Nonstationary Compound Weather Extremes in Canada based on Large Ensemble Climate Simulations
Bibliographic record
Abstract
Compound weather extremes including warm-wet and warm-dry events can lead to catastrophes such as wildfires, droughts and flooding. We use three large ensembles (3 × 50 members) of climate simulations to study the non-stationarity of compound events based on an ensemble pooling approach: the Canadian Regional Climate Model Large Ensemble (CanRCM4-LE), and the Canadian Large Ensembles Adjusted Datasets (CanLEAD1&2). The CanLEAD products include daily precipitation, maximum and minimum temperature from CanRCM4-LE that are bias-corrected using a novel statistical approach, which preserves the multivariate structure of the climate variables and corrects for univariate biases. Each ensemble member is validated against the NRCANmet observed data over Canada for 1951-2000 using a hierarchical Bayesian framework. Additionally, the performance of the models to mimic the dependence structure of the observation is tested using copulas. Extreme climate indices are estimated for a baseline period and changes in extremes are explored across four future warming scenarios corresponding to +1.5°C, +2.0°C, +3.0°C and +4.0°C warming above the pre-industrial period of 1850-1900. The ensemble pooling approach allows for the quantification of changes in the dependence structure and its subsequent effects on compound extremes in the future. Results show that the CanLEAD products can reduce warm and wet biases in CanRCM4-LE over the majority of Canadian regions in all seasons except for winter. The ensembles unanimously project significant warming and wetting trends over most of southern Canada excluding the Canadian Prairies in summer, which show a drying trend towards the end of the 21st century. The overall trend shows an increase in hot extremes in central and southeastern Canada and a significant increase in wet extremes in western coastal regions. Results from compound extreme analysis show that there is significant under-estimation of extremes when the dependence between temperature and precipitation is ignored. For example, a 100-year hot and dry event under the assumption of independence becomes a ~60-year event when the dependence is characterized using copulas.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".