Atmos. Chem. Phys. Discuss., 13, 4989-5038, 2013
www.atmos-chem-phys-discuss.net/13/4989/2013/
doi:10.5194/acpd-13-4989-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
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This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
Pauci ex tanto numero: reducing redundancy in multi-model ensembles
E. Solazzo1, A. Riccio2, I. Kioutsioukis1, and S. Galmarini1
1European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
2Department of Applied Science, University of Naples "Parthenope", Napoli, Italy

Abstract. We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date no attempts in this direction are documented within the air quality (AQ) community, although 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 biases among models will determine a biased ensemble, making therefore essential the errors of the ensemble members to be independent so that bias can cancel out. 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.

Citation: Solazzo, E., Riccio, A., Kioutsioukis, I., and Galmarini, S.: Pauci ex tanto numero: reducing redundancy in multi-model ensembles, Atmos. Chem. Phys. Discuss., 13, 4989-5038, doi:10.5194/acpd-13-4989-2013, 2013.
 
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