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119 results for “atmospheric simulation”
Simulation Files for Organic Contaminants and Atmospheric Nitrogen at the Graphene–Water Interface
<p>This data set provides files needed to run the simulations described in the manuscript entitled "Organic contaminants and atmospheric nitrogen at the graphene–water interface: A simulation study" using the molecular dynamics software NAMD and LAMMPS. The output of the simulations, as well as scripts used to analyze this output, are also included. The files are organized into directories corresponding to the figures of the main text and supplementary information. They include molecular model structure files (NAMD psf), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD or LAMMPS configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and some trajectories in dcd format (downsampled). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts. A modified LAMMPS C++ source file is also included.</p>
Simulation data on the growth of atmospheric molecular clusters and particles
<p>This data set contains output data from cluster population simulations performed with Atmospheric Cluster Dynamics Code (ACDC) model, which simulates the formation of clusters from atmospheric vapors and the growth of these clusters by further molecular and cluster-cluster collisions. The data can be used for investigating the formation and growth of atmospheric particles from inorganic and organic vapors.</p> <p>The data is output of a computational process model, and hence does not represent a specific time period or location. Simulation sets are calculated for a one or two-component system containing a quasi-unary inorganic compound representing a mixture of sulfuric acid and ammonia (SA) and/or oxidized organic vapors corresponding to a low volatility organic compound (LVOC) and an extremely-low volatility organic compound (ELVOC). The external conditions in the simulations correspond to those in the CLOUD (Cosmics Leaving Outdoor Droplets) chamber at temperature of 5 C°.</p> <p>Data are provided for 14 simulations.</p> <p><strong>References</strong></p> <p>Kontkanen J, Stolzenburg D, Olenius T, Yan C, Dada L, Ahonen L, Simon M, Lehtipalo K, Riipinen I (2022) What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?. Environ. sci. Atmos. <a href="https://doi:10.1039/d1ea00103e">https://doi:10.1039/d1ea00103e</a> </p> <p>Olenius T, Riipinen I (2017) Molecular-resolution simulations of new particle formation: Evaluation of common assumptions made in describing nucleation in aerosol dynamics models. Aerosol Sci. Tech. 51:397 – 408. <a href="https://doi.org/10.1080/02786826.2016.1262530">https://doi.org/10.1080/02786826.2016.1262530</a></p> <p>Olenius T, Atmospheric Cluster Dynamics Code. <a href="https://github.com/tolenius/ACDC">https://github.com/tolenius/ACDC</a> </p> <p>McGrath MJ et al. (2012) Atmospheric Cluster Dynamics Code: a flexible method for solution of the birth-death equations. Atmos. Chem. Phys. 12:2345 – 2355. <a href="https://doi.org/10.5194/acp-12-2345-2012">https://doi.org/10.5194/acp-12-2345-2012</a></p> <p><strong>Data description</strong></p> <p>The data is in the form of text files. The provided data files (total compressed size ~10GB) correspond to simulation output from the ACDC model. Simulation sets are shown in the table below and further described in Kontkanen et al. (2022). For the interpretation of the model output, the interested user is referred to the manual of ACDC model (<a href="https://github.com/tolenius/ACDC">https://github.com/tolenius/ACDC</a>). </p> <table align="left"> <tbody> <tr> <td> <p>Simulation set</p> </td> <td> <p>Model compounds</p> </td> <td> <p>Vapor concentrations (cm<sup>-3</sup>)</p> </td> <td> <p>Method to retrieve evaporation rates</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>SA</p> </td> <td> <p><em>C</em><sub>SA </sub>= 8.0*10<sup>6</sup>, 2.0*10<sup>7</sup>, 4.7*10<sup>7</sup>, 1.1*10<sup>8</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>SA</p> </td> <td> <p><em>C</em><sub>SA </sub>= 2.0*10<sup>7</sup>, 4.7*10<sup>7</sup>, 1.1*10<sup>8</sup></p> </td> <td> <p>QC data and Kelvin eq.<br> <em>(non-classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>LVOC</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 5.0*10<sup>7</sup>, 1*10<sup>8</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>LVOC,<br> ELVOC</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 5.0*10<sup>7</sup>, 1*10<sup>8</sup><br> <em>C</em><sub>ELVOC </sub>= 1.0 *10<sup>7</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>LVOC,<br> SA</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 2.0*10<sup>7</sup>, 5.0*10<sup>7</sup>, 1*10<sup>8</sup><br> <em>C</em><sub>SA </sub>= 8.0*10<sup>6</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> </tbody> </table> <p> </p>
Analysis of regional CO2 contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ13C
<p>The data set complementary to manuscript "Analysis of regional CO<sub>2</sub> contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ<sup>13</sup>C" in <em>Atmospheric Chemistry and Physics</em> (<a href="https://acp.copernicus.org">https://acp.copernicus.org</a>).</p>
Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters
<p>This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters.</p> <p>Contact: Yohei Yamada (yoheiy@jamstec.go.jp)</p>
Relevant data for publication 'Atmospheric phosphorus deposition amplifies carbon sinks in simulations of a tropical forest in Central Africa' Goll et al.
<p>Plotting scripts and processed output from ORCHIDEE-CNP. The version of ORCHIDEE is available here: https://doi.org/10.14768/391825ae-d257-4365-9820-30ea1940914c</p>
NFFA-Europe|Pilot proporsal "NANO meets ASTRO: simulating the formation of silicon oxide nanoparticles in the atmosphere of dying stars" (PID: 140).
<p>XPS, IRRAS, QMS and OES data of the nanoparticles synthesized within the NFFA-Europe|Pilot proporsal "NANO meets ASTRO: simulating the formation of silicon oxide nanoparticles in the atmosphere of dying stars" (PID: 140).</p>
Data for Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing
<p>Data for<em> Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing </em>(https://doi.org/10.5194/acp-2022-484)</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Atmospheric CO2 simulations over Indian sites using STILT driven by WRF meteorology.
<p>This data contains atmospheric CO2 simulations at a temporal resolution of 3 hours over 15 Indian sites using a Lagrangian transport model, STILT driven by WRF meteorology during May 2017.</p> <p>Note: You are encouraged to contact the creator before using this data in any presentation or publication. </p> <p>Reference:</p> <p>Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, Saradambal Lekshmi, Gokul Udayakumar, Thara Anna Mathew, Aparnna Ravi, Manoj M G. How critical is the accuracy of the atmospheric transport modelling to improve the urban CO2 emission in India? - A Lagrangian-based approach. (2024), JGR Atmospheres. [Under review]</p> <p> </p>
Atmospheric CO2 simulations over Indian sites using STILT driven by ECMWF meteorology.
<p>This data contains atmospheric CO2 simulations at a temporal resolution of 3 hours over 15 Indian sites using a Lagrangian transport model, STILT driven by ECMWF meteorology during May 2017.</p> <p>Note: You are encouraged to contact the creator before using this data in any presentation or publication. </p> <p>Reference:</p> <p>Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, Saradambal Lekshmi, Gokul Udayakumar, Thara Anna Mathew, Aparnna Ravi, Manoj M G. How critical is the accuracy of the atmospheric transport modelling to improve the urban CO2 emission in India? - A Lagrangian-based approach. (2024), JGR Atmospheres. [Under review]</p> <p> </p>
Input data for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019.
<p>This dataset provides input data (fluxes, background concentrations, and observations) for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019 using chemical transport models (CTMs). While some components of the dataset are available in other repositories, this compilation serves to 1) streamline the data collection process for other users and 2) bypass the need to perform data aggregation.</p> <p>Here is a description of each dataset:</p> <p><strong>cams73_latest_co2_conc_surface_inst_2019*.nc</strong></p> <p>CO2 mole fractions from the CAMS global inversion-optimised product v20r2 (Chevallier et al., 2010).</p> <p>The data are provided at a resolution of 3.75° in longitude and 1.9° in latitude, with a 3-hourly temporal resolution. </p> <p><strong>monitor_CO2_CIF_2019.nc</strong></p> <div> <div> <div> <div> <div> <div> <p>Observed CO2 atmospheric mixing ratios in Europe, compiled in version V8 of the ICOS GlobalView Obspack (ICOS RI et al., 2023), include continuous measurements from 58 stations across Europe, incorporating both ICOS and non-ICOS facilities.</p> <p>The original dataset has been aggregated and adapted to match the format of the monitor files used in the Community Inversion Framework (CIF; Berchet et al., 2021).</p> </div> </div> </div> </div> </div> </div> <p><strong>EDGARv4.3_BP2021_CO2_EU2_2019.nc</strong></p> <p>Anthropogenic CO2 fluxes (European, hourly) obtained from EDGAR-v4.2 and BP.</p> <p>The anthropogenic CO2 emissions are based on the spatial distribution from the EDGAR-v4.2 inventory, national and annual budgets from British Petroleum (BP) statistics, and hourly temporal profiles derived using the COFFEE approach (Steinbach et al., 2011, available on the ICOS Carbon Portal). This data is provided at a 0.1° × 0.1° horizontal resolution and hourly temporal resolution.</p> <p><strong>FG2.TRENDY11.ORC3.S3.3H_NBP_resp_2019.nc</strong></p> <p>NBP CO2 fluxes (global, 3-hourly) obtained from ORCHIDEE simulations. </p> <p>The ORCHIDEE-TRENDY simulation is conducted as part of the TRENDY model intercomparison project (e.g., Sitch et al., 2015; Friedlingstein et al., 2022). This simulation uses inputs provided by the project, including the CRUERA atmospheric climate forcing (global, 6-hourly, 0.5-degree resolution), LUH2 land-use change dataset, global atmospheric CO2 concentration data, and nitrogen fertilizer input datasets. All TRENDY simulations adhere to a standardized protocol: a model spin-up phase using recycled forcing data from 1901-1920, with other inputs from 1700, continues until the model's carbon pools reach equilibrium (340 years of spin-up for ORCHIDEE). This is followed by a transient simulation from 1700-1900, varying CO2 and land-use data while recycling climate forcing, and a historical simulation from 1901-2020 with all data inputs varied.</p> <p><strong>FR2.ORC3v7267.CRUERA3.NBP_3H.2019.nc</strong></p> <p>NBP CO2 fluxes (Europe, 3-hourly) obtained from ORCHIDEE simulations. </p> <p>The ORCHIDEE-VERIFY simulation is performed as part of the VERIFY project over the European region. This simulation is driven by the CRUERA dataset, which is derived from the ERA5-Land dataset (originally global, 1-hourly, at 0.1-degree resolution), transformed to the VERIFY region of interest (35°N to 73°N, 25°W to 45°E, 3-hourly, at 0.125-degree resolution), and re-aligned with the CRU observation dataset (for air temperature, shortwave radiation, humidity, and precipitation). The Hilda+ dataset is used for land use, and the EMEP model outputs are used for nitrogen inputs. The VERIFY simulation follows the general protocol used in the TRENDY project.</p> <p><strong>FR2.ORC3v7267.CRUERA3.hetero_resp_3H.2019.nc</strong></p> <p>Heterotrophic respiration CO2 fluxes (Europe, 3-hourly) obtained from ORCHIDEE simulations as described in the previous section.</p> <p><strong>Becker_coastal_fluxes_RF_v2021_2_2019.nc</strong></p> <p>Ocean CO2 fluxes (Europe, daily). </p> <p>The ocean fluxes come from a hybrid product combining the University of Bergen coastal ocean flux estimate and the Rödenbeck global ocean estimate (Rödenbeck et al., 2014). This data is provided at a 0.125° × 0.125° horizontal resolution and at a daily temporal resolution.</p> <p> </p> <p><em><strong>References</strong></em> </p> <p> </p> <p>Berchet, A., Sollum, E., Pison, I., Thompson, R. L., Thanwerdas, J., Fortems-Cheiney, A., Peet, J. C. A. v., Potier, E., Chevallier, F., Broquet, G., and Berchet, A.: The Community Inversion Framework: codes and documentation, https://doi.org/10.5281/zenodo.6304912, 2022</p> <p>Chevallier, F., Ciais, P., Conway, T. J., Aalto, T., Anderson, B. E., Bousquet, P., Brunke, E. G., Ciattaglia, L., Esaki, Y., Fröhlich, M., Gomez, A., Gomez-Pelaez, A. J., Haszpra, L., Krummel, P. B., Langenfelds, R. L., Leuenberger, M., Machida, T., Maignan, F., Matsueda, H., Morguí, J. A., Mukai, H., Nakazawa, T., Peylin, P., Ramonet, M., Rivier, L., Sawa, Y., Schmidt, M., Steele, L. P., Vay, S. A., Vermeulen, A. T., Wofsy, S., and Worthy, D.: CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements, Journal of Geophysical Research: Atmospheres, 115, https://doi.org/10.1029/2010JD013887, 2010</p> <p>Friedlingstein, P., O’Sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I. T., Olsen, A., Peters, G. P.,Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Alkama, R., Arneth, A., Arora,V. K., Bates, N. R., Becker, M., Bellouin, N., Bittig, H. C., Bopp, L., Chevallier, F., Chini, L. P., Cronin, M., Evans, W., Falk, S., Feely, R. A., Gasser, T., Gehlen, M., Gkritzalis, T., Gloege, L., Grassi, G., Gruber, N., Gürses, O., Harris, I., Hefner, M., Houghton, R. A.,Hurtt, G. C., Iida, Y., Ilyina, T., Jain, A. K., Jersild, A., Kadono, K., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken,J. I., Landschützer, P., Lefèvre, N., Lindsay, K., Liu, J., Liu, Z., Marland, G., Mayot, N., McGrath, M. J., Metzl, N., Monacci, N. M.,Munro, D. R., Nakaoka, S.-I., Niwa, Y., O’Brien, K., Ono, T., Palmer, P. I., Pan, N., Pierrot, D., Pocock, K., Poulter, B., Resplandy, L.,Robertson, E., Rödenbeck, C., Rodriguez, C., Rosan, T. M., Schwinger, J., Séférian, R., Shutler, J. D., Skjelvan, I., Steinhoff, T., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tanhua, T., Tans, P. P., Tian, X., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf,G. R., Walker, A. P., Wanninkhof, R., Whitehead, C., Willstrand Wranne, A., Wright, R., Yuan, W., Yue, C., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2022, Earth System Science Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022,https://essd.copernicus.org/articles/14/4811/2022/, publisher: Copernicus GmbH, 2022</p> <p>ICOS RI, Bergamaschi, P., Colomb, A., De Mazière, M., Emmenegger, L., Kubistin, D., Lehner, I., Lehtinen, K., Lund Myhre, C., Marek, M., Platt, S. M., Plaß-Dülmer, C., Schmidt, M., Apadula, F., Arnold, S., Blanc, P.-E., Brunner, D., Chen, H., Chmura, L., Conil, S., Couret, C., Cristofanelli, P., Delmotte, M., Forster, G., Frumau, A., Gheusi, F., Hammer, S., Haszpra, L., Heliasz, M., Henne, S., Hoheisel, A., Kneuer, T., Laurila, T., Leskinen, A., Leuenberger, M., Levin, I., Lindauer, M., Lopez, M., Lunder, C., Mammarella, I., Manca, G., Manning, A., Marklund, P., Martin, D., Meinhardt, F., Müller-Williams, J., Necki, J., O’Doherty, S., Ottosson-Löfvenius, M., Philippon, C., Piacentino, S., Pitt, J., Ramonet, M., Rivas-Soriano, P., Scheeren, B., Schumacher, M., Sha, M. K., Spain, G., Steinbacher, M., Sørensen, L. L., Vermeulen, A., Vítková, G., Xueref-Remy, I., di Sarra, A., Conen, F., Kazan, V., Roulet, Y.-A., Biermann, T., Heltai, D., Hensen, A., Hermansen, O., Komínková, K., Laurent, O., Levula, J., Pichon, J.-M., Smith, P., Stanley, K., Trisolino, P., ICOS Carbon Portal, ICOS Atmosphere Thematic Centre, ICOS Flask And Calibration Laboratory, and ICOS Central Radiocarbon Laboratory: European Obspack compilation of atmospheric carbon dioxide data from ICOS and non-ICOS European stations for the period 1972-2023;<br>obspack_co2_466_GLOBALVIEWplus_v8.0_2023-04-26, https://doi.org/10.18160/CEC4-CAGK, 2023</p> <p>Rödenbeck, C., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., Cassar, N., Reum, F., Keeling, R. F., and Heimann, M.: Interannual sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme, Biogeosciences, 11, 4599–4613, https://doi.org/10.5194/bg-11-4599-2014, 2014</p> <p>Sitch, S., Friedlingstein, P., Gruber, N., Jones, S. D., Murray-Tortarolo, G., Ahlström, A., Doney, S. C., Graven, H., Heinze, C., Huntingford,C., Levis, S., Levy, P. E., Lomas, M., Poulter, B., Viovy, N., Zaehle, S., Zeng, N., Arneth, A., Bonan, G., Bopp, L., Canadell, J. G.,Chevallier, F., Ciais, P., Ellis, R., Gloor, M., Peylin, P., Piao, S. L., Le Quéré, C., Smith, B., Zhu, Z., and Myneni, R.: Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://bg.copernicus.org/articles/12/653/2015/, publisher: Copernicus GmbH, 2015.</p> <p>Steinbach, J., Gerbig, C., Rödenbeck, C., Karstens, U., Minejima, C., and Mukai, H.: The CO2 release and Oxygen uptake from Fossil Fuel Emission Estimate (COFFEE) dataset: effects from varying oxidative ratios, Atmospheric Chemistry and Physics, 11, 6855–6870,1160 https://doi.org/10.5194/acp-11-6855-2011, 2011</p> <p> </p> <p> </p> <p> </p>
Data for: "Simulation of uranium plasma plume dynamics in atmospheric oxygen produced via femtosecond laser ablation"
<p>Data generated by 2D reactive, compressible, multi-species fluid model of uranium femtosecond laser ablation in an atmospheric oxygen environment. The dataset consists of a series of plain text tabular data files recorded at several time points during the simulation run. The data files are numbered according to the simulation time in nanoseconds (FFF-0100.txt is the data at 100 ns) and are given at intervals of 50 ns for the first 500 ns of simulation time, and every 100 ns thereafter, up to the total simulation time of 10000 ns. The initial conditions are provided in the first data file (FFF-0000.txt). Each data file contains spatially-resolved values of the fluid moments along with the molar concentrations of each species considered in the model (total of 30 species), given in a column format delimited by spaces.</p> <p>For details on the model implementation and simulation conditions, please refer to the associated manuscript.</p>
Data from: Numerical Simulation of the Atmospheric Signature of Artificial and Natural Seismic Events
<p>This data is related to the seismic hammer experiment discussed in "Numerical Simulation of the Atmospheric Signature of Artificial and Natural Seismic Events" by Martire et al. (2018, DOI will be added upon acceptance of the manuscript).</p> <p>The .zip file contains 3 .mseed files, and 1 .txt file. The .mseed are the raw seismometer signals. The .txt details the position of the sensor.</p> <p>Remaining data used in our paper can be found in the repository related to "Detection of Artificially Generated Seismic Signals using Balloon-borne Infrasound Sensor" by Krishnamoorthy et al. (2018, DOI 10.1002/2018GL077481). That repository has DOI 10.6084/m9.figshare.6137507.</p>
Global FLEXPART-ERA5 simulations using 30 million atmospheric parcels since 1980
<h2><strong>Abstract</strong></h2> <p>This database compiles the outputs of the global experiment performed with the Lagrangian particle dispersion model FLEXPART since 1980. The experiment was conducted using the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) and homogeneously dividing the atmosphere into 30 million particles. The database can be used to investigate global moisture and heat transport and to establish sink-source relationships.</p> <h2><strong>Input data</strong></h2> <p>The data employed for FLEXPART running was the ERA5 reanalysis dataset from the ECMWF (Hersbach et al., 2020). To feed the model, the input data was downloaded and pre-processed by using the software Flex_extract v7.1 (Tipka et al., 2020).</p> <p>The original available ERA5 resolution is 0.1-degree and 1-hour. For this experiment, ERA5 input data was retrieved for the global area (90ᵒS to 90ᵒN and 180ᵒW to 180ᵒE) at a 0.5-degree horizontal resolution for 137 level from the surface to 1 hPa and a 3-hour temporal resolution (00, 03, 06, 09, 12, 15,18 and 21 UTC).</p> <p>The data is stored in individual GRIB files for each time step, following the name criteria "EAYYMMDDHH". The size of each file is approximately 530 MB. The variables included in each file are: temperature, specific humidity, u- and v-wind components, Eta-coordinate vertical velocity, divergence, specific cloud liquid water content, specific cloud ice water content, and the logarithm of surface pressure on model levels; and 2m temperature an dew-point temperature, 10m u and v wind component, geopotential, land-sea mask, mean sea level pressure, snow depth, the standard deviation of orography, surface pressure, total cloud cover, convective precipitation, large-scale precipitation, surface sensitive heat flux, eastward and northward turbulent surface stress and surface net solar radiation at the surface level.</p> <h2><strong>Software and running</strong></h2> <p>The software used for the simulations is the Lagrangrian particle dispersion model FLEXPART on version 10.4 (Pisso et al., 2019). The software is configured for a global experiment, and the simulations were obtained from 1980 to the present with a temporal resolution of 3-h. For the experiment, 30 million particles were homogeneously distributed on the global area, and their trajectories were followed according to the model configuration specified in the COMMAND and RELEASES files. The complete period is distributed in individual annual experiments, with each annual experiment obtained continuously running the model from October of the previous year to December of that year.</p> <h2><strong>Outputs characteristics</strong></h2> <p>The outputs were stored in individual GRIB files for each time step, with the file name following the naming convention "partposit_YYYYMMDDHH". Each file has a size of 1,76 GB, and the total size of the annual experiment is 6 TB. Each file contains information about each particle of the experiment: the particle identification number (particle ID), the particle's position (latitude, longitude, and altitude), topographic height, potential vorticity, specific humidity, air density, atmospheric boundary layer height, and temperature. The file corresponding to the 1st January 2023 at 00UTC is provided in this repository as an example. Due to the size of each file, the complete dataset is accessible by personal contact (see <em>Data Access</em> section). </p> <h2><strong>Post-process and applications</strong></h2> <p>The dataset presented here allows for the analysis of moisture and heat transport in the atmosphere for any region of the world up to 3-h temporal resolution and different horizontal resolutions. The transport may be established between sources and sinks, both in a forward or backward tracking in time. Currently, two open-source post-processing options developed within the EPhyslab-UVigo group are available for the analysis of these data: TROVA (Fernadez-Alvarez et al., 2022) and LATTIN (Perez-Alarcón et al., 2024) with different moisture tracking calculation options, and the latter including tools for heat transport analysis. Both options allow different methodologies (those most widely used) for the moisture transport analysis. The studies can be configured for any region of the planet, specifying it by a NetCDF 2-D mask, and the moisture transport can be set for different time periods (from 1 to 15 days, being from 8 to 10 days the periods most commonly applied according to the mean residence time of water vapor in the atmosphere). For further discussion on the residence time of water vapor in the atmosphere and its application for Lagrangian studies see Gimeno et al. (2021) and Nieto and Gimeno (2019).</p> <h2><strong>Example of application</strong></h2> <p>J. C. Fernández-Álvarez, M. Vázquez, A. Pérez-Alarcón, R. Nieto, L. Gimeno (2023) Comparison of moisture sources and sinks estimated with different versions of FLEXPART and FLEXPART-WRF models forced with ECMWF reanalysis data, Journal of Hydrometeorology, doi: 10.1175/JHM-D-22-0018.1.</p> <p>A. Pérez-Alarcón, R. Sorí, M. Stojanovic, M. Vázquez, R.M. Trigo, R. Nieto, L. Gimeno (2024) Assessing the Increasing Frequency of Heat Waves in Cuba and Contributing Mechanisms, Earth Systems and Environment, DOI: 10.1007/s41748-024-00443-8</p> <h2><strong>Validation</strong></h2> <p>The moisture transport analysis provided by this dataset was validated by Fernández-Alvarez et al. (2023) through an in-depth comparison with different versions of the model, horizontal resolutions and input data, including the ERA-Interim reanalysis from the ECMWF, which has been widely used for this purpose over the past decades.</p> <h2><strong>Data Access</strong></h2> <p>Data access is available by contacting the EPhysLab group via: rnieto[at]uvigo.gal or l.gimeno[at]uvigo.gal</p>
Relative Random Errors in the Convective Atmospheric Boundary Layer Estimated by the Relaxed Filtering Method from Large Eddy Simulations
<p>Data supporting the paper "How representative are uncrewed aircraft system measurements of the convective boundary layer?" by Brian R. Greene, Leia M. Otterstatter, and Scott T. Salesky, submitted to Geophysical Research Letters in 2024. Data are postprocessed from large-eddy simulations of the convective atmospheric boundary layer that are used to produce the figures within the paper. Details on the production of these files are included in the supplementary informatin of this paper.</p>
Supplementary data for the manuscript entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" (JGR Atmospheres)
<p>This dataset contains additional material to reproduce the simulation and some of the figures of the manuscipt entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" in the Journal of Geophysical Reasseach - Atmospheres.</p>
Top-of-the-atmosphere radiative forcing by aerosol due to continuous OCS injection near the tropical tropopause simulated by EMAC
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figure 4.</p> <p>The dataset contains additional stratospheric aerosol forcing for injections of 6 Tg S a<sup>-1</sup> OCS for several years over 5 tropical cities at the tropopause (97 hPa) calculated with the EMAC (ECHAM5/MESSy Atmospheric Chemistry) CCM (e.g. Brühl et al., 2018; Schallock et al., 2023). Data are given for a four year time series starting in January 2017.</p> <p>- - - - - - - - - - - -</p> <p>File format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> time, 10 hourly, as 'day since 1997-01-01' (note that the two variables named time4 and time6 are identical)</p> <p>Parameters:</p> <p> SOLFORCCSO: instantaneous solar radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS were fixed to observations</p> <p> TOTFORCCSO: instantaneous total radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS were fixed to observations</p> <p> SOLFORCCSO_FREE: instantaneous solar radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS allowed to increase from downward transport</p> <p> TOTFORCCSO_FREE: instantaneous total radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS allowed to increase from downward transport</p> <p>- - - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Brühl, C., Schallock, J., Klingmüller, K., Robert, C., Bingen, C., Clarisse, L., Heckel, A., North, P., and Rieger, L.: Stratospheric aerosol radiative forcing simulated by the chemistry climate model EMAC using Aerosol CCI satellite data, Atmos. Chem. Phys., 18, 12845-12857, 10.5194/acp-18-12845-2018, 2018</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl<br> sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>Schallock, J., Brühl, C., Bingen, C., Höpfner, M., Rieger, L., and Lelieveld, J.: Reconstructing volcanic radiative forcing since 1990, using a comprehensive emission inventory and spatially resolved sulfur injections from satellite data in a chemistry climate model, Atmos. Chem. Phys., 23, 1169-1207, 10.5194/acp-23-1169-2023, 2023.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) , EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-268, 2023.</p>
Simple Biosphere model version 4.2 (SiB4) simulations for the present day atmosphere with 500 ppt OCS and the two OCS geoengineering scenarios with 4.8 ppb and 35.5 ppb OCS.
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figures 1 and 2.</p> <p>Simple Biosphere model version 4.2 (SiB4, Haynes et al., 2019; Sellers et al., 1986) was used to calculate (i) the average increase in evapotranspiration anticipated under an elevated OCS scenario for the years 2000-2021 on a 0.5 ° latitude x 0.5 ° longitude grid and (ii) OCS uptake by plants and soils, per month, at baseline (500 ppt) and elevated (4.8 and 35.5 ppb) OCS levels averaged over the years 2000-2021.</p> <p>- - - - - - - - - - -</p> <p><em>File 1: vonHobe_et_al_2023_CarbonylSulfideGeoengineeringScenarios_DeltaEvapotranspiration_GloballyGridded_SiB4.nc</em></p> <p>File Format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> latitude</p> <p> longitude</p> <p>Parameters:</p> <p> percent_diff_et: relative increase in % of evapotranspiration in a scenario where 20% of terrestrial plants exhibit a 50% increase in stomatal conductance under high OCS</p> <p>- - - - - -</p> <p><em>File 2: vonHobe_et_al_2023_CarbonylSulfideGeoengineeringScenarios_BiosphereUptake_MonthlyIntegrated_SiB4.csv</em></p> <p>File Format:</p> <p> comma delimited text file (.csv)</p> <p>Index Variable:</p> <p> time: monthly, format m/dd/yy</p> <p>Parameters:</p> <p> ocs_veg_base: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 500 ppt</p> <p> ocs_soil_base: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 500 ppt</p> <p> ocs_veg_4.8ppb: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocs_soil_4.8ppb: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocs_veg_35.5ppb: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 35.5 ppb</p> <p> ocs_soil_35.5ppb: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 35.5 ppb</p> <p>- - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Haynes, K. D., Baker, I. T., Denning, A. S., Stöckli, R., Schaefer, K., Lokupitiya, E. Y., and Haynes, J. M.: Representing<br> Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple<br> Biosphere Model (SiB4), Journal of Advances in Modeling Earth Systems, 11, 4423-4439, 10.1029/2018ms001540, 2019.</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>Sellers, P. J., Mintz, Y., Sud, Y. C., and Salcher, A.: A Simple Biosphere Model (SiB) for Use within General Circulation Models, Journal of the Atmospheric Sciences, 43, 505-531, 1986.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) ,</p>
Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Earth Syst. Dynam.</i>, 2023). For details see the README.md file.</p>
Simulated top-of-atmosphere (120 km) downward and upward solar and thermal-infrared irradiances and ice cloud optical thickness; calculated solar, TIR and net cloud radiative effect. Simulated with ice crystal properties for aggregates, droxtals, and plates based on Yang (2013).
<p>This dataset consists of three .nc files for ice crystal shapes of aggregates, plates, and droxtals. The files include ice cloud optical thickness <span class="math-tex">\(\tau\)</span> (550nm), the simulated upward and downward irradiances <span class="math-tex">\(F\)</span> at the top-of-atmosphere (with and without the presence of the ice cloud), and the calculated ice cloud radiative effect <span class="math-tex">\(\Delta F\)</span> (solar [0.3-3.5 <span class="math-tex">\(\mu\)</span>m], thermal-infrared [3.5-75 <span class="math-tex">\(\mu\)</span>m], and net). The data set allows the user to extract <span class="math-tex">\(\Delta F\)</span> values for their parameter combinations. The available cloudy and cloud-free irradiances further allow to calculate the cirrus radiative effect (RE) by scaling the 'cloudy' RE with the required cloud cover. This serves as a first-approximation because, as 3D effects are neglected.</p>
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
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