Atmospheric circulation modulates the spatial variability of temperature in the Atlantic–Arctic region
Bibliographic record
Abstract
The Arctic region has experienced significant warming during the past two decades with major implications on the cryosphere. The causes of Arctic amplification are still an open question within the scientific community, attracting recent interest. The goal of this study is to quantify the contribution of atmospheric circulation on temperature variability in the Atlantic–Arctic region at decadal to intra‐annual timescales from 1951 to 2014. Daily 20th Century reanalyses geopotential height anomalies at 500 hPa were clustered into different weather regimes to assess their contribution to observed temperature variability. The results show that in winter, 25% of the warming (cooling) in the North Atlantic Ocean (northeastern Canada) is due to temporal decreases of high geopotential anomalies in Greenland. This regime influences air mass migration patterns, bringing less cold (warm) air masses into these regions. Additionally, atmospheric warming or cooling has been attributed to a change in nearby oceanic basin surface conditions because of sea ice decline. In summer, about 15% of the warming observed in Norwegian/Greenland Seas is related to an increase in temporal anticyclonic patterns. This ratio reaches 37% in Norway due to an amplification from downwards solar radiation. This study allows for better understanding how natural climate variability modulates the regional signature of climate change and estimating the uncertainties in climate projections.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".