Sensitivity of the North Atlantic climate to Greenland Ice Sheet melting during the Last Interglacial
Bibliographic record
Abstract
Abstract. During the Last Interglacial (LIG; ~130 thousand years BP), part of the Greenland Ice Sheet (GIS) melted under the influence of a warmer than present-day climate. However, the impact of this melting on the LIG climate in the North Atlantic region is unknown. Using the LOVECLIM earth system model of intermediate complexity we have systematically tested the sensitivity of the LIG climate to increased freshwater runoff from the GIS. Moreover, additional experiments have been performed to investigate the impact of an idealized reduction of both altitude and extent of the GIS on the LIG climate. By comparing the simulated deep ocean circulation with proxy-based reconstructions, the most realistic simulated climate could be discerned. The resulting climate is characterized by a shutdown of deep convection in the Labrador Sea and a subsequent cooling here by ~6 °C and ~2 °C over the southern part of Baffin Island and the North Atlantic Ocean between 40° N and 60° N. The reduction of altitude and extent of the GIS results in a local warming of up to 6 °C and a reduction in deep convection and accompanying cooling in the Nordic Seas. Combining model results and proxy-based reconstructions enabled us to constrain the possible melt rate of the GIS to a flux between 0.052 Sv and 0.13 Sv. A further comparison of simulated summer temperatures with both continental and oceanic proxy-records reveals that the partial melting of the GIS during the LIG could have delayed maximum summer temperatures in the western part of the North Atlantic region relative to the insolation maximum.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".