Climate - cryosphere interactions for past, present and future climates with the comprehensive Earth system model AWI-ESM
Bibliographic record
Abstract
The Alfred Wegener Institute Earth System Model (AWI-ESM) is applied to study the role of the Greenland Ice Sheet (GIS) in different future warming scenarios. The model includes an interactive Ice Sheet Model (ISM) to capture ice sheet dynamics and enhance the representation of spatial freshwater discharge. The ISM leads to a strong decadal variability to the freshwater release, resulting in intervals in which it reduces the surface runoff by high accumulation rates. This compensating effect is missing in climate models without dynamic ice sheets. To investigate processes involved in the buildup phase of ice sheets, transient simulations of the Last Glacial Inception (LGI) are performed. These cooling climate simulations complement the future warming scenarios. The model is run asynchronously from 125 ka to 110 ka with prescribed orbital and greenhouse gas forcing. Large-scale glaciation occurs in a simulation with implemented anomaly coupling. This anomaly coupling is applied to compensate for model biases in high-latitude near-surface air temperature. Large areas of Quebec and Baffin Island glaciate, mainly driven by the thickening of snowfields. In order to assess the effects of heat and freshwater fluxes by iceberg melting on deep-ocean characteristics, multi-centennial simulations under fixed pre-industrial forcing are run with a fully coupled ESM including interactive icebergs. Compared to simulations without interactive icebergs, the results show a cooling of deep ocean water masses and enhanced deep water formation in the continental shelf area of the Ross Sea, a process commonly underestimated by current climate models. The results emphasize the importance of realistically representing both heat and freshwater fluxes in the high southern latitudes.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".