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2,152 results for “outputs”
Model output for a storyline analysis of hurricane Irma's precipitation under various levels of climate warming
<p>Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the Community Atmosphere Model, we explore how Irma's precipitation would change under various levels of climate warming. Analysis is focused on a 48-hour period where the simulated hurricane tracks reasonably represent Irma's observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3-7%/K compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10-13%/K and 17-21%/K, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16-26%/K, with local maxima increasing by 18-43%/K. All percent changes increase monotonically with warming level.</p>
Model outputs from a NEMO-PISCES configuration of the tropical Atlantic Ocean
<p>Model outputs from a NEMO-PISCES configuration of the tropical Atlantic Ocean (35°S-35°N, 100°W-20°E), from 1998 to 2007. It contains monthly climatologies (10-years averages) of several ocean and biogeochemical variables, as well as monthly Chlorophyll, for 4 sensitivity experiments. These 4 simulations are described and analyzed in the paper: "On the importance of riverine organic matter for the Amazon plume: a modeling study" by Gévaudan et al., under review in JGR: Oceans. A preprint is available here: https://doi.org/10.22541/essoar.172081327.75230541/v2.</p>
RAPID input and output files corresponding to "Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset consists of output files of the study reported in:</p> <ul> <li>Tom, M., David, C.H., Marlis, K.M., Zimdars, P.A., Bonassies, Q., Wade, J., Cerbelaud, A., Pavelsky T., Huang, T. (In Review), Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary<br></strong><br>This dataset contains the results of RAPID river discharge simulations (January 1980, February 1980) for the Mississippi river basin using the CURRNT framework. The surface and subsurface runoff data were retrieved from NASA GLDAS Phase 2 dataset (Rodell et al., 2004) at a 3-hourly temporal resolution.<strong><br></strong></p> <p><strong>Software</strong></p> <p>The software used to produce the files in this dataset is available at <a href="https://github.com/czarmanu/currnt" target="_blank" rel="noopener">https://github.com/czarmanu/currnt</a>.</p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to Mississippi River Basin.</p> <p><strong>Description of files</strong></p> <p>All files below were prepared by Manu Tom, using the software mentioned above.<br><br>1980-01</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for January 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the initial state of RAPID (zeros populated for Qout).</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for January 1980 from RAPID corresponding to the downstream point of each reach.</li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for January 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-02</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for February 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-01-31.</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for February 1980 from RAPID corresponding to the downstream point of each reach. </em></li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for February 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-03</p> <ul> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-03.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-02-29.<br><br></em></li> </ul> <p><strong>Other necessary links associated with this dataset:</strong></p> <p>RAPID model (David et al., 2011): <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RRR: RAPID model pre-processor (David et al., 2019): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS VIC 3H v2.0 outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0</a></p> <p>NSIDC Earthaccess library: <a href="https://github.com/nsidc/earthaccess">https://github.com/nsidc/earthaccess</a><br><br><strong>References</strong></p> <p>David, C. H., Maidment, D. R., Niu, G. Y., Yang, Z. L., Habets, F., and Eijkhout, V. (2011), River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>David, C. H., Hobbs, J., Turmon, M., Emery, C., Reager, J. T., Famiglietti, J. (2019). Analytical propagation of runoff uncertainty into discharge uncertainty through a large river network. Geophys. Res. Lett. 46, 8102–8113, <a href="https://doi.org/10.1029/2019GL083342.492">https://doi.org/10.1029/2019GL083342.492</a></p> <p>Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., et al. (2004). The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p>
ICON-Coast model output for a study on the impact of Arctic coastal erosion on sea water carbonate saturation.
<p>Primary output of the ocean-biogeochemistry model ICON-Coast that has been used to create the figures in a manuscript on the impact of Arctic coastal erosion on sea water carbonate saturation.</p>
Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean
<p>Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean and notebooks created for analyses and visualization.</p> <p>Configuration names:</p> <ul> <li>ne30_n</li> <li>ne30x4_n</li> <li>ne30x4_t</li> <li>ne30x8_t</li> </ul> <p>Variables at single level: PHIS,PRECC,PRECL,PS,TREFHT,LHFLX,SWCF,LWCF,TMQ,SNOWHLND</p> <p>Variables at pressure levels:U,V,OMEGA,RELHUM,Z3,Q</p>
Climate model (CM2.6) and regional model (ACM) processed output used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key processed output from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README_v2.txt</em> for more details.</p>
Output of WRF BEP+BEM model and Pseudo Global Warming composites
<p>Output for the submitted article "Assessing the intensity of heatwaves in a warming climate at the urban scale: A case study of the Metropolitan Area of Barcelona"</p> <p>Sergi Ventura1, JR Miro2, Ricard Segura-Barrero1, Fei Chen3, Alberto Martilli4, Changhai Liu5, Kyoko Ikeda5, Gara Villalba1,6,*</p> <p><br>1 Sostenipra Research Group (SGR 01412), Institute of Environmental Sciences and Technology (MDM-2015-0552), Z Building, Universitat Autònoma de Barcelona (UAB), Campus UAB, 08193 Bellaterra, Barcelona, Spain<br>2 Department of Territory and Sustainability, Meteorological Service of Catalonia, Generalitat de Catalunya, Barcelona, Spain<br>3 Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China<br>4 Research Center for Energy, Environment and Technology, CIEMAT, Madrid, Spain<br>5 National Center for Atmospheric Research, Boulder, CO 80301, USA<br>6 Department of Chemical, Biological and Environmental Engineering, Universitat Autònoma de Barcelona (UAB), Campus UAB, 08193 Bellaterra, Barcelona, Spain</p> <p><br>* Corresponding author: gara.villalba@uab.cat</p> <p>All rights lie with the authors.</p> <p> </p> <p>We use WRF BEP+BEM to investigate the sensitivity of multiple heat wave periods to climate change under the SSP370 scenario. The simulations show mean temperature increases of 2.5 ºC by the mid-21st-century and 4.2 ºC by the end of the century in the Metropolitan Area of Barcelona, a Mediterranean region in northeastern Spain.</p> <p>This Zenodo repository contains the following:</p> <ul> <li> <p><strong>met_em initial and boundary conditions for the Metropolitan Area of Barcelona</strong>:<br><code>met_em</code> files including the initial and boundary conditions used for modeling a historical heatwave (2020), as well as projected PGW-MID (2070) and PGW-END (2100) periods.</p> </li> <li> <p><strong>wrfout_d03, model output</strong>:<br>WRF output at 1 km resolution for the Metropolitan Area of Barcelona on <strong>August 1st, 2020</strong> (a historical heatwave day), representing the historical, mid-century (2070), and end-of-century (2100) periods.</p> </li> <li> <p><strong>PGW composites</strong>:<br>NetCDF-format plots of <strong>relative humidity (RH)</strong> and <strong>temperature (T)</strong> maximum and minimum values. These are provided for the historical control period (CTL), mid-century (MID), and end-of-century (END), across the four analyzed synoptic weather patterns (SS, SA, DA, and DAU).</p> </li> </ul> <p> </p> <p> </p>
SCAR DistAnt Ecological Model Output Repository
<p>This repository provides access to a collection of ecological model outputs (species distribution and similar models) from Antarctica and the Southern Ocean. It is a project of the SCAR <a href="https://scar.org/science/egabi/home/" rel="nofollow">Expert Group on Biodiversity Informatics</a> in conjunction with <a href="https://www.belspo.be/belspo/impuls/project_en.stm#ADVANCE" rel="nofollow">ADVANCE</a> (Royal Belgian Institute of Natural Sciences) and the Integrated Digital East Antarctica program at the <a href="https://www.antarctica.gov.au/science/" rel="nofollow">Australian Antarctic Division</a>.</p> <p><strong>Please note:</strong> the inclusion of a layer in this collection is not an endorsement of its quality or suitability for your intended purpose. Users should consult the associated publication for details on the source data and modelling processes. We encourage users to contact the original publication authors to discuss their intended use of these layers.</p> <p>Model outputs are provided as cloud-optimized geotiffs (COGs), with generally one file per species. The COG will have multiple bands if the original model predictions include uncertainty estimates or multiple model output variables. Each output has been kept on its original coordinate reference system (map projection) and spatial resolution.</p> <p>There is a <a href="https://github.com/SCAR/distant/blob/master/metadata.csv">table of minimal metadata</a> that describes the layers in the collection. This metadata is intended to be sufficient for users to find potential layers of interest, and make an initial evaluation of their high-level characteristics such as taxonomic details, spatial coverage and resolution, and model output types. Our metadata is NOT intended to provide a comprehensive description of each layer: users are referred to the original publication for that level of detail (see the layer’s <code>reference</code> entry).</p> <div> <h3>Citing</h3> The layers in this collection have been re-released under their original license where applicable, or a CC-BY licence otherwise. Please cite when using, and also cite the original data sources used. For example:</div> <div> <p>Kovacs J (2020) A model of my favourite Southern Ocean species. <em>Journal of Southern Ocean Stuff</em> <strong>123</strong>:1–10. Data obtained from the SCAR DistAnt Ecological Model Output Repository, doi:10.5281/zenodo.10910076</p> </div> <p>Individual data sources might have varying licence conditions: consult the <code>licence</code> field for details.</p>
Archived Model Output and Code for "Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations "
<div> <p>This is an archive of CAM6 simulation output used in the paper Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations, submitted to the AGU Journal. Codes used to read the nc file is also attached.</p> </div>
An axonal brake on striatal dopamine output by cholinergic interneurons
<h3><strong>ABSTRACT</strong></h3> <p>Depolarisation of distal axons is necessary for somatic action potentials to be translated into axonal neurotransmitter release. Here, we show that activation of striatal cholinergic interneurons (ChIs) and nicotinic receptors (nAChRs) on mouse DA axons transiently prevents the release of dopamine (DA) by subsequent stimuli for ~100 ms. Previous studies have shown that nAChRs on DA axons can drive ectopic action potentials in DA axons to trigger DA release. We demonstrate <em>ex vivo </em>that a lower level of activation of ChIs is needed to suppress DA release than to trigger it, an effect that is not due to DA depletion, but to restricted re-activation of DA axons. This axonal brake on DA output is stronger and more persistent in dorsal than ventral striatum. <em>In vivo</em>, we reveal a predominant depression of DA release by endogenous acetylcholine, as antagonism of nAChRs in dorsal striatum conversely elevated tonic DA detected with optic-fibre photometry of GRAB<sub>DA2m</sub> sensor and promoted conditioned place-preference. Our findings reveal that ChIs acting via nAChRs limit activation of DA axons by subsequent DA neuron activity, uncoupling DA axons from ascending action potentials and generating a dynamic inverse scaling of DA release according to ChI activity.</p> <p> </p> <h3><strong>FILE DESCRIPTIONS</strong></h3> <p>This repository contains the following files:</p> <ul> <li>Key Resources Table (.xlsx) - Table containing details on key resources (antibodies, mouse lines, virus strains, and software), and the persistent identifiers for protocols and code used and generated in this study. </li> <li>Source Data Folder (.zip): <ul> <li>_README_Source_Data (.txt) with detailed information about each dataset.</li> <li>Individual tabular datasets corresponding to panels shown in the Main Figures 1 to 5 (.csv).</li> <li>Excel spreadsheet containing all tabular datasets plotted in Main Figures 1 to 5 (.xlsx)</li> </ul> </li> <li>Supplementary Data Folder (.zip): <ul> <li>_README_Supplementary_Data (.txt) with detailed information about each dataset.</li> <li>Individual tabular datasets corresponding to panels shown in the Supplementary Figures 1 to 6 (.csv).</li> <li>Excel spreadsheet containing all tabular datasets plotted in Supplementary Figures 1 to 6 (.xlsx)</li> </ul> </li> </ul>
Geoclaw inputs and processed outputs used in building tsunami ML surrogates for nearshore and onshore approximation
<p>This dataset contains some input files needed for GeoClaw tsunami simulation and post-processed outputs used for training the nearshore and onshore surrogates discussed in the article - Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates available as preprint (https://doi.org/10.5194/nhess-2024-72) and project repo - https://github.com/naveenragur/tsunami-surrogates</p> <p>The GeoClaw simulation requires the following files: </p> <p><strong><a href="../api/records/10817116/draft/files/geoclaw_dtopo_files.tar.gz/content" target="_blank" rel="noopener noreferrer">geoclaw_dtopo_files.tar.gz</a></strong><strong>: </strong>input bathymetry and topography elevation in asc format from multiple sources of datasets.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_his.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_his.tar.gz</a></strong>: tsunami displacement(dtopo) input files in tt3 format for historic earthquake scenarios.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_typeA.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_typeA.tar.gz</a>:</strong> tsunami displacement(dtopo) input files in tt3 format for DOE type A earthquake scenarios.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_typeB.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_typeB.tar.gz</a>:</strong> tsunami displacement(dtopo) input files in tt3 format for DOE type A earthquake scenarios.</p> <p>The machine learning training and testing requires:</p> <p><strong><a href="../api/records/10817116/draft/files/procesed_tsunami_data.tar/content" target="_blank" rel="noopener noreferrer">procesed_tsunami_data.tar</a>: </strong>the post-processed outputs for the three test location i.e. (1) the waveforms( time series recorded for the water level at the offshore and the nearshore gauge locations and (2) the maximum inundation depth recorded at the fixed grids onshore.</p> <p><a href="https://zenodo.org/uploads/14902165"><strong>tsunami-surrogates-nhess-2024-72.tar.gz</strong></a><a href="https://zenodo.org/uploads/14902165"> </a>: Model code, scripts and notebooks used in the study</p>
Input and Output Data for local earthquake tomography in the central Dead Sea Fault using PyVoroTomo
<p>Data to reproduce wave velocity models for the central DSF.</p> <p>eventsAndArrivals.h5 - 2 csv files (keys: events, arrivals)</p> <p>stations_sub.h5 - csv file containing station data</p> <p>gitter.csv - 1D velocity model by Gitterman et al. (2002)</p> <p>3d_Vp_Vs_VpVs_models.nc - velocity models for vp, vs, and vp/vs and their uncertainty.</p> <p>relocated seismicity.h5 - relocated seismicity, arrivals used, and stations used (keys: events, arrivals, stations)</p>
Scripts, figures and output for "Oxygen intrusions sustain aerobic nitrite oxidation in anoxic marine zones" by Pearse J. Buchanan & colleagues.
<p>Scripts for producing the visualisations, analysis and model results presented in the paper entitled "<em>Oxygen intrusions sustain </em><em>aerobic nitrite-oxidizing bacteria in anoxic </em><em>marine zones</em>" by Pearse James Buchanan & colleagues. </p> <p>Figures.tar holds the figures made with the python scripts <br>Chemostat_model.tar holds the python code required for running the chemostat model<br>Chemostat_output.tar holds the output of our experiments<br>OxicMAGS.txt and mumax_MAGS_gRodon.txt are required data files for fig5.py</p> <p>Other python scripts and figures seen in the full paper are held at https://doi.org/10.5281/zenodo.15139207 and were completed using jupyter notebooks on a memory intensive instance on the Expanse Super computer in San Diego. Please see those files for the remaining analysis.</p> <p><br>Contact pearse.buchanan@csiro.au or ezakem@carnegiescience.edu for any questions<br> </p> <p> </p>
Support for a novel, simple method for calculating word frequency of output on language production tasks
<p>Pre-recorded presentation for the International Workshop on Language Production 2021</p>
WBM Output daily discharge and water temperature all GCMs
<p>The WBM model was run at daily temporal resolution over a 6 arc min. spatial grid river network (WECC Domain v.3a) using the monthly time series (2006-2099) CMIP-5 climate drivers as an input:</p> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <p>In addition the model was run using the NASA MERRA2 climate drivers for historical time series (1980-2018).</p> <p>Two WBM output variables (Discharge and Water Temperature) have been extracted (sampled) for the 715 power plants of the WECC database using their WBM river-network (snapped, Lat Lon) locations which were derived from their original site locations. Extracted point data represents daily time series for 55 years (2006-2099) of CMIP-5 WBM simulations and 39 years (1980-2018) in the case of the historical MERRA-2 run.</p> <p>Post-processing of this data is done for statistical quantification of water temperature and discharge pertinent to usage of water resources by WECC power plants as cooling agents of thermoelectric electricity production (e.g. cooling towers) that may have some regulatory limitations to the heating of river waters from return flows of the power plant cooling technological processes.</p>
Aerosol-light interactions reduce the carbon budget imbalance - DGVM output
<p>DGVM (JULES and ORCHIDEE_DF) output associated with the publication 'Aerosol-light interactions reduce the carbon budget imbalance' (O'Sullivan et al., 2021 - Environmental Research Letters).</p> <p>Files contain monthly data over the period 1901-2017. There are 7 simulations for each model, with varying surface solar radiation in each case. Atmospheric CO<sub>2</sub> and all climate variables (except for incoming short-wave radiation) vary throughout all simulations.</p> <p>Historical: total and diffuse shortwave radiation depend on the solar zenith angle, temporally varying cloud cover and aerosol optical depth of the whole atmospheric column.</p> <p>FixedAero: Time-invariant (1901-1920 cycle) tropospheric aerosol and no stratospheric aerosol influence on total and diffuse shortwave radiation</p> <p>FixedTropAero: Time-invariant (1901-1920 cycle) tropospheric aerosol influence on total and diffuse shortwave radiation</p> <p>FixedStratAero: no stratospheric aerosol influence on total and diffuse shortwave radiation</p> <p>FixedAero_DF: Time-invariant (1901-1920 cycle) tropospheric aerosol and no stratospheric aerosol influence on diffuse shortwave radiation</p> <p>FixedTropAero_DF: Time-invariant (1901-1920 cycle) tropospheric aerosol influence on diffuse shortwave radiation</p> <p>FixedStratAero_DF: no stratospheric aerosol influence on diffuse shortwave radiation</p> <p> </p>
Data underlying figures in Beyond Data: sharing related research outputs to make data reusable
<p>Data that underlies figures in forthcoming article Beyond Data: sharing related research outputs to make data reusable, to be published in the journal Learned Publishing. Data in these files was retrieved from the DataCite REST API and the DataCite metadata schema. Zipped file contains identical data in 2 formats: individual CSV files (one per figure, plus a file with the overall count of DataCite DOIs by resource type and year) and single XLSX file with multiple tabs. </p>
Quantifying research interests in 7,521 mammalian species with h-index: a case study (scopus output)
<p>Dataset of bibliometric data collected from Scopus for the manuscript 'Quantifying research interests in 7,521 mammalian species with h-index: a case study'.</p>
Output of aquaplanet idealized GCM simulations under perpetual solstice-like forcing from Singh 2019, J. Atmos. Sci.
<p># Overview</p> <p>This directory contains the output from simulations in the idealized aquaplanet GCM that were originally reported in Singh 2019, J. Atmos Sci. The contents are as follows:</p> <p>```<br> ├── Omega0.125_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega0.25_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega0.5_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega0.66_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega0.75_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega1.5_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega1_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega2_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega3_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega4_y0005-y0010.mean.1x5days.zonal_avg.nc<br> ├── Omega8_y0005-y0010.mean.1x5days.zonal_avg.nc<br> └── README.md<br> ```</p> <p>where `Omega*x` indicates the rotation rate multiplicative factor, with 1x being Earth's rotation rate. All simulations are performed with time-invariant northern solstice insolation. A full description of the simulation setups is available in Singh 2019.</p> <p># References</p> <p>Idealized GCM formulation: Frierson, Dargan M. W., Isaac M. Held, and Pablo Zurita-Gotor. “A Gray-Radiation Aquaplanet Moist GCM. Part I: Static Stability and Eddy Scale.” Journal of the Atmospheric Sciences 63, no. 10 (October 1, 2006): 2548–66. https://doi.org/10.1175/JAS3753.1.</p> <p>Simulations: Singh, Martin S. “Limits on the Extent of the Solsticial Hadley Cell: The Role of Planetary Rotation.” Journal of the Atmospheric Sciences 76, no. 7 (April 30, 2019): 1989–2004. https://doi.org/10.1175/JAS-D-18-0341.1.</p>
Output of dry idealized GCM simulations under perpetual solstice-like forcing
<p>This repository contains the output from simulations in the Caltech idealized dry GCM that were originally reported in Hill, Bordoni, and Mitchell 2021, J. Atmos Sci. The simulations use a Newtonian cooling approximation of radiative transfer, and the Newtonian relaxation temperature field is an approximation of Earth's northern solstice. Simulations are run at three different planetary rotation rates, 1/4, 1, and 2x Earth's rotation rate. A fourth simulation is run at Earth's rotation rate but with the `delta_h` parameter that controls the magnitude of the meridional temperature gradients set to 1/6 rather than 1/15 as for the other simulations.</p> <p>Idealized GCM formulation: Schneider, Tapio. “The Tropopause and the Thermal Stratification in the Extratropics of a Dry Atmosphere.” Journal of the Atmospheric Sciences 61, no. 12 (June 1, 2004): 1317–40. https://doi.org/10.1175/1520-0469(2004)061<1317:TTATTS>2.0.CO;2.</p> <p>Simulations: Hill, Spencer A., Simona Bordoni, and Jonathan L. Mitchell. “Solsticial Hadley Cell Ascending Edge Theory from Supercriticality.” Journal of the Atmospheric Sciences 78, no. 6 (June 1, 2021): 1999–2011. https://doi.org/10.1175/JAS-D-20-0341.1.</p>
ScienceDex guides
Understand access before you commit
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.