Spatial variations in the warming trend and the transition to more severe weather in midlatitudes
Bibliographic record
Abstract
Due to various feedback processes called Arctic amplification, the high-latitudes' response to increases in radiative forcing is much larger than elsewhere in the world, with a warming more than twice the global average. Since the 1990's, this rapid warming of the Arctic was accompanied by no-warming or cooling over midlatitudes in the Northern Hemisphere in winter (the hiatus). The decrease in the thermal contrast between Arctic and midlatitudes has been connected to extreme weather events in midlatitudes via, e.g., shifts in the jet stream towards the equator and increases in the probability of high-latitude atmospheric blocking. Here we present an observational attribution study showing the spatial structure of the response to changes in radiative forcing. The results also connect the hiatus with diminished contrast between temperatures over regions in the Arctic and midlatitudes. Recent changes in these regional warming trends are linked to international actions such as the Montreal Protocol, and illustrate how changes in radiative forcing can trigger unexpected responses from the climate system. The lesson for climate policy is that human intervention with the climate is already large enough that even if stabilization was attained, impacts from an adjusting climate are to be expected.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".