University thesis:
Dissertation, Universität Bremen, 2024
Footnote:
Description:
In the atmosphere, carbon monoxide is a trace gas with a relatively short lifetime in the order of a few months. On a global scale, it affects the climate, because most carbon monoxide is eventually oxidized to the greenhouse gas carbon dioxide. That reaction is also the largest sink of hydroxyl radicals and, therefore, prolongs greenhouse gas lifetimes. Close to the sources of carbon monoxide, concentrations can be high and adversely impact local air quality, because it is a precursor for tropospheric ozone. For these reasons, knowledge about the global distribution of carbon monoxide and its sources is important. Inverse modeling is a powerful top-down technique to constrain trace gas emissions, or refine existing bottom-up source estimates, based on observational data. However, inverse modeling of atmospheric chemistry is not trivial and requires the use of sophisticated systems, which combine elaborate models with a plethora of input data, including remote sensing observations and boundary conditions for the model, e.g. meteorology or prior emission estimates. In this work, such an inverse modeling system will be improved by testing, updating, and revising most of its components. Most prominently, observations from a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), are introduced into the system. In recent years, the data quality and resolution of satellite instruments have been steadily improving. These improvements inevitably also lead to an increase in the amount of data to be handled. For inverse modeling systems, large observational datasets can become problematic due to computational constraints. In this work, methods for handling those datasets are developed. To investigate the capabilities and limitations of the new observational dataset, multiple inversion experiments are conducted. These experiments target carbon monoxide emissions from three categories, biomass burning, fossil fuel, and secondary production, for the second half of the year 2018 on a global scale. The results suggest that the emissions, especially in the southern hemisphere, are well constrained by the TROPOMI observations. However, the inversion experiments also reveal biases in the optimized emissions, especially in the northern tropics. These biases are linked to an imbalanced prior budget, i.e. to the boundary conditions of the model before the observations are considered. The budget and the biases are improved in multiple steps, most notably by revising the assumed hydroxyl radical distribution and the meteorology.