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Dataset results
238 results for “Atmosphere modeling”
Representing surface heterogeneity in land-atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime - E3SM SCM data and code
<p>This dataset contains post-processed E3SM single-column model output and code used to produce the figures in the manuscript that we are targeting Geoscientific Model Development to submit. </p>
Unified Model Atmospheric Forecast Model Data for Machine Learning Cloud-Base Height
<p>Unified Model data, in pp format, for machine learning of cloud-base height based on profiles of temperature, humidity, pressure and cloud fraction. The model configuration is Global Atmosphere 6, running with a resolution of N320 (which is coarser than what was running operationally at the time). Each simulation is run for 24 hours, re-initialising every 24 hours. A separate data file is provided every 6 hours. Data points are on a latitude-longitude grid in the horizontal and on a stretched grid in the vertical. See https://gmd.copernicus.org/articles/10/1487/2017/ for details of the model configuration.</p> <p>Data from January 2016 is for training.</p> <p>Data from July 2017 is for development/validation</p> <p>Data from October 2017 is for final testing.</p> <p> </p>
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models
<p>Trained machine learning models and scaling values used in the paper "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model."</p>
Monthly-Mean Model Output for Paper Titled "Do Nudging Tendencies Depend on the Nudging Timescale Chosen in Atmospheric Models?"
<p>These tarballs contains monthly-mean model output, which was primarily what was presented in the AGU JAMES paper titled "Do nudging tendencies depend on the nudging timescale chosen in atmospheric models?". Also included are the scripts used to set up these simulations, allowing reproducibility of the portion of the paper that used 3-hourly output. The 3-hourly output was not included, as it totaled ~7TB.</p>
Atmospheric hydroxyl distribution from the EMAC model (MOM kinetic chemistry mechanism)
<p>This dataset contains the output from the simulations with the EMAC model implementing the MOM kinetic chemistry mechanism presented in <a href="https://doi.org/10.5194/acp-16-12477-2016">Lelieveld et al. (2016)</a> study. In addition to the computed atmospheric hydroxyl (OH) and hydroperoxyl (HO2) radicals abundance distributions, we add related model fields facilitating usage/comparison of these results with other estimates.</p><p>The data containers format is netCDF v.4 (compressed); please refer to the container variables/attributes for the extended information. We present here the actual model output (weekly averages for the 2013−2014 period, in EMAC-MOM__*.nc) and the monthly "climatology" fields (average, SD, minima and maxima of the time steps falling in particular month, in EMAC-MOM__*--clim.nc, respectively).</p><p>See the .README.pdf file for additional notes.</p><p>Changes w.r.t. initial version from 2020/09/22:</p><p>2020/10/30: Updated attributes and DOIs in .nc/.jnl files, added OH "climatology", updated README.</p><p>2020/10/31: Updated description and "climatology" (SD fields were missing).</p><p>2022/06/19: Added hydroperoxyl (HO2) fields, updated species average concentration plot sample script.</p><p>2023/10/26: Dataset title adjusted for clarity</p>
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
<p>We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations.</p> <p>Due to storage limitations, the full dataset is much too large to be published (~50TB). Instead, a subset consisting of 6-hourly warm season (May-September) 2-meter temperature, precipitation, and 500hPa height is presented. If you wish to access the full dataset (as presented in Michaelis et al. 2019), please contact one of the authors.</p>
Model simulation results for "Enhanced seasonal amplitude of atmospheric CO2 by the changing Southern Ocean carbon sink"
<p>This dataset contains the seasonal variations of monthly mean atmospheric CO<sub>2</sub> concentration derived from GEOS-Chem model simulations during 2000-2016. Monthly terrestrial CO2 fluxes derived from CLM4.5-CN, used as an input dataset for the GEOS-Chem simulations, are also included.</p> <p>There are six sets of GEOS-Chem simulation results; "ctrl", "BIOfix", "OCNfix", and "FFfix" are the main experiments to evaluate the effects of changes in terrestrial CO<sub>2</sub> fluxes, air-sea CO<sub>2</sub> fluxes, and fossil fuel CO<sub>2</sub> emissions on the seasonal amplitude of atmospheric CO<sub>2</sub> over the globe; "ALLfix" and "OCNfix_SO" are additional experiments for identifying the effects of changes in the other factors (i.e., atmospheric transport and biomass burning) and regional changes in air-sea fluxes in the Southern Ocean. </p> <p>Detailed explanations for each simulation are described in the main text.</p> <p>*We recommend contacting us first if you want to utilize the dataset for study (yjm921@gmail.com).</p>
Data & model products from "Identification of carbon dioxide in an exoplanet atmosphere"
<p>Associated Publication: <a href="https://www.nature.com/articles/s41586-022-05269-w">https://www.nature.com/articles/s41586-022-05269-w</a><br> <br> OVERVIEW: Carbon dioxide (CO2) is a key chemical species that is found in a wide range of planetary atmospheres. In the context of exoplanets, CO2 is an indicator of the metal enrichment (i.e., elements heavier than helium, also called “metallicity”), and thus formation processes of the primary atmospheres of hot gas giants. It is also one of the most promising species to detect in the secondary atmospheres of terrestrial exoplanets. Previous photometric measurements of transiting planets with the Spitzer Space Telescope have given hints of the presence of CO2, but have not yielded definitive detections due to the lack of unambiguous spectroscopic identification. Here we present the detection of CO2 in the atmosphere of the gas giant exoplanet WASP-39b from transmission spectroscopy observations obtained with JWST as part of the Early Release Science Program (ERS). The data used in this study span 3.0 - 5.5 µm in wavelength and show a prominent CO2 absorption feature at 4.3 µm (26σ significance). The overall spectrum is well matched by one-dimensional, 10x solar metallicity models that assume radiative-convective-thermochemical equilibrium and have moderate cloud opacity. These models predict that the atmosphere should have water, carbon monoxide, and hydrogen sulfide in addition to CO2, but little methane. Furthermore, we also tentatively detect a small absorption feature near 4.0 µm that is not reproduced by these models.</p>
Additional Figures for winning models for sample in A Comparative L-dwarf Sample Exploring the Interplay Between Atmospheric Assumptions and Data Properties
<p>Additional Figures for winning models for sample in <em>A Comparative L-dwarf Sample Exploring the Interplay Between Atmospheric Assumptions and Data Properties (<a href="https://arxiv.org/pdf/2209.02754.pdf">https://arxiv.org/pdf/2209.02754.pdf</a>).</em></p> <p>Model naming key: NC = cloud-free, d2_89 = power-law deck cloud</p> <p>SDSS J1416+1348A: Winning model: power-law deck cloud</p> <p>Spectral Type Comparison J1526+2043 Winning model: Cloud-free</p> <p>Temperature Comparisons</p> <p>J1539-0520 Winning model: Power-law deck cloud and cloud-free tied.</p> <p>J0539-0059 Winning model: Power-law deck cloud and cloud-free tied. </p> <p><br> </p>
Modeling the hydrological cycle in the atmosphere of Mars: Influence of a bimodal size distribution of aerosol nucleation particles
<p>Data from figures.</p>
Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study
<p>Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input–output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ± 40 % variation range for the UK emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub>, from which regression coefficients for each input-output combination and each model grid (>10,000 across the UK) were calculated. Surface concentrations of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO<sub>3</sub> and particulate SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup> to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (± 4 %, ± 10 % and ± 20 % respectively). The uncertainties in the surface concentrations of NH<sub>3</sub> and NO<sub>x</sub> and the depositions of NH<sub>x</sub> and NO<sub>y</sub> were dominated by the uncertainties in emissions of NH<sub>3</sub>, and NO<sub>x</sub> respectively, whilst concentrations of SO<sub>2</sub> and deposition of SO<sub>y</sub> were affected by the uncertainties in both SO<sub>2</sub> and NH<sub>3</sub> emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and HNO<sub>3</sub>) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.</p> <p>The dataset contains model outputs used for the sensitivity and uncertainty analyses.</p>
The Model Grid for The atmosphere of HD 149026b: Low metal-enrichment and weak energy transport
<p>This grid contains cloud-free 1-dimensional radiative-convective-thermochemical equilibrium atmosphere models created using the Python-based code <a href="https://natashabatalha.github.io/picaso/">PICASO</a>. The parameters varied for this grid are the atmospheric metallicity (<em>[M/H]</em>), Carbon-to-Oxygen ratio (<em>C/O</em>), heat redistribution factor (<em>rfacv</em>), and the intrinsic temperature of the planet (<em>Tint</em>). The ranges of these parameters have been outlined in the paper. </p> <p>The profile and spectra are provided for each model as a .dat file. Each profile contains the temperature and abundance for a variety of chemicals at each of the 91 pressure levels modeled for the atmosphere. The spectra file contains the wavelength in microns, transit depth, eclipse depth, and emission flux from the planet in ergs/s/cm^3. The isolated planetary thermal emission spectrum needs to be multiplied by 1e6 to be in ppm. There are four types of models, ones with VO, ones with TiO, ones with TiO and VO, and ones without TiO or VO. The files are labeled based on each of the 4 atmospheric parameters and whether they contain TiO and VO.</p> <p>Note on TiO: The inclusion of gaseous TiO in the atmosphere was found to cause strong inversions in the temperature-pressure profile and a worse fit of the thermal emission spectrum to the data. This finding has been described in the paper.</p>
Modeling the impacts of Antarctic Sea Ice Decline: Responses of Atmospheric Dynamics
Open the record for dataset details and reuse information.
Limits of the Habitable Zone CO2 atmosphere with 3D Climate Modelling
<p>Water is crucial for life, regardless of the environment. That's why in search of extraterrestrial life, we focus on planets in the habitable zone (HZ), where liquid water can exist. The size of this zone depends on factors like the star type and planet size. Using the Generic PCM model (https://lmdz-forge.lmd.jussieu.fr/mediawiki/Planets/index.php/Overview_of_the_Generic_PCM), we've defined the limits of the HZ for atmospheres dominated by CO2 in 3D for the first time. You can access the dataset from the simulations in ".nc" file format. Temporal evolution of variables such as surface temperature, surface pressure, water vapour, liquid water, ice etc. are in a 3D grid for different orbital distances and with different surface pressures are present in the dataset. We have used the correlated-k table published in Zenodo for the simulations (https://doi.org/10.5281/zenodo.10978791).</p>
Correlated-k table for H2 dominated atmospheres for 3D Climate Modelling
<p>Correlated-k table for H2 dominated atmospheres with a variable amount of water vapour built by incorporating absorption data files from the HITRAN database and using the exo\_k code by J.Leconte. This correlated-k tables are created to use with Generic-PCM model to simulate the atmospheres of H2 dominated planets.</p> <p>The calculation of radiative transfer can be performed using the correlated-k method, which efficiently determines net radiative fluxes by categorizing spectral lines into infrared and visible bands (IR x VI) and assigning the average absorption coefficients to each band. These coefficients are pre-calculated based on detailed line-by-line radiative transfer calculations. The correlated-k method is an economic alternative to the line-by-line method, making it possible to accurately and rapidly compute atmospheric radiation.</p> <p> </p>
Correlated-k table for CO2 dominated atmospheres for 3D Climate Modelling
<p>Correlated-k table for CO2 dominated atmospheres with a variable amount of water vapour built by incorporating absorption data files from the HITRAN database and using the exo\_k code by J.Leconte. This correlated-k tables are created to use with Generic-PCM model to simulate the atmospheres of CO2 dominated planets.</p> <p>The calculation of radiative transfer can be performed using the correlated-k method, which efficiently determines net radiative fluxes by categorizing spectral lines into infrared and visible bands (IR x VI) and assigning the average absorption coefficients to each band. These coefficients are pre-calculated based on detailed line-by-line radiative transfer calculations. The correlated-k method is an economic alternative to the line-by-line method, making it possible to accurately and rapidly compute atmospheric radiation.</p> <p> </p>
Limits of the Habitable Zone for H2 atmosphere with 3D climate Modelling
<p>Water is crucial for life, regardless of the environment. That's why in search of extraterrestrial life, we focus on planets in the habitable zone (HZ), where liquid water can exist. The size of this zone depends on factors like the star type and planet size. Using the Generic PCM model (https://lmdz-forge.lmd.jussieu.fr/mediawiki/Planets/index.php/Overview_of_the_Generic_PCM), we've defined the limits of the HZ for atmospheres dominated by H2 in 3D for the first time. You can access the dataset from the simulations in ".nc" file format. Temporal evolution of variables such as surface temperature, surface pressure, water vapour, liquid water, ice etc. are in a 3D grid for different orbital distances and with different surface pressures are present in the dataset. We have used the correlated-k table published in Zenodo for the simulations (https://doi.org/10.5281/zenodo.10978762).</p>
Simulation results with the EULAG research model for the publication: "Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost"
<p>Supplementary material for the publication</p> <ul> <li>Mark Schlutow, Tobias Stacke, Tom Doerffel, et al. Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost. ESS Open Archive . January 24, 2024. <a href="https://doi.org/10.22541/essoar.170612558.81370785/v1">https://doi.org/10.22541/essoar.170612558.81370785/v1</a></li> </ul> <p>The material contains all simulation results and raw outputs that are necessary to reproduce the figures and statistics of the publication. </p>
The SPHINX M-dwarf Spectral Grid. I. Benchmarking New Model Atmospheres to Derive Fundamental M-Dwarf Properties
<p><strong>NEW UPDATE:::::::::::::VERSION 4</strong></p> <p>MODEL GRID AND SUPPLEMENTARY FIGURES for <strong>The SPHINX M-dwarf Spectral Grid. I. Benchmarking New Model Atmospheres to Derive Fundamental M-Dwarf Properties:</strong></p> <p><strong>(1) MODEL GRID:</strong> Zip file titled 'SPHINX_MODELS_MLT_1.zip' contains a directory (376MB on disk) that is divided into three folders: ATMS, SPECTRA, and ABUNDANCES. All models here assume mixing length parameter of 1. For more info, we direct the reader to the paper.</p> <p>The ATMS directory contains thermal profiles/atmospheres of all models. (Temperature in K, Pressure in bars)</p> <p>The SPECTRA directory contains synthetic spectra. (Wavelength--0.1 to 20 microns, Flux in W/m2/m, R~250)</p> <p>The ABUNDANCES directory contains mixing ratios of all atomic and molecular species included in these models.</p> <p><strong>(2) SUPPLEMENTARY FIGURES:</strong></p> <p>The plot files are named as 'target name' _ corner.</p> <p>Each file is a corner plot of posterior probability distributions from grid-model fit of low-resolution spectra of benchmark M dwarfs using the SPHINX model grid. We also include posterior distributions of Starfish (Czekala et al.2015) hyperparameters to properly constrain model and data systematics. The vertical blue lines in the plots indicate values from observations (empirically derived [M/H] from Mann et al. 2013 and interferometrically measured radii from Boyajian et al. 2012b).</p> <p>File named Gl436_mixinglength shows difference in grid model fit assuming mixing length parameter 1 vs 0.5.</p> <p>File named Gl725B_spot shows difference in grid model fit assuming stellar photospheric heterogeneity vs without.</p> <p><strong>If you use our stellar atmosphere models, please remember to cite both the zenodo doi for the open source data as well as the paper. Thank you!</strong></p> <p> </p> <p>--> link to <a href="https://iopscience.iop.org/article/10.3847/1538-4357/acabc2/meta">published paper</a></p>
Geostatistical inverse modeling with large atmospheric data: data files for a case study from OCO-2
<p>The files in this data repository provide the inputs required to run an inverse modeling case study. This case study will estimate CO<sub>2</sub> fluxes across North America for July 2015 using synthetic observations that have been created to resemble observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite.</p> <p>This data repository is specifically linked to a GitHub code repository (http://doi.org/10.5281/zenodo.3241524 or <a href="https://github.com/greenhousegaslab/geostatistical_inverse_modeling">https://github.com/greenhousegaslab/geostatistical_inverse_modeling</a>). That GitHub repository provides scripts for constructing a geostatistical inverse model that will estimate greenhouse gas fluxes or air pollution emissions using atmospheric observations. The GitHub repository includes a case study that can be run out-of-the-box; the case study provides users an opportunity to test out and explore the inverse modeling code. All of the input data files for that case study are provided for download here.</p> <p>Here is a brief explanation of the different files included in this data repository, but refer to the linked GitHub repository for greater details. All of these files are in a ".mat" file that can be read into Matlab using the <em>load</em> function or can be read into R using the <em>R.matlab</em> package.</p> <ul> <li><strong>H.tar.gz</strong>: This tar file contains the <strong>H</strong> matrices or sensitivity matrices required by the inverse model. These inputs were generated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model as part of NOAA's CarbonTracker-Lagrange program (<a href="https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/">https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/</a>). The <strong>H</strong> matrix is too large to store in a single file. We have therefore split up the matrix into 328 different files (all contained within H.tar.gz). Each file contains a vertical strip of the <strong>H</strong> matrix that corresponds to a different time period of fluxes to be estimated as part of the inverse model.</li> <li><strong>Z.mat</strong>: This file contains the synthetic OCO-2 observations used in the case study. </li> <li><strong>areas_us.mat</strong>: This file lists the area of each model grid box used in the case study in units of meters<sup>2</sup>. This file only includes grid box area for model grid boxes that fall within the continental United States. We estimate CO<sub>2</sub> fluxes across terrestrial North America on a 1 degree latitude by 1 degree longitude grid as part of the case study. Each of these model grid boxes will have a different area, depending upon the latitude of that model grid box. </li> <li><strong>distmat.mat</strong>: This file contains a matrix that lists the distance (in kilometers) between the center of each model grid box used in the case study. </li> <li><strong>land_mask.mat</strong>: We only estimate CO<sub>2</sub> fluxes for terrestrial regions of North America as part of the case study. This land mask is used to convert the fluxes estimated by the inverse model to a latitude-longitude grid that can then be plotted.</li> <li><strong>H_all_OCO2.mat</strong>: This file contains the H matrices summed across differnt time periods. I.e., this file is the sum of all the individual H files contained within H.tar.gz.</li> <li><strong>Xvar.tar.gz</strong>: This file contains different environmental variables from ERA5 meteorology that have been reformatted to match the H footprint matrices. These different variables can be used as predictors of CO2 fluxes in an inverse model. The different variables included in this file are as follows: <ul> <li>Xvar_e.mat Evaporation</li> <li>Xvar_msdwswrf.mat Mean surface downward short-wave radiation flux</li> <li>Xvar_q.mat Specific humidity</li> <li>Xvar_stl1.mat Soil temperature level 1</li> <li>Xvar_stl3.mat Soil temperature level 3</li> <li>Xvar_swvl1.mat Volumetric soil water layer 1</li> <li>Xvar_t2m.mat 2 metre temperature</li> <li>Xvar_tp.mat Total precipitation</li> <li>Xvar_mer.mat Mean evaporation rate</li> <li>Xvar_pev.mat Potential evaporation</li> <li>Xvar_r.mat Relative humidity</li> <li>Xvar_swvl3.mat Volumetric soil water layer 1</li> <li>Xvar_tcc.mat Total cloud cover</li> </ul> </li> </ul>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.