On the choice of ensemble mean for estimating the forced signal in the presence of internal variability

Leela M. Frankcombe, Matthew H. England, Jules B. Kajtar, Michael E. Mann, Byron A. Steinman

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)5681-5693
Number of pages13
JournalJournal of Climate
Volume31
Issue number14
DOIs
StatePublished - Jul 1 2018

Bibliographical note

Funding Information:
This work was supported by the Australian Research Council (ARC) through grants to L. M. F. (DE170100367) and to M. H. E. through the ARC Centre of Excellence in Climate System Science (CE110001028). J. B. K. is supported by the Natural EnvironmentResearch Council (Grant NE/N005783/1).B.A. S. was supported by the U.S. National Science Foundation (EAR-1447048). We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and thank the climate modeling groups for producing and making available their model output. HadISST data were provided by the Met Office Hadley Centre (www.metoffice.gov.uk/hadobs) and GISTEMP by NASA Goddard Institute for Space Studies (data.giss.nasa.gov/gistemp).

Funding Information:
Acknowledgments. This work was supported by the Australian Research Council (ARC) through grants to L. M. F. (DE170100367) and to M. H. E. through the ARC Centre of Excellence in Climate System Science (CE110001028). J. B. K. is supported by the Natural Environment Research Council (Grant NE/N005783/1). B. A. S. was supported by the U.S. National Science Foundation (EAR-1447048). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and thank the climate modeling groups for producing and making available their model output. HadISST data were provided by the Met Off ice Hadley Centre (www.metoffice.gov.uk/hadobs) and GISTEMP by NASA Goddard Institute for Space Studies (data.giss.nasa.gov/gistemp).

Keywords

  • Climate variability
  • Decadal variability
  • Interdecadal variability
  • Model evaluation/performance
  • Multidecadal variability

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