Identification of Forcing Mechanisms Driving Interannual Sea Level Variations along the US West Coast
Bibliographic record
Abstract
The US West Coast is subject to extreme sea level variations on interannual timescales that can exceed the long-term sea level rise trend by an order of magnitude or more. It is well established that sea level anomalies along the US West Coast are correlated with the multivariate El Nino/Southern Oscillation index, the Pacific Decadal Oscillation index, and the Pacific-North America index. Each of these indices are themselves associated with large-scale atmospheric forcing anomalies which then begs the question: exactly which atmospheric forcing anomalies are responsible for driving the observed interannual coastal sea surface variations? In this study we employ a global ocean state estimate from the ECCO Consortium and its adjoint to 1) identify the dynamical sensitivity pathways that link atmospheric forcing anomalies to US West Coast sea level anomalies and 2) quantify the relative contributions of local and nonlocal wind stress, buoyancy forcing, and air/sea freshwater fluxes to past interannual sea level variations. The main findings build on earlier research (e.g., Verdy et al. 2014) that show that on timescales longer than 1 day, nonlocal winds are overwhelming responsible for driving sea level anomalies. Extending previous work, we find that the relevant wind stress anomalies can be split into two well-defined regions: a near-coastal box spanning the equator to the US/Canadian border (where alongshore wind stress anomalies generate coastally-trapped waves) and an offshore box spanning the entire Pacific Ocean between 10S and 10N (where zonal wind stress anomalies generate equatorial Kelvin waves. Finally, we describe the implications for improving predictions of US West Coast sea level anomalies.
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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) | 0.001 | 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".