Marine Heatwaves in the Arctic Ocean: drivers, feedback mechanisms and interactions with sea ice
Bibliographic record
Abstract
Arctic regions are warming at a rate faster than the global average. Superimposed on this trend, marine heatwaves and other extreme events are becoming more frequent and intense. Simultaneously the sea ice phenology with which these events interact is also changing. While sea ice can absorb atmospheric heat by melting and therefore acts as a heat buffer for the ocean, meltwater-induced stratification and albedo changes can provoke positive feedbacks on the heat content of the upper ocean. Disentangling those effects is key to better understanding and predicting the present and future state of the Arctic Ocean, including how it responds to forcing by extreme events. Using a three-dimensional regional ice-ocean coupled numerical model, we calculate a two-layer heat budget for the surface mixed layer of the Arctic Ocean, using a novel approach for the treatment of residuals. We present a statistical overview of the dominant drivers of marine heatwaves at the regional scale as well as more in-depth analyses of specific events in key regions of interest. The characteristics of marine heatwaves under different sea ice conditions is also considered, to identify anomalous ice-ocean interactions. Finally, potential feedback mechanisms are investigated to verify their existence and quantify their importance.
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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".