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695 results for “model output”
Datasets and model outputs used for validating WASP for SURFEX v8.0
<p>These datasets contain the model outputs used for validating the WASP SURFEX v8.0 parameterization of turbulent fluxes at sea. The file LION_BUOY.tar contains (ascii format) the tubrulent fluxes computed from the surface parameters recorded at the Météo-France LION buoy (in the centre of the Gulf of Lion, NW Mediterranean Sea) at an hourly frequency between 2001 and 2014, using the bulk softwares COARE 3.0 with various wave representation and WASP with waves. The file AROME_MED.tar contains the outputs of the AROME operational model with ECUME or WASP representation of the turbulent fluxes, on the case study of October 2016, that were used to validate the parameterization as presented in the paper. The file AROME_OI.tar contains the outputs of AROME OI on the case of the tropical cyclone Batsirai in the Indian Ocean, in February 2022. The file ARPEGE_CLIM_AMIP contains the outputs of the low-resolution, AMIP runs performed using the ECUME and WASP parameterization.</p>
Forcing data, evaluation data, model output and analysis scripts used in LPJ-GUESS/LSM description paper
<p>This archive contains:</p> <p>- model_output.tar.gz: Model output<br> - extracted_fluxes.tar.gz: Sensible heat, latent heat and CO2 fluxes extracted from the FLUXNET2015 dataset, used to evaluate the model output<br> - extracted_climate.tar.gz: Climate data extracted from the FLUXNET2015 dataset, used to force the simulations<br> - scripts.tar.gz: Python scripts used to analyze the data and produce the results reported in the model description paper</p> <p>The climate forcing data has been extracted from the FLUXNET2015 dataset [1]. Input and output data is stored in netCDF files. Evaluation data is stored in python serialized files (pickle).</p> <p>[1] Pastorello, G., Trotta, C., Canfora, E. <em>et al.</em> The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. <em>Sci Data</em> <strong>7, </strong>225 (2020). https://doi.org/10.1038/s41597-020-0534-3</p>
Input data and model output for study about wind changes and impact on the Subtropical Front
<p>This dataset contains:</p> <p>Model data for the CONTROL simulation (CONTROL.gz)</p> <p>Model data for the SHIFT simulation (SHIFT.gz) where the westerly winds have been shifted by 1degree per decade</p> <p>Model data for the INCREASE simulation (INCREASE.gz) where the westerly winds have been incresaed by 1 percent per decade</p> <p>Reference dataset are provided (Argo.gz and Modiz.gz)</p>
Bering10K BEST_NPZ ROMS model: Carbon dioxide removal simulation output
<p>This dataset accompanies our manuscript:</p> <p>Wang H, Pilcher DJ, Kearney KA, Cross JN, Shugart OM, Eisaman MD, Carter BR. Simulated impact of ocean alkalinity enhancement on atmospheric CO2 removal in the Bering Sea. Submitted to Earth's Future, in review</p> <p>The dataset includes selected output variables (temperature, salinity, and carbonate system variables) from two 10-year simulations of the Bering10K ROMS application with BEST_NPZ biogeochemistry. The simulations represent a control run and a point source alkalinity enhancement; see paper for further details. The source code used to run this this simulation is available on Github (https://github.com/beringnpz/roms-bering-sea) and archived at DOI: 10.5281/zenodo.7062782.</p>
Model outputs in the northern shelf of the South China Sea
<p>The data used in the figure of manuscript are available in Figure.rar file. The 201706XX_Chla_budget_2.mat files are the model outputs of the chlorophyll budget terms after process. The Biogeo*.mat files are the model outputs of the chlorophyll biological terms after process. The nutrient*.mat files are the model outputs of the nutrients (no3 and po4).</p>
Model output dataset used in "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China"
<p>This dataset accompanies the paper by Zhang et al. "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China".</p> <p>Three heavy precipitation events were modeled using the WRF v3.9 model:</p> <p>(1) The_21_July_Beijing_Rainstorm_Simulation<br> (2) The_30_July_Ningxia_rainstorm_Simulation<br> (3) The_19_June_Jiangxi_rainstorm_Simulation</p> <p>Furthermore, three cases were designed for each heavy precipitation event: (1) control experiment (DEFAULT), using the default M-O option (<em>C<sub>zil</sub></em> ~ 0); (2) constant <em>C<sub>zil</sub></em> (CZIL0.01, CZIL0.05, CZIL0.1, CZIL0.3, CZIL0.5 and CZIL0.8), with <em>C<sub>zil</sub></em> values of 0.01, 0.05, 0.1, 0.3, 0.5, and 0.8; (3) a dynamic canopy-height dependent <em>C<sub>zil</sub></em> (NEWCZIL).</p> <p>Plain Language Summary for this paper:<br> Over recent decades, the frequent occurrence of heavy precipitation events has caused devastating ecological and socioeconomic impacts, such as agriculture losses, infrastructure damage, and casualties. High-resolution atmospheric modeling at a convection-permitting grid spacing (≤4 km) provides valuable applications for predicting heavy precipitation. Precipitation can be strongly affected by the energy and moisture exchanges between land surface and atmosphere. However, the representation of land-atmosphere interactions in atmospheric models and the responses of precipitation to land-atmosphere exchange efficiency remain great uncertainties. This study performed 3-km high-resolution atmospheric modeling with a dynamic vegetation-type-dependent land-atmosphere exchange scheme for three typical heavy precipitation events that occurred over areas with different dominant land-cover types. The results showed that land-atmosphere exchange efficiency mainly affected the precipitation intensity as well as the onset and peak time of precipitation. The dynamic exchange scheme modifies the efficiency of land-atmosphere exchanges to match local land cover conditions and could reproduce well the field observations, especially the intensity and location of the heaviest rainfall which usually serve as the most concerned variable in a major rainstorm event. Our findings show that the dynamical scheme could help achieve more accurate precipitation simulations.</p>
MIROC4-ACTM: Model setup, input and output data for CH4 LETKF (Bisht et al., GMD-D, 2022)
<p>Details in :</p> <p>Bisht, J. S. H., Patra, P. K., Takigawa, M., Sekiya, T., Kanaya, Y., Saitoh, N., and Miyazaki, K.: Estimation of CH<sub>4</sub> emission based on advanced 4D-LETKF assimilation system, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-719, 2022.</p>
Output climate model parameters reported in Izquierdo et al., (2022)
<p>Data set containing an ensemble of model parameters for each of the candidate climate models in Table 1 of Izquierdo et al., (2022). These ensembles are stored as Python objects using the Pickle module, identified by the accumulation and lag sub models they contain and the number of steps used in the Markov chain Monte Carlo algorithm. From each Python object, it can be extracted the distribution of accumulation and retreat rates with time following the scripts and notebooks of the repository referenced in the open research section of the paper. </p> <p>The folders in this repository refer to the ensembles of all candidate models dependent of insolation values (insolation), ensembles of all candidate models dependent on obliquity values (obliquity, ensembles of best fit models (files that are not within a folder) and ensembles of best fit models with the addition of an age prior of 2 My. </p>
Model outputs in the northern shelf of the South China Sea
<p>The data used in the figure of manuscript are available in Figure.rar file. The 201706XX_Chla_budget_2.mat files are the model outputs of the chlorophyll budget terms after process. The Biogeo*.mat files are the model outputs of the chlorophyll biological terms after process. The nutrient*.mat files are the model outputs of the nutrients (no3 and po4). *NoR means the NoRiver run. *ZMix means the ZMix run.</p>
MAgPIE model runs outputs: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>Each folder contains the fulldata.gdx and the configuration files for each MAgPIE run based on the nine crop impact models and 5 gcms used in the paper.</p>
Ensembled projection outputs of Lake Sunapee using multiple General Circulation models and Lake models
<p>This dataset contains the ensemble projection output of Lake Sunapee, NH. The conception and methods of the dataset is explained in the manuscript "Uncertainty in projections of future lake thermal dynamics is differentially driven by global climate models and lake models."</p>
Model output for: Rate of mass loss across the instability threshold for Thwaites Glacier determines rate of mass loss for entire basin
<p><strong>Results from “Rate of mass loss across the instability threshold for Thwaites Glacier determines rate of mass loss for entire basin.”</strong></p> <p>The tar files herein contain multi-resolution grounding line position data and 4 km resolution output of modeled fields for all model runs. The region of interest represented is the Thwaites catchment in West Antarctica. The files for modeled fields have been coarsened or “flattened” to 4 km from their original adaptive mesh refinement (AMR) structure.</p> <p>Metadata contents</p> <p><em>1. Grounding line position data – text files</em></p> <p><em>2. Modeled Fields – HDF5 files</em></p> <p><em>3. BISICLES grid and coordinate system</em></p> <p> </p> <p><em>1. Grounding line position data</em></p> <p>Tar Files with "GLposition" in the title contain the annual grounding line positions, at cell faces, over the discretized Thwaites catchment for the specified model run. Each tar file contains a series of text files for a particular model run that used a specific background melt rate. The background marine melt rate is specified in the third field of the tar file name as delimited by the underscore character (“_”). Additionally, the last year of anomalous forcing before it was turned off leaving only the background marine melting is listed in the third field.</p> <p>nonuniformMM indicates the non-uniform background marine melting.</p> <p>uniformMM indicates the uniform background marine melting.</p> <p>260 and 270 are the last model years where anomalous marine melting were applied.</p> <p>After untarring a file, the naming convention for the individual text files is seen to be similar to the name of the respective tar file. The first field as delimeted by the underscore character contains either “glnonMM” or “gluniMM” followed by the last year of anomalous forcing used; e.g “gluniMM260”. The second field indicates the model year. Note that the last year forced is included for all runs.</p> <p>The text files contain three columns of data: an indicator of model resolution followed by <em>x- </em>and<em> y-</em>coordinates, respectively. Location coordinate units are meters and are BISICLES physical coordinates (see 3. BISICLES grid and coordinate system).</p> <p>For the first column:</p> <p>1 is 2 km resolution</p> <p>2 is 1 km resolution</p> <p>3 is 500 m resolution</p> <p>4 is 250 m resolution</p> <p>Zero (0) would be the base resolution of 4 km, however, all grounded ice was tagged to refine to level 1 so it does not appear in these files. Additionally, if a region was refined at a high resolution, then the grounding line positions for this region are not reported at any lower resolutions below this.</p> <p> </p> <p><em>2. Modeled fields</em></p> <p>Modeled fields are 4 km resolution in Chombo HDF5 file format. Each tar file contains the annual data as individual HDF5 files for the specified model run. The second field of the tar file as delimited by the underscore character specifies the background melt rate used and the last year of anomalous marine forcing (ramp).</p> <p>NonUniformMM260 indicates the non-uniform background marine melt rate with ramp shutoff after year 260.</p> <p>NonUniformMM270 same as above but ramp shutoff at year 270</p> <p>UniformMM260 indicates the spatially uniform background marine melt rate with ramp shutoff after year 270</p> <p>UniformMM270 same as above but ramp shutoff at year 270</p> <p>HDF5 files: The third field as delimited by the period (“.”) character shows the background melt rate used in individual HDF5 files and the fifth field indicates the model year.</p> <p>Contents of HDF5 files (field name: variable)</p> <p>xVel: velocity in the direction of the x-axis (m/a)</p> <p>yVel: velocity in the direction of the y-axis (m/a)</p> <p>Z_surface: upper ice surface elevation (masl)</p> <p>Z_bottom: underside surface ice elevation (masl)</p> <p>Z_base: bed elevation (masl)</p> <p>basal_friction: Basal friction coefficients</p> <p>div_uh: mass divergence</p> <p>mask: differentiates physical setting of cells (Note that coarsening introduces averages of numbers below at interfaces)</p> <p> grounded ice = 1</p> <p> floating ice = 2</p> <p> ocean = 4</p> <p> rock = 8</p> <p>basalThicknessSource: melt rate (m/a)</p> <p>surfaceThicknessSource: accumulation rate (m/a)</p> <p>surfaceThicknessBalance: sum of melt rate and accumulation rate (m/a)</p> <p> </p> <p><em>3. BISICLES grid and coordinate system</em></p> <p>The BISICLES model uses cell-centered grids with each cell represented by (i, j) pairs that begin numbering at (0,0) typically in the lower left hand corner of a domain. This project maintained the number ordering for the continental dataset such that (i = 366, j = 561) is the lower left cell for the included 4 km resolution HDF5 files and (i = 504, j=732) is the upper right cell. As the resolution is 4 km, this is noted as dx = 4000 in the HDF5 files.</p> <p>Since the data is located at cell centers, the physical coordinates relative to the BISICLES grid for a variable at (i, j) in meters is:</p> <p>(dx*(i + 0.5), dx*(j + 0.5)) = (BISICLES_X, BISICLES_Y)</p> <p>where dx is the cell resolution</p> <p>The translation from BISICLES physical coordinates (m) to polar stereographic projection in meters (standard parallel at -71 degrees) is as follows:</p> <p>(BISICLES_X – 3071500, BISICLES_Y – 3072500)</p> <p><br> </p> <p><br> </p> <p><br> </p> <p><br> </p> <p> </p>
Realistic and idealized model output of surface currents and tracers used in frontal maintenance analysis.
<h1>Realistic model</h1> <p><em>./surface_child.zarr</em> directory is a zarr dataset containing a realistic model model based on the Regional Ocean Modeling System \citep[ROMS][]{haidvogel.ea:08} configured with a high-resolution ($\sim$300~m), two-way nested child domain within a coarser ($\sim$1.5~km in the region of nesting) model of the Texas-Louisiana continental shelf, described by \citet{schlichting.ea:23}. The coarser parent model includes the entire coastlines of Louisiana and Texas, and has been run over multiple years of simulated time to examine, for example, interannual variability in harmful algal blooms \citep{thyng.ea:13} along the Texas coast, and the extent of the Mississippi-Atchafalaya plume \citep{zhang.ea:12}. Both models have 30 vertical layers, with resolution focused near the surface. The mean water depth in the nested region is about 50~m. </p> <h1>Idealized model</h1> <p><em>./reduced_channel_files</em> contain six resolution cases spanning 200 m to 10 km horizontal resolution. The idealized ROMS model domain for all of them is 100~km by 300~km, with no-slip walls along the short (north/south) boundaries, and re-entrant along the long (east/west) boundaries. Horizontal resolution is varied from 200~m to 10~km. The initial condition is designed to roughly mirror conditions in the Mississippi-Atchafalaya river plume in the northern Gulf of Mexico, which is a location of strong buoyancy forcing, energetic eddy activity, and intense frontal processes \citep[see][]{hetland:17}. The channel has a constant depth of 50~m, with a constant rotation throughout the domain, $f=10^{-4}$~s$^{-1}$. Initial tracer distributions of temperature, $T$, and salinity, $S$, are defined by constant values specifying the vertical and horizontal density gradients, $N^2 = -g \rho_z \rho_0^{-1} = 10^{-4}~\mathrm{s}^{-2}$ and $M^2 = g \rho_y \rho_0^{-1} = 10^{-6}~\mathrm{s}^{-2}$:<br>\begin{align}<br> T &= T_0 + \frac{N^2}{g \rho_0 \alpha} z\\<br> S &= S_0 + \frac{M^2}{g \rho_0 \beta} y \quad ,<br>\end{align}<br>where $\alpha$=0.17~$^\circ$C$^{-1}$ and $\beta$=0.76~g$^{-1}$~kg are the linear thermal and haline expansion coefficients in a linearized equation of state, <br>\begin{equation}<br> \rho = \rho_0 (1 - \alpha T + \beta S) \quad ,<br>\end{equation}<br>where $\rho_0$ is a reference density.<br>A small perturbation to the initial $u$ velocity (in the periodic direction) with a period of 100~km in both the $x$- and $y$-directions and an amplitude of 0.01~m~s$^{-1}$ is applied to supply perturbations on which instabilities can form and grow. The simulations include a weak near inertial (0.9$f$) wind stress in the $x$-direction,<br>\begin{equation}<br> \label{eqn:roms_wind}<br> \tau^x = \tau^x_0 \mathrm{sin}(0.9 f\,t) \quad ,<br>\end{equation}<br>to mimic the diurnal winds observed in the northern Gulf of Mexico.</p>
Model outputs from Chen et al. (2024)
<p>This repository contains the model outputs from the revised GEOS-Chem performed by Chen et al. (2024).</p>
Model output for: "Freshwater input from glacier melt outside Greenland alters modeled northern high-latitude ocean circulation"
<p>NEMO-ANHA4 and OGGM model output files used in the article: "Freshwater input from glacier melt outside Greenland alters modeled northern high-latitude ocean circulation".</p> <p> </p> <p>halfsolid.zip and noOGGM.zip contain output of the runs called <em>halfsolid</em> and <em>noOGGM</em> in the article. output_data_OGGM.zip contains the output files of OGGM runs.</p>
Output Data from the CLASS model, the CLASS-L model, and the LES.
<p>These data sets are for the work documented in Modifications to the CLASS Boundary Layer Model for Improved Interaction between the Mixed Layer and Clouds (https://doi.org/10.1029/2023MS004202).</p> <p>Please download these data first to run "Plotcode_Open.m" available at https://doi.org/10.5281/zenodo.11186607.</p>
Model output for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the model output fields used in : "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". <br><br>daily_surface_fields.tar: contains daily output of sea surface salinity, surface u and v velocity components, sea-surface height and mixed layer depths. Files are split in two: 2000-01-01:2009-12-31 and 2010-01-01:2019-12-31 as specified by each filename.</p> <p>monthly_3D_tracers.tar: contains monthly mean output of temperature and salinity for the full 3D field (lat,lon,depth). Files are split in two: 2000-01-01:2009-12-31 and 2010-01-01:2019-12-31 as specified by each filename.</p> <p> </p> <p> </p>
Output files of the Wd_WD model from Baïsset et al. paper entitled « Weakening induced by phase nucleation in metamorphic rocks: insights from numerical models »
<p>Here you can find the Output files of the Wd_WD Model.</p> <p>These files can be read using the files "ReadHDF5_Baisset2024.m" provided with the code here: </p> <p>https://doi.org/10.5281/zenodo.11473988</p>
Output files of the reference model from Baïsset et al. paper entitled « Weakening induced by phase nucleation in metamorphic rocks: insights from numerical models »
<p>Here you can find the Output files of the Reference Model.</p> <p>These files can be read using the files "ReadHDF5_Baisset2024.m" provided with the code here: </p> <p>https://doi.org/10.5281/zenodo.11473988</p> <p> </p>
Fire Weather Index for Europe from Downscaled and Bias-Corrected CMIP6 Model Outputs
<p>This dataset contains the Canadian Forest Fire Weather Index (FWI) calculated from six downscaled and bias-corrected CMIP6 model outputs. The models included are:</p> <ul> <li>ACCESS-CM2 (Ziehn et al. 2020)</li> <li>CanESM5 (Swart et al. 2019)</li> <li>CNRM-ESM2-1 (Séférian et al. 2019)</li> <li>EC-EARTH3 (EC-Earth Consortium 2019)</li> <li>MPI-ESM1-2-HR (von Storch et al. 2017)</li> <li>MRI-ESM2-0 (Yukimoto et al. 2019)</li> </ul> <p>The dataset encompasses four Shared Socio-economic Pathway (SSP) projections:</p> <ul> <li>SSP1-2.6</li> <li>SSP2-4.5</li> <li>SSP3-7.0</li> <li>SSP5-8.5</li> </ul> <p>Each model output has been downscaled to a resolution of 0.0703135°, corresponding to approximately 9km×9km grids before the FWI calculation. The data covers Europe spatially and temporally spans from 1950 to 2080, offering comprehensive insights into past, present, and future fire weather conditions.</p> <p>This dataset supports the manuscript titled <strong>"The fire weather in Europe: large-scale trends towards higher danger" </strong>by Hetzer et al., currently under review in ERL. Detailed instructions for accessing the data can be found in the included README file. </p> <p>Note: Downloads are password protected. Please use "FWI_2024" for access. </p> <p>Funding: The authors acknowledge the financial support of the European Union’s Horizon 2020 research and innovation action for the FirEUrisk project under grant agreement ID: 101003890.</p> <p> </p> <p> </p> <p> </p> <p> </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.