Tropical cyclones expand faster at warmer relative sea surface temperature
Bibliographic record
Abstract
Tropical cyclones are expected to intensify more rapidly with warming, but relatively little work has examined whether they could expand more rapidly with warming, too. Recent theory predicts that peak expansion rate should increase with sea surface temperature (SST), and physical arguments suggest this dependence should be specifically on the relative SST, i.e. the SST difference from the tropical mean. We test this hypothesis with historical observational data, in which SST variations are primarily variations in relative SST. Both average and peak expansion rates are found to systematically increase with relative SST globally across the Northern Hemisphere (27.2 and 37.5 km/d/K) and within each individual basin. Results are robust across both reanalysis and Best Track observational datasets. Uniform-SST aquaplanet simulations show a much weaker dependence of maximum expansion rate on absolute SST, suggesting that the dominant dependence is on relative SST. Hence, mean global warming is not expected to strongly change storm size dynamics, but patterns of sea surface warming may play an important role in determining how storm size, and hence coastal risk, may change in the future. This work can also help improve forecasting of the wind field and its hazards and impacts at landfall.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".