Future changes in intense precipitation over Canada assessed from multi‐model NARCCAP ensemble simulations
Bibliographic record
Abstract
Abstract Annual maxima (AM) series of precipitation from 15 simulations of the North American Regional Climate Change Assessment Program (NARCCAP) have been analysed for gridpoints covering Canada and the northern part of United States. The NARCCAP Regional Climate Models' simulations have been classified into the following three groups based on the driving data used at the RCMs boundaries: (1) NCEP (6 simulations); (2) GCM‐historical (5 simulations); and (3) GCM‐future (4 simulations). Historical simulations are representative of the 1968‐2000 period while future simulations cover the 2041‐2070 period. A reference common grid has been defined to ease the comparison. Multi‐model average intensities of AM precipitation of 6‐, 12‐, 24‐, 72‐, and 120‐h for 2‐, 5‐, 10‐, and 20‐year return periods have been estimated for each simulation group. Comparison of results from NCEP and GCM‐historical groups shows good overall agreement in terms of spatial distribution of AM intensities. Comparison of GCM‐future and GCM‐historical groups clearly shows widespread increases with median relative changes across all gridpoints ranging from 12 to 18% depending on durations and return periods. Fourteen Canadian climatic regions have been used to define regional projections and average regional changes in intense precipitation have been estimated for each duration and return period. Uncertainties on these regional values, resulting from inter‐model variability, were also estimated. Results suggest that inland regions (e.g. Ontario and more specifically Southern Ontario, the Prairies, Southern Quebec) will experience the largest relative increases in AM intensities while coastal regions (e.g. Atlantic Provinces and the West Coast) will experience the smallest ones. These projections are most valuable inputs for the assessment of future impact of climate change on water infrastructures and the development of more efficient adaptation strategies. Copyright © 2011 Royal Meteorological Society
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".