Scaling of precipitation extremes with surface temperature in western Canada: Understanding the control factors using a convection‐permitting climate model
Bibliographic record
Abstract
Abstract The Clausius–Clapeyron (CC) relationship can shed light on understanding how precipitation extremes may change in a warming climate. We evaluated relationships between hourly extreme precipitation and surface temperature using observed and simulated datasets and found the convection‐permitting climate model is able to capture the observed relationships between precipitation extremes and temperature (PT scaling) in western Canada. In the current climate, the intensity of extreme precipitation increases with temperature approximately at the CC scale in the Prairie, North, and Mountain regions, whereas a clear sub‐CC scale can be seen in the Coast region. All four regions show a negative scaling rate at warm temperature bins. In the future climate, precipitation extremes in each region get intensified with warming at a larger scale rate than that in the current climate. A super‐CC and even double‐CC rate can be seen in the Coast, Prairie, and North regions. A similar scaling method has been applied for the corresponding column integrated water vapour, vertical velocity, and the convective available potential energy. We found the PT scaling pattern is physically governed by the ascending velocity of air and the amount of atmospheric water vapour. Approximately 99% of the variation of precipitation extremes can be explained by the vertical velocity and 95% by the precipitable water for the current climate in western Canada. In the future, more than 97% of the variation of precipitation extremes can be explained by the precipitable water and more than 99% by the vertical velocity.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".