Distinct and reproductible northem hemisphere winter teleconnection pattern during strong El Niño events : relative roles of Sea Surface Temperature forcing and atmospheric nonlinearities
Bibliographic record
Abstract
The strengthening and north-eastward shift of El Niño Northern hemisphere winter teleconnections relative to those of La Niña is a well-known asymmetry of ENSO (El Niño Southern Oscillation). It is generally attributed to atmospheric nonlinearities associated with the Sea Surface Temperature (SST) threshold for tropical deep convection. Here, we re-examine these teleconnection asymmetries in the context of ENSO SST pattern diversity. We find that the asymmetries are mainly attributable to strong El Niño events (eg. 1982-83, 1997-98, 2015-16), both in observations and in ensemble simulations with the atmospheric component of the CNRM-CM6 model. This strong El Niño teleconnection pattern also results in specific impacts, characterized by enhanced rainfall along the United States (US) west coast and warm anomalies over Canada and the Northern US. Our ensemble simulations further indicate that moderate “Eastern Pacific” El Niño events exhibit teleconnection patterns that are similar to those of “Central Pacific” El Niño, or to the opposite of La Niña events. We also find that the teleconnection spread between ensemble members or events is reduced for strong El Niño relative to moderate El Niño or La Niña events, with important implications for predictability. Sensitivity experiments in which the atmospheric model is forced by the opposite of observed SST anomalies are used to assess the mechanisms inducing the strong El Niño teleconnection pattern. In addition to the well-known influence of atmospheric nonlinearities, these experiments reveal an important contribution from the Eastward-shifted SST pattern during strong El Niño events.
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 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 | 0.000 | 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".