Multi-annual predictability of the Atlantic Meridional Overturning Circulation
Bibliographic record
Abstract
Decadal climate predictions have the main feature of being initialized, hence lying midway between initialized seasonal forecasts and forced multi-decadal projections. \nThe North Atlantic is among the few places where decadal variations are considered potentially predictable with an added value of the initialization due to the Atlantic Meridional Overturning Circulation (AMOC), which exhibits slow multi-annual fluctuations. A correct representation of this process is fundamental to skillfully predict climate variability in the Northern Hemisphere at these timescales. \nIn this thesis, AMOC predictability is investigated in the CMCC-CM2-SR5 (CMCC Coupled Model v2 in standard resolution) decadal system. \nThe ability of the model to forecast the AMOC is evaluated in both a deterministic and probabilistic way, comparing a set of hindcasts initialized between 1960 and 2018 with observations, ocean reconstructions, and a non-initialized historical simulation. \nSpecial attention is devoted to the analysis of AMOC biases. \nIndeed, it is documented that predictions suffer from initial shocks and tend to drift towards the model's equilibrium state. \nWe find that the potential predictability of the system is high up to a ten-year forecast range, but this is not reflected in the AMOC transport forecast skill, which undergoes a sudden reduction after the first year. \nAn interesting finding is that the drift of the model is start-date dependent: we leverage on this feature to propose a new post-processing approach for the drift adjustment, different from the usual one in which drifts are treated as stationary. \nThe experimented approach significantly increases the forecast skill. \nFurthermore, we identify a reduction of convection in the Labrador Sea, a feature that previous studies linked with the model drift of the AMOC. \nFurther research with an increased ensemble size of both initialized and historical simulations and with a multi-model set is envisaged.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".