Articles | Volume 21, issue 13
https://doi.org/10.5194/acp-21-10707-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-10707-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jul 2021
Research article |
| 14 Jul 2021
| 14 Jul 2021
Sensitivity to the sources of uncertainties in the modeling of atmospheric CO2 concentration within and in the vicinity of Paris
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX,
France
now at: Origins S.A.S., Suez Group, Tour CB21, 16 Place de l'Iris, 92040 La
Défense, Paris CEDEX, France
François-Marie Bréon
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX,
France
Grégoire Broquet
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX,
France
Thomas Lauvaux
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX,
France
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX,
France
now at: Institute of Environment and Ecology, Tsinghua Shenzhen
International Graduate School, Tsinghua University, Shenzhen 518055, China
Michel Ramonet
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX,
France
Irène Xueref-Remy
Institut Méditerranéen de Biodiversité et d'Ecologie
marine et continentale (IMBE), Aix-Marseille Université, CNRS, IRD,
Avignon Université, Aix-en-Provence, France
formerly at: Laboratoire des Sciences du Climat et de l'Environnement
(LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX, France
Simone Kotthaus
Institut Pierre-Simon Laplace (IPSL), CNRS, École Polytechnique,
Institut Polytechnique de Paris, 91128 Palaiseau CEDEX, France
Martial Haeffelin
Institut Pierre-Simon Laplace (IPSL), CNRS, École Polytechnique,
Institut Polytechnique de Paris, 91128 Palaiseau CEDEX, France
Philippe Ciais
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX,
France
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Cited
13 citations as recorded by crossref.
- Global warming will largely increase waste treatment CH4 emissions in Chinese megacities: insight from the first city-scale CH4 concentration observation network in Hangzhou, China C. Hu et al. 10.5194/acp-23-4501-2023
- Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities? D. Mallia et al. 10.1029/2023JD038686
- Can we use atmospheric CO2 measurements to verify emission trends reported by cities? Lessons from a 6-year atmospheric inversion over Paris J. Lian et al. 10.5194/acp-23-8823-2023
- Effect of meteorological data assimilation using 3DVAR on high-resolution simulations of atmospheric CO2 concentrations in East Asia M. Seo & H. Kim 10.1016/j.apr.2023.101759
- Identification of spikes in continuous ground-based in situ time series of CO2, CH4 and CO: an extended experiment within the European ICOS Atmosphere network P. Cristofanelli et al. 10.5194/amt-16-5977-2023
- Sustained Reductions of Bay Area CO2 Emissions 2018–2022 N. Asimow et al. 10.1021/acs.est.3c09642
- High‐Resolution Lagrangian Inverse Modeling of CO2 Emissions Over the Paris Region During the First 2020 Lockdown Period K. Nalini et al. 10.1029/2021JD036032
- Worldwide Evaluation of CAMS-EGG4 CO2 Data Re-Analysis at the Surface Level D. Custódio et al. 10.3390/toxics10060331
- Evaluation of light atmospheric plume inversion methods using synthetic XCO2 satellite images to compute Paris CO2 emissions A. Danjou et al. 10.1016/j.rse.2023.113900
- Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants J. Dumont Le Brazidec et al. 10.5194/gmd-16-3997-2023
- A Gradient-Descent Optimization of CO2–CO–NOx Emissions over the Paris Megacity─The Case of the First SARS-CoV-2 Lockdown C. Abdallah et al. 10.1021/acs.est.3c00566
- Trend analysis and outlier distribution of CO2 and CH4: A case study at a rural site in northern Spain I. Pérez et al. 10.1016/j.scitotenv.2022.153129
- Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment J. Lian et al. 10.1021/acs.est.1c04973
13 citations as recorded by crossref.
- Global warming will largely increase waste treatment CH4 emissions in Chinese megacities: insight from the first city-scale CH4 concentration observation network in Hangzhou, China C. Hu et al. 10.5194/acp-23-4501-2023
- Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities? D. Mallia et al. 10.1029/2023JD038686
- Can we use atmospheric CO2 measurements to verify emission trends reported by cities? Lessons from a 6-year atmospheric inversion over Paris J. Lian et al. 10.5194/acp-23-8823-2023
- Effect of meteorological data assimilation using 3DVAR on high-resolution simulations of atmospheric CO2 concentrations in East Asia M. Seo & H. Kim 10.1016/j.apr.2023.101759
- Identification of spikes in continuous ground-based in situ time series of CO2, CH4 and CO: an extended experiment within the European ICOS Atmosphere network P. Cristofanelli et al. 10.5194/amt-16-5977-2023
- Sustained Reductions of Bay Area CO2 Emissions 2018–2022 N. Asimow et al. 10.1021/acs.est.3c09642
- High‐Resolution Lagrangian Inverse Modeling of CO2 Emissions Over the Paris Region During the First 2020 Lockdown Period K. Nalini et al. 10.1029/2021JD036032
- Worldwide Evaluation of CAMS-EGG4 CO2 Data Re-Analysis at the Surface Level D. Custódio et al. 10.3390/toxics10060331
- Evaluation of light atmospheric plume inversion methods using synthetic XCO2 satellite images to compute Paris CO2 emissions A. Danjou et al. 10.1016/j.rse.2023.113900
- Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants J. Dumont Le Brazidec et al. 10.5194/gmd-16-3997-2023
- A Gradient-Descent Optimization of CO2–CO–NOx Emissions over the Paris Megacity─The Case of the First SARS-CoV-2 Lockdown C. Abdallah et al. 10.1021/acs.est.3c00566
- Trend analysis and outlier distribution of CO2 and CH4: A case study at a rural site in northern Spain I. Pérez et al. 10.1016/j.scitotenv.2022.153129
- Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment J. Lian et al. 10.1021/acs.est.1c04973
Latest update: 20 Nov 2024
Short summary
Currently there is growing interest in monitoring city-scale CO2 emissions based on atmospheric CO2 measurements, atmospheric transport modeling, and inversion technique. We analyze the various sources of uncertainty that impact the atmospheric CO2 modeling and that may compromise the potential of this method for the monitoring of CO2 emission over Paris. Results suggest selection criteria for the assimilation of CO2 measurements into the inversion system that aims at retrieving city emissions.
Currently there is growing interest in monitoring city-scale CO2 emissions based on atmospheric...
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