Constraints on Carbon Monoxide Emissions Based on Tall Tower Measurements in the U.S. Upper Midwest
Bibliographic record
Abstract
We interpret a full year of high-frequency CO measurements from a tall tower in the U.S. Upper Midwest with a time-reversed Lagrangian Particle Dispersion Model (STILT LPDM) and an Eulerian chemical transport model (GEOS-Chem CTM) to develop top-down constraints on U.S. CO sources in 2009. Our best estimate is that anthropogenic CO emissions in the U.S. Upper Midwest in 2009 were 2.9 Tg, 61% lower (a posteriori scale factor of 0.39) than our a priori prediction based on the U.S. EPA's National Emission Inventory for 2005 (NEI 2005). If the same bias applies across the contiguous U.S., the inferred CO emissions are 26 Tg/y, compared to the a priori estimate of 66 Tg/y. This discrepancy is significantly greater than would be expected based solely on emission decreases between 2005 and 2009 (EPA estimate: 23% decrease). Model transport error is an important source of uncertainty in the analysis, and we employ an ensemble of sensitivity runs using multiple meteorological data sets and model configurations to assess its impact on our results. A posteriori scale factors for the U.S. anthropogenic CO source from these sensitivity runs range from 0.22 to 0.64, corresponding to emissions of 1.6-4.8 Tg/y for the U.S. Upper Midwest and 15-42 Tg/y for the contiguous U.S. The data have limited sensitivity for constraining biomass + biofuel burning emissions and photochemical CO production from precursor organic compounds. Our finding of a NEI 2005 overestimate of CO emissions is consistent with recent assessments for individual cities and with earlier analyses based on the NEI 1999, implying the need for a better mechanism for refining such bottom-up emission estimates in response to top-down constraints.
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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 | 0.004 |
| 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 | 0.002 |
| 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".