The impact of ENSO and the NAO on extreme winter precipitation in North America in observations and regional climate models
Bibliographic record
Abstract
The relationship between winter precipitation in North America and indices of the North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) is evaluated using non-stationary generalized extreme value distributions with the indices as covariates. Both covariates have a statistically significant influence on precipitation that is well simulated by two regional climate models (RCMs), CanRCM4 and CRCM5. The observed influence of the NAO on extreme precipitation is largest in eastern North America, with the likelihood of a negative phase extreme rainfall event decreased in the north and increased in the south under the positive phase of the NAO. This pattern is generally well simulated by the RCMs although there are some differences in the extent of influence, particularly south of the Great Lakes. A La Niña-magnitude extreme event is more likely to occur under El Niño conditions in California and the southern United States, and less likely in most of Canada and a region south of the Great Lakes. This broad pattern is also simulated well by the RCMs but they do not capture the increased likelihood in California. In some places the extreme precipitation response in the RCMs to external forcing from a covariate is of the opposite sign, despite use of the same lateral boundary conditions and dynamical core. This demonstrates the importance of model physics for teleconnections to extreme precipitation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".