Global Analysis of Marine Heatwave physical Processes
Bibliographic record
Abstract
Marine heatwaves (MHWs) are periods of extreme warm temperatures in the ocean and have been seen to exert substantial pressure to marine ecosystems around the world. For instance, they may drive widespread marine species die-offs, force coastal marine ecosystem regime shifts, promote toxic algal blooms, and/or alter the distribution of commercial fisheries on a scale of weeks to months. Recent studies have indicated a significant increase in MHW frequency and intensity throughout the 20th century, a trend which is likely to aggravate in the 21st century, according to future projections. Therefore, it is crucial to understand what are the climate drivers and physical processes governing MHWs in different regions of the global ocean and how these may change under the climate change regime. Here, we perform a mixed layer heat budget analysis, using a global ocean reanalysis product, to diagnose the relative role of ocean advection and atmosphere fluxes on the development of past MHWs around the world. Significant events are first identified using a consistent framework. Then, the heat budget results reveal that certain physical processes tend to be dominant in different regions, which can be traced back to specific local-scale dynamics. The global scale of this analysis provides a significant addition to the current literature which has, so far, been focused on the examination of the underlying mechanisms behind individual events. It also contributes to a better understanding of the variability and processes governing MHWs, offering also a potential ability for future event predictability.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".