McGill paleoclimate model ice sheet sensitiivity to ice flow rate and discharge parameters
Bibliographic record
Abstract
Sensitivity studies of the ice sheet model and forcing in the McGill Paleoclimate Model (MPM) are presented. The MPM is a five component (atmosphere, ocean, sea ice, land surface, ice sheet) sectorially averaged Earth system Model of Intermediate Complexity (EMIC). The ice flow rate factor is found to have a large effect on both the ice volume and the extent of the southern margin. Values corresponding to equivalent temperatures of -7°C and -4°C are most appropriate for the initiation of glaciation (formation of Laurentide and Fennoscandian ice sheets) during the last glacial period. The formulation for the lateral ice discharge is improved to allow for a more realistic description of the east-west length scale of the ice sheets. Significant increases in both the ice volume and growth rate are obtained. To improve the ice sheet-atmosphere coupling in the MPM, a high resolution nested land surface component is introduced. By calculating the land surface processes on the higher resolution of the ice sheet component, a more accurate representation of the dependence of the surface air temperature on the surface elevation of the ice sheet can be included. This removes grid related artifacts from the equilibrium ice sheets determined by the model. It is shown that this high resolution nested component does not significantly affect the growth rate of the ice sheets during the initiation phase of glaciation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".