ENSO Influences Subsurface Marine Heatwave Occurrence in the Kuroshio Extension
Bibliographic record
Abstract
Abstract Extreme ocean temperature events, also known as marine heatwaves (MHWs), can have devastating consequences for ecosystems, communities, and economies. However, the ability to understand and predict MHWs beneath the sea surface is limited by a scarcity of subsurface observations. Here, we combined in situ temperature observations from a High‐Resolution eXpendable BathyThermograph (HR‐XBT) transect in the northwest Pacific Ocean with satellite observations to produce a multidecadal (1993–2022) subsurface temperature time series with 10‐day temporal resolution. This novel time series was used to examine MHWs between the surface and 800‐m deep in the Kuroshio‐Kuroshio Extension region east of Japan. The length of this 30‐year time series also permitted exploration of long‐term trends and interannual variability in subsurface temperature. Variability in the Kuroshio‐Kuroshio Extension system is found to exert a strong control on the occurrence of MHWs along the transect. Throughout the upper 800‐m of the water column, Kuroshio warming drove a significant increase in Kuroshio MHW days per year. Notably, the largest mean MHW event intensities were observed in the subsurface at every location along the transect rather than at the sea surface. Strengthening of the Kuroshio Extension and its southern recirculation gyre during El Niño drove a significant increase in subsurface MHWs where the intensified current system intersected the transect. In contrast, surface MHW occurrence along the transect was not influenced by the El Niño‐Southern Oscillation (ENSO). Clearly, relying only on sea surface temperature observations does not provide the full picture of MHWs in this highly dynamic 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".