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1,574 results for “atmospheres”
Attached_eddy_model_in_the_atmosphere
<p>This archive contains data files used in the paper titled "Asymptotic Limits of the Attached Eddy Model Derived from an Adiabatic Atmosphere". <br>If you have any questions, feel free to contact yueqin@bu.edu.</p>
No increase is detected and modelled for the seasonal cycle amplitude of δ13C of atmospheric carbon dioxide: scripts and data to prepare figures
<p>The file contains the scripts and data to plot the graphics displayed in Joos et al., No increase is detected and modelled for the seasonal cycle amplitude of δ13C of atmospheric carbon dioxide, Biogeosciences, in press, November 2024.</p>
Data from: Adaptations and responses of the common dandelion to low atmospheric pressure in high altitude environments
<p>Atmospheric pressure is an important, yet understudied factor that may shape plant ecology and evolution.</p> <p>By growing plants under controlled conditions at different experimental stations in the Swiss alps, we evaluated the impact of ecologically realistic atmospheric pressures between 660 and 950 hPa on the growth and defence of different dandelion populations.</p> <p>Low atmospheric pressure was associated with reduced root growth and defensive sesquiterpene lactone production. Defence suppression only occurred in populations originating from lower altitudes. Populations from higher altitudes constitutively produced less sesquiterpene lactones and did not suffer from suppression under low atmospheric pressure.</p> <p><em>Synthesis</em>. We conclude that atmospheric pressure modulates root growth and defence traits, and that evolutionary history shapes plant phenotypic responses to atmospheric pressure. Our findings have important implications for our understanding of altitudinal gradients and the future use of plants as a source of food and bioactive metabolites in extraterrestrial habitats.</p>
Model data for: Drawdown of atmospheric pCO2 via dynamic particle export stoichiometry in the ocean twilight zone
<p>Understanding the global carbon cycle is key to understanding the climate system. One of the large unknowns is the processes happening in the twilight zone of the ocean. Here, we focus on how elemental stoichiometry of particulate organic matter in the twilight zone affects the strength of the biological pump and atmospheric CO2. We show through modeling that atmospheric CO2 is very sensitive to the change in C:P ratio in the twilight zone. Numerous model studies study the link between the carbon cycle and flexible elemental stoichiometry of organic matter in the surface ocean. However, our model study is unique. It investigates the effects of stoichiometric changes both at the surface and in the subsurface ocean that also involve stoichiometric interaction between phytoplankton and zooplankton.<br> <br> We use a 3D numerical model to illustrate how C:P variability in the twilight zone can significantly modulate the strength of carbon sequestration and atmospheric CO2. We used the biogeochemical model MOPS (Kriest and Oschlies, Geosci. Model Dev., 8, 2929–2957, 2015) coupled to ECCO Transport Matrices.</p> <p>This repository contains model input and output files for each sensitivity run outlined in the paper. The run ID corresponds to different sensitivity run. See README for more details. </p>
A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
<p>Data used to generate the figures in "A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model" 2021. The zip files contains the hybrid forecasts and regridded ERA5 data used to verify the forecasts as well as the SPEEDY-LLR and ML-only runs. </p>
Ground thermal image temperatures (Campi Flegrei and Vesuvius) and atmospheric temperature and pressure
<p>The dataset contains the ground thermal image temperatures measured by the thermal infrared camera network of Campi Flegrei and Vesuvius (Italy) and the temperature and pressure acquired by a local meteo station. The data refer to the period Jun 25, 2016-May 29, 2020.</p>
Data used in the publication "Streamer discharges in the atmosphere of Primordial Earth"
<p>Simulation data used to create Figs. 2 and 4 of "Streamer discharges in the atmosphere of Primordial Earth"</p>
Speciation data for "Genesis of a CO2-rich and H2O-depleted atmosphere from the Earth's early global magma ocean"
<p>Here we present the speciation statistics for volatile-bearing pyrolite melts. For computational details and nomenclature, please refer to the manuscript, "Genesis of a CO2-rich and H2O-depleted atmosphere from the Earth’s early global magma ocean" by N.V. Solomatova and R. Caracas.</p> <p>The data is organized by composition and temperature.</p> <p>File names contain the lattice parameter (e.g., a16.0) in Angstroms, indicating that the cell is 16x16x16 Angstroms in size, for example. The corresponding volumes and densities are tabulated in each folder (e.g., "4CO_4000K_densities.dat"). </p> <p>The first column of each *stat.dat file contains the species type. Long species (e.g., Na_1Ca_2Fe_4Mg_30Al_3Si_24O_93C_4) typically represent the silicate melt and short species (e.g., O_1C_1 and O_2C_1) represent the vapor phase. Underscores represent subscripts (e.g., O_2C_1 denotes a CO2 molecule). The second column is the lifetime of the species in femtoseconds. The third column is the percent of the total. The fourth column is the number of elements in each species (e.g., O_1C_1 has 2 elements and O_2C_1 has 3 elements). Note that the majority of the "H_1" species represent free protons within the melt phase.</p>
Supporting data for Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions
<p>Supporting data for "Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions" by Michael F. Howland, Aditya S. Ghate, Jesús Bas Quesada, Juan José Pena Martínez, Wei Zhong, Felipe Palou Larrañaga, Sanjiva K. Lele, and John O. Dabiri</p> <p>See README for data description.</p> <p>The code is available at: https://github.com/FPAL-Stanford-University/PadeOps</p>
Comparison of atmospheric refractivity estimation methods and their influence on radar wave propagation predictions
<p>Environmental predictions in the marine atmospheric surface layer (MASL) are imperative to optimize X-band radar system performance in marine environments. Evaporation ducts (ED) lead to anomalous propagation where characterization of EDs in the MASL occurs primarily through two methods: <i>in-situ </i>measurements and numerical modeling. This study investigates differences in co-located and synchronous refractivity estimations from the CASPER-East campaign. Propagation predictions are generated for refractive profiles from <i>in-situ </i>measurements, Monin-Obukov boundary layer similarity theory, and numerical weather prediction forecasts. Variations in evaporation duct height (EDH) are found to be a primary driver of differences in propagation between the estimated refractivity profiles, where location of the EDH relative to the transmitter changes the sensitivity of propagation predictions to EDH estimates. Differences in propagation are large when EDH estimates span the transmitter height and the lowest EDH across the methods is small, regardless of how much variation there is in EDH estimates. When the lowest EDH is small and EDH estimates span the transmitter height there are differences in physical regimes causing large propagation discrepancies – e.g., leakage into versus trapping within the duct. Variation in EDH between the methods is greatest in stable environments. M-deficit and curvature of the refractive profiles also influence propagation specifically in scenarios when EDH spans the transmitter. When all EDHs are below the transmitter, EDH variance is the primary contributor to propagation variance, but M-deficit and profile curvature variance play a secondary role. M-deficits and curvature between the methods agree most often during periods of atmospheric stability.</p>
INMET data - "Intensification of heatwaves by soil moisture–atmosphere coupling in Southeast Brazil"
<p>Time series of Daily Maximum Temperature values from two meteorological stations from INMET (Brazilian National Institute of Meteorology) located in São Paulo and Curitiba.</p>
Datasets for :Atmospheric chemistry of oxalate: Insight into the role of relative humidity and acidity from high-resolution observation.
<p>The datasets in this file were used to generate figures for the paper titled"Atmospheric chemistry of oxalate: Insight into the role of relative humidity and acidity from high-resolution observation."</p> <p>The datasets include the hourly concentration of atmospheric composition and meteorological parameters in Nanjing Area.</p>
A complete view of the atmospheric hydrologic cycle
<p>fig01.zip: Inside this zip there are 4 files which were used to make Figure 1. The 3 csv files are used to plot the trajectory pathways from their starting positions to the ending locations. The variable 'ntopo' in the single netcdf file represents the ocean basin and global landmass mask.</p> <p>fig02_03_04.zip: In this zip there are 192 netcdf files and were used to produce Figure 2, 3 and 4. Each file is for a month and consists of evaporation, precipitation, meridional and zonal overturning stream function within and between the three major ocean basins and the global landmass. Here atl = Atlantic Ocean, pac=Pacific Ocean,ind = Indian Ocean, and lnd = global landmass.</p> <p>fig05.zip: In this zip there are also 192 netcdf files that were used to produce Figure 5.</p> <p>fig06.zip: Consists of 16 nectdf files that represent average residence time (days) of the atmospheric waters travelling from the surface evaporative regions to the precipitation areas. This residence time was calculated from the points where net evaporation exceeds a monthly mean value of 0.2 mm/day.</p> <p>table1.zip: This zip file was used to calculate the atmospheric freshwater transport within and between the ocean basins and land.</p>
Trajectories and metadata for Bach et al.: 'Holistic re-evaluation of Southern Ocean Iron Fertilization for atmospheric CO2 removal'
<p>Trajectory and metadata files from a Southern Ocean surface particle release experiment using Connectivity Modeling System (Paris et al. 2013, <a href="https://github.com/beatrixparis/connectivity-modeling-system">https://github.com/beatrixparis/connectivity-modeling-system</a>) run offline in the MOM01 model (Stewart et al., 2017; Spence et al., 2017; Morrison et al., 2020), a global 0.1° ocean sea-ice model, based on version 5 of the Modular Ocean Model (MOM) code (mom-ocean.github.io) (Griffies, 2012).</p> <p> Here particle trajectories are are split into 28 netcdf files (numbered traj_file_01.nc through traj_file_28.nc), and uploaded altogether as a single .tar.gz compressed file. Metadata files are also uploaded containing information on the CMS setup and release locations.</p> <p>Netcdf files contain particle trajectory positions, along with temperature, salinity, status in or out of the mixed layer, release date, and particle exit status (see <a href="https://github.com/beatrixparis/connectivity-modeling-system/blob/master/User-Guide-v2.pdf">CMS user guide</a> for details on trajectory files). Other CMS setup files included in each release directory are nest_1.nml, runconf.list, ibm.list, and releaseFile_matchtransport. These input files contain information needed to reproduce the experiment using the MOM01 model output, and are explained in detail in the <a href="https://github.com/beatrixparis/connectivity-modeling-system/blob/master/User-Guide-v2.pdf">CMS user guide</a>.</p> <p> </p> <p>Citation of associated paper: Bach, L., V. Tamsitt, K. Baldry, J. McGee, E. Laurenceau-Cornec, R. Strzepek, Y. Xie, P. W. Boyd, 2021. Holistic re-evaluation of Southern Ocean Iron Fertilization for atmospheric CO<sub>2</sub> removal, <em>submitted </em></p> <p> </p> <p>References:</p> <p>Griffies, S. M. (2012). Elements of the modular ocean model (MOM). <em>GFDL Ocean Group Tech. Rep</em>, 7(620), 47.</p> <p> Morrison, A. K., Hogg, A. M., England, M. H., & Spence, P. (2020). Warm Circumpolar Deep Water transport towards Antarctic driven by local dense water export in canyons. <em>Science Advances</em>, 6(18), eaav2516.</p> <p> Paris, C. B., Helgers, J., van Sebille, E., & Srinivasan, A. (2013). Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean. <em>Environmental Modelling and Software</em>, 42, 47-54.</p> <p>Spence, P., Holmes, R. M., Hogg, A. M., Gries, S. M., Stewart, K. D., & England, M. H. (2017). Localized rapid warming of West Antarctic subsurface waters by remote winds. <em>Nature Climate Change</em>, 7(8), 595.</p> <p>Stewart, K., Hogg, A. M., Gries, S., Heerdegen, A., Ward, M., Spence, P., & England, M. H. (2017). Vertical resolution of baroclinic modes in global ocean models. <em>Ocean Modelling</em>, 113, 50-65.</p>
Amplification of the divergent component from balanced motions and forward kinetic energy cascade in an ocean-atmosphere simulation
<p>This dataset contains animations of:</p> <ol> <li> <p>The temporal evolution of ocean currents partitioned into balanced motions (BMs) and non-BMs (including internal gravity waves and low-frequency wind-driven currents), displayed in the physical domain for COAS (top row) and Ocean-forced (bottom row). Both simulations illustrate kinetic energy (KE) together with velocity gradients—vorticity ($\zeta$) and horizontal divergence ($\delta$)—all normalized by the Coriolis frequency.</p> </li> <li> <p>The temporal evolution of low-frequency (lf) and high-frequency (hf) non-BM currents in the physical domain for COAS (top row) and Ocean-forced (bottom row).</p> </li> </ol>
Water "Pump" in the Atmosphere of Mars: Modeling Vertical Transport to the Thermosphere
<p>Recent studies link the observed hydrogen escape in the Martian thermosphere to the water of lower atmospheric origin. However, the cold mesosphere hinders penetration of vapor into the upper atmosphere. We present results of simulations with the Max Planck Institute general circulation model (MPI-MGCM) implementing a state-of-the-art hydrological cycle scheme. The simulations reveal a seasonal water ``pump” mechanism responsible for the upward transport of vapor. It takes place in high latitudes of the southern hemisphere at perihelion, when the upward branch of the meridional circulation is particular strong. A combination of the mean vertical flux with variations induced by solar tides facilitates penetration of water across the “bottleneck” at approximately 60 km. The meridional circulation then transports water across the globe to the northern hemisphere. Since the intensity of the meridional cell is tightly controlled by airborne dust, the water abundance in the thermosphere strongly increases during dust storms.</p>
Figure data of "Measurement report: Molecular-level investigation of atmospheric cluster ions at the tropical high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes"
<p>This dataset involves the data that is used for the figures in "Measurement report: Molecular-level investigation of atmospheric cluster ions at the tropical high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes".</p>
Assessing the atmospheric response to subgrid surface heterogeneity in CESM2 - code, output, and analysis script
<p>This dataset contains the raw model output, analysis scripts, and CESM source code (containing modifications to include subgrid-scale surface heterogeneity impacts in CAM) that were used to produce the figures in a manuscript targeting the Journal of Advances in Modeling Earth Systems (JAMES) for submission, "Assessing the atmospheric response to subgrid surface heterogeneity in CESM2". For additional information on CESM2 and the most up to date source code, see https://github.com/ESCOMP/CESM. </p> <p>The pre-print of the submitted article is available here: <a href="https://doi.org/10.1002/essoar.10512837.1">https://doi.org/10.1002/essoar.10512837.1</a></p>
Data and code for Seeley and Wordsworth, "Moist convection is most vigorous at intermediate atmospheric humidity"
<p>Data and code for Seeley and Wordsworth, "Moist convection is most vigorous at intermediate atmospheric humidity"</p>
MUA PRR_lidar temperature profiles for atmospheric refraction correction in lunar laser ranging
<p>This PRR lidar temperature data are used for atmospheric refraction corrrection in millimeter-level precision lunar laser ranging.</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.