Variability of Atmospheric CO2 Over the Arctic Ocean: Insights From the O-Buoy Chemical Observing Network
Bibliographic record
Abstract
As the Arctic climate rapidly warms, there is a critical need for understanding variability and change in the Arctic carbon cycle, but a lack of long-term observations has hindered progress. This work analyzes and interprets measurements of atmospheric carbon dioxide (CO2) mixing ratios from long-term on-ice measurements (the O-Buoy Network), as well as coastal observatories from 2009-2016. The on-ice measurements show smaller seasonal amplitudes when compared to the coastal observatories, in contrast to the general observation of poleward increases of seasonal cycle amplitude. Average on-ice mixing ratios were lower than their coastal counterparts during the winter and spring months, contradicting the expectation that wintertime presents a poleward increasing gradient of CO2. We compare the observations to CO2; simulated in an updated version of the GEOS-Chem 3-D chemical transport model, which includes new tracers of airmass history and CO2; sources and sinks. The model reproduces the observed features of the seasonal cycle and shows that terrestrial biosphere fluxes and synoptic transport explain most CO2; variability over the surface of the Arctic Ocean. Interannually, the coastal observations were more comparable in overall CO2; growth than concurrent measurements over sea ice. We find evidence indicating the presence of ocean gas exchange in and around sea ice during periods where this growth discrepancy occurs. Periods with large spatial gradients are examined, showing that release of CO2; from Arctic waters in years with low sea ice concentration could possibly contribute to the greater interannual increase of CO2; over sea ice compared to land.
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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 | 0.001 |
| 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 | 0.000 |
| 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".