A process-based analysis of Arctic Ocean warming in response to increasing CO2
Bibliographic record
Abstract
Abstract Using an ensemble of atmosphere–ocean general circulation models (AOGCMs) in an idealized climate change experiment, this study evaluates the contribution of different ocean processes to Arctic Ocean warming. On the AOGCM-mean, the Arctic Ocean warming is greater than the global ocean warming in the volume mean, and at most depths within the upper 2000~m. The Arctic warming is greatest a few 100~m below the surface and is dominated by the import of extra heat which is added to the ocean at lower latitude and is conveyed to the Arctic via the large-scale barotropic ocean circulation. The change in strength of this circulation in the North Atlantic is relatively small and not correlated with the Arctic Ocean warming. The Arctic Ocean warming is opposed and substantially mitigated by the weakening of the Atlantic meridional overturning circulation (AMOC), though the magnitude of this effect has a large model spread. By reducing the northward transport of heat, the AMOC weakening causes a redistribution of heat from high latitude to low latitude. Within the Arctic Ocean, the propagation of heat anomalies is influenced by broadening of cyclonic circulation in the east and weakening of anticyclonic circulation in the west. On the model-mean, the Arctic Ocean warming is most pronounced in the Eurasian Basin, with large spread across the AOGCMs, and accompanied by subsurface cooling by diapycnal mixing and heat redistribution by mesoscale eddies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".