AIRBORNE OBSERVATIONS AND REGIONAL FLUX ESTIMATES OF GREENHOUSE GASES
Bibliographic record
Abstract
Methane is the second most important long-lived greenhouse gas. However, it is typically emitted to the atmosphere by spatially and temporally heterogeneous sources, meaning that local measurements cannot easily be extrapolated to represent global scales. As a consequence, its global sources and sinks are generally poorly quantified. This thesis focuses on the use of airborne observations to improve flux estimates of methane at regional scales. A commercially available cavity-enhanced absorption spectrometer has been modified here for airborne measurements of methane and carbon dioxide. An algorithm employing the system's simultaneous water vapour measurement has been derived, using laboratory experiments, to determine dry air mole fractions without the need for sample drying. The system was found to be relatively independent of the aircraft's motion and its measurements were found to be accurate to within 1.28 ppb (1 standard deviation repeatability at 1Hz of 2.48 ppb) for methane and 0.17 ppm (1 standard deviation repeatability at 1Hz of 0.66 ppm) for carbon dioxide.This new measurement capability has been deployed during three international field campaigns, data from which is used in this thesis. The composition of boreal biomass burning was measured in eastern Canada. Methane emission factors showed a high degree of variability (range 1.8 $\pm$ 0.2 to 8.5 $\pm$ 0.9 g (kg dry matter)$^{-1}$), accentuating the challenges with using a purely bottom-up approach to determine total methane emissions and that top-down constraints are needed. Two case studies have shown that an aircraft mass balance approach can be a valuable tool for deriving regional scale top-down flux estimates, when a suitable sampling strategy can be employed under appropriate atmospheric conditions. First, this technique was applied to the European Arctic wetlands; and second, its suitability to derive emissions from a megacity was investigated using London, UK as a test case. On both occasions, the derived fluxes were found to be in good agreement with coincident surface observations within the aircraft's sampling domain. In the case of the Arctic wetlands the excellent agreement with seasonally averaged surface observations allowed this information to be used for the evaluation of land surface models. Two commonly used models, the Joint UK Land Environment Simulator and Hybrid8 were found to underestimate the methane emission flux for this region by an order of magnitude, highlighting the large uncertainties present in future methane emission scenarios at regional scales under a changing climate.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".