Gas dynamics within landfast sea ice of an Arctic fjord (NE Greenland) during the spring–summer transition
Bibliographic record
Abstract
Sea ice is an active component of the Earth’s climate system, interacting with both the atmosphere and the ocean. Arctic sea ice is commonly covered by melt ponds during late spring and summer, strongly affecting sea ice physical and optical properties. How melt pond formation affects sea ice gas dynamics and exchanges between sea ice and the atmosphere, with potential feedbacks on climate, is not well known. Here we measured concentrations of N2, O2, and Ar, total alkalinity, and dissolved inorganic carbon within sea ice of Young Sound, NE Greenland, to examine how melt pond formation and meltwater drainage through the ice affect its physical properties and gas composition, including impacts on CO2 exchange with the atmosphere. Sea ice gas composition was controlled mainly by physical processes, with most of the gas initially in gaseous form in the upper ice layer. A minor contribution from biological processes was associated with positive estimates of net community production (up to 2.6 µmol Lice−1 d−1), indicating that the ice was net autotrophic. As the sea ice warmed, the upper ice gas concentrations decreased, suggesting a release of gas bubbles to the atmosphere. However, as melt ponds formed, the ice surface became strongly depleted in gases. Due to melt pond development, meltwater permeated through the ice, resulting in the formation of an underwater ice layer also depleted in gases. Sea ice, including brine, slush, and melt ponds, was undersaturated in CO2 compared to the atmosphere, supporting an uptake of up to −4.26 mmol m−2 d−1 of atmospheric CO2. As melt pond formation progressed, however, this uptake weakened in the strongly altered remaining ice surface (the “white ice”), averaging −0.04 mmol m−2 d−1. This study reveals the importance of melt pond formation and dynamics for sea ice gas composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".