Sea‐salt aerosol distribution during the Last Glacial Maximum and its implications for mineral dust
Bibliographic record
Abstract
Sea‐salt aerosols and mineral dust in ice cores are complementary in their use as indicators of past climate because of their differing dependence on the various factors affecting their deposition rates, such as winds, precipitation, and soil properties. Here sea‐salt aerosol distributions for the Last Glacial Maximum (LGM) and for the modern climate are simulated using an online passive aerosol model in the Canadian Centre for Climate Modelling and Analysis second‐generation general circulation model. Comparison of simulated deposition rates of sea salt from the open ocean with polar ice core concentrations indicates that a 75‐fold enhancement, beyond that indicated by the model, is necessary for consistency with the Greenland ice core observations and an approximate tenfold additional enhancement is necessary for Antarctica. However, considering sea ice as a possible sea‐salt aerosol source allows greater simulated LGM deposition in Greenland, though there is a great deal of uncertainty in the possible magnitude of this. Using very simple models for which 1–2% of the total modern sea‐salt aerosol comes from ice‐covered areas, the ice core data can be accommodated by a sevenfold to tenfold overall additional LGM source enhancement. The fact that these additional enhancement factors are similar to the enhancement factor previously determined for mineral dust using the same general circulation model suggests that some combination of increased LGM surface winds and transport or deposition to ice core locations is necessary for agreement with the ice core data.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".