Late Pleistocene glacial terminations accelerated by proglacial lakes
Bibliographic record
Abstract
During the glacial cycles of the past 800 000 years, Eurasia and North America were periodically covered by large ice sheets, causing up to 100 m of sea-level change. While Late Pleistocene glacial cycles typically lasted 80 000–120 000 years, the termination phases were completed in only 10 000 years. During these glacial terminations, the North American and Eurasian ice sheets retreated, which created large proglacial lakes in front of the ice-sheet margin. Proglacial lakes accelerate deglaciation as they facilitate the formation of ice shelves at the southern margins of the North American and Eurasian ice sheets. These ice shelves are characterized by basal melting, low surface elevations, and negligible friction at the base. Here, we use an ice-sheet model to quantify the (combined) effects of proglacial lakes on Late Pleistocene glacial terminations by examining their interplay with glacial isostatic adjustment (GIA) and basal sliding. We find that proglacial lakes accelerate the deglaciation of ice sheets mainly because there is an absence of basal friction underneath ice shelves. If friction underneath grounded ice is applied to floating ice, full deglaciation is postponed by a few millennia, resulting in more ice remaining during interglacial periods and no extensive ice shelves forming. Additionally, the large uncertainty in melt rates underneath lacustrine ice shelves translates to an uncertainty in the timing of the termination of up to a millennium. Proglacial lakes are created by depressions in the landscape that remain after an ice sheet has retreated. The depth, size, and timing of proglacial lakes depend on the rate of bedrock rebound. We find that if bedrock rebounds within a few centuries (rather than a few millennia), the mass loss rate of the ice sheet is substantially reduced. This is because fast bedrock rebound prevents the formation of extensive proglacial lakes. Additionally, a decrease in ice thickness is partly compensated for by faster bedrock rebound, resulting in a higher surface elevation; lower temperatures; and a higher surface mass balance, which delays deglaciation. We find that a very long bedrock relaxation time does not substantially affect terminations, but it may lead to a delayed onset of the next glacial period. This is because inception regions, such as northwestern Canada, remain below sea level throughout the preceding interglacial period.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".