The POLARCAT Model Intercomparison Project (POLMIP): overview and evaluation with observations
Bibliographic record
Abstract
Abstract. A model intercomparison activity was inspired by the large suite of atmospheric chemistry observations made during the International Polar Year (2008) in the Arctic. Nine global and two regional chemical transport models have performed simulations for 2008 using a common emissions inventory to quantify the differences in model chemistry and transport schemes. This paper summarizes the models and compares their simulations of ozone and its precursors, and presents an evaluation of the simulations using a variety of surface, balloon, aircraft and satellite observations. Despite using the same emissions, large differences are seen among the models. Differences in a number of model parameters are identified as contributing to differences in the modelled chemical species, including cloud fields and photolysis rates. The largest differences among models, and between models and observations, are in NOy partitioning (PAN vs. HNO3) and in oxygenated volatile organic compounds (VOCs) such as acetaldehyde and acetone. Comparisons to surface site measurements of ethane and propane indicate that the emissions of these species are significantly underestimated. While limited in spatial and temporal coverage, the aircraft measurements provide a simultaneous evaluation of many species. Satellite observations of NO2 from OMI have been used to evaluate the models over source regions, indicating anthropogenic emissions are underestimated in East Asia, but fire emissions are generally overestimated. The emission factors for wildfires in Canada are evaluated using the correlations of VOCs to CO in the model output in comparison to enhancement factors derived from aircraft observations, showing reasonable agreement for methanol and acetaldehyde, but underestimate of ethanol, propane and acetone, while overestimating ethane emission factors.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".