Regional climate change trends and uncertainty analysis using extreme indices: A case study of Hamilton, Canada
Bibliographic record
Abstract
This study aims to provide a deeper understanding of the level of uncertainty associated with the development of extreme weather frequency and intensity indices at the local scale. Several different global climate models, downscaling methods, and emission scenarios were used to develop extreme temperature and precipitation indices at the local scale in the Hamilton region, Ontario, Canada. Uncertainty associated with historical and future trends in extreme indices and future climate projections were also analyzed using daily precipitation and temperature time series and their extreme indices, calculated from gridded daily observed climate data along with and projections from dynamically downscaled datasets of CanRCM4 and PRECIS, and the statistically downscaled CIMP5 ensemble. A bias correction technique was applied to all raw daily temperature and precipitation time series prior to calculation of the indices. All climate models predicted increasing trends for extreme temperature indices, maximum 1-day and 5-day precipitation (RX1day and RX5day), total wet day precipitation (PRCPTOT), very heavy precipitation days (R20mm), Summer Days (SU), and Tropical Nights (TR) and decreasing trend for Forest Days (FD) and Ice Days (ID) in 2020s, 2050s, and 2080s compared to present. CanRCM4 model did consistently project values in the upper range of the CMIP5 ensemble while the PRECIS ensemble was more in-line with the CMIP5 mean values. This difference may however be a function of different emission scenarios used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".