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Volume 14, issue 8
Atmos. Chem. Phys., 14, 3855–3864, 2014
https://doi.org/10.5194/acp-14-3855-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 14, 3855–3864, 2014
https://doi.org/10.5194/acp-14-3855-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 17 Apr 2014

Research article | 17 Apr 2014

Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

A. L. Ganesan1,*, M. Rigby2, A. Zammit-Mangion3, A. J. Manning4, R. G. Prinn1, P. J. Fraser5, C. M. Harth6, K.-R. Kim7, P. B. Krummel5, S. Li8, J. Mühle6, S. J. O'Doherty2, S. Park9, P. K. Salameh6, L. P. Steele5, and R. F. Weiss6 A. L. Ganesan et al.
  • 1Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
  • 2School of Chemistry, University of Bristol, Bristol, UK
  • 3School of Geographical Sciences, University of Bristol, Bristol, UK
  • 4Hadley Centre, Met Office, Exeter, UK
  • 5Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia
  • 6Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
  • 7GIST College, Gwangju Institute of Science and Technology, Kwangju, South Korea
  • 8Research Institute of Oceanography, Seoul National University, Seoul, South Korea
  • 9Department of Oceanography, Kyungpook National University, Sangju, South Korea
  • *now at: School of Chemistry, University of Bristol, Bristol, UK

Abstract. We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods.

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