Drivers of ocean heat content variability and predictability in the North Atlantic
Bibliographic record
Abstract
This PhD thesis focuses on the variability and predictability of the upper ocean heat content (OHC) in the North Atlantic (NA) basin, investigating the drivers of decadal predictive skill of OHC in climate models and potential differences among them. The NA basin is characterized by prominent decadal variability driven by internal and external sources, which modulates the local warming rates and the Northern Hemisphere climate. However, the contributions of the internal and external drivers of variability in the region remain largely unknown. This thesis aims at investigating how internal and forced variability contribute to local trends, as well as their roles in the local prediction skill using both climate model simulations and ocean reanalysis, to account for the observational and model uncertainty. A first analysis based on historical and decadal prediction simulations with the EC-Earth3 climate model shows that internal variability is essential to understand the spatial pattern of North Atlantic OHC trends, contributing decisively to the local trends, and providing high levels of predictive skill in the Eastern Subpolar NA, the Irminger-Iceland Sea, and to a lesser extent in the Labrador Sea (LS). Skill and trends in other NA areas were mostly externally forced. Large observational uncertainties affect the evaluation of trends, interannual variability and predictability in the Central Subpolar NA, the only region exhibiting a cooling during the study period, for which results should be taken cautiously. The analysis is further expanded by including seven additional climate models, to understand if they provided similar skill, or if intrinsic model characteristics, like key mean state biases, could influence the relative role of external forcings and internal variability. Particular attention is given to the LS and its surroundings, since it is found to be a region with low observational uncertainties and high inter-model spread of OHC decadal prediction skill. Large incertainties in the representation of the forced signals and the occurrence of initialization shocks in some models prevent us from reaching definitive conclusions about the origins of the LS decadal prediction skill. This PhD thesis highlights the necessity of using several climate models when assessing OHC predictability. Relying on a single model can lead to misleading conclusions about the true sources of predictive skill.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".