Modelling large-scale ice-sheet–climate interactions following glacial inception
Bibliographic record
Abstract
Abstract. We have coupled the FAMOUS global AOGCM (atmosphere-ocean general circulation model) to the Glimmer thermomechanical ice-sheet model in order to study the development of ice-sheets in north-east America (Laurentia) and north-west Europe (Fennoscandia) following glacial inception. This first use of a coupled AOGCM–ice-sheet model for a study of change on long palæoclimate timescales is made possible by the low computational cost of FAMOUS, despite its inclusion of physical parameterisations similar in complexity to higher-resolution AOGCMs. With the orbital forcing of 115 ka BP, FAMOUS–Glimmer produces ice caps on the Canadian Arctic islands, on the north-west coast of Hudson Bay and in southern Scandinavia, which grow to occupy the Keewatin region of the Canadian mainland and all of Fennoscandia over 50 ka. Their growth is eventually halted by increasing coastal ice discharge. The expansion of the ice-sheets influences the regional climate, which becomes cooler, reducing the ablation, and ice accumulates in places that initially do not have positive surface mass balance. The results suggest the possibility that the glaciation of north-east America could have begun on the Canadian Arctic islands, producing a regional climate change that caused or enhanced the growth of ice on the mainland. The increase in albedo (due to snow and ice cover) is the dominant feedback on the area of the ice-sheets and acts rapidly, whereas the feedback of topography on SMB does not become significant for several centuries, but eventually has a large effect on the thickening of the ice-sheets. These two positive feedbacks are mutually reinforcing. In addition, the change in topography perturbs the tropospheric circulation, producing some reduction of cloud, and mitigating the local cooling along the margin of the Laurentide ice-sheet. Our experiments demonstrate the importance and complexity of the interactions between ice-sheets and local climate.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".