Drivers of diffusive CH <sub>4</sub> emissions from shallow subarctic lakes on daily to multi-year timescales
Bibliographic record
Abstract
Abstract. Lakes and reservoirs contribute to regional carbon budgets via significant emissions of climate forcing trace gases. Here, for improved modelling, we use 8 years of floating chamber measurements from three small, shallow subarctic lakes (2010–2017, n=1306) to separate the contribution of physical and biogeochemical processes to the turbulence-driven, diffusion-limited flux of methane (CH4) on daily to multi-year timescales. Correlative data include surface water concentration measurements (2009–2017, n=606), total water column storage (2010–2017, n=237), and in situ meteorological observations. We used the last to compute near-surface turbulence based on similarity scaling and then applied the surface renewal model to compute gas transfer velocities. Chamber fluxes averaged 6.9±0.3 mg CH4 m−2 d−1 and gas transfer velocities (k600) averaged 4.0±0.1 cm h−1. Chamber-derived gas transfer velocities tracked the power-law wind speed relation of the model. Coefficients for the model and dissipation rates depended on shear production of turbulence, atmospheric stability, and exposure to wind. Fluxes increased with wind speed until daily average values exceeded 6.5 m s−1, at which point emissions were suppressed due to rapid water column degassing reducing the water–air concentration gradient. Arrhenius-type temperature functions of the CH4 flux (Ea′=0.90±0.14 eV) were robust (R2≥0.93, p<0.01) and also applied to the surface CH4 concentration (Ea′=0.88±0.09 eV). These results imply that emissions were strongly coupled to production and supply to the water column. Spectral analysis indicated that on timescales shorter than a month, emissions were driven by wind shear whereas on longer timescales variations in water temperature governed the flux. Long-term monitoring efforts are essential to identify distinct functional relations that govern flux variability on timescales of weather and climate change.
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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 | 0.001 |
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".