Assessing past and future hazardous freezing rain and wet snow events in Manitoba using a pseudo-global warming approach
Bibliographic record
Abstract
Freezing precipitation is a major hazard across Canada. Usually occurring in the form of freezing rain and/or wet snow and can damage transportation networks, infrastructure, and vegetation. Under future warming climatic conditions, the characteristics of this precipitation may change but there is great uncertainty. This thesis characterizes damaging freezing precipitation events within Manitoba and examines their future occurrence within a warmer climate. A total of 10 events were identified, 8 of which were within the WRF period; 5 of these had both freezing rain and wet snow, and the other 3 had freezing rain exclusively. These were characterized using data from the Japanese 55-year Reanalysis (JRA-55), several Environment and Climate Change Canada (ECCC) datasets, and two 4 km Weather Research and Forecasting (WRF) simulations from the National Center for Atmospheric Research (NCAR) from October 2000 to September 2013 (Liu et al. 2017). These were a retrospective control (CTRL) and a pseudo-global warming (PGW) simulation covering CONUS and much of Canada. Large scale and local factors were associated with these events. Most (9 of 10) showed consistent large scale forcing: a midlatitude cyclone with 500 hPa trough and jet exit enhancing lift, low surface pressure centre nearby, and an atmospheric river. Local factors, such as the elevated terrain of Riding Mountain, influenced 2 events in CTRL and 3 in PGW by altering surface temperature and/or winds to be favourable for freezing precipitation. This terrain is also somewhat co-located with areas of severe ice loading, as shown by the Canadian Standards Association (2015). In the PGW simulations, these events changed significantly. The 3 events with freezing rain exclusively were in December and January. Of these, 2 (1) had increased (decreased) in extent, precipitation accumulation, and duration. There was no wet snow in these events in CTRL, but it was present in PGW. The other 5 events that had both wet snow and freezing rain, and none had wet snow exclusively. Of these, 1 increased in extent, duration, and accumulation, and another increased in extent, but had similar duration and lesser accumulation. The other 3 events were reduced.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".