skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

This content will become publicly available on November 1, 2020

Title: High-Resolution Tropical Channel Model Simulations of Tropical Cyclone Climatology and Intraseasonal-to-Interannual Variability

Abstract

We tailored a tropical channel configuration of the Weather Research and Forecasting (WRF) Model to study tropical cyclone (TC) activity and associated climate variabilities. This tropical channel model (TCM) covers from 30°S to 50°N at 27-km horizontal resolution, with physics parameterizations carefully selected to achieve more realistic simulations of TCs and large-scale climate mean states. We performed 15-member ensembles of retrospective simulations from 1982 to 2016 hurricane seasons. A thorough comparison with observations demonstrates that the TCM yields significant skills in simulating TC activity climatology and variabilities in each basin, as well as TC physical structures. The correlation of the ensemble averaged accumulated cyclone energy (ACE) with observations in the western North Pacific (WNP), eastern North Pacific (ENP), and North Atlantic (NAT) is 0.80, 0.64, and 0.61, respectively, but is insignificant in the north Indian Ocean (NIO). Moreover, the TCM-simulated modulations of El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO) on the large-scale environment and TC genesis also agree well with observations. To examine the TCM’s potential for seasonal TC prediction, the model is used to forecast the 2017 and 2018 hurricane seasons, using bias-corrected sea surface temperatures (SSTs) from the CFSv2 seasonal prediction results. The TCM accurately predictsmore » the hyperactive 2017 NAT hurricane season and near-normal WNP and ENP hurricane seasons when initialized in May. In addition, the TCM accurately predicts TC activity in the NAT and WNP during the 2018 season, but underpredicts ENP TC activity, in association with a poor ENSO forecast.« less

Authors:
 [1];  [2];  [3];  [4]
  1. Department of Oceanography, Texas A&M University, College Station, Texas, and Physical Oceanography Laboratory/CIMST, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
  2. Department of Oceanography, and Department of Atmospheric Sciences, Texas A&M University, College Station, Texas, and Physical Oceanography Laboratory/CIMST, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
  3. Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California
  4. Department of Atmospheric Sciences, Texas A&M University, College Station, Texas
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
National Science Foundation (NSF)
OSTI Identifier:
1580965
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Volume: 32; Journal Issue: 22; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English

Citation Formats

Fu, Dan, Chang, Ping, Patricola, Christina M., and Saravanan, R. High-Resolution Tropical Channel Model Simulations of Tropical Cyclone Climatology and Intraseasonal-to-Interannual Variability. United States: N. p., 2019. Web. doi:10.1175/jcli-d-19-0130.1.
Fu, Dan, Chang, Ping, Patricola, Christina M., & Saravanan, R. High-Resolution Tropical Channel Model Simulations of Tropical Cyclone Climatology and Intraseasonal-to-Interannual Variability. United States. doi:10.1175/jcli-d-19-0130.1.
Fu, Dan, Chang, Ping, Patricola, Christina M., and Saravanan, R. Fri . "High-Resolution Tropical Channel Model Simulations of Tropical Cyclone Climatology and Intraseasonal-to-Interannual Variability". United States. doi:10.1175/jcli-d-19-0130.1.
@article{osti_1580965,
title = {High-Resolution Tropical Channel Model Simulations of Tropical Cyclone Climatology and Intraseasonal-to-Interannual Variability},
author = {Fu, Dan and Chang, Ping and Patricola, Christina M. and Saravanan, R.},
abstractNote = {We tailored a tropical channel configuration of the Weather Research and Forecasting (WRF) Model to study tropical cyclone (TC) activity and associated climate variabilities. This tropical channel model (TCM) covers from 30°S to 50°N at 27-km horizontal resolution, with physics parameterizations carefully selected to achieve more realistic simulations of TCs and large-scale climate mean states. We performed 15-member ensembles of retrospective simulations from 1982 to 2016 hurricane seasons. A thorough comparison with observations demonstrates that the TCM yields significant skills in simulating TC activity climatology and variabilities in each basin, as well as TC physical structures. The correlation of the ensemble averaged accumulated cyclone energy (ACE) with observations in the western North Pacific (WNP), eastern North Pacific (ENP), and North Atlantic (NAT) is 0.80, 0.64, and 0.61, respectively, but is insignificant in the north Indian Ocean (NIO). Moreover, the TCM-simulated modulations of El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO) on the large-scale environment and TC genesis also agree well with observations. To examine the TCM’s potential for seasonal TC prediction, the model is used to forecast the 2017 and 2018 hurricane seasons, using bias-corrected sea surface temperatures (SSTs) from the CFSv2 seasonal prediction results. The TCM accurately predicts the hyperactive 2017 NAT hurricane season and near-normal WNP and ENP hurricane seasons when initialized in May. In addition, the TCM accurately predicts TC activity in the NAT and WNP during the 2018 season, but underpredicts ENP TC activity, in association with a poor ENSO forecast.},
doi = {10.1175/jcli-d-19-0130.1},
journal = {Journal of Climate},
number = 22,
volume = 32,
place = {United States},
year = {2019},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on November 1, 2020
Publisher's Version of Record

Save / Share: