The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

karolina pantazatou profile pic

Karolina Pantazatou

Doctoral student

karolina pantazatou profile pic

Greenhouse gas observation network design for Africa

Author

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

Summary, in English

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.

Department/s

  • ICOS Sweden
  • Dept of Physical Geography and Ecosystem Science

Publishing year

2020-10-19

Language

English

Pages

1-30

Publication/Series

Tellus. Series B: Chemical and Physical Meteorology

Volume

72

Issue

1

Document type

Journal article

Publisher

Taylor & Francis

Topic

  • Climate Research
  • Meteorology and Atmospheric Sciences

Keywords

  • Greenhouse Gases
  • observation network design
  • Bayesian inversion
  • Lagrangian particle dispersion model

Status

Published

ISBN/ISSN/Other

  • ISSN: 1600-0889