Contribution of Greenland ice sheet melting to sea level rise during the last interglacial period: an approach combining ice sheet modelling and proxy data
Bibliographic record
Abstract
Abstract. In the context of global warming, the contribution of the two major ice sheets, Antarctica and Greenland, to global sea level rise is a subject of key importance for the scientific community (4th assessment report of the Intergovernmental Panel on climate change, IPCC-AR4, Meehl et al., 2007). By the end of the next century, a 3–5 °C warm up is expected in Greenland. Similar temperatures in this region were reached during the last interglacial (LIG) period due to a change in orbital configuration rather than to anthropogenic forcing. Ice core evidence suggests that the Greenland Ice Sheet (GIS) has survived this warm period but great uncertainties remain about the total Greenland ice reduction during the LIG and its sea level rise contribution. In order to improve our confidence in future state projections, we first intend to reconstruct the past states of the GIS using ice sheet modelling, and confront the simulations with paleo data. The chosen methodoly of paleoclimate reconstruction is strongly based on proxy data. Proxy data are also used to constrain the ice sheet model during the calibration phase. Our estimates of Greenland melting contribution to sea level rise during the LIG period range from 0.65 to 1.5 m of sea level equivalent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".