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.
53
datasets available to search
ShareScore release 0.9.0
Dataset results
53 results for “Regional climate model”
Data and Code for "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" Paper Submission to AGU GRL
<p>This contains the emergent constraint code, the offline CMIP6 hydrology data, and the shapefiles for each region used in the paper "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" submitted to AGU GRL.</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>
Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations
<p>Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"</p>
Lagrangian Heavy Precipitation Events in convection-permitting Regional Climate Models over the Alps and in the Mediterranean
<p>The csv datafile contains a set of heavy precipitation events identified in cpRCMs.</p> <p>Each of the entries represents an event and is described with detailed properties:</p> <p>'Start Date [YYYYMMDD.HOUR/24]', 'Latitude [°]', 'Longitude [°]',<br> 'Duration [h]', 'Volume [km² h]', 'P99(pr) [mm h-1]',<br> 'P90(pr) [mm h-1]', 'P75(pr) [mm h-1]',<br> 'P50(pr) [mm h-1]', 'P25(pr) [mm h-1]',<br> 'P10(pr) [mm h-1]', 'Total Precipitation [m3]',<br> 'Maximum Precipitation [mm h-1$]',<br> 'Mean Precipitation [mm h-1$]', 'Direction [°]',<br> 'Distance Traveled [km]', 'Eccentricity [-]', 'Track Eccentricity [-]',<br> 'Mean Ellipsicity [-]', 'Track Ellipsicity [-]', 'Mean Major Angle [°]',<br> 'Track Major Angle [°]', 'Mean Major Axis [-]', 'Track Major Axis [-]',<br> 'My Orientation [°]', 'My Track Orientation [°]', 'max(Elevation) [m]',<br> 'min(Elevation) [m]', 'Start Year [YYYY]', 'Start Month [MM]',<br> 'LandFallSea [-]', 'Scenarios', 'Models', 'situations', 'Ensemble',<br> 'Speed [km h$^{-1}$]', 'Mean(Area) [km²]', 'orographic [-]',<br> 'Severity [-]', 'I/O OBS [-]', 'Region [-]', 'orographic1500 [-]',<br> 'orographic2000 [-]', 'orographic2500 [-]', 'orographic3000 [-]']</p>
The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales
<p>This is the companion data for the manuscript titled 'The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales', submitted to the Journal of Advances in Modeling Earth Systems in September 2022.</p> <p>derecho_simulation_result: this folder contains part of the SCREAM RRM outputs I ran on NERSC Cori in 2021-2022 corresponding to the simulation in Table 1 of the manuscript.</p> <p>wrf_simulation_result: this folder contains part of the WRF outputs run by Jianfeng Li from PNNL (jianfeng.li@pnnl.gov) in 2022 corresponding to the simulations in Table 4 of the manuscript.</p> <p>plot_script: this folder includes python scripts to plot figures shown in the manuscript.</p> <p>ASOS_station: this txt file contains the processed ASOS station wind speed used in the manuscript.</p> <p><br> Unfortunately, all model outputs are large (~ 3.8 TB for SCREAM RRM and 3.1 TB for WRF). Therefore, I only provide the variables (i.e., OLR, precipitation, composite radar reflectivity, and 10-m wind speed) used directly to generate figures in this repository. All model outputs are archived on tape at NERSC.</p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu).</p>
The evaluation of climate change competitiveness via DEA models and Shannon's entropy: EU regions case
<p>Supplementary materials for the article titled “The evaluation of climate change competitiveness via DEA models and Shannon’s entropy: EU regions case”.</p> <p><span>The data was collected under project provided by the National Science Centre, Poland; Grant No. 2019/35/B/HS5/01548.</span></p> <div> <div> <div> <p> </p> </div> </div> </div> <p> </p>
A Factor Two Difference in 21st-Century Greenland Ice Sheet Surface Mass Balance Projections from Three Regional Climate Models for a Strong Warming Scenario (SSP5-8.5)
<p>1km regridded Greenland Ice Sheet SMB / Runoff / Melt projection until 2100. Projections from MAR, RACMO, HIRHAM forced by CESM2 (SSP5-8.5).</p>
Biasadjusted Regional Climate Model Data for Europe - Temperature
<p>This repository contains the bias-adjusted temperature data used in the production of numbers and figures contained in our research article entitled "Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change".</p> <p>Ferdini S., von Cossel M., Wulfmeyer V., Warrach-Sagi K. (2023) Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change. <em>Global Change Biology - Bioenergy.</em></p>
Biasadjusted Regional Climate Model Data for Europe - Precipitation
<p>This repository contains the bias-adjusted precipitation data used in the production of numbers and figures contained in our research article entitled "Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change".</p> <p>Ferdini S., von Cossel M., Wulfmeyer V., Warrach-Sagi K. (2023) Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change. <em>Global Change Biology - Bioenergy.</em></p>
Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks [datasets]
<p>Outputs used in:</p> <p><em>van der Meer, M., de Roda Husman, S., Lhermitte, S.: </em>Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks</p> <ul> <li>MAR(ACCESS1-3)_monthly_SMB.nc: MAR outputs with monthly values of SMB and components over the Antarctic ice sheet (1980--2100)</li> <li>MAR(ACCESS1-3)-stereographic_monthly_GCM_like.nc: MAR outputs upscaled to GCM resolution (1980--2100)</li> <li>ACCESS1-3-stereographic_monthly_cleaned.nc: GCM monthly outputs over the Antarctic ice sheet (1980--2100)</li> </ul> <p>The up-to-date working versions of our experiments and source code can be found and are available on our GitHub: <a href="https://github.com/marvande/RCM-Emulator">https://github.com/marvande/RCM-Emulator</a> and at this link: <a href="https://doi.org/10.5281/zenodo.7875967">https://doi.org/10.5281/zenodo.7875967</a></p> <p>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. You should also refer to and cite the following paper:</p> <p><strong>Cite as: </strong>Marijn van der Meer, Sophie de Roda Husman, S Lhermitte. Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks. <em>Authorea.</em> December 27, 2022 <br> DOI: <a href="https://doi.org/10.22541/essoar.167214210.02213149/v1">10.22541/essoar.167214210.02213149/v1</a> </p>
WRF model configuration and data used for the NHESS manuscript "Heat wave characteristics: evaluation of regional climate model performances for Germany"
<p>The file contains:</p> <ul> <li>the namelist.input document with the description of the WRF model configuration used in Warscher et al. (2019)</li> <li>WRF simulation outputs from the reanalysis run: daily values of maximum temperature for the time period 1980-2009 from the innermost (5 km grid resolution) and second innermost (15 km) domain; from both domains the same section, relevant for the study, was taken; the data was bilineraily interpolated to 12.5 km horizontal grid resolution to match the EUR-11 CORDEX format</li> </ul>
Investigating the local temperature response to forest management with a regional climate model
<p>Data used in the manuscript "Investigating the local temperature response to forest management with a regional climate model" which will be submitted to Journal of Geophysical Research: Atmospheres.</p>
Data from: A model-based evaluation of the effects of irrigation expansion on regional and global land surface climate
Open the record for dataset details and reuse information.
Climate model output from a study of tropical cyclones over the Shanghai region under climate change based on a convection-permitting modelling
Open the record for dataset details and reuse information.
Regional Atmospheric Climate Model 2 (RACMO2), version 2.3p2
<p>In the 1990s the KNMI developed in cooperation with the Danish Meteorological Institute the research model RACMO based on the High Resolution Limited Area Model (HIRLAM) numerical weather prediction model. In 1993 UU/IMAU started to modify the model such that it better represented the extreme conditions over glacier surfaces. This first version of RACMO, RACMO1, combined the dynamical core of the HIRLAM model with ECHAM4 physics. The polar modified version of RACMO1 was mainly applied to the Antarctic Ice Sheet.</p> <p>The second version, RACMO2, combines the dynamical core of the HIRLAM model with the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecast System (ISF) physics. RACMO versions 2.0 and 2.1 included HIRLAM version 5.0.6 and ISF cycle CY23r4, while version 2.3 includes HIRLAM version 6.3.7 and cycle CY33r1. Due to the rapid increase in computer capacity over the years, these versions of RACMO have not only been applied to the Greenland and Antarctic Ice Sheets, but also at higher resolution to smaller areas such as Dronning Maud Land and Patagonia.</p> <p>For the RACMO model in general the grids are defined over the equator and then rotated to the area of interest. Grid distance is defined in fraction of degrees, which results in near equidistant grid points as long as the domain is small enough. Note that the domain is thus not on a (polar) stereographic projection plane. In the vertical, the model adopts a system of hybrid sigma levels, which evolve from terrain-following sigma levels close to the surface to pure pressure levels at higher elevation. The actual number of horizontal grid points varies per model run; in most simulations, 40 vertical layers were used.</p> <p>Since RACMO is a regional model, it needs external information at the lateral boundaries and sea surface. At the lateral boundary zone of the model, the temperature, specific humidity, zonal and meridional wind components, and the surface pressure are relaxed towards the fields of a global model every 6 model hours, as are the sea surface temperature and sea ice concentration. RACMO is not forced at the model top. The interior of the model is not nudged towards observations and allowed to evolve freely.</p>
A companion dataset to the paper Scenarios of future climate zone changes in Europe based on EURO-CORDEX regional model ensemble by Holtanová et al., to be submitted to Regional Environmental Change
<p>The content of the dataset is described in the metadata.txt file. </p>
Validation Data used for manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model"
<p>those are the processed data that used for model-data comparison in the manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model", including Lake Surface Temperature and Lake Surface Ice Cover from Great Lakes Surface Environmental Analysis (GLSEA), Surface Air temperature and Precipitation from Climatic Research Unit (CRU). </p>
WRF model configuration and data used for the NHESS manuscript "Droughts in Germany: Performance of Regional Climate Models in reproducing observed characteristics"
<p>The file contains:</p> <ul> <li>the namelist.input document with the description of the WRF model configuration used in Warscher et al. (2019)</li> <li>WRF simulation outputs from the reanalysis run: monthly values for the time period 1980-2009 of precipitation, maximum and minimum temperature (needed for the SPEI calculation) from the innermost (5 km grid resolution) and second innermost (15 km) domain; from both domains the same section, relevant for the study, was taken; the data was bilineraily interpolated to 12.5 km horizontal grid resolution to match the EUR-11 CORDEX format</li> </ul> <p> </p>
Supporting Information: Vortex streets to the lee of Madeira in a km-resolution regional climate model
<p>This is the Supporting Information for the manuscript <em>Vortex streets to the lee of Madeira in a km-resolution regional climate model</em>.</p> <p>It is posted as a preprint on EGUsphere for the journal Weather and Climate Dynamics: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-965/.</p>
Kilometre-scale regional climate model simulations of two atmospheric river case studies in West Antarctica
<p>Regional climate model simulations produced using the MetUM, HCLIM and Polar-WRF models at 1 km horizontal grid spacing. The data span two case studies in which an atmospheric river made landfall over the Amundsen Sea Embayment and Thwaites / Pine Island ice shelves. The first is a winter case (23-30 June 2020) and the second a summer case (3-9 February 2020). </p> <p>Data are gridded, in native model coordinates, and saved as netcdf.</p> <p>Data produced by:</p> <p>HCLIM: José Abraham Torres</p> <p>MetUM: Ella Gilbert</p> <p>Polar-WRF: Denys Pishniak</p> <p>Data were produced to support the analysis presented in Gilbert et al. (2024) [preprint] . The research was funded by the PolarRES project, which is funded under the EU's Horizon 2020 programme call H2020-LC-CLA-2018-2019-2020 under grant agreement 101003590. MetUM simulations were performed on the ARCHER2 UK National Supercomputer. </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.