Abstract
Abstract. We present a method to derive atmospheric-observation-based estimates of carbon dioxide (CO2) 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 CO2. 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 between 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 CO2 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 CO2 yr−1 (DALEC prior) and 64±85 Tg CO2 yr−1 (JULES prior), where negative values represent an uptake of CO2. These biospheric CO2 estimates show that annual UK biospheric sources and sinks are roughly in balance. These annual mean estimates consistently indicate a greater net release of CO2 than the prior estimates, which show much more pronounced uptake in summer months.
Highlights
There are significant uncertainties in the magnitude and spatiotemporal distribution of global carbon dioxide (CO2) fluxes to and from the atmosphere, those due to terrestrial ecosystems (Le Quéré et al, 2018)
Diurnal ranges for Gross primary productivity (GPP) are more similar in magnitude; Joint United Kingdom (UK) Land Environment Simulator (JULES) exhibits a stronger sink in spring with maximum uptake in June
We have developed a framework for estimating net biospheric CO2 fluxes in the UK that takes advantage of recent innovation in atmospheric inverse modelling and a relatively dense regional network of measurement sites
Summary
There are significant uncertainties in the magnitude and spatiotemporal distribution of global carbon dioxide (CO2) fluxes to and from the atmosphere, those due to terrestrial ecosystems (Le Quéré et al, 2018). Biosphere models and land surface models can be used to estimate carbon fluxes using coupled representations of biogeophysical and biogeochemical processes, driven by observations of meteorology and ecosystem parameters (Potter, 1999; Clark et al, 2011; Bloom et al, 2016). Such models describe processes to varying degrees of complexity, with poorly described errors, and are driven by observational data at differing temporal and spatial resolutions; predictions of biogenic greenhouse gas (GHG) fluxes have poorly quantified biases and can vary significantly between models (Todd-Brown et al, 2013; Atkin et al, 2015)
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