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opencc-by-nc-4.0Mar 2022View details →

Bias-corrected CORDEX dataset for the Carpathian Region

This dataset contains bias-corrected regional climate model (RCM) daily outputs for the following variables under the RCP8.5 scenario: - tas - tasmin - tasmax The reference dataset is CARPATCLIM (Szalai et al., 2013) which covers the the Carpathian Region for the period 1961-2010. 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): - ALADIN - CCLM - HIRHAM - RACMO - RCA4 - RegCM - REMO - WRF   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): - 1976-2005 - 2021-2050 - 2070-2099   File format: NetCDF All data have been created following the work of Mezghani et al. (2017). 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. International Journal of Climatology, 42(12): 6631–6646. https://doi.org/10.1002/joc.7654     References: 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 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 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 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

opencc-by-4.0May 2017View details →

Supplementary material 3 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235

Weighted mean SDMs for individual algorithms and evaluation statistics (biomod2) :

opencc-by-4.0May 2017View details →

Supplementary material 2 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235

Full readout for the MaxEnt northern quoll SDM :

opencc-by-4.0May 2017View details →

Supplementary material 1 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235

GIS data sets used in variable assessments and map of Pilbara vegetation systems :

opencc-by-nc-4.0Sep 2024View details →

A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics

Motivation 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.  Data Record CMIP6 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. Wet-bulb and wet-bulb globe temperature are calculated using the Davies-Jones[1] and Liljegren[2]  approach respectively. 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. 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: <ul> <li> "VAR" represents the variable (Ta, Tw, WBGT for dry-bulb, wet-bulb, and wet-bulb globe temperature, respectively), </li> <li> "GCM" denotes the CMIP6 GCM name, </li> <li> "X" indicates the warming target compared to the preindustrial period, </li> <li> "yyyy" represents the year index (0001-0027) of the 27-year sample </li> </ul> Table 1 CMIP6 GCMs used for generating the dataset for Ta, Tw and WBGT. <div> <table> <tbody> <tr> <td> GCM </td> <td> Realization </td> <td> GCM grid spacing </td> <td> Ta </td> <td> Tw </td> <td> WBGT </td> </tr> <tr> <td> ACCESS-CM2 </td> <td> r1i1p1f1 </td> <td> 1.25ox1.875o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> BCC-CSM2-MR </td> <td> r1i1p1f1 </td> <td> 1.1ox1.125o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> CanESM5 </td> <td> r1i1p2f1 </td> <td> 2.8ox2.8o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> CMCC-CM2-SR5 </td> <td> r1i1p1f1 </td> <td> 0.94ox1.25o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> CMCC-ESM2 </td> <td> r1i1p1f1 </td> <td> 0.94ox1.25o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> CNRM-CM6-1 </td> <td> r1i1p1f2 </td> <td> 1.4ox1.4o </td> <td> ✓ </td> <td> ✓ </td> <td> </td> </tr> <tr> <td> EC-Earth3 </td> <td> r1i1p1f1 </td> <td> 0.7ox0.7o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> GFDL-ESM4 </td> <td> r1i1p1f1 </td> <td> 1.0ox1.25o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> HadGEM3-GC31-LL </td> <td> r1i1p1f3 </td> <td> 1.25ox1.875o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> HadGEM3-GC31-MM </td> <td> r1i1p1f3 </td> <td> 0.55ox0.83o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> KACE-1-0-G </td> <td> r1i1p1f1 </td> <td> 1.25ox1.875o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> KIOST-ESM </td> <td> r1i1p1f1 </td> <td> 1.9ox1.9o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> MIROC-ES2L </td> <td> r1i1p1f2 </td> <td> 2.8ox2.8o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> MIROC6 </td> <td> r1i1p1f1 </td> <td> 1.4ox1.4o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> MPI-ESM1-2-HR </td> <td> r1i1p1f1 </td> <td> 0.93ox0.93o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> MPI-ESM1-2-LR </td> <td> r1i1p1f1 </td> <td> 1.85ox1.875o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> </tbody> </table> </div> ERA5 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.   Data Access 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. Dataset Validation 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. 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.

publicOct 2018View details →

Data from: Correction for bias in meta-analysis of little-replicated studies

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publicDec 2014View details →

Data from: Bayesian long branch attraction bias and corrections

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publicAug 2015View details →

Data from: Short tree, long tree, right tree, wrong tree: new acquisition bias corrections for inferring SNP phylogenies

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publicApr 2022View details →

Bias-corrected VIC historical runoff data (1950-2013) for the Central Sierra Nevada

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OCO-2 Gridded bias-corrected XCO2, SIF, and other select fields aggregated as Level 3 daily files V4 (OCO2GriddedXCO2_SIF)

Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) bias corrected data.This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.

OCO-2 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V11r (OCO2_L2_Lite_SIF) at GES DISC

Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r. The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.

OCO-2 Gridded bias-corrected XCO2 and other select fields aggregated as Level 3 daily files V4 (OCO2GriddedXCO2)

ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020

This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated.

OCO-2 Gridded bias-corrected XCO2 and other select fields aggregated as Level 4 daily files V3 (OCO2GriddedXCO2)

OCO-2 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V11.2r (OCO2_L2_Lite_SIF) at GES DISC

Version 11.2r is the current version of the data set. The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.

OCO-3 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Forward Processing V11 (OCO3_L2_Fwd_SIF) at GES DISC

Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.

OCO-3 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing v10.4r (OCO3_L2_Lite_FP) at GES DISC

Version 10.4r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10.4r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.

OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V11.1r (OCO2_L2_Lite_FP) at GES DISC

Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The OCO-2 Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.

OCO-3 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V10r (OCO3_L2_Lite_SIF) at GES DISC

Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.

OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V10r (OCO2_L2_Lite_FP) at GES DISC

Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.The OCO-2 Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files.In early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.