Runtime Bias Corrected Driving Data for Regional Climate Models -Regional-scale impacts
Bibliographic record
Abstract
An empirical runtime bias correction (ERBC) technique is applied to a global model used to drive two RCMs: CRCM5 (Ouranos) and CanRCM5 (CCCma). While the ERBC is constructed to improve the climatological annual cycle, due to its application at runtime, it also indirectly improves time-dependent circulation dynamics and maintains physical coherence among adjusted atmospheric variables. We analyze the regional impacts of this prognostic bias correction on the climatology of atmospheric and oceanic driving data, for the historical period (1981-2010) and the end-of-century (2071-2100) climate change signal, in RCM simulations of North America. Three 10-member ensembles are created using three configurations of CanESM5 as driving data: the original CanESM5 (no correction); CanESM5 with only diagnostically corrected sea surface temperature (SST) and sea ice concentration (SIC); and CanESM5 with ERBC-corrected atmosphere and adjusted SST/SIC. Results show clear advantages of using adjusted atmospheric and oceanic driving data, especially in regions with substantial raw biases. Significant improvements are noted in representing key meteorological phenomena, notably nor'easters (extratropical cyclones) and the North American monsoon, due to better oceanic data and enhanced representation of regional circulation. In future projections, the ERBC is found to alter the climate change response of both RCMs, offering the potential for uncertainty reduction in future projection of such regional scale features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".