masses, the variation in aerosol concentration and type is mostly responsible for these shifts in cloud microphysical state and sensitivity to aerosol. With these insights, the remote sensing of cloud droplets in such clouds can be used to infer either CCN spectra (when in the FTL) or vertical velocity (when in the PBL). In conclusion, we show that a coordinated measurement of aerosol and cloud properties, together with the novel analysis approaches presented here, allows for the determination of the drivers of droplet formation in orographic clouds and their sensitivity to aerosol and vertical velocity variations. [ABSTRACT FROM AUTHOR]

Remote sensing has been a key resource for developing extensive and detailed datasets for studying and constraining aerosol-cloud-climate interactions. However, aerosol-cloud collocation challenges, algorithm limitations, as well as difficulties in unraveling dynamic from aerosol-related effects on cloud microphysics, have long challenged precise retrievals of cloud droplet number concentrations. By combining a series of remote sensing techniques and in situ measurements at ground level, we developed a semiautomated approach that can address several retrieval issues for a robust estimation of cloud droplet number for non-precipitating Planetary Boundary Layer (PBL) clouds. The approach is based on satellite retrievals of the PBL cloud droplet number (Ndsat) using the geostationary meteorological satellite data of the Optimal Cloud Analysis (OCA) product, which is obtained by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The parameters of the retrieval are optimized through closure with droplet number obtained from a combination of ground-based remote sensing data and in situ observations at ground level. More specifically, the remote sensing data are used to retrieve cloud-scale vertical velocity, and the in situ aerosol measurements at ground level were used constrain as input to a state-of-the-art droplet activation parameterization to predict the respective Cloud Condensation Nuclei (CCN) spectra, cloud maximum supersaturation and droplet number concentration (Nd), accounting for the effects of vertical velocity distribution and lateral entrainment. Closure studies between collocated Nd and Nd sat are then used to evaluate exising droplet spectral width parameters used for the retrieval of droplet number, and determine the optimal values for retrieval. This methodology, used to study aerosol-cloud interactions for non-precipitating clouds formed over the Athens Metropolitan Area (AMA), Greece, during the springtime period from March to May 2020, shows that droplet closure can be achieved to within ±33.4%, comparable to the level of closure obtained in many in situ studies. Given this, the ease of applying this approach with satellite data obtained from SEVIRI with high temporal (15 min) and spatial resolution (3.6 km Ã--4.6 km), opens the possibility of continuous and reliable Ndsat, giving rise to high value datasets for aerosol-cloud-climate interaction studies. [ABSTRACT FROM AUTHOR]

Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement: Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months. [ABSTRACT FROM AUTHOR]

The 2015 Paris agreement was established to limit Greenhouse gas (GHG) global warming below 1.5°C above preindustrial era values. Knowledge of climate sensitivity to GHG levels is central for formulating effective climate policies, yet its exact value is shroud in uncertainty. Climate sensitivity is quantitatively expressed in terms of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR), estimating global temperature responses after an abrupt or transient doubling of CO2. Here, we represent the complex and highly-dimensional behavior of modelled climate via low-dimensional emergent networks to evaluate Climate Sensitivity (netCS), by first reconstructing meaningful components describing regional subprocesses, and secondly inferring the causal links between these to construct causal networks. We apply this methodology to Sea Surface Temperature (SST) simulations and investigate two different metrics in order to derive weighted estimates that yield likely ranges of ECS (2.35–4.81°C) and TCR (1.53-2.60°C). These ranges are narrower than the unconstrained distributions and consistent with the ranges of the IPCC AR6 estimates. More importantly, netCS demonstrates that SST patterns (at "fast" timescales) are linked to climate sensitivity; SST patterns over the historical period exclude median sensitivity but not low-sensitivity (ECS < 3.0°C) or very high sensitivity (ECS ≥ 4.5°C) models. The Earth's surface temperature response to increasing CO2 emissions remains uncertain in climate models within the CMIP6 project. The authors present a network-based constraint of climate sensitivity of sea surface temperatures that can be used to evaluate and weight CMIP6 models. [ABSTRACT FROM AUTHOR]

The Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISHTO) campaign took place in autumn 2021 at the NCSR Demokritos background high-altitude Helmos Hellenic Atmospheric Aerosol and Climate Change station (HAC) 2 to study the interactions between aerosols and clouds. The current study presents the chemical characterization of the non-refractory (NR) PM 1 aerosol fraction using a time-of-flight aerosol chemical speciation monitor (ToF-ACSM). A comparative offline aerosol filter analysis by a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) showed consistent results regarding the species determined. Source apportionment applied on both datasets (ACSM-ToF and offline AMS analysis on filter extracts) yielded the same factors for the organic aerosol (one primary and two secondary factors). Additionally, the positive matrix factorization (PMF) model was applied on the total PM 1 fraction by the ToF-ACSM (including both organic and inorganic ions). Five different types were identified, including a primary organic factor; ammonium nitrate; ammonium sulfate; and two secondary organic aerosols, one more oxidized and one less oxidized. The prevailing atmospheric conditions at the station, i.e., cloud presence, influence of emissions from the planetary boundary layer (PBL), and air mass origin, were also incorporated in the study. The segregation between PBL and free-troposphere (FT) conditions was made by combining data from remote sensing and in situ measurement techniques. The types of air masses arriving at the site were grouped as continental, marine, dust, and marine–dust based on back-trajectory data. Significant temporal variability in the aerosol characteristics was observed throughout the campaign; in September, air masses from within the PBL were sampled most of the time, resulting in much higher mass concentrations compared to October and November when concentrations were reduced by a factor of 5. Both in-cloud and FT measurement periods resulted in much lower concentration levels, while a similar composition was observed in PBL and FT conditions. We take advantage of using a recently developed "virtual-filtering" technique to separate interstitial from activated aerosol sampled from a PM 10 inlet during cloudy periods. This allows the determination of the chemical composition of the interstitial aerosol during in-cloud periods. Ammonium sulfate, the dominant PMF factor in all conditions, contributed more when air masses were arriving at (HAC) 2 during dust events, while a higher secondary organic aerosol contribution was observed when air masses arrived from continental Europe. [ABSTRACT FROM AUTHOR]

This paper presents the findings from a collaborative interlaboratory comparison exercise designed to assess oxidative potential (OP) measurements conducted by 20 laboratories worldwide. This study represents an innovative effort as the first exercise specifically aimed at harmonising this type of OP assay, setting a new benchmark in the field. Over the last decade, there has been a noticeable increase in OP studies, with numerous research groups investigating the effects of exposure to air pollution particles through the evaluation of OP levels. However, the absence of standardised methods for OP measurements has resulted in variability in results across different groups, rendering meaningful comparisons challenging. To address this issue, this study engages in an international effort to compare OP measurements using a simplified method (with a dithiothreitol (DTT) assay). Here, we quantify the OP in liquid samples to focus on the protocol measurement itself, while future ILCs should aim to assess the full-chain process, including the sample extraction. We analyse the similarities and discrepancies observed in the results, identifying the critical parameters (such as the instrument used, the use of a simplified protocol, the delivery and analysis time) that could influence OP measurements, and provide recommendations for future studies and interlaboratory comparisons. Even if other crucial aspects, such as sampling PM methods, sample storage, extraction methods and conditions, and the evaluation of other OP assays, still need to be standardised. This collaborative approach enhances the robustness of the OP-DTT assay and paves the way for future studies to build on a unified framework. This pioneering work concludes that interlaboratory comparisons provide essential insights into the OP metric and are crucial to move toward the harmonisation of OP measurements. [ABSTRACT FROM AUTHOR]

Recent years have shown that secondary ice production (SIP) is ubiquitous, affecting all clouds from polar to tropical regions. SIP is not described well in models and may explain biases in warm mixed-phase cloud ice content and structure. Through modeling constrained by in-situ observations and its synergy with radar we show that SIP in orographic clouds exert a profound impact on the vertical distribution of hydrometeors and precipitation, especially in seeder-feeder cloud configurations. The mesoscale model simulations coupled with a radar simulator strongly support that enhanced aggregation and SIP through ice-ice collisions contribute to observed spectral bimodalities, skewing the Doppler spectra toward the slower-falling side at temperatures within the dendritic growth layer, ranging from −20 °C to −10 °C. This unique signature provides an opportunity to infer long-term SIP occurrences from the global cloud radar data archive, particularly for this underexplored temperature regime. [ABSTRACT FROM AUTHOR]

Accurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms. Plain Language Summary: Being able to correctly simulate the amount of ice and liquid in clouds is essential for accurate predictions of the cloud radiative forcing in the climatologically sensitive polar regions. A number of collisional processes between ice and liquid particles in clouds, known as secondary ice production, can significantly enhance the ice crystal number concentrations contained in them. This enhancement is often accompanied by a decrease in the cloud liquid water content, resulting in less opaque clouds to incoming solar radiation, which, in turn, can cause a cloud‐induced warming at the surface. Currently most global climate models are missing the description of the most important secondary ice production processes, which can lead to a biased radiative impact of clouds at the surface. To address this, we propose using a machine learning algorithm trained on high‐resolution model outputs to include the effect of ice multiplication in large‐scale climate models. The machine learning framework effectively captures the physical processes underlying secondary ice production in stratiform clouds using only a few inputs readily available in model frameworks. This approach has the potential to improve model predictions bringing them closer to the observed cloud phase partitioning. Key Points: A random‐forest parameterization for secondary ice production is developed using outputs from a 10‐km horizontal grid spacing simulationCloud phase partitioning agrees within a factor of 3, with radiative biases below 3 Wm−2 compared to the detailed microphysics simulationThe scheme can be adjusted to coarser resolutions typical of climate models without losing computational efficiency and numerical stability [ABSTRACT FROM AUTHOR]

Aerosol-cloud interactions, and particularly ice crystals in mixed-phase clouds (MPC), stand as a key source of uncertainty in climate change assessments. State-of-the-art laboratory-based parameterizations were introduced into a global chemistry-transport model to investigate the contribution of mineral dust, marine primary organic aerosol (MPOA), and terrestrial primary biological aerosol particles (PBAP) to ice nucleating particles (INP) in MPC. INP originating from PBAP (INPPBAP) are found to be the primary source of INP at low altitudes between -10°C and -20°C, particularly in the tropics, with a pronounced peak in the Northern Hemisphere (NH) during boreal summer. INPPBAP contributes about 27% (in the NH) and 30% (in the SH) of the INP population. Dust-derived INP (INPD) show a prominent presence at high altitudes in all seasons, dominating at temperatures below -25°C, constituting 68% of the INP average column burden. MPOA-derived INP (INPMPOA) dominate in the Southern Hemisphere (SH), particularly at subpolar and polar latitudes at low altitudes for temperatures below -16°C, representing approximately 46% of INP population in the SH. When evaluated against available global observational INP data, the model achieves its highest predictability across all temperature ranges when both INPD and INPMPOA are included. The additional introduction of INPPBAP slightly reduces model skills for temperatures lower than -16oC; however, INPPBAP are the main contributors to warm-temperature ice nucleation events. Therefore, consideration of dust and marine and terrestrial bioaerosol as INP precursors is required to simulate ice nucleation in climate models. In this respect, emissions, ice-nucleating activity of each particle type and its evolution during atmospheric transport require further investigations. [ABSTRACT FROM AUTHOR]

Given the importance of aerosols, clouds and their interactions in the climate system, it is imperative that the global Earth system models accurately represent processes associated with them. This is an important prerequisite if we were to narrow the uncertainties in future climate projections. In practice, this means that the continuous model evaluations and improvements grounded in observations are necessary. Numerous studies in the last few decades have shown both the usability and the limitations of utilizing satellite-based observations in understanding and evaluating aerosol-cloud interactions, particularly under varying meteorological and satellite sensor sensitivity paradigms. Furthermore, the vast range of spatio-temporal scales at which aerosol and cloud processes occur adds another dimension to the challenges while evaluating climate models. In this context, the aim of this study is two-fold. 1) We evaluate the most recent, significant changes in the representation of aerosol and cloud processes implemented in the EC-Earth3-AerChem model in the framework of the EU project FORCeS compared to its previous CMIP6 version. We focus particularly on evaluating cloud physical properties and radiative effects, wherever possible, using a satellite simulator. We report overall improvements in EC-Earth3-AerChem model. In particular, the strong warm bias chronically seen over the Southern Ocean is reduced significantly. 2) A statistical, maximum covariance analysis is carried out between aerosol optical depth (AOD) and cloud droplet (CD) effective radius based on the recent EC-Earth3-AerChem/FORCeS simulation to understand to what extent the Twomey effect can manifest itself in the larger spatio-temporal scales. We focus on the three oceanic low-level cloud regimes that are important due to their strong net cooling effect and where pollution outflow from the nearby continent is simultaneously pervasive. We report that the statistical covariability between AOD and CD effective radius is indeed dominantly visible even at the climate scale when the aerosol amount and composition are favourably preconditioned for allowing aerosol-cloud interactions. Despite this strong covariability, our analysis shows a strong cooling/warming in shortwave cloud radiative effects at the top of the atmosphere in our study regions associated with an increase/decrease in CD effective radius. And this cooling/warming can be attributed to the increase/decrease in low cloud fraction, in line with the previous observational studies. [ABSTRACT FROM AUTHOR]

The Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISHTO) campaign took place in autumn 2021 at the NCSR Demokritos background high-altitude Helmos Hellenic Atmospheric Aerosol & Climate Change station (HAC)2 to study the interactions between aerosols and clouds. The current study presents the chemical characterization of the Non-Refractory (NR) PM1 aerosol fraction using a Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM). A comparative offline aerosol filter analysis by a High-Resolution Time-of-Flight Aerosol Mass Spectrometry (HR-ToF-AMS) showed consistent results regarding the species determined. Source apportionment applied on both datasets (ACSM-ToF and offline AMS analysis on filter extracts) yielded the same factors for the organic aerosol (one primary and two secondary factors). Additionally, the Positive Matrix Factorization (PMF) model was applied on the total PM1 fraction by the ToF-ACSM (including both organic and inorganic ions). Five different types were identified, including a primary organic factor, ammonium nitrate, ammonium sulphate, and two secondary organic aerosols; one more and one less oxidized. The prevailing atmospheric conditions at the station, i.e. cloud presence, influence from emissions from the Planetary Boundary Layer (PBL) and air mass origin were also incorporated in the study. The segregation between PBL and Free Troposphere (FT) conditions was made by combining data from remote sensing and in-situ measurement techniques. The types of air masses arriving at the site were grouped as continental, marine, dust and marine-dust based on back trajectories data. Significant temporal variability in the aerosol characteristics was observed throughout the campaign; in September, air masses from within the PBL were sampled most of the time, resulting in much higher mass concentrations compared to October and November when concentrations were reduced by a factor of 5. Both in-cloud and FT measurement periods resulted in much lower concentration levels, while similar composition was observed in PBL and FT conditions. We take advantage of using a recently developed "virtual filtering" technique to separate interstitial from activated aerosol sampled from a PM10 inlet during cloudy periods. This allows the determination of the chemical composition of the interstitial aerosol during in-cloud periods. Ammonium sulphate, the dominant PMF factor in all conditions, contributed more when air masses were arriving at (HAC)2 during Dust events, while higher secondary organic aerosol contribution was observed when air masses arrived from continental Europe. [ABSTRACT FROM AUTHOR]

The purpose of this study is to understand the drivers of cloud droplet formation in orographic clouds. We used a combination of modeling, in-situ and remote sensing measurements at the high-altitude Helmos Hellenic Atmospheric Aerosol and Climate Change station ((HAC)2), which is located at the top of Mt. Helmos (1314 metres above sea level), Greece during the Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISTHO) campaign in Fall 2021 (https://calishto.panacea-ri.gr/) to examine the origins of the aerosols (i.e., local aerosol from the Planetary Boundary Layer (PBL), or long-range transported aerosol from the Free Tropospheric Layer (FTL) contributing to the Cloud Condensation Nuclei (CCN), their characteristics (hygroscopicity, size distribution and mixing state), as well as the vertical velocities distributions and resulting supersaturations. We found that the characteristics of the PBL aerosol were considerably different from FTL aerosol and use the aerosol particle number and equivalent mass concentration of the black carbon (eBC) in order to determine when the (HAC)2 was within the FTL or PBL based on timeseries of the height of the PBL. During the (HAC)2 cloud events we sample a mixture of interstitial aerosol and droplet residues, which we characterize using a new approach that utilizes the in-situ droplet measurements to determine time periods where the aerosol sample is purely interstitial. From the dataset we determine the properties (size distribution and hygroscopicity) of the pre-cloud, activated and interstitial aerosol. The hygroscopicity of activated aerosol is found to be higher than that of the interstitial or pre-cloud aerosol. A series of closure studies with the droplet parameterization shows that cloud droplet concentration (N d) and supersaturation can be predicted to within 25 % of observations when the aerosol size distributions correspond to pre-cloud conditions. Analysis of the characteristic supersaturation of each aerosol population indicates that droplet formation in clouds is aerosol-limited when formed in FTL airmasses – hence droplet formation is driven by aerosol variations, while clouds formed in the PBL tend to be velocity limited and droplet variations are driven by fluctuations in vertical velocity. Given that the cloud dynamics do not vary significantly between airmasses, the variation in aerosol concentration and type is mostly responsible for these shifts in cloud microphysical state and sensitivity to aerosol. With these insights, remote sensing of cloud droplets in such clouds can be used to infer either CCN spectra (when in the FTL) or vertical velocity (when in the PBL). In conclusion, we show that a coordinated measurement of aerosol and cloud properties, together with the novel analysis approaches presented here allow for the determination of the drivers of droplet formation in orographic clouds and their sensitivity to aerosol and vertical velocity variations. [ABSTRACT FROM AUTHOR]

Aerosol-cloud interactions in mixed-phase clouds (MPCs) are one of the most uncertain drivers of the hydrological cycle and climate change. A synergy of in-situ, remote sensing and modelling experiments was used to determine the source of ice nucleating particles (INPs) for MPCs at Mount Helmos in the Eastern Mediterranean. The influences of boundary layer turbulence, vertical aerosol distributions and meteorological conditions were also examined. When the observation site is in the Free Troposphere (FT), approximately 1 in 106 aerosol particles serve as INPs. The INP abundance spans three orders of magnitude and increases following the order of marine aerosols, continental aerosols, and finally, dust plumes. Biological particles are important INPs observed in continental and marine aerosols, whereas they play a secondary yet important role even during Saharan dust events. Air masses in the planetary boundary layer (PBL) show both enriched INP concentrations and higher proportion of INPs in comparison to total aerosol particles, different from cases in the FT. The presence of precipitations/clouds enriches INPs in the FT but decreases INPs in the PBL. Additionally, new INP parameterizations, incorporating the ratio of fluorescent-to-nonfluorescent or coarse-to-fine particles and predicting >90% of the observed INPs within an uncertainty range of a factor of 10, exhibit better performance than current widely-used parameterizations, and allow ice formation in models to respond to variations in dust and biological particles. The improved parameterizations can help MPC formation simulations in regions with various INP sources or different regions with prevailing INP source. [ABSTRACT FROM AUTHOR]

This study explores the differences in performance and results by various versions of the ISORROPIA thermodynamic module implemented within the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. Three different versions of the module were used, ISORROPIA II v1, ISORROPIA II v2.3, and ISORROPIA-lite. First, ISORROPIA II v2.3 replaced ISORROPIA II v1 in EMAC to improve pH predictions close to neutral conditions. The newly developed ISORROPIA-lite has been added to EMAC alongside ISORROPIA II v2.3. ISORROPIA-lite is more computationally efficient and assumes that atmospheric aerosols exist always as supersaturated aqueous (metastable) solutions, while ISORROPIA II includes the option to allow for the formation of solid salts at low RH conditions (stable state). The predictions of EMAC by employing all three aerosol thermodynamic models were compared to each other and evaluated against surface measurements from three regional observational networks in the polluted Northern Hemisphere (Interagency Monitoring of Protected Visual Environments (IMPROVE), European Monitoring and Evaluation Programme (EMEP), and Acid Deposition Monitoring Network of East Asia (EANET)). The differences between ISORROPIA II v2.3 and ISORROPIA-lite were minimal in all comparisons with the normalized mean absolute difference for the concentrations of all major aerosol components being less than 11 % even when different phase state assumptions were used. The most notable differences were lower aerosol concentrations predicted by ISORROPIA-lite in regions with relative humidity in the range of 20 % to 60 % compared to the predictions of ISORROPIA II v2.3 in stable mode. The comparison against observations yielded satisfactory agreement especially over the USA and Europe but higher deviations over East Asia, where the overprediction of EMAC for nitrate was as high as 4 µ g m -3 (∼20%). The mean annual aerosol pH predicted by ISORROPIA-lite was on average less than a unit lower than ISORROPIA II v2.3 in stable mode, mainly for coarse-mode aerosols over the Middle East. The use of ISORROPIA-lite accelerated EMAC by nearly 5 % compared to the use of ISORROPIA II v2.3 even if the aerosol thermodynamic calculations consume a relatively small fraction of the EMAC computational time. ISORROPIA-lite can therefore be a reliable and computationally efficient alternative to the previous thermodynamic module in EMAC. [ABSTRACT FROM AUTHOR]

While the number and types of indoor air pollutants is rising, much is suspected but little is known about the impact of their potentially synergistic interactions, upon human health. Gases, particulate matter, organic compounds but also allergens and viruses, fall within the 'pollutant' definition. Distinct populations, such as children and allergy and asthma sufferers are highly susceptible, while a low socioeconomic background is a further susceptibility factor; however, no specific guidance is available. We spend most of our time indoors; for children, the school environment is of paramount importance and potentially amenable to intervention. The interactions between some pollutant classes have been studied. However, a lot is missing with respect to understanding interactions between specific pollutants of different classes in terms of concentrations, timing and sequence, to improve targeting and upgrade standards. SynAir‐G is a European Commission‐funded project aiming to reveal and quantify synergistic interactions between different pollutants affecting health, from mechanisms to real life, focusing on the school setting. It will develop a comprehensive and responsive multipollutant monitoring system, advance environmentally friendly interventions, and disseminate the generated knowledge to relevant stakeholders in accessible and actionable formats. The aim of this article it to put forward the SynAir‐G hypothesis, and describe its background and objectives. [ABSTRACT FROM AUTHOR]

New particle formation (NPF) substantially contributes to global cloud condensation nuclei (CCN), and their climate impacts. Individual NPF events are also thought to increase local CCN, cloud droplet number (CDN), and cloud albedo. High resolution simulations however go against the latter, showing that radiatively important stratiform clouds can experience a systematic and substantial decrease in CDN during and after NPF events. CDN drops because particles too small to act as CCN uptake condensable material, and stunt the growth of particles that would otherwise form droplets. Convective clouds however experience modest increases in CDN—consistent with established views on the NPF‐cloud link. Together, these results reshape our conceptual understanding of NPF impacts on clouds, as the newly discovered duality of responses would drive cloud systems in a fundamentally different manner than thought. Plain Language Summary: Most studies assume that cloud condensation nuclei (CCN) changes from new particle formation (NPF) events directly reflect cloud droplet number (CDN) responses in clouds and ignore the growth of pre‐existing particles and their contribution to CCN. High resolution state‐of‐the‐art simulations over Europe portray that while convective clouds experience modest increases in CDN, the radiatively important stratiform clouds may present a systematic and substantial decrease in droplet number during and after new particle formation (NPF) events. Consequently, it is evident that NPF exhibits a duality in response—which depending on the local conditions may vitally change the manner in which cloud systems may respond. Key Points: New particle formation (NPF) events have always been thought to increase the concentration of particles that form cloud droplets thus always lead to climate coolingΤhrough high resolution modeling it is showed that stratiform clouds influenced by NPF events may experience systematic reductions in droplet leading to local warming from reductions in cloud albedo, while droplet number is always enhanced in convective cloudsThese effects combined could bear important impacts on cloud properties and structure following NPF events [ABSTRACT FROM AUTHOR]

Wildfires are a significant source of organic aerosol during summer, with major impacts on air quality and climate. However, studies in Europe suggest a surprisingly low (less than 10%) contribution of biomass burning organic aerosol to average summertime fine particulate matter levels. In this study we combine field measurements and atmospheric chemical transport modeling, to demonstrate that the contribution of wildfires to fine particle levels in Europe during summer is seriously underestimated. Our work suggests that the corresponding contribution has been underestimated by a factor of 4–7 and that wildfires were responsible for approximately half of the total OA in Europe during July 2022. This discrepancy with previous work is due to the rapid physicochemical transformation of these emissions to secondary oxidized organic aerosol with an accompanying loss of its organic chemical fingerprints. These atmospheric reactions lead to a regionally distributed background organic aerosol that is responsible for a significant fraction of the health-related impacts caused by fine particles in Europe and probably in other continents. These adverse health effects can occur hundreds or even thousands of kilometers away from the fires. We estimate that wildfire emissions are responsible for 15–22% of the deaths in Europe due to exposure to fine particulate matter during summer. [ABSTRACT FROM AUTHOR]

China has long-term high PM2.5 levels, and its Oxidative Potential (OP) is worth studying as it may unravel the impacts of aerosol pollution on public health better than PM2.5 alone. OP and PM2.5 are influenced by meteorological factors, anthropogenic emissions sources and atmospheric aging. Although their impact on PM2.5 have been studied, OP measurements only recently became available and on a limited scale, as they require considerable technical expertise and resources. For this, the joint relationship between PM2.5 and OP for a wide range of meteorological conditions and emissions profiles remain elusive. Towards this, we estimated PM2.5 and OP over China using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from WRF weather forecast model. It was found that higher values of PM2.5 and OP were primarily concentrated in urban agglomerations in the central and eastern regions of China, while lower values were found in the western and northeastern regions. Furthermore, the probability density function revealed that about 40 % of areas in China had an annual average PM2.5 concentrations exceeding the Chinese concentrations limit; 36 % of the regions have OP below 1 푛푚표푙 푚푖푛−1 푚−3, 41 % have OP between 1 and 2 푛푚표푙 푚푖푛−1 푚−3, and 23 % have OP above 2 푛푚표푙 푚푖푛−1 푚−3. Analysis of the simulations indicate that meteorological conditions and anthropogenic emission contributed 46 % (65 %) and 54 % (35 %) to the PM2.5 concentration (OP) variability. The emission sensitivity analysis also highlighted PM2.5 and OP levels are mostly determined by secondary aerosol formation and biomass burning. [ABSTRACT FROM AUTHOR]

China has long-term high PM2.5 levels, and its Oxidative Potential (OP) is worth studying asit may unravel the impacts of aerosol pollution on public health better than PM2.5 alone. OP and PM2.5 are influenced by meteorological factors, anthropogenic emissions sources and atmospheric aging. Although their impact on PM2.5 have been studied, OP measurements only recently became available and on a limited scale, as they require considerable technical expertise and resources. For this, the joint relationship between PM2.5 and OP for a wide range of meteorological conditions and emissions profiles remain elusive. Towards this, we estimated PM2.5 and OP over China using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from WRF weather forecast model. It was found that higher values of PM2.5 and OP were primarily concentrated in urban agglomerations in the central and eastern regions of China, while lower values were found in the western and northeastern regions. Furthermore, the probability density function revealed that about 40% of areas in China had an annual average PM2.5 concentrations exceeding the Chinese concentrations limit; 36% of the regions have OP below 1 41% have OP between 1 and, and 23% have OP above 2. Analysis of the simulations indicate that meteorological conditions and anthropogenic emission contributed 46% (65%) and 54% (35%) to the PM2.5 concentration (OP) variability. The emission sensitivity analysis also highlighted PM2.5 and OP levels are mostly determined by secondary aerosol formation and biomass burning. [ABSTRACT FROM AUTHOR]

Single garlic is known to have many benefits as an alternative therapy for various types of metabolic syndrome. The bioactive compounds, allicin, and alliin, in garlic are unstable and easily degraded in digestion. Chitosan-alginate microencapsulation is thought to increase stability and protect active compound so its therapeutic effect is more optimal. This study aimed to characterize the microencapsulation chitosan-alginate of single garlic extract (MCA-SGE), as well as to examine the antioxidant activity and kinetic release of MCA-SGE in vitro. The research procedure includes the steps of single garlic extraction, preparation of MCA-SGE, characterization of MCA-SGE (PSA, SEM, and FTIR) as well as biological testing of MCA-SGE through antioxidant activity and kinetic release tests. PSA results showed the mean particle size of MCASGE was 439.0 ± 1.9 nm or 0.4 m with a polydispersity index (PDI) value of 0.579 ± 0.046 and a zeta potential value of 15.4 ± 0.3 mV. The SEM results showed that the morphology of MCA-SGE was spherical with a smooth surface and a micrometre size of 0.4 - 0.7 µm. The FTIR results describe a shift in absorption and addition of SGE functional groups after encapsulation. The results of the antioxidant activity test showed the antioxidant activity of MCA -SGE was 65%, while SGE was 55%. The results of the kinetic release showed that more allicin and alliin were released by SGE than MCA-SGE during the 4 -hour kinetic release simulation. MCA-SGE has the potential to be used as a drug delivery system with controlled release. [ABSTRACT FROM AUTHOR]

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-cloud in situ measurements taken during nine flights over 4 d. 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 ice production through ice–ice collisions or droplet shattering may have explained the high ice fraction, as opposed to rime splintering that would have significantly reduced 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 (ca. 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 supports the universality of this relationship. [ABSTRACT FROM AUTHOR]

A large part of the uncertainty in climate projections comes from uncertain aerosol properties and aerosol–cloud interactions as well as the difficulty in remotely sensing them. The southeastern Atlantic functions as a natural laboratory to study biomass-burning smoke and to constrain this uncertainty. We address these gaps by comparing the Weather Research and Forecasting with Chemistry Community Atmosphere Model (WRF-CAM5) to the multi-campaign observations ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS), CLARIFY (CLoud–Aerosol–Radiation Interaction and Forcing), and LASIC (Layered Atlantic Smoke Interactions with Clouds) in the southeastern Atlantic in August 2017 to evaluate a large range of the model's aerosol chemical properties, size distributions, processes, and transport, as well as aerosol–cloud interactions. Overall, while WRF-CAM5 is able to represent smoke properties and transport, some key discrepancies highlight the need for further analysis. Observations of smoke composition show an overall decrease in aerosol mean diameter as smoke ages over 4–12 d, while the model lacks this trend. A decrease in the mass ratio of organic aerosol (OA) to black carbon (BC), OA:BC , and the OA mass to carbon monoxide (CO) mixing ratio, OA:CO , suggests that the model is missing processes that selectively remove OA from the particle phase, such as photolysis and heterogeneous aerosol chemistry. A large (factor of ∼2.5) enhancement in sulfate from the free troposphere (FT) to the boundary layer (BL) in observations is not present in the model, pointing to the importance of properly representing secondary sulfate aerosol formation from marine dimethyl sulfide and gaseous SO 2 smoke emissions. The model shows a persistent overprediction of aerosols in the marine boundary layer (MBL), especially for clean conditions, which multiple pieces of evidence link to weaker aerosol removal in the modeled MBL than reality. This evidence includes several model features, such as not representing observed shifts towards smaller aerosol diameters, inaccurate concentration ratios of carbon monoxide and black carbon, underprediction of heavy rain events, and little evidence of persistent biases in modeled entrainment. The average below-cloud aerosol activation fraction (NCLD/NAER) remains relatively constant in WRF-CAM5 between field campaigns (∼0.65), while it decreases substantially in observations from ORACLES (∼0.78) to CLARIFY (∼0.5), which could be due to the model misrepresentation of clean aerosol conditions. WRF-CAM5 also overshoots an observed upper limit on liquid cloud droplet concentration around NCLD= 400–500 cm -3 and overpredicts the spread in NCLD. This could be related to the model often drastically overestimating the strength of boundary layer vertical turbulence by up to a factor of 10. We expect these results to motivate similar evaluations of other modeling systems and promote model development to reduce critical uncertainties in climate simulations. [ABSTRACT FROM AUTHOR]

Water is a key component of atmospheric aerosol, affecting many aerosol processes including gas-to-particle partitioning of semi-volatile compounds. Water related to secondary organic aerosol (SOAW) is often neglected in atmospheric chemical transport models and is not considered in gas-to-particle partitioning calculations for inorganic species. We use a new inorganic aerosol thermodynamics model, ISORROPIA-lite, which considers the effects of SOAW, to perform chemical transport model simulations for 1 year over the continental United States to quantify its effects on aerosol mass concentration and composition. SOAW can increase average fine aerosol water levels by up to a factor of 2 when secondary organic aerosol (SOA) is a major PM 1 component. This is often the case in the south-eastern US, where SOA concentrations are higher. Although the annual average impact of this added water on total dry PM 1 concentrations due to increased partitioning of nitrate and ammonium is small (up to 0.1 µ g m -3), total dry PM 1 increases of up to 2 µ g m -3 (with nitrate levels increases of up to 200 %) can occur when RH levels and PM 1 concentrations are high. [ABSTRACT FROM AUTHOR]

Ice-nucleating particles (INPs) initiate primary ice formation in Arctic mixed-phase clouds (MPCs), altering cloud radiative properties and modulating precipitation. For atmospheric INPs, the complexity of their spatiotemporal variations, heterogeneous sources, and evolution via intricate atmospheric interactions challenge the understanding of their impact on microphysical processes in Arctic MPCs and induce an uncertain representation in climate models. In this work, we performed a comprehensive analysis of atmospheric aerosols at the Arctic coastal site in Ny-Ålesund (Svalbard, Norway) from October to November 2019, including their ice nucleation ability, physicochemical properties, and potential sources. Overall, INP concentrations (NINP) during the observation season were approximately up to 3 orders of magnitude lower compared to the global average, with several samples showing degradation of NINP after heat treatment, implying the presence of proteinaceous INPs. Particle fluorescence was substantially associated with INP concentrations at warmer ice nucleation temperatures, indicating that in the far-reaching Arctic, aerosols of biogenic origin throughout the snow- and ice-free season may serve as important INP sources. In addition, case studies revealed the links between elevated NINP and heat lability, fluorescence, high wind speeds originating from the ocean, augmented concentration of coarse-mode particles, and abundant organics. Backward trajectory analysis demonstrated a potential connection between high-latitude dust sources and high INP concentrations, while prolonged air mass history over the ice pack was identified for most scant INP cases. The combination of the above analyses demonstrates that the abundance, physicochemical properties, and potential sources of INPs in the Arctic are highly variable despite its remote location. [ABSTRACT FROM AUTHOR]

This study explores the differences in performance and results by various versions of the ISORROPIA thermodynamic module implemented within the global atmospheric chemistry model EMAC. Three different versions of the module were used, ISORROPIA II v1, ISORROPIA II v2.3, and ISORROPIA-lite. First, ISORROPIA II v2.3 replaced ISORROPIA II v1 in EMAC to improve pH predictions close to neutral conditions. The newly developed ISORROPIA-lite has been added to EMAC alongside ISORROPIA II v2.3. ISORROPIA-lite is more computationally efficient and assumes that atmospheric aerosols exist always as supersaturated aqueous (metastable) solutions while ISORROPIA II includes the option to allow the formation of solid salts at low RH conditions (stable state). The predictions of EMAC by employing all three aerosol thermodynamic models were compared to each other and evaluated against surface measurements from three regional observational networks (IMPROVE, EMEP, EANET) in the polluted Northern Hemisphere. The differences between ISORROPIA II v2.3 and ISORROPIA-lite were minimal in all comparisons with the normalized mean absolute difference for the concentrations of all major aerosol components being less than 10 % even when different phase state assumptions were used. The most notable differences were lower aerosol concentrations predicted by ISORROPIA-lite in regions with relative humidity in the range of 20% to 60% compared to the predictions of ISORROPIA II v2.3 in stable mode. The comparison against observations yielded satisfactory agreement especially over the US and Europe, but higher deviations over East Asia, where the overprediction of EMAC for nitrate was as high as 4 μg m-3 (~ 20%). The mean annual aerosol pH predicted by ISORROPIAlite was on average less than a unit lower than ISORROPIA II v2.3 in stable mode, mainly for coarse mode aerosols over Middle East. The use of ISORROPIA-lite accelerated EMAC by 5 % compared to the use of ISORROPIA II v2.3 even if the aerosol thermodynamic calculations consume a relatively small fraction of the EMAC computational time. ISORROPIA-lite can therefore be a reliable and computationally effective replacement of the previous thermodynamic module in EMAC. [ABSTRACT FROM AUTHOR]

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, among them meteorological radars, offer the possibility of studying clouds and precipitation in extended areas over long time periods and are highly valuable to understand the spatiotemporal 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 spatiotemporal features in the radar time series that point to the occurrence of distinct mechanisms during different stages of the event. By focusing on three 30 min 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 that could 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 1 to 4 orders of magnitude. While a robust proof of occurrence of a given SIP mechanism cannot be easily established, the multi-sensor data provide various independent elements each supporting the proposed interpretations. [ABSTRACT FROM AUTHOR]

Heterogeneous ice nucleation impacts the hydrological cycle and climate through affecting cloud microphysical state and radiative properties. Despite decades of research, a quantitative description and understanding of heterogeneous ice nucleation remains elusive. Parameterizations are either fully empirical or heavily rely on classical nucleation theory (CNT), which does not consider molecular-level properties of the ice-nucleating particles – which can alter ice nucleation rates by orders of magnitude through impacting pre-critical stages of ice nucleation. The adsorption nucleation theory (ANT) of heterogeneous droplet nucleation has the potential to remedy this fundamental limitation and provide quantitative expressions in particular for heterogeneous freezing in the deposition mode (the existence of which has even been questioned recently). In this paper we use molecular simulations to understand the mechanism of deposition freezing and compare it with pore condensation freezing and adsorption. Based on the results of our case study, we put forward the plausibility of extending the ANT framework to ice nucleation (using black carbon as a case study) based on the following findings: (i) the quasi-liquid layer at the free surface of the adsorbed droplet remains practically intact throughout the entire adsorption and freezing process; therefore, the attachment of further water vapor to the growing ice particles occurs through a disordered phase, similar to liquid water adsorption. (ii) The interaction energies that determine the input parameters of ANT (the parameters of the adsorption isotherm) are not strongly impacted by the phase state of the adsorbed phase. Thus, not only is the extension of ANT to the treatment of ice nucleation possible, but the input parameters are also potentially transferable across phase states of the nucleating phase at least for the case of the graphite/water model system. [ABSTRACT FROM AUTHOR]

We present a comprehensive review of methods and applications of molecular simulations of interfacial systems. We give a detailed overview of the main techniques and major challenges in the following aspects of solid and fluid surfaces: adsorption at solid surfaces, interfacial transport and surface-to-bulk partitioning. We summarise methods to estimate macroscopic properties interfaces (adsorption isotherms, surface tension and contact angle) and ways to extract quantitative information about fluctuating liquid surfaces. We demonstrate the usage of these methods on recent applications from the fields of atmospheric science, material science and biophysics. The two main goals of this review are: (i) to provide guidance in practical questions, such as choosing software, force field, level of theory, system geometry, and finding the appropriate selective surface analysis methods based on the type of the interface and the nature of the physical problem to be studied; and (ii) to highlight domains where molecular simulations can bring about substantial advances in our understanding in important questions of applied science as a function of future method development and adaptation for applied fields. [ABSTRACT FROM AUTHOR]

Primary emissions from wood and pellet stoves were aged in an atmospheric simulation chamber under daytime and nighttime conditions. The aerosol was analyzed with online aerosol mass spectrometry and offline Fourier transform infrared spectroscopy (FTIR). Measurements using the two techniques agreed reasonably well in terms of the organic aerosol (OA) mass concentration, OA:OC trends, and concentrations of biomass burning markers – lignin-like compounds and anhydrosugars. Based on aerosol mass spectrometry, around 15 % of the primary organic aerosol (POA) mass underwent some form of transformation during daytime oxidation conditions after 6–10 h of atmospheric exposure. A lesser extent of transformation was observed during the nighttime oxidation. The decay of certain semi-volatile (e.g., levoglucosan) and less volatile (e.g., lignin-like) POA components was substantial during aging, highlighting the role of heterogeneous reactions and gas–particle partitioning. Lignin-like compounds were observed to degrade under both daytime and nighttime conditions, whereas anhydrosugars degraded only under daytime conditions. Among the marker mass fragments of primary biomass burning OA (bbPOA), heavy ones (higher m/z) were relatively more stable during aging. The biomass burning secondary OA (bbSOA) became more oxidized with continued aging and resembled that of aged atmospheric organic aerosols. The bbSOA formed during daytime oxidation was dominated by acids. Organonitrates were an important product of nighttime reactions in both humid and dry conditions. Our results underline the importance of changes to both the primary and secondary biomass burning aerosols during their atmospheric aging. Heavier fragments from aerosol mass spectrometry seldom used in atmospheric chemistry can be used as more stable tracers of bbPOA and, in combination with the established levoglucosan marker, can provide an indication of the extent of bbPOA aging. [ABSTRACT FROM AUTHOR]

A large part of the uncertainty in climate projections comes from poorly understood or constrained aerosol properties (e.g., particle size, composition, mixing state, aging processes) and aerosol-cloud interactions, as well as the difficulty in remotely sensing them. This is an issue especially in remote regions such as the southeast Atlantic, which exhibits large model spread due to the seasonal coexistence of extensive cloud cover and regional biomass burning smoke. Here we address these gaps by comparing the WRF-CAM5 model to multi-campaign observations (ORACLES, CLARIFY, and LASIC) of the southeastern Atlantic region in August 2017 to evaluate a broad swath of the model's aerosol properties, processes, and transport, and the degree to which aerosol interactions with clouds are captured. Building on earlier work showing strong performance in model advection and mixing, we find that biomass-burning smoke aerosol size and composition are generally well-captured in the marine free troposphere, except for a likely overprediction of dust in the accumulation mode (7–17 % modeled dust fraction which is not present in the observations). Evaluating smoke aging trends, the model shows a steady increase in aerosol mean diameter and an unchanging composition as smoke ages, deviating from the observed trends that show a rise and subsequent fall in mean diameter over 4–12 days and a decreasing OA : BC ratio beyond 3 days. Both results are likely due to missing processes in the model that remove OA from the particle phase such as photolysis and heterogeneous aerosol chemistry. The observed composition change from the free-troposphere to the marine boundary layer (MBL) is not fully captured in the model, especially the observed enhancement of sulfate from 11 % to 37 % by mean mass fraction in ORACLES, and from 11 % to 26 % in CLARIFY. This points to the importance of properly representing sulfate formation from marine dimethyl sulfide (DMS) emissions and in smoke-free parcels. Additionally, the model does not capture the occurrence of an Aitken mode during clean and medium-smoke conditions in the boundary layer, likely pointing to misrepresentation of new particle formation. The model shows a persistent overprediction of aerosols in the MBL, especially for clean conditions, that multiple pieces of evidence link to weaker aerosol removal in the modeled MBL than reality. This evidence includes the model not representing observed shifts in the aerosol size distribution towards smaller sizes, the model not capturing the relative concentrations of carbon monoxide compared to black carbon, model underprediction of heavy rain events, and little evidence of persistent biases in modeled entrainment. Average below-cloud aerosol activation fraction (NCLD/NAER) remains relatively constant in WRF-CAM5 between field campaigns (~0.65), while it decreases substantially in observations from ORACLES (~0.78) to CLARIFY (~0.5), which could be due to the model misrepresentation of clean aerosol conditions. WRF-CAM5 also overshoots an observed upper limit on liquid cloud droplet concentration around NCLD=400–500 cm-3 observed in both ORACLES and CLARIFY and also overpredicts the spread in NCLD. This could be related to the model often drastically overestimating the strength of boundary layer vertical turbulence by up to a factor of 10 and having a bimodal—rather than the observed unimodal—probability distribution of updraft turbulent kinetic energy. We expect these results to motivate similar evaluations of other modeling systems and promote model development in these critical areas to reduce uncertainties in climate simulations. [ABSTRACT FROM AUTHOR]

A large part of the uncertainty in climate projections comes from poorly understood or constrained aerosol properties (e.g., particle size, composition, mixing state, aging processes) and aerosol-cloud interactions, as well as the difficulty in remotely sensing them. This is an issue especially in remote regions such as the southeast Atlantic, which exhibits large model spread due to the seasonal coexistence of extensive cloud cover and regional biomass burning smoke. Here we address these gaps by comparing the WRF-CAM5 model to multi-campaign observations (ORACLES, CLARIFY, and LASIC) of the southeastern Atlantic region in August 2017 to evaluate a broad swath of the model's aerosol properties, processes, and transport, and the degree to which aerosol interactions with clouds are captured. Building on earlier work showing strong performance in model advection and mixing, we find that biomassburning smoke aerosol size and composition are generally well-captured in the marine free troposphere, except for a likely overprediction of dust in the accumulation mode (7-17% modeled dust fraction which is not present in the observations). Evaluating smoke aging trends, the model shows a steady increase in aerosol mean diameter and an unchanging composition as smoke ages, deviating from the observed trends that show a rise and subsequent fall in mean diameter over 4-12 days and a decreasing OA:BC ratio beyond 3 days. Both results are likely due to missing processes in the model that remove OA from the particle phase such as photolysis and heterogeneous aerosol chemistry. The observed composition change from the free-troposphere to the marine boundary layer (MBL) is not fully captured in the model, especially the observed enhancement of sulfate from 11% to 37% by mean mass fraction in ORACLES, and from 11% to 26% in CLARIFY. This points to the importance of properly representing sulfate formation from marine dimethyl sulfide (DMS) emissions and in smoke-free parcels. Additionally, the model does not capture the occurrence of an Aitken mode during clean and medium-smoke conditions in the boundary layer, likely pointing to misrepresentation of new particle formation. The model shows a persistent overprediction of aerosols in the MBL, especially for clean conditions, that multiple pieces of evidence link to weaker aerosol removal in the modeled MBL than reality. This evidence includes the model not representing observed shifts in the aerosol size distribution towards smaller sizes, the model not capturing the relative concentrations of carbon monoxide compared to black carbon, model underprediction of heavy rain events, and little evidence of persistent biases in modeled entrainment. Average below-cloud aerosol activation fraction (NCLD/NAER) remains relatively constant in WRF-CAM5 between field campaigns (~0.65), while it decreases substantially in observations from ORACLES (~0.78) to CLARIFY (~0.5), which could be due to the model misrepresentation of clean aerosol conditions. WRF-CAM5 also overshoots an observed upper limit on liquid cloud droplet concentration around NCLD=400-500 cm-3 observed in both ORACLES and CLARIFY and also overpredicts the spread in NCLD. This could be related to the model often drastically overestimating the strength of boundary layer vertical turbulence by up to a factor of 10 and having a bimodal--rather than the observed unimodal--probability distribution of updraft turbulent kinetic energy. We expect these results to motivate similar evaluations of other modeling systems and promote model development in these critical areas to reduce uncertainties in climate simulations. [ABSTRACT FROM AUTHOR]

Aerosol forcing uncertainty represents the largest climate forcing uncertainty overall. Its magnitude has remained virtually undiminished over the past 20 years despite considerable advances in understanding most of the key contributing elements. Recent work has produced modest increases only in the confidence of the uncertainty estimate itself. This review summarizes the contributions toward reducing the uncertainty in the aerosol forcing of climate made by satellite observations, measurements taken within the atmosphere, as well as modeling and data assimilation. We adopt a more measurement‐oriented perspective than most reviews of the subject in assessing the strengths and limitations of each; gaps and possible ways to fill them are considered. Currently planned programs supporting advanced, global‐scale satellite and surface‐based aerosol, cloud, and precursor gas observations, climate modeling, and intensive field campaigns aimed at characterizing the underlying physical and chemical processes involved, are all essential. But in addition, new efforts are needed: (a) to obtain systematic aircraft in situ measurements capturing the multi‐variate probability distribution functions of particle optical, microphysical, and chemical properties (and associated uncertainty estimates), as well as co‐variability with meteorology, for the major aerosol airmass types; (b) to conceive, develop, and implement a suborbital (aircraft plus surface‐based) program aimed at systematically quantifying the cloud‐scale microphysics, cloud optical properties, and cloud‐related vertical velocities associated with aerosol‐cloud interactions; and (c) to focus much more research on integrating the unique contributions of satellite observations, suborbital measurements, and modeling, to reduce the persistent uncertainty in aerosol climate forcing.Plain Language Summary: Aerosols, such as airborne wildfire smoke, desert dust, volcanic and pollution particles, affect Earth's climate by reflecting (some also absorb) sunlight. These aerosol particles also play key roles in cloud formation and evolution, further affecting the planet's energy balance. The magnitudes of these effects, and even the underlying mechanisms, represent the largest uncertainty in climate modeling. Despite two decades of advances in many aspects of aerosol‐climate science, aerosol climate forcing uncertainty is virtually undiminished. Yet, reducing this uncertainty is critical for any effort to attribute, mitigate, or predict climate changes. We adopt a measurement‐oriented perspective to assess the strengths and limitations of measurement and modeling programs, and conclude that current and planned efforts need to continue. However, in addition, new efforts are needed: (a) to obtain aircraft in situ measurements that capture systematically aerosol particle properties for the major aerosol airmass types, globally, (b) to conceive, develop, and implement an aircraft and surface‐based program aimed at filling gaps in our understanding of the interactions between aerosol particles and clouds, along with (c) much more research focused on integrating the unique contributions of satellite observations, suborbital measurements, and modeling, to reduce the uncertainty in our understanding of Earth's changing climate.Key Points: Aerosol climate forcing uncertainty is virtually undiminished despite two decades of advances in many aspects of aerosol‐climate scienceThis review concludes that current and planned aerosol modeling, satellite and ground‐based observation programs remain essentialNew, systematic aircraft aerosol‐particle and cloud‐process measurements are also needed, along with better model‐measurement integration [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Heterogeneous ice nucleation impacts the hydrological cycle and climate through affecting cloud microphyiscal state and radiative properties. Despite decades of research, a quantitative description and understanding of heterogeneous ice nucleation remains elusive. Parameterizations are either fully empirical or heavily rely on classical nucleation theory (CNT), which does not consider molecular level properties of the ice nucleating particles - which can alter ice nucleation rates by orders of magnitude through impacting pre-critical stages of ice nucleation. The Adsorption Nucleation Theory (ANT) of heterogeneous droplet nucleation has the potential to remedy this caveat and provide quantitative expressions in particular for heterogeneous freezing in the deposition mode (the existence of which has even been questioned recently). In this paper we use molecular simulations to understand the mechanism of deposition freezing and compare it with pore condensation freezing and adsorption. We put forward the plausibility of extending the ANT framework to ice nucleation (using black carbon as a case study) based on the following findings: i) The quasi-liquid layer at the free surface of the adsorbed droplet remains practically intact throughout the entire adsorption and freezing process, therefore the attachment of further water vapor to the growing ice particles occurs through a disordered phase, similar to liquid water adsorption. ii) The interaction energies that determine the input parameters of ANT (the parameters of the adsorption isotherm) are not strongly impacted by the phase state of the adsorbed phase. Thus, not only the extension of ANT to the treatment of ice nucleation is possible, but the input parameters are also potentially transferable across phase states of the nucleating phase. [ABSTRACT FROM AUTHOR]

Heterogeneous ice nucleation impacts the hydrological cycle and climate through affecting cloud microphyiscal state and radiative properties. Despite decades of research, a quantitative description and understanding of heterogeneous ice nucleation remains elusive. Parameterizations are either fully empirical or heavily rely on classical nucleation theory (CNT), which does not consider molecular level properties of the ice nucleating particles - which can alter ice nucleation rates by orders of magnitude through impacting pre-critical stages of ice nucleation. The Adsorption Nucleation Theory (ANT) of heterogeneous droplet nucleation has the potential to remedy this caveat and provide quantitative expressions in particular for heterogeneous freezing in the deposition mode (the existence of which has even been questioned recently). In this paper we use molecular simulations to understand the mechanism of deposition freezing and compare it with pore condensation freezing and adsorption. We put forward the plausibility of extending the ANT framework to ice nucleation (using black carbon as a case study) based on the following findings: i) The quasi-liquid layer at the free surface of the adsorbed droplet remains practically intact throughout the entire adsorption and freezing process, therefore the attachment of further water vapor to the growing ice particles occurs through a disordered phase, similar to liquid water adsorption. ii) The interaction energies that determine the input parameters of ANT (the parameters of the adsorption isotherm) are not strongly impacted by the phase state of the adsorbed phase. Thus, not only the extension of ANT to the treatment of ice nucleation is possible, but the input parameters are also potentially transferable across phase states of the nucleating phase. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Multimodal 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 con figuration. 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. [ABSTRACT FROM AUTHOR]

Accurately capturing cloud condensation nuclei (CCN) concentrations is key to understanding the aerosol–cloud interactions that continue to feature the highest uncertainty amongst numerous climate forcings. In situ CCN observations are sparse, and most non-polarimetric passive remote sensing techniques are limited to providing column-effective CCN proxies such as total aerosol optical depth (AOD). Lidar measurements, on the other hand, resolve profiles of aerosol extinction and/or backscatter coefficients that are better suited for constraining vertically resolved aerosol optical and microphysical properties. Here we present relationships between aerosol backscatter and extinction coefficients measured by the airborne High Spectral Resolution Lidar 2 (HSRL-2) and in situ measurements of CCN concentrations. The data were obtained during three deployments in the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project, which took place over the southeast Atlantic (SEA) during September 2016, August 2017, and September–October 2018. Our analysis of spatiotemporally collocated in situ CCN concentrations and HSRL-2 measurements indicates strong linear relationships between both data sets. The correlation is strongest for supersaturations (S) greater than 0.25 % and dry ambient conditions above the stratocumulus deck, where relative humidity (RH) is less than 50 %. We find CCN–HSRL-2 Pearson correlation coefficients between 0.95–0.97 for different parts of the seasonal burning cycle that suggest fundamental similarities in biomass burning aerosol (BBA) microphysical properties. We find that ORACLES campaign-average values of in situ CCN and in situ extinction coefficients are qualitatively similar to those from other regions and aerosol types, demonstrating overall representativeness of our data set. We compute CCN–backscatter and CCN–extinction regressions that can be used to resolve vertical CCN concentrations across entire above-cloud lidar curtains. These lidar-derived CCN concentrations can be used to evaluate model performance, which we illustrate using an example CCN concentration curtain from the Weather Research and Forecasting Model coupled with physics packages from the Community Atmosphere Model version 5 (WRF-CAM5). These results demonstrate the utility of deriving vertically resolved CCN concentrations from lidar observations to expand the spatiotemporal coverage of limited or unavailable in situ observations. [ABSTRACT FROM AUTHOR]

Ice nucleating particles (INPs) initiate primary ice formation in Arctic mixed-phase clouds (MPCs), altering cloud radiative properties and modulating precipitation. For atmospheric INPs, the complexity of their spatiotemporal variations, heterogeneous sources and evolution via intricate atmospheric interactions challenge the understanding of their impact on microphysical processes in Arctic MPCs and induce an uncertain representation in climate models. In this work, we performed a comprehensive analysis of atmospheric aerosols at the Arctic coastal site in Ny-Ålesund (Svalbard, Norway) from October to November 2019, including their ice nucleation ability, physicochemical properties and potential sources. Overall, INP concentrations NINP) during the observation season were approximately up to three orders of magnitude lower compared to the global average, with several samples showing degradation of NINP after heat treatment, implying the presence of proteinaceous INPs. Particle fluorescence was substantially associated with INP concentrations at warmer ice nucleation temperatures, indicating that in the far-reaching Arctic, aerosols of biogenic origin throughout the snow- and ice-free season may serve as important INP sources. In addition, case studies revealed the links between elevated NINP to heat-lability, fluorescence, high wind speeds originating from the ocean, augmented concentration of coarse-mode particles and abundant organics. Backward trajectory analysis demonstrated a potential connection between high-latitude dust sources and high INP concentrations, while prolonged air mass history over the ice pack was identified for most scant INP cases. The combination of the above analyses demonstrates the abundance, physicochemical properties and potential sources of INPs in the Arctic are highly variable despite its remote location. [ABSTRACT FROM AUTHOR]