Using reduced-complexity volcanic aerosol and climate models to produce large ensemble simulations of Holocene temperature
Bibliographic record
Abstract
Volcanic eruptions are one of the most important drivers of climate variability, but climate model simulations typically show stronger surface cooling than proxy-based reconstructions. Uncertainties associated with eruption source parameters, aerosol–climate modelling, and internal climate variability might explain those discrepancies, but their quantification using complex global climate models is computationally expensive. In this study, we combine a reduced-complexity volcanic aerosol model (EVA_H) and a climate model (FaIR) to simulate global-mean surface temperature from 6755 BCE to 1900 CE (8705 to 50 BP) accounting for volcanic forcing, solar irradiance, orbital, ice sheet, greenhouse gases, land-use forcing, and anthropogenic aerosols and ozone forcing for the historical period (1750–1900 CE). The negligible computational cost of the models enables us to use a Monte Carlo approach to propagate uncertainties associated with eruption source parameters, aerosol and climate modelling, and internal climate variability. Averaging over the last 9000 years, we obtain a global-mean volcanic forcing of −0.15 W m −2 and an associated surface cooling of 0.12 K. Averaged over the 14 largest eruptions (injecting more than 20 Tg of SO 2 ) of 1250–1900 CE, the mean temperature response in tree-ring-based reconstructions is in good agreement with the our simulations, scaled to Northern Hemisphere summer temperature. For individual eruptions, discrepancies between the simulated and reconstructed surface temperature response are almost always within uncertainties. At multimillennial timescales, our simulations reproduce the Holocene global warming trend typically derived from simulations and data assimilation products but exhibit some discrepancies on centennial to millennial timescales. In particular, the Medieval Climate Anomaly to Little Ice Age transition is weaker in our simulations, and we also do not capture a relatively cool period between 3000 and 1000 BCE (5000 and 3000 BP), visible in climate reanalyses. We discuss how uncertainties in land-use forcing and model limitations might explain these differences. Our study demonstrates the value of reduced-complexity volcanic aerosol–climate models to simulate climate at annual to multimillennial timescales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".