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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-66
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-66
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 05 Jun 2019

Submitted as: development and technical paper | 05 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Bayesian spatiotemporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields

Luke M. Western1, Zhe Sha2,3, Matthew Rigby1, Anita L. Ganesan2, Alistair J. Manning4, Kieran M. Stanley1, Simon J. O'Doherty1, Dickon Young1, and Jonathan Rougier3 Luke M. Western et al.
  • 1School of Chemistry, University of Bristol, Bristol, UK
  • 2School of Geographical Sciences, University of Bristol, Bristol, UK
  • 3School of Mathematics, University of Bristol, Bristol, UK
  • 4Hadley Centre, Met Office, Exeter, UK

Abstract. We present a method to infer spatially and spatiotemporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model. The method allows fast computation of spatial emissions using a hierarchical Bayesian framework as an alternative to Markov chain Monte Carlo algorithms. The spatial emissions follow a Gaussian process with a Matérn correlation structure which can be represented by a Gaussian Markov random field through a stochastic partial differential equation approach. The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields. Combining an autoregressive temporal correlation and the Matérn field provides a full spatiotemporal correlation structure. We first demonstrate the method on a synthetic data example and follow this using a well-studied test case of inferring UK methane emissions from tall tower measurements of atmospheric mole fraction. Results from these two test cases show that this method can accurately estimate regional greenhouse gas emissions, accounting for spatiotemporal uncertainties that have traditionally been neglected in atmospheric inverse modelling.

Luke M. Western et al.
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Luke M. Western et al.
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Short summary
Assessments of greenhouse gas emissions using atmospheric measurements and meteorological models, or top-down methods, are important to verify national inventories, or produce a stand-alone estimate where no inventory exists. We present a novel top-down method to estimate emissions. This approach uses a fast method, called an integrated nested Laplacian approximation, to estimate how these emissions are correlated with other emissions in different locations, and at different times.
Assessments of greenhouse gas emissions using atmospheric measurements and meteorological...
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