1. Introduction
Increasingly, atmospheric models are incorporating more complex and physically realistic microphysical models within their frameworks. These schemes vary in their
complexity from the number of moments of the droprice spectra predicted to the number of classes of hydrometeors defined. At the same time, accurate and efficient
computations of the atmospheric radiative budget are necessary for reasons ranging from
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climate considerations e.g., Curry and Ebert, 1990; Royer et al., 1990; Curry, 1995 to Ž
. Ž
the detailed simulation of clouds on the Large Eddy Simulation LES scale e.g., .
Stevens et al., 1996 . Since real clouds interact strongly with both solar and infrared radiation, it is desirable for current radiation schemes to interact realistically with the
clouds predicted in these models. Microphysical schemes that are coupled to dynamical models can generally be
divided into two classes: the first is normally referred to as a bulk or explicit Ž
. microphysical scheme e.g., Walko et al., 1995 and the second is that of a binned
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microphysical scheme e.g., Feingold et al., 1994 . Bulk microphysical schemes vary in complexity. However, they are all built upon the same underlying assumption, which
fixes the functional form of the hydrometeor size spectra. In many cases, this functional Ž
. form is assumed to be the generalized gamma distribution function Walko et al., 1995 .
Using this function, either one or two moments of the hydrometeor spectra are Ž .
prognosed. In most cases, one-moment schemes predict the total mass mixing-ratio r
l
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of a given hydrometeor class e.g., Rutledge and Hobbs, 1984; Walko et al., 1995 while Ž
two-moment schemes also predict the number concentration e.g., Ferrier, 1994; Meyers .
et al., 1997 . The number of hydrometeor classes used in a model is somewhat dependent upon the class distinctions made by the developer; however, most follow a
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similar construction e.g., Ferrier, 1994; Walko et al., 1995; Meyers et al., 1997 . Binned microphysical models make no assumptions about the functional form of the
hydrometeor size spectrum. Instead, a preset number of bins are defined for each hydrometeor class and the number concentration and mass are then predicted for each
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bin Feingold et al., 1994; Kogan et al., 1995; Reisin et al., 1996 . Such models allow for a much more realistic evolution of the hydrometeor spectra, but at a high computa-
tional cost. In each of these modeling frameworks, assumptions must be made about the shape
and growth characteristics of the various ice species that exist within the model. A successful radiative transfer scheme should include appropriate parameterizations of the
cloud optical properties for each hydrometeor class, whether water or ice. The purpose of the present paper is twofold. First, we present the cloud microphysi-
cal-radiative transfer coupling developed for use in the Regional Atmospheric Modeling Ž
. System RAMS . We illustrate the parameterization’s flexibility and its potential accu-
racy. Second, we use this parameterization to examine the importance of various microphysical parameter choices to the computation of the radiative heat budget of the
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mixed-phase Arctic cloudy boundary layer BL and the surface. Few studies have examined the radiative influence of mixed-phase clouds in general, and those that have
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considered simple, idealized cloudy situations e.g., Sun and Shine, 1994 . Nonetheless, these studies have illustrated that ice-phase optical effects cannot necessarily be ignored
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Ž Sun and Shine, 1994 and may even have a significant impact on climate Sun and
. Shine, 1995 . Mixed-phase clouds cover the Arctic Ocean throughout a large portion of
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the year e.g., Intrieri et al., 1999 and, thus, have a significant impact on the radiative Ž
budget of the Arctic Ocean. This is quite important as many studies e.g., Curry and .
Ebert, 1990; Royer et al., 1990; Curry, 1995; Lynch et al., 1995 have illustrated the possible sensitivity of the Arctic system to alterations in cloud radiative properties,
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particularly the frequently observed low-lying Arctic stratus clouds ASC . Since
microphysical data are sparse for ASC, we use microphysical information derived from Ž
. RAMS bin microphysical simulations of ASC Harrington et al., 1999 , which compare
favorably with observations in our radiative computations. We pick cases that span a large range of liquid and ice water paths, covering the ranges observed in the Arctic.
This model information is useful since the simulated clouds behave like observed ASC and have microphysical structures similar to available observations. However, it must
always be kept in mind that this information is derived from a numerical model. Thus, our results should be viewed as an attempt to assess the possible qualitatiÕe impacts of
cloud microstructure on the Arctic radiation budget.
2. Bulk model parameterization
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