Disentangling the Coupled Atmosphere‐Ocean‐Ice Interactions Driving Arctic Sea Ice Response to CO<sub>2</sub> Increases
Bibliographic record
Abstract
Abstract A novel decomposition of the ocean heat energy that contributes to sea ice melt and growth (ocean‐ice and frazil heat) into components that are driven by surface heat flux and ocean circulation changes is used to isolate the evolving roles of the atmosphere and ocean in the Arctic sea ice loss from CO2 increases. A sea ice volume budget analysis is used to separate the impacts of the anomalous frazil/ocean‐ice heat from those of atmosphere‐ice heat on the evolving Arctic sea ice volume. The role of atmosphere‐ocean coupling in augmenting or curtailing the atmosphere‐ and ocean‐driven sea ice losses is further isolated by comparing the ice volume budget and the anomalous frazil/ocean‐ice heat components in partially and fully coupled experiments. Atmosphere‐ice heat fluxes drive most of Arctic sea ice loss in the first decade following CO2 increase by increasing the sea ice top face melt in summer, while ocean circulation changes drive the loss over the longer term through the anomalous increase of heat transport into the Arctic, which drive decreases in frazil ice growth and sea ice extent in winter. Atmosphere‐ocean coupling in the subpolar Atlantic further supports a negative feedback that attenuates the ocean‐driven sea ice losses over time; by accelerating the weakening of the Atlantic meridional overturning circulation, it causes a large cooling of the subpolar Atlantic and attenuation of the anomalous heat transport into the Arctic in winter, allowing for a seasonal Arctic sea ice in the fully coupled experiment, while the Arctic completely becomes ice free in the partially coupled experiment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".