Assimilating the ICE‐6G_C Reconstruction of the Latest Quaternary Ice Age Cycle Into Numerical Simulations of the Laurentide and Fennoscandian Ice Sheets
Bibliographic record
Abstract
We analyze the effects of nudging 100 kyr numerical simulations of the Laurentide and Fennoscandian ice sheets toward the glacial isostatic adjustment‐based (GIA‐based) ICE‐6G_C reconstruction of the most recent ice age cycle. Starting with the ice physics approximations of the PISM ice sheet model and the SeaRISE simulation protocols, we incorporate nudging at characteristic time scales, τf, through anomalous mass balance terms in the ice mass conservation equation. As should be expected, these mass balances exhibit physically unrealistic details arising from pure GIA‐based reconstruction geometry when nudging is very strong (τf=20 years for North America), while weakly nudged (τf=1,000 years) solutions deviate from ICE‐6G_C sufficiently to degrade its observational fit quality. For reasonable intermediate time scales (τf=100 years and 200 years), we perturbatively analyze nudged ice dynamics as a superposition of “leading‐order smoothing” that diffuses ICE‐6G_C in a physically and observationally consistent manner and “higher‐order” deviations arising, for instance, from biases in the time dependence of surface climate boundary conditions. Based upon the relative deviations between respective nudged simulations in which these biases follow surface temperature from ice cores and eustatic sea level from marine sediment cores, we compute “ice core climate adjustments” that suggest how local paleoclimate observations may be applied to the systematic refinement of ICE‐6G_C. Our results are consistent with a growing body of evidence suggesting that the geographical origins of Meltwater Pulse 1B (MWP1b) may lie primarily in North America as opposed to Antarctica (as reconstructed in ICE‐6G_C).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".