Greenhouse gas observation network design for Africa. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Nickless, Alecia; Scholes, Robert J.; Vermeulen, Alex; Beck, Johannes; López-Ballesteros, Ana; Ardö, Jonas; Karstens, Ute; Rigby, Matthew; Kasurinen, Ville; Pantazatou, Karolina; Jorch, Veronika; Kutsch, Werner
• Is Part Of: Tellus. Volume 72:Issue 1(2020)
• Page Start: 1
• Page End: 30
• Publication Date: 2020-01-01
• Subjects: greenhouse gases -- observation network design -- Bayesian inversion -- Lagrangian particle dispersion model; Atmospheric chemistry -- Periodicals; Atmospheric physics -- Periodicals; Meteorology -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; Chimie de l'atmosphère -- Périodiques; Météorologie physique -- Périodiques; Météorologie -- Périodiques; Air -- Pollution -- Meteorological aspects; Atmospheric chemistry; Atmospheric physics; Meteorology; Meteorologie; Chimie de l'atmosphère; Météorologie physique; Météorologie; Meteorology; Electronic journals; Computer network resources; Periodicals; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 551.505
• DOI: 10.1080/16000889.2020.1824486
• Languages: English
• ISSNs: 0280-6509

Abstract

Abstract: 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 N2 O. 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 N2 O 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 N2 O 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 N2 O. 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.