Modelling Northern Hemisphere ice sheets distribution during MIS5 and MIS7 glacial inceptions
Bibliographic record
Abstract
Abstract. The present manuscript compares the last two glacial inceptions, Marine Isotope Stage 5 (MIS5, 125–115 kyr BP) and MIS7 (236–229 kyr BP) with the aim to detect the relative impact of external forcing (orbitals and GHG) and ice-albedo feedbacks on the ice sheets growth and distribution in the Northern Hemisphere high latitudes. In order to investigate the differences between those two states, we combine atmosphere-ocean coupled model experiments and off-line ice-sheet-model simulations. In particular, we use a low resolution coupled Atmosphere-Ocean-Sea-ice general circulation model to simulate the mean climate of the four time periods associated with the inception states of MIS5 and MIS7 (i.e. 236, 229, 125 and 115 kyr BP). The four mean states are then use to force a 3-D thermodynamical ice sheet model by means of two types of ice sheet experiments, i.e., steady-state and transient experiments. Our results show that steady-state ice experiments underestimate the ice volume at both 229 kyr BP and 115 kyr BP. On the other hand, the simulated pre-inception ice distributions at 236 kyr BP and 125 kyr BP are in good agreement with observations indicating that during these periods feedbacks associated with external forcing dominate over other processes. However, if proper ice-elevation and albedo feedbacks are not taken into consideration, the evolution towards glacial inception in terms of ice volume and extent is hardly simulated. The experimental setup chosen allows us to conclude that, depending on the mean background climate state, the effect of model biases on climate are more important during a cold inception, such as MIS7, than during a warm inception, such as MIS5. The last results suggest to be cautious when tuning and calibrating Earth System Models on a specific time period, mainly for the purpose of ice sheet-climate coupling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".