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18 results for “Cloud condensation nuclei”
Cloud Condensation Nuclei number concentrations over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Cloud Condensation Nuclei (CCN) are a subclass of atmospheric aerosol particles, which can be activated to cloud droplets at a certain supersaturation, with respect to water. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as CCN. It was found that CCN are relevant for the Earth’s radiation budget, by affecting cloud albedo and lifetime. When giving a number concentration of CCN, also the supersaturation at which it was measured has to be given.</p> <p>With additional information on particle number size distribution, the hypothetical diameter of particle activation (critical diameter) was derived. Further, the particle hygroscopicity parameter (kappa) was calculated using the critical diameter. Values of kappa can be a proxy for bulk chemical composition of the sampled CCN population.</p> <p>Our dataset gives CCN number concentrations measured by a CCN counter (type CCN-100 by DMT, Boulder, US) operated at five different levels of supersaturation (0.15%, 0.2%, 0.3%, 0.5%, 1%) during the Antarctic Circumnavigation Expedition (ACE) cruise over the Southern Ocean, as part of the ACE-SPACE project. Temporal coverage is from December 20, 2016 to March 19, 2017. We give 5-minute averaged and quality controlled CCN number concentrations, critical diameter and kappa values.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS015.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS020.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS030.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS050.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS100.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS015.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS020.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS030.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS050.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS100.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS015.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS020.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS030.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS050.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS100.csv, data file, comma-separated values</li> <li>data_file_header_number_concentration.txt, metadata, text</li> <li>data_file_header_critical_diameter.txt, metadata, text</li> <li>data_file_header_hygroscopicity_parameter.txt, metadata, text</li> <li>change_log.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p>The files listed above contain Cloud Condensation Nuclei (CCN) number concentration (N_CCN), critical diameter (D_crit) and particle hygroscopicity parameter (KAPPA) values for the Antarctic Circumnavigation Expedition from in-situ measurements. Each file contains only N_CCN, D_crit or KAPPA values for one of the five measured levels of supersaturation (SS), e.g., N_CCN at SS=0.15% in ACESPACE_cloud_condensation_nuclei_number_concentration_SS015.csv or N_CCN at SS=0.2% in ACESPACE_cloud_condensation_nuclei_number_concentration_SS020.csv etc. In addition, for each N_CCN value the respective temperature of the CCNCs measurement column (T_col) is given. Values are from 1 Hz measurements and averaged to represent 5-minute intervals.</p> <p>For every given value of CCN number concentration, the respective supersaturation level is given, although files only contain values for one level only. Additionally, longitude and latitude for the ship’s position at the start time of the averaging period are given.</p> <p>For latitude and longitude nan values are given, in cases where positioning data was not available for the given time period. There are no nan values for CCN number concentration included, in a way that only quality assured data is given.</p> <p><strong>Change log</strong></p> <p>v1.1 - data files updated</p> <ul> <li>change dataset title to specify ACE cruise</li> <li>change time resolution to 5 minutes</li> <li>addition of critical diameter data</li> <li>addition of hygroscopicity parameter data</li> <li>create separate data_file_headers</li> <li>add change log</li> </ul> <p>v1.0 - initial release of dataset</p>
Data for the 'Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation'
<p>All numerical data used in the manuscript <strong>“Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation” </strong>by G. S. Fanourgakis et al. ACP (2019) are categorized and provided in a number of files. All files are in the hdf format. A readme file is also provided.</p> <p>These data files have been created by G. S. Fanourgakis (fanourg@uoc.gr)</p> <p>Details on the data are provided in Fanourgakis et al. Atmos. Chem. Phys. 2019 https://doi.org/10.5194/acp-2018-1340 (e-mail to <a href="mailto:mariak@uoc.gr">mariak@uoc.gr</a> ; <a href="mailto:athanasios.nenes@epfl.ch">athanasios.nenes@epfl.ch</a> )</p> <p>For an in-depth understanding of the description below, a study of the above mentioned manuscript is required.</p> <p>(A) Station model results</p> <p>The station results can be found in files with filenames of the form:</p> <p>station $MODEL.nc</p> <p>The “$MODEL” (as well as all names starting with “$”) indicates a variable, and more specifically one of the models participated in the present study. The values of this variable are tabulated in Table 1 in the readme file.</p> <p>In each file a number of computational results are provided by the specified model for all nine (9) stations that provided observational data. The name of the variable is formed as:</p> <p>st $STATION $FIELDhour st $STATION $FIELD month</p> <p>where all possible values of the variables $STATION and $FIELD are tabulated in Tables 2 and 3 in the readme file, respectively. The extension _hour denotes that hourly values for the field are provided, while the extension _month the monthly average of this quantity. For example, the variable</p> <p>st Finokalia CCN02 hour</p> <p>found in the file station_TM4-ECPL.nc, contains the hourly values of the CCN<sub>0<em>.</em>2 </sub>at the Finokalia station as computed by the TM4-ECPL model. In a similar way, in the file station_EMAC.nc, the variable below gives the monthly values of dust at Vavihill as computed with the EMAC model.</p> <p>st Vavihill DU month</p> <p>Notice also that in all files hourly and monthly data are provided for the time period from 1-1-2011 up to 31-12-2015 (60 months and 43,824 hours)</p> <p>(B) Station observational results</p> <p>There is one file that contains all observational data from Schmale et al., SCIENTIFIC DATA | 4:170003 | DOI: 10.1038/sdata.2017.3, 2017 (<a href="mailto:julia.schmale@psi.ch">julia.schmale@psi.ch</a>) and the data that were computed based on the observations (i.e. number of cloud droplets) (contact person: athanasios.nenes@epfl.ch). The file is</p> <p>station observations.nc</p> <p>while the following fields are contained in there:</p> <p>st $STATION $FIELDhour</p> <p>st $STATION $FIELD month</p> <p>The values of variables are given in the Tables 2 and 3 in the readme file. The time period covered is from 1-1-2011 up to 31-12-2015. Notice that due to the lack of observations a lot of data are missing. For missing observational data the value -9999.999 is given. Contact person for the observational data is Julia Schmale (julia.schmale@psi.ch).</p> <p>(C) Station Multi-model Median</p> <p>Monthly averages of the models can be found in the file</p> <p>station MMM.nc</p> <p>The following fields can be found in the file</p> <p>st $STATION $FIELD month median</p> <p>st $STATION$FIELD month quart25</p> <p>st $STATION$FIELD month quart75</p> <p>where the values of the variables $STATION and $FIELD can be found in Tables 2 and 3, respectively. The extension median corresponds to the multi-model median, while the quart25 and quart75 to the 25 % and 75 % quartiles, respectively.</p> <p>(D) Global model results</p> <p>In the following single file can be found for each of the models the surface distribution of various fields.</p> <p>results global models year2011.nc</p> <p>They correspond to the annual mean of the year 2011. The resolution of the grid is 1<sup>◦ </sup>× 1<sup>◦</sup>. The file contains the following variables:</p> <p>$FIELD $MODEL</p> <p>The $FIELD and $MODEL can be found in Tables 3 and 1, respectively.</p> <p>(E) Global average results</p> <p>In the file</p> <p>surface_ global_average_year2011.nc</p> <p>can be found in 5<sup>◦</sup>×5<sup>◦ </sup>resolution, the Multi-model median of surface distribution of the various fields denoted in Table 3 and their corresponding diversity. The names of the variables are formed as:</p> <p>med $FIELD</p> <p>div $FIELD</p> <p>where, ‘med’ stands for median and ‘div’ for diversity calculated as standard deviation divided by the mean of the model results.</p> <p>Tables and details on the fields provided are given in the readme file.</p>
Size-resolved cloud condensation nuclei data collected during the CalWater 2015 field campaign
<p>This repository contains raw and processed data for the size-resolved cloud condensation nuclei instrument deployed during the Calwater-2015 field campaign. It also contains the averaged cluster data presented in the paper "Classification of aerosol population type and cloud condensation nuclei properties in a coastal California littoral environment using an unsupervised cluster model" by Atwood et al. (2019). Details about the datafiles are provided in README.md file in markdown format.</p>
Dataset for "Method to retrieve cloud condensation nuclei number concentrations using lidar measurements"
<p>This repository contains the source data for the manuscript "<strong>Method to retrieve cloud condensation nuclei number concentrations using lidar measurements</strong>" published in <em>Atmospheric Measurement Techniques</em>. In situ measured data from five filed campaigns and corresponding theoretical simulated CCN number concentrations, lidar extinction and backscatter are included.</p>
Tracking the photomineralization mechanism in irradiated lab-generated and field-collected brown carbon samples and its effect on cloud condensation nuclei abilities
<p>Data set of the data presented in figures and tables in our manuscript on the photomineralization of brown carbon samples: ammonium sulfate-methylglyoxal solutions, Suwannee River fulvic acid isolates, firewood smoke and ambient aerosols from Padua, Italy.</p>
Experimental CCN properties of 6 pollenkitts and two mixtures with ammonium sulfate reported in the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Critical dry particle size Dp<sub>50</sub> measured for supersaturations 0.1–1.4% with a DMT CCN Counter (CCN-100). Size and composition resolved, and size-averaged hygroscopicity values calculated using the method presented by Rose et al. (2010), Cloud condensation nuclei in polluted air and biomass burning smoke near the mega-city Guangzhou, China - Part 1: Size-resolved measurements and implications for the modeling of aerosol particle hygroscopicity and CCN activity, <em>Atmospheric Chemistry and Physics</em>, <em>10</em>, 3365–3383.</p>
Experimental surface tensions of aqueous poplar and ragweed pollenkitts reported in the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Surface tensions measured with a ramé-hart goniometer (Model 250) for aqueous solutions of varying concentrations comprising poplar and ragweed pollenkitts and their mixtures with ammonium sulfate</p>
Critical droplet properties for particles containing 6 pollenkitts evaluated with 4 Köhler models for the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Critical droplet supersaturation and surface tension, calculated with 3 thermodynamic Köhler models presented in Prisle et al., <em>Surfactants in cloud droplet activation: mixed organic-inorganic particles</em>, Atmospheric Chemistry and Physics, 10, 5663-5683, doi:10.5194/acp-10-5663-2010, and with the model presented in Prisle et al., <em>A simple representation of surface active organic aerosol in cloud droplet formation</em>, Atmospheric Chemistry and Physics, 11, 4073-4083, doi: 10.5194/acp-11-4073-2011, for particles of varying size comprising 6 pollenkitts and mixtures with ammonium sulfate.</p>
Data for: Multi-campaign ship and aircraft observations of marine cloud condensation nuclei, and droplet concentrations
<p class="MsoNormal"><span>In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns, ACTIVATE, NAAMES, CAMP2EX, ORACLES, SOCRATES, MARCUS, and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements are collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, representing a range of clean to polluted conditions in various climate regimes. With the large range of environmental conditions sampled, this collection of data is ideal for testing satellite remote detection methods of CDNC and CCN in marine environment. Remote measurement methods are key to expanding the available data, in these difficult to reach regions of the Earth, and improving our understanding of aerosol-cloud interactions. Additional particle composition and continental tracers are included to identify potential contributing CCN source. Several of these campaigns, include both High Spectral Resolution Lidar and polarimetric imaging measurements that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates.</span></p>
Data for: Multi-campaign ship and aircraft observations of marine cloud condensation nuclei, and droplet concentrations
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Effect of Long-Range Transported Fire Aerosols on Cloud Condensation Nuclei Concentrations and Cloud Properties at High Latitudes
<p>The tab-delimited files contain Aerosol chemical composition data, size distribution data measured by AMS, ACSM, MAAP, and SMPS, and DMPS at the SMEAR IV station (Puijo) and the Zepplein observatory (Zep) . Please read the file named 'Information.txt' for more details about the files.</p>
Data from: Supersaturation and critical size of cloud condensation nuclei in marine stratus clouds
<p>Observations of marine stratus clouds in clean air off the Californian coast reveal a functional relationship between the number of cloud condensation nuclei (CCN) and supersaturation. Satellite-derived liquid droplet density estimates the number density of CCN. Combining the estimated supersaturation using Kohler theory, global maps of supersaturation and the critical activation size of CCN are estimated. Here, we show that high supersaturation >0.5% persists over the oceans with a critical CCN size of 25-30 nm, which is smaller than the conventional wisdom of 60 nm. Independent support for such high supersaturation in the marine cloud is obtained from CCN measurements provided by the "Atmospheric Tomography Mission". Higher supersaturation implies smaller activation size for CCN making cloud formation more sensitive to changes in aerosol nucleation.</p>
Archived Model Output and Code for "Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations "
<div> <p>This is an archive of CAM6 simulation output used in the paper Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations, submitted to the AGU Journal. Codes used to read the nc file is also attached.</p> </div>
Data for "Kinetic limitations affect cloud condensation nuclei activity measurements under low supersaturation"
<p>Data for the manuscript "Kinetic limitations affect cloud condensation nuclei activity measurements under low supersaturation" by Tao et al.</p>
Data from: Supersaturation and critical size of cloud condensation nuclei in marine stratus clouds
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Water uptake, cloud condensation nuclei and surface tension: results from the MadFACTS campaign
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Dataset for "Roles of marine biota in the formation of atmospheric bioaerosols, cloud condensation nuclei, and ice-nucleating particles over the North Pacific Ocean, Bering Sea, and Arctic Ocean"
<p>Atmospheric and oceanic observations were conducted over the North Pacific Ocean, Bering Sea, and Arctic Ocean during a cruise (MR19-03C) in early autumn of 2019. The dataset includes trace gases, chemical composition, fluorescent particles in the ambient and bioindicators in the surface seawater, observed along the ship track (time stamp and coordinates are included). This dataset is for Kawana et al., "Roles of marine biota in the formation of atmospheric bioaerosols, cloud condensation nuclei, and ice-nucleating particles over the North Pacific Ocean, Bering Sea, and Arctic Ocean", Atmospheric Chemistry and Physics, 2024 (in press).</p>
Particle Number Size Distribution of Wintertime Alpine Aerosols and Their Activation as Cloud Condensation Nuclei in the Guanzhong Plain, Northwest China
<p>This is the drawing data of manuscript entitled 'Characteristics of Particle Number Size Distribution of Alpine Aerosols on Mt. Hua and Their Activation as Cloud Condensation Nuclei.</p>
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