Estimating local atmosphere-surface fluxes using eddy covariance and numerical Ogive optimization
Bibliographic record
Abstract
Abstract. Regardless of study type, site topography, homogeneity and large-scale meteorological flows, estimating ecosystem-scale surface-fluxes using the micrometeorological eddy covariance method inevitably leads to questions concerning inclusion or exclusion of advective flux contributions, in providing representative results. For process-oriented studies in which fluxes are linked to local physical parameters and up-scaled through numerical modeling efforts, advection represents a site-specific component which interferes with our ability to isolate local biochemical processes of interest, as represented by turbulent fluxes. Yet, outside of discarding data reflecting excessive advective interference, no method currently exists to disentangle these contributions on flux estimates. Here, we present a novel comprehensive numerical scheme to identify and separate out advective contributions to exchanges in the surface layer. Comparison between the presented method and conventional methodology on observations of sensible heat, latent heat and CO2-fluxes from a number of sites suggests the presence of absolute flux thresholds at |QSENS|=30 Wm−2, |QLAT|=16 Wm−2 and |FCO2|=2.0 μmol m−2 s−1 marking clear shifts in the influence of advection. Above the thresholds, the relative difference of flux estimates δ remained fixed at δ =5–25% suggesting arguably negligible advection influence. Below the thresholds, however, relative difference rises to δSENS=⟨ 51%|88%|225%⟩, δLAT=⟨14%|28%|99%⟩ and δCO2=⟨ 41%|83%|521%⟩, where bracketed values are the 13.6th percentile, 50th percentile (the median) and the 86.4th percentile respectively, suggesting non-negligible relative influence of advection on low flux estimates. The thresholds thus serve as lower limits to local-scale flux resolvability by conventional methodology. The presented method is shown to allow for flux estimation during severe signal disruption and to yield fewer estimates for an enclosed gas analyzer during low-flux conditions suggesting the presence of a lower detection limit with this particular instrument setup, as well as a superiority of open path gas analyzers, in low-flux environments. Overall the notion of a dynamic and generally non-negligible overlap of advective and turbulent frequency-wise flux contributions is confirmed, suggesting the inevitable indiscriminate inclusion/exclusion of both when setting a fixed averaging time according to conventional methodology, leading ultimately to misrepresentation of actual local fluxes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".