Simulated Changes in the Freezing Rain Climatology of North America under Global Warming Using a Coupled Climate Model
Bibliographic record
Abstract
A precipitation typing algorithm was applied to climate model simulations in order to investigate the effect of global warming on the occurrence of freezing rain over North America. The model used in the study was the Canadian Centre for Climate Modelling and Analysis' CGCM3. Two realizations of the present-day (1981–2000) climate and two realizations of a global warming (2081–2100) simulation were run using scenario A2 from the Special Report on Emissions Scenarios (SRES) prepared by the Intergovernmental Panel on Climate Change (IPCC). The algorithm was applied to the four twenty-year periods in order to determine the change in the number and distribution of freezing rain events. The model results indicate that the present-day freezing rain maximum over eastern North America will shift poleward and weaken with the result that freezing rain events will decrease significantly in the eastern United States and the Atlantic Provinces. To the north of the maximum and over central Canada there will be modest increases in freezing rain. When averaged over North America, there will be an overall decrease in freezing rain events with global warming.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".