Investigating the Mechanisms of Sea Level Change Using 30+ Years of Satellite Altimetry and the ECCO Ocean State Estimate
Bibliographic record
Abstract
We present a new effort to elucidate the origins, pathways, and mechanisms of large-scale oceanic changes in heat, freshwater, and mass that have contributed to more than three decades of observed sea level rise. Although global mean sea level rise is largely attributable to increases in ocean heat content and mass linked to global warming, the expression of these changes across the ocean is heterogeneous and remains incompletely understood. Regional variations are strongly shaped by natural modes of climate variability such as the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), which can at times exceed the magnitude of the global trend. Such regional signals often arise from the redistribution of heat, freshwater, and mass rather than net changes in their global budgets. Identifying the regions and mechanisms through which global warming influences sea level is therefore essential for advancing physical understanding and predictive capability. Our approach leverages the Estimating the Circulation and Climate of the Ocean (ECCO) ocean general circulation model, which assimilates the complete altimetry record along with a broad suite of other observations. The framework enables interpretation of observed changes in a dynamically consistent manner. Unlike traditional methods that rely on simplified dynamics (e.g., geostrophy) or statistical relationships (e.g., correlation), ECCO provides a comprehensive synthesis of the ocean state constrained by the full governing equations of motion. We will employ ECCO’s adjoint model to trace and quantify the drivers of sea level change and use passive tracers to delineate circulation pathways, thereby distinguishing the effects of external forcing from those of internal redistribution. The methodology provides a uniquely powerful means to attribute observed sea level variations to their causal mechanisms. We illustrate the approach with examples using ECCO Modeling Utilities (EMU), a recently developed, intuitive menu-driven toolkit that enables analysis of ECCO’s underlying model without requiring specialized modeling expertise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".