The Changing CO<sub>2</sub>Sink in the Western Arctic Ocean From 1994 to 2019
Bibliographic record
Abstract
Abstract The Arctic Ocean has turned from a perennial ice‐covered ocean into a seasonally ice‐free ocean in recent decades. Such a shift in the air‐ice‐sea interface has resulted in substantial changes in the Arctic carbon cycle and related biogeochemical processes. To quantitatively evaluate how the oceanic CO2sink responds to rapid sea ice loss and to provide a mechanistic explanation, here we examined the air‐sea CO2flux and the regional CO2sink in the western Arctic Ocean from 1994 to 2019 by two complementary approaches: observation‐based estimation and a data‐driven box model evaluation. ThepCO2observations and model results showed that summer CO2uptake significantly increased by about 1.4 ± 0.6 Tg C decade−1in the Chukchi Sea, primarily due to a longer ice‐free period, a larger open area, and an increased primary production. However, no statistically significant increase in CO2sink was found in the Canada Basin and the Beaufort Sea based on both observations and modeled results. The reduced sea ice coverage in summer in the Canada Basin and the enhanced wind speed in the Beaufort Sea potentially promoted CO2uptake, which was, however, counteracted by a rapidly decreased air‐seapCO2gradient therein. Therefore, the current and future Arctic Ocean CO2uptake trends cannot be sufficiently reflected by the air‐seapCO2gradient alone because of the sea ice variations and other environmental factors.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".