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Geoscientific Instrumentation, Methods and Data Systems An interactive open-access journal of the European Geosciences Union
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Volume 5, issue 1
Geosci. Instrum. Method. Data Syst., 5, 1-15, 2016
https://doi.org/10.5194/gi-5-1-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Instrum. Method. Data Syst., 5, 1-15, 2016
https://doi.org/10.5194/gi-5-1-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 19 Jan 2016

Research article | 19 Jan 2016

Designing optimal greenhouse gas monitoring networks for Australia

T. Ziehn1, R. M. Law1, P. J. Rayner2, and G. Roff3 T. Ziehn et al.
  • 1CSIRO Oceans and Atmosphere Flagship, Aspendale, VIC 3195, Australia
  • 2School of Earth Sciences, University of Melbourne, Melbourne, VIC 3010, Australia
  • 3Australian Bureau of Meteorology, Docklands, VIC 3008, Australia

Abstract. Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.

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This study investigates the optimal location of greenhouse gas (GHG) measurement stations in Australia in order to derive GHG flux estimates from concentration measurements. We find that an optimal network designed for CO2 also performs well for other GHGs such as CH4 and N2O due to large similarities in the flux pattern for each of the three GHGs. Economic costs (i.e. maintenance costs) can be halved by selecting stations closer to the base laboratory with only a slight decrease in performance.
This study investigates the optimal location of greenhouse gas (GHG) measurement stations in...
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