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617 results for “Climate models”
Climate model data for "Atmosphere-ocean feedback from wind-driven sea spray aerosol production"
<p>Data from atmosphere-only and coupled climate model simulations performed for "Atmosphere-ocean feedback from wind-driven sea spray aerosol production". Files are in netCDF format.</p>
Data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J.Carter et. al.
<p>The data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J.Carter et. al. The datasets are dictionaries and are saved with .npy extensions. The datasets can be loaded in Python with expressions like: 'scenario_base = np.load(f"{filepath}scenario_base_hierarchical.npy",allow_pickle="TRUE").item()'.</p>
Model output for "Climate variability leads to multiple oxygenation episodes across the Great Oxidation Event"
<div> <p>This repository contains all the model output presented in Garduno et al. (2024). Climate variability leads to multiple oxygenation episodes across the Great Oxidation Event. Submitted to Geophysical Research Letters.</p> <h1>Model output organization</h1> <p>There are seven zip files containing the model output from different simulations described in the main manuscript. See the main manuscript and <a href="https://github.com/DanyIvan/climate_goe_over_time" target="_blank" rel="noopener">code repository</a> for more details.:</p> <ul> <li>`o2_flux_constant_1.8e12.zip`: model output for simulation in which the O2 input flux is kept constant at a value of 1.8e12 molecules/cm^2/s</li> <li>`o2_flux_constant_2.2e12.zip`: model output for simulation in which the O2 input flux is kept constant at a value of 2.2e12 molecules/cm^2/s</li> <li>`linear_o2_flux_increase.zip`: model output for simulation in which the O2 input flux is linearly increased</li> <li>`linear_o2_flux_increase_change_during_glaciations.zip`: model output for simulation in which the O2 input flux is linearly increased, superimposing a 60% decrease during glaciations and a 60% increase after glaciations.</li> <li>`linear_ri_flux_decrease.zip`: model output for simulation in which the reductant input flux is linearly decreased and the O2 input flux is kept constant</li> <li>`linear_ri_flux_decrease_change_during_glaciations.zip`: model output for simulation in which the reductant input flux is linearly decreased and the O2 input flux is kept constant with a 60% decrease during glaciations and a 60% increase after glaciations.</li> <li>`stability_analysis.zip`: model output for stability analysis of steady states.</li> </ul> <p><br>Each of these folders contains model output every 1e5 years. The files are numbered from 1 to 4998. `1` corresponds to the output at 1e5 years, `2` corresponds to the output at 2e5 years, and so on.</p> <p>There are also files containing the O2 fluxes (`o2_flux.txt`) and reductant input (`ri_flux.txt`) used at each 1e5 output step.</p> <h1>Reading data</h1> <p>The data files are Fortran binary files. You can read the with the <a href="https://github.com/Nicholaswogan/PhotochemPy/blob/5bf449fa26370eb2d21d45ae59eacbfebf9705a4/PhotochemPy/io.py#L45" target="_blank" rel="noopener">read_evolve_output</a> function implemented in <a href="https://github.com/Nicholaswogan/PhotochemPy/" target="_blank" rel="noopener">PhotochemPy</a>.</p> <p>You can also use the Python functions provided in the paper's code repository: <a href="https://github.com/DanyIvan/climate_goe_over_time/blob/main/read_output.py" target="_blank" rel="noopener">https://github.com/DanyIvan/climate_goe_over_time/blob/main/read_output.py</a></p> <h1>Names and units</h1> <p>The model output files contain the mixing ratios for all modeled species species. They also contain information about:</p> <p>- 'T_time': temperature profile (K) at the output time step.<br>- 'edd_time': eddy diffusivity profile (cm^2/s) at the output time step.<br>- 'press_time': pressure profile (bar) at the output time step.<br>- 'h2osat': saturation vapor pressure profile (bar) at the output time step.<br>- 'time': time (s) at the output time step<br>- 'den': air number density (molecules/cm^3)</p> </div>
Supporting datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS"
<p>This archive contains datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS".</p>
Daily climate and rainfall data for Niger 1983-2021, for use in SARRA-O crop simulation model
<p>This dataset contains daily rainfall and climate data for Niger, that can be used as input of the <a href="https://github.com/SARRA-cropmodels/SARRA-O">SARRA-O spatialized crop simulation model</a>. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.</p> <p>The archive contains :</p> <ul> <li>AgERA5 (doi:<a href="https://doi.org/10.24381/cds.6c68c9bb">10.24381/cds.6c68c9bb</a>) climatic data for Niger, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²) at 0.1° spatial resolution from 01/01/1981 to 31/12/2021</li> <li>TAMSAT v3.0 (doi:<a href="http://doi.org/10.1038/sdata.2017.63">10.1038/sdata.2017.63</a>) satellite rainfall estimation data for Niger (mm), with daily geotiff files at 0.0375° spatial resolution from 01/01/1983 to 31/12/2021</li> <li>CHIRPS v2.0 (doi:<a href="https://doi.org/10.1038/sdata.2015.66">10.1038/sdata.2015.66</a>) satellite rainfall estimation data for Niger (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/1981 to 31/12/2022</li> </ul> <p>This data has been extracted from their original sources using the <a href="https://github.com/SARRA-cropmodels/SARRA-data-download">SARRA-data-downloader tool</a>, on June 14th and 15th, 2023.</p> <p>The applicable licences are the licences of the respective datasets.</p>
Daily climate and rainfall data for northern Cameroon 2020-2022, for use in SARRA-Py crop simulation model
<p>This dataset contains daily rainfall and climate data for north Cameroon, that can be used as input of the SARRA-Py spatialized crop simulation model. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.</p> <p>The archive contains :</p> <ul> <li>AgERA5 (doi:<a href="https://doi.org/10.24381/cds.6c68c9bb">10.24381/cds.6c68c9bb</a>) climatic data for north Cameroon, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²/d) at 0.1° spatial resolution from 01/01/2020 to 31/12/2022</li> <li>CHIRPS v2.0 (doi:<a href="https://doi.org/10.1038/sdata.2015.66">10.1038/sdata.2015.66</a>) satellite rainfall estimation data for north Cameroon (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/2020 to 31/12/2022</li> </ul> <p>This data has been extracted from their original sources using the <a href="https://github.com/SARRA-cropmodels/SARRA-data-download">SARRA-data-downloader tool</a>.</p> <p>The applicable licences are the licences of the respective datasets.</p>
The dataset of predicting the potential habitat suitability of Saussurea species in China under future climate change using the optimized Maximum Entropy (MaxEnt) model
<p><strong>Description:</strong></p> <p>This dataset accompanies the study on the Saussurea species, renowned for its biodiversity and medicinal significance in high-elevation regions, which faces endangerment due to climate change and human activities. Despite its importance, conservation research on Saussurea has been limited. To address this gap, the study employed the optimized MaxEnt model to simulate Saussurea's habitat suitability and analyze key environmental factors influencing its distribution.</p> <p>The dataset includes:</p> <ol> <li><strong>Model and Parameter Optimization Code</strong>: The code used for optimizing the MaxEnt model parameters, ensuring reproducibility of the habitat suitability models.</li> <li><strong>Saussurea Distribution Points</strong>: Georeferenced points indicating the observed locations of Saussurea species.</li> <li><strong>Current Environmental Variables</strong>: Data on key environmental factors influencing Saussurea distribution, such as Elevation, Isothermality (Bio3), and Temperature Annual Range (Bio7).</li> <li><strong>Future Environmental Variables: </strong>Data on key environmental factors influencing Saussurea distribution under SSP126, SSP245, SSP370 and SSP585 in 2020-2100s.</li> </ol>
Climate model (CM2.6) and regional model (ACM) outputs 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 variables 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.txt</em> for more details.</p>
Nonlinear sensitivity of glacier-mass balance to climate attested by temperature-index models; synthetic data
<p>Synthetic data and results of the PDD model used in the paper <a href="https://doi.org/10.5194/tc-2022-210">https://doi.org/10.5194/tc-2022-210</a></p>
Data set for: Efficient and Stable Coupling of the SuperdropNet Deep Learning-based Cloud Microphysics (v0.1.0) to the ICON Climate and Weather Model (v2.6.5)
<p>This dataset provides the experiment results for the publication " Efficient and Stable Coupling of the SuperdropNet Deep<br> Learning-based Cloud Microphysics (v0.1.0) to the ICON Climate and Weather Model (v2.6.5) ", to be submitted.</p>
Climate-economy model scenario runs and sensitivity analysis using DICE-2016
<p>This paper assesses the prospects for climate stabilization from both positive and normative economic perspectives, and with an eye to the conditions necessary for collective action across the three domains: domestic, international, and intergenerational. While it is well-established that international freeriding and transaction costs pose major impediments to successful environmental agreements, this analysis identifies the intergenerational domain as the source of intractability due to long delays between enduring mitigation costs and enjoying their eventual climate benefits. This lag causes the net benefits for median-aged voters' to be negative over their expected remaining lifespans. Drawing on estimates from several Integrated Assessment Models of the benefits and costs of climate stabilization actions, the analysis concludes that a program of domestic and international climate actions will be hopelessly stymied by the failure of the actions to pass individual and collective rationality tests. However, the dire implications of this conclusion leaves the door open to the possibility that some change in circumstances could undercut this conclusion. The assignment of rights, in particular, has that potential. Indeed, these circumstances echo the canonical insights from Ron Coase's observation in The Problem of Social Cost (1960) that the arrangement of rights can have large effects on welfare when transaction costs for an externality are high. Current climate rights amount to a de facto open access right to pollute the atmosphere. Were a right to a stable climate for both for current and future generations recognized, added weight or leverage would apply in support of climate stabilization policies and international agreements. These legal changes could represent a counterweight to offset the inadequacy of support from the current self-interested generation. Indeed, some recent climate litigation argues that many nations' constitutions already encompass an affirmative right to a stable climate, a proposition that could represent an inimitable means to break the climate impasse.</p>
Climate-economy model scenario runs and sensitivity analysis using DICE-2016
Open the record for dataset details and reuse information.
Southern Ocean Carbon and Climate Observations and Modeling project (SOCCOM)
SOCCOM (Southern Ocean Carbon and Climate Observations and Modeling project) is a NSF project sampling the Southern Ocean and its influence on climate.Additional Data LinksCLIVAR P16S_2014 Pigment AnalysisCLIVAR P16S_2014 POC dataCLIVAR P16S_2014 Supporting Documentation
Potential Impacts of Climate Change on World Food Supply: Datasets from a Major Crop Modeling Study
The Potential Impacts of Climate Change on World Food Supply: Datasets from a Major Crop Modeling Study contain projected country and regional changes in grain crop yields due to global climate change. Equilibrium and transient scenarios output from General Circulation Models (GCMs) with three levels of farmer adaptations to climate change were utilized to generate crop yield estimates of wheat, rice, coarse grains (barley and maize), and protein feed (soybean) at 125 agricultural sites representing major world agricultural regions. Projected yields at the agricultural sites were aggregated to major trading regions, and fed into the Basic Linked Systems (BLS) global trade model to produce country and regional estimates of potential price increases, food shortages, and risk of hunger. These datasets are produced by the Goddard Institute for Space Studies (GISS) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
AVISO Level 4 Absolute Dynamic Topography for Climate Model Comparison Standard Error
These data are the standard error calculated from the AVISO Level 4 Absolute Dynamic Topography for Climate Model Comparison Number of Observations data set ( in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/aviso/L4/abs_dynamic_topo ). This data set is not meant to be used alone, but with the absolute dynamic topography data. These data were generated to help support the CMIP5 (Coupled Model Intercomparison Project Phase 5) portion of PCMDI (Program for Climate Model Diagnosis and Intercomparison). The dynamic topograhy are from sea surface height measured by several satellites, Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2 and referenced to the geoid. These data were provided by AVISO (French space agency data provider), which are based on a similar dynamic topography data set they already produce( http://www.aviso.oceanobs.com/index.php?id=1271 ).
On the deep carbon cycle in numerical modelling of mantle convection: Implications for the long-term climate evolution
Open the record for dataset details and reuse information.
Extreme events changes over China under 1.5-4°C global warming targets: projected by an ensemble of regional climate model simulations
<p>This file is for the upload of data for 2019JD031057R.</p>
interactive and prescribed snow depth driven climate model simulations output
<p>compressed dataset of model simulation output for current and future climates</p>
High Mountain Asia COAWST Hourly 4km Regional Climate Model Simulations V001
This data product contains either hourly accumulated or hourly snapshots of modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.
High Mountain Asia MAR V3.5 Regional Climate Model Output V001
This data set provides modeled surface and atmospheric fields from the Modèle Atmosphérique Régionale (MAR) regional climate model (version 3.5) over the Himalayan region at 10 km spatial resolution. Modeled parameters include surface mass and energy balance components, near-surface atmospheric properties, and snowpack properties.
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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.