Drivers of Marine Heatwaves in the Arctic Ocean
Bibliographic record
Abstract
Abstract Among the documented consequences of anthropogenic global warming are the increased frequency and duration of marine heatwaves in the global ocean. The literature dedicated to Arctic marine heatwaves corroborates those results, but fails to identify the heat sources and sinks. Because of the numerous feedbacks impacting polar regions, understanding the processes triggering and dissipating those extreme events is particularly important to predict their occurrence in a fast changing ocean. A three‐dimensional regional ice‐ocean numerical model is used to calculate a surface mixed layer heat budget and to investigate mechanisms generating and dissipating marine heatwaves. The majority of the marine heatwaves are onset by surface heat fluxes and decayed by bottom and surface heat fluxes. The dominant processes are spatially and seasonally heterogeneous: lateral heat flux can become the primary process when advecting heat anomalies at the main Arctic gateways or by triggering temperature extremes in winter. Using a Reynolds decomposition, it can be determined that the shoaling of the surface mixed layer induced by ice melt can significantly lengthen and intensify Arctic marine heatwaves. In winter, the analysis of marine heatwaves poses unique challenges, with the long term freshening of the Arctic inducing a positive trend of 0.1°C per decade for the freezing point. Arctic marine heatwaves are expected to keep increasing in duration and intensity due to the increased trend of the primary process, the surface heat flux, and their dissipation by bottom heat flux provides a pathway for heat from the atmosphere to the Arctic subsurface water masses.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".