Linking Atmospheric Waviness to Extreme Temperatures Across the Northern Hemisphere: Comparison of Different Waviness Metrics
Bibliographic record
Abstract
Abstract Large meanders in the atmospheric jet stream (Rossby waves) are strongly related to extreme temperature events. It has been theorized that changes in such waves, or “waviness,” may have played a role in observed changes in the frequency of extreme temperature events. There is, however, no consensus on how atmospheric waviness will change in response to climate change; the existence of different metrics used to quantify waviness may be contributing to this lack of consensus. Here, we perform a waviness metric comparison, focusing on their association with extreme daily temperatures across the Northern Hemisphere midlatitudes. Using a probability ratio, we quantify the association between high waviness and colocated extreme temperatures. We find large differences in association strength across different metrics, highlighting that different metrics are associated with very different aspects of waviness, and are not all equally associated with extremes. A composite analysis highlights some differences. We find that the local wave activity (LWA) metric has the strongest associations with temperature extremes, particularly when separated into its cyclonic and anticyclonic components. We also conduct similar analysis using a neural network model to simulate links between metrics and temperature anomalies. We find that probability ratios can be increased through this method of including nonlinear, and not just colocated, connections, but the result of the LWA metric providing the strongest connection to extremes holds. We also show that a simple geopotential height zonal anomaly method provides overall very high associations and may be sufficient for many studies connecting waviness to extremes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".