The Dynamical Influence of Separate Teleconnections from the Pacific and Indian Oceans on the Northern Annular Mode
Bibliographic record
Abstract
Abstract The northern annular mode (NAM) influences wintertime climate variability in the Northern Hemisphere, and understanding the processes controlling its sign and amplitude is of critical importance. Mounting evidence supports a robust teleconnection between the El Niño–Southern Oscillation (ENSO) and the NAM, while internal variability generated in the tropical Indian Ocean (TIO) may be associated with a NAM response of the opposite sign. This study uses a coupled ocean–atmosphere model to separate the influence on the NAM from teleconnections driven by ENSO and the TIO. In composites constructed using a long preindustrial control integration, increased December–February precipitation in the central/eastern Pacific drives a negative late-winter NAM response. When isolated from ENSO variability, increased precipitation over the western-central TIO drives a strong and persistent positive NAM response throughout the winter. Opposite linear interference of the anomalous wave teleconnections explains most of the opposite-signed planetary wavedriving of the NAM responses. The case with combined ENSO and TIO variability yields cancellation of the wave interference and a weak NAM response. This mechanism is confirmed using experiments where the tropical ocean is nudged separately over the Pacific and TIO to the large-amplitude 1997/98–1998/99 ENSO cycle. The phases of the Rossby wave and NAM responses in these two cases are of opposite sign, providing strong evidence that internal variability over the TIO can induce teleconnections independent of—and with opposite sign to—those associated with ENSO.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".