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

  1. H. W. Barker1,†,*,
  2. J. N. S. Cole2,
  3. J.-J. Morcrette3,
  4. R. Pincus4,
  5. P. Räisänen5,
  6. K. von Salzen6,†,
  7. P. A. Vaillancourt7,†

Article first published online: 17 SEP 2008

DOI: 10.1002/qj.303

Issue

Quarterly Journal of the Royal Meteorological Society

Quarterly Journal of the Royal Meteorological Society

Volume 134, Issue 635, pages 1463–1478, July 2008 Part B

Additional Information

How to Cite

Barker, H. W., Cole, J. N. S., Morcrette, J.-J., Pincus, R., Räisänen, P., von Salzen, K. and Vaillancourt, P. A. (2008), The Monte Carlo Independent Column Approximation: an assessment using several global atmospheric models. Quarterly Journal of the Royal Meteorological Society, 134: 1463–1478. doi: 10.1002/qj.303

Author Information

  1. 1

    Environment Canada, Toronto, Ontario, Canada

  2. 2

    University of British Columbia, Vancouver, British Columbia, Canada

  3. 3

    European Centre for Medium-Range Weather Forecasts, Reading, Berkshire, UK

  4. 4

    NOAA-CIRES Climate Diagnostics Center, Boulder, Colorado, USA

  5. 5

    Finnish Meteorological Institute, Helsinki, Finland

  6. 6

    Environment Canada, Victoria, British Columbia, Canada

  7. 7

    Environment Canada, Montreal, Quebec, Canada

  1. †

    The contribution of H. W. Barker, K. von Salzen and P. A. Vaillancourt was written in the course of their employment by Environment Canada.

*Environment Canada, Cloud Physics and Severe Weather Research Section (ARMP), 4905 Dufferin St., Toronto, ON, Canada M3H 5T4.

Publication History

  1. Issue published online: 17 SEP 2008
  2. Article first published online: 17 SEP 2008
  3. Manuscript Accepted: 9 JUL 2008
  4. Manuscript Revised: 2 JUL 2008
  5. Manuscript Received: 17 MAR 2008

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Keywords:

  • radiative flux profiles;
  • ICA

Abstract

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.

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