Holocene temperature trends in the extratropical Northern Hemisphere based on inter‐model comparisons
Bibliographic record
Abstract
ABSTRACT Large uncertainties exist in Holocene climate estimates, especially for the early Holocene when large‐scale reorganization occurred in the climate system. To improve our understanding of these uncertainties, we compare four Holocene simulations performed with the LOVECLIM, CCSM3, HadCM3 and FAMOUS climate models. The simulations are generally consistent for the large‐scale Northern Hemisphere extratropics, while the multi‐simulation consistencies are heterogeneous on the sub‐continental scale. Consistently simulated temperature trends are found in Greenland, northern Canada, north‐eastern and north‐western Europe, and central‐west Siberia. These Holocene temperatures show a pattern of an early Holocene warming, mid‐Holocene warmth and gradual decrease towards the pre‐industrial in winter, and the extent of early Holocene warming varies spatially, with 9 °C warming in northern Canada compared with 3 °C warming in central‐west Siberia. In contrast, mismatched temperatures are detected: in Alaska, the warm early Holocene winter in LOVECLIM primarily results from strongly enhanced southerly winds induced by the ice sheets; in eastern Siberia, the intense early‐Holocene summer warmth anomaly in CCSM3 is caused by large negative albedo anomalies due to overestimated snow cover at 0 ka; in the Arctic, cool winter conditons in FAMOUS can be attributed to extensive sea ice coverage probably due to simplified sea ice representations. Thus, the Holocene temperature trends in these regions remain inconclusive.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".