Addressing a sensible heat bias in open-path eddy covariance carbon dioxide flux measurements
Bibliographic record
Abstract
Open-path (OP) infrared gas analyzers (IRGA) are widely used for CO2 eddy covariance flux measurements in diverse ecosystems, including in arid desert environments. These high sensible heat and low CO2 flux conditions can lead to a systematic bias in the estimation of the carbon exchange. Numerous studies using both open- and closed-path IRGAs report large overestimates of CO2 uptake in the OP measurements, which persists for all seasons and is not driven by biological activity, but rather by instrumentation artefacts. Despite the attempts to address these biases, their origin and the appropriate correction approaches remain unresolved. Sensor-path heat exchange has been considered as a potential source of the bias. Consequently, later models OP gas analyzers have eliminated the self-heating effects, yet they still exhibit apparent CO2 uptake. In this study we consider the influence of ambient air temperature on the absorption in the CO2 spectral band typically used in non-dispersive broadband IRGAs as the source of the bias. We show the results from simulations of infrared transmission in the CO2 spectral band using high resolution molecular transmission (HITRAN) database. We evaluated the temperature sensitivity of an IRGA by simulating integrated absorption spectra for a typical interference optical filter with a 100 nm passband where the CO2 density was kept constant, and the gas mixture temperature was varied between 244 and 385 K. The data show that if the absorption is not corrected for temperature of the air in the optical sensing path a bias is introduced. The bias causes underestimation of CO2 density at warmer temperatures and overestimation of CO2 density at low temperatures. We conclude that OP gas analyzer measurements need to be corrected for the effects of changes in air temperature in the sensing path. We demonstrate that the correction is not universal, but rather instrument specific and depends on the actual pass band of the specific interference filter used.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".