Impact of Internal Climate Variability on the Relationship between Spring Northern Tropical Atlantic SST Anomalies and Succedent Winter ENSO: The Role of the North Pacific Oscillation
Bibliographic record
Abstract
Abstract Previous studies suggested that spring sea surface temperature anomalies (SSTAs) in the northern tropical Atlantic (NTA) have a marked influence on the succedent winter El Niño–Southern Oscillation (ENSO). In this study, we examine the spring NTA SSTA–winter ENSO connection in a 50-member large-ensemble simulation conducted with the Canadian Centre for Climate Modelling and Analysis second-generation Canadian Earth System Model (CanESM2) and a 100-member ensemble simulation conducted with the Max Planck Institute Earth System Model (MPI-ESM). The observed out-of-phase relation of spring NTA SSTA with winter ENSO can be captured by the multimember ensemble means of the large-ensemble simulations from both models. However, the relation shows a large diversity among different ensemble members attributing to the internal climate variability. The preceding winter North Pacific Oscillation (NPO) is suggested to be an important source of the internal climate variability that modulates the spring NTA SSTA–ENSO connection. The modulation of the winter NPO on the subsequent spring NTA SSTA–winter ENSO relation is seen in both climate modeling and observational datasets. When winter NPO and spring NTA SSTA indices have the same (opposite) sign, the linkage between the spring NTA SSTA and the following winter ENSO tends to be weak (strong). The NPO modulates the spring NTA SSTA–winter ENSO relation mainly via changing the zonal wind anomalies over the tropical western-to-central Pacific induced by the spring NTA SSTA. In addition, our analysis indicates that winter NPO may have a marked effect on the predictability of winter ENSO based on the condition of spring NTA SSTA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".