Freezing Rain Events that Impacted the Province of New Brunswick, Canada, and Their Evolution in a Warmer Climate
Bibliographic record
Abstract
Winter storms in eastern Canada can bring heavy precipitation, including large amounts of freezing rain. The resulting ice accumulation on structures such as trees and power lines can lead to widespread power outages and damage to infrastructure. The objective of this study is to provide a better understanding of the processes that led to extreme freezing rain events over New Brunswick (NB), Canada, during past events and how they may change in the future. To accomplish this, freezing rain events that affected the power network over NB were identified and analysed using high-resolution convection-permitting simulations. These simulations were produced from 2000 to 2013 climate data and using the pseudo global warming (WRF-PGW) approach, assuming warmer climate conditions. Our results show that through the process of cold air damming, the Appalachians enhance the development of strong temperature inversions, leading to an increase in the amount of freezing rain in central and southern NB. The occurrence of freezing rain events generally decreases by 40% in southern and eastern NB, while the occurrence of long-duration events (>6 h) increases slightly in northwestern NB in the WRF-PGW simulation. Overall, key local orographic effects that influence atmospheric conditions favorable for freezing precipitation were identified. This knowledge will enable us to better anticipate the impact of climate change on similar storms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".