EGU25-13641, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-13641
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Scalable Approaches for Hierarchical Non-Gaussian Inverse Modelling for Emissions Estimation
Stephen Pearson, Luke Western, Anita Ganesan, and Matt Rigby
Stephen Pearson et al.
  • University of Bristol, School of Geographical Sciences, Bristol, United Kingdom of Great Britain – England, Scotland, Wales (s.pearson@bristol.ac.uk)

Bayesian inverse modelling systems are a valuable tool for quantifying sources and sinks of greenhouse gases using atmospheric observations. They are increasingly used to verify national emission inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC). Recent studies have noted the value of hierarchical Bayesian inverse methods for improved uncertainty quantification in inverse modelling systems, and the importance of including physical constraints such as non-negativity in emissions. However, systems that exhibit these properties can suffer from severe computational bottlenecks, exacerbated by the growing volume of atmospheric observations and the demand for higher spatiotemporal resolution in emission estimates. As a result, spatial dimension reduction and spatiotemporal independence are often placed on emissions estimates, to make the algorithms computationally feasible. This research aims to explore these limits, before introducing novel approaches to address them.

We use a hierarchical Bayesian approach, using Markov chain Monte Carlo (MCMC) sampling, to explore the limits of the spatiotemporal resolution of flux estimates considering different multivariate Gaussian correlations. We demonstrate this by quantifying emissions of methane in the UK. In addition, we demonstrate the utility of a multivariate log-normal emissions distribution, simultaneously maintaining the non-negativity of emissions, as well as the explicit representation of emissions covariance. The results are compared with the emissions calculated using a spatially and temporally independent emissions prior, demonstrating the developments associated with a multivariate approach.

The computational costs associated with MCMC sampling means that the potential for extending the approach to large spatiotemporal parameter spaces is limited. Therefore, alternative inferential methods are explored, with a focus on sequential algorithms - such as Kalman filtering - that are augmented for non-Gaussian, hierarchal inference, in higher dimensional parameter spaces. This work provides the foundation for developing scalable non-Gaussian hierarchical frameworks that combine computational feasibility with improved emissions estimates.

How to cite: Pearson, S., Western, L., Ganesan, A., and Rigby, M.: Scalable Approaches for Hierarchical Non-Gaussian Inverse Modelling for Emissions Estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13641, https://doi.org/10.5194/egusphere-egu25-13641, 2025.

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