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41 results for “CORDEX”
Ensemble calculations of "98perc_sfcWindmax" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>98perc_sfcWindmax</p> <p><strong>Definition:</strong> Average of the annual 98<sup>th</sup> percentile of the daily maximum wind speed over a 30-year time-period.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily maximum near-surface wind speed (sfcWindmax).</p> <p>Results (ensemble mean and ensemble standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) time periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The EURO-CORDEX climate model simulations used are:</p> <ul> <li>SMHI-RCA4/ ICHEC-EC-EARTH, SMHI-RCA4/ MOHC-HadGEM2-ES</li> <li>CLMcom-CCLM4-8-17/ ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/ MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ ICHEC-EC-EARTH, KNMI-RACMO22E/ MOHC-HadGEM2-ES</li> </ul>
Ensemble calculations of "Fmax" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>Fmax</p> <p><strong>Definition:</strong> Average of the annual maximum of the daily maximum wind speed over a 30-year time-period.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily maximum near-surface wind speed (sfcWindmax).</p> <p>Results (ensemble mean and ensemble standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) time periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The EURO-CORDEX climate model simulations used are:</p> <ul> <li>SMHI-RCA4/ ICHEC-EC-EARTH, SMHI-RCA4/ MOHC-HadGEM2-ES</li> <li>CLMcom-CCLM4-8-17/ ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/ MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ ICHEC-EC-EARTH, KNMI-RACMO22E/ MOHC-HadGEM2-ES</li> </ul>
RoCliB - Bias corrected CORDEX RCM dataset over Romania
<p>This dataset contains a set of four climate variables from 10 General Circulation Models (GCMs), dynamically downscaled in the EURO-CORDEX initiative by several Regional Climate Models (RCMs) and adjusted (bias-corrected) over Romania for the period 1971–2100. The climate models data were obtained from the <a href="https://cordex.org/data-access/">EURO-CORDEX archive</a>. Two climate change scenarios were selected, namely the moderate (RCP4.5) and business-as-usual scenario (RCP8.5). The multivariate bias correction by the N-dimensional probability density method (MBCn) was used to bias correct the RCMs outputs [1], using as reference the ROCADA gridded dataset [2].</p> <p>Characteristic:</p> <ul> <li><strong>Climate variables</strong>: air temperature (tasAdjust - Celsius degree), maximum air temperature (tasmaxAdjust - Celsius degree), minimum air temperature (tasminAdjust - Celsius degree) and precipitation (prAdjust - mm)</li> <li><strong>Bias-correction method:</strong> multivariate bias correction (N-pdft)</li> <li><strong>The reference period used for bias correction: </strong>1971-2005</li> <li><strong>The observational dataset used as a reference for bias correction: </strong>ROCADAv1</li> <li><strong>Temporal resolution:</strong> daily</li> <li><strong>Temporal extent</strong>: <ul> <li>Historical: 1971-2005;</li> <li>RCP4.5 and RCP8.5: 2006-2100.</li> </ul> </li> <li><strong>Spatial resolution:</strong> 0.1 degrees (~10km)</li> <li><strong>Spatial extent:</strong> from 20.1 to 29.8°E and 43.5 to 48.4°N</li> <li><strong>File format: n</strong>etCDF, CF-1.4-compliant format using netCDF4 compression</li> <li><strong>Coordinate system: </strong>WGS 1984 (EPSG:4326)</li> <li><strong>Naming conventions: </strong><em>variablename</em>_ROU-11_<em>cmip5experiment</em>_<em>globalmodel</em>_<em>run</em>_r<em>egionalmodel</em>_<em>rcmversionid</em>_<em>timefrequency</em>_<em>starttime-endtime</em><em>.</em>nc</li> <li><strong>RMCs</strong> (Institution or working group, RCM Model, GCM Institute, GCM Driving): <ul> <li>Climate Limited-area Modelling Community (CLMcom) CCLM4-8-17 CNRM-CERFACSCNRM-CM5</li> <li>Royal Netherlands Meteorological Institute (KNMI) RACMO22E CNRM-CERFACS CNRM-CM5</li> <li>Swedish Meteorological and Hydrological Institute (SMHI) RCA4CNRM-CERFACS CNRM-CM5</li> <li>Climate Limited-area Modelling Community (CLMcom) CCLM4-8-17 ICHECEC-EARTH</li> <li>Swedish Meteorological and Hydrological Institute (SMHI) RCA4I CHECEC-EARTH</li> <li>Royal Netherlands Meteorological Institute (KNMI) RACMO22E ICHECEC-EARTH</li> <li>Danish Meteorological Institute (DMI) HIRHAM5 ICHECEC-EARTH</li> <li>Climate Limited-area Modelling Community (CLMcom) CCLM4-8-17 MPI-MMPI-ESM-LR</li> <li>Swedish Meteorological and Hydrological Institute (SMHI) RCA4 MPI-MMPI-ESM-LR</li> <li>Climate Service Center Germany (GERICS) REMO2015 NCC NorESM1-M</li> </ul> </li> </ul> <p><strong>The terms of use</strong> for RoCliB datasets are the same as those from the original EURO-CORDEX simulations obtained from ESGF servers: <a href="https://is-enes-data.github.io/cordex_terms_of_use.pdf">https://is-enes-data.github.io/cordex_terms_of_use.pdf</a>.</p> <p><strong>To access and visualize</strong> relevant facts and statistics about climate change based on the RoCliB datasets use <a href="http://suscap.meteoromania.ro/en/roclib">http://suscap.meteoromania.ro/en/roclib</a>.</p> <p><strong>Acknowledgement</strong><br> This work was supported by a grant from the Romanian National Authority for Scientific Research and Innovation, CCCDI-UEFISCDI, project number COFUND-SUSCROP-SUSCAP-2, within PNCDI III. We also acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5.</p>
EURO-CORDEX Precipitation Intensity-Duration-Frequency Curves
<p>This dataset provides preliminary precipitation intensity-duration-frequency (IDF) values calculated from the <a href="https://cds.climate.copernicus.eu/datasets/projections-cordex-domains-single-levels">CMIP5 based EURO-CORDEX regional climate model simulations</a>. Values for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece are provided for a historical and future period for the RCP 2.6, 4.5 and RCP 8.5 atmospheric greenhouse gas concentration scenarios for a total of 123 simulations.</p> <p>Technical Info</p> <p>The return period values are computed for exceedance periods of 2, 5, 10, 25, 50, 100, 200, and 500-years and durations of 3, 6, 12, and 24-hours using the generalized extreme value (GEV) distribution with the <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.genextreme.html">scipy.stats.genextreme </a>function from the Python SciPy library. Computations are performed from the output of a combination of 123 regional climate models (RCMs), global climate models (GCMs), climate change scenarios, and ensemble members. The historical data cover the period 1976 through 2005 and the future 2041 through 2070 for the RCP 2.6, 4.5 and 8.5 atmospheric greenhouse gas concentration scenarios. Included in the analysis, there are 39 unique RCM-GCM combinations with outputs from 48 historical, 15 RCP 2.6, 13 RCP 4.5, and 47 RCP 8.5 simulations.</p> <p>Data description: This dataset contains EURO CORDEX return period estimates computed for exceedance periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-years and 3-, 6-, 12-, and 24-hour durations using the generalized extreme value (GEV) distribution. The data includes values based on outputs from a total of 123 simulation combinations, including 39 RCM–GCM configurations, three climate change scenario combinations for a historical period (1976-2005) and future period (2041-2070), and three ensemble members.</p> <p>Format:</p> <p>The format of this dataset is organized in one ZIP files: IDF_{Cityname}.zip. The zip file contains 48 csv files for each RCM-GCM combination and ensemble member that include IDFs for each available RCP scenario.</p>
Ensemble calculations of "Tn10p" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>Tn10p</p> <p><strong>Definition:</strong> Average number of days that the daily minimum temperature is below the 10th percentile of daily minimum temperatures of a five day window.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p> <p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The bias-corrected EURO-CORDEX climate model simulations used are:</p> <ul> <li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li> <li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li> </ul>
Ensemble calculations of "Ice Days" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>Ice days</p> <p><strong>Definition:</strong> Number of days with daily maximum temperature below 0°C.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p> <p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The bias-corrected EURO-CORDEX climate model simulations used are:</p> <ul> <li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li> <li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li> </ul>
Ensemble calculations of "RX1day" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>RX1day</p> <p><strong>Definition:</strong> Greatest one-day precipitation amount.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p> <p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The bias-corrected EURO-CORDEX climate model simulations used are:</p> <ul> <li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li> <li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li> </ul>
CNRM-ARPEGE v6.2.4 contribution to Antarctic Cordex
<p>This data set is a contribution to Antarctic Polar Cordex using the stretched grid capacity of CNRM-ARPEGE atmospheric GCM. Model outputs have been interpolated from the native ARPEGE grid, with horizontal resolution varying between 35kms (near the stretching pole) to 45 kms on the Antarctic continent, to the ANTi-44 domain (actual lon/lat). The data and metadata format respect almost all of Cordex/CMIP conventions (variables names, units, file names...). The data set consists in six simulations of 30 years time slots : 1981-2010 for "historical" simulations and 2071-2100 for future projections using radiative forcing from RCP8.5 scenario :</p> <p>- ARP-AMIP : amip-style control run driven by observed SST and sea-ice (1981-2100)</p> <p>- ARP-NOR-OC : Future projection driven by NorESM1-M RCP8.5 climate change signal on SST and sea-ice (2071-2100)</p> <p>- ARP-MIR-OC : Future projection driven by MIROC-ESM RCP8.5 climate change signal on SST and sea-ice (2071-2100)</p> <p>More details on these three simulations are given in Beaumet et al., 2019 (<strong><a href="https://dx.doi.org/10.5194/tc-13-3023-2019">10.5194/tc-13-3023-2019) </a></strong></p> <p>- ARP-AMIP-AC : Driven by observed SST and sea-ice + run-time flux bias correction*</p> <p>- ARP-NOR-AOC : Driven by same SST and sea-ice as NOR-OC + run-time flux bias correction*</p> <p>- ARP-MIR-AOC : Driven by same SST and sea-ice as MIR-OC + run-time flux bias correction*</p> <p>Empirical run-time bias correction uses correction terms derived from the climatological mean of tendency errors of a simulation nudged towards climate reanalysis (here ERA-Interim). The method is presented first in Guldberg et al., 2005 (10.1111/j.1600-0870.2005.00120.x) and Krinner et al., 2019 (10.1029/2018MS001438). The method applied with ARPEGE over Antarctica and the evalution of the simulation are presented in this paper : https://doi.org/10.5194/tc-2020-307 (In review)</p> <p>Outputs are available at daily time scale for near-surface atmospherique mean (tas), min (tasmin) and max (tasmax) temperature, total precipitation (pr), snowfall (prsn), snowmelt(snm), surface snow sublimation (sbl_i) and surface runoff (mrros).</p> <p>If you consider using these data, please email me (Julien.Beaumet@univ-grenoble-alpes.fr) to see how I can help and/or be involved.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
CORDEX AFR44 Model Evaluation Data for the Melka-Wakena Watershed, Ethiopia
<ul> <li> <p>#Overview<br>This dataset supports the study on Africa-CORDEX Regional Climate Models' performance evaluation in simulating air temperature (tasmax and tasmin) and precipitation in the Melka-Wakena watershed, Ethiopia. <br>The evaluation spans from 1991 to 2005 and includes observed daily data and raw CORDEX AFR44 daily data for tasmax, tasmin, and precipitation. The dataset was analyzed using scatter plots, empirical cumulative distribution functions (ECDF), Taylor diagrams, and multi-metric performance evaluations.</p> <p>#Files and Structure<br>1. Data/<br>>>This directory contains all input and processed datasets used in the study.</p> <p>#Observed_Data/</p> <p>>>Observed_Tasmax.csv: Observed daily maximum temperature (tasmax) data.<br>>>Observed_Tasmin.csv: Observed daily minimum temperature (tasmin) data.<br>>>Observed_Precipitation.csv: Observed daily precipitation data.</p> <p>#CORDEX_Raw_Data/</p> <p>>>CORDEX_Tasmax_Raw.csv: Daily tasmax data from CORDEX AFR44 models.<br>>>CORDEX_Tasmin_Raw.csv: Daily tasmin data from CORDEX AFR44 models.<br>>>CORDEX_Precipitation_Raw.csv: Daily precipitation data from CORDEX AFR44 models.</p> <p>#Processed_CORDEX_Data/</p> <p>>>CORDEX_Tasmax_Processed.csv: Preprocessed daily tasmax data for analysis (e.g., aggregated and formatted).<br>>>CORDEX_Tasmin_Processed.csv: Preprocessed daily tasmin data for analysis.<br>>>CORDEX_Precipitation_Processed.csv: Preprocessed daily precipitation data for analysis.</p> <p>2. Scripts/<br>This directory contains Python scripts used for preprocessing, evaluation, and visualization of the data.</p> <p>#Data_Preprocessing_Scripts/</p> <p>>>Preprocess_Tasmax.py: Script to preprocess daily tasmax data.<br>>>Preprocess_Tasmin.py: Script to preprocess daily tasmin data.<br>>>Preprocess_Precipitation.py: Script to preprocess daily precipitation data.</p> <p>#Evaluation_Scripts/</p> <p>>>Scatter_Plot_Script.py: Script for scatter plot visualizations comparing observed and model data.<br>>>ECDF_Script.py: Script for generating empirical cumulative distribution functions (ECDF).<br>>>Taylor_Diagram_Script.py: Script for generating Taylor diagrams to evaluate model performance.<br>>>Performance_Metrics_Script.py: Script to compute evaluation metrics.<br>>>Approach_Comparison_Script.py: Script for comparing different model evaluation approaches using multi-metric weighted ranking.</p> <p>#Metadata<br>>>Study Area: Melka-Wakena watershed, Ethiopia.<br>>>Time Period: 1991–2005.<br>#Data Source:<br>>>Observed data from local meteorological stations.<br>>>CORDEX AFR44 model data downloaded from the Earth System Grid Federation (ESGF).</p> <p>#Variables:<br>Tasmax: Daily maximum temperature (°C).<br>Tasmin: Daily minimum temperature (°C).<br>Precipitation: Daily precipitation (mm/day).<br>Evaluation Metrics: RMSE, MAE, R², NSE, Percent Bias (PBIAS) ,and others.</p> <p>#How to Use<br>Download the dataset:<br>All required data files are organized in the Data/ folder.</p> <p>#Run the preprocessing scripts:<br>>>If using new datasets, preprocess the raw data using the scripts in Data_Preprocessing_Scripts/. This step formats the data and ensures compatibility with the evaluation scripts.</p> <p>#Conduct evaluation:</p> <p>>>Use the Evaluation_Scripts/ to replicate scatter plots, ECDF, Taylor diagrams, and compute performance metrics.<br>>>Use Approach_Comparison_Script.py for multi-metric weighted ranking comparisons of model performance.</p> <p>#Citation<br>>>When using this dataset, please cite the following:</p> <p>#The dataset:<br>"Dataset for CORDEX AFR44 Model Evaluation in the Melka-Wakena Watershed, Ethiopia."<br>DOI: https://doi.org/10.5281/zenodo.14208274</p> <p>#The source of CORDEX data:<br>>>CORDEX AFR44 data, available from the Earth System Grid Federation (ESGF).</p> <p>Contact<br>For questions or additional information, contact:</p> <p>Tadele: t4shgeresu@gmail.com.</p> </li> </ul>
Bias-corrected EURO-CORDEX RCM simulations for the OPTAIN case studies
<p>Bias-corrected EURO-CORDEX RCM simulations are available on a daily timescale for:</p> <p>-period 1981-2099/2100,</p> <p>-6 RCM,</p> <p>-3 scenarios (RCPs 2.6, 4.5 and 8.5),</p> <p>-7 variables (mean, minimum and maximum temperature, precipitation, solar radiation, wind speed at 2 m and relative humidity) and</p> <p>-18 domains and 23 locations within these domains.</p> <p>Bias correction and further downscaling to 0.1° was done using <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> reanalysis data with non-parametric empirical quantile mapping. Moreover, the interpolation of gridded bias-corrected climate model simulations to the locations was made using universal kriging.</p> <p><strong>Organization of the data</strong></p> <p>The name of the files are <em>domain</em>-<em>type</em>.zip, where <em>type</em> is gridded (NetCDF) or point (csv). Each zip file contains multiple files, organized in subfolders: <em>experiment</em>/<em>modelNumber</em>/<em>variable</em>.nc for gridded and <em>experiment</em>/<em>modelNumber</em>/<em>variable-pilotFieldNumber</em>.txt for point data, where <em>experiment </em>is rcp26, rcp45 or rcp85.</p> <p><em>domain and pilotFieldNumber</em></p> <table> <tbody> <tr> <td> <p><strong>domain</strong></p> </td> <td> <p><strong>domain </strong><strong>location (min and max. Longitude, min and max latitude</strong><strong>)</strong></p> </td> <td> <p><strong>pilotFieldNumber</strong></p> </td> <td> <p><strong>pilot field </strong><strong>location (longitude, latitude)</strong></p> </td> <td> <p><strong>case study</strong><strong> number</strong></p> </td> <td> <p><strong>country</strong></p> </td> <td> <p><strong>Name (OPTAIN case study)</strong></p> </td> </tr> <tr> <td> <p>01</p> </td> <td> <p>50.95 51.45 14.55 15.05</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>1</p> </td> <td> <p>DEU</p> </td> <td> <p>Schoeps</p> </td> </tr> <tr> <td> <p>02</p> </td> <td> <p>46.35 47.05 6.55 7.15</p> </td> <td> <p>2</p> </td> <td> <p>46.816667 6.95</p> </td> <td> <p>2</p> </td> <td> <p>CHE</p> </td> <td> <p>Petite Glane</p> </td> </tr> <tr> <td> <p>02_1</p> </td> <td> <p>46.75 47.25 7.25 7.75</p> </td> <td> <p>1</p> </td> <td> <p>46.983333 7.466667</p> <p> </p> </td> </tr> <tr> <td> <p>02_34</p> </td> <td> <p>47.35 47.85 8.35</p> </td> <td> <p>3</p> <p>4</p> </td> <td> <p>47.433333 8.516667</p> <p>47.683333 8.616667</p> </td> </tr> <tr> <td> <p>02_5</p> </td> <td> <p>46.15 46.65 5.95 6.45</p> </td> <td> <p>5</p> </td> <td> <p>46.4 6.233333</p> </td> </tr> <tr> <td> <p>03a</p> </td> <td> <p>46.65 47.15 17.45 17.95</p> </td> <td> <p>1</p> <p>2</p> <p>3</p> <p>4</p> </td> <td> <p>46.92649 17.68246</p> <p>46.9166 17.68976</p> <p>46.91283 17.69754</p> <p>46.91283 17.69723</p> </td> <td> <p>3a</p> </td> <td> <p>HUN</p> </td> <td> <p>Csorsza</p> </td> </tr> <tr> <td> <p>03b</p> </td> <td> <p>46.45 46.95 16.65 17.15</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>3b</p> </td> <td> <p>HUN</p> </td> <td> <p>Felso Valicka</p> </td> </tr> <tr> <td> <p>04</p> </td> <td> <p>52.35 52.85 18.45 18.95</p> </td> <td> <p>1</p> </td> <td> <p>52.597469 18.728617</p> </td> <td> <p>4</p> </td> <td> <p>POL</p> </td> <td> <p>Upper Zglowiaczka</p> </td> </tr> <tr> <td> <p>05</p> </td> <td> <p>46.35 46.85 15.35 15.85</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>5</p> </td> <td> <p>SVN</p> </td> <td> <p>Pesnica</p> </td> </tr> <tr> <td> <p>06</p> </td> <td> <p>46.45 46.95 16.15 16.65</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>6</p> </td> <td> <p>HUN/SVN</p> </td> <td> <p>Kebele/Kobiljski</p> </td> </tr> <tr> <td> <p>07</p> </td> <td> <p>49.85 50.35 4.75 5.25</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>7</p> </td> <td> <p>BEL</p> </td> <td> <p>La Wimbe</p> </td> </tr> <tr> <td> <p>08</p> </td> <td> <p>55.15 55.75 23.55 24.05</p> </td> <td> <p>1</p> <p>2</p> </td> <td> <p>55.522057 23.799235</p> <p>55.42233194 23.82580339</p> </td> <td> <p>8</p> </td> <td> <p>LTU</p> </td> <td> <p>Dotnuvele</p> </td> </tr> <tr> <td> <p>09</p> </td> <td> <p>45.45 45.95 9.65 10.15</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>9</p> </td> <td> <p>ITA</p> </td> <td> <p>Cherio</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>59.45 59.95 10.75 11.25</p> </td> <td> <p>1</p> <p>2</p> <p>3</p> <p>4</p> <p>5</p> <p>6</p> <p>7</p> <p>8</p> </td> <td> <p>59.71949 10.83576</p> <p>59.6833306 10.8833298</p> <p>59.6833306 10.8833298</p> <p>59.665 10.9475</p> <p>59.665 10.9475</p> <p>59.841012 10.903597</p> <p>59.757631 11.072031</p> <p>59.539623 10.856447</p> </td> <td> <p>10</p> </td> <td> <p>NOR</p> </td> <td> <p>Krogstad</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>46.45 46.95 17.55 18.05</p> </td> <td> <p>1</p> <p>2</p> </td> <td> <p>46.658333 17.75583</p> <p>46.656944 17.75833</p> </td> <td> <p>11</p> </td> <td> <p>HUN</p> </td> <td> <p>Tetves</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>49.35 49.85 14.75 15.25</p> </td> <td> <p>1</p> </td> <td> <p>49.616837 15.078266</p> </td> <td> <p>12</p> </td> <td> <p>CZE</p> </td> <td> <p>Cechticky</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>55.85 56.35 25.85 26.45</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>13</p> </td> <td> <p>LVA</p> </td> <td> <p>Dviete</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>59.75 60.25 17.55 18.05</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>14</p> </td> <td> <p>SWE</p> </td> <td> <p>Ingvastaan Lehstaan</p> </td> </tr> </tbody> </table> <p> </p> <p><em>modelNumber</em></p> <table> <tbody> <tr> <td> <p><strong>modelNumber</strong></p> </td> <td> <p><strong>Driving Model (GCM)</strong></p> </td> <td> <p><strong>Ensemble</strong></p> </td> <td> <p><strong>RCM </strong></p> </td> <td> <p><strong>End date</strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>EC-EARTH</p> </td> <td> <p>r12i1p1</p> </td> <td> <p>CCLM4-8-17</p> </td> <td> <p>31.12.2100</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>EC-EARTH</p> </td> <td> <p>r3i1p1</p> </td> <td> <p>HIRHAM5</p> </td> <td> <p>31.12.2100</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>HadGEM2-ES</p> </td> <td> <p>r1i1p1</p> </td> <td> <p>HIRHAM5</p> </td> <td> <p>30.12.2099</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>HadGEM2-ES</p> </td> <td> <p>r1i1p1</p> </td> <td> <p>RACMO22E</p> </td> <td> <p>30.12.2099</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>HadGEM2-ES</p> </td> <td> <p>r1i1p1</p> </td> <td> <p>RCA4</p> </td> <td> <p>30.12.2099</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>MPI-ESM-LR</p> </td> <td> <p>r2i1p1</p> </td> <td> <p>REMO2009</p> </td> <td> <p>31.12.2100</p> </td> </tr> </tbody> </table> <p> </p> <p><em>variable</em></p> <table> <tbody> <tr> <td> <p><strong>variable</strong></p> </td> <td> <p><strong>description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>Tmean</p> </td> <td> <p>Mean temperature</p> </td> <td> <p>°C</p> </td> </tr> <tr> <td> <p>Tmin</p> </td> <td> <p>Min temperature</p> </td> <td> <p>°C</p> </td> </tr> <tr> <td> <p>Tmax</p> </td> <td> <p>Max temperature</p> </td> <td> <p>°C</p> </td> </tr> <tr> <td> <p>prec</p> </td> <td> <p>Precipitation</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>solarRad</p> </td> <td> <p>Solar radiation</p> </td> <td> <p>MJ/m2</p> </td> </tr> <tr> <td> <p>windSpeed</p> </td> <td> <p>Wind speed at 2m</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>relHum</p> </td> <td> <p>Relative humidity</p> </td> <td> <p>%</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Methodolody</strong></p> <p>Bias correction was done using non-parametric empirical quantile mapping with modified method from R package <a href="https://cran.r-project.org/web/packages/qmap/index.html">qmap</a>. Parameters selected were: corrections for each day of the year using a moving windows for a 31 days; 100 quantiles; wet days corrections for precipitation. The reference period is 1981-2010.</p> <p>The interpolation of gridded bias-corrected climate model simulations to the location was made using universal kriging with R packages <a href="https://cran.r-project.org/web/packages/automap/index.html">automap</a> and <a href="https://cran.r-project.org/web/packages/gstat/index.html">gstat</a> with (external) variables x, y, x2, y2, x*y, z, where x is latitude, y is longitude, and z is elevation. For Digital Elevation Model <a href="https://webmap.ornl.gov/wcsdown/dataset.jsp?dg_id=10008_1">Shuttle Radar Topography Mission</a> was used. If there was an error using above mentioned variables, the number of variables was reduced to x, y, x*y, z and if there was still an error to x, y, z.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862756.</p>
CORDEX GCM source.grids for interpolating CORDEX data for ATLAS
<p>This is the list of all source.grid files used for the conservative interpolation of all the outputs from regional climate models from the CORDEX experiment used in ATLAS (https://www.ipcc.ch/report/ar5/wg1/atlas-of-global-and-regional-climate-projections/)</p>
Bias-corrected CORDEX daily precipitation dataset for the Carpathian Region
<p>This dataset contains bias-corrected regional climate model (RCM) daily outputs for daily precipitation under the RCP8.5 scenario.</p> <p><br> The reference dataset is CARPATCLIM (Szalai et al., 2013) which covers the the Carpathian Region for the period 1961-2010.</p> <p>The dataset contains bias corrected daily outputs of the following high-resolution (0.11o) RCMs from the framework of EURO-CORDEX (Jacob et al., 2014) and Med-CORDEX (Ruti et al., 2016):<br> - ALADIN<br> - CCLM<br> - HIRHAM<br> - RACMO<br> - RCA4<br> - RegCM<br> - REMO<br> - WRF</p> <p> </p> <p>The dataset covers the following periods with grid spacing of 0.11o on a regular lon/lat grid (between latitudes 44°N and 50°N, and longitudes 17°E and 27°E):</p> <p>- 1976-2005</p> <p>- 2021-2050</p> <p>- 2070-2099</p> <p> </p> <p>File format: NetCDF</p> <p>All data have been created following the work of Mezghani et al. (2017).</p> <p>Using the dataset please cite the following reference paper (also further details are given there): <a href="https://doi.org/10.28974/idojaras.2020.1.2">https://doi.org/10.28974/idojaras.2020.1.2</a>.</p> <p> </p> <p>References:<br> Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O.B., Bouwer, L.M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevel, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B. and Yiou, P. (2014) EURO-CORDEX New high resolution climate change projections for European impact research. Reg. Environ. Change, 14, 563–578. https://doi.org/10.1007/s10113-013-0499-2</p> <p><br> Mezghani, A., Dobler, A., Haugen, J.E., Benestad, R.E., Parding, K.M., Piniewski, M., Kardel, I. and Kundzewicz, Z.W. (2017) CHASE-PL Climate Projection dataset over Poland – bias adjustment of EURO-CORDEX simulations. Earth Syst. Sci. Data, 9, 905–925. https://doi.org/10.5194/essd-9-905-2017</p> <p><br> Ruti, P.M., Somot, S., Giorgi, F., Dubois, C., Flaounas, E., Obermann, A., Dell'Aquila, A., Pisacane, G., Harzallah, A., Lombardi, E., Ahrens, B., Akhtar, N., Alias, A., Arsouze, T., Aznar, R., Bastin, S., Bartholy, J., Béranger, K., Beuvier, J., Bouffies-Cloché, S., Brauch, J., Cabos, W., Calmanti, S., Calvet, J.-C., Carillo, A., Conte, D., Coppola, E., Djurdjevic, V., Drobinski, P., Elizalde-Arellano, A., Gaertner, M., Galán, P., Gallardo, C., Gualdi, S., Goncalves, M., Jorba, O., Jordi, G., L'Heveder, B., Lebeaupin-Brossier, C., Li, L., Liguori, G., Lionello, P., Maciás, D., Nabat, P., Onol, B., Raikovic, B., Ramage, K., Sevault, F., Sannino, G., Struglia, M.V., Sanna, A., Torma, C. and Vervatis, V. (2016) MED-CORDEX initiative for Mediterranean climate studies. Bulletin of the American Meteorological Society, 97, 1187–1208. https://doi.org/10.1175/BAMS-D-14-00176.1</p> <p><br> Szalai, S., Auer, I., Hiebl, J., Milkovich, J., Radim, T., Stepanek, P., Zahradnicek, P., Bihari, Z., Lakatos, M., Szentimrey, T., Limanowka, D., Kilar, P., Cheval, S., Deak, Gy., Mihic, D., Antolovic, I., Mihajlovic, V., Nejedlik, P., Stastny, P., Mikulova, K., Nabyvanets, I., Skyryk, O., Krakovskaya, S.,Vogt, J., Antofie, T. and Spinoni, J. (2013) Climate of the Greater Carpathian Region. Final Technical Report. http://www.carpatclim-eu.org<br> </p>
CORDEX-FPS-Aerosol Protocol 1B output
<p>CORDEX-FPS-Aerosol has set up a dedicated protocol of simulations (called protocol 1B) to evaluate the impact of aerosols on regional climate using non interactive aerosol climatologies (still widely used by the climate community).</p> <p>This protocol relies on three simulations for each regional climate model: a historical run (1971-2000) and two future RCP8.5 simulations (2021-2050), a first one with evolving aerosols (RCMevol or evoaer), and a second one with the same aerosols as in the historical period (RCMcst or cstaer). The historical run and one of the two scenario runs are already published on ESGF (official CORDEX simulations). The present data on Zenodo provides the output for the new scenario simulation designed in the FPS-Aerosols, that is to say either RCMevol if the official CORDEX simulation has constant aerosols of RCMcst if the CORDEX simulation has already evolving aerosols.</p> <p>9 GCM-RCM pairs are available : REMO2015/EC-EARTH, ALADIN63/CNRM-CM5, ALADIN/HadGEM2-ES, ALADIN/NorESM1-M, ALADIN/MPI-ESM-LR, WRF/CCSM4, COSMO/MPI-ESM-M, RegCM/EC-EARTH and RACMO/EC-EARTH.</p> <p>Note that the two other simulations used in FPS-Aerosols are available on the Earth System Grid Federation (ESGF), except for RegCM and WRF for which all simulations have been included in this Zenodo repository.</p>
Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain". Training predictors from the ERA5 reanalysis, projecting predictors from CORDEX EUR11, and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rds" format, to be read from R, except for CORDEX files in NetCDF.</p>
Ensemble calculations of "torro17" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>torro17</p> <p><strong>Definition:</strong> Number of days per year with daily maximum wind speed equal or greater than 17 m/s, average over a 30-year time-period.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily maximum near-surface wind speed (sfcWindmax).</p> <p>Results (ensemble mean and ensemble standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) time periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The EURO-CORDEX climate model simulations used are:</p> <ul> <li>SMHI-RCA4/ ICHEC-EC-EARTH, SMHI-RCA4/ MOHC-HadGEM2-ES</li> <li>CLMcom-CCLM4-8-17/ ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/ MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ ICHEC-EC-EARTH, KNMI-RACMO22E/ MOHC-HadGEM2-ES</li> </ul>
Bias-corrected EURO-CORDEX daily temperature and precipitation dataset for Hungary
<p>These datasets contain bias-corrected regional climate model outputs for daily precipitation, near surface mean-, minimum- and maximum temperature for the historical period 1993-2005 on a regular 0.11°x0.11° lon/lat grid (between latitudes 45.6825°N and 48.6525°N, and longitudes 15.9075°E and 22.9475°E).</p> <p> </p> <p>The datasets contain daily outputs of the following high-resolution (0.11°) regional climate models from the framework of EURO-CORDEX (Jacob et al., 2014):</p> <p>-CCLM</p> <p>-HIRHAM</p> <p>-RACMO</p> <p>-RCA</p> <p>-REMO</p> <p> </p> <p>The reference dataset is HUCLIM, which covers Hungary for the period 1971-2022 (downloaded in 2023) produced by the HungaroMet Hungarian Meteorological Service.</p> <p>File format: NetCDF</p> <p>All bias-corrected data produced by the use of HUCLIM have been created following the work of Mezghani et al. (2017).</p> <p> </p> <p>References:</p> <p>Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O.B., Bouwer, L.M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevel, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B. and Yiou, P. (2014) EURO-CORDEX New high resolution climate change projections for European impact research. Reg. Environ. Change, 14, 563–578. <a href="https://doi.org/10.1007/s10113-013-0499-2" target="_blank" rel="noopener">https://doi.org/10.1007/s10113-013-0499-2</a></p> <p>Mezghani, A., Dobler, A., Haugen, J.E., Benestad, R.E., Parding, K.M., Piniewski, M., Kardel, I. and Kundzewicz, Z.W. (2017) CHASE-PL Climate Projection dataset over Poland – bias adjustment of EURO-CORDEX simulations. Earth Syst. Sci. Data, 9, 905–925. <a href="https://doi.org/10.5194/essd-9-905-2017" target="_blank" rel="noopener">https://doi.org/10.5194/essd-9-905-2017</a></p> <p> </p>
Multivariate projected ensemble of "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>Multivariate statistically downscaled projected ensemble for different combinations of GCM-RCMs of both stochastic and deterministic runs for eight historical runs, eight RCP85 runs and one RCP26 run. Selection of good perfoming GCM-RCM combinations in NetCDF format. Variables: precipitation, water vapour pressure, radiation, wind speed, and, maximum, mean and minimum temperature.</p>
Ensemble calculations of "Consecutive Frost Days" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>Consecutive Frost days</p> <p><strong>Definition:</strong> Maximum number of days with daily minimum temperature below 0°C.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p> <p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The bias-corrected EURO-CORDEX climate model simulations used are:</p> <ul> <li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li> <li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li> </ul>
Ensemble calculations of "Consecutive Summer Days" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>Consecutive Summer days</p> <p><strong>Definition:</strong> Maximum number of days with daily maximum temperature above 25°C.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p> <p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The bias-corrected EURO-CORDEX climate model simulations used are:</p> <ul> <li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li> <li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li> </ul>
Ensemble calculations of "Summer Days" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>Summer days</p> <p><strong>Definition:</strong> Number of days with daily maximum temperature above 25°C.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p> <p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The bias-corrected EURO-CORDEX climate model simulations used are:</p> <ul> <li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li> <li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li> </ul>
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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.