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

Fu, Dan; Chang, Ping; Patricola, Christina M.; Saravanan, R.

doi:10.1175/jcli-d-19-0130.1

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. In this work 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. Themore »« less

Authors:

Fu, Dan ^[1]; Chang, Ping ^[1]; Patricola, Christina M. ^[2]; Saravanan, R. ^[3]

Texas A & M Univ., College Station, TX (United States); Ocean Univ. of China, Qingdao (China); Qingdao National Lab. for Marine Science and Technology (China)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Texas A & M Univ., College Station, TX (United States)

Publication Date:: Thu Oct 24 00:00:00 EDT 2019

Research Org.:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: National Science Foundation (NSF); National Natural Science Foundation of China (NSFC); Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division

OSTI Identifier:: 1580965

Grant/Contract Number:: AC02-05CH11231; AGS-1462127; 41490644; U1406401

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

Subject:: 54 ENVIRONMENTAL SCIENCES; Tropical cyclones; Ensembles; Model evaluation/performance; Nonhydrostatic models; Interannual variability; Intraseasonal variability

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.

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                    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.  https://doi.org/10.1175/jcli-d-19-0130.1

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                    Fu, Dan, Chang, Ping, Patricola, Christina M., and Saravanan, R. Thu .  
"High-Resolution Tropical Channel Model Simulations of Tropical Cyclone Climatology and Intraseasonal-to-Interannual Variability".  United States.  https://doi.org/10.1175/jcli-d-19-0130.1.  https://www.osti.gov/servlets/purl/1580965.

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@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. In this work 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         = {Thu Oct 24 00:00:00 EDT 2019},

  month        = {Thu Oct 24 00:00:00 EDT 2019}

}

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https://doi.org/10.1175/jcli-d-19-0130.1

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Cited by: 7 works

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FIG. 1: TCM model domain and orography (m).

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