Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
doi:10.5194/acp-2017-10
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
03 Feb 2017
Review status
This discussion paper is under review for the journal Atmospheric Chemistry and Physics (ACP).
Data Assimilation using an Ensemble of Models: A hierarchical approach
Peter Rayner School of Earth Sciences, University of Melbourne, Melbourne, Australia
Abstract. One characteristic of biogeochemical models is uncertainty about their formulation. Data assimilation should take this uncertainty into account. A common approach is to use an ensemble of models. We must assign probabilities not only to the parameters of the models but the models themselves. The method of hierarchical modelling allows us to calculate these probabilities. This paper describes the approach, develops the algebra for the most common case then applies it to the TRANSCOM intercomparison. We see that the discrimination among models is unrealistically strong, due to optimistic assumptions inherent in the underlying inversion. The weighted ensemble means and variances from the hierarchical approach are quite similar to the conventional values because the best model in the ensemble is also quite close to the ensemble mean. The approach can also be used for cross-validation in which some data is held back to test estimates obtained with the rest. We demonstrate this with a test of the TRANSCOM inversions holding back the airborne data. We see a slight decrease in the tropical sink and a notably different preferred order of models.

Citation: Rayner, P.: Data Assimilation using an Ensemble of Models: A hierarchical approach, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-10, in review, 2017.
Peter Rayner
Peter Rayner
Peter Rayner

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Short summary
This work extends previous calculations of carbon dioxide sources and sinks to take account of the varying quality of atmospheric models. It uses an extended version of Bayesian statistics which includes the model as one of the unknowns. I performed the work as an example of including the model in the description of the uncertainty.
This work extends previous calculations of carbon dioxide sources and sinks to take account of...
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