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Volume 13, issue 16
Atmos. Chem. Phys., 13, 8315–8333, 2013
https://doi.org/10.5194/acp-13-8315-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 13, 8315–8333, 2013
https://doi.org/10.5194/acp-13-8315-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 22 Aug 2013

Research article | 22 Aug 2013

Pauci ex tanto numero: reduce redundancy in multi-model ensembles

E. Solazzo1, A. Riccio2, I. Kioutsioukis1,3, and S. Galmarini1 E. Solazzo et al.
  • 1European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
  • 2Department of Applied Science, University of Naples "Parthenope", Napoli, Italy
  • 3Region of Central Macedonia, Thessaloniki, Greece

Abstract. We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date, no attempts in this direction have been documented within the air quality (AQ) community despite the extensive use of ensembles in this field. Common biases and redundancy are the two issues directly deriving from lack of independence, undermining the significance of a multi-model ensemble, and are the subject of this study. Shared, dependant biases among models do not cancel out but will instead determine a biased ensemble. Redundancy derives from having too large a portion of common variance among the members of the ensemble, producing overconfidence in the predictions and underestimation of the uncertainty. The two issues of common biases and redundancy are analysed in detail using the AQMEII ensemble of AQ model results for four air pollutants in two European regions. We show that models share large portions of bias and variance, extending well beyond those induced by common inputs. We make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble with the advantage of being poorly correlated. Selecting the members for generating skilful, non-redundant ensembles from such subsets proved, however, non-trivial. We propose and discuss various methods of member selection and rate the ensemble performance they produce. In most cases, the full ensemble is outscored by the reduced ones. We conclude that, although independence of outputs may not always guarantee enhancement of scores (but this depends upon the skill being investigated), we discourage selecting the members of the ensemble simply on the basis of scores; that is, independence and skills need to be considered disjointly.

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