Maya Harms

Sulfur hexafluoride (SF6) is a highly potent greenhouse gas (GHG). Despite its high global warming potential (GWP), it continues to be produced and used in Germany. The reported emission estimates can be used to calculate expected concentrations at measurements sites. Within the PARIS (Process Attribution of Regional Emissions) project we used the operational numerical weather prediction model ICON (ICOsahedral Nonhydrostatic) and its extension module for aerosol and trace gases (ART) as an Eulerian forward model to calculate the expected mixing concentrations response of Germany's largest point source of SF6. We compared the modelled concentration peaks that occur when the modelled plume crosses the measurement site of the Taunus observatory (TOB) with the respective observed signals (requiring background subtraction). The 4-year period of 2020-2023 was covered, and the uncertainty of the meteorological transport was estimated using a 20-member ensemble in our limited area model for Europe, which was run with a horizontal grid resolution of 6.5 km and 74 vertical levels.The model predicts well when peaks are measured but weWe found that most observed peaks at TOB are considerably higher than in the model, suggesting that prior emissions estimates were too low. This indicates that the independent, observation-based emission estimate of our ICON-ART based system is in the range of double-digit tons, which is considerably higher than the self-reported SF6 emission estimate for this point source, also if the model uncertainties are taken into account.

Observation based quantification of surface CO2 fluxes relies on the consistent integration of atmospheric observations with numerical transport models. We present the development and demonstration of an ensemble-based data assimilation system that couples atmospheric CO2 observations to the ICON-ART modeling framework using a Local Ensemble Transform Kalman Filter (LETKF). Starting with a flux estimate provided by CarbonTracker Europe High-Resolution we start with a dynamic model with hourly resolution with a focus on fluxes in Europe for 2021. We then create an ensemble of perturbed prior fluxes within assumed uncertainties using prescribed spatial and temporal correlation structures. We simulate the transport of these ensemble members in ICON-ART in limited area mode, while varying the meteorological conditions to represent meteorological uncertainties. Subsequently, we use the LETKF to update the state vector of concentrations and CO2 fluxes daily, resulting in an posterior estimate of surface CO2 fluxes over Europe. This work provides the foundation for an ICON-ART-based CO2 flux assimilation system and establishes a technical basis for future extensions toward longer assimilation periods, refined error modeling, and the assimilation of anthropogenic emission signals.