Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling
We present a city-scale inversion over Cape Town, South Africa. Measurement sites for atmospheric CO<sub>2</sub> concentrations were installed at Robben Island and Hangklip lighthouses, located downwind and upwind of the metropolis. Prior estimates of the fossil fuel fluxes were obtai...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-04-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/18/4765/2018/acp-18-4765-2018.pdf |
| _version_ | 1811343881144369152 |
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| author | A. Nickless A. Nickless P. J. Rayner F. Engelbrecht F. Engelbrecht E.-G. Brunke B. Erni B. Erni R. J. Scholes |
| author_facet | A. Nickless A. Nickless P. J. Rayner F. Engelbrecht F. Engelbrecht E.-G. Brunke B. Erni B. Erni R. J. Scholes |
| author_sort | A. Nickless |
| collection | DOAJ |
| description | We present a city-scale inversion over Cape Town, South Africa. Measurement
sites for atmospheric CO<sub>2</sub> 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.</br></br>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 CO<sub>2</sub>
budget needs to be better understood. Additional measurements of
Δ<sup>14</sup>C and <i>δ</i><sup>13</sup>C isotope measurements would be a
beneficial component of an atmospheric monitoring programme aimed at MRV of
CO<sub>2</sub> for any city which has significant biogenic influence, allowing
improved separation of contributions from NEE and fossil fuel fluxes to the
observed CO<sub>2</sub> concentration. |
| first_indexed | 2024-04-13T19:37:58Z |
| format | Article |
| id | doaj.art-883dd6296d4e438daf74a85eacfc32dc |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-04-13T19:37:58Z |
| publishDate | 2018-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-883dd6296d4e438daf74a85eacfc32dc2022-12-22T02:32:58ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242018-04-01184765480110.5194/acp-18-4765-2018Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modellingA. Nickless0A. Nickless1P. J. Rayner2F. Engelbrecht3F. Engelbrecht4E.-G. Brunke5B. Erni6B. Erni7R. J. Scholes8Department of Statistical Sciences, University of Cape Town, Cape Town, 7701, South AfricaNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UKSchool of Earth Sciences, University of Melbourne, Melbourne, VIC 3010, AustraliaCSIR Natural Resources and the Environment – Climate Studies, Modelling and Environmental Health, P.O. Box 395, Pretoria, 0001, South AfricaUnit for Environmental Sciences and Management, North-West University, Potchefstroom, 2520, South AfricaSouth African Weather Service c/o CSIR, P.O. Box 320, Stellenbosch, 7599, South AfricaDepartment of Statistical Sciences, University of Cape Town, Cape Town, 7701, South AfricaCentre for Statistics in Ecology, the Environment and Conservation, University of Cape Town, Cape Town, 7701, South AfricaGlobal Change Institute, University of the Witwatersrand, Johannesburg, 2050, South AfricaWe present a city-scale inversion over Cape Town, South Africa. Measurement sites for atmospheric CO<sub>2</sub> 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.</br></br>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 CO<sub>2</sub> budget needs to be better understood. Additional measurements of Δ<sup>14</sup>C and <i>δ</i><sup>13</sup>C isotope measurements would be a beneficial component of an atmospheric monitoring programme aimed at MRV of CO<sub>2</sub> for any city which has significant biogenic influence, allowing improved separation of contributions from NEE and fossil fuel fluxes to the observed CO<sub>2</sub> concentration.https://www.atmos-chem-phys.net/18/4765/2018/acp-18-4765-2018.pdf |
| spellingShingle | A. Nickless A. Nickless P. J. Rayner F. Engelbrecht F. Engelbrecht E.-G. Brunke B. Erni B. Erni R. J. Scholes Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling Atmospheric Chemistry and Physics |
| title | Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling |
| title_full | Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling |
| title_fullStr | Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling |
| title_full_unstemmed | Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling |
| title_short | Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling |
| title_sort | estimates of co sub 2 sub fluxes over the city of cape town south africa through bayesian inverse modelling |
| url | https://www.atmos-chem-phys.net/18/4765/2018/acp-18-4765-2018.pdf |
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