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A Semiempirical Cloudiness Parameterization for Use in Climate Models

Kuan-Man XuDepartment of Atmospheric Science, Colorado State University, Fort Collins. Colorado

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David A. RandallDepartment of Atmospheric Science, Colorado State University, Fort Collins. Colorado

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Abstract

Data produced from explicit simulations of observed tropical cloud systems and subtropical stratocumuli are used to develop a “semiempirical” cloudiness parameterization for use in climate models. The semiempirical cloudiness parameterization uses the large-scale average condensate (cloud water and cloud ice) mixing ratio as the primary predictor. The large-scale relative humidity and cumulus mass flux are also used in the parameterization as secondary predictors. The cloud amount is assumed to vary exponentially with the large-scale average condensate mixing ratio. The rate of variation is, however, a function of large-scale relative humidity and the intensity of convective circulations. The validity of such EL semiempirical approach and its dependency on cloud regime and horizontal-averaging distance are explored with the simulated datasets.

Abstract

Data produced from explicit simulations of observed tropical cloud systems and subtropical stratocumuli are used to develop a “semiempirical” cloudiness parameterization for use in climate models. The semiempirical cloudiness parameterization uses the large-scale average condensate (cloud water and cloud ice) mixing ratio as the primary predictor. The large-scale relative humidity and cumulus mass flux are also used in the parameterization as secondary predictors. The cloud amount is assumed to vary exponentially with the large-scale average condensate mixing ratio. The rate of variation is, however, a function of large-scale relative humidity and the intensity of convective circulations. The validity of such EL semiempirical approach and its dependency on cloud regime and horizontal-averaging distance are explored with the simulated datasets.

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