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238 results for “Atmosphere modeling”
Data for: Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model
<p>This dataset, provided in NetCDF format, supports the research presented in the paper titled "Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model." Please contact Dr. Sun (ltsun@rams.colostate.edu) if you have any questions.</p>
Model for random atmospheric inhomogeneities in engine noise auralization: Audio files for validation
<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with the following publication: A. Prescher, A. Moreau, S. Schade, "<a href="https://doi.org/10.1007/s13272-024-00764-4" target="_blank" rel="noopener"><em>Model for random atmospheric inhomogeneities in engine noise auralization</em></a>", CEAS Aeronautical Journal, 2024.</p> <p>Selected binaural audio files to illustrate the impact of random atmospheric inhomogenities on the noise characteristics of a turbofan engine.</p> <p>The corresponding time signals and spectrograms are available in the associated paper in Figure 7.</p>
Data for impacts of topography-based subgrid scheme and downscaling of atmospheric forcing on modeling land surface processes in the conterminous US
<p>The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). A topography-based subgrid structure and methods of downscaling of atmospheric forcing from the atmospheric grid to the subgrids of the land model grid (TGUs) have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve representation of the effects of small-scale topography-induced land surface heterogeneity on land surface processes. This study evaluates the impacts of the topography-based subgrid structure and downscaling of atmospheric forcing on modeling land surface processes in E3SM over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions.</p> <p>The data utilized to evaluate effects of the topography-based subgrid structure and downscaling of atmospheric forcing in land surface modeling include a TGU level land surface data file, atmospheric forcing to drive the land model, ELM user name list configuration parameters, regionalization variables (topographic regions, snow fraction regions, water versus energy limited regions, and regions of season of maximum precipitation), model restart files for both ELM configurations, and model outputs (grid and subgrid levels), model outputs aggregated to TGUs and grid levels.</p> <p>The data files include:</p> <ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">DELM.zip</a>: Directory containing the following files relevant to the D ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_all_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_disag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_disag_daily_20220512.nc</li> <li>TGU-level monthly output file: tgu_level_output_all_disag_20221109.nc</li> </ol> <li> <a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">NoDELM.zip</a>: Directory containing the following files relevant to the NoD ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_no_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_nodisag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_nodisag_daily_20220512.nc </li> <li>TGU-level monthly output file: tgu_level_output_no_disag_20221109.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">shared.zip</a>: Directory containing the following files relevant to both the D ELM and NoD ELM configurations.</li> <ol> <li>Subgrid-based surface data file: MASKED.half_degree_merge.surfdata_0.5x0.5_simyr1850_c200924.pft17.10262022v2.nc</li> <li>Regionalization file used to generate regions based on snow fraction, water versus energy limited state, and seasons of maximum precipitation: half_deg_budyko_curve_analysis_20230104_disag.nc</li> <li>Topographic ratio file used to generate topography-based regions: grd_level_output_nodisag_run_11_new_20221109.nc</li> <li>TGU-level surface elevation data file where surface elevation data are derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]: half_deg_subgrids_with_PFTs_and_stat_20210403.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">SNOTEL_files.zip</a>: Directory containing the following SNOTEL data related files used to evaluate model performance.</li> <ol> <li> SNOTEL list of stations file: SNOTEL_halfdegree_intersect4.csv</li> <li>SNOTEL data files/folders: csv</li> </ol> </ol> <p> </p> <p><strong>References</strong></p> <p>Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union <strong>89</strong>(10): 93-94.</p> <p>Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes <strong>27</strong>(15): 2171-2186.</p> <p> </p>
Revisiting the impacts of Stochastic Multicloud model on the MJO using low-resolution ECHAM6.3 atmosphere model
<p>There are the corresponding source codes and input data used to run the numerical experiments. Analysis scripts and model results are also included.</p>
Some atmospheric model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain 2-D fields of atmospheric T and P maps, as climatologies averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, and from the CESM Large Ensemble control simulation (LENS), the basis for two of the plots in our manuscript that is, as of January 2019, in review at JAMES. All of these data files are binary, written sequentially, 288 x 192. Also included are some PowerPoint-generated pdf images of the two fields.</p>
Dataset - Assessing the Added Value of the Intermediate Complexity Atmospheric Research Model (ICAR) for Precipitation in Complex Topography
<p>Abstract. The coarse grid spacing of global circulation models necessitates the application of downscaling techniques to investigate the local impact of a changing global climate. Difficulties arise for data sparse regions in complex topography which are computationally demanding for dynamic downscaling and often not suitable for statistical downscaling due to the lack of high quality observational data. The Intermediate Complexity Atmospheric Research Model (ICAR) is a physics-based model that can be applied without relying on measurements for training and is computationally more efficient than dynamic downscaling models. This study presents the first in-depth evaluation of multi-year precipitation time series generated with ICAR on a 4 × 4 km<sup>2</sup> grid for the South Island of New Zealand for an eleven-year period, ranging from 2007 until 2017. It focuses on complex topography and evaluates ICAR at 16 weather stations, eleven of which are situated in the Southern Alps between 700 m MSL and 2150 m MSL. ICAR is assessed with standard skill scores and the effect of model top elevation, topography, season, atmospheric background state and synoptic weather patterns on these scores are investigated. The results show a strong dependence of ICAR skill on the choice of the model top elevation, with the highest scores obtained for 4 km above topography. Furthermore, ICAR is found to provide added value over its ERA-Interim reanalysis forcing data set for alpine weather stations, improving mean squared errors (MSE) by up to 53 % and 30 % on median. It performs similarly during all seasons with an MSE minimum during winter, while flow linearity and atmospheric stability were found to increase skill scores. ICAR scores are highest during weather patterns associated with flow perpendicular to the Southern Alps and lowest for flow parallel to the alpine range. While measured precipitation is underestimated by ICAR, these results show the skill of ICAR in a real-world application, and may be improved upon by further observational calibration or bias correction techniques.<br> Based on these findings ICAR shows the potential to generate downscaled fields for long term impact studies in data sparse regions with complex topography.</p>
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>
Atmospheric and sea ice model fields from the perturbed parameter ensemble E3SMv0-HILAT used to examine emergent relationships among climate variables in the Arctic
<p>These files contain time series of several sea ice ad atmospheric fields produced in an ensemble of perturbed parameter simulations using the E3SMv0-HiLAT model. The time series are used to produced seasonal means, which are used to examine emerging relationships in the ensemble discussed in our manuscript, as of September 2019, in review at JGR</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>
Dataset for "Estimating the offshore wind power potential of Portugal by utilizing gray-zone atmospheric modeling" article
<p>This dataset is used for analysis and visualization, that supports the article titled "Estimating the offshore wind power potential of Portugal by utilizing gray-zone atmospheric modeling", which has been accepted for publication in the Journal of Renewable Sustainable Energy.</p>
Code and Data for Sturm and Silva (2024) A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correction
<p>This record contains the Julia photochemical model (https://doi.org/10.5281/zenodo.13385541) output in csv format used for training XGBoost in ProjectionConservationRF.py to emulate ozone photochemical formation. Nonphysical predictions that violate conservation of atoms are corrected using a closed-form, constrained least-squares approach that factors in uncertainty and scale using species-level weights. The file ozoneNOx_visualization.py contains an example and visualization for a smaller system, the primary photolytic cycle from which the Leighton relationship can be derived.</p> <p>The corresponding preprint is available here: <a href="https://doi.org/10.48550/arXiv.2408.16109">https://doi.org/10.48550/arXiv.2408.16109</a></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>
Chemical Networks and Model Output and for "Evidence of Photochemistry in an Exoplanet Atmosphere"
<p>The volume mixing ratio output of the key sulphur species computed by photochemical models for producing Fig. 1 </p> <p>Synthetic spectra in Fig. 2</p> <p>The photochemical networks used in each model.</p>
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
Model output data for 3D Climate modelling of LP 890-9 c with a modern Venus-like atmosphere
<p>We make available the output data from 3D climate modelling of LP 890-9 c with a modern Venus-like atmosphere. The data here has been produced for the publication submitted to Monthly Notices of the Royal Astronomical Society: Letters under the title: «3D Global Climate Model of an Exo-Venus: a modern Venus-like Atmosphere for the Nearby Super-Earth LP 890-9 c». The data includes the temperature profiles, emission (thermal) phase curves and transmission spectra files calculated for JWST/NIRSpec Prism. We also make available larger versions of the synthetic observable figures. Proper credit should be given to the authors. For further information, please get in touch with the corresponding author (Diogo Quirino) at: dfquirino@fc.ul.pt</p>
Preliminary data of drifting snow mass flux from the lower SPC at MOSAiC (2020-01-26 to 2020-02-04) for the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>Preliminary data of lower SPC massflux from MOSAiC, for the time period 2020-01-26 -- 2020-02-04.</p> <p>1-h averaged time series of mass flux (kg/m²/h) to compare with the ALPINE3D simulation results.</p> <p>Will soon be replaced with a DOI / Repositiry at the Arctic Data Centre from BAS.</p>
Self-consistent Models of Y Dwarf Atmospheres with Water Clouds and Disequilibrium Chemistry
<p>This data set consists of 1d radiative-convective equilibrium models intended for Y Dwarfs or cool giant planets with negligible levels of external irradiation, computed using coolTLUSTY and a recently updated set of molecular absorption cross sections. Models span effective temperatures of 200 - 600 K, metallicities of [-0.5], [0], and [+0.5] dex, and surface gravities of log10(g / [cm/s^2]) = 3.5 - 5.0, and are computed for both cloudy and clear cases, in thermochemical equilibrium and with nonequilibrium carbon and nitrogen chemistry due to vertical mixing. Models extend from 0.5 microns to 300 microns with 30,000 frequency points evenly spaced in ln(frequency), which corresponds to an average resolving power of R ~ 4340.</p> <p>See README.txt for a description of file formats, naming conventions, and decompressed sizes.</p> <p>A more thorough summary of the assumptions made in computing these models and a walk-through of their properties can be found in Lacy & Burrows 2023 "Self-consistent Models of Y Dwarf Atmospheres with Water Clouds and Disequilibrium Chemistry" accepted for publication in the Astrophysical Journal and available on arXiv.</p> <p>If you make use of these models please cite that publication, along with this zenodo data set.</p>
Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"
<p>Model outputs associated with “Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"</p>
Comparison between ozone column depths and methane lifetimes computed by 1-D and 3-D models at different atmospheric O2 Levels
<p>Recently, Cooke et al. (2022) used a 3-D coupled chemistry-climate model (WACCM6) to calculate ozone column depths at varied atmospheric O<sub>2</sub> levels. They argued that previous 1-D photochemical model studies, e.g., Segura et al. (2003), may have overestimated the ozone column depth at low pO<sub>2</sub>, and hence also overestimated the lifetime of methane. We have compared new simulations from an updated version of the Segura et al. model with those from WACCM6, together with some results from another 1-D and 3-D model. The discrepancy in ozone column depths is likely due to multiple interacting parameters, including lower boundary conditions, vertical and meridional transport rates, and different chemical mechanisms, especially the treatment of O<sub>2</sub> photolysis in the Schumann-Runge (SR) bands (175-205 nm). The discrepancy in tropospheric OH concentrations and methane lifetime between WACCM6 and the 1-D model at low pO<sub>2</sub> is reduced when absorption from CO<sub>2</sub> and H<sub>2</sub>O in this wavelength region is included in WACCM6. Including scattering in the SR bands may further reduce this difference. Resolving these issues can be accomplished by developing an accurate parameterization for O<sub>2</sub> photolysis in the SR bands and then repeating these calculations in the various models. Work is already underway to this end.</p>
Climate model results for H2-H2O atmospheres
<p>This dataset is a realization of a greenhouse climate model for H2-H2O atmospheres</p>
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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.