Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network

Abstract

We present a method to derive atmosphericobservation-based estimates of carbon dioxide (CO 2 ) fluxes at the national scale, demonstrated using data from a network of surface tall-tower sites across the UK and Ireland over the period 2013-2014. The inversion is carried out using simulations from a Lagrangian chemical transport model and an innovative hierarchical Bayesian Markov chain Monte Carlo (MCMC) framework, which addresses some of the traditional problems faced by inverse modelling studies, such as subjectivity in the specification of model and prior uncertainties. Biospheric fluxes related to gross primary productivity and terrestrial ecosystem respiration are solved separately in the inversion and then combined a posteriori to determine net ecosystem exchange of CO 2 . Two different models, Data Assimilation Linked Ecosystem Carbon (DALEC) and Joint UK Land Environment Simulator (JULES), provide prior estimates for these fluxes. We carry out separate inversions to assess the impact of these different priors on the posterior flux estimates and evaluate the differences be-tween the prior and posterior estimates in terms of missing model components. The Numerical Atmospheric dispersion Modelling Environment (NAME) is used to relate fluxes to the measurements taken across the regional network. Posterior CO 2 estimates from the two inversions agree within estimated uncertainties, despite large differences in the prior fluxes from the different models. With our method, averaging results from 2013 and 2014, we find a total annual net biospheric flux for the UK of 8 ± 79 Tg CO 2 yr −1 (DALEC prior) and 64 ± 85 Tg CO 2 yr −1 (JULES prior), where negative values represent an uptake of CO 2 . These biospheric CO 2 estimates show that annual UK biospheric sources and sinks are roughly in balance. These annual mean estimates consistently indicate a greater net release of CO 2 than the prior estimates, which show much more pronounced uptake in summer months.

The regional European atmospheric transport inversion comparison, EUROCOM: first results on European-wide terrestrial carbon fluxes for the period 2006–2015

“…We use the results from an ensemble of six inversions (one for each participating system), covering a large spectrum of inversion characteristics (prior constraints, inversion technique, transport models, etc. ): PYVAR-CHIMERE (Broquet et al, 2011;Fortems-Cheiney et al, 2019, developed at LSCE, France); LUMIA (Lund University Modular Inversion Algorithm) (Monteil and Scholze, 2019), developed at Lund University (Sweden) as part of the EUROCOM project; CarboScope-Regional (Kountouris et al, 2018a, b, developed at MPI-Jena, Germany); FLEXINVERT (Thompson and Stohl, 2014, from NILU, Norway); NAME-HB (White et al, 2019b, from the University of Bristol, United Kingdom) and CarbonTracker Europe (Peters et al, 2010;van der Laan-Luijkx et al, 2017), from the University of Wageningen, the Netherlands.…”

Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment

“…Gas fields or geological storage sites can cover areas of tens to hundreds of square kilometres. Unless there is a high density of sensors (≈ 100 m scale, van Leeuwen et al, 2013;Jenkins et al, 2016), the sensitivity of detection will be poor (Wilson et al, 2014;Luhar et al, 2014). It is however relatively straightforward to effectively extend the methodology to when the emission is from an area rather than a point source.…”

“…Calibration of the transport model from observations can be done within the classic inverse theory framework of Tarantola (2005). This framework is in turn seated within a Bayesian paradigm, which underpins several of the inversion systems in place today (see, for example, Flesch et al, 2004;Humphries et al, 2012;Hirst et al, 2013;Ganesan et al, 2014;Luhar et al, 2014;Houweling et al, 2017;White et al, 2019). Inference in such cases is often done using sampling techniques such as Markov chain Monte Carlo (MCMC) or importance sampling (e.g.…”

Greenhouse gas observation network design for Africa

“…For the purpose of the network design we solved for the total flux, but a real-world inversion, aimed at modelling the observed GHG mole fractions at a site, would most likely solve for the component fluxes separately (e.g. fossil fuel and biogenic fluxes) (Chevallier et al, 2014;Kaminski and Rayner, 2017;Nickless et al, 2018aNickless et al, , 2019bWhite et al, 2019). We solve for the uncertainty reduction of gridded fluxes at a relatively high resolution in order to allow the network design to take into account nuances in atmospheric transport that are due to large scale topography, and information on localised emissions.…”