Drivers of Marine Heatwaves in the Northwest Atlantic: The Role of Air–Sea Interaction During Onset and Decline
Bibliographic record
Abstract
Marine heatwaves (MHWs) are increasing in duration and intensity at a global scale and are projected to continue to increase due to the anthropogenic warming of the climate. Because MHWs may have drastic impacts on fisheries and other marine goods and services, there is a growing interest in understanding the predictability and developing practical predictions of these events. A necessary step toward prediction is to develop a better understanding of the drivers and processes responsible for the development of MHWs. Prior research has shown that air–sea heat flux and ocean advection across sharp thermal gradients are common physical processes governing these anomalous events. In this study we apply various statistical analyses and employ the self-organizing map (SOM) technique to determine specifically which of the many candidate physical processes, informed by a theoretical mixed-layer heat budget, have the most pronounced effect on the onset and/or decline of MHWs on the Northwest Atlantic continental shelf. It was found that latent heat flux is the most common driver of the onset of MHWs. Mixed layer depth (MLD) also strongly modulates the onset of MHWs. During the decay of MHWs, atmospheric forcing does not explain the evolution of the MHWs well, suggesting that oceanic processes are important in the decay of MHWs. The SOM analysis revealed three primary synoptic scale patterns during MHWs: low-pressure cyclonic Autumn-Winter systems, high-pressure anti-cyclonic Spring-Summer blocking, and mild but long-lasting Summer blocking. Our results show that nearly half of past MHWs on the Northwest Atlantic shelf are initiated by positive heat flux anomaly into the ocean, but less than one fifth of MHWs decay due to this process, suggesting that oceanic processes, e.g., advection and mixing are the primary driver for the decay of most MHWs.
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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".