Ocean Heat Content in the Iberian-Biscay-Ireland regional seas
Bibliographic record
Abstract
Abstract. The ocean has the largest heat capacity of any single component of the climate system and plays a key dominant role in global heat content changes. Several recent studies have found that the Ocean Heat Content (OHC) has increased during the last decades, not only at a global scale, but also at regional/basin scales. To analyse OHC variability in the Iberia-Biscay-Ireland (IBI) region, several Copernicus Marine model and observed derived products are used together to provide multi-product estimations of OHC anomalies over the water column (at layers upper 150 m, 700 m, and 2000 m). This work provides an exhaustive analysis of spatial and temporal variability of OHC in the Northeast Atlantic region providing an analysis of area-averaged time series, trend maps, and trends linked with the main water masses found in the IBI region. The analysis of trends reveals that, despite a significant warming of the region in the period 1993–2020 (at rates of 0.4 W/m2), the thermohaline variability of subsurface water masses mainly dominates the OHC variability over the upper 2000 m. Additionally, vertical profiles of OHC trends are investigated, linking them with the vertical distribution of water masses; and describing how coupled inter-annual variability of Sub-Artic Intermediate Water, Mediterranean Outflow Water, and Labrador Sea Water accounts for an important part of the total OHC variability in the region.
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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 | 0.000 |
| 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 | 0.001 | 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".