Abstract
Abstract. We present a method to infer spatially and spatio-temporally 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 spatio-temporal 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 spatio-temporal uncertainties that have traditionally been neglected in atmospheric inverse modelling.
Highlights
Emissions of greenhouse gases, ozone-depleting substances and air pollutants are increasingly inferred indirectly from atmospheric trace gas concentration observations and chemical transport models
Inverse methods often assume that uncertainties in the likelihood and prior probabilities are known exactly and Gaussian (e.g. Stohl et al, 2009; Brioude et al, 2013)
We begin by introducing the model and the inferred latent parameters, followed by an introduction to Gaussian Markov random fields and how they are useful for efficient calculation of spatial and spatio-temporal correlation structures
Summary
Ozone-depleting substances and air pollutants are increasingly inferred indirectly from atmospheric trace gas concentration observations and chemical transport models. These “top-down” or “inverse” methods complement inventory- or process-model-based “bottom-up” techniques that are used, for example, in national reporting of greenhouse gas emissions to the United Nations Framework Convention on Climate Change Top-down methods rely on some form of statistical inference, or inverse theory, to infer emissions at global (e.g. Saunois et al, 2016) to regional (e.g. Brunner et al, 2017) scales. They require a chemical transport model to provide the relationship between atmospheric mole fraction and emissions.
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