Predictability of North Atlantic Sea Surface Temperature and Upper-Ocean Heat Content
Bibliographic record
Abstract
Abstract Understanding the extent to which Atlantic sea surface temperatures (SSTs) are predictable is important due to the strong climate impacts of Atlantic SST on Atlantic hurricanes and temperature and precipitation over adjacent landmasses. However, models differ substantially on the degree of predictability of Atlantic SST and upper-ocean heat content (UOHC). In this work, a lower bound on predictability time scales for SST and UOHC in the North Atlantic is estimated purely from gridded ocean observations using a measure of the decorrelation time scale based on the local autocorrelation. Decorrelation time scales for both wintertime SST and UOHC are longest in the subpolar gyre, with maximum time scales of about 4–6 years. Wintertime SST and UOHC generally have similar decorrelation time scales, except in regions with very deep mixed layers, such as the Labrador Sea, where time scales for UOHC are much larger. Spatial variations in the wintertime climatological mixed layer depth explain 51%–73% (range for three datasets analyzed) of the regional variations in decorrelation time scales for UOHC and 26%–40% (range for three datasets analyzed) of the regional variations in decorrelation time scales for wintertime SST in the extratropical North Atlantic. These results suggest that to leading order decorrelation time scales for UOHC are determined by the thermal memory of the ocean.
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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 | 0.002 |
| 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 | 0.001 | 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".