Revisiting the nonlinear relationship between<scp>ENSO</scp>and winter extreme station precipitation in North America
Bibliographic record
Abstract
ABSTRACT While researchers have identified teleconnections between El Niño‐Southern Oscillation ( ENSO ) and extended winter precipitation extremes in North America using generalized extreme value ( GEV ) models, the regional form of the statistical relationship remains an open question. Past work has shown that relatively warm ENSO conditions may be needed to trigger a nonlinear response over North America. However, studies that stratify winters into La Niña, neutral, and El Niño phases have found that precipitation extremes in neutral/La Niña winters respond differently than in El Niño winters, whereas studies that stratify ENSO data into cold/warm conditions have not found evidence for a coherent nonlinear response. Data and methodological differences have made direct comparison between results difficult. In this study, evidence for a nonlinear association between ENSO and precipitation extremes is reassessed by fitting stationary and linear/nonlinear GEV regression models, with the Niño3.4 index as a covariate, to 1‐, 5‐, and 10‐day extended winter precipitation maxima. Two types of nonlinear model – both one‐sided GEV regressions where linear relationships are allowed to differ above/below a Niño3.4 index threshold – are considered. In the first, following past work, the breakpoint is fixed at zero, that is stratifying data into warm/cold conditions. In the second, to see whether there is evidence for a differential neutral/La Niña and El Niño response, the breakpoint is allowed to vary freely. Due to the non‐nested nature of the set of models, the Akaike Information Criterion is used to assess the relative support for each model. Depending on accumulation time‐scale, the strength of evidence favours nonlinear models at 28%–30% of stations when the Niño3.4 breakpoint is free to vary, versus just 3% with a fixed breakpoint. Optimum Niño3.4 breakpoints are positive (> +0.4°C) in the majority of the nonlinear models, confirming that ENSO /precipitation relationships differs between La Niña/neutral and El Niño winters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".