Coupled ice sheet–climate modeling under glacial and pre-industrial boundary conditions
Bibliographic record
Abstract
Abstract. We studied the climate of the last glacial maximum (LGM) in a set of coupled ice sheet–climate model experiments. They are based on the standard Paleoclimate Modelling Intercomparison Project Phase 2 (PMIP-2) experiments and extend the PMIP-2 (and PMIP-3) protocol by explicitly modeling the ice sheets. This adds a new layer of complexity and yields a set of ice sheets and climate that interact and are consistent with each other. We studied the behavior of the ice sheets and the climate system and compared our results to proxy data. The setup consists of the atmosphere-ocean-vegetation general circulation model ECHAM5/MPIOM/LPJ bidirectionally coupled with the Parallel Ice Sheet Model (PISM). We validated the setup by comparing the LGM experiment results with proxy data and by performing a pre-industrial control run. In both cases, the results agree reasonably well with reconstructions and observations. This shows that the model system adequately represents large, non-linear climate perturbations. Under LGM boundary conditions, the surface air temperature decreases by 3.5 K, and the precipitation north of 45° N by 0.12 m yr−1 (−18%) compared to the pre-industrial conditions. The North Atlantic Deep Water cell strengthens from 17.0 to 22.1 Sv (1 Sv = 106 m3 s−1) and the deep water formation shifts from the Labrador and GIN Seas to southeast of Iceland. Under LGM boundary conditions, different ice sheet configurations imply different locations of deep water formation. The major ice streams form in topographic troughs. In large parts, the modeled ice stream locations agree with sedimentary seafloor deposits. Most ice streams show recurring surges. The Hudson Strait Ice Stream surges with an ice volume equivalent to about 5 m sea level and a recurrence interval of about 7000 yr.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".