A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields

[1] Radiative transfer schemes in large-scale models tightly couple assumptions about cloud structure to methods for solving the radiative transfer equation, which makes these schemes inflexible, difficult to extend, and potentially susceptible to biases. A new technique, based on simultaneously sampling cloud state and spectral interval, provides radiative fluxes that are guaranteed to be unbiased with respect to the benchmark Independent Column Approximation and works equally well no matter how cloud structure is specified. Fluxes computed in this way are subject to random, uncorrelated errors that depend on the distribution of cloud optical properties. Seasonal forecasts, however, are not sensitive to this noise, making the method useful in weather and climate prediction models.

[1]  Jean-Jacques Morcrette,et al.  The Overlapping of Cloud Layers in Shortwave Radiation Parameterizations , 1986 .

[2]  G. Stephens.  Radiative Transfer through Arbitrarily Shaped Optical Media. Part I: A General Method of Solution , 1988 .

[3]  A. Slingo.  A GCM Parameterization for the Shortwave Radiative Properties of Water Clouds , 1989 .

[4]  Q. Fu,et al.  Parameterization of the Radiative Properties of Cirrus Clouds , 1993 .

[5]  Robert F. Cahalan,et al.  Independent Pixel and Monte Carlo Estimates of Stratocumulus Albedo , 1994 .

[6]  Interactions of Radiation and Convection in Simulated Tropical Cloud Clusters , 1995 .

[7]  H. Barker,et al.  Accounting for subgrid‐scale cloud variability in a multi‐layer 1d solar radiative transfer algorithm , 1999 .

[8]  Sally A. McFarlane,et al.  Albedo bias and the horizontal variability of clouds in subtropical marine boundary layers: Observations from ships and satellites , 1999 .

[9]  Piotr K. Smolarkiewicz,et al.  CRCP: a cloud resolving convection parameterization for modeling the tropical convecting atmosphere , 1999 .

[10]  Qiang Fu,et al.  The sensitivity of domain‐averaged solar fluxes to assumptions about cloud geometry , 1999 .

[11]  Jean-Jacques Morcrette,et al.  On the Effects of the Temporal and Spatial Sampling of Radiation Fields on the ECMWF Forecasts and Analyses , 2000 .

[12]  S. Klein,et al.  Unresolved spatial variability and microphysical process rates in large‐scale models , 2000 .

[13]  Brian Cairns,et al.  Absorption within inhomogeneous clouds and its parameterization in general circulation models , 2000 .

[14]  Robin J. Hogan,et al.  Deriving cloud overlap statistics from radar , 2000 .

[15]  William D. Collins,et al.  Parameterization of Generalized Cloud Overlap for Radiative Calculations in General Circulation Models , 2001 .

[16]  D. Randall,et al.  A cloud resolving model as a cloud parameterization in the NCAR Community Climate System Model: Preliminary results , 2001 .

[17]  A. Tompkins.  A Prognostic Parameterization for the Subgrid-Scale Variability of Water Vapor and Clouds in Large-Scale Models and Its Use to Diagnose Cloud Cover , 2002 .

[18]  Gerald G. Mace,et al.  Cloud-Layer Overlap Characteristics Derived from Long-Term Cloud Radar Data , 2002 .

[19]  E. Clothiaux,et al.  Assessing 1D atmospheric solar radiative transfer models: Interpretation and handling of unresolved clouds , 2003 .