The Monte Carlo Independent Column Approximation: an assessment using several global atmospheric models

The Monte Carlo Independent Column Approximation (McICA) computes domain‐average, broadband radiative flux profiles within conventional global climate models (GCMs). While McICA is unbiased with respect to the full ICA, it generates, as a by‐product, random noise. If this by‐product leads to statistically significant impacts on GCM simulations, it could limit the usefulness of McICA. This paper assesses the impact of McICA's random noise on six GCMs. To this end, the GCMs performed ensembles of 14‐day long simulations for various renditions of McICA, each with differing amounts of random noise. As seen in the past, low‐cloud fraction and surface temperature were affected most by noise. However, all GCM simulations using operationally viable renditions of McICA showed no statistically significant impacts, even for precipitation ‐ a highly intermittent variable that one might expect to be sensitive to random fluctuations. Two GCMs showed statistically significant responses using an academic version of McICA that generates overly large sampling noise. Time series analyses of high‐resolution (i.e. typically 2‐hourly) data revealed that fluctuations associated with most variables and GCMs are immune to McICA noise. Moreover, the nature of these fluctuations can vary substantially among GCMs and most often they overwhelm any noise impacts. Overall, the results presented here corroborate a range of previous studies done on one GCM at a time: random noise produced by recommended versions of McICA has statistically insignificant effects on GCM simulations. Copyright © 2008 Royal Meteorological Society and Her Majesty in Right of Canada.

[1]  J. Morcrette,et al.  Impact of a New Radiation Package, McRad, in the ECMWF Integrated Forecasting System , 2008 .

[2]  S. Klein,et al.  Using Stochastically Generated Subcolumns to Represent Cloud Structure in a Large-Scale Model , 2005 .

[3]  H. Barker,et al.  The Monte Carlo Independent Column Approximation's Conditional Random Noise: Impact on Simulated Climate , 2005 .

[4]  Eugene E. Clothiaux,et al.  Atmospheric radiative transfer through global arrays of 2D clouds , 2005 .

[5]  G. Stephens.  Cloud Feedbacks in the Climate System: A Critical Review , 2005 .

[6]  D. Randall,et al.  Stochastic generation of subgrid‐scale cloudy columns for large‐scale models , 2004 .

[7]  Howard W. Barker,et al.  Evaluation and optimization of sampling errors for the Monte Carlo Independent Column Approximation , 2004 .

[8]  H. Barker,et al.  Neglect by GCMs of subgrid‐scale horizontal variations in cloud‐droplet effective radius: A diagnostic radiative analysis , 2004 .

[9]  Akio Arakawa,et al.  CLOUDS AND CLIMATE: A PROBLEM THAT REFUSES TO DIE. Clouds of many , 2022 .

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

[11]  J. Morcrette,et al.  A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields , 2003 .

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

[13]  H. Storch,et al.  Statistical Analysis in Climate Research , 2000 .

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

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

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

[17]  W. Rossow,et al.  Implementation of Subgrid Cloud Vertical Structure inside a GCM and Its Effect on the Radiation Budget , 1997 .

[18]  H. Barker.  A parameterization for computing grid-averaged solar fluxes for inhomogeneous marine boundary layer , 1996 .

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

[20]  Q. Fu,et al.  On the correlated k-distribution method for radiative transfer in nonhomogeneous atmospheres , 1992 .

[21]  G. Stephens,et al.  Statistical radiative transport in one-dimensional media and its application to the terrestrial atmosphere , 1991 .

[22]  J. Curry,et al.  Cloud overlap statistics , 1989 .

[23]  G. Stephens.  Radiative Transfer through Arbitrarily Shaped Optical Media. Part II. Group Theory and Simple Closures , 1988 .

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

[25]  W. Weaver,et al.  Two-Stream Approximations to Radiative Transfer in Planetary Atmospheres: A Unified Description of Existing Methods and a New Improvement , 1980 .

[26]  D. Schertzer,et al.  m Discrete Angle Radiative Transfer Numerical Results and Meteorological Applications , 2007 .

[27]  Anthony B. Davis,et al.  3D Radiative Transfer in Cloudy Atmospheres , 2005 .

[28]  Filipe Aires,et al.  Inferring instantaneous, multivariate and nonlinear sensitivities for the analysis of feedback processes in a dynamical system: Lorenz model case‐study , 2003 .

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

[30]  J. Wiscombe,et al.  The Delta-Eddington Approximation for a Vertically Inhomogeneous Atmosphere , 1977 .