Linear interference and the Northern Annular Mode response to tropical SST forcing: Sensitivity to model configuration
Bibliographic record
Abstract
Abstract Interannual variability in tropical sea surface temperatures (SSTs) associated with the El Niño–Southern Oscillation is linked to teleconnections with the Northern Annular Mode (NAM). Previous work highlighted that the sign and amplitude of the NAM response to tropical SSTs are controlled by the total wave activity entering the subpolar stratosphere, which depends on the linear interference of planetary wave anomalies with the climatological stationary wave field. This study uses multiple configurations of atmospheric general circulation models to assess the robustness of these linkages to details of the tropical SST forcing and model configuration. Across 23 cases with idealized SST forcing, the amplitudes of the tropical and extratropical wave responses are found to scale approximately linearly with forcing strength. But wave amplitude alone is not sufficient to predict the NAM response. Instead, the spatial structure of the wave response (and hence the linear interference) provides the best explanation of the NAM response in all cases. Linear interference explains most of the total wave activity response even in cases with stronger nonlinear contributions, due to consistent cancellation between quasi‐stationary wave nonlinearity and nonlinearity arising from transient waves. Within this limited set of experiments, there is no evidence for a consistent sensitivity of the NAM response to horizontal resolution or to vertical resolution in the stratosphere. These findings reveal that linear interference provides a robust and reproducible mechanism linking midlatitude wave responses to zonal mean circulation (NAM) responses across a wide variety of forcing cases.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".