Pacific Decadal Oscillation and Sea Level Variability in the Bohai, Yellow, and East China Seas
Bibliographic record
Abstract
Abstract Sea level variability off East China has been investigated based primarily on 10 years of Ocean Topography Experiment (TOPEX)/Poseidon altimetry data. The altimetric annual harmonic has a magnitude of 10 to 30 cm in amplitude and is highest in summer, agreeing well with independent tide-gauge data. After the inverse barometer effect is removed, the annual sea level cycle can be approximately accounted for by the steric height variation. Significant interannual sea level change was also observed from altimetry and tide-gauge data, with a range of ∼10 cm. The interannual and longer-term sea level variability in the altimetric data are negatively correlated (significant at the 95% confidence level) with the Pacific decadal oscillation (PDO), attributed in part to steric height change. The altimetric sea level rise rate is 0.64 cm yr−1 for the period from 1992 to 2002, consistent with the tide-gauge rate of 0.6 cm yr−1. These values are much larger than the rate of 0.24 cm yr−1 observed at the same tide gauges but for the period from 1980 to 2002, implying the sensitivity to the length of data as a result of the decadal variability. The potential role of the PDO in the interannual and longer-term sea level variability is discussed in terms of regional manifestations such as the ocean temperature and salinity and the Kuroshio transport.
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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".