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Climate Dynamics

, Volume 45, Issue 11–12, pp 3169–3181 | Cite as

Weighting climate model ensembles for mean and variance estimates

  • Ned Haughton
  • Gab Abramowitz
  • Andy Pitman
  • Steven J. Phipps
Article

Abstract

Projections based on climate model ensembles commonly assume that each individual model simulation is of equal value. When combining simulations to estimate the mean and variance of quantities of interest, they are typically unweighted. Exceptions to this approach usually fall into two categories. First, ensembles may be pared down by removing either poorly performing model simulations or model simulations that are perceived to add little additional information, typically where multiple simulations have come from the same model. Second, weighting methodologies, usually based on model performance differences, may be applied, and lead to some improvement in the projected mean. Here we compare the effect of three different weighting techniques—simple averaging, performance based weighting, and weighting that accounts for model dependence—on three ensembles generated by different approaches to model perturbation. We examine the effect of each weighting technique on both the ensemble mean and variance. For comparison, we also consider the effect on the CMIP5 ensemble. While performance weighting is shown to improve the estimate of the mean, it does not appear to improve estimates of ensemble variance, and may in fact degrade them. In contrast, the model independence weighting approach appears to improve both the ensemble mean and the variance in all ensembles.

Keywords

Ensemble Member Internal Variability Error Correlation Weighting Approach Performance Weighting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. All CMIP5 data was accessed via the NCI ESGF node. This research was undertaken with the assistance of resources provided at the Australian National University through the National Computational Merit Allocation Scheme supported by the Australian Government.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Ned Haughton
    • 1
  • Gab Abramowitz
    • 1
  • Andy Pitman
    • 1
  • Steven J. Phipps
    • 1
  1. 1.Climate Change Research Centre Level 4, Mathews BuildingUniversity of New South WalesSydneyAustralia

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