Estimating regional scale methane flux and budgets using CARVE aircraft measurements over Alaska
Bibliographic record
Abstract
Abstract. Methane (CH4) is the second most important greenhouse gas but its emissions from northern regions is still poorly constrained. In this study, we analyze a subset of in situ CH4 aircraft observations made over Alaska during the growing seasons of 2012–2014 as part of the Carbon in Arctic Reservoir Vulnerability Experiment (CARVE). Surface CH4 fluxes are estimated using an atmospheric particle transport model which quantitatively links surface emissions from Alaska and the western Yukon with observations of enhanced CH4 in the boundary layer. We estimate that between May and September, 2.1 ± 0.5 Tg, 1.7 ± 0.4 Tg and 2.0 ± 0.3 Tg of CH4 were emitted from the region of interest for 2012–2014, respectively. The predominant sources of the CH4 budget were two broadly classed eco-regions within our domain, with CH4 from the tundra region accounting for over half of the overall budget, despite only representing 18 % of the total surface area. Boreal regions, which cover a large part of the study region, accounted for the remainder of the emissions. Simple multiple linear regression analysis revealed that overall, CH4 flux were largely driven by soil temperature and elevation. In regions specifically dominated by wetlands, soil temperature and moisture at 10 cm depth were important explanatory variables while in regions that were not wetlands, soil temperature and moisture at 40 cm depth were more important, reflecting the depth at which methanogenesis occurs. Although similar variables have been found in the past to control CH4 emissions at local scales, this study shows that they can be used to generate a statistical model to estimate the regional scale CH4 budget.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".