A method for estimating and assessing modes of interannual variability in coupled climate models

The seasonal mean of a climate variable consists of: slow-external; slow-internal; and intraseasonal components. Using an analysis of variance-based method, the interannual variability of the seasonal mean from an ensemble of coupled atmosphere-ocean general circulation model (CGCM) realisations is...

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Bibliographic Details
Main Authors: Grainger, Simon, Frederiksen, Carsten Segerlund, Zheng, Xiaogu
Format: Article in Journal/Newspaper
Language:English
Published: Australian Mathematical Society 2016
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Online Access:https://journal.austms.org.au/ojs/index.php/ANZIAMJ/article/view/9445
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Summary:The seasonal mean of a climate variable consists of: slow-external; slow-internal; and intraseasonal components. Using an analysis of variance-based method, the interannual variability of the seasonal mean from an ensemble of coupled atmosphere-ocean general circulation model (CGCM) realisations is separable into these three components. Eigenvalue decomposition is applied to the covariance matrices to obtain, for each component, the dominant modes of variability (eigenvectors) and their associated variance (eigenvalues) for the climate variable. Here, a method is described that assesses the modes of interannual variability in CGCMs against those obtained from reanalysis data based on observations. A metric is defined based on the pattern correlation between the observed and modelled modes of variability, and the ratio of their associated variances. This metric is applied to monthly mean southern hemisphere 500 hPa geopotential height from the second half of the 20th century. It is shown that CGCMs have clear differences in the slow-component of modes of interannual variability, related to external forcings and/or slowly-varying internal variability. References C. S. Frederiksen and X. Zheng. Coherent structures of interannual variability of the atmospheric circulation: the role of intraseasonal variability. Frontiers in Turbulence and Coherent Structures, World Scientific Lecture Notes in Complex Systems, Vol. 6, Eds Jim Denier and Jorgen Frederiksen, World Scientific Publications, 87–120, 2007. doi:10.1142/6320 C. E. Leith. The standard error of time-average estimates of climatic means. J. Appl. Meteor., 12:1066–1069, 1973. doi:10.1175/1520-0450(1973)012<1066:TSEOTA>2.0.CO;2 X. Zheng and C. S. Frederiksen. Variability of seasonal-mean fields arising from intraseasonal variability: part 1, methodology. Clim. Dynam., 23:177–191, 2004. doi:10.1007/s00382-004-0428-7 C. S. Frederiksen and X. Zheng. Variability of seasonal-mean fields arising from intraseasonal variability. Part 3: Application to SH winter and summer circulations. Clim. Dynam., 28:849–866, 2007. doi:10.1007/s00382-006-0214-9 S. Grainger, C. S. Frederiksen and X. Zheng. A method for evaluating the modes of variability in general circulation models. ANZIAM J., 50:C399–C412, 2008. http://journal.austms.org.au/ojs/index.php/ANZIAMJ/article/view/1431 G. A. Meehl, C. Covey, K. E. Taylor, T. Delworth, R. J. Stouffer, M. Latif, B. McAvaney and J. F. B. Mitchell. The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88:1383–1394, 2007. doi:10.1175/BAMS-88-9-1383 K. E. Taylor, R. J. Stouffer and G. A. Meehl. An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc. 93:485–498, 2012. doi:10.1175/BAMS-D-11-00094.1 X. Zheng, M. Sugi and C. S. Frederiksen. Interannual variability and predictability in an ensemble of climate simulations with the MRI-JMA AGCM. J. Meteor. Soc. Jap., 82:1–18, 2004. doi:10.2151/jmsj.82.1 H. von Storch and F. W. Zwiers. Statistical Analysis in Climate Research. Cambridge University Press, 484pp, 1999. doi:10.1017/cbo9780511612336 S. Grainger, C. S. Frederiksen and X. Zheng. Modes of interannual variability of Southern Hemisphere atmoshperic circulation in CMIP3 models: assessment and projections. Clim. Dynam. 41:479–500, 2013. doi:10.1007/s00382-012-1659-7 S. Grainger, C. S. Frederiksen and X. Zheng. Estimating components of covariance between two climate variables using model ensembles. ANZIAM J., 52:C318–C332, 2011. http://journal.austms.org.au/ojs/index.php/ANZIAMJ/article/view/3928 G. P. Compo, J. S. Whitaker, P. D. Sardeshmukh, N. Matsui, R. J. Allan, X. Yin, B. E. Gleason, R. S. Vose, G. Rutledge, P. Bessemoulin, S. Bronnimann, M. Brunet, R. I. Crouthamel, A. N. Grant, P. Y. Groisman, P. D. Jones, M. C. Kruk, A. C. Kruger, G. J. Marshall, M. Maugeri, H. Y. Mok, O. Nordli, T. F. Ross, R. M. Trigo, X. L. Wang, S. D. Woodruff and S. J. Worley. The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc. 137:1–28, 2011. doi:10.1002/qj.776 N. A. Rayner, D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent and A. Kaplan. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108(D14):4407, 2003. doi:10.1029/2002JD002670