Articles | Volume 18, issue 7
Atmos. Chem. Phys., 18, 4765–4801, 2018
Atmos. Chem. Phys., 18, 4765–4801, 2018

Research article 09 Apr 2018

Research article | 09 Apr 2018

Estimates of CO2 fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling

Alecia Nickless1,2, Peter J. Rayner3, Francois Engelbrecht4,5, Ernst-Günther Brunke6, Birgit Erni1,7, and Robert J. Scholes8 Alecia Nickless et al.
  • 1Department of Statistical Sciences, University of Cape Town, Cape Town, 7701, South Africa
  • 2Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
  • 3School of Earth Sciences, University of Melbourne, Melbourne, VIC 3010, Australia
  • 4CSIR Natural Resources and the Environment – Climate Studies, Modelling and Environmental Health, P.O. Box 395, Pretoria, 0001, South Africa
  • 5Unit for Environmental Sciences and Management, North-West University, Potchefstroom, 2520, South Africa
  • 6South African Weather Service c/o CSIR, P.O. Box 320, Stellenbosch, 7599, South Africa
  • 7Centre for Statistics in Ecology, the Environment and Conservation, University of Cape Town, Cape Town, 7701, South Africa
  • 8Global Change Institute, University of the Witwatersrand, Johannesburg, 2050, South Africa

Abstract. We present a city-scale inversion over Cape Town, South Africa. Measurement sites for atmospheric CO2 concentrations were installed at Robben Island and Hangklip lighthouses, located downwind and upwind of the metropolis. Prior estimates of the fossil fuel fluxes were obtained from a bespoke inventory analysis where emissions were spatially and temporally disaggregated and uncertainty estimates determined by means of error propagation techniques. Net ecosystem exchange (NEE) fluxes from biogenic processes were obtained from the land atmosphere exchange model CABLE (Community Atmosphere Biosphere Land Exchange). Uncertainty estimates were based on the estimates of net primary productivity. CABLE was dynamically coupled to the regional climate model CCAM (Conformal Cubic Atmospheric Model), which provided the climate inputs required to drive the Lagrangian particle dispersion model. The Bayesian inversion framework included a control vector where fossil fuel and NEE fluxes were solved for separately.

Due to the large prior uncertainty prescribed to the NEE fluxes, the current inversion framework was unable to adequately distinguish between the fossil fuel and NEE fluxes, but the inversion was able to obtain improved estimates of the total fluxes within pixels and across the domain. The median of the uncertainty reductions of the total weekly flux estimates for the inversion domain of Cape Town was 28 %, but reach as high as 50 %. At the pixel level, uncertainty reductions of the total weekly flux reached up to 98 %, but these large uncertainty reductions were for NEE-dominated pixels. Improved corrections to the fossil fuel fluxes would be possible if the uncertainty around the prior NEE fluxes could be reduced. In order for this inversion framework to be operationalised for monitoring, reporting, and verification (MRV) of emissions from Cape Town, the NEE component of the CO2 budget needs to be better understood. Additional measurements of Δ14C and δ13C isotope measurements would be a beneficial component of an atmospheric monitoring programme aimed at MRV of CO2 for any city which has significant biogenic influence, allowing improved separation of contributions from NEE and fossil fuel fluxes to the observed CO2 concentration.

Short summary
Carbon dioxide emissions and uptake were estimated for Cape Town, South Africa. We placed two high-precision analysers in lighthouses located on either end of Cape Town (Robben Island and Hangklip). The Cape Point GAW station provided background measurements. We were able to improve the agreement between modelled and observed concentrations, relative to initial estimates provided. This methodology could potentially be scaled up to provide monitoring and verification of city emissions.
Final-revised paper