Impact of the Laurentide Ice Sheet deglaciation on early to mid-Holocene climate evolution
Bibliographic record
Abstract
We simulate the early to mid-Holocene climate evolution enforced by the Laurentide Ice Sheet (LIS) and associated freshwater flux from the melting ice sheet with the coupled global atmosphere-ocean-vegetation model of intermediate complexity, CLIMBER-2, to investigate the impact of the LIS deglaciation on climatic conditions over circumpolar and other regions. The modeling results show that before 7,000 years ago, the air temperature and precipitation over North Atlantic and Northern Europe (50N-70N) were substantially influenced by the presence of the Laurentide ice sheet. In addition, The spatio-temporal pattern of peak warming show that the timing of peak warming in north Canada and part of North Africa is between 8 and 7 kyr BP, which is earlier than that in southern Greenland (7-6 kyr BP). The simulated delayed warming in north Canada and southern Greenland agree with reconstructions for these regions. Elsewhere, peak warming is no delayed in 9-8 kyr BP, showing no significant effect of the LIS, and suggesting that summer temperatures are controlled here by orbital forcing throughout the Holocene. The spatio-temporal pattern of peak summer precipitation is complicated than that in temperature structure. The model results suggest that the coupling of the extra feedbacks and forcings of Laurentide Ice Sheet plays an important role in early to mid-Holocene climate evolution, at least on the circumpolar regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".