Designing optimal greenhouse gas monitoring networks for Australia

Geoscientific Instrumentation, Methods and Data Systems

R. M.Law¹  G.Roff¹  T.Ziehn¹  P. J.Rayner¹ 

DOI  :  10.5194/gi-5-1-2016

学科分类：天文学(综合)

来源: Copernicus Publications

【 摘 要 】

Atmospheric transport inversion is commonly used to infer greenhouse gas(GHG) flux estimates from concentration measurements. The optimal location ofground-based observing stations that supply these measurements can bedetermined by network design. Here, we use a Lagrangian particle dispersionmodel (LPDM) in reverse mode together with a Bayesian inverse modellingframework to derive optimal GHG observing networks for Australia. Thisextends the network design for carbon dioxide (CO₂) performed byZiehn et al. (2014) to also minimise the uncertainty on the flux estimates formethane (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 becausethe flux uncertainties for each GHG are dominated by regions of biologicallyproductive land. There is little penalty, in terms of flux uncertaintyreduction, for the combined network compared to individually designednetworks. The location of the stations in the combined network is sensitiveto variations in the assumed data uncertainty across locations. A simpleassessment of economic costs has been included in our network designapproach, considering both establishment and maintenance costs. Our resultssuggest that, while site logistics change the optimal network, there is only asmall impact on the flux uncertainty reductions achieved with increasingnetwork size.

【 授权许可】

Unknown