Frozen ponds: production and storage of methane during the Arctic winter in a lowland tundra landscape in northern Siberia, Lena River delta
Bibliographic record
Abstract
Abstract. Lakes and ponds play a key role in the carbon cycle of permafrost ecosystems, where they are considered to be hotspots of carbon dioxide CO2 and methane CH4 emission. The strength of these emissions is, however, controlled by a variety of physical and biogeochemical processes whose responses to a warming climate are complex and only poorly understood. Small waterbodies have been attracting an increasing amount of attention since recent studies demonstrated that ponds can make a significant contribution to the CO2 and CH4emissions of tundra ecosystems. Waterbodies also have a marked effect on the thermal state of the surrounding permafrost; during the freezing period they prolong the period of time during which thawed soil material is available for microbial decomposition. This study presents net CH4 production rates during the freezing period from ponds within a typical lowland tundra landscape in northern Siberia. Rate estimations were based on CH4 concentrations measured in surface lake ice from a variety of waterbody types. Vertical profiles along ice blocks showed an exponential increase in CH4 concentration with depth. These CH4 profiles were reproduced by a 1-D mass balance model and the net CH4 production rates were then inferred through inverse modeling. Results revealed marked differences in early winter net CH4 production among various ponds. Ponds situated within intact polygonal ground structures yielded low net production rates, of the order of 10-11 to 10-10 mol m-2 s-1 (0.01 to 0.14 mgCH4 m-2 day-1). In contrast, ponds exhibiting clear signs of erosion yielded net CH4 production rates of the order of 10-7 mol m-2 s-1 (140 mg CH4 m-2 day-1). Our results therefore indicate that once a particular threshold in thermal erosion has been crossed, ponds can develop into major CH4 sources. This implies that any future warming of the climate may result in nonlinear CH4 emission behavior in tundra ecosystems.
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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 | 0.001 | 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 | 0.000 |
| 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 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".