Future Changes in Precipitation Extremes Over Canada: Driving Factors and Inherent Mechanism
Bibliographic record
Abstract
Abstract In this study, future changes in precipitation extremes over 10 climatic regions in Canada and their mechanism under Representative Concentration Pathways (RCPs) throughout the 21st century are investigated by using the Providing Regional Climates for Impacts Studies (PRECIS) model. The performance of PRECIS in hindcasting total and extreme precipitation for the historical period is first evaluated through two experiments driven by the boundary conditions from both ERA‐Interim (1979–2011) and HadGEM2‐ES (1959–2005). The validation results indicate that PRECIS can reasonably reproduce both the magnitudes and spatial patterns of precipitation extremes over Canada. Changes in total and extreme precipitation for two future periods are analyzed to explore how regional climate over different climatic regions would respond to global warming. Mechanism governing changes in precipitation extremes is explored through a comprehensive analysis of potential climate factors and their correlations and interactions with precipitation extremes. There are obvious increasing trends over most regions for the magnitude of precipitation extremes except for the duration indices. Averages of projected precipitation extremes over the climatic regions in Canada are projected to increase under RCP4.5. Such increases under RCP8.5 would be amplified due to higher greenhouse gas emissions. The projected changes in total precipitation are dominated by changes in wind velocity and relative humidity (e.g., changes in horizontal water vapor flux that would have significant effects on the occurrence of precipitation in Canada). In addition, the changes in the majority of precipitation extremes are commonly attributed to the changes in the saturation vapor pressure due to warmer temperature as described by the Clausius‐Clapeyron equation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".