ENSO response to tropical volcanic eruptions: the role of the season
Bibliographic record
Abstract
Stratospheric volcanic aerosol can have major impacts on the global climate. However, these impacts are eruption specific, as they critically depend on the characteristics of the eruption, such as magnitude, location and timing. Towards understanding these criticalities, only a handful of studies have either assessed the effects of eruptions at distinct times throughout the year or the location of eruption. To our knowledge, no study has hitherto considered the combined of the timing and location of an eruption. Here we investigate variations in the impact of volcanic eruptions linked on the timing of the eruption in relation to the seasons of the year and the location (Northern or Southern Tropical eruption), focusing on ENSO dynamics. In doing so, we use the Norwegian Earth System Model (NorESM) to perform a set of sensitivity experiments in which the tropical volcanic eruptions are set to go off at the beginning of each season. These experiments are meant to shed light on the role of the season in shaping the ENSO response to volcanic eruption, elucidating the nuanced role of volcanic forcing in modulating ENSO variability and enhancing our predictive capabilities of this influential climate phenomenon. In our contribution, we will describe first results from our experiments showing how boreal spring and summer Northern Tropical eruptions lead to El Niño-like conditions in the winter of the eruption, followed by strong La Niña conditions. On the other hand, boreal fall and winter eruptions causes a weak La Niña on the first 6-8 months, followed by El Niño conditions in the winter after the eruption. For Southern Tropical eruptions the response is muted and only fall and winter eruptions show El Niño conditions in the second winter of the eruption. To better understand the different responses, we will also interpret the model results within the framework of a simple delayed oscillator of ENSO.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".