Projected Changes in Precipitation Extremes for Western Canada based on High-Resolution Regional Climate Simulations
Bibliographic record
Abstract
Abstract An analysis of changes in precipitation extremes in western Canada is presented, based upon an ensemble of high-resolution regional climate projections. The ensemble is composed of four independent, identically configured Community Earth System Model (CESM) integrations that were dynamically downscaled to 10-km resolution, using the WRF Model in two different configurations. Only the representative concentration pathway 8.5 (RCP8.5) scenario is considered. Changes in extremes are found to generally follow changes in the (seasonal) mean, but changes in mean and extreme precipitation differ strongly between seasons and regions (where extremes are defined as the seasonal maximum of daily precipitation). At the end of the twenty-first century, the highest projected increase in precipitation extremes is approximately 30% in winter away from the coast and in fall at the coast. Changes in winter are consistent between models; however, changes in summer are not: CESM is characterized by a decrease in summer precipitation (and extremes), while one WRF configuration shows a significant increase and another no statistically significant change. Nevertheless, the fraction of convective precipitation (extremes) in summer increases by 20%–30% in all models. There is also evidence that the climate change signal in summer is sensitive to the choice of the convection scheme. A comparison of CESM and WRF shows that higher resolution clearly improves the representation of winter precipitation (extremes), while summer precipitation does not appear to be sensitive to resolution (convection is parameterized in both models). To increase the statistical power of the extreme value analysis that has been performed, a novel method for combining data from climatologically similar stations was employed.
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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 | 0.001 | 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".