Antarctic Sea Ice Trends: Insights from a Suite of Climate Models
Bibliographic record
Abstract
Background: Antarctic sea ice concentration has been observed to increase from 1978 to 2015, in contrast with the decrease that most climate models show. Here, we aim to examine the respective roles of natural variability and anthropogenic forcing in shaping Antarctic sea ice trend. Method: To do so, we use the GFDL-CM2 coupled climate model with varying horizontal resolutions in the ocean (1°, 0.25° and 0.10°) that displays a range of behaviours in natural variability with the representation of Weddell Sea polynyas, and different intensities in the decrease of sea ice under climate change. Results: In the 0.10° model, a sea ice trend of similar sign and magnitude to that observed over the satellite record is found between two occurrences of the Weddell Sea polynya. In the 1° and 0.25° models, which do not simulate any polynya, no equivalent trend of what the satellite record shows is found. Under increasing CO2 forcing, all models show a surface cooling on a short time scale (years) south of 50°S, followed by a warming on a longer time scale (decades), consistent with the delayed warming mechanism of Ferreira et al. (2015). Of all models, the higher resolution model shows the strongest surface warming and decrease in sea ice, suggesting an important role for mesoscale eddies in the response of Antarctic sea ice to climate change. Conclusion: We conclude that the Weddell Sea polynya is key to the representation of the sea ice trend and that the disagreement between models and observations might partly arise from a desynchronization of the polynya cycles or a too weak natural variability of sea ice in models compared to observations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".