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Greenhouse gas observation network design for Africa

Nickless, Alecia ; Scholes, Robert J. ; Vermeulen, Alex LU orcid ; Beck, Johannes ; López-Ballesteros, Ana ; Ardö, Jonas LU orcid ; Karstens, Ute LU orcid ; Rigby, Matthew ; Kasurinen, Ville and Pantazatou, Karolina LU orcid , et al. (2020) In Tellus. Series B: Chemical and Physical Meteorology 72(1). p.1-30
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 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,... (More)
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. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Greenhouse Gases, observation network design, Bayesian inversion, Lagrangian particle dispersion model
in
Tellus. Series B: Chemical and Physical Meteorology
volume
72
issue
1
pages
30 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85092915013
ISSN
1600-0889
DOI
10.1080/16000889.2020.1824486
language
English
LU publication?
yes
id
8fd44d17-ad21-4a19-bbe9-c8be2632513f
date added to LUP
2020-10-26 22:23:33
date last changed
2022-04-19 01:26:47
@article{8fd44d17-ad21-4a19-bbe9-c8be2632513f,
  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 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.}},
  author       = {{Nickless, Alecia and Scholes, Robert J. and Vermeulen, Alex and Beck, Johannes and López-Ballesteros, Ana and Ardö, Jonas and Karstens, Ute and Rigby, Matthew and Kasurinen, Ville and Pantazatou, Karolina and Jorch, Veronika and Kutsch, Werner}},
  issn         = {{1600-0889}},
  keywords     = {{Greenhouse Gases; observation network design; Bayesian inversion; Lagrangian particle dispersion model}},
  language     = {{eng}},
  month        = {{10}},
  number       = {{1}},
  pages        = {{1--30}},
  publisher    = {{Taylor & Francis}},
  series       = {{Tellus. Series B: Chemical and Physical Meteorology}},
  title        = {{Greenhouse gas observation network design for Africa}},
  url          = {{http://dx.doi.org/10.1080/16000889.2020.1824486}},
  doi          = {{10.1080/16000889.2020.1824486}},
  volume       = {{72}},
  year         = {{2020}},
}