Localised intermittent upwelling intensity has increased along South Africa’s south coast due to<i>El Niño</i>–Southern Oscillation phase state
Bibliographic record
Abstract
The El Niño–Southern Oscillation (ENSO) phase state is reported to drive interannual variability in sea temperatures along South Africa’s south coast through its influence on wind-induced upwelling processes. Whether ENSO drives the intensity of localised, abrupt, intermittent upwelling is less well known. To explore this relationship, we used an index of localised, extreme (>2 °C anomaly), intermittent upwelling intensity, derived from in situ sea temperature data within the Tsitsikamma National Park Marine Protected Area, and quantified the relationship between annual cumulative upwelling intensities (1991–2013) with an annual ENSO index, namely the Southern Oscillation Index. We found that ENSO phase state modulates the cumulative intensity of extreme intermittent upwelling events during an annual period, with more and greater events during La Niña phases compared with El Niño phases. Furthermore, these extreme upwelling events have increased with time along South Africa’s south coast as ENSO phase state becomes more intense and variable. Our findings support the emerging notion that the biological effects of climate change may be manifested through increased environmental variability rather than long-term mean environmental changes as ENSO is predicted to remain the dominant driver of local climate patterns in the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".