Revisiting the mechanisms of ENSO response to tropical volcanic eruptions
Bibliographic record
Abstract
Large explosive volcanic eruptions can have major impacts on global climate, affecting both radiative balance and inducing interannual-to-decadal dynamical alterations of the atmospheric and ocean circulation. Despite some discrepancies across studies regarding the response of ENSO to volcanism based on paleoclimate data, the majority of ENSO reconstructions display an El Niño–like warming in the year of eruption, while none display a significant La Niña–like response, Furthermore, there has been an emerging consensus from the numerous coupled General Circulation Model studies investigating the impact of tropical volcanism on ENSO, with the overwhelming majority displaying an El Niño–like warming occurring in the year following the eruption. However, the mechanisms that trigger a change in the ENSO state following volcanic eruptions are still debated. The center of the argument is understanding how volcanism can affect the trade winds along the equatorial Pacific.Here, we shed light on the processes that govern the ENSO response to tropical volcanic eruptions through a series of sensitivity experiments with an Earth System Model where a uniform stratospheric volcanic aerosol loading is imposed over different parts of the tropics. Three tropical mechanisms are tested: the “ocean dynamical thermostat” (ODT); the cooling of the Maritime Continent; and the cooling of tropical northern Africa (NAFR). We find that the NAFR mechanism plays the largest role, while the ODT mechanism is absent in our simulations as La Niña-like rather than El-Niño-like conditions develop following a uniform radiative forcing over the equatorial Pacific.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".