A history-matching analysis of Antarctic Ice Sheet evolution since the last interglacial
Bibliographic record
Abstract
One technique to explicitly quantify uncertainties of glacial systems is a history-matching analysis (HMA) of a model against a large observational database. This is achieved by ruling out simulations that are inconsistent with an observational constraint database. A comprehensive database (“AntICE2”) was compiled for state-space estimation of past Antarctic Ice Sheet (AIS) changes and to evaluate model reconstructions. This research applies a HMA on a 3D glacial systems model (GSM) for Antarctica against the AntICE2 observational constraint database. A HMA represents a crucial steppingstone towards a comprehensive Bayesian calibration. A HMA consists of identifying model reconstructions that are consistent with observations given uncertainties in the model and data. Our HMA extensively samples model uncertainties against fits to observational data through Markov Chain Monte Carlo methods using Bayesian artificial neural network emulators of the full GSM. This methodology produced several large ensembles exceeding 40,000 simulations that were evaluated against observational constraints. The terminal large ensemble consisting of 9,293 members represents the culmination of this research. The GSM simulation output is scored against the AntICE2 database to evaluate the model reconstruction. The HMA rules simulations as being broadly inconsistent with the AntICE2 database based on being within a 3σ or 4σ threshold of each various observational data type. The simulations from the full ensemble that are tentatively not inconsistent with the observational constraint database are classified as the not-ruled-out-yet (NROY) sub-ensemble. The HMA of the AIS since the last interglacial and the resulting NROY sub-ensemble addresses several outstanding research questions. Considering the extent to which uncertainties across the glacial system and data were incorporated in the HMA, the NROY sub-ensemble should approximately bracket the past evolution of the actual ice sheet. The NROY simulations have excess Last Glacial Maximum (LGM) volumes ranging between 9.2 to 26.5 meters equivalent sea level. This range has upper limits that are considerably higher than past studies and this addresses in large part inferential deficits in the LGM sea-level budget. Moreover, the NROY sub-ensemble represents an envelop of chronologies which can be used as input boundary conditions for general circulation models and glacial isostatic adjustment models to better understand past atmospheric and oceanic circulation, and sea-level change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".