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Title: Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model

Abstract

Abstract. A stochastic deep convection parameterization is implemented into the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1.0 (EAMv1). This study evaluates its performance in simulating precipitation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. The well-known problem of “too much light rain and too little heavy rain” is alleviated, especially over the tropics. As a result, the contribution from different rain rates to the total precipitation amount is shifted toward heavier rain. The less frequent occurrence of convection contributes to suppressed light rain, while more intense large-scale and convective precipitation contributes to enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. The sensitivity of the rainfall intensity PDF to the model vertical resolution is examined. The relationship between precipitation and dilute convective available potential energy in the stochastic simulation agrees better with that in the Atmospheric Radiation Measurement (ARM) observations compared with the standard model simulation. The annual mean precipitation is largely unchanged with the use of the stochastic scheme except over the tropical western Pacific, where a moderate increasemore » in precipitation represents a slight improvement. The responses of precipitation and its extremes to climate warming are similar with or without the stochastic deep convection scheme.« less

Authors:
; ; ; ; ; ORCiD logo; ORCiD logo
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC); USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1771346
Alternate Identifier(s):
OSTI ID: 1785914
Report Number(s):
LLNL-JRNL-820861
Journal ID: ISSN 1991-9603
Grant/Contract Number:  
SC0019373; AC52-07NA27344; 2017YFA0604000; 41975126; 41605074; AC02-05CH11231
Resource Type:
Published Article
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online) Journal Volume: 14 Journal Issue: 3; Journal ID: ISSN 1991-9603
Publisher:
Copernicus GmbH
Country of Publication:
Germany
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Wang, Yong, Zhang, Guang J., Xie, Shaocheng, Lin, Wuyin, Craig, George C., Tang, Qi, and Ma, Hsi-Yen. Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model. Germany: N. p., 2021. Web. doi:10.5194/gmd-14-1575-2021.
Wang, Yong, Zhang, Guang J., Xie, Shaocheng, Lin, Wuyin, Craig, George C., Tang, Qi, & Ma, Hsi-Yen. Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model. Germany. https://doi.org/10.5194/gmd-14-1575-2021
Wang, Yong, Zhang, Guang J., Xie, Shaocheng, Lin, Wuyin, Craig, George C., Tang, Qi, and Ma, Hsi-Yen. Thu . "Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model". Germany. https://doi.org/10.5194/gmd-14-1575-2021.
@article{osti_1771346,
title = {Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model},
author = {Wang, Yong and Zhang, Guang J. and Xie, Shaocheng and Lin, Wuyin and Craig, George C. and Tang, Qi and Ma, Hsi-Yen},
abstractNote = {Abstract. A stochastic deep convection parameterization is implemented into the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1.0 (EAMv1). This study evaluates its performance in simulating precipitation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. The well-known problem of “too much light rain and too little heavy rain” is alleviated, especially over the tropics. As a result, the contribution from different rain rates to the total precipitation amount is shifted toward heavier rain. The less frequent occurrence of convection contributes to suppressed light rain, while more intense large-scale and convective precipitation contributes to enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. The sensitivity of the rainfall intensity PDF to the model vertical resolution is examined. The relationship between precipitation and dilute convective available potential energy in the stochastic simulation agrees better with that in the Atmospheric Radiation Measurement (ARM) observations compared with the standard model simulation. The annual mean precipitation is largely unchanged with the use of the stochastic scheme except over the tropical western Pacific, where a moderate increase in precipitation represents a slight improvement. The responses of precipitation and its extremes to climate warming are similar with or without the stochastic deep convection scheme.},
doi = {10.5194/gmd-14-1575-2021},
journal = {Geoscientific Model Development (Online)},
number = 3,
volume = 14,
place = {Germany},
year = {Thu Mar 18 00:00:00 EDT 2021},
month = {Thu Mar 18 00:00:00 EDT 2021}
}

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https://doi.org/10.5194/gmd-14-1575-2021

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