2017
DOI: 10.1002/qj.3178
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Regime dependence of ice cloud heterogeneity – a convective life‐cycle effect?

Abstract: Cloud condensate varies on scales smaller than those typically resolved by global weather and climate models. In order to accurately predict the radiative and microphysical process rates representative of the entire model grid box, the effect of the subgrid‐scale heterogeneity of cloud must be taken into account. In this study, observed ice water content retrieved from A‐Train satellite observations is used to explore how spatial ice condensate variability, characterized by the fractional standard deviation (F… Show more

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Cited by 4 publications
(4 citation statements)
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References 35 publications
(68 reference statements)
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“…Another challenge is to refine many of the assumptions made in the radiation scheme that determine the radiative effect of clouds, such that they agree as well as possible with the latest observations, and are consistent with the IFS cloud scheme and the observational forward operators in the data assimilation system. This could include switching from EXP‐EXP to EXP‐RAN overlap, changing the fractional standard deviation of in‐cloud water content from 1 to a value closer to observations (e.g., Ahlgrimm & Forbes, ; Shonk et al, ), representing convective clouds in the radiation scheme, and making use of an ice optics parameterization that represents more recent findings that ice particles are optically rough (Baran et al, ; Yi et al, ). Although not affordable operationally, ecRad also has the option to represent 3‐D effects for the first time in a global model.…”
Section: Discussionmentioning
confidence: 99%
“…Another challenge is to refine many of the assumptions made in the radiation scheme that determine the radiative effect of clouds, such that they agree as well as possible with the latest observations, and are consistent with the IFS cloud scheme and the observational forward operators in the data assimilation system. This could include switching from EXP‐EXP to EXP‐RAN overlap, changing the fractional standard deviation of in‐cloud water content from 1 to a value closer to observations (e.g., Ahlgrimm & Forbes, ; Shonk et al, ), representing convective clouds in the radiation scheme, and making use of an ice optics parameterization that represents more recent findings that ice particles are optically rough (Baran et al, ; Yi et al, ). Although not affordable operationally, ecRad also has the option to represent 3‐D effects for the first time in a global model.…”
Section: Discussionmentioning
confidence: 99%
“…From Atmospheric Radiation Measurement ground‐based observations, Ahlgrimm and Forbes () confirm that the FSD of isolated cirrus clouds, at 80‐km scale, falls largely within the range of parameterized values from Hill et al (). Ahlgrimm and Forbes () refined the formulation by Hill et al (), wherein FSD is dependent on total water and is enhanced for convective situations based on the detrainment ratio.…”
Section: Methodsmentioning
confidence: 99%
“…To quantify f σ , the ratio of the standard deviation of in‐cloud water content to the mean in‐cloud water content, the cloud water content sub‐grid variability needs to be provided. The sub‐grid variability of cloud water content may be assumed or diagnosed (e.g., Ahlgrimm & Forbes, 2017). Some GCMs (e.g., Bogenschutz et al., 2012) have adopted the probability density function (PDF)‐based boundary layer cloud parameterization scheme (Golaz et al., 2002) to treat shallow convection.…”
Section: Model Data and Methodsmentioning
confidence: 99%