Changing Northern Hemisphere weather linked to warming amplification in High Mountain Asia
Bibliographic record
Abstract
High Mountain Asia is a global warming hotspot, yet its influence on Northern Hemisphere extreme weather-related synoptic temperature variability remains unclear. Combining observations and numerical model simulations, we show that amplified High Mountain Asia warming has substantially enhanced summer synoptic temperature variability (>21%) in Canada and Russia while reducing winter variability (>19%) in Eastern Europe and the Nordic Seas during 1940–2022. These changes are primarily driven by altered high-frequency temperature advection. High Mountain Asia warming modifies horizontal temperature gradients, strengthening them in Canada and Russia in summer but weakening them in the Nordic Seas and Eastern Europe in winter. These patterns arise from hemispheric teleconnections that redistribute temperature and modulate atmospheric circulation stability via changes in jet streams, Rossby waves, and air-sea interactions. Our findings highlight High Mountain Asia warming’s far-reaching impacts on Northern Hemisphere weather variability, extending beyond its well-known local climate effects. From 1940-2022, High Mountain Asia warming influenced Northern Hemisphere weather by increasing summer synoptic temperature variability in Canada and Russia, but decreasing winter synoptic temperature variability in Eastern Europe and the Nordic Seas, according to observational and modeling analysis.
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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 | 0.000 |
| 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) | 0.002 | 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".