United States Heat Wave Frequency and Arctic Ocean Marginal Sea Ice Variability
Bibliographic record
Abstract
Abstract Recent studies point to a significant rise in the number of summer extreme weather events that correspond with the presence of amplified, quasistationary midtropospheric planetary waves, weakened atmospheric circulation in the Northern Hemisphere, and coincide with reduced summer Arctic sea ice cover. This study explores potential connections between 1979 and 2016 summer heat wave frequency across the USA and regional Arctic sea ice extent (SIE) in various Arctic basins. Most notable SIE interannual relationships exist across the southern Plains and southeastern US during low Hudson Bay summer SIE. Locally increased frequencies of summer heat waves coincide with unseasonably warm conditions developed and sustained by the presence of an omega blocking pattern situated over the southern US throughout summer. The block appears following anomalous atmospheric warming and reduced mean zonal winds observed throughout spring (March–May) over northeastern Canada, the northwestern Atlantic basin, and Greenland. Spring preconditioning of summer ice melt is favored by the presence of strong negative phase of the North Atlantic Oscillation and positive Greenland Blocking Index. Summer synoptic flow related to Hudson Bay ice melt over North America appears to be influenced by the background state of atmospheric variability, namely, the positive phase of the Atlantic Multidecadal Oscillation. Antecedent local humidity, soil moisture, and precipitation conditions are shown to influence the “flavor” of the heat waves, which are more likely to be oppressive in the southeastern US and extreme across the southern Plains during summers experiencing low Hudson SIE.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".