- 1Institute of Environmental Physics (IUP-UB), University of Bremen, Bremen, Germany (rasmus.nuess@uni-bremen.de)
- 2Environmental Chemical Processes Laboratory (ECPL), University of Crete, Heraklion, Greece
- 3Center for the Study of Air Quality and Climate Change (C-STACC), Institute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, Greece
- 4Meteorology and Air Quality, Wageningen University and Research, Wageningen, the Netherlands
- 5Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
- 6Center of Marine Environmental Science (MARUM), University of Bremen, Germany
- 7Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Nicosia, Cyprus
Inverse modeling can provide valuable insights into the sources of atmospheric tracers based on observations and a set of boundary conditions. However, biases in these boundary conditions can lead to biases in the optimized emissions. In this study, we present a series of global inversion experiments of carbon monoxide (CO) emissions using the TM5-4dvar inverse modeling suit constrained by satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) and surface flask measurements from NOAA. These experiments are designed to systematically assess the impact of different boundary conditions, with a particular focus on hydroxyl radical (OH) distributions, a key determinant of both the sources and sinks of CO.
Methyl chloroform (MCF) measurements are commonly used to constrain global atmospheric OH climatological fields. We find that our OH fields that are modeled with global atmospheric chemistry calculations are biased high. Despite this bias, these OH fields often provide more realistic lateral distributions than climatological OH fields, particularly in the tropical boundary layer. Another critical boundary condition for inverse modeling of CO is its secondary production from Volatile Organic Compounds (VOCs) and methane. Due to the challenges of directly measuring secondary CO production, model-based estimates are used instead.
Our results show that combining modeled secondary CO production estimates with modeled OH fields leads to a closed budget, reducing aliasing across emission categories and enhancing confidence in the optimized anthropogenic and biomass burning emissions. Although the individual budget terms of both the secondary production and the chemical loss of CO may be overestimated, their combined effect yields realistic steady-state CO mixing ratios, as validated by TROPOMI CO observations. This study emphasizes the critical need for improved OH fields to accurately estimate CO emissions, and advances the understanding of potential biases in future inversions.
How to cite: Nüß, J. R., Daskalakis, N., Gkouvousis, A., Kanakidou, M., Krol, M. C., and Vrekoussis, M.: Exploring the influence of OH fields and secondary CO production on CO emission estimates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9998, https://doi.org/10.5194/egusphere-egu25-9998, 2025.
