Investigation of CH4 emissions from boreal wetlands by remote sensing during the airborne CoMet 2.0 campaign
Bibliographic record
Abstract
Methane (CH4) is a potent greenhouse gas, and its atmospheric concentration has been rising due to emissions from both anthropogenic and natural sources. Recognizing its significant impact on climate change, several international treaties, such as the Global Methane Pledge, have identified CH4 reduction as a key mitigation target. Accurately quantifying its emissions is essential for understanding their respective contributions to climate change and improving global CH4 budgets. In particular, natural sources such as wetlands play a crucial role but remain highly uncertain, making it difficult to predict future CH4 dynamics. Wetlands are recognised as the largest natural CH4 source. Nevertheless, they also represent the most significant source of uncertainty in CH4 emissions estimates. In August/September 2022, the German Aerospace Center conducted an airborne campaign over Canada. The objective of the mission was to measure the weighted column-integrated dry-air molar mixing ratios of CH4. These were measured below the aircraft along its flight track over boreal wetlands. The measurements were taken using an IPDA lidar, which was mounted on board the research aircraft. Several flights over the Hudson Bay Lowlands, which is one of the largest Arctic-boreal wetlands in the world with significant contribution to the global CH4 budget were conducted as part of the campaign. The objective of this study was to quantify regional and large scale (between 36 km and 889 km) gradients in CH4 concentration using lidar. These gradients are used in a mass balance approach to calculate CH4 fluxes. In this approach, a designated upwind and downwind region is established within the area of interest. Inside these regions, the difference between the inflow and outflow of methane concentration is measured to assess the increase in methane concentration. As this method requires, next to the methane concentration, also information about the transport, wind information is taken from a numerical weather prediction model. Data from three individual flights were analysed in detail. Not all of the three flights show a general accumulation of CH4 along the wind direction. Furthermore, the circumstances for calculating emissions vary between the three flights. The flights differ in their flight pattern, the wind speed, the homogeneity of the wind, and the CH4 distribution. Nevertheless, it was possible to calculate fluxes that differed for the various flights. Backward trajectories from HYSPLIT showed the history of air parcels along the wind. In order to explain the results, the backward trajectories and emission estimates from WetCHARTs are used. Overall this study showed that a maximum gradient of up to 11 ppb (across a distance of approximately 600 km) could be observed, representing a relative increase of 0.55%. The mass balance approach typically works best for ideal conditions: well mixed boundary layer, homogeneous wind perpendicular to upwind and downwind region, uniform methane source distribution and therefore lower upwind concentration than downwind concentrations.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".