Articles | Volume 19, issue 5
https://doi.org/10.5194/acp-19-2991-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-19-2991-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Mar 2019
Research article |
| 07 Mar 2019
| 07 Mar 2019
Characterizing uncertainties in atmospheric inversions of fossil fuel CO2 emissions in California
Department of Physics, Imperial College London, London, UK
Heather Graven
Department of Physics, Imperial College London, London, UK
Alistair J. Manning
Hadley Centre, Met Office, Exeter, UK
Emily White
School of Chemistry, University of Bristol, Bristol, UK
Tim Arnold
National Physical Laboratory, London, UK
School of Geosciences, University of Edinburgh, University of Edinburgh, Edinburgh, UK
Marc L. Fischer
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Seongeun Jeong
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Xinguang Cui
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Matthew Rigby
School of Chemistry, University of Bristol, Bristol, UK
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Cited
15 citations as recorded by crossref.
- Changes to Carbon Isotopes in Atmospheric CO2 Over the Industrial Era and Into the Future H. Graven et al. 10.1029/2019GB006170
- The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2018 A. Petrescu et al. 10.5194/essd-13-2363-2021
- Spatial Distribution of the Annual Atmospheric Carbon Dioxide in the Contiguous USA and Their Controlling Factors M. Uddin & O. Smirnov 10.1007/s10666-021-09780-8
- Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation K. Hu et al. 10.3390/rs16183394
- Atmospheric inverse estimates of CO emissions from Zhengzhou, China H. Fan et al. 10.1016/j.envpol.2020.115164
- Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO2 Emissions S. Jeong et al. 10.1021/acs.est.4c09395
- The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2020 M. McGrath et al. 10.5194/essd-15-4295-2023
- A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks L. Nayagam et al. 10.1088/1748-9326/ad0f74
- Continuous CH4 and δ13CH4 measurements in London demonstrate under-reported natural gas leakage E. Saboya et al. 10.5194/acp-22-3595-2022
- The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps G. Hurtt et al. 10.1088/1748-9326/ac7407
- Spatial Modeling: an Approach for Estimating the Effect of Industrial Emissions on the Atmospheric Carbon Dioxide M. Uddin & N. Reid 10.1007/s12061-023-09532-z
- Impact of emissions from a single urban source on air quality estimated from mobile observation and WRF-STILT model simulations H. Fan et al. 10.1007/s11869-021-01023-9
- The impact of temporal variability in prior emissions on the optimization of urban anthropogenic emissions of CO2, CH4 and CO using in-situ observations I. Super et al. 10.1016/j.aeaoa.2021.100119
- Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2 I. Super et al. 10.5194/gmd-13-2695-2020
- Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon H. Graven et al. 10.1029/2018EF001064
14 citations as recorded by crossref.
- Changes to Carbon Isotopes in Atmospheric CO2 Over the Industrial Era and Into the Future H. Graven et al. 10.1029/2019GB006170
- The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2018 A. Petrescu et al. 10.5194/essd-13-2363-2021
- Spatial Distribution of the Annual Atmospheric Carbon Dioxide in the Contiguous USA and Their Controlling Factors M. Uddin & O. Smirnov 10.1007/s10666-021-09780-8
- Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation K. Hu et al. 10.3390/rs16183394
- Atmospheric inverse estimates of CO emissions from Zhengzhou, China H. Fan et al. 10.1016/j.envpol.2020.115164
- Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO2 Emissions S. Jeong et al. 10.1021/acs.est.4c09395
- The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2020 M. McGrath et al. 10.5194/essd-15-4295-2023
- A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks L. Nayagam et al. 10.1088/1748-9326/ad0f74
- Continuous CH4 and δ13CH4 measurements in London demonstrate under-reported natural gas leakage E. Saboya et al. 10.5194/acp-22-3595-2022
- The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps G. Hurtt et al. 10.1088/1748-9326/ac7407
- Spatial Modeling: an Approach for Estimating the Effect of Industrial Emissions on the Atmospheric Carbon Dioxide M. Uddin & N. Reid 10.1007/s12061-023-09532-z
- Impact of emissions from a single urban source on air quality estimated from mobile observation and WRF-STILT model simulations H. Fan et al. 10.1007/s11869-021-01023-9
- The impact of temporal variability in prior emissions on the optimization of urban anthropogenic emissions of CO2, CH4 and CO using in-situ observations I. Super et al. 10.1016/j.aeaoa.2021.100119
- Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2 I. Super et al. 10.5194/gmd-13-2695-2020
1 citations as recorded by crossref.
Latest update: 17 Jun 2025
Short summary
We investigate potential errors and uncertainties related to the spatial and temporal prior representation of emissions and modelled atmospheric transport for the inversion of California's fossil fuel CO2 emissions. Our results indicate that uncertainties in posterior total state fossil fuel CO2 estimates arising from the choice of prior emissions or atmospheric transport model are on the order of 15 % or less for the ground-based network in California we consider.
We investigate potential errors and uncertainties related to the spatial and temporal prior...
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