Using CO<sub>2</sub>:CO correlations to improve inverse analyses of carbon fluxes
Bibliographic record
Abstract
Observed correlations between atmospheric concentrations of CO 2 and CO represent potentially powerful information for improving CO 2 surface flux estimates through coupled CO 2 ‐CO inverse analyses. We explore the value of these correlations in improving estimates of regional CO 2 fluxes in east Asia by using aircraft observations of CO 2 and CO from the TRACE‐P campaign over the NW Pacific in March 2001. Our inverse model uses regional CO 2 and CO surface fluxes as the state vector, separating biospheric and combustion contributions to CO 2 . CO 2 ‐CO error correlation coefficients are included in the inversion as off‐diagonal entries in the a priori and observation error covariance matrices. We derive error correlations in a priori combustion source estimates of CO 2 and CO by propagating error estimates of fuel consumption rates and emission factors. However, we find that these correlations are weak because CO source uncertainties are mostly determined by emission factors. Observed correlations between atmospheric CO 2 and CO concentrations imply corresponding error correlations in the chemical transport model used as the forward model for the inversion. These error correlations in excess of 0.7, as derived from the TRACE‐P data, enable a coupled CO 2 ‐CO inversion to achieve significant improvement over a CO 2 ‐only inversion for quantifying regional fluxes of CO 2 .
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".