Bias-corrected high-resolution temperature and precipitation projections for Canada
Bibliographic record
Abstract
High-resolution precipitation and temperature projections are indispensable for informed decision-making, risk assessment, and planning. Here, we have developed an extensive database (SPQM-CMIP6-CAN) of high-resolution (0.1°) precipitation and temperature projections extending till 2100 at a daily scale for Canada. We employed a novel Semi-Parametric Quantile Mapping (SPQM) methodology to bias-correct the Coupled Model Intercomparison Project, Phase-6 (CMIP6) projections for four Shared Socio-economic Pathways. SPQM is simple, yet robust, in reproducing the observed marginal properties, trends, and variability according to future scenarios, while maintaining a smooth transition from observations to projected simulations. The SPQM-CMIP6-CAN database encompasses 693 simulations derived from 34 diverse climate models for precipitation. Similarly, for temperature projections, our database comprises 581 simulations from 27 climate models. These projections are valuable for hydrological, environmental, and ecological studies, offering a comprehensive resource for analyses within these domains. Furthermore, these projections serve as a vital tool for the quantification of uncertainties arising from climate models, their variant configurations, and future scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".