Examining Possible Retreat Scenarios for the Greenland Ice Sheet during the MIS-11c Interglacial
Bibliographic record
Abstract
The interglacial period spanning ca. 423 to 398 ka and known as Marine Isotope Stage (MIS) 11c has been the subject of much study, due largely to the unique evolution of global temperatures, greenhouse gas levels, and sea levels relative to other interglacials of the late Pleistocene. Particularly concerning is some geological evidence and prior modeling studies which have suggested that a large majority of the Greenland ice sheet (GrIS) disappeared during this period, despite global mean air temperatures only modestly higher than those of the preindustrial period. However, uncertainty is high as to the extent and spatiotemporal evolution of this melt due to a dearth of direct geological constraints. Our study therefore endeavors to better constrain these large uncertainties by using spatiotemporally interpolated climate forcing from CESM v1.2 time slice simulations and an ensemble of ice sheet model parameter vectors derived from a GrIS history matching over the most recent glacial cycle from the Glacial Systems Model (GSM). The use of different ice sheet initialization states from simulations of the previous glacial-interglacial transition helps to capture the large initial condition uncertainty. Two different regional present-day climate modeling datasets are utilized for anomaly correction of CESM precipitation and temperature fields. Preliminary analysis indicates that the most robust retreat across most ensemble members happens in the northern, western, and central portions of the ice sheet, while the higher terrain of the south and east retain substantial amounts of ice. This is broadly consistent with indications that ice may have survived the MIS-11c interglacial at the Summit ice core location, but not at DYE-3. Simulations indicate a maximum MIS-11c sea level contribution from the GrIS centered between 408 and 403 ka, with minimum GrIS volumes reaching between 25% and 70% of modern-day values. In part due to the prior constraint of ice-sheet model ensemble parameters from history matching, ensemble parameters controlling downscaling and climate forcing bias correction are the largest parametric sources of output variance in our simulations. Though CESM uncertainties are unassessed in this study, it is likely they dominate given that the choice of present-day reference temperature climatology for anomaly correction of the climate model output has the largest effect on the GrIS melt response in our simulations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".