A parametrization of 3‐D subgrid‐scale clouds for conventional GCMs: Assessment using A‐Train satellite data and solar radiative transfer characteristics
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ABSTRACTA stochastic algorithm for generating 3‐D cloud fields based on profiles of cloud fraction
and mean cloud water content is presented and assessed using cloud properties inferred from A‐Train satellite data. The ultimate intention is to employ the algorithm, along with 3‐D radiative transfer (RT) models, in Global Climate Models (GCMs). The algorithm approaches cloud fields as whole objects demarcated by contiguous layers with
. This contrasts with conventional GCM radiation routines that deal with clouds on a per‐(arbitrary) layer basis. A‐Train cloud data for August 2007 were partitioned into ∼29,000 domains, each ∼280 km long, to represent nominal GCM columns. For each A‐Train/stochastic pair of domains, profiles of domain‐averaged fluxes were computed by a 1‐D broadband solar RT model in Independent Column Approximation mode. Globally averaged, mean bias error for upwelling radiation at top‐of‐atmosphere (TOA) is 6.8 W m−2. Upon advancing the RT model to 2‐D, differences between 1‐D and 2‐D upwelling fluxes at TOA for A‐Train domains differed from corresponding differences for model‐generated domains by ∼1 W m−2, on average, with differences for the model domains exhibiting stronger dependence on solar zenith angle
. Moving on to 3‐D RT for model domains, 1‐D–3‐D differences became slightly stronger functions of
thanks mostly to accentuated 3‐D effects at small
. Simple parametrizations for the stochastic algorithm's variables that govern horizontal and vertical structure of clouds should be adequate to capture the ramifications of systematic neglect of 3‐D solar RT in GCMs.
