Rapid neoglaciation on Ellesmere Island promoted by enhanced summer snowfall in a transient climate model simulation of the middle-late-Holocene
Bibliographic record
Abstract
Arctic neoglaciation following the Holocene Thermal Maximum is an important feature of late-Holocene climate. We investigated this phenomenon using a transient 6000-year simulation with the CESM-CAM5 climate model driven by orbital forcing, greenhouse gas concentrations, and a land use reconstruction. During the first three millennia analyzed here (6–3 ka), mean Arctic snow depth increases, despite enhanced greenhouse forcing. Superimposed on this secular trend is a very abrupt increase in snow depth between 5 and 4.9 ka on Ellesmere Island and the Greenland coasts, in rough agreement with the timing of observed neoglaciation in the region. This transition is especially extreme on Ellesmere Island, where end-of-summer snow coverage jumps from nearly 0 to virtually 100% in 1 year, and snow depth increases to the model’s imposed maximum within 15 years. This climatic shift involves more than the Milankovitch-based expectation of cooler summers causing less snow melt. Coincident with the onset of the cold regime are two consecutive summers with heavy snowfall on Ellesmere Island that help to short-circuit the normal seasonal melt cycle. These heavy snow seasons are caused by synoptic-scale, cyclonic circulation anomalies over the Arctic Ocean and Canadian Archipelago, including an extremely positive phase of the Arctic Oscillation. Our study reveals that a climate model can produce sudden climatic transitions in this region prone to glacial inception and exceptional variability, due to a dynamic mechanism (more summer snowfall induced by an extreme circulation anomaly) that augments the traditional Milankovitch thermodynamic explanation of orbitally induced glacier development.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".