New perspectives on the skill of modelled sea ice trends in light of recent Antarctic sea ice loss
Bibliographic record
Abstract
Most climate models do not reproduce the 1979–2014 increase in Antarctic sea ice area (SIA). This was a contributing factor in successive Intergovernmental Panel on Climate Change reports allocating low confidence to model projections of sea ice over the 21st century. However, due to the rapid declines in Antarctic sea ice since 2016, the linear trend in annual mean Antarctic SIA is no longer positive. We therefore investigate what impact this has on the evaluation of trends from the CMIP6 multi-model ensemble and show that the recent rapid declines bring observed SIA trends back into line with the models. More generally, the level of agreement between observed and modelled linear trends depends both on the length of the time series examined ('timescale') and the exact years ('time period'). Our novel result that trends over the full satellite era 1979–2023 do not disagree between observations and models could imply that models are better able to represent changes over longer timescales than previously thought. However, this is not the only interpretation. One confounding aspect is the abrupt nature of recent change, as a result of which the full time series does not appear particularly linear. This presentation will discuss these aspects and the implications for future research priorities.
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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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".