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Title: Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging: RESOLUTION ADAPTABILITY OF ZM SCHEME

Abstract

Realistic modeling of cumulus convection at fine model resolutions (a few to a few tens of km) is problematic since it requires the cumulus scheme to adapt to higher resolution than they were originally designed for (~100 km). To solve this problem, we implement the spatial averaging method proposed in Xiao et al. (2015) and also propose a temporal averaging method for the large-scale convective available potential energy (CAPE) tendency in the Zhang-McFarlane (ZM) cumulus parameterization. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with both spatial and temporal averaging at 4-32 km resolution is assessed using the Weather Research and Forecasting (WRF) model, by comparing with Cloud Resolving Model (CRM) results. We find that the original ZM scheme has very poor resolution adaptability, with sub-grid convective transport and precipitation increasing significantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves the total transport of moist static energy and total precipitation. With the temporal averaging method, the resolution adaptability of the scheme is further improved, with sub-grid convective precipitation becoming smaller than resolved precipitation for resolution higher than 8 km, which ismore » consistent with the results from the CRM simulation. Both the spatial distribution and time series of precipitation are improved with the spatial and temporal averaging methods. The results may be helpful for developing resolution adaptability for other cumulus parameterizations that are based on quasi-equilibrium assumption.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [2]; ORCiD logo [4]; ORCiD logo [2]; ORCiD logo [2]
  1. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland WA USA; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing China
  2. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland WA USA
  3. Scripps Institution of Oceanography, University of California, San Diego CA USA
  4. NASA Langley Research Center, Hampton VA USA
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1415700
Report Number(s):
PNNL-SA-125729
Journal ID: ISSN 1942-2466; KP1703020
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Advances in Modeling Earth Systems; Journal Volume: 9; Journal Issue: 7
Country of Publication:
United States
Language:
English
Subject:
cumulus convection; zhang-mcfarlane; cloud resolving model; CRM

Citation Formats

Yun, Yuxing, Fan, Jiwen, Xiao, Heng, Zhang, Guang J., Ghan, Steven J., Xu, Kuan-Man, Ma, Po-Lun, and Gustafson, William I. Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging: RESOLUTION ADAPTABILITY OF ZM SCHEME. United States: N. p., 2017. Web. doi:10.1002/2017MS001035.
Yun, Yuxing, Fan, Jiwen, Xiao, Heng, Zhang, Guang J., Ghan, Steven J., Xu, Kuan-Man, Ma, Po-Lun, & Gustafson, William I. Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging: RESOLUTION ADAPTABILITY OF ZM SCHEME. United States. doi:10.1002/2017MS001035.
Yun, Yuxing, Fan, Jiwen, Xiao, Heng, Zhang, Guang J., Ghan, Steven J., Xu, Kuan-Man, Ma, Po-Lun, and Gustafson, William I. Wed . "Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging: RESOLUTION ADAPTABILITY OF ZM SCHEME". United States. doi:10.1002/2017MS001035.
@article{osti_1415700,
title = {Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging: RESOLUTION ADAPTABILITY OF ZM SCHEME},
author = {Yun, Yuxing and Fan, Jiwen and Xiao, Heng and Zhang, Guang J. and Ghan, Steven J. and Xu, Kuan-Man and Ma, Po-Lun and Gustafson, William I.},
abstractNote = {Realistic modeling of cumulus convection at fine model resolutions (a few to a few tens of km) is problematic since it requires the cumulus scheme to adapt to higher resolution than they were originally designed for (~100 km). To solve this problem, we implement the spatial averaging method proposed in Xiao et al. (2015) and also propose a temporal averaging method for the large-scale convective available potential energy (CAPE) tendency in the Zhang-McFarlane (ZM) cumulus parameterization. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with both spatial and temporal averaging at 4-32 km resolution is assessed using the Weather Research and Forecasting (WRF) model, by comparing with Cloud Resolving Model (CRM) results. We find that the original ZM scheme has very poor resolution adaptability, with sub-grid convective transport and precipitation increasing significantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves the total transport of moist static energy and total precipitation. With the temporal averaging method, the resolution adaptability of the scheme is further improved, with sub-grid convective precipitation becoming smaller than resolved precipitation for resolution higher than 8 km, which is consistent with the results from the CRM simulation. Both the spatial distribution and time series of precipitation are improved with the spatial and temporal averaging methods. The results may be helpful for developing resolution adaptability for other cumulus parameterizations that are based on quasi-equilibrium assumption.},
doi = {10.1002/2017MS001035},
journal = {Journal of Advances in Modeling Earth Systems},
number = 7,
volume = 9,
place = {United States},
year = {Wed Nov 01 00:00:00 EDT 2017},
month = {Wed Nov 01 00:00:00 EDT 2017}
}