Modelling sea-level reconstructions from southern Greenland: Implications for glacially-induced faulting and the response of the ice sheet to the Younger Dryas cold interval
Bibliographic record
Abstract
Understanding the past evolution of the Greenland ice sheet (GrIS) is important for accurately simulating its future behavior and thus its contribution to global mean sea level rise. Data and models related to glacial isostatic adjustment (GIA) have provided critical constraints on past GrIS evolution. These models are necessary to interpret a variety of data, including past sea-level changes and geodetic observations of current land motion and gravity changes. In all studies to date, paleo sea level data from southern Greenland have presented the greatest challenge to GIA models. Poor data-model fits in this region have led to the hypothesis of glacially-induced faulting during periods of rapid ice loss (with associated tsunami hazard).In this study, we seek to determine if quality fits to the southern Greenland relative sea level (RSL) data can be obtained by improving the GIA model and exploring the parameter space more fully than past efforts. Specifically, we consider two recent advancements in model development: new 3-D models of earth viscosity structure based on the joint inversion of regional geophysical datasets, and GrIS reconstructions output from a leading glacial systems model. The improved 3-D earth models result in a larger RSL fall compared to past 1-D earth modelling and so that amplitude of the measured signal can be accurately simulated at most sites in southern Greenland. However, the rate and timing of RSL fall are generally too late and too slow to match many of the mid-Holocene sea-level index points. We seek to improve this aspect of the model fits by varying the ice history model. A two-step approach is used: (1) manually adjust the timing and rate of ice retreat in a chosen model to identify if plausible variations in these aspects can capture RSL data, and (2) assuming (1) is satisfied, seek to produce a glaciologically consistent ice history by varying parameters within the glacial systems model (e.g., climate forcing). In this presentation, we will provide an update on the status of our sensitivity analysis and the implications for glacially-induced faulting and the ice sheet response to the Younger Dryas cold interval.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".