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122 results for “Emission Modelling”
Bern3D model output related to: Hysteresis of the Earth system under positive and negative CO2 emissions
<p>The data below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations used in Jeltsch-Thömmes et al., Environ. Res. Lett. 15 (2020) 124026, https://doi.org/10.1088/1748-9326/abc4af</p> <p><br> The data are provided as .csv and .nc files<br> There are different types of data</p> <p><br> 1) TIMESERIES DATA (Fig. 1 and 2)<br> =================================<br> The name of the files indicates the variable:<br> co2_ts.csv change in atm. co2 [ppm]<br> cumulativeEmissions_ts.csv cumulative emissions [GtC]<br> cumulativeAOflux_ts.csv cumulative atm-ocean C flux [GtC]<br> cumulativeABflux_ts.csv cumulative atm-land C flux [GtC]<br> sat_ts.csv change in surface air temperature [degC]<br> ohc_ts.csv change in ocean heat content [10^24 J]<br> amoc_ts.csv change in Atlantic meridional overturning circulation strength [Sv]<br> seaice_ts.csv fraction of pre-industrial sea-ice area remaining [fraction of PI]<br> <br> The first row in the .csv files contains the header, which indicates the experiment. The naming convention is as follows:<br> c4k#_###</p> <p>c4 indicates the maximum co2 as times pre-industrial (4 times)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> ### indicates the rate of CDR:<br> 010: 0.1% yr^-1<br> 010: 0.3% yr^-1<br> 010: 0.5% yr^-1<br> 010: 0.7% yr^-1<br> 100: 1% yr^-1<br> 200: 2% yr^-1<br> 400: 4% yr^-1<br> 600: 6% yr^-1</p> <p><br> 2) HYSTERESIS DATA (Fig. 3)<br> ===========================<br> The name of the files indicates the variables:<br> cumulativeEmissions_sat.csv cumulative emissions and change in surface air temperature [degC]<br> cumulativeEmissions_OHCsurf.csv cumulative emissions and change in upper ocean heat content (0-700 m) [10^24 J]<br> cumulativeEmissions_o2thermo.csv cumulative emissions and change in thermocline (200-600 m) o2 [mmol m^-3]<br> cumulativeEmissions_OM_arag.csv cumulative emissions and fraction of water in the uppermost 175 m with omegar_aragonite saturation state >3 [fraction]</p> <p>each file contains the time (simulation year) as well as cumulative emissions (cumuEmis) and the respective variable (same naming as in filename) for all the experiments (see timeseries data for naming convention)</p> <p><br> 3) SPATIAL DATA (Fig. 4 and 5)<br> ==============================<br> All data for Fig. 4 and 5 are contained in one single .nc file (fig4_5_data.nc) with a varibale for each map shown in Fig. 4 and 5:<br> c4k2_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=2 degC, in [degC]<br> c4k3_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=3 degC, in [degC]<br> c4k5_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=5 degC, in [degC]<br> <br> c4k2_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=2 degC, in [mmol m^-3]<br> c4k3_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=3 degC, in [mmol m^-3]<br> c4k5_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=5 degC, in [mmol m^-3]<br> <br> c4k3_100_Om_arag_up mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the up-path, ECS=3 degC, [unitless]<br> c4k3_100_Om_arag_do mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the down-path, ECS=3 degC, [unitless]</p> <p> </p> <p> </p> <p><br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename')</p> <p><br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch</p>
Dataset for "Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux"
<p>This dataset provides measured and upscaled forest floor methane (CH4) fluxes and soil moisture.</p> <p>This dataset is related to the following manuscript:</p> <p>Vainio et al., Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux, Biogeosciences, in review. (The discussion preprint is available at https://doi.org/10.5194/bg-2020-263.)</p>
Daily European biospheric methane emissions estimated with the ecosystem model JSBACH-HIMMELI.
<p>Daily estimates of European biospheric methane emissions from JSBACH-HIMMELI model from year 1990 to year 2023. JSBACH-HIMMELI is an ecosystem process model based on JSBACH land surface model, YASSO soil carbon model and HIMMELI methane emission model. The gridded fluxes are available with a resolution of 0.1x0.1 degrees and in units of mol m-2 s-1 (m-2 refers to grid cell area). The gridded flux file contains a variable for methane fluxes, including a sum of methane fluxes from peatlands, inundated lands and mineral soils. More information of the model set-up is documented in Petrescu, A. M. R., et al., The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019, Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, 2023, Tyystjärvi, V., 2024. Future methane fluxes of peatlands are controlled by management practices and fluctuations in hydrological conditions due to climatic variability. EGUsphere 1–37. https://doi.org/10.5194/egusphere-2023-3037 and Raivonen, M. et al., 2017. HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands. Geoscientific Model Development 10, 4665–4691. <a href="https://doi.org/10.5194/gmd-10-4665-2017">https://doi.org/10.5194/gmd-10-4665-2017</a></p>
Modelling CO2 emissions of cultivated and rewetted peat soils with SWAP-ANIMO - Dataset
<p><span>Three locations in Europe (wet river valley (Denmark), coastal peatland (The Netherlands) and broad river floodplain (Switzerland)) were selected for which two to three years of measurements of hydrological variables and CO2 exchange fluxes were available for some period between 2015 and 2023. The hydrology, grass growth and CO2 fluxes of these sites were modelled with the SWAP-ANIMO model using the available measurement period for model input and calibration. Model simulations were used to improve the understanding of the hydrological drivers of each site and to obtain estimates of the different pools contributing to the measured CO2 fluxes using a period of 10 years (2014-2023). Rewetting was considered either by calibration on direct measurements of an actual rewetting measure (Denmark, The Netherlands) or extrapolation of the reference simulation (Switzerland). Also, the potential impact of climate change on the rate of peat oxidation was modelled for these sites for both the reference and rewetting measure. </span></p> <p><span>The dataset contains the relevant detailed, daily model output of the 10 year simulation period and aggregated, yearly model output of the scenario simulations which are detailed in the corresponding report (van de Craats et al., 2024), available at <a href="https://doi.org/10.5281/zenodo.14041243">https://doi.org/10.5281/zenodo.14041243</a>.</span></p>
PACT-1D model version including polar halogen emissions - output files
<p>This dataset includes all output files created from the PACT-1D model (v1.0) developed with Arctic chlorine and bromine emission parameterizations. The PACT-1D source code used to create these output files is available at: https://doi.org/10.5281/zenodo.5654589.</p> <p> </p>
Estimating drivers and pathways for hydroelectric reservoir methane emissions using a new mechanistic model (estimated methane emissions for hydropower reservoir surfaces and potential dam emissions)
<p>Methane emissions data from hydropower reservoir surfaces and dams, as estimated with the ResME model. Emissions estimates available for hydropower reservoirs in the GRanD database (Lehner et al., 2011). </p> <p> </p> <p>References:</p> <p>Lehner, B., Liermann, C. Reidy, Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., and Wisser, D. (2011). High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers in Ecology and the Environment, 9 (9): 494-502. https://doi.org/10.1890/100125.</p>
Extended Files for "Lessons from Hubble & Spitzer: 1D Self-Consistent Model Grids for 19 Hot Jupiter Emission Spectra"
<p>This directory includes extended files for Wiser et al. 2024, "Lessons from Hubble & Spitzer: 1D Self-Consistent Model Grids for 19 Hot Jupiter Emission Spectra." </p> <p>Files:</p> <ul> <li><strong>Extended Planet Figures:</strong> For each of the 20 planets discussed in the manuscript, <em>[PlanetName].pdf </em>includes figures showing the secondary eclipse spectra and parameter estimations for each model scenario. A file for Kepler-13Ab illustrates our grid models' inability to explain the WFC3 and Spitzer observations simultaneously. </li> <li><strong>Internal Temperature Tests:</strong><em> InternalTemperatureTests.pdf</em> includes figures illustrating our inability to constrain an atmosphere's internal temperature with these model grids and the WFC3 and Spitzer observations.</li> <li><strong>Parameter Estimates .csv Tables:</strong> <ul> <li>Both .csv files include planet and star parameters (temperatures, radii, mass, logg, semimajor axis) and parameter ranges for each planet grid. They also include parameter estimations for each model scenario. Listed are the medians of each parameter's posterior probability distribution and pos/neg values encompassing the one-sigma confidence region. This information is shown visually in the extended planet figures. </li> <li><em>fiducial_stats.csv</em> includes parameter estimations for the fiducial model scenario. For planets with multiple solutions (as described in Wiser et al. 2024), multiple rows detail each solution. There is also a "limit" flag for parameters with an upper limit, lower limit, or unconstrained (UL, LL, or UC, respectively).</li> <li><em>nonfiducial_stats.csv </em>includes parameter estimates for all other model scenarios. This table does not account for multiple solutions or "limit" flags. </li> </ul> </li> <li>For complete model grids, please contact the authors. </li> </ul>
Dataset for "Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions"
<p>This dataset is a part of the paper "Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions", accepted for publication in the Journal of Advances in Modeling Earth Systems (JAMES).</p> <h2>Contents</h2> <p>This dataset includes:</p> <ul> <li><strong>lhs-gen-190.csv</strong>: Training input LHS samples generated by <code>lhsgen.py</code>.</li> <li><strong>lhs-gen-50-test.csv</strong>: Test input LHS samples generated by <code>lhsgen.py</code>.</li> <li><strong>190-elm-samples.csv</strong>: Training input perturbed parameter samples for performing ELM simulations.</li> <li><strong>50-elm-test-samples.csv</strong>: Test input perturbed parameter samples for performing ELM simulations.</li> <li><strong>train_CH-CHA.csv</strong>: Contains the five ELM simulation output flux values for 240 samples (190 train + 50 test).</li> <li><strong>lhsgen.py</strong>: Script for generating Latin Hypercube Samples.</li> <li><strong>gpr-fit-new.py</strong>: Script for fitting Gaussian Process Regression (GPR) models.</li> <li><strong>sobol-new.py</strong>: Script for performing Sobol sensitivity analysis.</li> </ul> <h2>Usage</h2> <ol> <li><strong>lhsgen.py</strong>: <ul> <li>Use this script to generate the Latin Hypercube Samples for parameter sampling.</li> </ul> </li> <li><strong>gpr-fit-new.py</strong>: <ul> <li>This script fits GPR models using the training samples provided in <code>lhs-gen-190.csv</code>.</li> <li>It tests the models using the input testing samples in <code>lhs-gen-50-test.csv</code>.</li> <li>The fitted GPR models are stored as <code>.joblib</code> files in the <code>gpr_models</code> directory.</li> <li>Corresponding cross-validation and R-squared values are stored in <code>.xlsx</code> files.</li> </ul> </li> <li><strong>sobol-new.py</strong>: <ul> <li>This script performs Sobol sensitivity analysis using the fitted GPR models by reading the .joblib files.</li> <li>The Sobol indices are written to <code>.xlsx</code> files in the <code>results</code> directory.</li> </ul> </li> </ol>
Modeling the Emission of Energetic Neutral Atoms in Titan's Dynamic Magnetospheric Environment
<p>Data for the manuscript "Modeling the Emission of Energetic Neutral Atoms in Titan's Dynamic Magnetospheric Environment" by Tippens et al., (2024). See README.txt for a description of the data files included here.</p>
Fossil Fuel CO₂ Emissions for the OCO2 Model Intercomparison Project (MIP)
<p>These are fossil CO<sub>2</sub> fluxes updated through August 2024 for atmospheric CO<sub>2</sub> modeling. They were constructed primarily to be used for the OCO2 Model Intercomparison Project (MIP).</p> <ul> <li>For 2000-2022, they're based on <a href="https://db.cger.nies.go.jp/dataset/ODIAC/DL_odiac2023.html">ODIAC 2023</a>, which in turn uses BP's energy use statistics for 2021 and 2022.</li> <li>ODIAC monthly emissions have been disaggregated to hourly using the TIMES emission factors for day of week and time of day (<a href="https://urldefense.us/v3/__https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JD018196__;!!PvBDto6Hs4WbVuu7!YsQP_T-Vf3Fv83toql-90HY0NO5e92fR0D9kAi10tTzUd0Ugum9d3CTUMBp22qA0M-vYoU_fvd4$">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JD018196</a>).</li> <li>For 2023 onwards, ODIAC's 2022 emissions have been scaled by the ratio of that month to 2022 emissions reported by <a href="https://www.nature.com/articles/s41597-020-00708-7">Carbon Monitor</a>, downloaded on October 15, 2024 from <a href="https://carbonmonitor.org">https://carbonmonitor.org/</a>. <ul> <li>ODIAC does not have sectoral decomposition to the degree provided by Carbon Monitor, so total ODIAC emissions for each region have been scaled by the total emission change between 2022 and each extended year reported by Carbon Monitor, i.e., power, ground transport, etc. have <strong>not</strong> been separately scaled.</li> <li>Carbon Monitor data are daily, but ODIAC emissions are monthly. So Carbon Monitor data have been aggregated to monthly totals before deriving scaling factors between 2022 and the extended years.</li> <li>Carbon Monitor reports international aviation emissions by country of origin, while ODIAC reports aviation emissions on a grid. Since there is no way to derive the points of emission for Carbon Monitor aviation emissions , all Carbon Monitor international aviation was aggregated to create a single number for each month, then that number was used to scale ODIAC's bunker fuel for each month in 2023-2024.</li> <li>CarbonMonitor data used for deriving 2023 and later emissions are now included in this dataset for convenience as netcdf files (converted from original CSV files).</li> </ul> </li> <li>Hourly global totals are given in the files as a check, in case you want to verify your units and file reading.</li> </ul> <p>These files can be downloaded from the browser, or from the command line following guides such as <a href="https://ict.ipbes.net/ipbes-ict-guide/data-and-knowledge-management/technical-guidelines/zenodo#b.-programmatically-using-r" target="_blank" rel="noopener">this</a>.</p>
Paranal Airglow Line And Continuum Emission (PALACE) model: data and code of v1.0
<p>This data and code release is related to the article "PALACE v1.0: Paranal Airglow Line And Continuum Emission model", which has been published in Geoscientific Model Development, Vol. 18, 2025 (https://doi.org/10.5194/gmd-18-4353-2025).<br>The release consists of three .zip files:<br>PMD.zip: 436 ASCII files with data that were used to build and evaluate the model. More details in PMD/pmd_README.txt.<br>PALACE.zip: Python/Cython code for the calculation of the model. More details in PALACE/README.txt.<br>test.zip: Test output of the code for the default parameters (.dat file in alternative ASCII format).</p>
Tables and Data for "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado"
<p>These are data sets and tables used in the paper "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado" to be submitted to Earth and Space Science. The monitor site 2016 and 2017 counts files have gridded HYSPLIT back trajectory counts for the 4 highest ozone concentration days at each site, as described in the manuscript.</p>
Dataset: A unified modelling framework for projecting sectoral greenhouse gas emissions
<p>Data provided includes results from the unified framework described in "A unified modelling framework for projecting sectoral greenhouse gas emissions". Contains posterior draws of emission intensities and resulting emissions for 173 countries, five main sectors up to the year 2050. Historical GHG emissions data based on <a href="https://doi.org/10.5194/essd-13-5213-2021">Minx et al (2021)</a>.</p> <p>Code for the processing of results can be found on <a href="https://github.com/oDNAudio/GHG_sector_projections">Github</a>.</p>
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands
<p>This repository contains CLM5 model soil nitrogen (N2O, NO and NH3) output and metadata, and soil N<sub>2</sub>O fluxes from the Energy Farm ERW field trials used in the paper "Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands" (https://gmd.copernicus.org/preprints/gmd-2023-47/).</p>
The Styrene, Benzene, Toluene, Ethylbenzene and Xylenes (SBTEX) hourly gridding modeled emission and concentration in the U.S. Gulf region
<p>This Data includes three parts, the emission data, concentration data, and evaluation process for Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX). We did the imputation for the missing emissions in the 2011 National Emission Inventory (NEI) and used the SMOKE model system (<a href="https://www.cmascenter.org/smoke/">https://www.cmascenter.org/smoke/</a>) to generate the hourly gridding (12x12km) explicit emission data (2012_May_to_SEP_SBTEX_emission_12K.zip). Then, we drove the CTM model (Comprehensive Air Quality Model with Extensions, CAMx model <a href="https://www.camx.com/">https://www.camx.com/</a>) with flexi-nesting to (4x4km) and the reactive tracer method, which considers the chemical decay, physical transport, and dry/wet deposition processes, with the complete imputed spatiotemporal SBTEX emission data to simulate the individual hourly SBTEX concentration accurately. The concentration data (2012_May_to_SEP_SBTEX_concentration_4K.zip) is hourly gridding modeled concentration data in netCDF format for Gulf region states in the US for 2012 May to Sep. The concentration and emission data are in netCDF format and have two scenarios, original emission data (Base case) and complete imputed emission (Adj case). In the concentration data, BENZENE and BENZ are two variables to represent Benzene species; however, only "BENZ" in Adj case is recommended to use. The Adj case results are also recommended for other species. The file (Model_evaluation_support_document) has the codes for evaluating the model result with observation data <a href="https://www.epa.gov/amtic/amtic-ambient-monitoring-archive-haps">https://www.epa.gov/amtic/amtic-ambient-monitoring-archive-haps</a>. The SBTEX concentration can support any SBTEX-related human health studies in the Gulf region.In this release version (Aug. 30, 2023), we have included the complete 2012 SBTEX concentration data (Adj case) at a 4km x 4km resolution in three different formats: ioapi (2012_SBTEX_conc_ioapi.zip), netCDF-CF (2012_SBTEX_conc_NetCDF_CF.zip), and csv (2012_SBTEX_conc_Hourly_csv_data.zip). Additionally, we have provided the Readme files for both concentration and emission data: "Readme_for_2012_SBTEX_conc_4km_conc.txt" and "Readme_for_2012_emission_12km.txt".</p>
A Novel Backtracing Model to Study the Emission of Energetic Neutral Atoms at Titan
<p>Data for the manuscript "A Novel Backtracing Model to Study the Emission of Energetic Neutral Atoms at Titan" by Tippens et al., (2023). See README.txt for a description of the data files included here.</p>
Modelling system for computing the tropospheric O3 and CH4 perturbations from South Korean Emissions (KORUS-AQ period)
Open the record for dataset details and reuse information.
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
Model simulation data used in "Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54" (Beer et al., Geosci. Model Dev., 2020)
<p>This dataset contains the output and the namelist setups of the EMAC-MADE3 global model simulations analysed and discussed in Beer et al. (<em>Geosci. Model Dev.</em>, 2020).</p>
Global High Resolution Dust Emission Inventory for Chemical Transport Models
<p><strong>Overview:</strong><br> ==================================================================================</p> <p>Offline dust emissions in 2016 are now available at 0.25° x 0.3125° resolution. This dataset is calculated using the native resolution <a href="http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-FP">GMAO meteorology (GEOS-FP) fields</a>. </p> <p>Codes and Instructions (README file in the GitHub repository) to generate these offline emissions can be found on <a href="https://github.com/Jun-Meng/geos-chem/tree/v11-01-Patches-UniCF-vegetation">GitHub</a>.</p> <p>The offline emissions in this database have no scale factor applied, so users should apply the required scale factor in their application. Suggested scale factor to make the global total annual dust emission to 2000 Tg is 5.7141e-4. </p> <p><br> <strong>Zip File Details:</strong><br> ===============================================================================</p> <p>2016.zip contains daily (366 in total) netCDF files (stored in monthly folders) of global gridded hourly mineral dust emission flux rate. </p> <p> </p> <p>Individual file: </p> <p>/YYYY/MM/dust_emissions_025x0.3125.YYYYMMDD.nc</p> <p> Resolution : 0.25 x 0.3125 grid (721 x 1152 boxes)<br> Units : kg m-2 s-1<br> Timestamps : Hourly, 2016<br> Compression : Level 1 (nccopy -d1)<br> Chunking : nccopy -c lon/1152,lat/721,time/24</p> <p> </p> <p>Variables in each file: </p> <p>EMIS_DST1, EMIS_DST2, EMIS_DST3 and EMIS_DST4 represent dust emission flux rate in four size bins (0.1-1.0, 1.0-1.8, 1.8-3.0, and 3.0-6.0 micro in radius). </p> <p> </p> <p>*<em>Version 2020_v1.0 of this dataset was produced to accompany the following manuscript:<br> Meng, Jun, R. V. Martin, P. Ginoux, M. Hammer, M. P. Sulprizio, D. A. Ridley, and A. van Donkelaar, Grid-independent high resolution dust emissions (v1.0) for chemical transport models: application to GEOS-Chem (version 12.5.0), Geoscientific Model Development, Submitted</em></p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.