Representing Sub-Grid Scale Cloud Variability with Monte- Carlo Independent Column Approximation and RRTMG in the National Center for Atmospheric Research Community Atmosphere Model, CAM3

The impact of representing inhomogeneous cloud structure in a global climate model (GCM) using a sub-column cloud generator and the Atmospheric Environmental Research, Inc. (AER) GCM radiation model, RRTMG, which has been modified to use, the Monte-Carlo Independent Column Approximation (McICA) (Pincus et al. 2003), has been examined in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, CAM3.0. The McICA method uses stochastically generated sub-grid cloud properties (Raisanen et al. 2004) that are randomly sampled during the spectral integration of the radiative transfer model. This approach provides results that are unbiased relative to the accurate, though computationally expensive, Independent Column Approximation. Random flux errors, which depend on cloud optical properties, cloud spatial structure and spectral sampling size, are introduced by McICA, and the sensitivity of GCM simulations to this random noise is being evaluated (Barker et al. 2006; Raisanen et al. 2005).