Disentangling the mechanisms of ENSO response to 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 oceanic 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 ENSO anomalies following volcanic eruptions are still debated. The center of the argument is understanding how volcanism affects the trade winds along the equatorial Pacific. We performed a series of sensitivity experiments using the Norwegian Earth System Model (NorESM1-M) designed to shed light on the processes that govern the ENSO response to volcanic eruptions as a function of the regional distribution of the aerosol forcing. Specifically, a uniform stratospheric volcanic aerosol loading was imposed over different parts of the tropics and extra-tropics to test the four main mechanisms invoked to explain the ENSO response to volcanic eruptions: 1) the ocean dynamical thermostat (ODT) mechanism; 2) the cooling of the Maritime Continent (MC) mechanism; 3) the cooling of tropical northern Africa (NAFR) mechanism; and 4) the Intertropical Convergence Zone shift mechanism. In this contribution, we will present results for NorESM1-M, illustrate their implications for understanding of forced ENSO dynamics and discuss how our approach can give benefit to multi-model assessments of ENSO response to volcanic forcing.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".