Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.668 IF 5.668
  • IF 5-year value: 6.201 IF 5-year
    6.201
  • CiteScore value: 6.13 CiteScore
    6.13
  • SNIP value: 1.633 SNIP 1.633
  • IPP value: 5.91 IPP 5.91
  • SJR value: 2.938 SJR 2.938
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 174 Scimago H
    index 174
  • h5-index value: 87 h5-index 87
Discussion papers
https://doi.org/10.5194/acp-2017-10
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-2017-10
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Submitted as: research article 03 Feb 2017

Submitted as: research article | 03 Feb 2017

Review status
This discussion paper is a preprint. A revision of the manuscript for further review has not been submitted.

Data Assimilation using an Ensemble of Models: A hierarchical approach

Peter Rayner 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.

Peter Rayner
Interactive discussion
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Interactive discussion
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Peter Rayner
Peter Rayner
Viewed  
Total article views: 776 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
542 174 60 776 28 61
  • HTML: 542
  • PDF: 174
  • XML: 60
  • Total: 776
  • BibTeX: 28
  • EndNote: 61
Views and downloads (calculated since 03 Feb 2017)
Cumulative views and downloads (calculated since 03 Feb 2017)
Viewed (geographical distribution)  
Total article views: 762 (including HTML, PDF, and XML) Thereof 755 with geography defined and 7 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 13 Nov 2019
Publications Copernicus
Download
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...
Citation