Local and Remote Atmosphere‐Ocean Coupling During Extreme Warming Events Impacting Subsurface Ocean Temperature in an Antarctic Embayment
Bibliographic record
Abstract
Abstract Coastal ocean temperatures can respond to different atmospheric and oceanic processes at local spatial scales or through remote teleconnections. This study focused on subsurface ocean temperatures (subT) at 10 m depth in Maxwell Bay, northern Antarctic Peninsula from February 2017 and January 2022. It investigated extreme warming events during austral summers and their interaction with atmospheric and oceanic conditions regionally and locally. The analysis identified active and extreme Marine Heat Waves (MHWs) in March 2017 and January‐February 2020 associated with a significantly negative Southern Annular Mode index observed 3–4 months before the temperature increase. In March 2017, temperatures exceeded the climatological mean by over 1°C. This anomaly was linked to a strengthened Amundsen Sea Low and a blocking anticyclone moving between the Scotia Sea and the South‐West Atlantic Ocean that deflected westerly winds and facilitated the anomalous transport of warmer northern air masses to the AP. In January‐February 2020, the highest recorded subT was observed (2.97°C), although air‐sea heat fluxes did not show a similar pattern. In February 2020, one of the most intense atmospheric heatwaves ever recorded in West Antarctica was observed. This heatwave corresponded with maximum subT and positive sea surface temperature anomalies extending throughout the western region of the Southern Ocean, related to an extremely negative Southern Annular Mode. This study provides valuable insights into the impact of strong MHWs, a phenomenon that has been less documented in Antarctic coastal regions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".