@article{a62a61a400334465bbb8847550c16d18,
title = "Hurricane annual cycle controlled by both seeds and genesis probability",
abstract = "Understanding tropical cyclone (TC) climatology is a problem of profound societal significance and deep scientific interest. The annual cycle is the biggest radiatively forced signal in TC variability, presenting a key test of our understanding and modeling of TC activity. TCs over the North Atlantic (NA) basin, which are usually called hurricanes, have a sharp peak in the annual cycle, with more than half concentrated in only 3 mo (August to October), yet existing theories of TC genesis often predict a much smoother cycle. Here we apply a framework originally developed to study TC response to climate change in which TC genesis is determined by both the number of pre-TC synoptic disturbances (TC “seeds”) and the probability of TC genesis from the seeds. The combination of seeds and probability predicts a more consistent hurricane annual cycle, reproducing the compact season, as well as the abrupt increase from July to August in the NA across observations and climate models. The seeds-probability TC genesis framework also successfully captures TC annual cycles in different basins. The concise representation of the climate sensitivity of TCs from the annual cycle to climate change indicates that the framework captures the essential elements of the TC climate connection.",
keywords = "Annual cycle, Hurricane, TC seeds, Tropical cyclone",
author = "Wenchang Yang and Hsieh, {Tsung Lin} and Vecchi, {Gabriel A.}",
note = "Funding Information: ACKNOWLEDGMENTS. The simulations presented in this article were performed on computational resources managed and supported by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering and the Office of Information Technology{\textquoteright}s High Performance Computing Center and Visualization Laboratory at Princeton University. This work is supported by NOAA/Ocean Climate Observation Program (OCO) (Award NA18OAR4310418), NOAA/Modeling, Analysis, Predictions, and Projections (MAPP) (Award NA18OAR4310273), and the Carbon Mitigation Initiative at Princeton University. Funding Information: The simulations presented in this article were performed on computational resources managed and supported by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering and the Office of Information Technology?s High Performance Computing Center and Visualization Laboratory at Princeton University. This work is supported by NOAA/Ocean Climate Observation Program (OCO) (Award NA18OAR4310418), NOAA/Modeling, Analysis, Predictions, and Projections (MAPP) (Award NA18OAR4310273), and the Carbon Mitigation Initiative at Princeton University. Publisher Copyright: {\textcopyright} 2021 National Academy of Sciences. All rights reserved.",
year = "2021",
month = oct,
day = "12",
doi = "10.1073/pnas.2108397118",
language = "English (US)",
volume = "118",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "National Academy of Sciences",
number = "41",
}