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1,574 results for “atmospheres”
Impact of atmospheric water-soluble iron on α-pinene-derived SOA formation and transformation in the presence of aqueous droplets
<p>The following data sets belong to the article "Impact of atmospheric water-soluble iron on α-pinene-derived SOA formation and transformation in the presence of aqueous droplets" The impact of water-soluble atmospheric iron on formation, growth and aging of secondary organic aerosol (SOA) is a controversial subject in the literature. Iron chemistry drives Fenton-like reactions in the aqueous phase which is dependent on pH. Flow reactor experiments in the dark and under humid conditions were conducted to investigate systematically the influence of ferrous iron in the aqueous phase on α-pinene SOA by online physical analysis and offline high-resolution mass spectrometry. In total 31 flow reactor experiments were conducted in four sets of experiments. All SMPS data and mass spectra in negative mode are uploaded sorted by set of experiments and figures in the paper. You can find the workflow of SMPS data analysis as well as the retention times of the target analysis in the paper and SI.</p>
On the increase of climate sensitivity and cloud feedback with warming in the Community Atmosphere Models
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2020). On the Increase of Climate Sensitivity and Cloud Feedback With Warming in the Community Atmosphere Models. <em>Geophysical Research Letters</em>, <em>47</em>(18), e2020GL089143. <a href="https://doi.org/10.1029/2020GL089143">https://doi.org/10.1029/2020GL089143</a></p> <p> </p> <p><strong>Upadated on July 3, 2024</strong>: add more timeseries of precipitation and energy fluxes for analysis in Bonan, Schneider, & Zhu (2024).</p> <p>David Bonan, Tapio Schneider, Jiang Zhu. Precipitation over a wide range of climates simulated with comprehensive GCMs. <em>ESS Open Archive .</em> April 25, 2024.<br><span>DOI: <a href="https://doi.org/10.22541/essoar.171405864.45942692/v1" target="_blank" rel="noopener noreferrer">10.22541/essoar.171405864.45942692/v1</a></span> </p>
Kilometre-scale regional climate model simulations of two atmospheric river case studies in West Antarctica
<p>Regional climate model simulations produced using the MetUM, HCLIM and Polar-WRF models at 1 km horizontal grid spacing. The data span two case studies in which an atmospheric river made landfall over the Amundsen Sea Embayment and Thwaites / Pine Island ice shelves. The first is a winter case (23-30 June 2020) and the second a summer case (3-9 February 2020). </p> <p>Data are gridded, in native model coordinates, and saved as netcdf.</p> <p>Data produced by:</p> <p>HCLIM: José Abraham Torres</p> <p>MetUM: Ella Gilbert</p> <p>Polar-WRF: Denys Pishniak</p> <p>Data were produced to support the analysis presented in Gilbert et al. (2024) [preprint] . The research was funded by the PolarRES project, which is funded under the EU's Horizon 2020 programme call H2020-LC-CLA-2018-2019-2020 under grant agreement 101003590. MetUM simulations were performed on the ARCHER2 UK National Supercomputer. </p>
Model simulation output for New Configuration for Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model
<p>Simulation output from the new configuration model used in the manuscript Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model</p>
Models and Data for "Approximate Atmospheric Scattering using PINNs"
Open the record for dataset details and reuse information.
Model Spectra for Morley et al. 2017 (Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST)
<p>README</p> <p>This file contains the model spectra presented in Morley et al. 2017, Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST. </p> <p>The models are organized as follows: </p> <p>##########################<br> ## Transmission spectra ##<br> ##########################</p> <p>transmission_spectra contains 4 folders:<br> alb0.0_massradiusrel includes models with Bond albedo=0.0, and planet masses assuming the Weiss & Marcy 2014 mass-radius relationship<br> alb0.3_massradiusrel includes models with Bond albedo=0.3, and planet masses assuming the Weiss & Marcy 2014 mass-radius relationship<br> alb0.0_measured_masses includes models with Bond albedo=0.0, and observed planet masses, as described in Morley et al. 2017<br> alb0.3_measured_masses includes models with Bond albedo=0.3, and observed planet masses, as described in Morley et al. 2017</p> <p>each file contains the wavelength and model transit depth from 0.3 to 250 microns, calculated at 1 cm-1 wavenumber resolution. </p> <p>The file name indicates the planet name, model surface pressure (in bar), Bond albedo, and the assumed composition (Earth-, Venus-, or<br> Titan-based compositions, calculated in chemical equilibrium at each layer of the model, as described in Morley et al. 2017). </p> <p>(e.g. trans_spect_massradiusrel_gj1132b_psurf0.1_alb0.0_chem_earth.p.txt is a model of GJ 1132b, assuming the Weiss/Marcy mass-radius relationship, with a surface pressure of 0.1 bar, Bond albedo of 0.0, and an Earth-based composition). </p> <p>##########################<br> ## Emission spectra ##<br> ##########################</p> <p>emission_spectra contains 4 folders:<br> alb0.0 includes models with Bond albedo=0.0<br> alb0.3 includes models with Bond albedo=0.3<br> alb0.7 includes models with Bond albedo=0.7<br> emission_spectra_dividestar includes models that have already been divided by a model stellar spectrum, for convenience. </p> <p><br> each file contains the wavelength and model thermal emission flux in W/m2/m from 0.3 to 250 microns, calculated at 1 cm-1 wavenumber resolution. </p> <p>The file name indicates the planet name, model surface pressure (in bar), Bond albedo, and the assumed composition (Earth-, Venus-, or<br> Titan-based compositions, calculated in chemical equilibrium at each layer of the model, as described in Morley et al. 2017). </p> <p>(e.g. gj1132b_psurf0.01_alb0.0_chem_earth.spec is a model of GJ 1132b with a surface pressure of 0.01 bar, Bond albedo of 0.0, and an Earth-based composition). </p> <p>The models in emission_spectra_dividestar instead contain the flux of the planet divided by the flux of the star. </p> <p> </p> <p>##########################<br> ## Eclipse Depths ##<br> ##########################</p> <p>eclipse_depths contains a file for each planet with calculated eclipse depths for each of the wide-band JWST MIRI filters (http://ircamera.as.arizona.edu/MIRI/pces.htm). </p> <p>(new version from Jan 29 2018 has fixed a bug in MIRI eclipse depths that affected the longest wavelength filter for GJ 1132b and LHS 1140b only). </p>
OpenFOAM test result data accompanying 'Numerical representation of mountains in atmospheric models'
<p>OpenFOAM test result data accompanying the PhD thesis, 'Numerical representation of mountains in atmospheric models'</p>
Selected CO2 Data from BErkeley Atmospheric CO2 Observation Network
<p>Selected CO<sub>2</sub>data from BErkeley Atmospheric CO<sub>2</sub> Observation Network (BEACO<sub>2</sub>N) for use in the characterization of the heterogeneity of greenhouse gas concentrations around the San Francisco Bay Area and the constraint of CO<sub>2 </sub>emissions from mobile sources.</p>
Spectrally resolved helium absorption from the extended atmosphere of a warm Neptune exoplanet
<p>Stellar heating causes atmospheres of close-in exoplanets to expand and escape. These extended atmospheres are difficult to observe because their main spectral signature – neutral hydrogen at ultraviolet wavelengths – is strongly absorbed by interstellar medium. We report the detection of the near-infrared triplet of neutral helium in the transiting warm Neptune-mass exoplanet HAT-P-11b using ground-based, high-resolution observations. The helium feature is repeatable over two independent transits, with an average absorption depth of 1.08±0.05%. Interpreting absorption spectra with 3D simulations of the planet’s upper atmosphere suggests it extends beyond 5 planetary radii, with a large scale height and a helium mass loss rate ≲ 3x10<sup>5</sup> g‧s<sup>−1</sup>. A net blueshift of the absorption might be explained by high-altitude winds flowing at 3 km‧s<sup>−1</sup>from day to night-side.</p>
Data used in "Seasonality of Intraseasonal Variability in CMIP5 and Nonhydrostatic Atmospheric Global Models" by Nakano and Kikuchi (2019) submitted to GRL
<p>PCs time series and NICAM-AMIP 2.5 degree gridded data used in Nakano and Kikuchi (2019) submitted to GRL.</p>
Influence of thermospheric impacts of solar activity on the general circulation and long-period planetary waves in the middle atmosphere
<p>This dataset contains model output files and examples of scripts for depicting figures related to the article "Influence of thermospheric impacts of solar activity on the general circulation and long-period planetary waves in the middle atmosphere " by A.V. Koval, N. M. Gavrilov, A. I. Pogoreltsev, N. O. Shevchuk.</p> <p><br> Contents:<br> Output data from model simulations averaged over 16 pairs of model runs with high and low solar activity:</p> <p><br> gh_m1_hsa5.dx, gh_m1_lsa5.dx – amplitudes of the geopotential height variations in g.p.m. at high and low solar activity caused by long-period PW modes with zonal wavenumber 1 averaged over 80 time subintervals selected from pairs of the MUAM runs (structure described in w1_tp1_hsa.ctl, w1_tp1_lsa.ctl).<br> gh_m2_hsa5.dx, gh_m2_lsa5.dx, gh_m3_hsa5.dx, gh_m3_lsa5.dx, gh_m4_hsa5.dx, gh_m4_lsa5.dx – the same but for zonal wavenumbers 2,3,4.<br> gh_m1_disp_dif5.dx – dispersion of differences in corresponding wave amplitudes between high and low solar activitiy (w1_tp1_disp.ctl).<br> gh_m2_disp_dif5.dx, gh_m3_disp_dif5.dx, gh_m4_disp_dif5.dx – the same but for zonal wavenumbers 2,3,4.<br> index_refr1_1_5.dx, index_refr1_2_5.dx, index_refr1_3_5.dx, index_refr1_4_5.dx – the zonal-mean quasi-geostrophic complex refractivity index squared (RI2) for PW modes with zonal wavenumbers 1-4 (ind_refr1_1.ctl).<br> uq_vwres1_pv_Jan_1_5.dx, uq_vwres1_pv_Jan_2_5.dx, uq_vwres1_pv_Jan_3_5.dx, uq_vwres1_pv_Jan_4_5.dx – the Eliassen-Palm flux for PW modes with zonal wavenumbers 1-4 (uqep1_vwres_1.ctl).<br> zw_a0_grads5_hsa.dx, tp_a0_grads5_hsa.dx – zonal-mean zonal wind in m/s and temperature in K for December – February averaged over 16-member ensemble of model runs for high solar activity (m0_zw5_hsa.ctl, m0_tp5_hsa.ctl).<br> zw_a0_grads5_lsa.dx, tp_a0_grads5_lsa.dx – the same but for the low solar activity<br> disp_U_dif5_.dx, disp_T_dif5_.dx – dispersion of differences in zonal wind and temperature between high and low solar activitiy for December – February averaged over 16-member ensemble of model runs (tp1_disp.ctl).<br> Additional data including outputs from separate model runs are available from the authors upon request.</p>
Atmospheric processing of loess dust particles in a polluted urban area of China
<p>STATEMENT</p> <p> </p> <p>This file is for the upload of data for 2018JD029956; the basic data for the article are available. The research data follows the FAIR Data Principles and demonstrate compliance with international standards for data repositories.</p> <p>Please contact Prof. Yang Chen (<a href="mailto:chenyang@cigit.ac.cn">chenyang@cigit.ac.cn</a>) if you have any questions.</p>
Data for the manuscript: TOWARDS AN INTEGRATED SOIL-PLANT-ATMOSPHERE MODELING ENVIRONMENT: IMPLEMENTATION OF A DYNAMIC PLANT UPTAKE MODULE FOR THE HYDRUS MODEL
<p>Data used in the manuscript for the theoretical and experimental validation, and for the Global Sensitivity Analysis. For a thorough description, please refer to the manuscript.</p>
Data for "Far-field Co-seismic Disturbances in Lithosphere, Atmosphere, and Ionosphere of Mw 7.4 Hualian Earthquake in 2024"
<p>Data for "Far-field Co-seismic Disturbances in Lithosphere, Atmosphere, and Ionosphere of Mw 7.4 Hualian Earthquake in 2024"</p> <p>1. MVP_LAI_data.mat: data from MVP-LAI system; time duration: 200 before to 1800s after the time of the earthquake </p> <p>format: [TEC, Doppler shift, Geomagnetic X component, Geomagnetic Y component, Geomagnetic Z component]</p> <p>2. mag_data.m: data from 16 permanent geomagntic stations; time duration: 00:00 UT on 3, Apri, 2025 to 1800s after the time of the earthquake </p> <p>format: [Geomagnetic X component, Geomagnetic Y component, Geomagnetic Z component, the longitude and latitude of stations],</p> <p>Lines 1-16 indicate an increasing order of epicentral distance</p> <p>3. tec_data.m: tec data from 20 ionospheric piercing points; time duration: 00:00 UT on 3, Apri, 2025 to 1800s after the time of the earthquake </p> <p>format: tec, the longitude and latitude of stations</p> <p>Lines 1-20 indicate an increasing order of epicentral distance<br> </p>
FESOM2.1 model data used in the paper 'Atmospheric blocking slows ocean-driven melting of Greenland's largest glacier tongue'
<p>This data set includes the data necessary to reproduce the findings of McPherson et al., in revision, and recreate the figures in the manuscript.</p> <p>The output of model simulations with the global ocean sea ice model FESOM2.1 is provided. </p> <p>In particular, the data set includes:</p> <ol> <li>long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (<a target="_blank" rel="noopener noreferrer">fesom.mean.1970_2021.t.s.u.v.mat</a>)</li> <li>air-sea heat flux anomaly and anomalous wind field at 10m, taking mean winter (DJF) conditions between 2014 - 2016 from the mean of the years 2017 - 2020 (fesom.anom.fh.wind.mat)</li> <li>Atlantic Water temperature and velocity anomalies, taking mean winter (DJF) conditions between 2014 - 2016 from the mean of the years 2017 - 2020 (fesom.anom.t.u.v.mat)</li> </ol> <p>Each file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas) needed for plotting the data.</p>
Consistent set of atmospheric and oceanic angular momentum, 1994 - 2022
<p>Data supplement for manuscript Kiani Shahvandi, M., Schindelegger, M., Börger, L., Mishra, S., & Soja, B. (2024). Revisiting the excitation of free core nutation. <em>Journal of Geophysical Research: Solid Earth</em>, 129, e2024JB029583. <a href="https://doi.org/10.1029/2024JB029583">https://doi.org/10.1029/2024JB029583</a></p> <p> </p> <p>Specifications for AAM:</p> <ul> <li>Data source: Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2)</li> <li>Temporal resolution & coverage: 3 hourly, 1994--2022</li> <li>Units for mass and motion terms: kg*m^2*s^(-1)</li> <li>Processing: Stationary signals due to atmospheric tides have been removed</li> <li>Other processing steps: Mass terms include angular momentum changes associated with the ocean's inverted barometer (IB) response</li> </ul> <p> </p> <p>Specifications for OAM:</p> <ul> <li>Derived from a barotropic ocean model simulation (the DEBOT model w/o data assimilation) using MERRA-2 atmospheric forcing</li> <li>Temporal resolution & coverage: 3 hourly, 1994--2022</li> <li>Units for mass and motion terms: kg*m^2*s^(-1)</li> <li>Processing: Stationary signals due to atmospheric tides have been removed</li> <li>Other processing steps: Angular momentum changes associated with the ocean's IB response are not part of OAM, but AAM (see above)</li> </ul> <p> </p> <p>For further information see <em>ReadMe.txt</em>.</p> <p> </p> <p>Terms of usage:</p> <p>If you use the AAM time series, please cite: </p> <p>Kiani Shahvandi, M., Schindelegger, M., Börger, L., Mishra, S., & Soja, B. (2024). Revisiting the excitation of free core nutation. <em>Journal of Geophysical Research: Solid Earth</em>, 129, e2024JB029583. <a href="https://doi.org/10.1029/2024JB029583">https://doi.org/10.1029/2024JB029583</a></p> <p> </p> <p>If you use the OAM time series, please cite: </p> <p>Harker, A.A., Schindelegger, M., Ponte, R.M., Salstein, D.A., 2021. Modeling ocean-induced rapid Earth rotation variations: an update. <em>J Geod</em> <strong>95</strong>, 110. https://doi.org/10.1007/s00190-021-01555-z</p> <p> </p> <p>Please cite both papers if you use the AAM and OAM time series.</p> <p> </p> <p>Contact: L. Börger (lboerger@igg.uni-bonn.de)</p>
[Simulation] Dynamics, Monitoring and Forecasting of Tephra in the Atmosphere.
<p><span><span>This repository </span><span>contains</span><span> the simulations</span><span> to produce Figures 1</span><span>5</span><span> and 1</span><span>7</span><span> in " </span><span>Dynamics, Monitoring and Forecasting of Tephra in the Atmosphere</span><span> " - Reviews of Geophysics, by </span></span><span><span>Pardini</span> </span><span><span>et al. (2024).</span></span><span> </span></p>
Datasets for "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the datasets for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations".</p>
STIM model output for "Aurorally-driven Supersonic Gravity Waves in Saturn's Atmosphere"
<p>Saturn Thermosphere-Ionosphere Model (STIM) output corresponding to the figures shown in the paper "Aurorally-driven Supersonic Gravity Waves in Saturn's Atmosphere".</p>
Reproduction package for the paper "The open-source sunbather code: Modeling escaping planetary atmospheres and their transit spectra"
<p>This is a reproduction package for the paper "The open-source sunbather code: modeling escaping planetary atmospheres and their transit spectra" by Dion Linssen, Jim Shih, Morgan MacLeod & Antonija Oklopčić (2024). It provides a front-to-end reproduction script to reproduce the results and Figures 1-5 of the paper. Figures 6&7 can be reproduced with the example notebook found in the sunbather installation.</p>
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
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.