@article{375e80c7f715474cae8176191e6e8f43,
title = "Moist static energy budget analysis of tropical cyclone intensification in high-resolution climate models",
abstract = "Tropical cyclone intensification processes are explored in six high-resolution climate models. The analysis framework employs process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled. These diagnostics include budgets of column moist static energy and the spatial variance of column moist static energy, where the column integral is performed between fixed pressure levels. The latter allows for the quantification of the different feedback processes responsible for the amplification of moist static energy anomalies associated with the organization of convection and cyclone spinup, including surface flux feedbacks and cloud-radiative feedbacks. Tropical cyclones (TCs) are tracked in the climate model simulations and the analysis is applied along the individual tracks and composited over many TCs. Two methods of compositing are employed: a composite over all TC snapshots in a given intensity range, and a composite over all TC snapshots at the same stage in the TC life cycle (same time relative to the time of lifetime maximum intensity for each storm). The radiative feedback contributes to TC development in all models, especially in storms of weaker intensity or earlier stages of development. Notably, the surface flux feedback is stronger in models that simulate more intense TCs. This indicates that the representation of the interaction between spatially varying surface fluxes and the developing TC is responsible for at least part of the intermodel spread in TC simulation.",
author = "Wing, {Allison A.} and Camargo, {Suzana J.} and Sobel, {Adam H.} and Daehyun Kim and Yumin Moon and Hiroyuki Murakami and Reed, {Kevin A.} and Vecchi, {Gabriel A.} and Wehner, {Michael F.} and Colin Zarzycki and Ming Zhao",
note = "Funding Information: Acknowledgments. This work is a contribution to the process-oriented diagnostic effort of the NOAA MAPP Model Diagnostics Task Force. This study was supported by NOAA{\textquoteright}s Climate Program Office{\textquoteright}s Modeling, Analysis, Predictions, and Projections program through Grants NA15OAR4310087, NA15OAR4310095, NA18OAR4310270, NA18OAR4310276, and NA18OAR4310277. Initial work on this study was performed while A. Wing was supported by a NSF AGS Postdoctoral Research Fellowship (AGS-1433251). Y. Moon was supported in part by a NSF AGS Postdoctoral Research Fellowship (AGS-1524270). D. Kim was also supported by the National Aeronautics and Space Administration{\textquoteright}s Modeling, Analysis, and Prediction program under Grant 80NSSC17K0227, the U.S. Department of Energy{\textquoteright}s Regional and Global Model Analysis program under Grant DE-SC0016223, and the Korean Meteorological Administration Research and Development Program under Grant KMI2018-03110. We thank Salvatore Pascale and Lucas Harris for helpful feedback on the manuscript, and Martin Singh and two anonymous reviewers for constructive comments and suggestions. We thank Michael Bosilovich for facilitating access to the GEOS M2-AMIP data and providing additional information about the simulation. The M2-AMIP data are now publicly available through the NASA Center for Climate Simulation (NCCS) DataPortal (https:// portal.nccs.nasa.gov/datashare/gmao_m2amip/). Publisher Copyright: {\textcopyright} 2019 American Meteorological Society.",
year = "2019",
month = sep,
day = "1",
doi = "10.1175/JCLI-D-18-0599.1",
language = "English (US)",
volume = "32",
pages = "6071--6095",
journal = "Journal of Climate",
issn = "0894-8755",
publisher = "American Meteorological Society",
number = "18",
}