Snowfall statistics for past and future climates calculated from two ensembles of the fifth-generation Canadian Regional Climate Model (CRCM5)
Bibliographic record
Abstract
This dataset contains extreme snowfall statistics from observations and regional climate model simulations. Simulated snowfall statistics are calculated using two ensembles of the fifth-generation Canadian Regional Climate Model (CRCM5). The first is a set of four simulations produced by Ouranos for the Coordinated Regional Climate Downscaling Experiment (CORDEX) at 0.22° horizontal grid spacing over the North American domain. The second ensemble is a set of 50 simulations from the ClimEx project, driven by 50 members of the CanESM2 large ensemble over the northeastern North America domain at 0.11° horizontal grid spacing. Observations are derived from the Global Historical Climatology Network daily (GHCNd) dataset. Statistics include mean annual snowfall, the 95th percentile of daily snowfall (SF95), and the mean annual number of daily snowfall events exceeding 10% of the climatological mean annual snowfall (TC10). Statistics are presented for four time periods, the 1980-2009 recent past climate and the 30-year periods during which the +2, +3, and +4°C global warming levels (relative to the 1850-1900 preindustrial climate) are attained. These data are associated with the manuscript “Changing Nature of High-Impact Snowfall Events in Eastern North America” by McCray et al. (2023), Journal of Geophysical Research: Atmospheres available at https://doi.org/10.1029/2023JD038804.
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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) | 0.000 | 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".