Satellite-Based CO2 Emission Analysis for France and Germany in 2022

Introduction
The increase in greenhouse gas (GHG) emissions, particularly CO2, is a significant driver of climate change. Accurate estimation of CO2 emissions is crucial for developing effective mitigation strategies. This study aims to compare reported CO2 emissions for the year 2022 with estimates derived from satellite observations, adjusted for atmospheric dispersion using the GEOS-Chem model.

Steps Followed

1. Data Collection:

   • Theoretical Data: I started by gathering the theoretical CO2 emissions data for France and Germany, expressed in kilotonnes (ktCO2).

   • Satellite Data: Then, I collected the CO2 concentration data in the dry air column (XCO2) from satellite observations.
     
     

2. Why Use the Dry Air Column (XCO2):

   • Definition of the Dry Air Column (XCO2): The average CO2 concentration in the dry air column, or XCO2, represents the average molar fraction of CO2 in an air column without water vapor. This means that water vapor present in the atmosphere is not accounted for in the CO2 measurement.
     
     

   • Reason for Use:
     Consistency of Measurements: Atmospheric humidity varies significantly in space and time. By using the dry air column, we obtain CO2 measurements that are not affected by variations in water vapor, ensuring consistency and comparability of the data.
     Accuracy of Estimates: Water vapor can interfere with CO2 measurements, potentially skewing the estimates. By excluding water vapor, we obtain more accurate measurements of CO2 concentrations.
     
     

3. Using the GEOS-Chem Model for Atmospheric Dispersion:

   • I installed and configured the GEOS-Chem atmospheric transport model.

   • Meteorological data from MERRA-2 was downloaded and used to drive the GEOS-Chem model.

   • I configured and ran the atmospheric transport simulations to trace CO2 emissions using the satellite and meteorological data.
     
     

4. How Dispersion Calculation Works:

   • Basic Principle: The calculation of atmospheric dispersion involves modeling the transport and diffusion of gases in the atmosphere. Dispersion models like GEOS-Chem take into account winds, turbulence, and chemical reactions to simulate how CO2 emissions move and disperse in the atmosphere.
     
     

   • Methodology:

     • Input Data: The models use meteorological data (such as winds and temperature) and CO2 emission data.

     • Simulation: The model calculates how CO2 particles move based on weather conditions, accounting for horizontal and vertical dispersion.

     • Inversion: By using inversion techniques, I estimated the sources of CO2 emissions based on the measured concentrations from satellite data. This involves matching observed concentrations with potential emissions that could have caused them.
       
       

5. Results Analysis:

   • The results from the GEOS-Chem simulations were used to correct the CO2 concentrations measured by satellites by incorporating atmospheric dispersion.

   • I extracted the corrected emission estimates for atmospheric dispersion for France and Germany.
     
     

Reliability of This Method

The combined approach I used, integrating satellite observations with atmospheric transport models, is generally more reliable than theoretical estimates alone for several reasons:

1. Direct Observations: Satellite observations provide continuous and global measurements of CO2 concentrations, capturing temporal and spatial variations with high precision.

2. Transport Modeling: Atmospheric transport models simulate the movement and dispersion of CO2 emissions, allowing us to trace back to specific emission sources.

3. Dispersion Corrections: By incorporating atmospheric dispersion into our calculations, we can correct CO2 measurements to obtain more accurate emission estimates at the source.

4. Cross-Validation: Using multiple data sources and methods allows for cross-validation, increasing the reliability of emission estimates.
   
   Carbon dioxide (CO2) is well-mixed in the atmosphere, meaning that its concentrations are relatively uniform on a global scale, despite some regional and seasonal variations. This relative homogeneity facilitates the analysis of its emissions and concentrations as local variations can be more reliably attributed to specific emission sources, such as anthropogenic activities. In this analysis, we used satellite CO2 data from Copernicus and theoretical emission data provided by Carbon Monitor. The corrected CO2 concentrations were calculated using the averages of the satellite data for the specific geographical regions of each country."

   Explanation:
   Despite CO2 being well-mixed in the atmosphere, the data used for each country can still be reliably attributed to their respective sources. This is achieved through the following process:

   The satellite data is segmented based on the specific geographical boundaries of each country. By selecting data points that fall within the latitudinal and longitudinal coordinates defining France and Germany, we ensure that the CO2 concentrations analyzed are geographically relevant to each country.

   By examining the data over a consistent time frame (e.g., the entire year of 2022), we can average out short-term variations and focus on the long-term trends and patterns that are indicative of each country's emission profile.

This graph compares the theoretical CO2 emissions with the corrected emissions accounting for atmospheric dispersion for France and Germany.

How to ?

Theoretical CO2 Emissions (Blue Bars): The blue bars represent the theoretical CO2 emissions in kilotonnes (ktCO2) for each country. These values are derived from reported emissions data without any corrections for atmospheric dispersion. For France, the theoretical emissions are approximately 266,256.89 ktCO2. For Germany, the theoretical emissions are approximately 548,925.60 ktCO2.

Corrected CO2 Emissions (Green Bars): The green bars represent the CO2 concentrations corrected for atmospheric dispersion, measured ppm. These corrected values are derived from satellite observations and adjusted using the GEOS-Chem model to account for atmospheric mixing and transport. For France, the corrected CO2 concentration is approximately 394.62 ppm. For Germany, the corrected CO2 concentration is approximately 394.75 ppm.

The theoretical emissions data shows the reported emissions without considering atmospheric dispersion. These values are based on national inventories and are subject to uncertainties due to assumptions and simplifications in the reporting process.

The corrected emissions data, on the other hand, incorporates real-time satellite measurements and adjusts for atmospheric transport and dispersion. This correction provides a more accurate representation of the actual emissions.



Data:
Lauwers, Sébastien (2024), “données satellitaires de CO2”, Mendeley Data, V1, doi: 10.17632/zcdzs9fynf.1
https://data.mendeley.com/drafts/zcdzs9fynf


References:

Copernicus Climate Change Service, Climate Data Store, (2018): "Carbon dioxide data from 2002 to present derived from satellite observations". Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.f74805c8 (Accessed on 12/07/2024)
https://doi.org/10.24381/cds.f74805c8

Knut Stamnes, Gary E. Thomas, & Jakob J. Stamnes. (2017). Radiative Transfer in the Atmosphere and Ocean. Cambridge University Press.

Liu, Z., Ciais, P., Deng, Z. et al. Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic. Nat Commun 11, 5172 (2020). https://doi.org/10.1038/s41467-020-18922-7

Liu, Z., Ciais, P., Deng, Z. et al. Carbon Monitor, a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production. Sci Data 7, 392 (2020). https://doi.org/10.1038/s41597-020-00708-7

Liu, Z., Deng, Z., Zhu, B. et al. Global patterns of daily CO2 emissions reductions in the first year of COVID-19. Nat. Geosci. (2022). https://doi.org/10.1038/s41561-022-00965-8

[for U.S. Carbon Monitor]: Hong, C., Ciais, P., Liu, Z., Boucher, O., Zhao, W., Gentine, P., Wang, Y., Deng, Z., and Davis, S.J.. Near-real-time and state-level monitoring of U.S. CO2 emissions. EarthArXiv preprint: https://doi.org/10.31223/X5BS5B

[for EU Carbon Monitor]: Ke, P., Deng, Z., Zhu, B. et al. Carbon Monitor Europe near-real-time daily CO2 emissions for 27 EU countries and the United Kingdom. Sci Data 10, 374 (2023). https://doi.org/10.1038/s41597-023-02284-y

National Aeronautics and Space Administration (NASA). (n.d.). Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Retrieved from NASA MERRA-2

GEOS-Chem. (n.d.). GEOS-Chem: A community atmospheric chemistry model. Retrieved from GEOS-Chem

Intergovernmental Panel on Climate Change (IPCC). (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Retrieved from IPCC