Potential linkage between the atmospheric summer circulation over Eurasia and preceding sea ice anomalies southwest of Greenland
Bibliographic record
Abstract
Sea ice alters the surface albedo and modulates the heat, moisture and momentum exchange between the ocean and the atmosphere. Various studies suggest an influence of the sea ice on the atmospheric circulation, whereby the focus is often on simultaneous connections and Arctic-wide sea ice conditions. Sea ice has a strong memory and we thus hypothesize a potential feedback on the atmosphere also at higher lags. Using ERA5 reanalysis data between 1983 and 2017, the present work investigates a potential connection of the summer atmospheric circulation over Eurasia to winter sea ice anomalies southwest of Greenland. Composites of the June-July geopotential height pattern show a wave-train structure throughout the troposphere and the resulting circulation anomalies are found to influence the two metre temperatures over northeastern Europe and northern Russia. These anomalies are significantly correlated with December-January sea ice anomalies. Persistent sea surface temperature (SST) anomalies associated with the strong ice memory indicates that the winter signal is partly stored in the Labrador Sea. The observations indicate a response in the June-July 500 hPa vertical velocity in proximity of the strongest SST anomalies that is dynamically consistent with the lower-level and upper-level divergence pattern. The result suggests that the vertical velocity potentially connects a vorticity forcing in the upper troposphere to near-surface conditions over the Labrador Sea that originate from the preceeding winter. A further analysis shows a particularly pronounced wave-train signal when the December-January ice anomalies appear in phase with a strong North Atlantic Oscillation (NAO) index. Those years are characterized by extensive and persistent SST anomalies in the North Atlantic bearing similarities with the tripole pattern that is known to be associated with the NAO. The SST signal is accompanied by widespread heat flux anomalies hinting at a further influence coming from the central North Atlantic. The study provides a first analysis of two possible factors that potentially contribute to the linkage between winter sea ice and the summer atmospheric circulation.
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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 | 0.001 |
| 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) | 0.001 | 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".