Sea level variation in the Arctic Ocean since 1979 based on ORAS5 data
Bibliographic record
Abstract
The Arctic is currently experiencing unprecedented changes across all components of the climate system, primarily driven by global warming. As an important indicator of climate change in the Arctic, sea level reflects variations in both the atmosphere and ocean. This paper analyzes the sea level variation of the Arctic Ocean over the past four decades using ORAS5 data, which is the product of the latest reanalysis-analysis system produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). ORAS5 accurately reproduces the main spatial features of the climatology and temporal evolution of sea surface height (SSH) in the Arctic Ocean, as observed by satellite altimeters, and reveals that seasonal variability is the most significant property of the sea level variation in this region. The seasonal cycle of SSH is closely linked to atmospheric circulation and sea ice formation. The first two dominant modes of the annual-mean SSH in the Arctic Ocean exhibit significant decadal variability. The first mode can be explained by the Ekman transport of wind related to the Arctic Oscillation (AO), which leads to antiphase changes in SSH on the continental shelves and in the deep basins. The second mode shows an antiphase oscillation of SSH between the Eurasian and Canadian Arctic Archipelago (CAA) sides and is driven by the wind anomaly associated with the Arctic dipole anomaly (DA). Due to the decadal variations associated with climate modes, particularly the AO, sea level in the Arctic Ocean has been continuously rising since the mid-1990s or early 2000s, with the most rapid sea level rise occurring in the Beaufort Sea.
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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 | 0.002 |
| 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".