Deriving daily carbon fluxes from hourly CO<sub>2</sub> mixing ratios measured on the WLEF tall tower: An upscaling methodology
Bibliographic record
Abstract
The temporal variation of the CO2 mixing ratio in the atmosphere at a given height results from several processes, including photosynthesis and respiration of the underlying ecosystems, the vertical mixing of the atmosphere near the surface and in the planetary boundary layer (PBL), and entrainment of the air above the PBL. Theoretically, if all atmospheric processes are modeled accurately, we can estimate the magnitude of ecosystem photosynthesis and respiration from the variations in the measured CO2 mixing ratio. Through analyzing the CO2 concentration measured at several heights (30 m, 122 m, and 396 m) on the Wisconsin tall tower, we demonstrate that it is possible to derive the daily carbon flux resulting from CO2 uptake from hourly CO2 mixing ratio data. At 30 m, the concentration‐derived daily gross primary productivity (GPP) is well correlated with measured daily GPP derived from flux measurements (r2 = 0.70), but the former was 20% larger than the latter. The correlation increased considerably for 10‐day averages (r2 = 0.87). As the variations at lower heights have larger diurnal CO2 amplitudes, the concentration‐derived GPP is more accurate at lower heights. The footprint distance of CO2 concentration during the daytime under the influence of the mixed layer is estimated to be of the order of 103 km, or a footprint area of 103–104 km2, which is much larger than that of CO2 fluxes measured using eddy covariance methods (typically 1 km2). The difference in these footprint areas may partly explain the differences between these two flux estimates at the Wisconsin tower. These differences also signify the importance of retrieving flux information from the mixing ratio as it provides a means to upscale from local sites to a region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".