On the Choice of Ensemble Mean for Estimating the Forced Signal in the Presence of Internal Variability

Leela M. Frankcombe Australian Research Council Centre of Excellence for Climate System Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

Search for other papers by Leela M. Frankcombe in
Current site
Google Scholar
PubMed
Close
,
Matthew H. England Australian Research Council Centre of Excellence for Climate System Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

Search for other papers by Matthew H. England in
Current site
Google Scholar
PubMed
Close
,
Jules B. Kajtar College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom

Search for other papers by Jules B. Kajtar in
Current site
Google Scholar
PubMed
Close
,
Michael E. Mann Department of Meteorology, and Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Michael E. Mann in
Current site
Google Scholar
PubMed
Close
, and
Byron A. Steinman Department of Earth and Environmental Sciences, and Large Lakes Observatory, University of Minnesota Duluth, Duluth, Minnesota

Search for other papers by Byron A. Steinman in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Leela M. Frankcombe, l.frankcombe@unsw.edu.au

Abstract

In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Leela M. Frankcombe, l.frankcombe@unsw.edu.au
Save
  • Alexander, L. V., and J. M. Arblaster, 2017: Historical and projected trends in temperature and precipitation extremes in Australia in observations and CMIP5. Wea. Climate Extremes, 15, 3456, https://doi.org/10.1016/j.wace.2017.02.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, E. A., and L. Polvani, 2013: Response of the midlatitude jets, and of their variability, to increased greenhouse gases in the CMIP5 models. J. Climate, 26, 71177135, https://doi.org/10.1175/JCLI-D-12-00536.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, P. T., Y. Ming, W. Li, and S. A. Hill, 2017: Change in the magnitude and mechanisms of global temperature variability with warming. Nat. Climate Change, 7, 743748, https://doi.org/10.1038/nclimate3381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheung, A. H., M. E. Mann, B. A. Steinman, L. M. Frankcombe, M. H. England, and S. K. Miller, 2017a: Comparison of low-frequency internal climate variability in CMIP5 models and observations. J. Climate, 30, 47634776, https://doi.org/10.1175/JCLI-D-16-0712.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheung, A. H., M. E. Mann, B. A. Steinman, L. M. Frankcombe, M. H. England, and S. K. Miller, 2017b: Reply to “Comment on ‘Comparison of low-frequency internal climate variability in CMIP5 models and observations.’” J. Climate, 30, 97739782, https://doi.org/10.1175/JCLI-D-17-0531.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chylek, P., J. D. Klett, G. Lesins, M. K. Dubey, and N. Hengartner, 2014: The Atlantic multidecadal oscillation as a dominant factor of oceanic influence on climate. Geophys. Res. Lett., 41, 16891697, https://doi.org/10.1002/2014GL059274.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cowtan, K., and Coauthors, 2015: Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures. Geophys. Res. Lett., 42, 65266534, https://doi.org/10.1002/2015GL064888.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frankcombe, L. M., M. H. England, M. E. Mann, and B. A. Steinman, 2015: Separating internal variability from the externally forced climate response. J. Climate, 28, 81848202, https://doi.org/10.1175/JCLI-D-15-0069.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, https://doi.org/10.1029/2010RG000345.

  • Haughton, N., G. Abramowitz, A. Pitman, and S. J. Phipps, 2015: Weighting climate model ensembles for mean and variance estimates. Climate Dyn., 45, 31693181, https://doi.org/10.1007/s00382-015-2531-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henley, B. J., J. Gergis, D. J. Karoly, S. Power, J. Kennedy, and C. K. Folland, 2015: A tripole index for the interdecadal Pacific oscillation. Climate Dyn., 45, 30773090, https://doi.org/10.1007/s00382-015-2525-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holmes, C. R., T. Woollings, E. Hawkins, and H. de Vries, 2016: Robust future changes in temperature variability under greenhouse gas forcing and the relationship with thermal advection. J. Climate, 29, 22212236, https://doi.org/10.1175/JCLI-D-14-00735.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huntingford, C., P. D. Jones, V. N. Livina, T. M. Lenton, and P. M. Cox, 2013: No increase in global temperature variability despite changing regional patterns. Nature, 500, 327330, https://doi.org/10.1038/nature12310.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kattsov, V. M., and J. E. Walsh, 2000: Twentieth-century trends of Arctic precipitation from observational data and a climate model simulation. J. Climate, 13, 13621370, https://doi.org/10.1175/1520-0442(2000)013<1362:TCTOAP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kattsov, V. M., J. E. Walsh, W. L. Chapman, V. A. Govorkova, T. V. Pavlova, and X. Zhang, 2007: Simulation and projection of Arctic freshwater budget components by the IPCC AR4 global climate models. J. Hydrometeor., 8, 571589, https://doi.org/10.1175/JHM575.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knight, J. R., 2009: The Atlantic multidecadal oscillation inferred from the forced climate response in coupled general circulation models. J. Climate, 22, 16101625, https://doi.org/10.1175/2008JCLI2628.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kravtsov, S., 2017: Comment on “Comparison of low-frequency internal climate variability in CMIP5 models and observations.” J. Climate, 30, 97639772, https://doi.org/10.1175/JCLI-D-17-0438.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kravtsov, S., and D. Callicutt, 2017: On semi-empirical decomposition of multidecadal climate variability into forced and internally generated components. Int. J. Climatol., 37, 44174433, https://doi.org/10.1002/joc.5096.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kravtsov, S., M. G. Wyatt, J. A. Curry, and A. A. Tsonis, 2015: Comment on “Atlantic and Pacific multidecadal oscillations and Northern Hemisphere temperatures.” Science, 350, 1326, https://doi.org/10.1126/science.aab3570.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maher, N., S. McGregor, M. H. England, and A. Sen Gupta, 2015: Effects of volcanism on tropical variability. Geophys. Res. Lett., 42, 60246033, https://doi.org/10.1002/2015GL064751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, M. E., 2008: Smoothing of climate time series revisited. Geophys. Res. Lett., 35, L16708, https://doi.org/10.1029/2008GL034716.

  • Mann, M. E., and K. A. Emanuel, 2006: Atlantic hurricane trends linked to climate change. Eos, Trans. Amer. Geophys. Union, 87, 233244, https://doi.org/10.1029/2006EO240001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, M. E., B. A. Steinman, and S. K. Miller, 2014: On forced temperature changes, internal variability, and the AMO. Geophys. Res. Lett., 41, 32113219, https://doi.org/10.1002/2014GL059233.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olonscheck, D., and D. Notz, 2017: Consistently estimating internal climate variability from climate model simulations. J. Climate, 30, 95559573, https://doi.org/10.1175/JCLI-D-16-0428.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Otterå, O. H., M. Bentsen, H. Drange, and L. Suo, 2010: External forcing as a metronome for Atlantic multidecadal variability. Nat. Geosci., 3, 688694, https://doi.org/10.1038/ngeo955.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., 2014: Arctic amplification decreases temperature variance in northern mid- to high-latitudes. Nat. Climate Change, 4, 577582, https://doi.org/10.1038/nclimate2268.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh, 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J. Geophys. Res. Atmos., 118, 24732493, https://doi.org/10.1002/jgrd.50188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steinman, B. A., L. M. Frankcombe, M. E. Mann, S. K. Miller, and M. H. England, 2015a: Response to comment on “Atlantic and Pacific multidecadal oscillations and Northern Hemisphere temperatures.” Science, 350, 1326, https://doi.org/10.1126/science.aac5208.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steinman, B. A., M. E. Mann, and S. K. Miller, 2015b: Atlantic and Pacific multidecadal oscillations and Northern Hemisphere temperatures. Science, 347, 988991, https://doi.org/10.1126/science.1257856.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swingedouw, D., J. Mignot, P. Ortega, M. Khodri, M. Menegoz, C. Cassou, and V. Hanquiez, 2017: Impact of explosive volcanic eruptions on the main climate variability modes. Global Planet. Change, 150, 2445, https://doi.org/10.1016/j.gloplacha.2017.01.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taschetto, A. S., A. Sen Gupta, N. C. Jourdain, A. Santoso, C. C. Ummenhofer, and M. H. England, 2014: Cold tongue and warm pool ENSO events in CMIP5: Mean state and future projections. J. Climate, 27, 28612885, https://doi.org/10.1175/JCLI-D-13-00437.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and D. J. Shea, 2006: Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett., 33, L12704, https://doi.org/10.1029/2006GL026894.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyatt, M. G., S. Kravtsov, and A. A. Tsonis, 2012: Atlantic multidecadal oscillation and Northern Hemisphere’s climate variability. Climate Dyn., 38, 929949, https://doi.org/10.1007/s00382-011-1071-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1019 201 17
PDF Downloads 842 145 20