Global Bias-Corrected CORDEX Datasets at Quarter Degree Resolution
Bibliographic record
Abstract
Abstract: 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 Size for the overall sum per GCM). TableOfContents: 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) TechnicalInfo: 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 Methods: 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: • Corrects biases across all percentiles of variable distributions • Preserves trends in these percentiles • Applies variable-specific treatments (e.g., handling drizzle issues for precipitation) • 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 Quality: 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/) Units: K; K; K; kg m-2 s-1; W m-2 GeoLocation: westBoundCoordinate: -165.0; westBoundCoordinateUnit: degrees East; eastBoundCoordinate: 179.0; eastBoundCoordinateUnit: degrees East; southBoundCoordinate: -55.0; southBoundCoordinateUnit: degrees North; northBoundCoordinate: 76.0; northBoundCoordinateUnit: degrees North Size: 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 Format: netCDF DataSources: See the file "GloBCORD-QD_Description.pdf" Contact: fuseini.yakubu (at) uni-hamburg.de; shabeh.hasson (at) uni-hamburg.de Webpage: 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.016 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".