Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.5194/acp-18-4765-2018 |
Estimates of CO2 fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling | |
Nickless, Alecia1,2; Rayner, Peter J.3; Engelbrecht, Francois4,5; Brunke, Ernst-Gunther6; Erni, Birgit1,7; Scholes, Robert J.8 | |
2018-04-09 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS |
ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2018 |
卷号 | 18期号:7页码:4765-4801 |
文章类型 | Article |
语种 | 英语 |
国家 | South Africa; England; Australia |
英文摘要 | 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 Delta C-14 and delta C-13 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. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000429473600004 |
WOS关键词 | ATMOSPHERIC TRANSPORT ; CARBON-CYCLE ; SURFACE FLUX ; PART 1 ; EMISSIONS ; SENSITIVITY ; SCALE ; QUANTIFICATION ; SYSTEM ; WATER |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/27264 |
专题 | 地球科学 |
作者单位 | 1.Univ Cape Town, Dept Stat Sci, ZA-7701 Cape Town, South Africa; 2.Univ Oxford, Nuffield Dept Primary Care Hlth Sci, Oxford OX2 6GG, England; 3.Univ Melbourne, Sch Earth Sci, Melbourne, Vic 3010, Australia; 4.CSIR, Nat Resources & Environm Climate Studies Modellin, POB 395, ZA-0001 Pretoria, South Africa; 5.North West Univ, Unit Environm Sci & Management, ZA-2520 Potchefstroom, South Africa; 6.CSIR, South African Weather Serv, POB 320, ZA-7599 Stellenbosch, South Africa; 7.Univ Cape Town, Ctr Stat Ecol Environm & Conservat, ZA-7701 Cape Town, South Africa; 8.Univ Witwatersrand, Global Change Inst, ZA-2050 Johannesburg, South Africa |
推荐引用方式 GB/T 7714 | Nickless, Alecia,Rayner, Peter J.,Engelbrecht, Francois,et al. Estimates of CO2 fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2018,18(7):4765-4801. |
APA | Nickless, Alecia,Rayner, Peter J.,Engelbrecht, Francois,Brunke, Ernst-Gunther,Erni, Birgit,&Scholes, Robert J..(2018).Estimates of CO2 fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling.ATMOSPHERIC CHEMISTRY AND PHYSICS,18(7),4765-4801. |
MLA | Nickless, Alecia,et al."Estimates of CO2 fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling".ATMOSPHERIC CHEMISTRY AND PHYSICS 18.7(2018):4765-4801. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论