CMIP5 drought projections in Canada based on the Standardized Precipitation Evapotranspiration Index
Bibliographic record
Abstract
Drought projections on seasonal to annual time scales are presented for Canada over the twenty-first century, based on the Standardized Precipitation Evapotranspiration Index (SPEI). Results make use of bias-corrected temperature and precipitation projections from 29 global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and include three different forcing scenarios (RCP2.6, RCP4.5 and RCP8.5). Large differences in projected drought changes are observed among different regions. On the annual time scale, southwestern Canada and the Prairies may experience an increase in drying under a warmer climate. On the other hand, coastal regions, including northern Canada, the northwest Pacific coast and the Atlantic region, show a small increase in wetness. Winter and spring SPEI results depict an increase in wetting, reflecting the projected country-wide winter and spring precipitation increases under climate change. For the most part, autumn and summer show increases in drying. The largest relative changes in both summer drying and winter wetting were found over northern regions, but the offsetting seasonal effects typically balance out to yield various degrees of wetting on the annual scale for this region. The projected drought responses are relatively modest in the weak forcing scenario (RCP2.6) for most Canadian regions. In addition, even for regions most affected, a marked increase in surface water deficit might not occur until the second half of this century. Inter-model variation (a crude measure of projection uncertainty) typically increases with forcing intensity and lead time, and is generally greater in northern and western Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".