Local sea level changes due to Greenland ice sheet mass changes from 1970 to 2
Bibliographic record
Abstract
The Greenland ice sheet is melting at an increasing rate and is predicted to have a large contribution to sea level change by 2100. Future climate over Greenland, which determines the ice sheet’s surface melt and marine-terminating glacier retreat, represents a major source of uncertainty for Greenland ice sheet evolution (ISMIP6). In this study, we explore the Greenland ice sheet contribution to sea level change from 1960 to 2100 and quantify how uncertainties in projected climate change and Earth rheological structure shape global and local sea level changes and their spatio-temporal variability.Ice load history is provided by simulations following the ISMIP6 protocol. To project regional sea level changes, we employ two different gravitationally self-consistent sea level models. We use the pseudo-spectral sea level model described in Gomez et al. (2010). To test the sensitivity of projections to surface resolution and Earth structure, the experiments are repeated with the finite volume sea level model SEAKON (Latychev 2005) that includes 3D variations in Earth structure and grid refinement capabilities to reach ~5 km surface resolution over Greenland.Results highlight the spatial variability of projected sea level for communities along the Greenlandic coastline, and contrast local changes to farfield sea level rise for Pacific Islands. With a spread of -1.00m to -2.96m sea level change by 2100 around Ilulissat, West Greenland, our results are up to three times the value provided by the NASA IPCC sea level tool (-0.8m) and emphasize the need for more studies addressing local sea level changes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".