Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

Monthly aerosol emissions and GHG concentration projections from 2020-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity

This repository holds the netcdf files for emissions and concentrations projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020. The details of these activity estimates are available from <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>. The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/tree/endof2020</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> to have a different timeframe.  Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a>

opencc-by-4.0Jan 2024View details →

Mineral spectral refractive index and bulk optical property dataset for aerosol studies

Version 1.3, updated 11/15/2024. Added a file with 27 regional dust sample mineral composition information 'NewRegionalSamples.xlsx', along with the refractive index data. All refractive index files here have 127 rows (wavelengths) and 27 columns (samples) 'kall27_coarse.dat' is the imaginary part of the coarse mode.  'kall27_fine.dat' is the imaginary part of the fine mode. 'nall27_coarse.dat' is the real part of the coarse mode. 'nall27_fine.dat' is the real part of the fine mode. Version 1.2, updated 04/23/2024. Major changes:  Changed all the data file names to new format: "mix"+{property name}+{number}, rearranged the number of mixing samples Updated all the bulk optical property data. This version use constant values of standard deviation in the lognormal size distribution settings for the coarse mode and the fine mode respectively. The phase matrices are separated from the other bulk properties due to their large file sizes. The readme file is updated correspondingly. The information of scattering angles (498 angles in total) is uploaded as "TAMUdust2020_Angle.dat". Added supplemental file data in 'Supplemental.tar.gz'. Additional refractive indices are zipped in 'AdditionalRefInd.tar.gz' Version 1.1, updated 03/14/2024. Major changes:  Added mixed bulk properties for "0 (99%coarse+1%fine)" and "11 (2.0 µm coarse+ 0.4 µm fine)"; Added "reff.dat" in the 'BulkProperties.tar.gz'. The data include four columns: fine mode fraction, bulk projected area <A>, bulk volume <V>, effective radius r_eff. The information is for mixed sample number 0 to 11, each corresponds to one row. Added refractive indices for chlorite, mica, smectite, pyroxene, vermiculite and pyroxenes. These groups can be applied in some other models. Version 1.0, uploaded 01/02/2024. This database include supplemental data and files for the publication of this paper: Sensitivities of Spectral Optical Properties of Dust Aerosols to their Mineralogical and Microphysical Properties. Yuheng Zhang, M. Saito, P. Yang, G. L. Schuster, and C. R. Trepte, J. Geophys. Res. Atmos. 2024.   ***************************************** The supplemental data include: 1) 'GroupRefInd.tar.gz' Mineral (group) refractive index files. E. g., 1All_Illite.dat contains the complex refractive index files of illite group. Format (from left to right columns): Wavelength (unit: µm), Real part (n), Imaginary part (k), standard deviation of n, standard deviation of k. The file 'fine_log.dat' includes the mean and standard deviation values of n and k for all the generated fine mode dust samples at 11,044 wavelengths from 0.2 to 50 micron. The file 'fine_log127.dat' only includes the values at 127 wavelengths from 0.2 to 50 micron (defined in 'swav.txt' and 'lwav.txt'), and is used for the bulk property computations. The files 'coarse_log.dat' and 'coarse_log127.dat' are for the coarse mode dust samples. 2) 'CompositionFraction.xlsx': Mineral composition data sources/references and composition data (mean and standard deviation values of each group). 'Vlog_coarse.dat': Randomly generated VOLUME FRACTION of 9 mineral groups for the coarse mode dust. Left to right: Illite, Kaolinite, Montmorillonite (Other clays), Quartz, Feldspar, Carbonate, Gypsum (Sulphate), Hematite, Goethite. 'Vlog_fine.dat': For the fine mode dust. 3) 'RefSources.xlsx': The data source references of mineral refractive indices. We didn't include the olivine, other silicates, soot and titanium-rich minerals in the paper, but the refractive indices are available for those who are interested. Chlorite, Mica and Vermiculite group are mentioned in some studies, and we included the refractive indices for these minerals as well. 4) 'DustSamples.tar.gz' Dust sample refractive index files. The files are enclosed in four folders: fine_sw/ fine_lw/ coarse_sw/ coarse_lw/. fine: fine mode. coarse: coarse mode. 'sw' means shortwave (< 4 µm, in total 76 wavelengths defined in 'swav.txt') while 'lw' means longwave (>= 4 µm, in total 51 wavelengths defined in 'lwav.txt'). All files start with 'rdn', which means that they are computed based on randomly generated composition (data given in sheet 2 of 'CompositionFraction.xlsx'). The four digit number after 'rdn' is the index of each dust sample. In total, there are 5,000 samples. The sample composition is the same for the same sample index in the same size mode (fine/coarse). Data file format (from left to right columns): real part, imaginary part. 5) 'BulkProperties.tar.gz' Bulk property files (excluding phase matrices) 'mixqx.dat' files format (from left to right columns): Extinction efficiency (Qext), Scattering efficiency (Qsca), Backscattering efficiency (Qbck), and Asymmetry coefficient (Qasy). To obtain asymmetry factor, use Qasy/Qsca. 'mixbkx.dat' files format (from left to right columns): P11(pi) P12(pi) P22(pi) P33(pi) P34(pi) P44(pi). 'x' refers to the number at the end of the file name. It can be 100 ~ 112, each represents a setting of coarse and fine mode effective radius and volume fraction (see details in "reff.dat") 'reff.dat' contains the effective radius information of the mixture. It has 7 columns: File number "x", Fine mode volume fraction, Fine mode effective radius (µm), Coarse mode effective radius (µm), Bulk projected area (µm^2), Bulk volume (µm^3), Bulk effective radius (µm). 6) 'PhaseMatrices.tar.gz' Phase matrices data 'mixphswx.dat' files contain phase matrix results at 532 nm (shortwave). From left to right: P11, P12, P22, P33, P34, P44. 'mixphlwx.dat' files contain phase matrix results at 10.5 µm (longwave). There are 635,000 rows in each data file. 635,000 rows = 127 wavelengths * 5,000 samples. Row 1~127 is sample 1, row 128~254 is sample 2, etc.. Suggest to use matlab function 'reshape(property, 127, 5000)' for each column when processing the data. 7) 'Supplemental.tar.gz' We also include data files mentioned in the supplemental file of the paper. The adjusted source data files of the nine mineral groups are included. The supplemental bulk property files are named based on the figure number. 8) 'AdditionalRefInd.tar.gz' We also include additional refractive indices for chlorite, smectite, vermiculite, mica, dolomite, titanium-rich minerals, pyroxenes and soot. These data can be useful in other models. For more detailed information and datasets, please contact: Yuheng Zhang, yuheng98@tamu.edu or yuhengz98@qq.com.

Sub-micron aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

Dataset abstract The authors would highly appreciate to be contacted if the data is used for any purpose. We measured sub-micrometer aerosol particles with two scanning mobility particle spectrometers (SMPSs) between 11 and 400 nm (file name ACESPACE_submicron_aerosol_particle_size_distribution.csv) in 100 bins, and 11 and 181 nm in 77 bins - so no data entry in the remaining 23 bins - (ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). Particles in this size range are important for cloud formation because a sub-set of them can act as cloud condensation nuclei (CCN). The time series of the size distribution shows that the particle population over the Southern Ocean can be quite variable featuring three dominant modes: a new particle formation mode (11 – 30 nm); an Aitken mode (20 – 70 nm); and an accumulation mode (> 70 nm). Often a concentration minimum between the Aitken and accumulation mode can be observed. It is known as Hoppel minimum (Hoppel and Frick, 1990; 10.1016/0960-1686(90)90020-N). Typically, particles larger than this minimum act as CCN. The variability of the particle size spectrum is a result of particle sources and atmospheric processes. Sea spray generation adds larger particles likely with a peak in the mode around 200 nm. Trace gas emissions from microbial communities in the ocean, such as dimethylsulfide (DMS) will be oxidized to either sulphuric acid or methanesulfonic acid in the atmosphere which condense onto pre-existing particles, hence growing those. Sulphuric acid can also form new particles (new particle formation mode). Rain and snow will remove particles larger than the Hoppel minimum. The data set can be used to explore the variability of the particle size distribution in three different oceans around Antarctica (Indian, Pacific, Atlantic Oceans) and from Cape Town to Europe in relation to weather patterns, air mass trajectories, microbial activity etc. It is best used in combination with CCN data to explore the importance of particles for cloud formation. This data set cannot be used to unambiguously determine sources of particles over the southern ocean or to trace anthropogenic impact in the region. The data have been cleaned from the influence of the exhaust of the research vessel. We give five-minute average data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/64. To derive the total particle number concentration between 11 and 400 nm one has to integrate over the diameter range taking into account the normalization by dlog(dp). Temporal coverage is from December 20, 2016 to April 10, 2017. The file “ACESPACE_submicron_aerosol_particle_size_distribution.csv” covers the entire time period except between 9 and 14 January 2017 due to instrument issues. The file “ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv” contains data for the period between 9 and 14 January 2017 and can be used to fill the above gap. The second data file stems from another SMPS with a smaller differential mobility analyser, hence the smaller diameter coverage. Dataset contents The data set contains two files with the size distribution of sub-micrometer aerosol particles. The rows are indexed by the time stamp, which is the end of the 5-minutes averaging interval. The columns are the normalized concentrations of particles in the respective size bin. See the data abstract for details. <ul> <li>ACESPACE_submicron_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv, data file, comma-separated values</li> <li>ACESPACE_particle_diameter_bins.csv, metadata, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> NaN values in a complete row denote missing values because of e.g., calibration periods, ship exhaust contamination, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. Dataset license This sub-micron aerosol particle size distribution dataset collected during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

Equivalent black carbon aerosol measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

Dataset abstract The authors would highly appreciate to be contacted if the data is used for any purpose. We measured equivalent black carbon (eBC) with an aethalometer (model AE33, Magee Scientific) at a time resolution of one second during the Antarctic Circumnavigation Expedition (ACE). We report five-minute averaged data, cleaned from exhaust gas influence. Temporal coverage is from December 20, 2016 to April 10, 2017. The mass concentration of eBC, reported in ng m-3, reflects how far fossil fuel combustion or biomass burning contribute to the aerosol population over the Southern Ocean and between South Africa and Europe. Over the Southern Ocean there are no sources of eBC, except for ship emissions and (sub-)Antarctic station emissions, and hence an enhancement of eBC points towards long-range influence from Africa, Australia, New Zealand and South America. When plotted against latitude, eBC concentrations drop south of 60°S, indicating a more pristine environment. Elevated concentration around the equator are likely influenced by biomass burning in tropical Africa. Dataset contents <ul> <li>ACESPACE_equivalent_black_carbon_aerosol.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> The data file listed above contains five-minute averaged values of equivalent black carbon (eBC) measured during the Antarctic Circumnavigation Expedition. Timestamps are the end of the five-minute period over which the eBC values were averaged. Latitude and longitude are average values of the position of the measurement during the five-minute interval. NaN values of eBC denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure or signal noise levels exceeding 200 ng/m3. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. Dataset license This equivalent black carbon dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

Coarse mode aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

Dataset abstract The authors would highly appreciate to be contacted if the data is used for any purpose. We measured coarse mode aerosol particle size distributions with an aerodynamic particle sizer (APS, model TSI 3321) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). The diameter range is 0.7 to 19 µm. Particles in this size range are indicative of primary sea spray aerosol, biological particles and potentially long-range transported mineral dust. These particles are also important for cloud formation as they act as cloud condensation nuclei or ice nucleating particles, the latter especially in the case of biological particles and mineral dust. Typically the instrument reports data starting from particles with a diameter greater than 500 nm, however, particle number concentrations in the channels below 723 nm were overestimated, which is a common artefact with this instrument. The data have been cleaned from the influence of the exhaust of the research vessel. Temporal coverage is from December 20, 2016 to April 10, 2017. We give five-minute averaged data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/32. To derive the total particle number concentration one has to integrate over the diameter range taking into account the normalization by dlog(dp). Dataset contents <ul> <li>ACESPACE_coarse_mode_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> NaN values in a complete row denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. Dataset license This coarse mode aerosol particle size distribution dataset collected during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

Aerosol particle number concentration measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

Dataset abstract The authors would highly appreciate to be contacted if the data is used for any purpose. We measured aerosol particle number concentration with a condensation particle counter CPC model TSI 3022 at a time resolution of 10 seconds during the Antarctic Circumnavigation Expedition (ACE). We report five-minute averaged data cleaned from exhaust gas influence. The lower cut-off of the CPC is 7 nm. Temporal coverage of the dataset is from December 20, 2016 to April 10, 2017. The total particle number concentration reflects aerosol particles from a variety of sources and processes. The concentrations include for example sea spray aerosol, long-range transported particles, newly formed particles and others. The variability in the concentration reflects processes such as wet removal through precipitation, new particle formation or sea spray formation. Dataset contents <ul> <li>ACESPACE_aerosol_particle_concentration.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> NaN values of aerosol particle number concentration denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. Dataset license This aerosol particle number concentration dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Dec 2019View details →

Non-refractory particulate sulfate and chloride data from a time of flight aerosol chemical speciation monitor around the Southern Ocean in the austral summer of 2016/17, during the Antarctic Circumnavigation Expedition (ACE).

Dataset abstract The Antarctic Circumnavigation Expedition (ACE) campaign was conducted between 20th December 2016 and 19th March 2017. The time of flight aerosol chemical speciation monitor (ToF-ACSM, Aerodyne Research Inc.) was deployed. It is capable of providing 10-minute resolution chemical compositions of NR-PM1 (non-refractory particulate matter with aerodynamic diameter smaller than 1 µm), including sulphate, nitrate, ammonium and organics. Chloride is refractory and can only be measured qualitatively, that is relative changes in intensity are trustworthy while absolute concentrations are a clear underestimation, because most of the chloride is in refractory form as part of sea salt in the marine environment. Since this ACSM dataset was collected on the ship, the ship exhaust will occasionally interfere with the natural signal. Therefore data gaps exist. The overall concentrations of particulate organics, nitrate and ammonium remained low, mostly below detection limit, except during the polluted periods. Thus, we do not report these three components. Only sulphate can be retrieved as a quantitative variable from this dataset. This dataset provides limited information on the chemical composition of sub-micron non-refractory aerosol in the Southern Ocean and gives hints on potential sources. Chloride clearly reflects the contribution of sea salt to the aerosol population. This can be checked by relating the particulate chloride to wind speed (Landwehr et al., 2019; 10.5281/zenodo.3379590) and particles with large diameters (Schmale et al., 2019; 10.5281/zenodo.2636709). Particulate sulphate may originate from a variety of sources: sea salt (minor contribution), anthropogenic emissions and natural marine emissions of dimethylsulfide, which is converted to SO2 and sulphuric acid in the atmosphere and can subsequently partition into the particle phase via gas-phase or aqueous phase reactions (Schmale et al., 2019). Dataset contents <ul> <li>raw_chl_SO4_mz_55_57_manual_with_flags.csv, data file, comma-separated values</li> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> <li>calibration_info.csv, metadata, comma-separated values</li> </ul>

opencc-by-4.0Aug 2018View details →

INTERACT-II (INTERcomparison of Aerosol and Cloud Tracking - II)

Following the previous efforts of INTERACT (INTERcomparison of Aerosol and Cloud Tracking), the INTERACT-II campaign used multi-wavelength Raman lidar measurements to assess the performance of an automatic compact micro-pulse lidar (MiniMPL) and two ceilometers (CL51 and CS135) in providing reliable information about optical and geometric atmospheric aerosol properties. The campaign took place at the CNR-IMAA Atmospheric Observatory (760 ma.s.l.; 40.60∘ N, 15.72∘ E) in the framework of ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 project. Co-located simultaneous measurements involving a MiniMPL, two ceilometers and two EARLINET multi-wavelength Raman lidars were performed from July to December 2016. All the data from the CIAO lidars, the MiniMPL and from theCHM15k, CS135 and the CT25K ceilometers, operating collocated and simultaneously during the INTERACT-II campaign, are provided here. Additional files for the correction of the MiniMPL incomplere overlap are also provided. The results of the campaign are described in detail in Madonna et al., 2018 (<a href="https://amt.copernicus.org/articles/11/2459/2018/">https://amt.copernicus.org/articles/11/2459/201</a>8/).

opencc-zeroOct 2023View details →

Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)

This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (Atmos. Chem. Phys., 2016). For details see the README.md file.

opencc-zeroOct 2023View details →

Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 1: Land transport and shipping" (Righi et al., Atmos. Chem. Phys., 2015)

This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (Atmos. Chem. Phys., 2015). For details see the README.md file.

opencc-by-4.0Oct 2023View details →

Sea Salt Aerosol Datasets

This collection contains data necessary to duplicate the plots found in Ackerman et al. (2023). Woodcock csv files come from the manual interpretation of Woodcock (1953). (Woodcock, A. H.: Salt nuclei in marine air as a function of altitude and wind force, J. Atmos. Sci., 10, 362–371, 1953.) Ackerman, K. L., Nugent, A. D., and Taing, C.: Mechanisms controlling giant sea salt aerosol size distributions along a tropical orographic coastline, Atmos. Chem. Phys. <a href="https://doi.org/10.5194/acp-23-13735-2023">https://doi.org/10.5194/acp-23-13735-2023</a>

opencc-by-4.0Dec 2023View details →

Meteograms of Ny-Ålesund for ICON-LEM maritime aerosols simulations

This data contains the simulation data as meteogram from ICON-LEM simulations with ca. 600 m resolution. The output location is Ny-Ålesund. The data is for the months Aug and Oct 2021. This data was used in the PhD thesis of Theresa Kiszler. Thesis title: "Improving our understanding of cloud phase-partitioning using long-term cloud-resolving simulations of Svalbard". The original simulation setup is is described in the method section of the paper "A Performance Baseline for the Representation of Clouds and Humidity in Cloud-Resolving ICON-LEM Simulations in the Arctic" by Kiszler et al. (2023).  <a href="https://doi.org/10.1029/2022MS003299">https://doi.org/10.1029/2022MS003299</a> The following adaptation has been made to the simulation settings: The CCN activation is based on a version by Segal and Khain (2006) using the lowest possible number concentration, i.e. maritime aerosols. The INP nucleation follows the paper by Phillips et al. (2008) only using dust as aerosol. The implementation of the mentioned schemes was not done by us, only the settings were changed to use these schemes instead of the default version.

opencc-by-4.0Sep 2024View details →

Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"

The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description.    <h2> </h2>

opencc-by-4.0Nov 2021View details →

Single aerosol measurements from a wideband integrated bioaerosol sensor, collected during the Antarctic Circumnavigation Expedition (ACE).

Dataset abstract This data set contains the time series of single particle data, measured by the wideband integrated bioaerosol sensor (WIBS-4, University of Hertfordshire, Hatfield, UK), during the Antarctic Circumnavigation Expedition (ACE), which was conducted between 20th of December 2016 and 19th of March 2017. WIBS provides aerosol optical diameter (5 μm - 14 μm), asymmetry factor and fluorescent signals on three different channels. WIBS measures single aerosol particles at a sampling rate of 125 Hz. More technical details about WIBS could be found in Kaye et al. (2005). Dataset contents <ul> <li>part_1_Cape_Town_Kerguelen.csv, data file, comma-separated values</li> <li>part_2_Kerguelen_Hobart.csv, data file, comma-separated values</li> <li>part_3_Hobart_Mertz.csv, data file, comma-separated values</li> <li>part_4_Mertz_Punta Arenas.csv, data file, comma-separated values</li> <li>part_5_Punta_Arenas_Cape_Town.csv, data file, comma-separated value</li> <li>part_6_Cape_Town_Bremerhaven.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> Dataset license This aerosol measurement dataset collected using a WIBS during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Jul 2021View details →

Number concentration and fluorescent class fraction of fluorescent and hyper-fluorescent aerosol particles measured during the Antarctic Circumnavigation Expedition

Dataset abstract This dataset consists of a 5-minute time series of number concentrations of fluorescent and total aerosol particles that were measured by wideband integrated by aerosol sensor during the Antarctic Circumnavigation Expedition in the austral summer of 2016/2017. Furthermore, the dataset includes the fraction of fluorescent classes of aerosol particles, according to the ABC classification of Perring et al. (2015). The dataset provides information on fluorescent and hyper-fluorescent aerosols which were obtained by considering low and high fluorescence thresholds. For this dataset, aerosol particles that have optical diameter greater than 1 μm are considered. Since the ship’s exhaust could considerably affect the fluorescence properties of aerosol particles, in this dataset we removed the periods where it is likely that the samples were contaminated by the ship's exhaust. Dataset contents <ul> <li>N_fluorescent.csv, data file, comma-separated values</li> <li>N_hyper_fluorescent.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> Dataset license This number concentration and fluorescent class fraction of fluorescent and hyper-fluorescent aerosol particles dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Jul 2019View details →

Data for the 'Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation'

All numerical data used in the manuscript “Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation” 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. These data files have been created by G. S. Fanourgakis (fanourg@uoc.gr) 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> ) For an in-depth understanding of the description below, a study of the above mentioned manuscript is required. (A) Station model results The station results can be found in files with filenames of the form: station $MODEL.nc The “$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. 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: st $STATION $FIELDhour st $STATION $FIELD month 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 st Finokalia CCN02 hour found in the file station_TM4-ECPL.nc, contains the hourly values of the CCN0.2 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. st Vavihill DU month 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) (B) Station observational results 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 station observations.nc while the following fields are contained in there: st $STATION $FIELDhour st $STATION $FIELD month 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). (C) Station Multi-model Median Monthly averages of the models can be found in the file station MMM.nc The following fields can be found in the file st $STATION $FIELD month median st $STATION$FIELD month quart25 st $STATION$FIELD month quart75 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. (D) Global model results In the following single file can be found for each of the models the surface distribution of various fields. results global models year2011.nc They correspond to the annual mean of the year 2011. The resolution of the grid is 1◦ × 1◦. The file contains the following variables: $FIELD $MODEL The $FIELD and $MODEL can be found in Tables 3 and 1, respectively. (E) Global average results In the file surface_ global_average_year2011.nc can be found in 5◦×5◦ 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: med $FIELD div $FIELD where, ‘med’ stands for median and ‘div’ for diversity calculated as standard deviation divided by the mean of the model results. Tables and details on the fields provided are given in the readme file.

opencc-by-4.0Aug 2024View details →

A European aerosol phenomenology – 9: LIGHT ABSORPTION PROPERTIES OF CARBONACEOUS AEROSOL PARTICLES ACROSS SURFACE EUROPE

Carbonaceous aerosols (CA), composed of black carbon (BC) and organic aerosols (OA), exert an important role on the climate system through their interaction with solar radiation. Light absorption properties of CA particles are of special interest due to their important contribution to global and regional warming. Among atmospheric particulate matter (PM), BC and the absorbing components of OA (or brown carbon, BrC) are characterized by the highest absorption efficiency but their role in the current climate change, especially that of BrC, is still uncertain. Here we present the absorption properties of BC and BrC PM at 44 sites across Europe using aethalometer data collected at different types of environment (6 traffic (TR), 16 urban (UB), 7 suburban (SUB), 10 regional background (RB) and 5 mountain (M) sites). The absorption Ångström exponent (AAE) method was used to assign total measured absorption to the contributions of BC (bAbs,BC) and BrC (bAbs,BrC) to total absorption (bAbs). The results showed a clear dependence of the absorption coefficients bAbs, bAbs,BC and bAbs,BrC on station settings as follows: TR > UB > SUB > RB > M, even if significant exceptions were observed. The relative contribution of bAbs,BrC to bAbs (%AbsBrC) at 370 nm was on average lower at traffic sites (11-20%) reaching at some SUB and RB sites median annual values that accounted for more than 30% and 10% of the absorption at 370 and 660 nm, respectively. The median AAE of CA particles was correspondingly low at TR sites (1.1-1.2) where internal combustion engines dominated the CA mass concentration. Low AAE were also observed at some remote RB and M sites, likely due to the lack of proximity from BrC sources or lack of sufficiently strong secondary processes resulting in BrC. On average, AAE was lower in Western Europe (<1.3) compared to Eastern Europe (>1.3), likely due to a more extensive use of coal and biomass burning in eastern countries. The median AAE of BrC PM (AAEBrC) showed a wide range of values, from 2.5 to 6, with no clear relationship with station background or region. Assessing the seasonal variability revealed, overall, an increase of bAbs, bAbs,BC, bAbs,BrC in winter, which was attributed to meteorological conditions and more heating related emissions. Accordingly, bAbs,BrC exhibited a stronger increase than bAbs,BC, resulting in higher AAE and %AbsBrC during the winter season. The diel cycles differed between bAbs,BC and bAbs,BrC, with bAbs,BC showing the bimodal peaks during the morning and evening rush hours, whereas bAbs,BrC, together with %AbsBrC, AAE and AAEBrC, peaked at night. Decade-long trend analysis performed for a subset of stations across Europe revealed a decrease of bAbs, driven by declining bAbs,BC, whereas, overall, bAbs,BrC, %AbsBrC and AAE increased with time. This strongly implies an efficient reduction of BC mass concentrations from traffic sources in Europe and a less effective reduction of emissions from BrC sources. The observed increasing trends of AAE reflected a progressive change in the chemical composition of CA particles driven by a relative increase/decrease of BrC/BC content in CA with time.

opencc-by-4.0Sep 2021View details →

Data supporting the study "An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021))

Data supporting the figures and findings presented in the study "An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021), Atmos. Chem. Phys..