Cold Weather Teleconnections from Future Arctic Sea Ice Loss and Ocean Warming
Bibliographic record
Abstract
Rapid Arctic warming and decline in sea ice have been observed in recent decades. These trends will likely continue, potentially changing winter extremes elsewhere in the Northern Hemisphere. We use coordinated Polar Amplification Model Intercomparison Project (PAMIP) experiments to decompose the Northern Hemisphere winter cold temperature responses to future Arctic sea-ice loss and sea surface temperature (SST) change, separately, at 2C global mean warming. Cold extremes (20-year return period) will generally become warmer at high- and mid-latitudes due to Arctic sea-ice loss, with the largest warming in East Canada. SST change will warm cold extremes everywhere, overwhelming simulated sea ice-induced cooling responses in, e.g., southwestern United States. In general, the SST-induced changes dominate over sea ice-induced changes, with exceptions in East Canada, Nunavut (Canada) and North Pacific Russia. Our results suggest that if climate models do not adequately capture the sea-ice and SST components, cold extremes will be biased.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".