Global Bias-Corrected CORDEX Datasets at Quarter Degree Resolution
Bibliographic record
Abstract
<strong>Abstract:</strong> This dataset provides globally consistent, bias-corrected climate data at 0.25° grid resolution, consisting of a set of five climate variables derived from four General Circulation Models (GCMs) participating in CMIP5 downscaled by 4 CORDEX Regional Climate Model (RCM) simulations and bias-corrected globally for the period 1979–2099 for 7 to 10 CORDEX domains. It includes data from two climate change scenarios, namely RCP2.6 and RCP8.5. The CMIP5 GCMs are: MOHC-HadGEM2-ES, MPI-M-MPI-ESM-LR and MR, and NCC-NorESM1-M. Available here are netCDF files per run, GCM and variable (see <strong>Size</strong> for the overall sum per GCM). <strong>TableOfContents:</strong> daily mean 2m-air temperature (tas); daily minimum 2m-air temperature (tasmin), daily maximum 2m-air temperature (tasmax); daily sum of precipitation (pr); daily mean surface downwelling shortwave radiation (rsds) <strong>TechnicalInfo:</strong> dimension: 1440 columns x 720 rows; temporalExtent_startDate_Historlcal: 1979-01-01 00:00:00; temporalExtent_endDate_Historical: 2005-12-31 23:59:59; temporalDuration_Historical: 27; temporalDurationUnit_Historical: a; temporalExtent_startDate_RCPs: 2006-01-01 00:00:00; temporalExtent_endDate_RCPs: 2099-12-31 23:59:59; temporalDuration_RCPs: 94; temporalDurationUnit_RCPs: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 0.25; spatialResolutionUnit: degree; horizontalResolutionXdirection: 0.25; horizontalResolutionXdirectionUnit: degree; horizontalResolutionYdirection: 0.25; horizontalResolutionYdirectionUnit: degree; verticalResolution: none; verticalResolutionUnit: none <strong>Methods: </strong> The ISIMIP3BASD v2.5 bias correction method (see Lange [2019; 2021]) was applied to adjust systematic biases while preserving the climate change signals. This parametric quantile mapping approach:<br> • Corrects biases across all percentiles of variable distributions<br> • Preserves trends in these percentiles<br> • Applies variable-specific treatments (e.g., handling drizzle issues for precipitation)<br> • Maintains physical relationships between variables (particularly for temperature variables) using the CHELSA-W5E5 observational reference dataset. The regional climate models (RCMs) used are: (listed are Institution/working group; RCM Models; Driving GCMs): Climate Service Center Germany (GERICS), Hamburg, Germany; REMO2015 v1; MPI-ESM-LR and NCC-NorESM1-M and MOHC-HadGEM2-ES Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy; RegCM4-4 v0 and RegCM4-7 v0; MPI-ESM-MR Centre pour l’Étude et la Simulation du Climat à l’Échelle Régionale (ESCER), Université du Québec à Montréal, Canada; CRCM5 v1; MPI-ESM-MR The historical runs begin 1979-01-01 and end 2005-12-31. All projection runs begin 2006-01-01 and end 2099-12-31. The routines (python) used to create and work with the data sets are available from this web page as well: Discontinuity_Analyses_GloBCORD-QD.py <strong>Quality:</strong> Not all of the domains have been downscaled by CORDEX RCMs. Therefore, data files for MPI-M-MPI-ESM-MR only contain 8 (rcp26: 7) CORDEX domains; all other files contain 10 domains (see also https://cordex.org/domains/cordex-domain-description/) <strong>Units:</strong> K; K; K; kg m-2 s-1; W m-2 <strong>GeoLocation:</strong> westBoundCoordinate: -165.0; westBoundCoordinateUnit: degrees East; eastBoundCoordinate: 179.0; eastBoundCoordinateUnit: degrees East; southBoundCoordinate: -55.0; southBoundCoordinateUnit: degrees North; northBoundCoordinate: 76.0; northBoundCoordinateUnit: degrees North <strong>Size:</strong> MOHC-HadGEM2-ES: 280.3 GByte, MPI-M-MPI-ESM-LR: 271.0 GByte, MPI-M-MPI-ESM-MR: 214.3GByte, NCC-NorESM1-M: 280.4 GByte <strong>Format:</strong> netCDF <strong>DataSources:</strong> See the file "GloBCORD-QD_Description.pdf" <strong>Contact:</strong> fuseini.yakubu (at) uni-hamburg.de; shabeh.hasson (at) uni-hamburg.de <strong>Webpage:</strong> https://www.geo.uni-hamburg.de/geographie/abteilungen/physische-geographie/arbeitsgruppen/ag-hareme.html
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".