Northern Hemisphere sea ice variability in a transient CGCM simulation of the past 2.6 Ma
Bibliographic record
Abstract
The recent sea ice changes in the Northern Hemisphere (NH), necessitate elucidating the sea ice variability over the past 2.6 million years (Ma), when the Earth’s glacial cycles transitioned from ∼41 to ∼100 kyr periodicity, following the Mid-Pleistocene Transition (MPT) period (0.7–1.2 Ma). Here, we analyze a coupled general circulation model (CGCM) simulation to understand how the NH sea ice responds to changes in the transient orbital, greenhouse gas (GHG), and ice-sheet forcings. We find that the Earth’s axial tilt (obliquity) and axial wobble (precession) strongly influence the variability in high-latitude (>70° N) perennial sea ice and mid-latitude (35° N–70° N) seasonal sea ice, respectively, by modifying the net surface shortwave radiation. Meanwhile, the GHG forcing affects the glacial–interglacial sea ice predominantly in the Labrador Sea, Irminger–Iceland basin sector, and Central North Pacific regions during the MPT and post-MPT (0.0–0.7 Ma) periods by modulating the downwelling longwave radiation. Additionally, we confirm that variability with longer periodicity (∼100 kyr) from GHG and ice-sheet forcings is most pronounced in NH sea ice during the MPT and post-MPT periods. For the past 2.6 million years, Earth’s axial tilt and wobble have influenced sea ice in the high-latitudes (>70° N) and mid-latitudes (35–70° N), respectively, while greenhouse gases mostly influenced sea ice in North Atlantic deep convection regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".