Variations of surface marine heatwaves in the Northwest Pacific during 1993–2019
Bibliographic record
Abstract
Parameters of surface marine heatwaves (MHWs) in the Northwest Pacific during 1993–2019 are derived from two sea surface temperature (SST) products: the Optimum Interpolation SST based on satellite remote sensing (OISST V2.1) and the Global Ocean Physics Reanalysis based on data-assimilative global ocean model (GLORYS12V1). Similarities and differences between the MHW parameters derived from the two datasets are identified. The spatial distributions of the mean annual MHW total days, frequency, duration, mean intensity and cumulative intensity, and interannual variations of these parameters are generally similar, while the MHW total days and duration from GLORYS12V1 are usually higher than that from OISST V2.1. Based on seasonal-mean values from GLORYS12V1, longer MHW total days (>7) have the largest spatial coverage in both the shelf and deep waters in summer, while the smallest coverage in spring. In selected representative regions, interannual variations of the MHW total days are positively correlated with the SST anomalies. In summer, the MHW total days have positive correlations with the Western Pacific Subtropical High intensity, and negative correlations with the East Asia Monsoon intensity, over nearly the whole South China Sea (SCS) and the low-latitude Pacific. In winter, positive correlations with both the Subtropical High and Monsoon intensities present over the western part of SCS. Strong El Niño is followed by longer MHW total days over the western half of SCS in winter, and over the whole SCS and low-latitude Pacific in summer of the next year. These correlation relationships are valuable for developing forecasts of MHWs in the region.
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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 | 0.000 | 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".