Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
119
datasets available to search
ShareScore release 0.9.0
Dataset results
119 results for “atmospheric simulation”
Model simulated potential natural vegetation state in the western US under preindustrial, historic, and future (RCP8.5) atmospheric conditions using multiple parameterizations of the dynamic vegetation model TRIFFID.
<p>DATA DESCRIPTION<br> Author contact information:<br> Linnia R. Hawkins<br> Oregon State University<br> lhawkins@oregonstate.edu; linnia.hawkins@gmail.com<br> Data supporting 2019 Journal of Advances in Modeling Earth Systems publication</p> <p>Simulations of the equilibrium vegetation distribution in the western US performed with the climate model HadAM3p-HadRM3p-MOSES2-TRIFFID</p> <p>step1: Identify Influential Parameters<br> All files labeled step1.<br> EXPERIMENT DESCRIPTION: Data used in step 1: identify influential parameters <br> sensitivity experiment adjusting one parameter at a time 38 individual parameters were adjusted to 7 values, equally spaced<br> over a defined plausible range. For reference nine simulations with the default model parameterization are included, initiated with unique initial potential temperature perturbations. </p> <p>The data contains the vegetation state variables at the end of four-year simulations (January 2004 to December 2007) during which two equilibrium time steps with the dynamic vegetation model TRIFFID (Cox et al., 2001). The results are averaged over three simulations initiated with unique atmospheric potential temperature perturbations.</p> <p>FILE DESCRIPTION:<br> NETCDF: Each netcdf file contains the fractional coverage (field1391), leaf area index (field1392), and the canopy height (field1393) for 5 plant functional types (PFTs: broadleaf, needleleaf, c3 grass, c4 grass, shrub) simulated for November 29, 2007. </p> <p> File labeling scheme: <br> step1_parameter_settingindex_3ICave.nc</p> <p> parameter: the name of the only model parameter adjusted<br> setting index: the index of the parameter setting (1-7)<br> index of 1 references the lowest plausible parameter setting<br> index of 7 references the highest plausible parameter setting<br> index of 4 references the parameter setting half way between the lowest and highest plausible parameter settings. <br> 3ICave: references that the results have been averaged over 3 initial conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> </p> <p>step2: ParameterSensitivity<br> all files labeled step2<br> EXPERIMENT DESCRIPTION:<br> Data used in step 2: parameter sensitivity <br> Perturbed Parameter Experiment (PPE) simultaneously adjusting 18 parameters.<br> Latin hypercube sampling was employed to generate 250 unique parameterizations references with a SETID (ranging from 1-359)<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p>FILE DESCRIPTION:<br> NETCDF: files contain either the fractional coverage (field1391) or the above ground biomass (field1512) for 5 plant functional types (PFTs) simulated for November 1907.</p> <p> File labeling scheme: <br> step2_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]</p> <p>TXT: files contain a list of the model parameterizations (for each PFT and variable) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p>RESTART: restart_region_TPPE_c374_1903-12-01.nc<br> The restart file contains the model state variables after spinup. This file was used to initiate all model simulations in step2.</p> <p>step3: ParameterSetSelection<br> All files labeled step3<br> EXPERIMENT DESCRIPTION:<br> Data used in step 3: parameter set selection<br> PPE simultaneously adjusting 10 parameters. <br> Latin hypercube sampling was employed to generate 140 unique model parameterizations, referenced with a SETID (ranging from 3-276).<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p><br> FILE DESCRIPTION:<br> NETCDF: files contain either the biomass (field1512) fractional coverage (field1391) canopy height (field1393) for 5 plant functional types (PFTs) simulated for November 1907 or the net primary productivity (NPP; item3262_monthly_mean) for December 1903 through November 1907. </p> <p> File labeling scheme: <br> step3_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable(s) contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> item3262_monthly_mean – Net primary productivity – units: (kgC/m2/sec) – all PFTs</p> <p>TXT: files contain a list of the model parameterizations (for each PFT) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p><br> production_runs<br> files labeled PI, historical, and future<br> EXPERIMENT DESCRIPTION:<br> Data simulated in the production runs. Model spinup was performed under preindustrial conditions with 10 unique model parameterizations (pset0-pset9). The resulting vegetation distribution for each parameterization after spinup are included and labeled PIrestarts. These restarts were used to initiate (or restart) the simulations under historic and future (RCP8.5) climate conditions. Files labeled historic contains the simulated vegetation state after 5 year simulations (2004-09-01 to 2009-08-30) with one TRIFFID equilibrium round occurring at the end. Files labeled future contain the simulated vegetation state after 5 year simulations (2054-09-01 to 2059-08-30) with one TRIFFID equilibrium round occurring at the end. </p> <p>FILE DESCRIPTION:<br> NETCDF: files contain the fractional coverage (field1391), leaf area index (field1392), canopy height (field1393), and biomass(field1512) for 5 plant functional types (in the order broadleaf, needleleaf, C3 grass, C4 grass, shrub).</p> <p><br> File labeling scheme:<br> pset* where * refers to the model parameterization 0-9<br> files were simulated with the model parameterization *, initiated with a unique initial condition (perturbation to the potential temperature field). </p> <p> Variables (PIrestart)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf] <br> field1391_1 – fractional coverage of PFT – units: fraction – [needleleaf]<br> field1391_2 – fractional coverage of PFT – units: fraction – [c3grass]<br> field1391_3 – fractional coverage of PFT – units: fraction – [c4grass]<br> field1391_4 – fractional coverage of PFT – units: fraction – [shrub]<br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf]<br> field1392_1 – leaf area index of PFT – units: m2/m2 – [needleleaf]<br> field1392_2 – leaf area index of PFT – units: m2/m2 – [c3grass]<br> field1392_3 – leaf area index of PFT – units: m2/m2 – [c4grass]<br> field1392_4 – leaf area index of PFT – units: m2/m2 – [shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf]<br> field1393_1 – canopy height of PFT – units: meters – [needleleaf]<br> field1393_2 – canopy height of PFT – units: meters – [c3grass]<br> field1393_3 – canopy height of PFT – units: meters – [c4grass]<br> field1393_4 – canopy height of PFT – units: meters – [shrub]<br> </p> <p> Variables (historic/future)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> <br> </p>
Post-processed SAM (System for Atmospheric Modeling) simulation output for "Tipping to an Aggregated State by Mesoscale Convective Systems"
<p>Statistics output files for all variables, for a select number of SAM (System for Atmospheric Modeling v. 6.11) simulation runs used in the study "Tipping to an Aggregated State by Mesoscale Convective Systems". The following simulations are included: DIU, OCEAN, DIU2OCEAN branch A1, DIU2OCEAN branch A2.</p>
Data for "Impact of prior terrestrial carbon flux on atmospheric CO2 concentration simulation"
<p>Data for "Impact of prior terrestrial carbon flux on atmospheric CO2 concentration simulation"</p>
FESOM output supporting: Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?
<p>AWI-CM1 and FESOM1.4 simulation results used in the manuscript "Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?".</p>
The simulated outputs analyzed in the article: "Understanding the influences of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere-ocean-wave-sea ice coupled model"
<p>In Ice-mass_[experiment] files, they include daily-averaged sea ice concentration and sea ice mass/area budgets.</p> <p>In Flux_[experiment] files, they include daily-averaged net ice surface flux, net shortwave/longwave radiation at the ice surface, latent/sensible heat flux at the ice surface, conductive heat flux at the top ice layer, and ice-ocean heat flux. </p>
Dataset of OFES2 ensemble simulations for Impact of atmospheric wind on SST in the northwestern Pacific
<p>Dataset to plot figures and make tables in Sasaki et al., Impact of atmospheric wind on sea surface temperature in the Kuroshio-Oyashio confluence and Oyashio southward intrusion regions.</p>
Evaluation of multi-season convection permitting atmosphere - mixed layer ocean simulations of the Maritime Continent.
<p>Supporting data for figures in GMD draft paper: Evaluation of multi-season convection permitting atmosphere - mixed layer ocean simulations of the Maritime Continent.</p>
NICOCO simulation data for the article "Diagnostic method for atmosphere–ocean coupling over tropical oceans at the sub-seasonal timescale"
<p>This data set includes data from an 8-year integration on the atmosphere-ocean coupled model NICOCO from 1 January 2010 to 31 December 2017. All outputs are daily averages on 1 x 1 degrees resolution. Output variables are sea surface temperature (K) and column water vapor (kg m-2). </p>
Data from: Wind-driven emission of marine ice nucleating particles in the Scripps Ocean-Atmosphere Research Simulator (SOARS)
Open the record for dataset details and reuse information.
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
Open the record for dataset details and reuse information.
Solar wind ENA precipitation in the Martian atmosphere: Monte Carlo simulation results
Open the record for dataset details and reuse information.
HIDRA simulations and post-processing scripts for JGR: SP manuscript: characterization of N+ abundances in the terrestrial polar wind using the multiscale atmosphere-geospace environment
Open the record for dataset details and reuse information.
Supporting Data for "A partial coupling method to isolate the roles of ocean and atmosphere in coupled climate simulations"
<p>Supporting data for "A partial coupling method to isolate the roles of the atmosphere and ocean in coupled climate simulations", submitted to Journal of Advances in modelling Earth system</p> <p>Monthly averaged variables for the 100 and 150 year fully coupled (b.e11.B1850C5CN.f09_g16.abrupt4xCO2.full.POP.100101_110012.nc) and partially coupled (b.e11.B1850C5CN.f09_g16.abrupt4xCO2.partial.POP.100101_115012.nc) abrupt CO2 quadrupling simulations and 40 yr ocean-driven partially coupled simulation (b.e11.B1850C5CN.f09_g16.prescribed.partial.POP.100101_115012.nc)</p> <p>Control simulation data is available on the NCAR HPSS /CCSM/csm/b.e11.B1850C5CN.f09_g16.005</p>
Atmospheric responses to partial SST perturbations simulated by MIROC5 and 6
<p>Ogura, T. and Webb, M. J., Positive low cloud feedback primarily caused by increasing longwave radiation from the sea surface in climate models.</p> <p>Files in netCDF format contain data from Figs.1-4 and Figs. S1-S6 in the above manuscript.</p> <p>All data are monthly climatology.</p> <p>Any queries please contact Tomoo Ogura ogura@nies.go.jp</p>
Simulation data for WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions
<p>This repository provides the test simulation data for "WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions" published in Geoscientific Model Development. The configurations for sensitivity experiments are described in this paper. Please contact the corresponding author Tzung-May Fu (fuzm@sustech.edu.cn) for more details.</p>
CESM Short lived Halogen simulation results for Roozitalab et al. (2023) in JGR-Atmospheres
<p>This dataset includes the data used for Roozitalab et al. (2023): "Measurements and modeling of the interhemispheric differences of atmospheric chlorinated very short-lived substances" in Journal of Geophysical Research - Atmospheres.</p>
Global variable-resolution simulations of extreme precipitation over Henan, China in 2021 with MPAS-Atmosphere v7.3
<p>This repository encompasses data and software to "Global variable-resolution simulations of the 2021 extreme precipitation event in Henan, China with MPAS-Atmosphere v7.3". The data and model are integral to our study, which focuses on simulating a significant rainstorm event in Henan, China, in July 2021.</p> <p>Contained within this archive are the following components:</p> <p>MPAS-Atmosphere v7.3: This is the core model employed in our study. MPAS v7.3 is a versatile global variable-resolution model, adept at simulating extreme weather events at varying scales.</p> <p>MPAS mesh data: The global meshes generated for the experiments.</p> <p>CMA Observation Data: Ground-based observational data from the China Meteorological Administration, crucial for the validation of our simulation results.</p> <p>ERA5 Reanalysis Data: These datasets are used to further validate the outcomes of our simulations, providing a comprehensive set of atmospheric parameters.</p> <p>GFS Data: The Global Forecast System (GFS) data serve as the input fields for the MPAS model, providing essential initial conditions for our simulations.</p> <p>This collection is aimed at offering researchers a holistic package for studying, replicating, or extending our findings on extreme weather phenomena. The data and model provided here are not only crucial for reproducing the results presented in our study but also offer a valuable resource for further research in atmospheric sciences and climate modeling.</p>
Code and Data for Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0
<p>Includes the code used for all simulations and grid configurations for the paper entitled "Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0" submitted to Geoscientific Model Development. Also included are the model output files for all cases and grid configurations used to generate the analysis and figures in the paper. </p>
Surface Kinetic energy from MITgcm-GEOS5 Coupled Ocean-Atmosphere Simulation
<p>Annual mean of surface kinetic energy computed from MITgcm-GEOS5 coupled ocean-atmosphere simulation, with a spacing grid of 4 km.</p> <p>The temporal coverage for the annual mean spans from March 01, 2020, to March 01, 2021.</p>
Simulation data for: 'Convective shutdown in the atmospheres of lava worlds'
<p>PROTEUS model outputs for HD 63433 d and TRAPPIST-1 c simulations, for MNRAS article.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.