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Abstract Aerosol‐cloud interactions (ACI) pose the largest uncertainty for climate projection. Among many challenges of understanding ACI, the question of whether ACI can be deterministically predicted has not been explicitly answered. Here we attempt to answer this question by predicting cloud droplet number concentration Nc from aerosol number concentration Na and ambient conditions using a data‐driven framework. We use aerosol properties, vertical velocity fluctuations, and meteorological states from the ACTIVATE field observations (2020–2022) as predictors to estimate Nc. We show that the campaign‐wide Nc can be successfully predicted using machine learning models despite the strongly nonlinear and multi‐scale nature of ACI. However, the observation‐trained machine learning model fails to predict Nc in individual cases while it successfully predicts Nc of randomly selected data points that cover a broad spatiotemporal scale. This suggests that, within a data‐driven framework, the Nc prediction is uncertain at fine spatiotemporal scales.

Abstract The Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) field campaign provides accurate data for aerosol characterization and trace gas profiles, and establishes knowledge of the relationships between aerosols and water. The dropsonde dataset provides an in situ characterization of the vertical thermodynamic structure of the atmosphere during 165 research flights by NASA Langley’s King Air research aircraft between February 2020 and June 2022 and four test flights between December 2019 and November 2021. The research flights covered the western North Atlantic region, off the coast of the Eastern United States and around Bermuda and covered all seasons. The dropsonde profiles provide observations of temperature, pressure, relative humidity, and horizontal and vertical winds between the surface and about 9 km. 801 dropsondes were released, of which 796 were processed and 788 provide complete profiles of all parameters between the flight level and the surface with normal parachute performance. Here, we describe the dataset, the processing of the measurements, general statistics, and applications of this rich dataset.

In this study, we propose a simple method to derive vertically resolved aerosol particle number concentration (Na) using combined polarimetric and lidar remote sensing observations. This method relies on accurate polarimeter retrievals of the fine-mode column-averaged aerosol particle extinction cross section and accurate lidar measurements of vertically resolved aerosol particle extinction coefficient such as those provided by multiwavelength high spectral resolution lidar. We compare the resulting lidar + polarimeter vertically resolved Na product to in situNa data collected by airborne instruments during the NASA aerosol cloud meteorology interactions over the western Atlantic experiment (ACTIVATE). Based on all 35 joint ACTIVATE flights in 2020, we find a total of 32 collocated in situ and remote sensing profiles that occur on 11 separate days, which contain a total of 322 cloud-free vertically resolved altitude bins of 150 m resolution. We demonstrate that the lidar + polarimeter Na agrees to within 106% for 90% of the 322 vertically resolved points. We also demonstrate similar agreement to within 121% for the polarimeter-derived column-averaged Na. We find that the range-normalized mean absolute deviation (NMAD) for the polarimeter-derived column-averaged Na is 21%, and the NMAD for the lidar + polarimeter-derived vertically resolved Na is 16%. Taken together, these findings suggest that the error in the polarimeter-only column-averaged Na and the lidar + polarimeter vertically resolved Na are of similar magnitude and represent a significant improvement upon current remote sensing estimates of Na.

A challenging aspect of conducting airborne in situ observations of the atmosphere is how to optimize flight plans for specific objectives and constraints associated with weather and flight restrictions. For aerosol-cloud interaction research, two recent campaigns utilized a “stairstepping” approach whereby an aircraft conducts level legs at various altitudes while moving forward with each subsequent leg: the 2019 MONterey Aerosol Research Campaign (MONARC) over the northeast Pacific and the 2020–2022 Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) over the northwest Atlantic. We examine the homogeneity of several atmospheric variables both vertically and horizontally in the marine boundary layer with a focus on the sub-cloud environment. In well-mixed boundary layers, there was generally good horizontal and vertical homogeneity in potential temperature, winds, water vapor mixing ratio, various trace gases, and many aerosol variables. Selected aerosol variables exhibited the most variability owing to sensitivity to humidity and near-cloud conditions (supermicrometer aerosol concentrations), coastal pollution gradients (e.g., organic aerosol mass), and small spatial scale phenomena such as new particle formation (aerosol number concentration for particles with diameter >3 nm). Illustrative cases are described when stairstepping can pose issues requiring extra caution for data analysis: (i) poor vertical mixing and layers decoupled from those below; (ii) multiple cloud layers; (iii) fluctuating cloud base/top and boundary layer top heights; and (iv) horizontal variability across specific features leading to sharp gradients such as right near coastlines and over the Gulf Stream with strong sea surface temperature changes. Results from this study provide a guide both for future studies aiming to examine these mission datasets and for designing new airborne campaigns.

This document is a correction notice for an article titled "Dropsonde observations during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment" published in Scientific Data. The correction addresses an issue with cell highlighting in Table 1, which was not included in the original version. The highlighting has been replaced with indicators (*) to indicate the relevant data points in the HTML and pdf versions of the article. The table caption has also been updated to reflect this change. The authors of the article are Holger Vömel, Armin Sorooshian, Claire Robinson, Taylor J. Shingler, Kenneth Lee Thornhill, and Luke D. Ziemba. [Extracted from the article]

Aerosol‐cloud interactions (ACI) pose the largest uncertainty for climate projection. Among many challenges of understanding ACI, the question of whether ACI can be deterministically predicted has not been explicitly answered. Here we attempt to answer this question by predicting cloud droplet number concentration Nc ${N}_{c}$ from aerosol number concentration Na ${N}_{a}$ and ambient conditions using a data‐driven framework. We use aerosol properties, vertical velocity fluctuations, and meteorological states from the ACTIVATE field observations (2020–2022) as predictors to estimate Nc ${N}_{c}$. We show that the campaign‐wide Nc ${N}_{c}$ can be successfully predicted using machine learning models despite the strongly nonlinear and multi‐scale nature of ACI. However, the observation‐trained machine learning model fails to predict Nc ${N}_{c}$ in individual cases while it successfully predicts Nc ${N}_{c}$ of randomly selected data points that cover a broad spatiotemporal scale. This suggests that, within a data‐driven framework, the Nc ${N}_{c}$ prediction is uncertain at fine spatiotemporal scales. Plain Language Summary: Ambient aerosol particles act as seeds for ice crystals and cloud droplets that form clouds. Both aerosols and clouds regulate the energy and water budgets of the Earth via radiative and cloud micro/macro‐processes. This is the so‐called aerosol‐cloud interactions (ACI). ACI remains the source of the largest uncertainty for accurate climate projections, due to incomplete understanding of nonlinear multi‐scale processes, limited observations across various cloud regimes, and insufficient computational power to resolve them in models. Quantifying the relation between the cloud droplet Nc $\left({N}_{c}\right)$ and aerosol Na $\left({N}_{a}\right)$ number concentration has been a central challenge of understanding and representing ACI. In this work, we tackle this challenge by predicting Nc ${N}_{c}$ from observations made during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) using machine learning models. We show that the climatological Nc ${N}_{c}$ can be successfully predicted despite the strongly nonlinear and multi‐scale nature of ACI. However, the observation‐trained machine learning model fails to predict Nc ${N}_{c}$ at fine spatiotemporal scales. Key Points: Three‐year in situ measurements (179 flights) provide adequate data to train and validate a random forest model (RFM) to study aerosol‐cloud interactionsThe RFM can successfully predict cloud droplet number concentration Nc ${N}_{c}$ and identify importance of key predictorsData‐driven Nc ${N}_{c}$ prediction in individual cases shows strong dependency on sampling strategy [ABSTRACT FROM AUTHOR]

The Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) field campaign provides accurate data for aerosol characterization and trace gas profiles, and establishes knowledge of the relationships between aerosols and water. The dropsonde dataset provides an in situ characterization of the vertical thermodynamic structure of the atmosphere during 165 research flights by NASA Langley's King Air research aircraft between February 2020 and June 2022 and four test flights between December 2019 and November 2021. The research flights covered the western North Atlantic region, off the coast of the Eastern United States and around Bermuda and covered all seasons. The dropsonde profiles provide observations of temperature, pressure, relative humidity, and horizontal and vertical winds between the surface and about 9 km. 801 dropsondes were released, of which 796 were processed and 788 provide complete profiles of all parameters between the flight level and the surface with normal parachute performance. Here, we describe the dataset, the processing of the measurements, general statistics, and applications of this rich dataset. [ABSTRACT FROM AUTHOR]

Cumulus clouds are common over maritime regions. They are important regulators of the global radiative energy budget and global hydrologic cycle, as well as a key contributor to the uncertainty in anthropogenic climate change projections due to uncertainty in aerosol–cloud interactions. These interactions are regionally specific owing to their strong influences on aerosol sources and meteorology. Here, our analysis focuses on the statistical properties of marine boundary layer (MBL) aerosol chemistry and the relationships of MBL aerosol to cumulus cloud properties just above cloud base as sampled in 2019 during the NASA Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP 2 Ex). The aerosol and clouds were sampled by instruments on the NASA P-3 aircraft over three distinct maritime regions around the Philippines: the West Pacific, the South China Sea, and the Sulu Sea. Our analysis shows three primary sources influenced the aerosol chemical composition: clean marine (ocean source), industrial (Southeast Asia, Manila, and cargo and tanker ship emissions), and biomass burning (Borneo and Indonesia). The clean marine aerosol chemical composition had low values of all sampled chemical signatures, specifically median values of 2.2 µg m -3 of organics (ORG), 2.3 µg m -3 of SO4 , 0.3 µg m -3 of NO3 , 1.4 µg m -3 of NH4 , 0.04 µg m -3 of Cl, and 0.0074 µg m -3 of refractory black carbon (BC). Chemical signatures of the other two aerosol source regions were industrial, with elevated SO4 having a median value of 6.1 µg m -3 , and biomass burning, with elevated median concentrations of ORG 21.2 µg m -3 and BC 0.1351 µg m -3. Based on chemical signatures, the industrial component was primarily from ship emissions, which were sampled within 60 km of ships and within projected ship plumes. Normalized cloud droplet size distributions in clouds sampled near the MBL passes of the P-3 showed that clouds impacted by industrial and biomass burning contained higher concentrations of cloud droplets, by as much as 1.5 orders of magnitude for diameters < 13 µm compared to clean marine clouds, while at size ranges between 13.0–34.5 µm the median concentrations of cloud droplets in all aerosol categories were nearly an order of magnitude less than the clean marine category. In the droplet size bins centered at diameters > 34.5 µm concentrations were equal to, or slightly exceeded, the concentrations of the clean marine clouds. These analyses show that anthropogenic aerosols generated from industrial and biomass burning sources significantly influenced cloud base microphysical structure in the Philippine region enhancing the small droplet concentration and reducing the concentration of mid-sized droplets. [ABSTRACT FROM AUTHOR]

Aerosol effects on micro/macrophysical properties of marine stratocumulus clouds over the western North Atlantic Ocean (WNAO) are investigated using in situ measurements and large-eddy simulations (LES) for two cold-air outbreak (CAO) cases (28 February and 1 March 2020) during the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE). The LES is able to reproduce the vertical profiles of liquid water content (LWC), effective radius reff and cloud droplet number concentration Nc from fast cloud droplet probe (FCDP) in situ measurements for both cases. Furthermore, we show that aerosols affect cloud properties (Nc, reff, and LWC) via the prescribed bulk hygroscopicity of aerosols (κ ¯) and aerosol size distribution characteristics. Nc, reff, and liquid water path (LWP) are positively correlated to κ ¯ and aerosol number concentration (Na) while cloud fractional cover (CFC) is insensitive to κ ¯ and aerosol size distributions for the two cases. The realistic changes to aerosol size distribution (number concentration, width, and the geometrical diameter) with the same meteorology state allow us to investigate aerosol effects on cloud properties without meteorological feedback. We also use the LES results to evaluate cloud properties from two reanalysis products, ERA5 and MERRA-2. Compared to LES, the ERA5 is able to capture the time evolution of LWP and total cloud coverage within the study domain during both CAO cases while MERRA-2 underestimates them. [ABSTRACT FROM AUTHOR]

Postfrontal clouds (PFC) are ubiquitous in the marine boundary layer, and their morphology is essential to estimating the radiation budget in weather and climate models. Here we examine the roles of sea surface temperature (SST) and meteorological factors in controlling the mesoscale morphology and evolution of shallow clouds associated with a cold-air outbreak that occurred on 1 March 2020 during phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE). Our results show that the simulated PFC structure and ambient conditions by the Weather Research and Forecasting (WRF) Model are generally consistent with observations from GOES-16 and dropsonde measurements. We also examine the thermodynamical and dynamical influences in the cloud mesoscale morphology using WRF sensitivity experiments driven by two meteorological forcing datasets with different domain-mean SST and spatial gradients, which lead to dissimilar values of hydrometeor water path and cloud core fraction. The SST from ERA5 leads to weaker stability and higher inversion height than the SST from FNL does. In addition, the use of large-scale meteorological forcings from ERA5 yields a distinctive time evolution of wind direction shear in the inner domain, which favors the formation and persistence of longer cloud rolls. Both factors contribute to a change in the time evolution of domain-mean water path and cloud core fraction of cloud streets. Our study takes advantage of the simulation driven by the differences between two large-scale forcing datasets to illustrate the importance of SST and wind direction shear in the cloud street morphology in a realistic scenario. [ABSTRACT FROM AUTHOR]

This study examines boundary layer turbulence derived from high temporal resolution meteorological measurements from 40 research flights over the western North Atlantic Ocean during the 2020 deployments of ACTIVATE. Frequency distributions of various turbulent quantities reveal stronger turbulence during the winter deployment than in summer and for cloud‐topped than in cloud‐free boundary layers during the summer deployment. Maximum turbulence kinetic energy (TKE) is most often within cloud from observations in winter and summer, whereas it is mostly below cloud in both seasons by a global model turbulence parameterization. Bivariate frequency distributions are consistent with the bivariate Gaussian probability distribution functions assumed for the closure of higher‐order turbulence/shallow convection parameterizations used by some global models. Turbulence simulated by the Community Atmosphere Model version 6 and the Energy Exascale Earth System Model Atmosphere Model version 2 using such parameterizations is not as strong as observed, with more TKE going into vertical wind perturbations rather than into zonal wind perturbations as observed, suggesting that the treatment of turbulence in Earth system models still needs to be further improved. Key Points: Maximum turbulence kinetic energy is most often within cloud in observations but mostly below cloud by a global model parameterizationObservations are similar to bivariate normal probability distribution functions assumed to close higher‐order turbulence parameterizationsBoundary layer turbulence simulated by global models is weaker than observed [ABSTRACT FROM AUTHOR]

Aerosol particles can be emitted, transported, removed, or transformed, leading to aerosol variability at scales impacting the climate (days to years and over hundreds of kilometers) or the air quality (hours to days and from meters to hundreds of kilometers). We present the temporal and spatial scales of changes in AOD (aerosol optical depth) and aerosol size (using Ångström exponent – AE; fine-mode fraction – FMF) over Korea during the 2016 KORUS-AQ (KORea–US Air Quality) atmospheric experiment. We use measurements and retrievals of aerosol optical properties from airborne instruments for remote sensing (4STAR; Spectrometers for Sky-Scanning Sun-Tracking Atmospheric Research) and in situ (LARGE; NASA Langley Aerosol Research Group Experiment) on board the NASA DC-8 and geostationary satellites (GOCI; Geostationary Ocean Color Imager; Yonsei aerosol retrieval – YAER, version 2) as well as from reanalysis (MERRA-2; Modern-Era Retrospective Analysis for Research and Applications, version 2). Measurements from 4STAR when flying below 1000 m show an average AOD at 501 nm of 0.36 and an average AE of 1.11 with large standard deviation (0.12 and 0.15 for AOD and AE, respectively), likely due to mixing of different aerosol types (fine and coarse mode). The majority of AOD due to fine-mode aerosol is observed at altitudes lower than 2 km. Even though there are large variations, for 18 out of the 20 flight days, the column AOD measurements by 4STAR along the NASA DC-8 flight trajectories match the South Korean regional average derived from GOCI. GOCI-derived FMF, which was found to be slightly low compared to AErosol RObotic NETwork (AERONET) sites (Choi et al., 2018), is lower than 4STAR's observations during KORUS-AQ. Understanding the variability of aerosols helps reduce uncertainties in the aerosol direct radiative effect by quantifying the errors due to interpolating between sparse aerosol observation sites or modeled pixels, potentially reducing uncertainties in the upcoming observational capabilities. We observed that, contrary to the prevalent understanding, AE and FMF are more spatially variable than AOD during KORUS-AQ, even when accounting for potential sampling biases by using Monte Carlo resampling. Averaging between measurements and models for the entire KORUS-AQ period, the reduction in correlation by 15 % is 65.0 km for AOD and shorter at 22.7 km for AE. While there are observational and model differences, the predominant factor influencing spatial–temporal homogeneity is the meteorological period. High spatiotemporal variability occurs during the dynamic period (25–31 May), and low spatiotemporal variability occurs during the blocking pattern (1–7 June). While AOD and FMF / AE are interrelated, the spatial variability and relative variability of these parameters in this study indicate that microphysical processes vary at scales shorter than aerosol concentration processes at which microphysical processes such as aerosol particle formation, growth, and coagulation mostly impact the dominant aerosol size (characterized by, e.g., FMF / AE) and to some degree AOD. In addition to impacting aerosol size, aerosol concentration processes such as aerosol emission, transport, and removal mostly impact the AOD. [ABSTRACT FROM AUTHOR]

A challenging aspect of conducting airborne in situ observations of the atmosphere is how to optimize flight plans for specific objectives and constraints associated with weather and flight restrictions. For aerosol-cloud interaction research, two recent campaigns utilized a "stairstepping" approach whereby an aircraft conducts level legs at various altitudes while moving forward with each subsequent leg: the 2019 MONterey Aerosol Research Campaign (MONARC) over the northeast Pacific and the 2020–2022 Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) over the northwest Atlantic. We examine the homogeneity of several atmospheric variables both vertically and horizontally in the marine boundary layer with a focus on the sub-cloud environment. In well-mixed boundary layers, there was generally good horizontal and vertical homogeneity in potential temperature, winds, water vapor mixing ratio, various trace gases, and many aerosol variables. Selected aerosol variables exhibited the most variability owing to sensitivity to humidity and near-cloud conditions (supermicrometer aerosol concentrations), coastal pollution gradients (e.g., organic aerosol mass), and small spatial scale phenomena such as new particle formation (aerosol number concentration for particles with diameter >3 nm). Illustrative cases are described when stairstepping can pose issues requiring extra caution for data analysis: (i) poor vertical mixing and layers decoupled from those below; (ii) multiple cloud layers; (iii) fluctuating cloud base/top and boundary layer top heights; and (iv) horizontal variability across specific features leading to sharp gradients such as right near coastlines and over the Gulf Stream with strong sea surface temperature changes. Results from this study provide a guide both for future studies aiming to examine these mission datasets and for designing new airborne campaigns. [ABSTRACT FROM AUTHOR]

Large-eddy simulation (LES) is able to capture key boundary layer (BL) turbulence and cloud processes. Yet, large-scale forcing and surface turbulent fluxes of sensible and latent heat are often poorly prescribed for LESs. We derive these quantities from measurements and reanalysis obtained for two cold-air outbreak (CAO) events during Phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) in February–March 2020. We study the two contrasting CAO cases by performing LES and test the sensitivity of BL structure and clouds to large-scale forcings and turbulent heat fluxes. Profiles of atmospheric state and large-scale divergence and surface turbulent heat fluxes obtained from ERA5 data agree reasonably well with those derived from ACTIVATE field measurements for both cases at the sampling time and location. Therefore, we adopt the time-evolving heat fluxes, wind, and advective tendencies profiles from ERA5 data to drive the LES. We find that large-scale thermodynamic advective tendencies and wind relaxations are important for the LES to capture the evolving observed BL meteorological states characterized by the hourly ERA5 data and validated by the observations. We show that the divergence (or vertical velocity) is important in regulating the BL growth driven by surface heat fluxes in LESs. The evolution of liquid water path is largely affected by the evolution of surface heat fluxes. The liquid water path simulated in LES agrees reasonably well with the ACTIVATE measurements. This study paves the path to investigate aerosol–cloud–meteorology interactions using LES informed and evaluated by ACTIVATE field measurements. [ABSTRACT FROM AUTHOR]