Summer Eurasian Heat Wave and its linkage to SST anomalies over North Atlantic and Barents-Kara Seas
Bibliographic record
Abstract
In our study, we aim to examine what factors lead to the summer heat waves over Eurasia and their variability. The analysis reveals that the summer heat waves over Eurasia show two kinds of spatial patterns: midlatitude and high latitude types. The mid-latitude heat wave mainly occurred over west Russia in the west of 55°E and in the south of 60°N, whereas the high-latitude type mainly occurred over west Russia in the east of 55°E and in the north of 55°N. We further analyzed the relationship of the two kinds of heat waves with atmospheric circulation patterns in the Atlantic-Eurasian sector and sea surface temperature (SST) anomalies over the North Atlantic and Arctic. The results show that the cold or warm SST anomalies over Barents-Kara Seas (BKS) can significantly influence the latitude and longitude of Russian heat waves, while the heat waves are also related to the latitude of positive SST anomalies over North Atlantic. A mid-latitude wave train propagating into Eurasia and mid-latitude Russian heat waves, which are related to the positive phase of the North Atlantic Oscillation (NAO), are seen when there are strong SST warming in the North Atlantic mid-high latitudes south of 60°N and SST cooling over BKS. In contrast, a high-latitude Russian heat wave can occur over west Russia when there are positive SST anomalies over Baffin Bay, Davis Strait and Labrador Sea north of 60°N and BKS, while this high-latitude wave train is related to the decay of Greenland blocking or the negative NAO phase via high-latitude wave train propagation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".