New insights on ENSO teleconnection asymmetry and ENSO forced atmospheric circulation variability over North America
Bibliographic record
Abstract
Abstract El Niño-Southern Oscillation (ENSO)-related sea surface temperature variability in the eastern equatorial Pacific drives an extratropical large-scale atmospheric response. The atmospheric response is a key driver of global climate variability, with the strongest impact occurring during Northern Hemisphere winter. The degree to which atmospheric circulation variability is altered during ENSO events, in comparison with atmospheric circulation variability during ENSO-neutral conditions, is the focus of this study. Two multi-century, CESM1-CAM4 simulations are compared: a fully coupled experiment (CTRL), and a partially decoupled experiment in which ENSO is dynamically suppressed (NoENSO) so that the mean state is not biased towards a particular ENSO phase. We present evidence that the rectification of ENSO and its teleconnections onto the mean state lead to an underestimation of the asymmetry of ENSO teleconnections. Analyses also show that ENSO displaces 500hPa geopotential height (Z500) variability away from the central northern U.S. and southern Canada, resulting in less variability during ENSO years than ENSO-neutral years. Additionally, we find that estimating the ENSO-forced change in Z500 variance compared to ENSO-neutral years requires a surprisingly large sample of ENSO-neutral years. The results imply that a substantially longer record–roughly an order of magnitude longer in length–is needed to fully capture the statistics of ENSO’s teleconnected impacts over North America than suggested in previous studies.
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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 | 0.002 |
| 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".