Extending the PAMIP protocol for assessing the climate response to regional sea ice change across all Arctic regions
Bibliographic record
Abstract
Climate models predict that sea ice cover will shrink--even disappear-- in most regions of the Arctic basin by the end of the century, inducing local and remote responses in the surface climate via dynamical changes in the atmospheric circulation. The atmospheric-only experiment designed and performed within PAMIP can help us elucidate the dynamical mechanisms, as well as assess the importance of sea ice loss in individual sectors of the Arctic in driving Northern Hemisphere climate change. Using the atmosphere-only EC-EARTH3.3 model and using the same protocol as for the pan-Arctic and regional Barents/Kara and Okhotsk future sea-ice experiments, we ran 5 complementary regional experiments that include other regions of the Arctic: Central Arctic, Hudson-Labrador-Baffin, Irminger-GIN, Bering-Chukchi, and Beaufort-East-Siberian-Laptev sectors. First. we compare the spatial pattern of climate anomalies in those simulations, and we discuss the contribution of sea ice loss in each region to climate change over Europe, Siberia and North America. One noticeable result that will be discussed is the strikingly different nature of the climate response to pan-Arctic sea ice loss when compared to any of the regional sea ice loss experiments; we conclude our presentation by discussing potential causes for this difference, and what it may imply when devising experiments with prescribed regional sea ice change in future projects.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".