Evaluation of Weather Noise and Its Role in Climate Model Simulations

Hua Chen Department of Atmospheric Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia

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Edwin K. Schneider Department of Atmospheric Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, and Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland

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Ben P. Kirtman Division of Meteorology and Physical Oceanography, Rosenstiel School for Atmospheric and Marine Science, University of Miami, Miami, Florida

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Ioana Colfescu Department of Atmospheric Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia

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Abstract

The relationship between coupled atmosphere–ocean general circulation model simulations and uncoupled simulations made with specified SST and sea ice is investigated using the Community Climate System Model, version 3. Experiments are carried out in a perfect model framework. Two closely related questions are investigated: 1) whether the statistics of the atmospheric weather noise in the atmospheric model are the same as in the coupled model, and 2) whether the atmospheric model reproduces the SST-forced response of the coupled model.

The weather noise in both the coupled and uncoupled simulations is found by removing the forced response, as determined from the uncoupled ensemble, from the atmospheric field. The weather-noise variance is generally not distinguishable between the coupled and uncoupled simulations. However, variances of the total fields differ between the coupled and uncoupled simulations, since there is constructive or destructive interference between the SST-forced response and weather noise in the coupled model but no correlation between the SST-forced and weather-noise components in the uncoupled model simulations.

Direct regression estimates of the forced response show little difference between the coupled and uncoupled simulations. Differences in local correlations are explained by weather noise because weather noise forces SST in the coupled simulation only.

The results demonstrate and explain an important intrinsic difference in precipitation statistics between the coupled and uncoupled simulations. This difference could have consequences for the design of dynamical downscaling experiments and for tuning general circulation models.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-12-00292.s1.

Corresponding author address: Hua Chen, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705. E-mail: chen@cola.iges.org

Abstract

The relationship between coupled atmosphere–ocean general circulation model simulations and uncoupled simulations made with specified SST and sea ice is investigated using the Community Climate System Model, version 3. Experiments are carried out in a perfect model framework. Two closely related questions are investigated: 1) whether the statistics of the atmospheric weather noise in the atmospheric model are the same as in the coupled model, and 2) whether the atmospheric model reproduces the SST-forced response of the coupled model.

The weather noise in both the coupled and uncoupled simulations is found by removing the forced response, as determined from the uncoupled ensemble, from the atmospheric field. The weather-noise variance is generally not distinguishable between the coupled and uncoupled simulations. However, variances of the total fields differ between the coupled and uncoupled simulations, since there is constructive or destructive interference between the SST-forced response and weather noise in the coupled model but no correlation between the SST-forced and weather-noise components in the uncoupled model simulations.

Direct regression estimates of the forced response show little difference between the coupled and uncoupled simulations. Differences in local correlations are explained by weather noise because weather noise forces SST in the coupled simulation only.

The results demonstrate and explain an important intrinsic difference in precipitation statistics between the coupled and uncoupled simulations. This difference could have consequences for the design of dynamical downscaling experiments and for tuning general circulation models.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-12-00292.s1.

Corresponding author address: Hua Chen, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705. E-mail: chen@cola.iges.org

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  • Barsugli, J. J., and D. S. Battisti, 1998: The basic effects of atmosphere–ocean thermal coupling on midlatitude variability. J. Atmos. Sci., 55, 477493.

    • Search Google Scholar
    • Export Citation
  • Bhatt, U. S., M. A. Alexander, D. S. Battisti, D. D. Houghton, and L. A. Keller, 1998: Atmosphere–ocean interaction in the North Atlantic: Near-surface climate variability. J. Climate, 11, 16151632.

    • Search Google Scholar
    • Export Citation
  • Bladé, I., 1997: The influence of midlatitude ocean–atmosphere coupling on the low-frequency variability of a GCM. Part I: No tropical SST forcing. J. Climate, 10, 20872106.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., and D. S. Battisti, 2000: An interpretation of the results from atmospheric general circulation models forced by the time history of the observed sea surface temperature distribution. Geophys. Res. Lett., 27, 767770.

    • Search Google Scholar
    • Export Citation
  • Brunet, G., and Coauthors, 2010: Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction. Bull. Amer. Meteor. Soc., 91, 13971406.

    • Search Google Scholar
    • Export Citation
  • Collins, W. D., and Coauthors, 2006a: The Community Climate System Model version 3 (CCSM3). J. Climate, 19, 21222143.

  • Collins, W. D., P. J. Rasch, B. A. Boville, J. J. Hack, J. R. McCaa, D. L. Williamson, and B. P. Briegleb, 2006b: The formulation and atmospheric simulation of the Community Atmosphere Model version 3 (CAM3). J. Climate, 19, 21442161.

    • Search Google Scholar
    • Export Citation
  • Danabasoglu, G., W. G. Large, J. J. Tribbia, P. R. Gent, B. P. Briegleb, and J. C. McWilliams, 2006: Diurnal coupling in the tropical oceans of CCSM3. J. Climate, 19, 23472365.

    • Search Google Scholar
    • Export Citation
  • Deser, C., A. Capotondi, R. Saravanan, and A. S. Phillips, 2006: Tropical Pacific and Atlantic climate variability in CCSM3. J. Climate, 19, 24512481.

    • Search Google Scholar
    • Export Citation
  • Dickinson, R. E., K. W. Oleson, G. Bonan, F. Hoffman, P. Thornton, M. Vertenstein, Z.-L. Yang, and X. Zeng, 2006: The Community Land Model and its climate statistics as a component of the Community Climate System Model. J. Climate, 19, 23022324.

    • Search Google Scholar
    • Export Citation
  • Fan, M., and E. K. Schneider, 2012: Observed decadal North Atlantic tripole SST variability. Part I: Weather noise forcing and coupled response. J. Atmos. Sci., 69, 3550.

    • Search Google Scholar
    • Export Citation
  • Folland, C. K., J. Shukla, J. Kinter, and M. J. Rodwell, 2002: The Climate of the Twentieth Century Project. CLIVAR Exchanges, Vol. 7 (2), International CLIVAR Project Office, Southampton, United Kingdom, 37–39.

  • Frankignoul, C., 1999: A cautionary note on the use of statistical atmospheric models in the middle latitudes: Comments on “Decadal variability in the North Pacific as simulated by a hybrid coupled model.” J. Climate, 12, 18711872.

    • Search Google Scholar
    • Export Citation
  • Gallimore, R. G., 1995: Simulated ocean–atmosphere interaction in the North Pacific from a GCM coupled to a constant-depth mixed layer. J. Climate, 8, 17211737.

    • Search Google Scholar
    • Export Citation
  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 2955.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, K., 1976: Stochastic climate models. Part I: Theory. Tellus, 28, 473485.

  • Holland, M. M., C. M. Bitz, E. C. Hunke, W. H. Lipscomb, and J. L. Schramm, 2006: Influence of the sea ice thickness distribution on polar climate in CCSM3. J. Climate, 19, 23982414.

    • Search Google Scholar
    • Export Citation
  • Hurrell, J., G. A. Meehl, D. Bader, T. L. Delworth, B. Kirtman, and B. Wielicki, 2009: A unified modeling approach to climate system prediction. Bull. Amer. Meteor. Soc., 90, 18191832.

    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., and J. Shukla, 2002: Interactive coupled ensemble: A new coupling strategy for CGCMs. Geophys. Res. Lett., 29, 13671370.

    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., D. M. Straus, D. Min, E. K. Schneider, and L. Siqueira, 2009: Toward linking weather and climate in the interactive ensemble NCAR climate model. Geophys. Res. Lett.,36, L13705, doi:10.1029/2009GL038389.

  • Kirtman, B. P., E. K. Schneider, D. M. Straus, D. Min, and R. Burgman, 2011: How weather impacts the forced climate response. Climate Dyn., 37, 23892416.

    • Search Google Scholar
    • Export Citation
  • Kitoh, A., and O. Arakawa, 1999: On overestimation of tropical precipitation by an atmospheric GCM with prescribed SST. Geophys. Res. Lett., 26, 29652968.

    • Search Google Scholar
    • Export Citation
  • Kumar, K., M. Hoerling, and B. Rajagopalan, 2005: Advancing dynamical prediction of Indian monsoon rainfall. Geophys. Res. Lett.,32, L08704, doi:10.1029/2004GL021979.

  • Leith, C. E., 1973: The standard error of time-averaged estimates of climatic means. J. Appl. Meteor., 12, 10661069.

  • Manabe, S., and R. J. Stouffer, 1996: Low-frequency variability of surface air temperature in a 1000-year integration of a coupled atmosphere–ocean–land surface model. J. Climate, 9, 376393.

    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., C. K. Folland, K. Maskell, and M. N. Ward, 1995: Variability of summer rainfall over tropical North Africa (1906–92): Observations and modelling. Quart. J. Roy. Meteor. Soc., 121, 669704.

    • Search Google Scholar
    • Export Citation
  • Saravanan, R., 1998: Atmospheric low-frequency variability and its relationship to midlatitude SST variability: Studies using the NCAR Climate System Model. J. Climate, 11, 13861404.

    • Search Google Scholar
    • Export Citation
  • Schneider, E. K., and M. Fan, 2007: Weather noise forcing of surface climate variability. J. Atmos. Sci., 64, 32653280.

  • Schneider, E. K., and M. Fan, 2012: Observed decadal North Atlantic tripole SST variability. Part II: Diagnosis of mechanisms. J. Atmos. Sci., 69, 5169.

    • Search Google Scholar
    • Export Citation
  • Wang, B., Q. Ding, X. Fu, I. Kang, K. Jin, J. Shukla, and F. Doblas-Reyes, 2005: Fundamental challenge in simulation and prediction of summer monsoon rainfall. Geophys. Res. Lett.,32, L15711, doi:10.1029/2005GL022734.

  • Wu, R., and B. P. Kirtman, 2004: Impacts of the Indian Ocean on the Indian summer monsoon–ENSO relationship. J. Climate, 17, 30373053.

    • Search Google Scholar
    • Export Citation
  • Wu, R., B. P. Kirtman, and K. Pegion, 2006: Local air–sea relationship in observations and model simulations. J. Climate, 19, 49144932.

    • Search Google Scholar
    • Export Citation
  • Wu, R., B. P. Kirtman, and K. Pegion, 2007: Surface latent heat flux and its relationship with sea surface temperature in the National Centers for Environmental Prediction Climate Forecast System simulations and retrospective forecasts. Geophys. Res. Lett.,34, L17712, doi:10.1029/2007GL030751.

  • Wu, Z., E. K. Schneider, and B. P. Kirtman, 2004: Causes of low frequency North Atlantic SST variability in a coupled GCM. Geophys. Res. Lett.,31, L09210, doi:10.1029/2004GL019548.

  • Xie, S.-P., K. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian Ocean capacitor effect on Indo-western Pacific climate during the summer following El Niño. J. Climate, 22, 730747.

    • Search Google Scholar
    • Export Citation
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