A comparison of large-scale atmospheric sulphate aerosol models (COSAM): overview and highlights
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The comparison of large-scale sulphate aerosol models study (COSAM) compared the performance of atmospheric models with each other and observations. It involved: (i) design of a standard model experiment for the world wide web, (ii) 10 model simulations of the cycles of sulphur and 222Rn/210Pb conforming to the experimental design, (iii) assemblage of the best available observations of atmospheric SO= 4, SO2 and MSA and (iv) a workshop in Halifax, Canada to analyze model performance and future model development needs. The analysis presented in this paper and two companion papers by Roelofs, and Lohmann and co-workers examines the variance between models and observations, discusses the sources of that variance and suggests ways to improve models. Variations between models in the export of SOx from Europe or North America are not sufficient to explain an order of magnitude variation in spatial distributions of SOx downwind in the northern hemisphere. On average, models predicted surface level seasonal mean SO= 4 aerosol mixing ratios better (most within 20%) than SO2 mixing ratios (over-prediction by factors of 2 or more). Results suggest that vertical mixing from the planetary boundary layer into the free troposphere in source regions is a major source of uncertainty in predicting the global distribution of SO= 4 aerosols in climate models today. For improvement, it is essential that globally coordinated research efforts continue to address emissions of all atmospheric species that affect the distribution and optical properties of ambient aerosols in models and that a global network of observations be established that will ultimately produce a world aerosol chemistry climatology.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 it