Mediterranean Marine heatwaves: On the comparison of the physical drivers behind the 2003 and 2015 events
Bibliographic record
Abstract
Over the last decade, an intensification of extreme warm temperature events, termed as marine heatwaves (MHWs), has been reported in the Mediterranean Sea, itself a “Hot Spot” region for climate change. In the summer of 2003, a major MHW occurred in the Mediterranean with abnormal surface temperature anomalies of 2-3 Cº persisting for over a month. In 2015, an undocumented but more intense summer MHW affected almost the entire Mediterranean Sea with regional temperatures anomalies reaching 4-5 Cº. Here, we apply a MHW detection algorithm for long-lasting and large-scale summer events, on the hindcast output of a fully-coupled regional climate model (RCSM). We first examine the spatial variability and temporal evolution of both the 2003 and 2015 events. Then a basin-scale analysis of the mixed layer heat budget during each MHW is performed. The ocean and atmospheric components’ contribution is investigated separately during the onset, peak, and decay phases of both events, in order to disentangle the dominant physical processes behind each event. On the large-scale, our results indicate a key role of the wind forcing and the air-sea heat fluxes, while advection processes become more important at local scales. This study provides a comparison of the underlying mechanisms behind the two most intense MHW detected in the Mediterranean Sea during the last decade, constituting key information for the marine ecosystems of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".