The tidal changes under sea-level rise in the marginal seas near China
Bibliographic record
Abstract
Tides significantly influence coastal sea-level variations, yet their response to future sea-level rise remains insufficiently understood. Using a validated regional ocean model (1/12°), we assess projected tidal changes in the marginal seas near China under present condition and multiple 1-meter sea-level rise scenarios. Spatially uniform 1-m sea-level rise produces nonuniform M2 amplitude changes (averaging 3.3% increase over the shelf and 5.1% along the coasts), together with shifting amphidromic system, and enhanced tidal energy flux and dissipation. Nonuniform sea-level rise pattern yields near-proportional local tidal responses. As a new contribution, tidal forcing changes at open boundaries, driven by global tidal adjustments under sea-level rise, exerts an important secondary influence, reducing the projected increases in M2 amplitude, energy flux, and bottom dissipation on the shelf by 17.2%, 41.9% and 41.2%, respectively. Small open-ocean perturbations (~0.1 cm in M2) amplify ~5 times on the shelf (~0.5 cm). SLR pattern and boundary effects superpose approximately linearly, giving a ~6-cm coastal M2 increase (~6% of 1-m SLR). These results highlight the importance of incorporating local sea-level rise and global tidal adjustments in regional tide simulations under sea-level rise with improved physical consistency. The quantified changes in amplitude, energy flux, and dissipation have broad downstream consequences, including extreme sea levels, internal-tide generation and ocean mixing, sediment transport, and ecosystem dynamics. The modelling approach and findings identified in this study can be readily extended to other tidally energetic marginal seas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".