Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging
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 »
- Authors:
-
- 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
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland WA USA
- Scripps Institution of Oceanography, University of California, San Diego CA USA
- NASA Langley Research Center, Hampton VA USA
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); Scientific Discovery through Advanced Computing (SciDAC); USDOE Office of Science (SC), Biological and Environmental Research (BER); Atmospheric System Research; National Natural Science Foundation of China (NSFC)
- OSTI Identifier:
- 1410703
- Alternate Identifier(s):
- OSTI ID: 1410704; OSTI ID: 1415700
- Report Number(s):
- PNNL-SA-125729
Journal ID: ISSN 1942-2466
- Grant/Contract Number:
- AC05-76RL01830; 91437101
- Resource Type:
- Journal Article: Published Article
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Additional Journal Information:
- Journal Name: Journal of Advances in Modeling Earth Systems Journal Volume: 9 Journal Issue: 7; Journal ID: ISSN 1942-2466
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; cumulus convection; zhang-mcfarlane; cloud resolving model; CRM; resolution adaptability; convective parameterizations; Zhang‐McFarlane cumulus scheme; scale awareness; high‐resolution climate model; convective precipitation
Citation Formats
Yun, Yuxing, Fan, Jiwen, Xiao, Heng, Zhang, Guang J., Ghan, Steven J., Xu, Kuan-Man, Ma, Po-Lun, and Gustafson, Jr., William I. Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging. 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, Jr., William I. Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging. United States. https://doi.org/10.1002/2017MS001035
Yun, Yuxing, Fan, Jiwen, Xiao, Heng, Zhang, Guang J., Ghan, Steven J., Xu, Kuan-Man, Ma, Po-Lun, and Gustafson, Jr., William I. 2017.
"Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging". United States. https://doi.org/10.1002/2017MS001035.
@article{osti_1410703,
title = {Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging},
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, Jr., 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},
url = {https://www.osti.gov/biblio/1410703},
journal = {Journal of Advances in Modeling Earth Systems},
issn = {1942-2466},
number = 7,
volume = 9,
place = {United States},
year = {Thu Nov 30 00:00:00 EST 2017},
month = {Thu Nov 30 00:00:00 EST 2017}
}
Web of Science
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