Variability of Atmospheric CO <sub>2</sub> Over the Arctic Ocean: Insights From the O‐Buoy Chemical Observing Network
Bibliographic record
Abstract
Abstract As the Arctic climate rapidly warms, there is a critical need for understanding variability and change in the Arctic carbon cycle, but sparse spatial coverage of observations has hindered progress. This work analyzes measurements of atmospheric CO 2 in the Arctic from long‐term on‐ice measurements (the O‐Buoy Network), as well as coastal observatories from 2009 to 2016. The on‐ice measurements showed smaller seasonal amplitudes than coastal observatories, in contrast to the general observation of poleward increases of seasonal cycle amplitude. Average on‐ice measurements were also lower than their coastal counterparts during winter and spring, contradicting the expectation that CO 2 increases poleward in boreal winter. We compared the observations to CO 2 simulated in an updated version of GEOS‐Chem 3‐D chemical transport model, which includes new tracers of airmass history and CO 2 sources and sinks. The model reproduced the observed features of the seasonal cycle and showed that terrestrial biosphere fluxes and synoptic transport explain most CO 2 variability (both synoptic and interannual) over the Arctic Ocean surface. The polar airmass partially isolates the Arctic Ocean surface air from terrestrial CO 2 exchange, which explains the reduced seasonal cycle amplitude and winter maxima. All Arctic coastal sites had similar CO 2 interannual variability, particularly in summer, which was largely reproduced by the model. The interannual variability observed over sea ice, however, was distinct from the coastal sites and not reproduced by the model. Air‐sea CO 2 exchange in and around sea ice, which was once thought to be negligible, may be an important driver of interannual variability over the Arctic Ocean.
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 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 | 0.000 | 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".