Impact of ocean heat transport on the natural and forced variability of Arctic sea-ice in the GFDL CM2-O model suite
Bibliographic record
Abstract
A recent study links an increase in the horizontal resolution of ocean models to improved representations of Arctic sea-ice and Ocean Heat Transport (OHT, Docquier et al. 2019). Here, the impact of horizontal resolution on meridional OHT and sea-ice is investigated over a broader range of resolutions, using the GFDL CM2-O climate model suite (1.0°, 0.25°, and 0.1°) in both preindustrial control and climate change simulations. Results show a direct link between OHT and sea-ice extent (SIE) in the Arctic. This link, however, is not monotonic with spatial resolution. While OHT increases and SIE decreases from the Low to the Medium resolution models, the reverse is true from the Medium to the High resolution models. Differences in OHT and SIE between the three models mostly arise from the preindustrial state. As the spatial resolution increases, the Irminger Current is favored at the expense of the North Atlantic Drift. This rerouting of water to the Western side of Greenland results in less heat delivered to the Arctic in the High resolution model than in its Medium counterpart. As a result, the Medium resolution model is in best agreement with observed SIE and Atlantic OHT. Concurrent with the change in the partitioning in volume is a change in deep convection centers from the Greenland-Irminger-Norwegian Seas in the Low resolution model to the Labrador Sea in the High resolution model. Results suggest a coupling between OHT into the Arctic and deep convection in the North Atlantic.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".