Impact of climate change on volcanic processes: current understanding and future challenges
Bibliographic record
Abstract
Abstract The impacts of volcanic eruptions on climate are increasingly well understood, but the mirror question of how climate changes affect volcanic systems and processes, which we term “climate-volcano impacts”, remains understudied. Accelerating research on this topic is critical in view of rapid climate change driven by anthropogenic activities. Over the last two decades, we have improved our understanding of how mass distribution on the Earth’s surface, in particular changes in ice and water distribution linked to glacial cycles, affects mantle melting, crustal magmatic processing and eruption rates. New hypotheses on the impacts of climate change on eruption processes have also emerged, including how eruption style and volcanic plume rise are affected by changing surface and atmospheric conditions, and how volcanic sulfate aerosol lifecycle, radiative forcing and climate impacts are modulated by background climate conditions. Future improvements in past climate reconstructions and current climate observations, volcanic eruption records and volcano monitoring, and numerical models all have a role in advancing our understanding of climate-volcano impacts. Important mechanisms remain to be explored, such as how changes in atmospheric circulation and precipitation will affect the volcanic ash life cycle. Fostering a holistic and interdisciplinary approach to climate-volcano impacts is critical to gain a full picture of how ongoing climate changes may affect the environmental and societal impacts of volcanic activity.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".