Evaluation and optimization of sampling errors for the Monte Carlo Independent Column Approximation

Abstract The Monte Carlo Independent Column Approximation (McICA) method for computing domain‐average broadband radiative fluxes is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. McICA's sampling errors are evaluated here using a global climate model (GCM) dataset and a correlated‐ k distribution (CKD) radiation scheme. Two approaches to reduce McICA's sampling variance are discussed. The first …