Large spread across AeroCom Phase II models in simulating black carbon in melting snow over Arctic sea ice
Bibliographic record
Abstract
Over two dozen global atmospheric chemistry models contributing to the Aerosol Comparisons between Observations and Models (AeroCom) project were used in this study to drive the Los Alamos sea ice model to simulate the black carbon (BC) concentration in melting snow on Arctic sea ice. Measurements of BC during the melting season show concentrations in the range 2.8–41.6 ng•g−1 (average: 15.3 ng•g−1) in the central Arctic Ocean and Canada Basin. Most results from models contributing to the Phase I project were within the 25th and 75th percentiles of the observations, and the multimodel mean was slightly lower than that of the observations. In contrast, there was larger divergence among the Phase II model simulations and the mean value of BC was overestimated. The multimodel mean bias was −3.1 (−11.2 to +6.7) ng•g−1 for Phase I models and +3.9 (−9.5 to +21.3) ng•g−1 for Phase II models. The differences between the models of the two phases were probably attributable to the updated aerosol scheme in the new contributions, in which removal processes are parameterized by considering the actual dimensions and chemical compositions of the particles. This means the removal mechanism acts in a way that is more selective and leads to more BC particles being transported to the Arctic. In addition, higher spatial resolution could be another important reason for overestimation of BC concentration in snow in Phase II models. Citation:Pan S F, Duan M K. Large spread across AeroCom Phase II models in simulating black carbon in melting snow over Arctic sea ice. Adv Polar Sci, 2020, 31(4): 291-298, doi: 10.13679/j.advps.2020.0026
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".