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40 results for “Bias-correction”
RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0 operated at Heidelberg University
<p>The data set contains bias-corrected column averaged dry air mole fractions (XCO2) retrieved with the RemoTeCv2.4.0 full-physics algorithm (Butz et al. 2011, Guerlet et al. 2013) applied on GOSAT TANSO-FTS Level 1B (L1B) data from 2009-04-18 to 2019-06-30. The GOSAT TANSO-FTS L1B data product is produced by JAXA/NOIES/MOE and provided by ESA. The XCO2 data together with related variables are aggregated as daily files, only good quality retrievals are included.</p> <p> </p> <p>If the data is used for publications, please contact andre.butz@uni-heidelberg.de to discuss potential co-authorship and technical details.</p> <p>To cite the data in publications:</p> <p>André Butz (2019), RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0, Institute of Environmental Physics, Heidelberg University, Heidelberg, Germany, Accessed: [Date], 10.5281/zenodo.5886662</p> <p> </p> <p>Summary:</p> <p>Shortname: REMOTEC_L2_CO2_GOSAT</p> <p>Longname: RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0</p> <p>DOI: 10.5281/zenodo.5886662</p> <p>Version: 2.4.0</p> <p>Format: netCDF</p> <p>Spatial Coverage: -180.0,-90.0,180.0,90.0</p> <p>Temporal Coverage: 2009-04-18 to 2019-06-30</p>
Bias-corrected monthly precipitation data over South Siberia for 1979-2019
<p>Bias-<strong>C</strong>orrected <strong>P</strong>recipitation data over <strong>S</strong>outh <strong>S</strong>iberia (<strong>CPSS 1.2</strong>) contains monthly precipitation data for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2019. CPSS data were combined from monthly total precipitation data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and precipitation data records from ground weather stations (Il’in et al., 2013). The ERA5 data were scaled according to the derived scale coefficient. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CPSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CPSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results. Data provided in NetCDF (Network Common Data Form) format.</p> <p>Copernicus Climate Change Service (C3S), 2017. <em>ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.</em> Copernicus Climate Change Service Climate Data Store (CDS), Available at <a href="https://cds.climate.copernicus.eu/cdsapp#!/home"><em>https://cds.climate.copernicus.eu/cdsapp#!/home</em></a></p> <p>Il’yin, B.M., Bulygina, O.N., Bogdanova, E.G, Veselov, V.M. and Gavrilova, S.Y., 2013. <em>Dataset of monthly precipitation totals, with the elimination of systematic errors of precipitation gauges</em>. Available at <a href="http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov"><em>http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov</em></a></p>
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>
RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 2009-2023, version 2.4.1 operated at Heidelberg University
<p>The data set contains bias-corrected column averaged dry air mole fractions (XCO2) retrieved with the RemoTeCv2.4.1 full-physics algorithm (Butz et al. 2011, Guerlet et al. 2013) applied on GOSAT TANSO-FTS Level 1B (L1B) data from 2009-04-18 to 2023-08-29. The GOSAT TANSO-FTS L1B data product is produced by JAXA/NOIES/MOE and provided by ESA. The XCO2 data together with related variables are aggregated as daily files, only good quality retrievals are included.</p> <p>If the data is used for publications, please contact andre.butz@uni-heidelberg.de to discuss potential co-authorship and technical details.</p> <p> </p> <p>Summary:</p> <p>Shortname: REMOTEC_L2_CO2_GOSAT</p> <p>Longname: RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.1</p> <p>DOI: 10.5281/zenodo.12773070</p> <p>Version: 2.4.1</p> <p>Format: netCDF</p> <p>Spatial Coverage: -180.0,-90.0,180.0,90.0</p> <p>Temporal Coverage: 2009-04-18 to 2023-08-29</p>
Phantom measurement data for 'Fast bias-corrected conductivity mapping using stimulated echoes', Iyyakkunnel et al. (2024)
<p>This dataset contains the phantom measurement data used in the article by Iyyakkunnel et al., titled "Fast Bias-Corrected Conductivity Mapping Using Stimulated Echoes," published in MAGMA, 2024 (doi: 10.1007/s10334-024-01194-3). In this study, the feasibility of using a stimulated echo sequence for electrical properties tomography (EPT) is demonstrated. The data were acquired with a 3T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany).<br>The dataset includes magnitude and phase measurements for the proposed Double-Angle Stimulated Echo (DA-STE) sequence, as well as reference measurements, including Double Angle measurements using a Gradient Echo sequence (GRE-DAM) for the B1+ magnitude, and a Single Echo Spin Echo sequence (SE) for the transceive phase.<br>For both the DA-STE and SE sequences, each measurement was repeated with inverted readout gradient polarities, denoted as LR (left-right) and RL (right-left) in the respective measurement folders. For each measurement, magnitude and phase data are provided in separate folders (in dicom (.dcm) format). Please note that for DA-STE, the two echo acquisitions are sequentially stored in the same measurement folder.<br>For further measurement details, please refer to the mentioned original article.</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>
Bias-corrected simluated wind power generation time series for Brazil
<p>Simulated and bias corrected wind power generation time series data sets for Brazil, its North-East and South, seven states and seven wind parks.</p> <p>The data sources, generation and validation of the datasets are described in the article "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil", preprint available on arXiv: arxiv.org/abs/1904.13083, final version DOI: <a href="https://doi.org/10.1016/j.energy.2019.116212">10.1016/j.energy.2019.116212</a></p> <p>Code for generating the datasets is available at github.com/KatharinaGruber/BrazilWindpower_biascorr</p> <p> </p> <p>The files "comp_*" contain comparisons of simulated and observed wind power generation time series with daily resolution for all regions.</p> <p>"comp_noc.RData" is for comparison of interpolation methods and contains time series generated with Nearest Neighbour interpolation (NN), Bilinear Interpolation (BLI) and Inverse Distance Weighting (IDW).</p> <p>"comp_wmsa.RData" is for comparison of wind speed mean approximation methods and contains time series generated with Nearest Neighbour interpolation (NN - no correction applied), mean approximation with measured data (IN) and mean approximation with the Global Wind Atlas (GWA).</p> <p>"comp_wsc.RData" is for comparison of spatiotemporal wind speed correction methods and contains time series generated with mean approximation with the Global Wind Atlas (wmsa) and combined mean approximation with the Global Wind Atlas and hourly and monthly mean approximation with measured data (wschm).</p> <p> </p> <p>The files "statpowlist_*" contain hourly simulated wind power generation time series for three interpolation methods (NN, BLI, IDW), two mean approximation methods (wsmaIN - measured data (INMET), wsmaWA - Global Wind Atlas) as well as for spatiotemporal (hourly and monthly) wind speed bias correction (wschm) for each wind park available in The Wind Power dataset.</p>
Bias-corrected monthly air temperature data over South Siberia for 1979-2020 (CTSS 1.0)
<p>Bias-<strong>C</strong>orrected Air <strong>T</strong>emperature data over <strong>S</strong>outh <strong>S</strong>iberia (<strong>CTSS 1.0</strong>) contains monthly air temperature at 2m for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2020. CTSS data were combined from monthly total air temperture data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and temperature data records from ground weather stations (Bulygina et al., 2014). The ERA5 data were scaled according to the derived correction coefficient. The additive coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CTSS reproduces the spatial variability of temperature more precisely than can be done from the weather station observation network. Data provided in NetCDF (Network Common Data Form) format.</p> <p>Copernicus Climate Change Service (C3S), 2017. <em>ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.</em> Copernicus Climate Change Service Climate Data Store (CDS), Available at: <a href="https://cds.climate.copernicus.eu/cdsapp#!/home"><em>https://cds.climate.copernicus.eu/cdsapp#!/home</em></a></p> <p><em>Bulygina O.N., Razuvaev V.N., Trofimenko L.T., Shvets N.V., 2014. Description of the monthly air temperature data at weather stations of Russia.</em> Certificate of state registration of the database No. 2014621485 Available at: <a href="http://meteo.ru/data/156-temperature"><em>http://meteo.ru/data/156-temperature</em></a></p>
Bias-corrected data from the preoperational MiKlip system for decadal climate predictions used in the PNRA-IPSODES project
<p>This dataset contains a selection of bias-corrected data from the preoperational MiKlip system for decadal climate predictions (Mueller et al., 2018) used within the Italian research project PNRA18_00199-IPSODES. The adopted method for bias correction is described in the file bias_correction.pdf. Also data from the assimilation run are provided. Nomenclature of variables follows that of the original MiKlip output.</p> <p>Mueller, W., et al. A Higher‐resolution Version of the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR). J. Adv. Model. Earth Syst. 10, 1383-1413 (2018)</p>
Bioclimatic outputs for Last Glacial Maximum South America for LPX, bias-corrected to pollen records
<p>LPX model output for South America for the Last Glacial Maximum. We provide outputs for LPX driven by four GCM simulated climates along with an ensemble average:</p> <ul> <li>MIROC.tar.gz: LPX driven by MIROC3.2</li> <li>FGOALS.tar.gz: driven by FGOALS-1.0g</li> <li>HAD.tar.gz: HadCM3M2</li> <li>CNRM.tar.gz: CNRM-CM33</li> <li>Ensemble.tar.gz: The mean of each output variable for the four models.</li> </ul> <p> </p> <p>Driving data comes from the Palaeoclimate Modelling Intercomparison Project Phase II (PMIP2)<sup>1,2</sup>. See Sato et al. <sup>3</sup> for modelling protocol.</p> <p>Each model’s directory contains “uncorrected” and “corrected” directories. With each of these are bioclimatic maps outputted from LPX and biome information:</p> <ul> <li>fpc.nc: fractional projected cover of all vegetation</li> <li>height.nc: mean height of vegetations</li> <li>gdd.nc: Growing Degree Days base 2</li> <li>tropical.nc: proportion of vegetated areas taken up by tropical trees and c4 grasses</li> <li>temperate.nc: proportion of vegetated areas taken up by temperate trees and c3 grasses</li> <li>evergreen.nc: proportion of tree cover that is composed of evergreen trees</li> <li>biome.nc: the assigned biomes from these data, based on a modified version of Sato et al. <sup>3</sup>. See “biomisation” below.</li> <li>cluster.nc: In “corrected” only. The spatial location of kmean clusters of fpc vs height. See <sup>4</sup> for details.</li> </ul> <p> </p> <p>“Uncorrected” is from Sato et al. <sup>3</sup>. “Corrected” is bias-corrected to match the 42 pollen-core observations taken from Marchant et al. <sup>5</sup> We do this by shifting the total vegetation cover and composition, height, and growing degree day (GDD) DVM output to the closest boundary of the corresponding biome of the pollen core in that specific location. We then extrapolate this correction between pollen-core locations across the Neotropics. See Kelley et al. <sup>4</sup> for details.</p> <p> </p> <p><strong>Biomeisation</strong></p> <p>"Biomeisation.png" displays the scheme. We primarily split biomes by FPCs of 0.3 and 0.6, with biomes > 0.6 split by a height of 10m. Forests (>0.6 FPC and > 10m) is split by GDD, Evergreen FPC (EG) and Tropical or temperate FPC (TR, TM). Likewise, we split FPCs> 0.6 and heights <10m into savanna, woodland and parkland using EG and TR. We additionally assign Tropical savanna >5m to Woodland/Tropical savanna. We divided desert, dry grassland and (shrub)-tundra by FPC of 0.3 and GDD of 350°C. See Kelley et al. <sup>4</sup> for details.</p>
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅱ
<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 Tmin in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmin(Tmin>2606)=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 Ⅰ
<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 Tmax in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmax(Tmax>2606)=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>
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>
Review data for: SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
<p>Climatology of snow water equivalent of Switzerland between winters 1962 and 2021. Obtained using quartile mapping between a model using data assimilation since 1998 and a model without data assimilation. This version of the dataset corresponds to the publication revision time. The publication has been submitted to GMD Copernicus journal as: <em>SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping</em></p>
Fire Weather Index for Europe from Downscaled and Bias-Corrected CMIP6 Model Outputs
<p>This dataset contains the Canadian Forest Fire Weather Index (FWI) calculated from six downscaled and bias-corrected CMIP6 model outputs. The models included are:</p> <ul> <li>ACCESS-CM2 (Ziehn et al. 2020)</li> <li>CanESM5 (Swart et al. 2019)</li> <li>CNRM-ESM2-1 (Séférian et al. 2019)</li> <li>EC-EARTH3 (EC-Earth Consortium 2019)</li> <li>MPI-ESM1-2-HR (von Storch et al. 2017)</li> <li>MRI-ESM2-0 (Yukimoto et al. 2019)</li> </ul> <p>The dataset encompasses four Shared Socio-economic Pathway (SSP) projections:</p> <ul> <li>SSP1-2.6</li> <li>SSP2-4.5</li> <li>SSP3-7.0</li> <li>SSP5-8.5</li> </ul> <p>Each model output has been downscaled to a resolution of 0.0703135°, corresponding to approximately 9km×9km grids before the FWI calculation. The data covers Europe spatially and temporally spans from 1950 to 2080, offering comprehensive insights into past, present, and future fire weather conditions.</p> <p>This dataset supports the manuscript titled <strong>"The fire weather in Europe: large-scale trends towards higher danger" </strong>by Hetzer et al., currently under review in ERL. Detailed instructions for accessing the data can be found in the included README file. </p> <p>Note: Downloads are password protected. Please use "FWI_2024" for access. </p> <p>Funding: The authors acknowledge the financial support of the European Union’s Horizon 2020 research and innovation action for the FirEUrisk project under grant agreement ID: 101003890.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Multi-model ensemble bias-corrected precipitation dataset for historical and future climate (1961–2099) in China
<p>本文基于耦合模式比较项目第六阶段(CMIP6)的27个全球气候模式(GCM),采用随机森林(RF)模型和EQM方法整合27个大气监测模型的降水模拟数据,进一步修正中国综合月降水数据。修正后的降水资料在月降水量和极端降水量方面均明显优于原GCM降水资料。数据以 GeoTIFF 格式,其中嵌入了具有 1° 空间分辨率的地理配准信息。LST在GeoTIFF中的单位是mm。压缩文件被命名为历史文件.zip、SSP126.zip、SSP245.zip 和 SSP585.zip。压缩文件中的每个文件都命名为“yyyymm.tif”,其中“yyyy”和“mm”分别表示年份和月份。例如,文件“196101.tif”存储了 1961 年 1 月中国每月降水量。</p>
High-resolution gridded climate data for Europe based on bias-corrected EURO-CORDEX: the ECLIPS-2.0 dataset
<p>We developed a new climate dataset for Europe referred to as ECLIPS (European CLimate Index ProjectionS), which contains gridded data for 80 annual, seasonal, and monthly climate variables for two past (1961-1990, 1991-2010) and five future periods (2011-2020, 2021-2140, 2041-2060, 2061-2080, 2081-2100). The future data are based on five Regional Climate Models (RCMs)driven by two greenhouse gas concentration scenarios, RCP 4.5 and 8.5.</p> <p>The ECLIPS dataset has two versions; ECLIPS 1.1 contains data with spatial resolution of 0.11° × 0.11°, which is the resolution of underlying RCMs. ECLIPS1.1 is available at <a href="https://doi.org/10.5281/zenodo.1181780">https://doi.org/10.5281/zenodo.1181780</a>.</p> <p>The ECLIPS 2.0 presented here contains a subset of climate indices of ECLIPS 1.1, downscaled to the resolution of 30 arcsec by means of the delta correction approach. Both ECLIPS versions were evaluated by testing their relationship with independent station data from the European Climate Assessment (ECA) dataset. Correlations of the empirical testing data to ECLIPS 1.1 ranged from 0.63 to 0.78,and to ECLIPS 2.0 from 0.78 to 0.93. suggesting substantial improvement due to downscaling. A large number of climate projections, time periods and indices as well as the availability of these data at two different spatial resolutions can support diverse studies across a range of disciplines and thus extend our understanding of climate-sensitive dynamics of many social-ecological systems</p> <p>The zipfile ECLIPS2.0 contains 5 folders with subfolders</p> <p>File naming system for the subfolders / folder are as follows</p> <p>ECLIPS2.0_196191: past climate 1961-1990: < climate index><period></p> <p>ECLIPS2.0_199110: past climate 1991-2010 < climate index><period></p> <p>ECLIPS2.0_45 : future climate RCP4.5 <subfolder-Model name> < climate index><period></p> <p>ECLIPS2.0_85 : future climate RCP8.5 <subfolder-Model name> < climate index><period></p> <p>Incase zpfile reader 7zip is not available, please install from here: <a href="https://www.7-zip.org/">https://www.7-zip.org/</a></p>
Bias-corrected CORDEX dataset for the Carpathian Region
<p>This dataset contains <strong>bias-corrected</strong> regional climate model (RCM) <strong>daily outputs</strong> for the following variables under the <strong>RCP8.5</strong> scenario:<br> <strong>- tas<br> - tasmin<br> - tasmax</strong></p> <p>The reference dataset is <strong>CARPATCLIM</strong> (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 <strong>high-resolution</strong> (0.11<sup>o</sup>) <strong>RCMs</strong> from the framework of<strong> EURO-CORDEX</strong> (Jacob et al., 2014) and <strong>Med-CORDEX</strong> (Ruti et al., 2016):<br> <strong>- ALADIN<br> - CCLM<br> - HIRHAM<br> - RACMO<br> - RCA4<br> - RegCM<br> - REMO<br> - WRF</strong></p> <p> </p> <p>The dataset covers the following periods with grid spacing of 0.11<sup>o</sup> on a regular lon/lat grid (between latitudes 44°N and 50°N, and longitudes 17°E and 27°E):</p> <p><strong>- 1976-2005</strong></p> <p><strong>- 2021-2050</strong></p> <p><strong>- 2070-2099</strong></p> <p> </p> <p>File format: NetCDF</p> <p>All data have been created following the work of Mezghani et al. (2017).</p> <p>Paper introducing present database: Torma, C. Z., & Kis, A. (2022): Bias-adjustment of high-resolution temperature CORDEX data over the Carpathian region: Expected changes including the number of summer and frost days. <em>International Journal of Climatology</em>, 42(12): 6631–6646. https://doi.org/10.1002/joc.7654</p> <p> </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</p>
A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics
<p><strong>Motivation</strong></p> <p>Increasing heat stress due to climate change poses significant risks to human health and can lead to widespread social and economic consequences. Evaluating these impacts requires reliable datasets of heat stress projections. </p> <p><strong>Data Record</strong></p> <p><strong>CMIP6</strong></p> <p>We present a global dataset projecting future dry-bulb, wet-bulb, and wet-bulb globe temperatures under 1-4°C global warming scenarios (at 0.5°C intervals) relative to the preindustrial era, using outputs from 16 CMIP6 global climate models (GCMs) (Table 1). All variables were retrieved from the historical and SSP585 scenarios which were selected to maximize the warming signal.</p> <p>Wet-bulb and wet-bulb globe temperature are calculated using the Davies-Jones[1] and Liljegren[2] approach respectively.</p> <p>The dataset was bias-corrected against ERA5 reanalysis by incorporating the GCM-simulated climate change signal onto the ERA5 baseline (1950-1976) at a 3-hourly frequency. It therefore includes a 27-year sample for each GCM under each warming target.</p> <p>The data is provided at a fine spatial resolution of 0.25° x 0.25° and a temporal resolution of 3 hours, and is stored in a self-describing NetCDF format. Filenames follow the pattern "VAR_bias_corrected_3hr_GCM_XC_yyyy.nc", where:</p> <ul> <li> <p>"VAR" represents the variable (Ta, Tw, WBGT for dry-bulb, wet-bulb, and wet-bulb globe temperature, respectively),</p> </li> <li> <p>"GCM" denotes the CMIP6 GCM name,</p> </li> <li> <p>"X" indicates the warming target compared to the preindustrial period,</p> </li> <li> <p>"yyyy" represents the year index (0001-0027) of the 27-year sample</p> </li> </ul> <p><strong>Table 1 </strong>CMIP6 GCMs used for generating the dataset for Ta, Tw and WBGT.</p> <div> <table> <tbody> <tr> <td> <p>GCM</p> </td> <td> <p>Realization</p> </td> <td> <p>GCM grid spacing</p> </td> <td> <p>Ta</p> </td> <td> <p>Tw</p> </td> <td> <p>WBGT</p> </td> </tr> <tr> <td> <p>ACCESS-CM2</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>BCC-CSM2-MR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.1ox1.125o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CanESM5</p> </td> <td> <p>r1i1p2f1</p> </td> <td> <p>2.8ox2.8o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CMCC-CM2-SR5</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.94ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CMCC-ESM2</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.94ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CNRM-CM6-1</p> </td> <td> <p>r1i1p1f2</p> </td> <td> <p>1.4ox1.4o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> </td> </tr> <tr> <td> <p>EC-Earth3</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.7ox0.7o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>GFDL-ESM4</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.0ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>HadGEM3-GC31-LL</p> </td> <td> <p>r1i1p1f3</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>HadGEM3-GC31-MM</p> </td> <td> <p>r1i1p1f3</p> </td> <td> <p>0.55ox0.83o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>KACE-1-0-G</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>KIOST-ESM</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.9ox1.9o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MIROC-ES2L</p> </td> <td> <p>r1i1p1f2</p> </td> <td> <p>2.8ox2.8o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MIROC6</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.4ox1.4o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-HR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.93ox0.93o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-LR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.85ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> </tbody> </table> </div> <p><strong>ERA5</strong></p> <p>We also provide hourly Tw and WBGT derived from ERA5 reanalysis during 1950-2023 to enable analyses of heat stress changes from historical period to a warmer climate.</p> <p><strong> </strong></p> <p><strong>Data Access</strong></p> <p>An inventory of the dataset is available in this repository. The complete dataset, approximately 57 TB in size, is freely accessible via Purdue Fortress' long-term archive through Globus. The bias-corrected CMIP6 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?origin_id=6538f53a-1ea7-4c13-a0cf-10478190b901&origin_path=%2F">Globus Link1</a>, and the ERA5 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?destination_id=63242aea-d3e0-4aa4-9372-0e19dd0c6539&destination_path=%2F">Globus Link2</a>. After clicking the link, users may be prompted to log in with a Purdue institutional Globus account. You can switch to your institutional account, or log in via a personal Globus ID, Gmail, GitHub handle, or ORCID ID. Alternatively, the dataset can be accessed by searching for the universally unique identifier (UUID)—"6538f53a-1ea7-4c13-a0cf-10478190b901" for CMIP6, and “63242aea-d3e0-4aa4-9372-0e19dd0c6539” for ERA5 dataset—in Globus.</p> <p><strong>Dataset Validation</strong></p> <p>We validate the bias-correction method and show that it significantly enhances the GCMs' accuracy in reproducing both the annual average and the full range of quantiles for all metrics within an ERA5 reference climate state. This dataset is expected to support future research on projected changes in mean and extreme heat stress and the assessment of related health and socio-economic impacts.</p> <p>For a detailed introduction to the dataset and its validation, please refer to our data descriptor currently under review at Scientific Data. We will update this information upon publication.</p> <p><strong><br><br><br></strong></p>
Bias-corrected VIC historical runoff data (1950-2013) for the Central Sierra Nevada
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