Impact of global ocean model resolution on sea-level variability with emphasis on interannual time scales
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. Four global ocean/sea-ice simulations driven by the same realistic 47-year daily atmospheric forcing were performed by the DRAKKAR group at 2°, 1°, &frac12°, and ¼° resolutions. Simulated mean sea-surface heights (MSSH) and sea-level anomalies (SLA) are collocated over the period 1993–2004 onto the AVISO dataset. MSSH fields are compared with an inverse estimate. SLA datasets are filtered and compared over various time and space scales with AVISO regarding three characteristics: SLA standard deviations, spatial correlations between SLA variability maps, and temporal correlations between observed and simulated band-passed filtered local SLA timeseries. Beyond the 2°−1° transition whose benefits are moderate, further increases in resolution and associated changes in subgrid scale parameterizations simultaneously induce (i) strong increases in SLA standard deviations, (ii) strong improvements in the spatial distribution of SLA variability, and (iii) slight decreases in temporal correlations between observed and simulation SLA timeseries. These 3 effects are not only clear on mesoscale (14–180 days) and quasi-annual (5–18 months) fluctuations, but also on the slower (interannual), large-scale variability ultimately involved in ocean-atmosphere coupled processes. Most SLA characteristics are monotonically affected by successive resolution increases, but irregularly and with a strong dependance on frequency and latitude. Benefits of enhanced resolution are greatest in the 1°−½° and ½°−¼° transitions, in the 14–180 day range, and within eddy-active mid- and high-latitude regions. In the real ocean, most eddy-active areas are characterized by a strong SLA variability at all timescales considered here; this localized, broad-banded temporal variability is only captured at ¼° resolution.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it