- 1Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany (josef.stauber@tum.de)
- 2Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
The Munich Urban Carbon Column network (MUCCnet) consists of five solar-tracking Fourier Transform spectrometers (EM27/SUN) measuring column-averaged mole fractions of carbon dioxide (XCO2), methane (XCH4), and carbon monoxide (XCO). They are strategically placed in the center of Munich and in every cardinal direction. Starting with one instrument in 2015, MUCCnet has been collecting data continuously with five instruments since 2020, allowing a detailed analysis of Munich's urban greenhouse gas emissions based on inverse methods. To this end, we use the Bayesian inversion approach: We compute the observed enhancements in dependence of the wind direction using one network site as background (observed background) and derive spatially resolved emissions. The forward model uses a Munich-specific inventory (100×100 m2 resolution) for anthropogenic fluxes and the Vegetation Photosynthesis and Respiration Model (VPRM) for biogenic fluxes. The transport is modeled with the Lagrangian particle dispersion model STILT.
A critical aspect of our analysis is the estimation of uncertainties within the inversion framework. Balancing the confidence in transport and measurements against prior information (inventories) is of great importance. Furthermore, we investigate the number and spatial distribution of state vector parameters based on the available degrees of freedom for signal. The choice of an appropriate background is crucial, since the urban enhancements for Munich are typically below 1 ppm, which is small (< 1%) compared to the background XCO2 concentrations (> 400 ppm). In addition to the observed background approach, we investigate background approaches derived from models (e.g. ICON-ART), and fitted, a posteriori backgrounds from the inversion approach.
Our inversion results represent spatially resolved, long-term, top-down CO2 emission estimates for Munich. Over a comprehensive measurement period of five years, we highlight seasonal and annual trends, providing valuable insights into Munich's CO2 emissions.
How to cite: Stauber, J., Chen, J., Klappenbach, F., Li, J., Luther, A., Makowski, M., Tang, H., Ponomarev, N., and Brunner, D.: Assessment of Munich’s CO2 emissions via Bayesian inversion using MUCCnet data from 2020-2025, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17841, https://doi.org/10.5194/egusphere-egu25-17841, 2025.
