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105 results for “cmip6”
CMIP6 projections
<p>Climate maps (raster layers .tif) of basic-ecvs with a spatial resolution of 5.5 km (1 km for Azores) for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon. This v2 includes the metadata.</p>
Climatological global-mean Sea Surface Temperature (SST) in AWI-CM-1-1-MR simulations for CMIP6, in preindustrial, present-day, +2°C, +3°C, and +4°C climates
<p>Daily climatologies of global-mean sea surface temperature (SST, parameter 'tos') free-running simulations performed using the coupled climate models AWI-CM-1-1-MR. The unstructured grid-ocean component FESOM was conservatively remapped to the ERA5 grid. Data was averaged across the 5 ensemble members and temporally averaged over 10-year long time periods: 1850-1859 for preindustrial climate, 2015-2024 for present-day, 2034-2043 for +2°C climate, 2061-2079 for +3°C climate, and 2091-2100 for +4°C climate. </p><p>Data is provided in .nc files, one for each climate.</p>
Optimally interpolated dissolved oxygen based on the World Ocean Database 2018 and CMIP6 models
<p>Optimal interpolation of observed and modeled dissolved oxygen data from WOD18 and CMIP6. Technical details are provided in the publication (Ito et al., 2023). </p><p>Ito, T., Garcia, H. E., Wang, Z., Minobe, S., Long, M. C., Cebrian, J., Reagan, J., Boyer, T., Paver, C., Bouchard, C., Takano, Y., Bushinsky, S., Cervania, A., and Deutsch, C. A.: Underestimation of global O2 loss in optimally interpolated historical ocean observations, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-72, in review, 2023.</p>
GloGEM CMIP6 global glacier projections
<p>The files contain the glacier evolution as modelled with the Global Glacier Evolution Model (GloGEM; Huss and Hock, 2015) under the CMIP6 (Coupled Model Intercomparison Project Phase 6) climate models. For details about the simulations, refer to Zekollari et al. (2024), where the model setup and the results are described.</p> <p>The files are provided at the regional scale, for each of the 19 glacier regions as defined in the Randolph Glacier Inventory v6.0 (RGI Consortium, 2017), for two variables (glacier volume and glacier area).</p> <p>The folder structure is as follows: ‘Variable/RGIXX’, with:</p> <ul> <li>‘Variable’: ‘Area’ or ‘Volume'</li> <li>‘RGIXX’: the region, where XX refers to the RGI v6.0 region number</li> </ul> <p>Every file contains the evolution (of Volume [km<sup>3</sup>] or Area [km<sup>2</sup>]) for a given Shared Socioeconomic Pathways (SSP), with ssp119.csv corresponding to SSP1-1.9, ssp126 corresponding to SSP1-2.6,…etc.</p> <p>If you use these data, please cite the dataset as following:</p> <ul> <li>Cite ZENODO</li> <li>Cite the corresponding publication (Zekollari et al., 2024)</li> </ul> <p> </p> <p><strong>Somes notes/remarks:</strong></p> <ul> <li>In these GloGEM simulations, every glacier is calibrated to match the glacier-specific observed mass changes by Hugonnet et al. (2021).</li> <li>Until 2020, the glacier evolution forcing is from ERA5, while from 2020 onwards, the forcing comes from the respective climate models. Very slight differences can exist in the modelled glacier evolution prior to 2020 for the different climate models, because of randomly generated sub-monthly air temperature variability used to estimate monthly positive degree days more accurately.</li> <li>In the manuscript that describes the glacier evolution (Zekollari et al., 2024), the results are shown for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (for 12 climate models). In the data provided here on ZENODO, the evolution as modelled under SSP1-1.9 is also included, but these simulations are not described in the manuscript (owing to limited climate model ensemble size, n=3).</li> <li>For SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, the considered climate models are the same as in Rounce et al. (2023).</li> <li>The glacier evolution was modelled with the same climate model forcing as OGGM v1.6.1. The OGGM v1.6.1 data is available from Schuster et al. (2023) and these projections are also described in Zekollari et al. (2024).</li> </ul> <p><strong>References</strong></p> <p>Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021.</p> <p>Huss, M. and Hock, R.: A new model for global glacier change and sea-level rise, Frontiers in Earth Science, 3, 54, https://doi.org/10.3389/feart.2015.00054, 2015.</p> <p>RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 6.0: Technical Report, Global Land Ice Measurements from Space, Colorado, USA. Digital Media, , https://doi.org/10.7265/N5-RGI-60, 2017.</p> <p>Rounce, D. R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., Berthier, E., Brinkerhoff, D., Compagno, L., Copland, L., Farinotti, D., Menounos, B., and McNabb, R. W.: Global glacier change in the 21st century: Every increase in temperature matters, Science, 379, 78–83, https://doi.org/10.1126/science.abo1324, 2023.</p> <p>Schuster, L., Schmitt, P., Vlug, A., and Maussion, F.: OGGM/oggm-standard-projections-csv-files: v1.0 (v1.0), https://doi.org/10.5281/zenodo.8286065, 2023.</p> <p>Zekollari, H., Huss, M., Schuster, L., Maussion, F., Rounce, D. R., Aguayo, R., Champollion, N., Compagno, L., Hugonnet, R., Marzeion, B., Mojtabavi, S., and Farinotti, D.: Twenty-first century global glacier evolution under CMIP6 scenarios and the role of glacier-specific observations, The Cryosphere, 18, 5045-5066, https://doi.org/10.5194/tc-18-5045-2024, 2024.</p> <p> </p>
Evaluation of Upper Tropospheric Geopotential Height Anomalies over the Tropical and Subtropical Oceans in CMIP6 Models Using GNSS Radio Occultation Observations
<p>The set-up of CESM2-CAM6 sensitivity experiments for winter season (Dec-Jan-Feb: DJF), with prognostic falling ice radiative effects on (SON) and off (NOS), is an updated two-moment stratiform cloud scheme (MG2, Gettelman & Morrison, 2015) in the CESM2 atmospheric component of CAM6. CESM2-CAM6 participated in CMIP6. Both the NOS and SON simulations were configured following the same approach as the CMIP6 "historical" run spanning from 1980 to 2014.</p> <p> </p> <p>The data are:</p> <p> </p> <p>TS: skin temperature (K)</p> <p>TAUX: zonal surface wind stress</p> <p>TAUY: meridinal surface wind stress</p> <p>DTCOND: moist condensation heating rate</p> <p>QRL: long wave heating rate</p> <p>OMEGA: vertical motion</p> <p>Z3: geopotential height</p> <p> </p>
NOAA-GFDL GFDL-ESM4 CMIP6 ScenarioMIP additional model output
<p>This repository contains annual 3D sea water age since surface contact files from the ScenarioMIP ssp585 simulation conducted with GFDL's ESM4.1 climate model (Dunne et al., 2020) that was contributed to the 6th Coupled Model Intercomparison Project (CMIP6).</p> <p>Both the ocean model native grid and the regridded output can be found in this repository. For the regridded output, the files have been regridded from MOM6 native grid to standard World Ocean Atlas 1x1 horizontal grid, and they have been remapped from the lagrangian vertical coordinate used by GFDL's MOM6 ocean model (Adcroft et al., 2019) to standard World Ocean Atlas depths.</p> <p>Files have undergone QA/QC. All data files are NetCDF.</p> <p>Enquiries should be directed to Jasmin.John (Jasmin.John@noaa.gov) or John Dunne (John.Dunne@noaa.gov)</p> <p>Anyone using these data should cite Dunne et al. (2020) and Adcroft et al. (2019). See references below.</p> <p>Other variables associated with these simulations, along with assigned DOI's, are available through the ESGF CMIP6 portal:</p> <p>https://esgf-node.llnl.gov/projects/cmip6/ </p> <p>______________________________________________________________________</p> <p>The organization of this repository is as follows:</p> <p>Experiments:</p> <p>The tar files in the repository follow this naming convention: <model>_<activity>_<MIP>_<experiment>_<component_and_grid>_<variable>.tar.gz</p> <p> </p> <p>E.g. </p> <p> </p> <p>GFDL_ESM4_CMIP6_ScenarioMIP_ssp585_ocean_annual_z_1x1deg_agessc.tar.gz (regridded)</p> <p>GFDL_ESM4_CMIP6_ScenarioMIP_ssp585_ocean_annual_z_agessc.tar.gz (native grid)</p> <p>The organization of above repository is as follows:</p> <p>Experiment: ssp585</p> <p>NOAA-GFDL/CMIP6/ScenarioMIP/GFDL-ESM4/ESM4_ssp585_D1/</p> <p>For each experiment, 3D annual sea water age data can be found in the sub-directory:</p> <p>/ocean_annual_z_1x1deg (for regridded output)</p> <p>/ocean_annual_z (for native grid output)</p> <p>_________________________________________________________________________</p> <p>NOTES:</p> <p>Provenance:</p> <p>The data provided here do not have metadata that can be used for provenance and traceability back to NOAA/GFDL. The data DOI assigned should be used when sharing and citing these data.</p> <p>_________________________________________________________________________</p>
Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850-2100)
<p>A long-term (1850-2100) monthly air temperature (tas) product with a spatial resolution of 0.5 degree. This is a merged product from 31 CMIP6 models using the Deep-learning model which reduce bias, spatial downscaling and data merge at the same time,. To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain one historical data (1850-2014) , four future scenarios data during 2015-2100 (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) and four future scenarios data in Australia . The dataset is stored in NetCDF format, containing the variable tas, representing air temperature, produced in centigrade (℃) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.75E to 179.75E, the latitude from -89.75N to 89.75N. </p>
multi-model metrics of daily sea-ice concentrations from CMIP6 models
<p>Multi-model metrics of daily mean sea-ice concentrations from 9 CMIP6 models:</p> <ul> <li>ACCESS-CM2</li> <li>CanESM5</li> <li>CNRM-ESM2.1</li> <li>EC-Earth3</li> <li>EC-Earth3-Veg</li> <li>IPSL-CM6A-LR</li> <li>MIROC6</li> <li>MRI-ESM2</li> <li>NorESM2-LM</li> </ul> <p> </p> <p>Metrics are mean, median, standard deviation, minimum and maximum sea-ice concentrations.</p>
Global and tropical band averages for a selection of CMIP5 and CMIP6 models: piControl and abrupt-4xCO2 experiments (compressed)
<p>This dataset provides post-processed spatial averages for a selection of 55 CMIP5 and CMIP6 models. The experiments contained in this dataset are only the pre-industrial controls (piControl) and the experiments with a four-fold increase in the atmospheric CO$_{2}$ concentration in relation to the pre-industrial level (abrupt-4xCO2). The spatial averages are global and tropical bands from x°S to x°N, where the x value is between 5 and 40 in increments of 5°. This dataset was created to study climate sensitivity in general and the effect of stratospheric circulation changes on the tropical equilibrium climate sensitivity. It contains the following variables:</p> <ul> <li>incoming (d) short-wave (SW, s) radiative flux (RF, r) at the top of the atmosphere (TOA, t): rsdt</li> <li>outgoing (u) SW RF at TOA: rsut</li> <li>outgoing long-wave (LW, l) RF at TOA: rlut</li> <li>net (n) RF at TOA: rnt*</li> <li>incoming SW RF at the surface (s): rsds</li> <li>outgoing SW RF at the surface: rsus</li> <li>incoming LW RF at the surface: rlds</li> <li>outgoing LW RF at the surface: rlus</li> <li>net RF at the surface: rns*</li> <li>sensible heat flux (hfs) at the surface: hfss</li> <li>latent heat flux (hfl) at the surface: hfls</li> <li>surface temperature (t): ts</li> <li>atmospheric temperature: ta</li> <li>specific humidity: hus</li> <li>zonal component of wind: ua</li> <li>meridional component of wind: va</li> <li>lagrangian tendency of pressure (vertical component of wind in pressure per time dimensions): wap</li> <li>surface pressure: ps</li> <li>geopotential height: zg</li> </ul> <p>*rnt and rns were calculated for the creation of this dataset using the model output rlut rsut, rsds, rsus, rlds, rlus.</p> <p>This version updates the previous version by providing the dataset in a compressed tarball. Use `tar -xzvf CMIP_data.tar.gz` to extract and decompress.</p>
CMIP6 model sea-ice concentration data
<p>Sea-ice concentration data from Climate System Models that participated in the sixth Coupled Model Intercomparison Project (CMIP6). <br> <br> Historical (1850-2014) and future projections (2015-2100) of sea-ice concentration were downloaded on the native model grid of 29 models, after which this data was regridded to a regular 1x1 degree latitude by longitude grid for multi-model comparison and calculation of statistics (mean, median, etc.).</p> <p>Future projections include the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios.</p> <p>Models included in the scenarios are shown in the excel spreadsheet.</p> <p> </p>
European winter windstorm days from ERA5 and CMIP6 models
European winter storm hazard days generated from ERA5 (1980–2010) and CMIP6 models (1980–2010 and 2070–2100) for the historical and Shared Socioeconomic Pathways SSP126, SSP245, SSP370, and SSP585 experiments, based on Severino et al., 2023.<br> For each storm day, the specific ensemble member of the climate model which has been used to model the data can be read in the "event_name" property (e.g. 'event_name': 'IPSL-CM6A-LR_ssp585_mem0' corresponds to a storm day which has been generated by the ensemble member 0 of the climate model IPSL-CM6A-LR for the ssp585 experiment).<br> <br> <a href="https://doi.org/10.5194/egusphere-2023-205">Severino, L. G., Kropf, C. M., Afargan-Gerstman, H., Fairless, C., de Vries, A. J., Domeisen, D. I. V., and Bresch, D. N.: Projections and uncertainties of future winter windstorm damage in Europe, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-205, 2023.</a>
North Atlantic average sea-surface temperature in a CMIP6 multi-model ensemble of historical and ssp585/ssp245 simulations
<p>This dataset contains North Atlantic average sea-surface temperatures and derived indices calculated for a multi-model ensemble of historical and future scenario (ssp585, ssp245) simulations contributing to the Coupled Model Intercomparison Project phase 6. A detailed description of the dataset is provided by Zanchettin, D., and Rubino, A., Accelerated North Atlantic surface warming reshapes the Atlantic Multidecadal Variability, Communications Earth & Environment, 2024, doi:10.1038/s43247-024-01804-x.</p> <p><br>The data are provided as netcdf files.</p> <p>The name of each file is structured as {model}_r{realization}_historical_{scenario}.nc where {model} is the model name, {realization} is a number corresponding to the historical realization, and {scenario} is either of the two scenarios considered (ssp585 and ssp245).</p> <p>Each file contains data for the following one dimensional variables:</p> <ul> <li>year: the sequence of years for which the data are provided</li> <li>NASST: annual-average spatially averaged North Atlantic sea-surface temperature</li> <li>state: slowly variable component of NASST obtained from a dlm decomposition of NASST</li> <li>strend: stochastic trend of NASST obtained from a dlm decomposition of NASST</li> <li>AMV: Atlantic Multidecadal Varibility index obtained as difference between NASST and state</li> </ul> <p> </p>
Cloud feedback maps from CMIP5 and CMIP6 models
<p>This repository contains maps of cloud feedbacks computed using up to three methodologies from two types of experiments conducted across CMIP5 and CMIP6 models.</p> <p>Methods:</p> <ul> <li>The approximate partial radiative perturbation technique (shortwave cloud feedbacks only), denoted with 'APRP' in filename. </li> <li>The adjusted change in cloud radiative effect, denoted with 'adjCRE' in filename. </li> <li>The cloud radiative kernel method, in files without 'APRP' or 'adjCRE' in the name. </li> </ul> <p>Experiments:</p> <ul> <li>amip+4K experiments, known as amip4K and amip-p4K in CMIP5 and CMIP6, respectively</li> <li>abrupt CO2 quadrupling experiments, known as abrupt4xCO2 and abrupt-4xCO2 in CMIP5 and CMIP6, respectively</li> </ul> <p>Data formats:</p> <ul> <li>For cloud feedbacks derived using APRP and adjCRE methods, all models' maps are contained within single netcdf files.</li> <li>For cloud feedbacks derived using cloud radiative kernels, Individual netcdf files are provided for each model, which are compressed into a zip file.</li> </ul>
NOAA-GFDL GFDL-ESM4 CMIP6 CMIP additional model output
<p>This repository contains annual 3D sea water age since surface contact files from a subset of simulations with GFDL's ESM4.1 climate model (Dunne et al., 2020) that were contributed to the 6th Coupled Model Intercomparison Project (CMIP6). </p> <p>Both the ocean model native grid and the regridded output can be found in this repository. For the regridded output, the files have been regridded from MOM6 native grid to standard World Ocean Atlas 1x1 horizontal grid, and they have been remapped from the lagrangian vertical coordinate used by GFDL's MOM6 ocean model (Adcroft et al., 2019) to standard World Ocean Atlas depths.</p> <p>Files have undergone QA/QC.</p> <p>All data files are NetCDF.</p> <p>Enquiries should be directed to Jasmin.John (Jasmin.John@noaa.gov) or John Dunne (John.Dunne@noaa.gov)</p> <p>Anyone using these data should cite Dunne et al. (2020) and Adcroft et al. (2019). See references below.</p> <p>Other variables associated with these simulations, along with assigned DOI's, are available through the ESGF CMIP6 portal:https://esgf-node.llnl.gov/projects/cmip6/ </p> <p>______________________________________________________________________</p> <p>The tar files in the repository follow this naming convention: <model>_<activity>_<MIP>_<experiment>_<component_and_grid>_<variable>.tar.gz</p> <p> </p> <p>E.g. </p> <p> </p> <p>GFDL_ESM4_CMIP6_CMIP_historical_ocean_annual_z_1x1deg_agessc.tar.gz (regridded)</p> <p>GFDL_ESM4_CMIP6_CMIP_historical_ocean_annual_z_agessc.tar.gz (native grid)</p> <p> </p> <p>The organization of above repository is as follows:</p> <p>Experiments:</p> <p>The concentration-forced piControl simulation: </p> <p>NOAA-GFDL/CMIP6/CMIP/GFDL-ESM4/ESM4_piControl_D</p> <p>The concentration-forced historical simulation: </p> <p>NOAA-GFDL/CMIP6/CMIP/GFDL-ESM4/ESM4_historical_D1</p> <p>The historical simulation was spawned from year 101 of the pre-industrial control simulation</p> <p>For each experiment, 3D annual sea water age data can be found in the sub-directory:</p> <p>/ocean_annual_z_1x1deg (for regridded output)</p> <p>/ocean_annual_z (for native grid output)</p> <p>_________________________________________________________________________</p> <p>NOTES:</p> <p>Provenance:</p> <p>The data provided here do not have metadata that can be used for provenance </p> <p>and traceability back to NOAA/GFDL.</p> <p>The data DOI assigned should be used when sharing and citing these data.</p> <p>_________________________________________________________________________</p> <p><br> </p>
Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations: video supplements
<p>These are video supplements cited in the manuscript "Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations" that has been submitted to Geoscientific Model Development (GMD; MS No.: gmd-2018-192).</p> <p>The animations (with a 10yr running window) show the development of temperature biases in the deep ocean over a period of 100 years in two pre-industrial configurations with the AWI Climate Model: AWI-CM-LR and AWI-CM-HR. Meridional biases along the 30.5°W transect through the Atlantic Ocean (S3 and S4; animated version of Fig.8 for LR and HR) and maps of along-isopycnal biases (sigma_1=31.8) are shown (S1 and S2). Time axes have been added compared to version 1.</p>
Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios
<p><strong>Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios</strong></p> <p>This dataset contains output ice sheet model runs forced by climate boundary conditions provided by CMIP6 climate model output. Each experiment set is archived in separate compressed tar.gz files. </p> <p>Description of the experiment sets, including the model setup, key parameters, climate forcings, and their main objectives are documented in Table 1 of Li, DeConto, Pollard (2023) Climate model differences contribute deep uncertainty in future Antarctic ice loss, Science Advances.</p> <p>Two kinds of output are included in each ice sheet run: fort.22 files contain time series of several key variables for the Antarctic Ice Sheet (area, volume, sea-level equivalent, etc.); fort.92.nc files contain 2D and 3D fields such as ice thickness and velocity at specific time slices.</p> <p> </p> <p> </p>
Dataset to Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution
<p>Six sensitivity experiments were conducted with the ocean-sea ice-biochemistry model FESOM2.1-REcoM3 to investigate the sensitivity of total alkalinity to changes in parametrizations. The results are reported in part two of the report:</p> <p>Hinrichs, C., & Hauck, J. (2022). OceanNETS Task 4.4 – Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution. <a href="https://oceanrep.geomar.de/id/eprint/56871/1/Deliverable_4.4_vfinal.pdf">https://oceanrep.geomar.de/id/eprint/56871/1/Deliverable_4.4_vfinal.pdf</a></p> <p>This item contains the data and plotting routine to reproduce Figure 14 in the report. The model data has been post-processed into global mean depth profiles.</p>
CMIP6 derived ensemble of global vapor pressure deficit, potential evapotranspiration, and reference evapotranspiration
<p>Climate change induced trends in long-term aridity—via changes to atmospheric water demand for have the potential to impact surface water availability across the globe by altering efficiency by which precipitation is converted to runoff. Quantification of aridity requires estimates of evaporative demand, often using vapor pressure deficit, potential evapotranspiration, and/or reference evapotranspiration, but no comprehensive estimate of these climate variables exists to date from the Coupled Model Intercomparison Project 6 (CMIP6). Here we present global monthly estimates of the Penman-Monteith short grass reference evapotranspiration, its advective and radiation components, Priestley-Taylor potential evapotranspiration, and vapor pressure deficit from 16 CMIP6 general circulation models (GCM) for the historical period and four future emission scenarios ranging from low to high projected emissions. The purpose of this dataset is to offer structured and well-documented estimates of historical and future projected evaporative demand derived from the state-of-the-science CMIP6 climate models for use in hydrologic and ecological analyses. We produce a single file for all monthly values of each variable for individual GCM/emission scenario combination gridded at the given GCMs native resolution. Produced alongside all of the files are descriptions of each of the variables and generated python scripts that contain the functions used to estimate vapor pressure deficit, potential evapotranspiration, and reference evapotranspiration.</p>
Observed Rainfall And CMIP6 Data
<p>The gridded rainfall datasets used in this analysis were sourced from the Indian Meteorological Department (IMD). These datasets were collected on a daily basis from 1951 to 2021 and were obtained from the National Climate Centre (NCC) of IMD from the following URL: https://www.imdpune.gov.in/lrfindex.php. The dataset covers the Indian River Basins (IRBs) and has been generated at high resolution by assimilating data from 6955 rain gauge stations spread across the IRBs.</p> <p>For the simulated daily precipitation spanning the years 2021 to 2100, four different Shared Socioeconomic Pathways (SSPs) were considered: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The simulated precipitation outputs were derived from the Coupled Model Intercomparison Project-6 (CMIP6) and were obtained from the following URL: https://esgf-node.llnl.gov/search/cmip6/. All 12 models of CMIP6 were downloaded, specifically using the variant label r1i1p1f1 as an initial condition.</p>
Clear-sky Outgoing Longwave Radiation Fluxes from Offline RTE-RRTMGP for AMIP Simulation of CMIP6 Models
<p>This study utilizes RTE-RRTMGP (<a href="https://doi.org/10.1029/2019MS001621">https://doi.org/10.1029/2019MS001621</a>) to compute clear-sky, aerosol-free outgoing longwave radiation (OLR) fluxes for AMIP simulation of CMIP6 models, using 36-year average of monthly-mean temperature and humidity profiles at 19 pressure level as an input. This dataset is used to investigate the cause of inter-model spread in OLR when the same set of radiative transfer schemes is used.</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.