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Volume 9, issue 1 | Copyright
Earth Syst. Dynam., 9, 135-151, 2018
https://doi.org/10.5194/esd-9-135-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 Feb 2018

Research article | 21 Feb 2018

Selecting a climate model subset to optimise key ensemble properties

Nadja Herger1, Gab Abramowitz1, Reto Knutti2,3, Oliver Angélil1, Karsten Lehmann4, and Benjamin M. Sanderson3 Nadja Herger et al.
  • 1Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, UNSW Sydney, Sydney, NSW 2052, Australia
  • 2Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
  • 3National Center for Atmospheric Research, Boulder, Colorado, USA
  • 4Satalia, Berlin, Germany

Abstract. End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history.

Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

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Users presented with large multi-model ensembles commonly use the equally weighted model mean as a best estimate, ignoring the issue of near replication of some climate models. We present an efficient and flexible tool that finds a subset of models with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments.
Users presented with large multi-model ensembles commonly use the equally weighted model mean as...
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