Greenhouse gas observation network design for Africa

Alecia Nickless*, Robert J. Scholes, Alex Vermeulen, Johannes Beck, Ana Lopez-Ballesteros, Jonas Ardö, Ute Karstens, Matthew L Rigby, Ville Kasurinen, Karolina Pantazatou, Veronika Jorch, Werner Kutsch

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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An optimal network design was carried out to prioritise the installation or refurbishment of greenhouse gas (GHG) monitoring stations around Africa. The network was optimised to reduce the uncertainty in emissions across three of the most important GHGs: CO2, CH4, and N2O. Optimal networks were derived using incremental optimisation of the percentage uncertainty reduction achieved by a Gaussian Bayesian atmospheric inversion. The solution for CO2 was driven by seasonality in net primary productivity. The solution for N2O was driven by activity in a small number of soil flux hotspots. The optimal solution for CH4 was consistent over different seasons. All solutions for CO2 and N2O placed sites in central Africa at places such as Kisangani, Kinshasa and Bunia (Democratic Republic of Congo), Dundo and Lubango (Angola), Zoétélé (Cameroon), Am Timan (Chad), and En Nahud (Sudan). Many of these sites appeared in the CH4 solutions, but with a few sites in southern Africa as well, such as Amersfoort (South Africa). The multi-species optimal network design solutions tended to have sites more evenly spread-out, but concentrated the placement of new tall-tower stations in Africa between 10ºN and 25ºS. The uncertainty reduction achieved by the multi-species network of twelve stations reached 47.8% for CO2, 34.3% for CH4, and 32.5% for N2O. The gains in uncertainty reduction diminished as stations were added to the solution, with an expected maximum of less than 60%. A reduction in the absolute uncertainty in African GHG emissions requires these additional measurement stations, as well as additional constraint from an integrated GHG observatory and a reduction in uncertainty in the prior biogenic fluxes in tropical Africa.
Original languageEnglish
Number of pages30
JournalTellus B
Issue number1
Publication statusPublished - 19 Oct 2020


  • greenhouse gases
  • observation network design
  • Bayesian inversion
  • Lagrangian particle dispersion model


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  • HPC (High Performance Computing) Facility

    Sadaf R Alam (Manager), Steven A Chapman (Manager), Polly E Eccleston (Other), Simon H Atack (Other) & D A G Williams (Manager)

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