New Insight Into Relative Sea Level Change and Its Constraints on Mantle Viscosity and Deglaciation History Since the Last Glacial Maximum
Bibliographic record
Abstract
Studies of glacial isostatic adjustment (GIA), the viscoelastic relaxation of the Earth's mantle stress induced by deglaciation following the last glacial maximum (LGM), have provide important constraints on Late Pleistocene deglaciation history and the viscoelastic structure of the Earth's mantle. Most GIA models assume a Newtonian viscosity in the mantle, but laboratory studies of rock deformation, observational studies of seismic anisotropy, and modeling studies of mantle dynamics show that the upper mantle viscosity is non-Newtonian and stress-dependent. With 3D finite element numerical modelling study, here we demonstrate that the mantle stress beneath glaciated regions increases significantly during rapid deglaciation around 15,000 years ago, leading to regionally reduced upper mantle viscosity by more than an order of magnitude, while the lithospheric stress keeps decreasing with time as ice sheets disappear. As the deglaciation slows down and especially after ice sheets in North America and Fennoscandia disappear, mantle stress decreases and upper mantle viscosity increases. This causes mantle viscosity to be time dependent. The predicted relative sea level (RSL) changes from non-Newtonian models have more rapid sea-level falls associated with the rapid deglaciation followed by a more gradual sea-level variation. This distinct feature may provide a diagnosis for distinguishing non-Newtonian and Newtonian rheology. RSL observations have been used to help construct deglaciation history since the LGM (i.e., ice model), together with observations of glacial isochrons. However, due to use of different RSL datasets and other assumptions, two widely used ice models, ICE-6G and ANU, differ significantly, suggesting the uncertainties in ice models. While ICE-6G shows >1 km thicker ice in the western Canada than the ANU ice model, the latter has > 1 km thicker ice in the eastern Canada. Approximating mantle viscosity structure as 1-D and two layers of viscosity (i.e., upper and lower mantles), for each of these two ice models and each of the six different RSL datasets published by different research groups, the GIA modeling indicates that the preferred mantle viscosity has significantly higher viscosity in the lower mantle. The spatial and temporal distributions of misfits to RSL data show that ICE-6G has significantly larger misfits to the farfield RSL data between 14,000 and 9,000 years ago and to RSL data in the eastern (e.g., St. Lawrence River) and northern Canada, compared with the ANU ice model. The misfit patterns provide guidance on revising ICE-6G to improve the fit to RSL data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".