Sea–air methane flux estimates derived from marine surface observations and instantaneous atmospheric measurements in the northern Labrador Sea and Baffin Bay
Bibliographic record
Abstract
Vast amounts of methane (CH 4 ) stored in submarine sediments are susceptible to release in a warming Arctic, further exacerbating climate change in a positive feedback. It is therefore critical to monitor CH 4 over pan-regional scales to detect early signs of CH 4 release. However, our ability to monitor CH 4 is hampered in remote northern regions by sampling and logistical constraints, and few good baseline data exist in many areas. From high-resolution atmospheric CH 4 measurements and discrete surface water samples, we estimated instantaneous sea–air CH 4 fluxes at various locations. We also created a baseline study of current background levels of CH 4 in North Atlantic waters based on the atmospheric CH 4 data over 22 d in summer 2021 on a roughly 5100 km voyage in the northern Labrador Sea and Baffin Bay between 55 and 72 ∘ N. In addition, we measured CH 4 concentrations across the water column at various stations. Measured atmospheric mixing ratios of CH 4 ranged from 1944 to 2012 ppbv, with a mean of 1966 ± 8 ppbv and a baseline of 1954–1981 ppbv. Dissolved CH 4 concentrations in the near-surface water peaked at 5.3 nmol L −1 within 1 km down-current of a known cold seep at Scott Inlet and were consistently oversaturated throughout the water column in Southwind Fjord, which is an area that has been recently affected by submarine landslides. Local sea–air CH 4 fluxes ranged from 0.003–0.119 µmol m −2 d −1 , indicating that the ocean released only small amounts of CH 4 to the atmosphere at all stations. Atmospheric CH 4 levels were also driven by meteorological, spatial, and temporal variations, and both onshore and ocean-based contributions to atmospheric CH 4 mixing ratios are likely. Coupled high-resolution measurements of marine and atmospheric CH 4 data have the potential to provide ongoing monitoring in a region susceptible to CH 4 releases, as well as critical validation data for global-scale measurements and modelling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".