Fluxes of Atmospheric Methane Using Novel Instruments, Field Measurements, and Inverse Modeling
Bibliographic record
Abstract
The atmospheric concentration of methane \\((CH_4)\\) - the most significant non-\\(CO_2\\) anthropogenic long-lived greenhouse gas - stabilized between 1999 and 2006 and then began to rise again. Explanations for this behavior differ but studies agree that more measurements and better modeling are needed to reliably explain the model-data discrepancies and predict future change. This dissertation focuses on measurements of \\(CH_4\\) and inverse modeling of atmospheric \\(CH_4\\) fluxes using field measurements at a variety of spatial scales. We first present a new fast-response instrument to measure the isotopic composition of \\(CH_4\\) in ambient air. The instrument was used to characterize mass fluxes and isofluxes (a isotopically-weighted mass flux) from a well-studied research fen in New Hampshire. Eddy-covariance and automatic chamber techniques produced consistent estimates of both the \\(CH_4\\) fluxes and their isotopic composition at sub-hourly resolution. We then characterize fluxes of \\(CH_4\\) from aircraft engines using measurements made with the same instrument during the Alternative Aviation Fuel Experiment (AAFEX), a study that aimed to determine the atmospheric impacts of alternative fuel use in the growing aviation industry. Emissions of \\(CO_2\\), \\(CH_4\\), and \\(N_2O\\) from different synthetic fuels were statistically indistinguishable from those of the widely used JP-8 jet fuel. We then present airborne observations of the long-lived greenhouse gas suite – \\(CO_2\\), \\(CH_4\\), \\(N_2O\\), and CO – during two aircraft campaigns, HIPPO and CalNex, made using a similar instrument built specifically for the NCAR HIAPER GV aircraft. These measurements are compared to data from other onboard sensors and show excellent agreement. We discuss the details of the end-to-end calibration procedures and the data quality-assurance and quality-control (QA/QC). Lastly, we quantify a top-down estimate of California’s \\(CH_4\\) emission inventory using the CalNex \\(CH_4\\) observations. Observed \\(CH_4\\) enhancements above background concentrations are simulated using a lagrangian transport model driven by validated meteorology. A priori source-specific emission inventories are optimized in a Bayesian inversion framework to show that California’s \\(CH_4\\) budget is 1.6 ± 0.34 times larger than the current estimate of California’s Air Resources Board (CARB), the body charged with enforcing the California Global Solutions Act and tracking emission changes over time. Findings highlight large underestimates of emissions from cattle and natural gas infrastructure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".