Influence of sea surface temperature variability on global temperature and precipitation extremes
Bibliographic record
Abstract
The HadISST1 data set was used to categorize seasonal patterns of observed global sea surface temperature (SST) variability between 1870 and 2006 using the method of Self‐Organizing Maps (SOM). Eight patterns represented the majority of global SST variations associated with the El Niño–Southern Oscillation (ENSO). Time series of the eight patterns exhibited periods with “preferred” SST states since the late 19th century, i.e., when one or more patterns occurred more frequently than in other periods. The eight patterns were used to investigate the global land‐based response of observed extreme temperature and precipitation indices from the HadEX data set to different nodes of SST variability between 1951 and 2003. Results showed very strong statistically significant opposite temperature and precipitation extremes associated with the first pattern (strong La Niña) and the last pattern (strong El Niño). Extreme maximum temperatures were significantly cooler during strong La Niña events than strong El Niño events over Australia, southern Africa, India, and Canada while the converse was true for United States and northeastern Siberia. These responses were larger when global warming was retained. Even intermediate patterns representing a shift from a weak El Niño to a weak La Niña with associated variability in the North Atlantic were linked with statistically significant increases in warm nights and warm days particularly across Scandinavia and northwest Russia. While the link between precipitation extremes and global SST patterns was less spatially coherent, there were large areas across North America and central Europe, which showed statistically significant differences in the response to opposite phases of the El Niño–Southern Oscillation. These results confirm that the variability of global SST anomaly patterns is important for the modulation of extreme temperature and precipitation globally.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".