Regional uncertainty in future cold extremes in North America
Bibliographic record
Abstract
North America has experienced high-impact cold air outbreak (CAO) events in recent decades despite a winter warming trend. Many studies agree CAOs will eventually become weaker and less frequent in the future, but it remains unclear when these changes will occur and how they will impact CAO frequency and strength on a regional scale. Here, we use a single model large ensemble (CESM2) under an aggressive emission scenario (SSP 3-7.0) to quantify and characterize the temporal and spatial impacts of internal variability in future CAO projections across North America. Our analysis reveals key regional differences: low-uncertainty regions include the Northeast US, Midwest, and Eastern Canada, where ensemble members show agreement on greatly reduced CAO frequency after 2050, while high-uncertainty regions encompass the Great Plains and parts of the southern US, where CAOs continue to occur across ensemble members despite strong warming. To better isolate the anthropogenic signal from internal variability, we apply a dynamical adjustment method that removes atmospheric dynamics contributions to temperature variability and show that its effectiveness in the uncertainty reduction varies across different regions. We leverage the large number of CAO events from the large ensemble to construct a non-stationary statistical model and estimate how the probability of occurrence of present-climate CAO events might change in the future. Our results emphasize on the persistence of CAOs in North America even if warming eliminates the average CAO. These findings underscore the importance of regional-scale uncertainty assessment for winter adaptation planning, particularly in high-uncertainty regions where the probability of worst-case CAO events remains comparable to current conditions through the end of the century.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".