Exploring Ice Sheet-Climate Feedbacks Across the Last Glacial Cycle: Insights from a Transient Sensitivity Ensemble 
Bibliographic record
Abstract
Ice sheet evolution profoundly influences the climate system through changes in orography, surface albedo, freshwater fluxes to the ocean, and ocean gateways. The changes to the climate system will, in turn, affect the ice sheets, leading to complex feedback loops. To date, the relative roles of these feedback loops have not been examined over a full glacial cycle. To address this, we employ the glacial earth system model of intermediate complexity LCice in transient simulations of the complete last glacial cycle. Through ensemble-based sensitivity experiments, we isolate the impact of individual ice sheet orography, albedo, meltwater input, Bering Strait opening/closure, and glacio-isostatic adjustment on the climate system and back onto the ice sheet evolution itself. To assess possible state dependencies, we compare the individual impact of ice-climate feedbacks on both the ice sheet growth and decay phase around MIS 5d (Last Glacial Inception) and MIS 2 (Last Glacial Maximum). The sensitivity of the North American and Eurasian ice sheets to some feedbacks changes from MIS 5d to MIS 2, suggesting a potential threshold behaviour and complex non-linear dynamics. Our analysis also examines which characteristics of last glacial cycle ice sheet evolution are relatively robust and which are more likely to be highly sensitive to incompletely resolved feedback loops. This work thereby not only improves our understanding of paleo ice/climate coupled dynamics but also identifies feedback pathways that are likely to generate the largest uncertainties in coupled ice and paleoclimate modelling.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".