Regional scenarios of change over Canada: future climate projections
Bibliographic record
Abstract
Abstract. This analysis documents projected changes in daily precipitation and temperature characteristics over Canada based on a 15-member ensemble which had been downscaled using the Canadian Regional Climate Model–CanRCM4 at 50 km resolution by the Canadian Centre for Climate Modelling and Analysis (CCCma) under Representative Concentration Pathway (RCP) 8.5. In this study, the historical CanRCM4 simulations are first compared against observations for validation purposes. Then, a multivariate bias correction algorithm is applied to the CanRCM4 outputs to adjust the data against the EU WATCH Forcing Data ERA-Interim reanalysis (WFDEI). We analyze changes in mean and extremes for two 30-year non-overlapping future periods: 2021–2050 and 2071–2100 relative to 1979–2008. The results indicate that daily mean precipitation is projected to increase over Canada, with larger increases expected in the 2080s. However, decreases are projected in summer precipitation over the Canadian Prairies by the year 2100. Mean air temperature is projected to intensify towards the northern high latitude regions, particularly in the winter season. Precipitation and temperature extreme events may increase more than the mean. By examining the behavior of precipitation distribution tails, the mean of the probability distributions of wet extremes over the Saskatchewan (SRB) and Mackenzie River basins (MRB) is projected to shift to the right with global warming. For temperature extremes, minimum temperature may warm faster compared to daily maximum temperatures, particularly in the winter and towards the Arctic region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".