Snow and Sea Ice Melt Enhance Under-Ice pCO2 Undersaturation in Arctic Waters
Bibliographic record
Abstract
The decline in Arctic summer sea ice alters air–sea gas exchange. Because the Arctic Ocean accounts for 5%–14% of global oceanic carbon uptake, understanding how sea ice melt impacts the ocean’s carbon sink capacity is central to constraining future fluxes. In this study, we focus on Young Sound-Tyrolerfjord in Northeast Greenland to examine the sea ice−ocean interaction during the transition from melt onset to melt pond drainage. High-frequency measurements of partial pressure of CO2 (pCO2) and seawater physical properties were taken 2.5 m below the sea ice. Our results reveal that pCO2 in the seawater was undersaturated (248–354 μatm) compared to the atmosphere (401 μatm), showing that the seawater has the potential to take up atmospheric CO2 as the sea ice breaks up. The pCO2 undersaturation was attributed to dilution resulting from mixing meltwater from snow and sea ice with the under-ice seawater. Additionally, the drainage of melt pond water that had been in contact with the atmosphere into the under-ice seawater further lowered pCO2. Melt pond drainage represents an initial connection between the atmosphere and under-ice seawater through meter-thick sea ice during the summer thaw. Our study demonstrates that snow and sea ice melt reduce pCO2 in under-ice seawater, enhancing its potential for atmospheric CO2 uptake during sea ice breakup.
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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 | 0.000 |
| 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 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".