Intraseasonal and Interannual Variability in North American Storm Tracks and Its Relationship to Equatorial Pacific Variability
Bibliographic record
Abstract
Abstract Extratropical cyclones play a principal role in wintertime precipitation and severe weather over North America. On average, the greatest number of cyclones track 1) from the lee of the Rocky Mountains eastward across the Great Lakes and 2) over the Gulf Stream along the eastern coastline of North America. However, the cyclone tracks are highly variable within individual winters and between winter seasons. In this study, the authors apply a Lagrangian tracking algorithm to examine variability in extratropical cyclone tracks over North America during winter. A series of methodological criteria is used to isolate cyclone development and decay regions and to account for the elevated topography over western North America. The results confirm the signatures of four climate phenomena in the intraseasonal and interannual variability in North American cyclone tracks: the North Atlantic Oscillation (NAO), the El Niño–Southern Oscillation (ENSO), the Pacific–North American pattern (PNA), and the Madden–Julian oscillation (MJO). Similar signatures are found using Eulerian bandpass-filtered eddy variances. Variability in the number of extratropical cyclones at most locations in North America is linked to fluctuations in Rossby wave trains extending from the central tropical Pacific Ocean. Only over the far northeastern United States and northeastern Canada is cyclone variability strongly linked to the NAO. The results suggest that Pacific sector variability (ENSO, PNA, and MJO) is a key contributor to intraseasonal and interannual variability in the frequency of extratropical cyclones at most locations across North America.
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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 | 0.000 | 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 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".