Different Effects of Two ENSO Types on Arctic Surface Temperature in Boreal Winter
Bibliographic record
Abstract
Abstract The present work investigates different responses of Arctic surface air temperature (SAT) to two ENSO types based on reanalysis datasets and model experiments. We find that eastern Pacific (EP) ENSO events are accompanied by statistically significant SAT responses over the Barents–Kara Seas in February, while central Pacific (CP) events coincide with statistically significant SAT responses over northeastern Canada and Greenland. These impacts are largely of opposite sign for ENSO warm and cold phases. During EP El Niño in February, the enhanced tropospheric polar vortex over Eurasia and associated local low-level northeasterly anomalies over the Barents–Kara Seas lead to anomalously cold SAT in this region. Simultaneously, the enhanced tropospheric polar vortex leads to enhanced sinking air motion and consequently reduced cloud cover. This in turn reduces downward infrared radiation (IR), which further reduces SAT in the Barents–Kara Seas region. Such a robust response cannot be detected during other winter months for EP ENSO events. During CP El Niño, the February SATs over northeastern Canada and Greenland are anomalously warm and coincide with a weakened tropospheric polar vortex and related local low-level southwesterly anomalies originating from the Atlantic Ocean. The anomalous warmth can be enhanced by the local positive feedback. Similar SAT signals as in February during CP ENSO events can also be seen in January, but they are less statistically robust. We demonstrate that these contrasting Arctic February SAT responses are consistent with responses to the two ENSO types with a series of atmospheric general circulation model experiments. These results have implications for the seasonal predictability of regional Arctic SAT anomalies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".