Can the Multiscale Modeling Framework (MMF) Simulate the MCS‐Associated Precipitation Over the Central United States?

Abstract Mesoscale convective systems (MCSs) are a major source of precipitation in many regions of the world. Traditional global climate models (GCMs) do not have adequate parameterizations to represent MCSs. In contrast, the Multiscalex Modeling Framework (MMF), which explicitly resolves convectio...

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Main Authors: Guangxing Lin, Jiwen Fan, Zhe Feng, William I. Gustafson Jr, Po‐Lun Ma, Kai Zhang
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2019-12-01
Series:Journal of Advances in Modeling Earth Systems
Online Access:https://doi.org/10.1029/2019MS001849
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spelling doaj-d18ad369550648738f8ec297ec6043cf2020-11-24T21:43:28ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662019-12-0111124669468610.1029/2019MS001849Can the Multiscale Modeling Framework (MMF) Simulate the MCS‐Associated Precipitation Over the Central United States?Guangxing Lin0Jiwen Fan1Zhe Feng2William I. Gustafson Jr3Po‐Lun Ma4Kai Zhang5Atmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USAAtmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USAAtmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USAAtmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USAAtmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USAAtmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USAAbstract Mesoscale convective systems (MCSs) are a major source of precipitation in many regions of the world. Traditional global climate models (GCMs) do not have adequate parameterizations to represent MCSs. In contrast, the Multiscalex Modeling Framework (MMF), which explicitly resolves convection within the cloud‐resolving model embedded in each GCM column, has been shown to be a promising tool for simulating MCSs, particularly over the Tropics. In this work, we use ground‐based radar‐observed precipitation, North American Regional Reanalysis data, and a high‐resolution Weather Research and Forecasting simulation to evaluate in detail the MCS‐associated precipitation over the central United States predicted by a prototype MMF simulation that has a 2° host‐GCM grid. We show that the prototype MMF with nudged winds fails to capture the convective initiation in three out of four major MCS events during May 201x1 and underpredicts the precipitation rates for the remaining event, because the model cannot resolve the mesoscale drylines/fronts that are important drivers for initiating convection over the Southern Great Plains region. By reducing the host‐GCM grid spacing to 0.25° in the MMF and nudging the winds, the simulation is able to better capture the mesoscale dynamics, which drastically improves the model performance. We also show that the MMF model performs better than the traditional GCM in capturing the precipitation intensity. Our results suggest that increasing resolution plays a dominant role in improving the simulation of precipitation in the MMF, and the cloud‐resolving model embedded in each GCM column further helps to boost precipitation rate.https://doi.org/10.1029/2019MS001849
collection DOAJ
language English
format Article
sources DOAJ
author Guangxing Lin
Jiwen Fan
Zhe Feng
William I. Gustafson Jr
Po‐Lun Ma
Kai Zhang
spellingShingle Guangxing Lin
Jiwen Fan
Zhe Feng
William I. Gustafson Jr
Po‐Lun Ma
Kai Zhang
Can the Multiscale Modeling Framework (MMF) Simulate the MCS‐Associated Precipitation Over the Central United States?
Journal of Advances in Modeling Earth Systems
author_facet Guangxing Lin
Jiwen Fan
Zhe Feng
William I. Gustafson Jr
Po‐Lun Ma
Kai Zhang
author_sort Guangxing Lin
title Can the Multiscale Modeling Framework (MMF) Simulate the MCS‐Associated Precipitation Over the Central United States?
title_short Can the Multiscale Modeling Framework (MMF) Simulate the MCS‐Associated Precipitation Over the Central United States?
title_full Can the Multiscale Modeling Framework (MMF) Simulate the MCS‐Associated Precipitation Over the Central United States?
title_fullStr Can the Multiscale Modeling Framework (MMF) Simulate the MCS‐Associated Precipitation Over the Central United States?
title_full_unstemmed Can the Multiscale Modeling Framework (MMF) Simulate the MCS‐Associated Precipitation Over the Central United States?
title_sort can the multiscale modeling framework (mmf) simulate the mcs‐associated precipitation over the central united states?
publisher American Geophysical Union (AGU)
series Journal of Advances in Modeling Earth Systems
issn 1942-2466
publishDate 2019-12-01
description Abstract Mesoscale convective systems (MCSs) are a major source of precipitation in many regions of the world. Traditional global climate models (GCMs) do not have adequate parameterizations to represent MCSs. In contrast, the Multiscalex Modeling Framework (MMF), which explicitly resolves convection within the cloud‐resolving model embedded in each GCM column, has been shown to be a promising tool for simulating MCSs, particularly over the Tropics. In this work, we use ground‐based radar‐observed precipitation, North American Regional Reanalysis data, and a high‐resolution Weather Research and Forecasting simulation to evaluate in detail the MCS‐associated precipitation over the central United States predicted by a prototype MMF simulation that has a 2° host‐GCM grid. We show that the prototype MMF with nudged winds fails to capture the convective initiation in three out of four major MCS events during May 201x1 and underpredicts the precipitation rates for the remaining event, because the model cannot resolve the mesoscale drylines/fronts that are important drivers for initiating convection over the Southern Great Plains region. By reducing the host‐GCM grid spacing to 0.25° in the MMF and nudging the winds, the simulation is able to better capture the mesoscale dynamics, which drastically improves the model performance. We also show that the MMF model performs better than the traditional GCM in capturing the precipitation intensity. Our results suggest that increasing resolution plays a dominant role in improving the simulation of precipitation in the MMF, and the cloud‐resolving model embedded in each GCM column further helps to boost precipitation rate.
url https://doi.org/10.1029/2019MS001849
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