Persistent cold air outbreaks over North America in a warming climate
Bibliographic record
Abstract
This study examines future changes of cold air outbreaks (CAOs) using a multi-model ensemble of global climate simulations from the Coupled Model Intercomparison Project Phase 5 and high resolution regional climate simulations. Overall, climate models agree on a dip in CAO duration across North America, but the percentage change is consistently smaller from western Canada to the upper mid-western US with historically more frequent CAO. By decomposing the changes of the probability density function of daily surface temperature into changes due to mean warming and changes in standard deviation (std) and skewness/higher order moments, the contributions of each factor to CAO changes are quantified. Results show that CAO changes can be explained largely by the mean warming, but the decrease in temperature std contributes to about 20% reduction of CAO from Alaska to northeastern US and eastern Canada possibly due to the Arctic amplification and weakening of storm track. A thermodynamical modulation of the skewness called the '0 °C mode' effect is found to operate prominently along the 0 °C isotherm hemispherically and reduce CAO in western and northeastern US with winter snow cover by up to 10%. This effect also produces a manifold increase in CAO events over the Arctic sea ice. An increased frequency in atmospheric blocking also contributes to increases in CAO duration over Alaska and the Arctic region. Regional simulations revealed more contributions of existing snowpack to CAO in the near future over the Rocky Mountain, southwestern US, and Great Lakes areas through surface albedo effects. Overall, the multi-model projections emphasize that cold extremes do not completely disappear in a warming climate. Concomitant with the relatively smaller reduction in CAO events in northwestern US, the top five most extreme CAO events may still occur, and wind chill will continue to have societal impacts in that region.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".