Low Antarctic continental climate sensitivity due to high ice sheet orography
Bibliographic record
Abstract
Abstract The Antarctic continent has not warmed in the last seven decades, despite a monotonic increase in the atmospheric concentration of greenhouse gases. In this paper, we investigate whether the high orography of the Antarctic ice sheet (AIS) has helped delay warming over the continent. To that end, we contrast the Antarctic climate response to CO2-doubling with present-day orography to the response with a flattened AIS. To corroborate our findings, we perform this exercise with two different climate models. We find that, with a flattened AIS, CO2-doubling induces more latent heat transport toward the Antarctic continent, greater moisture convergence over the continent and, as a result, more surface-amplified condensational heating. Greater moisture convergence over the continent is made possible by flattening of moist isentropic surfaces, which decreases humidity gradients along the trajectories on which extratropical poleward moisture transport predominantly occurs, thereby enabling more moisture to reach the pole. Furthermore, the polar meridional cell disappears when the AIS is flattened, permitting greater CO2-forced warm temperature advection toward the Antarctic continent. Our results suggest that the high elevation of the present AIS plays a significant role in decreasing the susceptibility of the Antarctic continent to CO2-forced warming.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".