Reconstructing the Atlantic Overturning Circulation Using Linear Machine Learning Techniques
Bibliographic record
Abstract
This paper examines the potential of reconstructing the Atlantic Meridional Overturning Circulation (AMOC) using surface data and linear machine learning algorithms. The algorithms are trained on pre-industrial control simulations with the aim of finding an algorithm that can reconstruct the AMOC robustly across multiple climate models. Predictors include a combination of surface temperature and surface salinity, as well as a combination of simultaneous and lagged values relative to the AMOC. For most climate models, the correlation skill of the AMOC reconstructions is greater than 0.7. This reconstruction model involves thousands of predictors and is therefore difficult to interpret. To improve interpretability, machine learning algorithms were applied to Laplacian eigenvectors, which are an orthogonal set of spatial patterns that can be ordered from largest to smallest spatial scale. The skill of the new algorithms is comparable to that based on gridded data, but the new algorithms have the advantage that dimension reduction can be more meaningfully interpreted. The most important predictors were simultaneous and lagged time series of area-averaged surface temperature, and a pattern that measures the east–west salinity difference over the basin surface lagged in time. These three predictors could recover a substantial fraction of the total skill from machine learning algorithms for most climate models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".