PRECIS‐projected increases in temperature and precipitation over Canada
Bibliographic record
Abstract
In this study, high‐resolution projections of temperature and precipitation changes over Canada were developed through the Providing Regional Climates for Impact Studies (PRECIS) model under Representative Concentration Pathways (RCPs). In detail, the PRECIS model was employed to conduct simulations for the historical period over the entirety of Canada, driven by the boundary conditions from both ERA‐Interim (1979–2011) and HadGEM2‐ES (1959–2005). The performance of PRECIS simulations in reproducing historical climatology of Canada was then validated through comparison with observed temperature and precipitation over the baseline period (1986–2005). The boundary conditions from HadGEM2‐ES under RCP4.5 and RCP8.5 was used to drive PRECIS for simulating climatic variables over Canada for the period of 2006–2099. Future climate projections of temperature and precipitation as well as their extreme indices over two time‐slices (i.e. 2046–2065 and 2076–2095) were extracted and analysed. The results could help investigate how the regional climate over Canada will respond to global warming as well as the spatio‐temporal characteristics of plausible climate changes in the Canadian context. The validation results demonstrate that the PRECIS model is effective in reproducing the historical climatological patterns of annual mean temperature and total precipitation across Canada. Projections of temperature and precipitation for the two future periods indicate that there will be an apparent increasing pattern over Canada. The projected changes derived in this study can provide decision‐makers with valuable information to evaluate possible impacts on economic, social and environmental sectors at regional and local scales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".