Using Ice Cores to Evaluate CMIP6 Aerosol Concentrations Over the Historical Era
Bibliographic record
Abstract
Abstract The radiative effect of anthropogenic aerosols is one of the largest uncertainties in Earth's energy budget over the industrial period. This uncertainty is in part due to sparse observations of aerosol concentrations in the pre‐satellite era. To address this lack of measurements, ice cores can be used, which contain the aerosol concentration record. To date, these observations have been under‐utilized for comparison to aerosol concentrations found in state‐of‐the‐art Earth system models (ESMs). Here we compare long term trends in concentrations of sulfate and black carbon (BC) between 15 ice cores and 11 ESMs over nine regions around the world during the period 1850–2000. We find that for sulfate concentration trends model results generally agree with ice core records, whereas for BC concentration the model trends differ from the records. Absolute concentrations of both investigated species are overestimated by the models, probably in part due to representation errors. However, we assume that biases in relative trends are not altered by these errors. Ice cores in the European Alps and Greenland record a maximum BC concentration before 1950, while most ESMs used in this study agree on a post‐1950 maximum. We source this bias to an error in BC emission inventories in Europe. Emission perturbation experiments using NorESM2‐LM support the observed finding that BC concentrations in Northern Greenland ice cores are recording European emissions. Errors in BC emission inventories have implications for all future and past studies where Coupled Model Intercomparison Project Phase 6 historical simulations are compared to observations relevant to aerosol forcing.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".