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Record W7123912849 · doi:10.24400/527896/a03-2025.4304

Investigating the Mechanisms of Sea Level Change Using 30+ Years of Satellite Altimetry and the ECCO Ocean State Estimate

2025· article· W7123912849 on OpenAlexaff
Ichiro Fukumori, S. Bulusu, Ou Wang, Ian Fenty

Bibliographic record

VenueCentre National d’Etudes Spatiales · 2025
Typearticle
Language
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsSemtech (Canada)
Fundersnot available
KeywordsAltimeterSea levelOcean currentClimate changeForcing (mathematics)Data assimilationOcean heat contentPacific decadal oscillationSea surface temperatureClimate model

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.261
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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