Investigating the Uncertainty in Global SST Trends Due to Internal Variations Using an Improved Trend Estimator
Bibliographic record
Abstract
Abstract A new criterion was proposed recently to measure the influence of internal variations on secular trends in a time series. When the magnitude of the trend is greater than a theoretical threshold that scales the influence from internal variations, the sign of the estimated trend can be interpreted as the underlying long‐term change. Otherwise, the sign may depend on the period chosen. An improved least squares method is developed here to further reduce the theoretical threshold and is applied to eight sea surface temperature (SST) data sets covering the period 1881–2013 to investigate whether there are robust trends in global SSTs. It is found that the warming trends in the western boundary regions, the South Atlantic, and the tropical and southern‐most Indian Ocean are robust. However, robust trends are not found in the North Pacific, the North Atlantic, or the South Indian Ocean. The globally averaged SST and Indian Ocean Dipole indices are found to have robustly increased, whereas trends in the zonal SST gradient across the equatorial Pacific, Niño 3.4 SST, and the Atlantic Multidecadal Oscillation indices are within the uncertainty range associated with internal variations. These results indicate that great care is required when interpreting SST trends using the available records in certain regions and indices. It is worth noting that the theoretical threshold can be strongly influenced by low‐frequency oscillations, and the above conclusions are based on the assumption that trends are linear. Caution should be exercised when applying the theoretical threshold criterion to real data.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".