Statistical characteristics of cloud variability. Part 2: Implication for parameterizations of microphysical and radiative transfer processes in climate models
Abstract
The effects of subgrid cloud variability on gridaverage microphysical rates and radiative fluxes are examined by use of longterm retrieval products at the Tropical West Pacific, Southern Great Plains, and North Slope of Alaska sites of the Department of Energy's Atmospheric Radiation Measurement program. Four commonly used distribution functions, the truncated Gaussian, Gamma, lognormal, and Weibull distributions, are constrained to have the same mean and standard deviation as observed cloud liquid water content. The probability density functions are then used to upscale relevant physical processes to obtain gridaverage process rates. It is found that the truncated Gaussian representation results in up to 30% mean bias in autoconversion rate, whereas the mean bias for the lognormal representation is about 10%. The Gamma and Weibull distribution function performs the best for the gridaverage autoconversion rate with the mean relative bias less than 5%. For radiative fluxes, the lognormal and truncated Gaussian representations perform better than the Gamma and Weibull representations. The results show that the optimal choice of subgrid cloud distribution function depends on the nonlinearity of the process of interest, and thus, there is no single distribution function that works best for all parameterizations. Examination of the scale (window size) dependencemore »
 Authors:

 Brookhaven National Lab. (BNL), Upton, NY (United States)
 Publication Date:
 Research Org.:
 Brookhaven National Lab. (BNL), Upton, NY (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC23)
 OSTI Identifier:
 1169559
 Report Number(s):
 BNL1073252015JA
Journal ID: ISSN 2169897X; R&D Project: 2016BNLEE631EECABudg; KP1703020
 DOE Contract Number:
 DESC00112704
 Resource Type:
 Journal Article
 Journal Name:
 Journal of Geophysical Research: Atmospheres
 Additional Journal Information:
 Journal Volume: 119; Journal Issue: 18; Journal ID: ISSN 2169897X
 Publisher:
 John Wiley & Sons, Inc.
 Country of Publication:
 United States
 Language:
 English
 Subject:
 54 ENVIRONMENTAL SCIENCES
Citation Formats
Huang, Dong, and Liu, Yangang. Statistical characteristics of cloud variability. Part 2: Implication for parameterizations of microphysical and radiative transfer processes in climate models. United States: N. p., 2014.
Web. doi:10.1002/2014JD022003.
Huang, Dong, & Liu, Yangang. Statistical characteristics of cloud variability. Part 2: Implication for parameterizations of microphysical and radiative transfer processes in climate models. United States. doi:10.1002/2014JD022003.
Huang, Dong, and Liu, Yangang. Wed .
"Statistical characteristics of cloud variability. Part 2: Implication for parameterizations of microphysical and radiative transfer processes in climate models". United States. doi:10.1002/2014JD022003.
@article{osti_1169559,
title = {Statistical characteristics of cloud variability. Part 2: Implication for parameterizations of microphysical and radiative transfer processes in climate models},
author = {Huang, Dong and Liu, Yangang},
abstractNote = {The effects of subgrid cloud variability on gridaverage microphysical rates and radiative fluxes are examined by use of longterm retrieval products at the Tropical West Pacific, Southern Great Plains, and North Slope of Alaska sites of the Department of Energy's Atmospheric Radiation Measurement program. Four commonly used distribution functions, the truncated Gaussian, Gamma, lognormal, and Weibull distributions, are constrained to have the same mean and standard deviation as observed cloud liquid water content. The probability density functions are then used to upscale relevant physical processes to obtain gridaverage process rates. It is found that the truncated Gaussian representation results in up to 30% mean bias in autoconversion rate, whereas the mean bias for the lognormal representation is about 10%. The Gamma and Weibull distribution function performs the best for the gridaverage autoconversion rate with the mean relative bias less than 5%. For radiative fluxes, the lognormal and truncated Gaussian representations perform better than the Gamma and Weibull representations. The results show that the optimal choice of subgrid cloud distribution function depends on the nonlinearity of the process of interest, and thus, there is no single distribution function that works best for all parameterizations. Examination of the scale (window size) dependence of the mean bias indicates that the bias in gridaverage process rates monotonically increases with increasing window sizes, suggesting the increasing importance of subgrid variability with increasing grid sizes.},
doi = {10.1002/2014JD022003},
journal = {Journal of Geophysical Research: Atmospheres},
issn = {2169897X},
number = 18,
volume = 119,
place = {United States},
year = {2014},
month = {9}
}