Attribution of Changes in Canadian Precipitation
Bibliographic record
Abstract
Total precipitation has increased over Canada, annually and seasonally. However, the drivers of this change have not been formally diagnosed. Globally, while changes in total precipitation have been attributed to anthropogenic forcing at larger scales, attribution at sub-continental scales has thus far been very limited. We perform a detection and attribution analysis using an optimal fingerprinting approach based on estimating equations to compare the observed changes in Canadian precipitation against CMIP6-model-based estimates of externally forced signals. For Canada as a whole and Northern Canada specifically, an anthropogenic forcing signal is detected in the observations, annually and for six-month warm and cool seasons over 1959-2018. For Southern Canada, observed records are longer and attribution is more robust at the century scale (1904-2018), where the observed increase in annual precipitation is attributed to anthropogenic forcing. Understanding the dominant role of anthropogenic forcing through a formal attribution analysis increases our confidence in the characterization of both past and future changes in precipitation over Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".