A sea ice-free Arctic during the Last Interglacial supports fast future loss
Bibliographic record
Abstract
The Last Interglacial (LIG) is a period of great importance as an analog for future climate change. Global sea level was 6-9 m higher than present. Stronger LIG summertime insolation at high northern latitudes drove Arctic land summer temperatures around 4-5 K higher than during the preindustrial era. Climate-model simulations have previously failed to capture these elevated temperatures. This may be because these models failed to correctly capture LIG sea ice changes. Here, we show that the latest version of the UK Hadley Center coupled ocean-atmosphere climate model (HadGEM3) simulates a much improved Arctic LIG climate, including the observed high temperatures. Improved model physics in HadGEM3, including a sophisticated sea ice melt-pond scheme, results in the first-ever simulation of the complete loss of Arctic sea ice in summer during the LIG. Our ice-free Arctic yields a compelling solution to the long-standing puzzle of what drove LIG Arctic warmth. The LIG simulation result is a new independent constraint on the strength of Arctic sea ice decline in climate-model projections, and provides support for a fast retreat of Arctic summer sea ice in the future.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".