Production and evaluation of an ocean reanalysis with an eddy-resolving version of the ocean model NEMO
Bibliographic record
Abstract
This thesis investigates the production and evaluation of three ocean reconstructions for 1993–2014 using an eddy-resolving configuration of the model NEMO. The experiments assess different nudging approaches toward a reference reanalysis, comparing results with that dataset and independent observations. All experiments accurately reproduce the observed mean climate state and variability of surface temperature and salinity, capturing key circulation patterns. The Surface Nudging experiment shows the best agreement with observations of the Atlantic Meridional Overturning Circulation (AMOC) at 26° N and the mixed layer depth in the Greenland–Iceland–Norwegian seas. The reference reanalysis shows inconsistencies in dynamic variables, notably an unrealistic evolution of mixed layer depth in the Labrador Sea and a decoupling of AMOC strength from convection. Results highlight the non-linear effects of different nudging choices and recommend optimizing nudging coefficients and improving sea-ice representation. This study contributes to improve the initialization of the IFS-NEMO climate prediction model.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".