Spatio-temporal variability of jet streams over North America and North Pacific Ocean
Bibliographic record
Abstract
This study examines the impact of climate change on jet stream features and their seasonal fluctuations across North America (NA) from 1984 to 2023. Maps and analyses were produced utilizing ERA5, ERA-Interim, and NCEP/NNCAR data for Temperature (T), Zonal wind (Uwnd), and Meridional wind (Vwnd). Results indicate two important places where jet streams are significantly affected by climate change: the North Pacific Ocean (NPO) and the eastern portion of North America (EPNA). The most varied jet stream trajectories in the NPO manifest during summer, but in EPNA, they peak in autumn. Jet streams are positioned lower and exhibit more velocity in winter, whereas they are situated higher and demonstrate less velocity in summer. In the last 40 years, jet streams have demonstrated cyclical patterns of 5, 7, and 10 years, exhibiting no altitude variations, while in other instances, they have shown fluctuations between 100 and 300 hectopascals in altitude. The winter and spring jet streams over the NPO ascended to elevated altitudes, diminishing variations, whereas the winter and autumn jet streams over EPNA descended, amplifying volatility. Seasonal analysis of temperature and zonal wind patterns revealed that rising temperatures were associated with increased zonal wind speeds across nearly all seasons. Concurrently, the jet stream cores exhibited a consistent upward and poleward shift toward higher latitudes. These currents convey moisture, influencing regional climatic patterns and resulting in occurrences such as atmospheric rivers. This study highlights the variable characteristics of jet streams and their essential function in regional climate.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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".