The critical role of cloud-infrared radiation feedback in tropical cyclone development.

clouds feedback hurricane radiation tropical cyclone

Journal

Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876

Informations de publication

Date de publication:
10 11 2020
Historique:
pubmed: 28 10 2020
medline: 28 10 2020
entrez: 27 10 2020
Statut: ppublish

Résumé

The tall clouds that comprise tropical storms, hurricanes, and typhoons-or more generally, tropical cyclones (TCs)-are highly effective at trapping the infrared radiation welling up from the surface. This cloud-infrared radiation feedback, referred to as the "cloud greenhouse effect," locally warms the lower-middle troposphere relative to a TC's surroundings through all stages of its life cycle. Here, we show that this effect is essential to promoting and accelerating TC development in the context of two archetypal storms-Super Typhoon Haiyan (2013) and Hurricane Maria (2017). Namely, this feedback strengthens the thermally direct transverse circulation of the developing storm, in turn both promoting saturation within its core and accelerating the spin-up of its surface tangential circulation through angular momentum convergence. This feedback therefore shortens the storm's gestation period prior to its rapid intensification into a strong hurricane or typhoon. Further research into this subject holds the potential for key progress in TC prediction, which remains a critical societal challenge.

Identifiants

pubmed: 33106402
pii: 2013584117
doi: 10.1073/pnas.2013584117
pmc: PMC7668167
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

27884-27892

Déclaration de conflit d'intérêts

The authors declare no competing interest.

Références

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Proc Natl Acad Sci U S A. 2020 Nov 10;117(45):27884-27892
pubmed: 33106402
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Proc Natl Acad Sci U S A. 2018 Mar 20;115(12):2930-2935
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Auteurs

James H Ruppert (JH)

Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA 16802; james.ruppert@psu.edu.
Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, PA 16802.

Allison A Wing (AA)

Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306.

Xiaodong Tang (X)

Key Laboratory of Mesoscale Severe Weather, Ministry of Education, and School of Atmospheric Sciences, Nanjing University, Nanjing 210093, China.

Erika L Duran (EL)

Earth System Science Center, University of Alabama in Huntsville/NASA Short-term Prediction Research and Transition (SPoRT) Center, Huntsville, AL 35805.

Classifications MeSH