Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
33
datasets available to search
ShareScore release 0.9.0
Dataset results
33 results for “CMIP5”
Amundsen Sea future MAR simulations forced by the CMIP5 multi-model mean
<p><strong>Amundsen Sea future MAR simulation forced by the CMIP5 multi-model mean (RCP8.5)</strong></p> <p>This future simulation is fully described in the following article:</p> <p>Donat-Magnin, M., Jourdain, N. C., Kittel, C., Agosta, C., Amory, C., Gallée, H., Krinner, G., and Chekki, M. Future surface mass balance and surface melt in the Amundsen sector of the West Antarctic Ice Sheet. <em>The Cryosphere</em>.</p> <p>The future is derived from the CMIP5 multi-model mean under the RCP8.5 scenario and covers the 2079-2108 period. The corresponding present-day simulation is available on <a href="http://doi.org/10.5281/zenodo.4308510">http://doi.org/10.5281/zenodo.4308510</a> and was thoroughly evaluated in the following TC paper: <a href="https://doi.org/10.5194/tc-14-229-2020">https://doi.org/10.5194/tc-14-229-2020</a></p> <p>See netcdf metadata for more information. Note that what is called runoff in the outputs is not actually a runoff (into the ocean) but more the net production of liquid water at the surface (which can either form ponds or flow into the ocean).</p> <p>Monthly files provided on MAR grid (see MAR_grid10km.nc). We also provide climatological (2079-2108 average) surface mass balance (SMB), surface melt rates and net liquid water production ("runoff") on a standard 8km WGS84 stereographic grid (see files ending as mean_polar_stereo.nc). Daily snowfall and surface melt rates are provided in ICE*nc.<br> <br> The interpolation to the stereographic grid is done using interpolate_to_std_polar_stereographic.f90. The fields are extrapolated to the ocean grid points so that ice sheet models with various ice-shelf extent can use this dataset.</p> <p>To extrapolate the SMB and surface melt projections to other warming scenarios or period, see eq. (2,3) in Donat-Magnin et al.</p> <p>The following variables are provided:</p> <ul> <li>CC Cloud Cover</li> <li>LHF Latent Heat Flux</li> <li>LWD Long Wave Downward</li> <li>LWU Long Wave Upward</li> <li>QQp Specific Humidity (pressure levels)</li> <li>QQz Specific Humidity (height levels)</li> <li>RH Relative Humidity</li> <li>SHF Sensible Heat Flux</li> <li>SIC Sea ice cover</li> <li>SP Surface Pressure</li> <li>ST Surface Temperature</li> <li>SWD Short Wave Downward</li> <li>SWU Short Wave Upward</li> <li>TI1 Ice/Snow Temperature (snow-layer levels)</li> <li>TTz Temperature (height levels)</li> <li>UUp x-Wind Speed component (pressure levels)</li> <li>UUz x-Wind Speed component (height levels)</li> <li>VVp y-Wind Speed component (pressure levels)</li> <li>VVz y-Wind Speed component (height levels)</li> <li>UVp Horizontal Wind Speed (pressure levels)</li> <li>UVz Horizontal Wind Speed (height levels)</li> <li>ZZp Geopotential Height (pressure levels)</li> <li>mlt Surface melt rate</li> <li>rfz Refreezing rate</li> <li>rnf Rainfall</li> <li>rof "Runoff" (i.e. net production of surface liquid water)</li> <li>sbl Sublimation</li> <li>smb Surface Mass Balance</li> <li>snf Snowfall</li> </ul>
CMIP5-historical dataset on Temperature and Precipitation
<p>CMIP5-historical dataset on Temperature and Precipitation</p>
CLynchy/CMIP5_patterns: First release of CMIP5 pattern library
<p>This is a working repository that contains CMIP5 patterns and pattern creation code.</p>
CMIP5 P50 Analysis v1.0 for Tuna Species: Source Data
<p><strong>Model results and data used to make future projections of the effects of climate change on the physiology of tuna in the global ocean</strong></p> <p>-------------------------------------------------</p> <p><strong>Description:</strong></p> <p>Coupled Model Intercomparison Project Phase 5 (CMIP5) model results were downloaded from here:<br> https://esgf-node.llnl.gov/search/cmip5/</p> <p>World Ocean Atlas (WOA) 2009 data were downloaded from here:<br> https://www.nodc.noaa.gov/OC5/WOA09/netcdf_data.html</p> <p>The model results and data should only be used to reproduce the analysis described in this publication:</p> <p>Mislan, K. A. S., C. A. Deutsch, R. W. Brill, J. P. Dunne, and J. L. Sarmiento. (2017) Projections of climate driven changes in tuna vertical habitat based on species-specific differences in blood oxygen affinity. Global Change Biology.</p> <p><strong>The Zenodo archive of the code is here:<br> https://doi.org/10.5281/zenodo.808742</strong></p> <p> </p> <p>-------------------------------------------------</p> <p><strong>Instructions:</strong></p> <p>Download the tar.gz file, unzip, and put the folders in the data folder of the CMIP5_p50_tuna code.</p>
An evaluation dataset for the skills of CMIP5 and CMIP6 models in simulating climate of China
<p>General circulation model (GCM) simulations archived by the Coupled Model Intercomparison Project (CMIP) are crucial tools for climate science. However, with various GCM results simulated by different countries and institutions, researchers have difficulty in choosing appropriate models for their unique study area. To this end, this dataset provieds Tayler skill scores of 28 GCMs in simulating temperature and precipitation of 631 reference sites across China under daily, monthly and seasonally scales. These scores are calculated based on the observations of meteorological stations and historical simulations of GCMs during 1970-2005. </p> <p>The dataset is very important for researchers to select locally appropriate GCMs. For example, researchers concerned with climate change of Beijing could firstly download the GCMs with sound performance at station 54511 (i.e., NorESM2-LM, INM-CM5-0 and MPI-ESM1-2-LR for temperature and NorESM1-M, IPSL-CM5A-LR and INM-CM4 for precipitation), and then conduct the further works of downscaling.</p>
Model simulation data used in "An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing" (Thor et al., GMD, 2022)
<p>This archive contains files that were used to produce the results published in the article "An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing" by Thor et al.</p> <p>The directory nml contains namelist setups (configuration files) used for each of the simulations that were performed for this study.<br> The used MESSy version is d2.54.0.3-pre2.55-02-2077-g6eca90858-dirty_6eca90858ecb4ee8fb8900681a94a79ac3d612af_2021-01-26T11:10:08+01:00_2021-02-18T09:14:01+0100 for the QCTM simulations and d2.54.0.3-pre2.55-02-1466-g1fb086944_1fb0869442bf4f1bb10a7dd5f36e4bde5c0cf6d7_2020-10-27T18:17:50+01:00_2020-10-27T18:24:16+0100 for the aerosol simulations (http://www.messy-interface.org).</p> <p>The directory figures contains ipython scripts that were used to produce the figures in the paper.</p>
Data used in "Seasonality of Intraseasonal Variability in CMIP5 and Nonhydrostatic Atmospheric Global Models" by Nakano and Kikuchi (2019) submitted to GRL
<p>PCs time series and NICAM-AMIP 2.5 degree gridded data used in Nakano and Kikuchi (2019) submitted to GRL.</p>
Transformed Eulerian mean data from CMIP5 EC-Earth v2.3 AMIP experiment
<p>Data are derived from EC-Earth atmosphere-only experiment outputs for CMIP5 (identifier SA07), following the procedure described in a paper currently in preparation.</p> <p>Files are organized in one .tar file for each variable and temporal aggregation (monthly and daily).</p> <p>The simulation was done by the Swedish Meteorological and hydrological Institute (SMHI) on resources provided by the Swedish National Infrastructure for Computing (SNIC).</p>
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅳ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Wind in historical periods, the value "2333" refers to no data. Set them to NaN before using, for example (Matlab): Wind(Wind==2333)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅲ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For precipitation in historical periods, the value "2333" refers to no data. Set them to NaN before using, for example (in Matlab):<br> Pr(Pr==2333)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
Data and code for GRL submission "Tropical rainfall linked to stronger future ENSO-NAO teleconnection in CMIP5 models"
<p>Data and python code used to plot figures in GRL submission "Tropical rainfall linked to stronger future ENSO-NAO teleconnection in CMIP5 models".</p>
GISS ModelE2 contributions to the CMIP5 archive
We present a description of the ModelE2 version of the Goddard Institute for Space Studies (GISS) General Circulation Model (GCM) and the configurations used in the simulations performed for the Coupled Model Intercomparison Project Phase 5 (CMIP5). We use six variations related to the treatment of the atmospheric composition, the calculation of aerosol indirect effects, and ocean model component. Specifically, we test the difference between atmospheric models that have noninteractive composition, where radiatively important aerosols and ozone are prescribed from precomputed decadal averages, and interactive versions where atmospheric chemistry and aerosols are calculated given decadally varying emissions. The impact of the first aerosol indirect effect on clouds is either specified using a simple tuning, or parameterized using a cloud microphysics scheme. We also use two dynamic ocean components: the Russell and HYbrid Coordinate Ocean Model (HYCOM) which differ significantly in their basic formulations and grid. Results are presented for the climatological means over the satellite era (1980-2004) taken from transient simulations starting from the preindustrial (1850) driven by estimates of appropriate forcings over the 20th Century. Differences in base climate and variability related to the choice of ocean model are large, indicating an important structural uncertainty. The impact of interactive atmospheric composition on the climatology is relatively small except in regions such as the lower stratosphere, where ozone plays an important role, and the tropics, where aerosol changes affect the hydrological cycle and cloud cover. While key improvements over previous versions of the model are evident, these are not uniform across all metrics.
Subset of GLACE-CMIP5 data used in Population Exposure to Future Heatwaves Influenced by Soil Drying
<p>This is a subset of GLACE-CMIP5 data which is linked to the manuscript titled with <em>Increasing Population Exposure to Future Heatwaves Influenced by Soil Drying. </em></p> <p>The HDF files are the values of contrast between CTL and ExpA for each grid.</p> <p>The XLSX files are the mean values for the globe, global land, and the seven domains (which will be described at the end) for both CTL and ExpA.</p> <p>Values for both present period (1980-2010) and future period (2070-2100) are included.</p> <p>Values for heatwave duration (HWD), heatwave magnitude (HWMt), heatwave amplitude (HWAt), and heatwave frequency (HWF) are included.</p> <p>Domain Definition: East, North, and central Asia (ENCA); Australia (AU); the US; West Europe (WE); East Europe and West Asia (EEWA); Southern and central Africa (SCAF, African continent below the Sahara Desert); South America (SA).</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.