MétaCan
Menu
Back to cohort
Record W4414195150 · doi:10.1073/pnas.2424385122

Tropical cyclones expand faster at warmer relative sea surface temperature

2025· article· en· W4414195150 on OpenAlexaff
Danyang Wang, Daniel R. Chavas, Benjamin A. Schenkel

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsImpact
FundersNational Science Foundation
KeywordsSea surface temperatureTropical cycloneNorthern HemisphereStorm trackStormGlobal warmingClimate changeAtlantic hurricane

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.289
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicTropical and Extratropical Cyclones ResearchFrench-language works237,207