The increasing variability of tropical cyclone lifetime maximum intensity
Bibliographic record
Abstract
This study investigates long-term changes in the variability of TC intensity of global tropical cyclones, a topic which has been relatively infrequently studied to date. Our study finds that the variability of global TC lifetime maximum intensity (LMI), as measured by the LMI standard deviation, increases during 1981-2016. The increasing trend in LMI variability is statistically significant for both the Northern and Southern Hemispheres, with three individual TC basins: the western North Pacific, the South Indian and the South Pacific also having statistically significant increases. This increasing trend primarily results from distinct changes in the relative percentages of TCs with different intensities. When comparing two periods: 1981-1998 and 1999-2016, the proportions of weak and strong TCs increase, whereas moderate TCs occur relatively less frequently. This bimodal pattern of observed LMI distribution change is further linked to opposite trends in the average intensities of TCs that undergo rapid intensification (RI) during their lifetime (RI TCs) and those that do not (non-RI TCs). The LMI distributions of RI and non-RI TCs migrate to higher and lower intensities, respectively. Our results demonstrate from an observational perspective that strong TCs have strengthened while weak TCs have weakened as the global climate has warmed since 1981.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".