On the Extreme Variability and Change of Cold-Season Temperatures in Northwest Canada
Bibliographic record
Abstract
Abstract The Mackenzie River basin (MRB) in northwestern Canada is a climatologically important region that exerts significant influences on the weather and climate of North America. The region exhibits the largest cold-season temperature variability in the world on both the intraseasonal and interannual time scales. In addition, some of the strongest recent warming signals have been observed over the basin. To understand the nature of these profound and intriguing observed thermal characteristics of the region, its atmospheric heat budget is assessed by using the NCEP–NCAR reanalysis dataset. The composite heat budgets and large-scale atmospheric conditions that are representative of anomalous winters in the region are examined in unison to study the processes that are responsible for the development of extreme warm/cold winters in the MRB. It is shown that the large winter temperature variability of the region is largely a result of the strong variability of atmospheric circulations over the North Pacific, the selective enhancement/weakening of latent heating of the cross-barrier flow for various onshore flow configurations, and synoptic-scale feedback processes that accentuate the thermal response of the basin to the changes in upwind conditions. The improved understanding of mechanisms that govern the thermal response of the basin to changes in the upstream environment provides a theoretical basis to interpret the climate change and modeling results for the region. In particular, the large recent warming trend observed for the region can be understood as the enhanced response of the basin to the shift in North Pacific circulation regime during the mid-1970s. The strong cold bias that affected the region in some climate model results can be attributed to the underprediction of orographic precipitation and associate latent heating of the cross-barrier flow, and the subsequent weakening of mean subsidence and warming over the basin in the models.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".