A new glacial isostatic adjustment model for Antarctica: calibrated and tested using observations of relative sea-level change and present-day uplift rates
Bibliographic record
Abstract
We present a glacial isostatic adjustment (GIA) model for Antarctica. This is driven by a new deglaciation history that has been developed using a numerical ice-sheet model, and is constrained to fit observations of past ice extent. We test the sensitivity of the GIA model to uncertainties in the deglaciation history, and seek earth model parameters that minimize the misfit of model predictions to relative sea-level observations from Antarctica. We find that the relative sea-level predictions are fairly insensitive to changes in lithospheric thickness and lower mantle viscosity, but show high sensitivity to changes in upper mantle viscosity and constrain this value (95 per cent confidence) to lie in the range 0.8–2.0 × 1021 Pa s. Significant misfits at several sites may be due to errors in the deglaciation history, or unmodelled effects of lateral variations in Earth structure. When we compare our GIA model predictions with elastic-corrected GPS uplift rates we find that the predicted rates are biased high (weighted mean bias = 1.8 mm yr−1) and there is a weighted root-mean-square (WRMS) error of 2.9 mm yr−1. In particular, our model systematically over-predicts uplift rates in the Antarctica Peninsula, and we attempt to address this by adjusting the Late Holocene loading history in this region, within the bounds of uncertainty of the deglaciation model. Using this adjusted model the weighted mean bias improves from 1.8 to 1.2 mm yr−1, and the WRMS error is reduced to 2.3 mm yr−1, compared with 4.9 mm yr−1 for ICE-5G v1.2 and 5.0 mm yr−1 for IJ05. Finally, we place spatially variable error bars on our GIA uplift rate predictions, taking into account uncertainties in both the deglaciation history and modelled Earth viscosity structure. This work provides a new GIA correction for the GRACE data in Antarctica, thus permitting more accurate constraints to be placed on current ice-mass change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".