Insights on the timing, global sulfate lifecycle and climate impact of Earth’s largest (pre-) historic volcanic eruptions
Bibliographic record
Abstract
Extratropical volcanic eruptions are commonly thought to be less effective at driving large-scale surface cooling than tropical eruptions, and only the latter are commonly thought to be able to distribute sulfate globally. Here, we test both of these assumptions using a network of ice cores from the polar regions of Antarctica and Greenland covering the past 15’000 years and climate-aerosol modeling. We employ state-of-the-art analyses of trace elements, cryptoptephra and sulphur isotopes (Burke et al., 2019) to gain new insights into the timing of past eruptions, their stratospheric sulphur mass injections and subsequent sulphate aerosol lifecycle. We use this information to estimate the climate impact potential due to negative radiative forcing caused by Earth’s largest volcanic eruptions since the last Glacial. Our analysis encompasses over 1’000 eruptions and include the caldera-forming eruptions of Okmok II (Alaska, 43 BCE, VEI=6, 53°N; McConnell et al., 2020), Aniakchak II (Alaska, 1600s BCE, VEI=6, 57°N), Crater Lake (Mazama, Oregon, 5600s BCE, VEI=7, 43°N) and Laacher See (Germany, c. 13 ka BP, VEI=6, 50°N). We use our reconstructed radiative forcing and the coupled earth system models MPI-ESM1.2 and CESM (version 1.2.2) to analyze the climatic impact caused by these eruptions and compare the simulated temperature response with temperature reconstructions based on ultra-long tree-ring chronologies. Finally, based on these comparisons, we propose a number of stratigraphic age tie-points to anchor ice-core chronologies from Greenland (GICC05) and Antarctica (WD2014) to the absolute dated tree-ring chronology. We thereby aim to improve proxy synchronization throughout the Holocene -- a prerequisite for detection and attribution studies -- and invite the paleo-climate community to update climate proxy records based on ice cores to the latest chronologies. The European Research Council Grant 820047 under the European Union’s Horizon 2020 research and innovation program funded the research project THERA - Timing of Holocene Volcanic eruptions and their radiative aerosol forcing. References: Burke, A., Moore, K. A., Sigl, M., Nita, D. C., McConnell, J. R., and Adkins, J. F.: Stratospheric eruptions from tropical and extra-tropical volcanoes constrained using high-resolution sulfur isotopes in ice cores, Earth Planet Sc Lett, 521, 113-119, 2019. McConnell, J. R., Sigl, M., Plunkett, G., Burke, A., Kim, W., Raible, C. C., Wilson, A. I., Manning, J. G., Ludlow, F. M., Chellman, N. J., Innes, H. M., Yang, Z., Larsen, J. F., Schaefer, J. R., Kipfstuhl, S., Mojtabavi, S., Wilhelms, F., Opel, T., Meyer, H., and Steffensen, J. P.: Extreme climate after massive eruption of Alaska’s Okmok volcano in 43 BCE and effects on the late Roman Republic and Ptolemaic Kingdom, Proceedings of the National Academy of Sciences, 117, 15443-15449, 2020.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".