Emissions and topographic effects on column CO<sub>2</sub> () variations, with a focus on the Southern California Megacity
Bibliographic record
Abstract
Abstract Within the California South Coast Air Basin (SoCAB), varies significantly due to atmospheric dynamics and the nonuniform distribution of sources. measurements within the basin have seasonal variation compared to the “background” due primarily to dynamics, or the origins of air masses coming into the basin. We observe basin‐background differences that are in close agreement for three observing systems: Total Carbon Column Observing Network (TCCON) 2.3 ± 1.2 ppm, Orbiting Carbon Observatory‐2 (OCO‐2) 2.4 ± 1.5 ppm, and Greenhouse gases Observing Satellite 2.4 ± 1.6 ppm (errors are 1σ). We further observe persistent significant differences (∼0.9 ppm) in between two TCCON sites located only 9 km apart within the SoCAB. We estimate that 20% (±1σ confidence interval (CI): 0%, 58%) of the variance is explained by a difference in elevation using a full physics and emissions model and 36% (±1σ CI: 10%, 101%) using a simple, fixed mixed layer model. This effect arises in the presence of a sharp gradient in any species (here we focus on CO2) between the mixed layer (ML) and free troposphere. Column differences between nearby locations arise when the change in elevation is greater than the change in ML height. This affects the fraction of atmosphere that is in the ML above each site. We show that such topographic effects produce significant variation in across the SoCAB as well.
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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 | 0.000 | 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 | 0.001 | 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 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".