Evaluation of Modeled Carbon Monoxide and Methane Columns in the High Arctic Using TCCON Measurements
Bibliographic record
Abstract
Abstract Methane (CH 4 ) and carbon monoxide (CO) are gases with important climate impacts as direct and indirect greenhouse gases, respectively. Methane has a warming potential 28 times that of carbon dioxide on a 100‐year timescale, and carbon monoxide is a precursor to ozone in the troposphere. Modeling trace gas concentrations in the Arctic atmosphere can be challenging due to Arctic conditions and sensitivity to long‐range transport, and comparing model outputs to remote sensing measurements is essential for ensuring that models are performing well. Ground‐based Arctic measurements are spatially sparse, so it is important to make use of all such available data sets. In this study, we assess eight atmospheric models, comparing their simulations of atmospheric CO and CH 4 column‐averaged dry‐air mole fractions for 2014 and 2015 with ground‐based retrievals of these species at three Arctic stations in the Total Carbon Column Observing Network (TCCON). The multi‐model mean had mean biases (± one standard deviation of the mean) of −5.4% ± 8% at Eureka, Canada, −6.5% ± 8% at Ny‐Ålesund, Norway, and −11% ± 7% at Sodankylä, Finland for CO, and mean biases of −0.25% ± 0.5% at Eureka, −0.90% ± 0.5% at Ny‐Ålesund, and −1.0% ± 0.5% at Sodankylä for CH 4 . Individual model mean biases range from −33% to +35% for CO and −2.5% to +1.9% for CH 4 . These results indicate that models could benefit from improvements targeting simulations of Arctic CO.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".