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238 results for “Atmosphere modeling”
Code and Data to support "Atmospheric circulation-constrained model sensitivity recalibrates Arctic climate projections"
<p><a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/sic.sep.5member.dat">sic.sep.5member.dat</a> contains direct binary data of spatial monthly averaged sea ice concentrations for 1979 January to 2020 December from the CESM2 wind-nudging runs.</p> <p><a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/cism2.exp.smb.01.nc">cism2.exp.smb.01.nc</a> to <a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/cism2.exp.smb.01.nc">cism2.exp.smb.05.nc</a> contain netcdf files of annual averaged surface mass balance output from the CESM2-CISM2 wind-nudging runs between 1979 and 2020.</p> <p>topal&ding_code1.py - data preparation Python code</p> <p>topal&ding_code2.py - creating the main text and supplementary figures.</p>
The simulated outputs analyzed in the article: "Understanding the influences of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere-ocean-wave-sea ice coupled model"
<p>In Ice-mass_[experiment] files, they include daily-averaged sea ice concentration and sea ice mass/area budgets.</p> <p>In Flux_[experiment] files, they include daily-averaged net ice surface flux, net shortwave/longwave radiation at the ice surface, latent/sensible heat flux at the ice surface, conductive heat flux at the top ice layer, and ice-ocean heat flux. </p>
Supporting data for Assessing clouds using satellite observations through three generations of global atmosphere models
<p>Monthly data from CAM4, CAM5, and CAM6 that are needed to reproduce the analysis and figures in the manuscript entitled: Assessing clouds using satellite observations through three generations of global atmosphere models by Brian Medeiros, Jonah Shaw, Jennifer Kay, and Isaac Davis.</p>
Data and Supplementary Plots for A Shallow Water Model Exploration of Atmospheric Circulation on Sub-Neptunes
<p>This repository contains geopotential maps, zonal wind plots, gifs, and data for the ensemble of possible sub-Neptunes presented in the main manuscript. The data, individual plots, and plot grids are based on the averages of the last 100 simulated days. The data are in the pickle format (see https://docs.python.org/3/library/pickle.html)<br> The gifs are based on the last 1000 simulated hours. These figures and gifs support the analysis presented in the main manuscript.</p>
1-D Model Files for Martian atmospheric chemistry of HCl: implications for the lifetime of atmospheric methane
<p>File containing 1-D model [netCDF] files used in the JGR: Planets manuscript "Martian atmospheric chemistry of HCl: implications for the lifetime of atmospheric methane".</p> <p><strong>MCD-model.zip</strong></p> <p>Contains data files where the 1-D model is driven using Mars Climate Database v5.3 standard climatological profiles of dust and ice aerosols and H2O. readme.txt provides details of the subdirectory structure.</p> <p><strong>TGO-model.zip</strong></p> <p>Contains data files where the 1-D model is driven using dust and ice aerosol profiles retrieved by the ACS TIRVIM and H2O profiles retrieved by either the NOMAD spectrometer or ACS NIR channel. readme.txt provides details of the directory structure.</p> <p><strong>matching_orbits.txt</strong></p> <p>Spatio-temporal details of the 77 ACS MIR HCl retrievals that we aim to reproduce, including the details of the approximately co-located ACS TIRVIM aerosol and NOMAD/ACS NIR H2O retrievals used to drive the 1-D model.</p>
Data and Code Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"
<p>Code and Data Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"<br> ===========================================================</p> <p>This directory contains the data and scripts used to create the plots from our publication as well as the source<br> code modifications necessary to run this test case within the CESM and MPAS models.</p> <p>Generating Plots<br> ---------------</p> <p>The `netcdf` directory contains the nominal half-degree runs necessary to generate nearly all of the plots from the paper. The one plot which is not reproducible from these data is the volume-integrated Eddy Kinetic Energy in the Spectral Element model. Storing high-resolution 4D wind fields requires a prohibitive amount of space. These data can be provided by the corresponding author, O.K. Hughes (owhughes@umich.edu). However, because this is several hundred GB of data I would strongly recommend generating these high-resolution runs yourself on your local system if you need them. Using 288 Intel Skylake cores (that is, 8 nodes each with two 18C processors) ran on the order of an hour.</p> <p><em>In order to generate the plots from the paper, you need only install NCL and then run</em> run.bash. Instructions for installing NCL<br> can be found in the `run.bash` script.</p> <p>Source Code Modifications<br> ----------------</p> <p><strong>CESM</strong><br> The `src` subdirectory contains the files `user_nl_cam` and `ic_baroclinic.F90`. Create a case using `--compset=FKESSLER` and `--run-unsupported` options when running `create_newcase`. If your case is located at `${CASE_DIR}`, then from within the directory containing this README, run `cp user_nl_cam ${CASE_DIR}/user_nl_cam`, and then run `cp ic_baroclinic.F90 ${CASE_DIR}/SourceMods/src.cam/`. Then build and run the model using the usual workflow.</p> <p><strong>MPAS</strong></p> <p>The MPAS code was run using a branch of the MPAS model provided by the model developers to the authors. While the source code modifications are provided in the `src` directory, I would strongly recommend contacting the corresponding author if you wish to run this test case in the MPAS codebase.</p>
Parameters for smooth exponential atmosphere density model based on Jacchia-77
<p>The data sets provided here can be used to derive the <a href="https://doi.org/10.1016/j.asr.2019.03.016">smooth exponential atmosphere density</a> profile, fitted to the Jacchia-77 atmosphere density model. Both the static (at <span class="math-tex">\(T_\infty = 750, 1000, 1250K\)</span>) and variable model (for <span class="math-tex">\(T_\infty \in [650, 1350] K\)</span>) parameters are available.</p> <p>The model was derived to increase the accuracy of semi-analytically propagated orbits, in particular highly eccentric ones.</p>
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
Open the record for dataset details and reuse information.
Comparison between ozone column depths and methane lifetimes computed by 1-D and 3-D models at different atmospheric O2 Levels
Open the record for dataset details and reuse information.
Photochemical model output data associated with: How to identify exoplanet surfaces using atmospheric trace species in hydrogen-dominated atmospheres
Open the record for dataset details and reuse information.
Regional Atmospheric Climate Model 2 (RACMO2), version 2.3p2
<p>In the 1990s the KNMI developed in cooperation with the Danish Meteorological Institute the research model RACMO based on the High Resolution Limited Area Model (HIRLAM) numerical weather prediction model. In 1993 UU/IMAU started to modify the model such that it better represented the extreme conditions over glacier surfaces. This first version of RACMO, RACMO1, combined the dynamical core of the HIRLAM model with ECHAM4 physics. The polar modified version of RACMO1 was mainly applied to the Antarctic Ice Sheet.</p> <p>The second version, RACMO2, combines the dynamical core of the HIRLAM model with the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecast System (ISF) physics. RACMO versions 2.0 and 2.1 included HIRLAM version 5.0.6 and ISF cycle CY23r4, while version 2.3 includes HIRLAM version 6.3.7 and cycle CY33r1. Due to the rapid increase in computer capacity over the years, these versions of RACMO have not only been applied to the Greenland and Antarctic Ice Sheets, but also at higher resolution to smaller areas such as Dronning Maud Land and Patagonia.</p> <p>For the RACMO model in general the grids are defined over the equator and then rotated to the area of interest. Grid distance is defined in fraction of degrees, which results in near equidistant grid points as long as the domain is small enough. Note that the domain is thus not on a (polar) stereographic projection plane. In the vertical, the model adopts a system of hybrid sigma levels, which evolve from terrain-following sigma levels close to the surface to pure pressure levels at higher elevation. The actual number of horizontal grid points varies per model run; in most simulations, 40 vertical layers were used.</p> <p>Since RACMO is a regional model, it needs external information at the lateral boundaries and sea surface. At the lateral boundary zone of the model, the temperature, specific humidity, zonal and meridional wind components, and the surface pressure are relaxed towards the fields of a global model every 6 model hours, as are the sea surface temperature and sea ice concentration. RACMO is not forced at the model top. The interior of the model is not nudged towards observations and allowed to evolve freely.</p>
Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>Gzipped CSV files containing convection scheme inputs and outputs used for training.</p> <p>Column format of each file:</p> <p>THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28</p> <p>where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg].</p> <p>Key:</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>All are 30 day simulations either for January "jan" or July "jul".</p> <p> </p>
Sample of initial condition data for the ABC simplified atmospheric model and data assimilation system (vn1.4da)
<p>The file contains a link to a sample of initial condition data for use with the ABC simplified atmospheric model and data assimilation system (vn1.4da).</p>
Supplement to "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions"
<p>This dataset contains data from two measurement campaigns in autumn 2018 and summer 2019 that were part of the BMWi project "MetPVNet", and serve as a supplement to the paper "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions", published in the special edition of "Advances in Science and Research", the proceedings of the 19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019.</p> <p>Data are resampled to one minute, and include:</p> <ol> <li>PV module temperature</li> <li>Ambient temperature</li> <li>Plane-of-array irradiance</li> <li>Windspeed</li> <li>Atmospheric thermal emission</li> </ol> <p>The data were used for the dynamic temperature model, as presented in the paper</p>
Lagrangian Atmospheric Model output for the Control, Onset and Development experiments
<p><span><span><span><span><span><span><span><span><span><span><span>This dataset consists of model outputs from the Control, Onset and Development experiments. Each experiment has six ensembles which were integrated for three years. 6-hourly instantaneous surface (1000hPa) zonal and meridional wind speed (m/s) between 31°S-31°N is recorded in uv1000hPa_{Control, Onset, Development}_ens{1, 2, 3, 4, 5, 6}.dat. 6-hourly accumulated precipitation (mm) between 32°S-32°N is stored in precip_{ Control, Onset, Development}_ens{1, 2, 3, 4, 5, 6}.dat. Note the precip.*dat records accumulated rainfall for the past 6 hours. The missing data is recorded as nan.</span></span></span></span></span></span></span></span></span></span></span></p>
Contrasting effects of Miocene and Anthropocene levels of atmospheric CO2 on silicon accumulation in a model grass
<p>Grasses are hyper-accumulators of silicon (Si) which they acquire from the soil and deposit in tissues to resist environmental stresses. Moreover, given the high metabolic costs of herbivore defensive chemicals and structural constituents (e.g. cellulose), grasses may substitute Si for these components when carbon (C) is limited. Indeed, high Si uptake grasses evolved in the Miocene when atmospheric CO<sub>2 </sub>concentration was much lower than present levels. It is; however, unknown how pre-industrial CO<sub>2</sub> concentrations affect Si accumulation in grasses. Using <em>Brachypodium distachyon</em>, we hydroponically manipulated Si-supply (0.0, 0.5, 1, 1.5, 2 mM) and grew plants under Miocene (200 ppm) and Anthropocene levels of CO<sub>2</sub> comprising ambient (410 ppm) and elevated (640 ppm) CO<sub>2</sub> concentrations. We showed that regardless of Si-treatments, the Miocene CO<sub>2</sub> levels increased foliar Si concentrations by 47% and 56% relative to plants grown under ambient and elevated CO<sub>2</sub>, respectively. This is due to higher accumulation overall, but also the reallocation of Si from the roots into the shoots. Our results suggest that grasses may accumulate high Si concentrations in foliage when carbon is less available (i.e. pre-industrial CO<sub>2</sub> levels) but this is likely to decline under future climate change scenarios, potentially leaving grasses more susceptible to environmental stresses</p>
Modeled Atmospheric Optical and Thermodynamic Responses to an Exceptional Trans-Atlantic Dust Outbreak
<p>Nine WRF-Chem 3.8.0 hindcasts, each utilizing a different dust emission configuration, from 1 March – 31 May 2015, coinciding with a Saharan air layer (SAL) dust outbreak during the 2015 Caribbean drought. WRF-Chem modeled aerosol optical depth (AOD) and Gálvez-Davison Index (GDI), a convective forecasting parameter, are provided in the files. These data correspond to the results published in "Modeled Atmospheric Optical and Thermodynamic Responses to an Exceptional Trans-Atlantic Dust Outbreak" by Paul Miller, Marcus Williams, and Thomas Mote, published the <em>Journal of Geophysical Research: Atmospheres</em>.</p>
Simulation data for WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions
<p>This repository provides the test simulation data for "WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions" published in Geoscientific Model Development. The configurations for sensitivity experiments are described in this paper. Please contact the corresponding author Tzung-May Fu (fuzm@sustech.edu.cn) for more details.</p>
MARCS (2011) model atmospheres
<p>This is a Python-pickled version of the MARCS 2011 model atmospheres.</p>
LMDZ6iso output - Antarctic water stable isotopes in the global atmospheric model LMDZ6: from climatology to boundary layer processes
<h3><strong>Update of the Simulation</strong></h3> <p dir="ltr">This archive contains the output of a revised simulation, addressing issues identified in the original simulation described in the following article:</p> <p><em>Dutrievoz, N., Agosta, C., Risi, C., Vignon, É., Nguyen, S., Landais, A., ... & Prié, F. (2025). Antarctic water stable isotopes in the global atmospheric model LMDZ6: From climatology to boundary layer processes. Journal of Geophysical Research: Atmospheres, 130(5), e2024JD042073.</em></p> <p>All information is available here:<strong> <a href="https://docs.google.com/document/d/1x4FSGtNiJGqsMoks38yATzTQJ7T8sO8iXqeWagTU5yE/edit?usp=sharing">README</a></strong></p> <p>Researchers and colleagues are welcome to contact me at <strong>niels.dutrievoz@lsce.ipsl</strong>.<strong>fr</strong> for any further information regarding this updated simulation or potential collaborations. Please feel free to reach out if you are interested in daily or hourly output data from this simulation.</p> <p dir="ltr">I look forward to any exchanges that might arise from this work.</p> <p> </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.