Lengthening of Warm Periods Increased the Intensity of Warm-season Marine Heatwaves Over the Past Four Decades
Bibliographic record
Abstract
Abstract Marine heatwaves (MHWs), periods of anomalously warm sea surface temperature (SST) which can have significant impacts on marine ecosystems, have increased in frequency and severity over recent decades. Many coastal systems (e.g. coral reefs) are particularly vulnerable to warm-season heat stress when temperature can exceed organisms’ thermal thresholds and lead to mass mortality. While many studies have examined the change of the warm-season heat stress occurrence over time, e.g., for coral reefs, there has been less analysis of the thermal structure of heat stress events. Here we examined the trend in the characteristics of warm-season heat stress (referred to as warm-season MHWs) at the global-scale from 1985 to 2019, using multiple metrics for each of duration, peak intensity, accumulated heat stress, heating rates and level of intensity. The results showed that warm-season MHWs have become more frequent, longer-lasting, featured higher peak intensity and accumulated heat stress across most of the ocean over the past four decades. Furthermore, decomposition of the trends in warm-season MHWs structure showed that the increased accumulated heat stress was predominantly driven by the increased duration rather than the increased intensity, especially in the western and central equatorial Pacific. The results contribute to improving the understanding of warm-season MHWs, which may help inform the prediction of their impacts on marine ecosystems as well as marine conservation and management under climate change.
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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 | 0.001 | 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) | 0.003 | 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".