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Discussion papers
https://doi.org/10.5194/acp-2018-839
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-839
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 21 Sep 2018

Research article | 21 Sep 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Quantifying the UK’s Carbon Dioxide Flux: An atmospheric inverse modelling approach using a regional measurement network

Emily D. White1, Matthew Rigby1, Mark F. Lunt1,2, Anita L. Ganesan3, Alistair J. Manning4, Simon O'Doherty1, Ann R. Stavert1,5, Kieran M. Stanley1, Mathew Williams2, T. Luke Smallman2, Edward Comyn-Platt6, Peter Levy7, Michel Ramonet8, and Paul I. Palmer2 Emily D. White et al.
  • 1School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
  • 2School of GeoSciences, University of Edinburgh, Edinburgh, EH9 3JN, UK
  • 3School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
  • 4Hadley Centre, Met. Office, Exeter, EX1 3PB, UK
  • 5Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale VIC 3195, Australia
  • 6Centre for Ecology and Hydrology, Wallingford, OX10 8BB, UK
  • 7Centre for Ecology and Hydrology (Edinburgh Research Station), Penicuik, E26 0QB, UK
  • 8Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, 91198, France

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 primary productivity. Two different models, CARDAMOM and 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±79TgCO2yr−1 (CARDAMOM prior) and −64±85TgCO2yr−1 (JULES prior), where -ve 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 are consistently higher than the prior estimates, which show much more pronounced uptake in the summer months.

Emily D. White et al.
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Emily D. White et al.
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Understanding carbon dioxide (CO2) fluxes from the terrestrial biosphere on a national scale is important for evaluating land use strategies to mitigate climate change. We estimate emissions of CO2 from the UK biosphere using atmospheric data in a top-down approach. Our findings show that bottom-up estimates from models of biospheric fluxes over-estimate the amount of CO2 uptake in summer. This suggests these models wrongly estimate or omit key processes e.g. land disturbance due to harvest.
Understanding carbon dioxide (CO2) fluxes from the terrestrial biosphere on a national scale is...
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